THE RELATIONSHIP BETWEEN CHILDREN’S
PSYCHOLOGICAL WELL-BEING, HABITUAL
PHYSICAL ACTIVITY, AND SEDENTARY
BEHAVIOURS
Toby G. Pavey
Submitted by Toby G. Pavey to the University of Exeter as a thesis
for the degree of Doctor of Philosophy in Sport and Health
Sciences, September 2009
This thesis is available for Library use on the understanding that it
is copyright material and that no quotation from the thesis may be
published without proper acknowledgement.
I certify that all material in this thesis which is not my own work
has been identified and that no material has previously been
submitted and approved for the award of a degree by this or any
other University.
…………………………(Signature)
Abstract
Well-being incorporates psychological, social and moral development, the capacity to
enter into and sustain satisfying relationships and limit distress and maladaptive
behaviour. The benefits of physical activity and dangers of a sedentary lifestyle to
health outcomes, e.g. cardio-vascular disease, obesity, diabetes and psychological
well-being are well documented children. Due to methodological and measurement
problems, research addressing the relationship between psychological well-being and
physical activity are inconsistent and weak. This series of studies aimed to overcome
the problems of previous research and investigated the relationship between children’s
psychological well-being and habitual physical activity. Previous research has
highlighted an association between children’s physical activity and individual
psychological well-being constructs. However, Masse et al. (1998) argued that
psychological well-being should contain the measurement of both positive (e.g. selfesteem) and negative (e.g. depression) psychological states. Subsequently, Parfitt and
Eston (2005) identified an association between children’s total physical activity and
psychological well-being (anxiety, depression, self-esteem). Limitations of previous
studies include the type of physical activity measures used and the measurement of
only global self-esteem, which itself is multidimensional. The purpose of this research
was to extend and expand on the limitations of the Parfitt and Eston’s (2005) study.
Psychological measures included both global, domain and sub-domain measurements
of self-esteem, with accelerometry providing estimates of total daily physical activity
and time spent in sedentary through to vigorous intensity activity. Study One assessed
relationships between psychological well-being and physical activity using the same
psychological constructs as Parfitt and Eston (2005), but with physical activity
intensity included. Results highlighted an association between self-esteem and time
spent in very light activity. Study Two assessed the current data’s applicability with
the proposed models and theories related to self-esteem. It was concluded that the
current data provided an adequate fit with the proposed models and theories of selfesteem. Study Three introduced the domain and sub-domain constructs of self-esteem
and a more valid measure of physical activity. Results highlighted associations at the
global, domain and sub-domain levels with predominately time spent in very light and
vigorous intensity activity. Very light intensity activity was associated with negative
effects while vigorous intensity activity was associated with positive effects. It was
hypothesised that if these cross-sectional relationships also existed longitudinally,
then an intervention study changing the time children spend in very light and vigorous
intensity activity may be beneficial to children’s psychological health. With the
relationship between children’s psychological well-being and physical activity clearly
highlighted, Study Four assessed the direction of this relationship and aimed to inform
a potential intervention study. Longitudinal data were collected over a 12-month
period and multi-level modelling was used to analyse the direction of the relationship.
Results highlighted a potential indirect effect of time accumulated in very light and
vigorous intensity activity on psychological well-being, which reinforced the previous
cross-sectional studies. Furthermore, a reciprocal effect was identified between
physical activity and the physical self-worth domain. It was concluded that
interventions decreasing time spent in very light and increasing vigorous intensity
activity may be beneficial to children’s psychological well-being. Study Five aimed to
have an impact on children’s psychological well-being, by reducing the time children
spent in very light intensity activity, through an increase in daily step counts.
Although the intervention decreased the time children spent in sedentary behaviour,
there was no influence on psychological well-being. However, there were several
methodological limitations that affected the intervention, limiting the conclusions that
can be drawn from this study, including a small sample size providing available data
for analysis. The results of this thesis show a relationship between children’s
psychological well-being and habitual physical activity intensity exists. Further
research manipulating the time children accumulate in very light (reduction) and
vigorous (increase) intensity activity, can potentially impact positively on the
psychological well-being of a normal population of children.
Contents
Page
List of Tables
List of Figures
Acknowledgements
1
2
4
1. Introduction
5
5
6
1.1 Rationale for programme of research
1.2 Structure of thesis
2. Children’s psychological well-being and habitual
physical activity
2.1 Anxiety
2.1.1 Measurement of anxiety
2.1.2 Non-clinical anxiety and physical activity
2.2 Depression
2.2.1 Measurement of depression
2.2.2 Non-clinical depression and physical activity
2.3 Self-esteem and self-concept
2.3.1 Self-esteem models
2.3.2 Development of self-esteem in children
2.3.3 Measurement of self-esteem
2.3.4 Self-esteem and physical activity
2.3.5 Physical self-worth
2.3.6 Measurement of physical self-worth
2.3.7 Physical self-worth and physical activity
2.4 Current trends in children’s physical activity
2.5 Gender difference in physical activity
2.6 Measurement of physical activity
2.6.1 Observation
2.6.2 Questionnaires and diaries
2.6.3 Doubly-labelled water
2.6.4 Direct and indirect calorimetry
2.6.5 Heart rate
2.6.6 Motion sensors
2.6.7 Pedometers
2.6.8 Accelerometers
2.6.8.1 Accelerometer cut-points
2.6.8.2 Epoch length
2.6.8.3 Accelerometer use, how many days?
2.6.8.4 Accelerometer limitations
2.6.8.5 Measurement of children’s physical activity
2.6.8.6 Accelerometer conclusions
2.6.9 Measurement of physical activity: combinations
2.7 Physical activity and psychological well-being
9
12
14
14
15
16
17
20
21
26
29
30
30
33
35
39
41
42
43
43
44
45
45
46
46
47
47
50
50
51
51
52
53
53
3. General methods section
3.1 Subject recruitment, sampling and power calculations
3.2 Assessment of psychological well-being
3.3 Internal consistency
3.4 Assessment of physical activity
3.5 Assessment of socio-economic status
3.6 Assessment of anthropometric measures
3.7 Normality of data
4. Study 1: The relationship between children’s psychological
well-being and time spent in physical activity
intensities
4.1 Introduction
4.2 Method
4.3 Results
4.4 Discussion
5. Study 2: The measurement and models of global and
physical self-esteem
5.1 Introduction
5.2 Method
5.3 Results
5.4 Discussion
5.4.1 Global self-esteem and global self-esteem domains
5.4.2 Physical self-worth and physical self-worth
sub-domains
5.4.3 Physical self-worth and mediation
5.4.4 Self-esteem and discrepancy scores
5.4.5 Conclusions
6. Study 3: The relationship between time spent in physical
activity intensities and expanded psychological
well-being of children
6.1 Introduction
6.2 Method
6.3 Results
6.4 Discussion
6.4.1 Total daily physical activity
6.4.2 Physical activity intensity and psychological
well-being
6.4.3 Body fat
6.4.4 Tertile analysis
6.4.5 Conclusions
56
56
57
60
61
62
62
62
64
64
66
67
68
70
70
72
74
79
79
80
80
81
82
83
84
85
86
92
93
93
93
94
95
7. Study 4: The direction of the relationship between children’s
physical activity and psychological well-being
97
7.1 Introduction
97
7.2 Method
7.3 Results
7.4 Discussion
7.4.1 Global level influences
7.4.2 Domain influences
7.4.3 Sub-domain influences
7.4.4 Reciprocal effects
7.4.5 Physical activity pathways
7.4.6 Conclusions
100
104
111
112
113
113
114
115
118
8. Study 5: An intervention to investigate whether reducing time
Spent in sedentary behaviours effects children’s
psychological well-being
119
8.1 Introduction
8.2 Method
8.3 Results
8.4 Discussion
8.4.1 Changes in time spent in physical activity intensities
8.4.2 Lack of changes in psychological well-being
constructs
8.4.3 Changes in total daily physical activity
8.4.4 Methodological Limitations
8.4.5 Conclusions
9. General Discussion
9.1 Main findings
9.1.1 The relationship between children’s psychological
well-being and habitual physical activity
9.1.2 The hierarchical model of self-esteem and the role of
importance
9.1.3 The direction of the relationship between children’s
psychological well-being and habitual physical activity
9.1.4 The lack of associations between total daily physical
activity and psychological well-being: an anomaly?
9.1.5 An intervention to improve children’s psychological
well-being
9.1.6 Overall summary and conclusions
9.2 Limitations
9.2.1 Questionnaires
9.2.2 Accelerometers
9.2.3 Sample size
9.3 Future directions
9.3.1 Further validation of the direction of the relationship
between children’s psychological well-being and
physical activity
9.3.2 Intervention Study
9.3.3 The relationship between children’s psychological
well-being and patterns of physical activity
119
123
128
135
135
136
137
137
139
140
140
140
142
144
146
148
149
149
149
150
151
152
152
153
154
References
156
Appendices
1. Questionnaires used within the research
A) State-Trait Anxiety Inventory for Children
B) Child Depression Inventory
C) Amalgamated Self-Perception Profile for Children and Child and Youth
Physical Self-Perception Profile
175
176
177
2. Information Sheets
A) Information sheet (chapters 3 – 7)
B) Information sheet home pack (chapters 3 – 7)
C) Information sheet intervention (Pedometer group)
D) Information sheet intervention (Control group)
184
185
188
190
193
3. Informed Consent
A) Informed consent (chapters 3 – 7)
B) Informed consent intervention (Pedometer group)
C) Informed consent intervention (Control group)
196
197
199
201
4. Intervention Materials
A) Accelerometer instructions and log sheet
B) Weekend log sheet
C) Powerpoint presentation
D) Pedometer steps bar chart example 1
E) Pedometer steps bar chart example 2
F) Examples of accelerometer outputs
203
204
206
207
210
211
212
5. Supplementary Data
A Power calculation graph (correlations)
B Power Calculation graph (ANOVA)
C Partial correlations controlling for SES
D Well-being scores by total daily activity and time spent in light
and moderate intensity activity (Study 3)
E Descriptive data Chapter 7 (boys)
F Descriptive data Chapter 7 (girls)
G Children’s physical activity and psychological health: the relevance
of intensity (journal article)
H Award certificate ‘Best oral presentation by a young investigator’
Children and Exercise XXIV The 24th Pediatric Work Physiology Meeting
213
214
215
216
179
217
218
219
220
227
LIST OF TABLES
Page
Chapter 2: Literature Review
2.1
Identified self-esteem domains during stages of life
2.2
Differences in published moderate and vigorous cut-points for children
Chapter 3: Generic Methods
3.1
Cronbach’s alpha values for anxiety, depression and self-esteem
(data collection one, chapter 4 & 5)
3.2
Cronbach’s alpha values for anxiety, depression and self-esteem
(data collection two, chapter 6)
3.3
Cronbach’s alpha values for anxiety, depression and self-esteem
(data collection three, incorporated in chapter 7)
3.4
RT3 activity count and physical activity intensity
3.5
Skewness and Kurtosis for psychological constructs
3.6
Skewness and Kurtosis for physical activity measures
Chapter 4: Study 1
4.1
Descriptive data for boys and girls
4.2
Correlation analyses for relationships between physical activity
intensity and psychological well-being
Chapter 5: Study 2
5.1
Self-esteem means and standard deviations for boys and girls
5.2
Cronbach’s alpha values for competence and importance ratings of
self-esteem
5.3
Forced entry regression and part correlations between
global self-esteem and global self-esteem domains
5.4
Forced entry regression and part correlations between
physical self-worth and physical self-worth sub-domains
5.5
Baron & Kenny (1986) test for mediation between physical self-worth
sub-domains and global self-esteem with physical self-worth domain
the mediating variable.
Chapter 6: Study 3
6.1
Descriptive data for boys and girls
6.2
Correlation analyses for relationships between physical activity
intensity and psychological well-being
6.3a Regression analyses showing relationship between well-being and
time spent in very light activity after controlling for body fat
6.3b Regression analyses showing relationship between well-being and
time spent in vigorous activity after controlling for body fat
6.4: Well-being scores by time spent in very light and vigorous activity
Chapter 8: Study 5
8.1
Recommended accelerometer compliance strategies
8.2
Intensity level conversion table (Actigraph)
8.3
Descriptive data experimental group
8.4
Descriptive data control group
25
49
60
60
60
62
63
63
67
68
74
74
75
76
77
87
88
89
89
91
126
127
134
134
1
LIST OF FIGURES
Page
Chapter 2: Literature Review
2.1
A Hierarchical Model of Self-concept
2.2
Harter’s (1985) Multidimensional Self-esteem Model for children
2.3
Example of the relationship between self-esteem and the
competence/importance discrepancy scores
2.4
Structure of Physical Self-worth
2.5
Physical self-worth model
2.6
Associations between physical self-worth sub-domains and
global self-esteem
2.7
Associations between physical self-worth sub-domains,
physical self-worth, and global self-esteem
23
25
27
31
32
34
34
Chapter 3: Generic Methods
3.1
Subject recruitment and sampling for study chapters
56
Chapter 5: Study 2
5.1
Normative data on the relationship between self-esteem and the
competence/importance discrepancy score
5.2
Path diagram of mediation model
5.3
Discrepancy scores for Global self-esteem level
5.4
Discrepancy scores for Physical self-worth level
71
73
78
78
Chapter 7: Study 4
7.1
Multi-level model panel design
7.2
The influence of self-esteem domains and physical activity on
psychological well-being
7.2.1 The influence of physical self-worth sub-domains and
physical activity on global self-esteem domains
7.2.2 The influence of physical activity on physical self-worth
sub-domains
7.2.3 The influence of psychological well-being constructs on
physical activity
7.3
Significant explanatory variables for changes in anxiety
7.3.1 Significant explanatory variables for changes in depression
7.3.2 Significant explanatory variables for changes in global self-esteem
7.4
Significant explanatory variables for changes in scholastic competence
7.4.1 Significant explanatory variables for changes in social acceptance
7.4.2 Significant explanatory variables for changes in physical appearance
7.4.3 Significant explanatory variables for changes in behavioural conduct
7.4.4 Significant explanatory variables for changes in physical self-worth
7.5
Significant explanatory variables for changes in sport/athletic
Competence
7.5.1 Significant explanatory variables for changes in attractive body
7.5.2 Significant explanatory variables for changes in strength competence
7.6
Significant explanatory variables for changes in average
daily activity
7.6.1 Significant explanatory variables for changes in time spent in
very light activity
99
102
103
103
104
105
105
106
107
107
107
107
108
109
109
109
110
110
2
7.6.2
7.6.3
7.6.4
7.7
Significant explanatory variables for changes in time spent in
light activity
Significant explanatory variables for changes in time spent in
moderate activity
Significant explanatory variables for changes in time spent in
vigorous activity
Time in very light intensity pathway and time in vigorous
intensity pathway
Chapter 8: Study 5
8.1
Intervention procedures timeline
8.2
Total daily physical activity across four time-points
8.3
Average time spent in sedentary behaviour across four time-points
For experimental and control groups
8.4
Average time spent in very light intensity activity across four
time-points for experimental and control groups
8.5
Average time spent in light intensity activity across four
time-points for experimental and control groups
8.6
Average time spent in moderate intensity activity across
four time-points
110
111
111
116
125
128
129
130
131
133
3
Acknowledgements
I would like to convey my utmost gratitude and respect to Gaynor Parfitt and Ann
Rowlands for their guidance, knowledge, assistance, help, and any other words that
will convey my thanks, during the construction of this thesis, and the related journal
articles, abstracts and conference presentations.
I would like to thank the School of Sport and Health Sciences for giving me the
opportunity to undertake the research contained within this thesis, and thank any other
members of staff who may have helped me during the last four years, including Jo
Welsman for her assistance with the multi-level modelling. I would also like to thank
the children, parents, teachers and schools involved, for their time and participation
within the studies, and the undergraduates who helped me with the data collection.
I would very much like to thank my Mum and Paul for their support and financial
backing, and my peers for problem solving and office banter.
4
CHAPTER 1 INTRODUCTION
1.1 Rationale for Area of Research
Well-being incorporates psychological, social and moral development, the capacity to
enter into and sustain satisfying relationships and limit distress and maladaptive
behaviour (NHS Health Advisory Service, 1995). The prevalence of mental health
problems in the UK is estimated to be approximately 7.7 % in children aged 5 – 10
years and 11.5 % in children aged 11 - 16 years (National Statistics, 2005) and these
problems can hinder normal development (Fox, 1999).
The benefits of physical activity and dangers of a sedentary lifestyle to health
outcomes, e.g. cardio-vascular disease, obesity, diabetes, bone health are well
documented in both adults and children (for reviews see: Biddle, Gorely, & Stensel,
(2004); Boreham & Riddoch, (2001); Livingstone, Robson, Wallace, & McKinley,
(2003)). Furthermore, these benefits include improved mood states, emotions and selfesteem (Biddle, Fox, & Boutcher, 2000). Physical activity guidelines for children
recommend at least 60 minutes of moderate to vigorous physical activity each day
(MVPA) (Department of Health, 2004), with some suggesting 90 minutes providing
greater benefits (Andersen et al., 2006). Beyond counselling, psychological and drug
therapy; physical activity, exercise and sports have been shown to be associated with
and help improvements in mental health disorders and general psychological wellbeing across all age groups (Fox, 1999).
For children, relationships identified between sedentary lifestyles, physical activity
and physical health are inconsistent and weak potentially due to methodological and
measurement problems (Boreham & Riddoch, 2001). These problems also affect
research addressing the relationship between psychological well-being and physical
activity (Parfitt & Eston, 2005). Hence, little is known about the relationship between
children’s psychological well-being and habitual physical activity. Parfitt and Eston
(2005) aimed to address the limitations associated with self-reported physical activity,
e.g. children’s ability to recall events, through assessing the relationship of habitual
physical activity, measured objectively using pedometry, with psychological wellbeing in children. Results highlighted a moderate to strong association between
psychological well-being and habitual physical activity.
5
While pedometers objectively measure total activity in the form of step counts, they
provide no information on the intensity or pattern of physical activity. Children’s
physical activity is sporadic and highly transitory (Bailey et al., 1995). It is suggested
that different intensities of physical activity (e.g. sedentary through to vigorous), may
have differing relationships with overall psychological well-being, and its various
constructs (Self-esteem, anxiety, depression). If relationships at different physical
activity intensities can be identified, this would provide useful information for the
future construction of intervention studies. The use of accelerometry to monitor
physical activity provides objective information on total daily physical activity, time
spent in different intensities and the pattern of physical activity. Therefore, the
purpose of this research is to build on the work of Parfitt and Eston (2005) and
investigate the effect of physical activity intensity on psychological well-being in
children.
1.2 Structure of Thesis
Chapter 2 provides a literature review of the area of research. Focusing particularly on
children and adolescents, this literature review discusses the development of
psychological well-being in the normal population. Although psychological wellbeing is multidimensional in nature, the main components which are often discussed
and studied in children are depression, anxiety and self-esteem. These three areas will
be further discussed in terms of their association with physical activity, with selfesteem expanded to incorporate the physical self-worth. As the measurement of
physical activity is a key component in this area of research, the use of various
measurement methods will also be addressed. Key research will be highlighted
throughout. This review develops the rationale for the series of studies in this thesis.
Chapter 3 provides a generic methods section, detailing the psychological well-being
questionnaires used, physical activity measures, assessment of socio-economic status,
anthropometric measures and the internal consistency and normal distribution of the
research data.
Chapter 4 (Study 1) is a replication of Parfitt and Eston’s (2005) study with physical
activity measured by accelerometry instead of pedometry, this enabled investigation
of the relationship between activity intensity and psychological well-being. Results
6
highlighted only one association (negative), between self-esteem and time spent in
very light intensity activity. As self-esteem is multidimensional and hierarchical, it
was logical for the next stage of the research to incorporate the domains of selfesteem and sub-domains of physical self-worth, as these are theorised (Fox & Corbin,
1989; Harter, 1985) to be more closely linked with physical activity.
Prior to examining the relationship between physical activity intensity and the
domains of self-esteem, and sub-domains of physical self-worth, it was important to
confirm the multidimensional nature of the construct with this population. Chapter 5
(Study 2) addresses the hierarchical models of self-esteem and physical self-worth, in
terms of applicability of the current data with proposed self-esteem models and theory
(Harter, 1985; Whitehead, 1995). The chapter also assessed the reliability of
importance discrepancies and their associations with global self-esteem and physical
self-worth. The chapter confirmed the adequate fit of the current data with the
proposed models of self-esteem.
Chapter 6 (Study 3) extends study one by expanding the global self-esteem measure to
incorporate domain level and physical self-worth sub-domain level constructs.
Physical activity was measured at two time-points to account for seasonal variation in
activity and thus provide a more valid physical activity measure. Physical activity was
shown to be associated with children’s psychological well-being, particularly for time
spent in very light (negative) and vigorous (positive) intensity activity.
With cross-sectional relationships identified, Chapter 7 (Study 4) focused on whether
the relationships also existed longitudinally and the direction of the relationships.
Three time-points of data and multi-level modelling analysis highlighted a reciprocal
relationship between psychological well-being and the habitual physical activity of
children. A reciprocal effect was found for time spent in the very light and vigorous
intensity activity. Based on these results, it was proposed that a decrease in very light
and an increase in vigorous intensity activity indirectly improves the psychological
well-being of children, through the positive effect on psychological constructs at each
level of the self-esteem hierarchy.
7
Based on the results of chapter 7 (Study 4), Chapter 8 (Study 5) formed a small scale
intervention study. The intervention aimed to reduce time spent in sedentary and very
light intensity activity by increasing children’s daily step counts using pedometry. It
was hypothesised that an outcome of reduced time in the lower physical activity
intensities would impact on psychological well-being, particularly the physical selfworth construct. Although there was a change in children’s physical activity, there
were no changes in psychological well-being constructs. However, results may have
been unduly influenced by methodological limitations.
A general summary and final conclusions are provided in Chapter 9. A discussion of
the theoretical and applied implications of the research is presented along with
potential limitations and proposals for where future research should be directed.
Study 3 formed the basis of a scientific paper published in a peer-reviewed journal
(Parfitt, Pavey & Rowlands, 2009, Acta Paediatrica) and was awarded ‘Best
Presentation by a Young Investigator’ at the 24th Pediatric Work Physiology Meeting
(Tallin, Estonia) international conference.
8
Chapter 2 Children’s Psychological Well-being and Habitual Physical Activity
It has been proposed that psychological distress and psychological well-being are
opposite poles on a single continuum of overall mental health (Mirowski & Ross,
1989). In contrast, it is argued that psychological distress and psychological wellbeing are independent constructs and should be measured on independent axes
(Keyes, 2005; Korkeila et al., 2003; Masse et al., 1998; Ryff, 1989). Psychological
distress is defined as a non-specific syndrome that covers constructs including anger,
cognitive problems, anxiety and depression (Korkeila, 2000), with anxiety and
depression identified as core distress syndromes (McDowell & Newell, 1996; Watson
& Kendall, 1989). Well-being refers to people’s evaluations of their lives that are both
cognitive and affective (Diener, 2000).
Adult inventories are readily available to measure either psychological distress
(Kessler et al., 2002; Poulin, Lemoine, Poirier, & Lambert, 2005) or psychological
well-being (Ryff, 1989). It is argued that the absence of mental illness does not equal
the presence of mental health (Keyes, 2005). The assessment of mental health needs
to include both negative (distress) and positive (well-being) measures, to fully depict
the mental health of a population.
Research by Masse et al., (1998) aimed to assess the independence of psychological
distress and well-being to mental health in an adult population. Psychological distress
was assessed by four factors: self depreciation; irritability; anxiety/depression; and
social disengagement. Well-being was assessed by six factors: self-esteem; social
involvement; mental balance; sociability; control of self and events; and happiness.
Results of structural equation modelling suggested that distress and well-being are not
opposite poles on the same axis or completely independent constructs, but are two
correlated and distinct components which may reflect the mental health of a general
population. Therefore, Masse et al. (1998) concluded that epidemiological assessment
of general population mental health should use measures of positive as well as
negative psychological states. Keyes (2005) further examined this argument, and
confirmed the distinct but correlated constructs, and proposed that mental health
should be viewed as a complete state.
9
Within the Masse et al. (1998) results, self-esteem was shown to have the highest
positive relationship with psychological well-being, with the joint anxiety/depression
measure negatively associated with well-being. Masse et al. (1998) argued that
anxiety and depression are strongly correlated, and in this case, part of the same
‘idiom’ of distress. However, a potential criticism of the Masse et al., (1998) study is
the joint scale of anxiety/depression. It can be argued that there are clear differences
between anxiety and depression with each having their own psychological and
somatic components (Brady & Kendall, 1992; Watson & Kendall, 1989). For
example, from a cognitive viewpoint, anxiety may be associated with anticipated
harm or danger, while depression centres on loss or failure (Brady & Kendall, 1992).
Furthermore from an emotion viewpoint, fear is predominant with anxiety, while
sadness is a key component for depression (Brady & Kendall, 1992).
Following the argument for the assessment of positive and negative states in adult
populations, it is proposed that the measurement of self-esteem, anxiety and
depression may provide a measure of psychological well-being, and thus an indication
of mental health, in the general population of children. This may be achieved through
the assessment of positive (self-esteem) and negative (anxiety and depression)
psychological states as proposed by Keyes (2005) Korkeila et al. (2003) and Masse et
al (1998). In adults and children, well-being incorporates psychological, social and
moral development, the capacity to enter into and sustain satisfying relationships and
limit distress and maladaptive behaviour (NHS Health Advisory Service, 1995).
Further for children, mental health problems can hinder normal development (Fox,
1999).
Research has shown from about the age of eight, children start to develop measurable
notions related to self-esteem (Harter, 1985), anxiety (Speilberger et al., 1973) and
depression (Kovac & Beck, 1977). However, children may find it difficult to relate to
other proposed (Masse et al., 1998) adult constructs of distress e.g. self-deprecation,
social disengagement, and irritability, due to their developing cognitive ability (of
children) (Harter, 1999). For similar reasons, children may also find it difficult to
relate to some adult notions of well-being e.g. control of self and events, social
involvement, and mental balance. This is highlighted by the lack of research literature
10
assessing these constructs in children, and may explain the lack of a mental health
scale for children.
There are however, inventories which assess children’s health-related quality of life
(HRQoL), of which positive and negative psychological states are sometimes
incorporated. Example include, the Child Health questionnaire (Landgraf, Abetz, &
Ware, 1996), and the Child Health and Illness Profile – Child Edition (Riley et al.,
2004). These type of inventories can be useful for identifying subgroups of children
who are at risk for health problems, and can be used to evaluate health service needs,
and the allocation of health-care resources (Children’s Health Committee, 2004).
HRQoL inventories may contain constructs such as physical well-being, relations with
parents, home life, social support, school environment, and financial resources.
Further, all the above HRQoL inventories contain a construct related to psychological
distress/well-being, which incorporate a measure of self-esteem, anxiety and
depression. However, this is a potential limitation of the HRQoL inventories for the
assessment of psychological well-being, as scores are grouped together with few
items contributing to constructs such as satisfaction, comfort or general mental
health/well-being. This is because the purpose of these inventories is to provide an
overall generic health-related quality of life indication, as opposed to a specific
psychological well-being measure.
One identified correlate of psychological well-being is that of the role of physical
activity. The benefits of physical activity to psychological health include improved
mood states, emotions and self-esteem (Biddle et al., 2000). Furthermore, the
psychological benefits of a physically active lifestyle are important because
psychological well-being contributes to overall health (Fox, 1999). Indeed, physical
activity has been shown to benefit self-esteem (Spence, McGannon, & Poon, 2005),
anxiety (Petruzzello, Landers, Hatfield, Kubitz, & Salazar, 1991), and depression
(North, McCullagh, & Tran, 1990) in adults and children (Ekeland, Heian, & Hagen,
2005; Larun, Nordheim, Ekeland, Hagen, & Heian, 2006). The following sections (2.1
– 2.3.7) will cover the psychological well-being constructs of anxiety, depression and
self-esteem, and their association with children’s physical activity.
11
2.1 Anxiety
Most people have anxious feelings or feelings of worry occasionally, and these are
normal responses to events in life, for example, the driving test or speaking in public
for the first time. Related to this are feelings of stress, which can arise from an
imbalance between one’s perceived capabilities and the perceived situational demands
(Cox, 1985). When these feelings of anxiety and stress persist and interfere with
personal and family relationships or work, these feelings may be classified as a
general anxiety disorder (Biddle & Mutrie, 2008). The prevalence of anxiety disorders
range from 6-12 per cent of the general population in Europe (Alonso et al., 2004).
For children, separation anxiety, panic disorders, social phobias, and obsessivecompulsive disorders represent more specific forms of anxiety (Spence, 1998).
Anxiety can be defined as either state (transitory) or trait (enduring) in nature and
pioneering work by Liebert and Morris (1967) identified worry and emotionality
(bodily tension) as major components of anxiety. Worry (cognitive anxiety) refers to
elements such as negative expectations, concerns about oneself, the current situation,
and potential consequences. Emotionality (somatic anxiety) refers to physiological –
affective elements of anxiety, such as autonomic arousal and unpleasant feelings such
as nervousness and tension (Morris, Davis, & Hutchings, 1981).
Although the worry and emotional components of anxiety are expected to co-vary
because stressful situations contain elements related to the arousal of each, worry and
emotionality are theorised to be conceptually independent (Morris et al., 1981). Worry
is aroused and maintained by situational factors that influence cognitive evaluations.
Arousal and maintenance of the emotionality component are of shorter duration and
consist of non-evaluative cues, such as discussion of a test, and the handing out of the
exam paper (Morris et al., 1981). Both worry and emotionality certainly appear to be
components of state anxiety, further both these components may be related to trait
anxiety, with the relatively stable tendency of individuals to experience worry and
emotionality across a variety of situations (Morris et al., 1981). Indeed, research has
shown that in a stressful situation, children with high trait anxiety show a
disproportionate rise in state anxiety, compared to children with low trait anxiety (Fox
& Houston, 1981; Houston, Fox & Forbes, 1984). As this thesis aims to assess a
12
chronic relationship between anxiety and physical activity, the emphasis will be on
trait anxiety within the study chapters.
It is suggested that control may be key in the development of anxiety in children and
adolescents (Weems & Silverman, 2006). Control can be defined as the ability to
personally influence events and outcomes in one’s environment (Chorpita & Barlow,
1998). Chorpita and Barlow (1998) suggest early childhood experiences with reduced
control may produce a cognitive style that increases the chances of interpreting events
as out of one’s control. This lack of control represents a psychological vulnerability
for the development of anxiety problems. Gray and McNaughton (2000) suggest
situations with low control are likely to increase anxiety due to heightened
behavioural inhibition system (BIS) activity. BIS is defined as a functional brain
system involving several areas of the brain. In situations of low control or increased
anxiety the BIS acts as the detection and preparation for danger. This can cause
narrowing of attention, inhibition of gross motor behaviours, increased stimulus
analysis, and increased exploration of the environment (Gray & McNaughton, 2000).
Further, the BIS acts to prime the hypothalamic motor system for possible rapid
action, represented by the fight or flight system (FFS). The FFS is related to actual
confrontation with danger and is associated with actions of escape, active avoidance,
and defensive aggression (Gray & McNaughton, 2000). Regardless of how the anxiety
is developed, symptoms can cause significant distress or a reduction of normal day-today functioning (Csoti, 2003).
Mechanisms (biochemical, physiological, and/or psychological) for the anxiety
reducing effects of physical activity can be linked to both acute and chronic physical
activity. Mechanisms to explain acute effects include increased beta endorphins and
parasympathetic activity, reduced muscle tension and excitability of the central
nervous system, and stimulation of brain monoamine and neurotrophins, thus,
suppressing FFS and BIS activity. (Dishman et al., 1997; Taylor, 2000). Cognitive
psychological explanations include the time-out or distraction hypothesis with
physical activity providing a distraction from anxiety (Paluska & Schwenk, 2000).
Chronic effects from physical activity include positive mood states, self-esteem and
perceptions of the physical self, perceptions of control over behaviours and outcomes,
13
and the development of enhanced coping resources and social networks (Taylor,
2000).
2.1.1Measurement of Anxiety
Self-report inventories for measuring anxiety symptoms in children are frequently
used (Muris, Merckelbach, Ollendick, King, & Bogie, 2002). These include, the
Revised Children’s Manifest Anxiety Scale (Reynolds & Richmond, 1978), the
Multidimensional Anxiety Scale for Children (March, Parker, Sullivan, Stallings, &
Conners, 1997), the Spence Children’s Anxiety Scale (Spence, 1998), and the StateTrait Anxiety Inventory for Children (STAIC; Speilberger, Edwards, Lushene,
Montuori, & Platzek, 1973).
The STAIC was developed from an adult version of Spielberger’s State Trait Anxiety
Inventory (STAI; Spielberger, Gorsuch & Lushene, 1970), with the STAI a popular
and widely used inventory in anxiety research (Balon, 2005; Bieling, Antony, &
Swinson, 1998). Furthermore, the STAI has been translated into many languages, with
its factor structure examined in a variety of populations (Bieling et al., 1998). The
STAIC is one of the most widely used children’s inventories, which has been shown
to have satisfactory psychometric properties (Kirisci & et al., 1996; Muris et al.,
2002). Both the STAI and STAIC contain a state anxiety and trait anxiety scale. It is
suggested that children who score highly on the trait anxiety scale would exhibit state
anxiety elevations more frequently and with greater intensity than those who score
low on the trait anxiety scale when exposed to a stressful situation (Endler &
Kocovski, 2001; Lau, Eley, & Stevenson, 2006; Speilberger et al., 1973). The
diagnosis properties of the STAIC for clinical purposes has come into question
(Stallings & March, 1995), with the suggestion that the STAIC is better suited as a
measure of anxious symptoms (Li & Lopez, 2004; Muris et al., 2002). As this thesis
aims to assess a normal population, the STAIC trait scale should provide an adequate
measure of children’s trait anxiety.
2.1.2 Non-clinical Anxiety and Physical Activity
Reviews of literature with adults suggest low-to-moderate anxiety reducing effects of
exercise and physical activity, with single activity sessions reducing state anxiety and
consistent periods of exercise reducing trait anxiety (Saxena, Van Ommeren, Tang, &
14
Armstrong, 2005; Taylor, 2000). Similar findings have also been found for children
and adolescents, with an association and reduction of anxiety related to physical
activity (Calfas & Taylor, 1994; De Moor, Beem, Stubbe, Boomsma, & De Geus,
2006; Mutrie & Parfitt, 1998; Strong et al., 2005). The comprehensive review paper
by Larun et al (2006) assessed the effects of physical activity and exercise
interventions in reducing or preventing anxiety in children and adolescents. The
authors concluded there was a small effect in favour of exercise in reducing anxiety
scores in the general population of children and adolescents. However, the ability to
draw further conclusions (e.g. mode, intensity of physical activity) was limited due to
the small number of studies included, the diversity of participants, differences in
interventions, and methods of measurement.
Indeed, there appears to be little
attention to the relationship between children’ physical activity and anxiety in the
research literature. Anxiety measures in the above reviews are usually encompassed
within mental health or quality of life inventories, or provide a joint
depression/anxiety construct. This may explain the lack of published studies focussing
specifically on anxiety. Nevertheless, physical activity and exercise have no known
negative effects on children’s health and might be an important method to improving
children’s anxiety levels (Larun et al., 2006).
2.2 Depression
Depression is the fourth leading contributor to the global burden of disease and has
been projected by the World Health Organisation (WHO) to be second only to
cardiovascular disease as the world’s main cause of death and disability by the year
2020 (WHO, 2004). Among adults, depressive disorders are quite common, and can
occur with other mental disorders, for example, anxiety, substance abuse and
schizophrenia (Cicchetti & Toth, 1998). Depressive disorders can provide mood
disturbances that involve loss of interest or pleasure in most activities, and feelings of
sadness. This is coupled with disturbances in energy, concentration, sleep, appetite,
and libido (Cicchetti & Toth, 1998).
In children and youth, depression impacts on growth and development, may inhibit
school performance, and affect peer and family relationships (Bhatia & Bhatia, 2007;
Glied & Pine, 2002). Significantly, when associated with maladjustment in
emotionally unstable individuals, depression is strongly associated with suicide, a
15
leading cause of death in adolescents and adults (Cicchetti & Toth, 1998). Depressive
disorders are neither normal developmental occurrences or short lived episodes that
dissipate over time (Kovacs, Gatsonis, Paulauskas, & Richards, 1989). Indeed,
depression can rise dramatically with the transition from childhood through to
adolescence, potentially due to hormonal changes during puberty (Angold, Costello,
Erkanli, & Worthman, 1999), and the increased capacity for abstract reasoning and
operational thought (Cole & Turner, 1993). Hankin et al. (1998) suggested that rates
of depression may rise up to six fold during adolescence, with a two to four fold risk
of depression continuing into adulthood (Brent & Birmaher, 2002). Furthermore, 40 –
70% of depressed children and adolescents develop an additional co-morbid disorder
(Cicchetti
&
Toth,
1998).
Further
disorders
include,
anxiety,
attention-
deficit/hyperactivity, oppositional defiance, and substance use (Bhatia & Bhatia,
2007).
For adults, cognitive theories of depression have hypothesised that the ways in which
individuals attend to, interpret, and remember negative life events contributes to the
development and maintenance of depression (Lakdawalla, Hankin, & Mermelstein,
2007). The review paper by Lakdawalla et al. (2007) assessed the application of
cognitive theory to the development of depression in child and adolescent
populations. Small to moderate effects were found in child and adolescent
populations, lending some support to cognitive theories of depression. However,
methodological,
statistical,
and
theoretical
limitations
presented
a
limited
understanding of how cognitive factors and processes present a risk for the
development of depression in children (Lakdawalla et al., 2007). Physical activity
may help to attenuate negative life events and promote positive life events through
positive feedback from others and an increased sense of self-worth, providing social
contact and the mastery of new skills, and acting as a diversion from negative
thoughts (Lawlor & Hopker, 2001).
2.2.1 Measurement of Depression
For clinical depression diagnosis, assessment using measurements such as self-report
inventories and structured clinical interviews is suggested (Kendall, Cantwell, &
Kazdin, 1989). Self-report inventories provide an inexpensive screening device for a
large number of participants, with the use of structured interviews for diagnostic
16
purposes for participants obtaining high scores from the initial screening (Fristad,
Emery, & Beck, 1997).
Several inventories exist for the assessment of depression in children. These include
The Centre for Epidemiological Studies Depression Scale (Weissman, Orvaschel, &
Padian, 1980), Depression Self-Rating Scale (Birleson, Hudson, Buchanan, & Wolff,
1987), Children’s Depression Scale (Lang & Tisher, 1978), and The Children’s
Depression Inventory (CDI; (Kovacs & Beck, 1977).
The CDI was developed from an adult version of the Beck Depression Inventory
(BDI;(Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) The BDI is a widely used
self-report measure of depressive symptoms in clinical and non-clinical populations
(Storch, Roberti, & Roth, 2004). The reliability and sound psychometric properties of
the BDI have been fully demonstrated providing a robust measure of depressive
symptoms (Bonilla, Bernal, Santos, & Santos, 2004; Kendall, Hollon, Beck, Hammen,
& Ingram, 1987; Richter, Werner, Heerlein, Kraus, & Sauer, 1998; Storch et al.,
2004). Similarly, the CDI is one of the most widely used depressive symptomology
self-rating inventories for children (Comer & Kendall, 2005). However, the use of the
CDI as a diagnostic tool has come into question, with the recommendation that the
CDI be used as a continuous measure of depressive symptoms and mood (Carey,
Faulstich, Gresham, Ruggiero, & Enyart, 1987; Comer & Kendall, 2005; Fristad et al.,
1997; Matthey & Petrovski, 2002). Given depression is an underlying aspect of
psychological well-being, the CDI provides a valid inventory for a general population
of children.
2.2.2 Non-clinical Depression and Physical Activity
It is suggested that depression can be defined along a continuum ranging from
feelings which are short lived and do not interfere very much with daily activities, to
longer lasting symptoms that suggest clinical diagnosis of a mental illness (Biddle &
Mutrie, 2008). It is these feelings of ‘getting the blues’ or ‘being fed up’ that are short
lived and usually and for the general population, physical activity and exercise may
protect against the development of more severe depressive symptoms.
17
A physically active lifestyle is shown to be associated with lower levels of depression
and depressed moods in adults (Biddle & Mutrie, 2008; Goodwin, 2003; Slawson,
2005), with the converse association for those with a sedentary lifestyle (Martinsen,
2008). These same associations are also found with children and adolescents. Crosssectional (Tomson, Pangrazi, Friedman, & Hutchinson, 2003), longitudinal (Birkeland
et al., 2008; Motl, Birnbaum, Kubik, & Dishman, 2004) and systematic review studies
with children (Calfas & Taylor, 1994; Larun et al., 2006), have reported depressive
symptoms to be negatively associated with physical activity and sports, with a small
effect for exercise in reducing depression.
However, there is still much debate as to the causal direction between physical
activity and depression due to the relatively few experimental studies conducted
(Biddle & Mutrie, 2008). The causal direction of increased physical activity reducing
depressive mood may be explained by the protection hypothesis, which is based on
psycho-physiological and psychological mechanisms providing a protective effect.
The psycho-physiological mechanisms propose that acute physiological responses
(e.g. increased monomine circulation, endorphin production, and lower hormonal
responses to stress), may mediate the relationship between depressed mood and
physical activity (Dishman et al., 1997; Oweis & Spinks, 2001). For the psychological
mechanisms, physical activity improves self-esteem, distracts from negative thoughts
and everyday stress, provides experience of mastery and control, and provides an
arena to improve social skills and form social networks (Goodman & Whitaker, 2002;
Salmon, 2001).
The converse relationship between depression and physical activity may be explained
by the inhibition hypothesis, which assumes a depressed mood may affect one’s
capability of being physically active (Birkeland, Torsheim, & Wold, 2008). The
inhibition hypothesis proposes that psychosocial dysfunction and functional
impairment are linked with a depressed mood (Georgiades, Lewinsohn, Monroe, &
Seeley, 2006). A negative pattern occurs with a depressed person lacking the energy
to be physically active (Goodwin, 2003), whereas a positive mood may supply
additional energy and motivation to engage in physical activity.
18
Tomson et al. (2003) assessed the relationship between childhood depression and
physical activity using 933 children aged 8 – 12 years old. Depression was selfreported via the Dimensions of Depression Profile for Children and Adolescents
(Harter & Nowakowski, 1987). Physical activity was measured via a proxy rating by
parents and teachers, with children classified as either ‘active’ or ‘not active’. Results
suggested the relative risk for depressive symptoms was 2.8 to 3.4 times higher for
inactive compared to active children. It was proposed that the elevated risk for
depressive symptoms for inactive children suggests it would be worthwhile to
promote physical activity and exercise interventions with children. Further, regular
physical activity may help to establish lifetime physical activity habits, and may serve
as a cost-effective component in the prevention of depression.
Limitations of the Tomson et al. (2003) study were acknowledged by the authors. The
study design was cross-sectional and thus did not provide causal information as either
depression or physical activity levels were not manipulated through intervention.
Also, the potential confounding effects of socio-economic status were not accounted
for. Probably the main limitation was the physical activity measure provided by the
adult proxy ratings. However, it was acknowledged that objective measures of
physical activity were warranted, but given the large number of participants was not
feasible.
Motl et al. (2004) assessed whether naturally occurring changes in physical activity
were related to depressive symptoms during early adolescence, while controlling for
potential confounding risk factors (including, socio-economic status, smoking, and
alcohol consumption). Using data from The Teens Eating for Energy and Nutrition at
School study (Lytle & Perry, 2001) 4594 12 – 13 year olds were assessed over a two
year period.
Physical activity was measured by a single-item measure of the
frequency of regular activity outside of school. Depressive symptoms were measured
using the Centre for Epidemiological Studies Depression scale (Radloff, 1977). The
results suggested that changes in physical activity were inversely related to a change
in depressive symptoms, or vice-versa. A change in the frequency of physical activity
by 1 SD was inversely related to a .25 SD change in depressive symptoms. The
magnitude of the relationship was reduced by one third when controlling for
confounding risk factors (including, sex, appearance smoking, alcohol, and SES). The
19
researchers acknowledged that it was not possible to infer causality, and it was
suggested that the findings were sufficiently strong enough to encourage long-term
controlled trial.
Compelling as the results may be, the physical activity measure was inadequate at
best. The number of participants was large, but the one question regarding physical
activity, can be interpreted differently by participants. The item asks the question ‘Do
you get some regular physical activity outside school? By regular we mean at least 3
times a week for at least 20 minutes at a time’ (pp. 337). This is very ambiguous and
open to many interpretations.
There is also the suggestion that lower levels of physical activity in childhood may be
a risk factor for the development of depression and depressed moods in adulthood. An
18-year longitudinal study (Camacho, Roberts, Lazarus, Kaplan, & Cohen, 1991)
suggested that physical inactivity during childhood puts adults from normal
populations at risk for depression, with higher physical activity levels lowering the
risk of later depression. Supporting this, lower levels of self-reported physical activity
in childhood may increase the risk of self-reported depression in adulthood by around
50% (Jacka et al., 2008). Birkeland et al. (2008) suggest that changes in leisure-time
physical activity and depressed mood are related over a ten year period (13-23 years
of age). However, there was no suggestion that high levels of leisure-time physical
activity during adolescence protected against later depressed mood.
2.3 Self-esteem and Self-concept
Theorists suggest that the sense of self may be central to the possession of mental and
physical health (Fox, 2000). However, confusion often surrounds the terminology of
self-esteem and self-concept, with these terms being used synonymously (Whitehead,
1995). Self-concept is a self-description. Individuals build up a picture of themselves
from identity statements such as ‘I am male’, ‘I am a brother’. Self-esteem, also
expressed as self-worth, is the evaluative element of self-concept, where individuals
formulate a judgement of their own worth (Fox, 1997). Sonstroem & Morgan (1989),
suggest that it is ‘almost impossible to consider a picture of oneself without
experiencing self-valuation’ (p.330), with the interchangeable nature of self-esteem
and self-concept being accepted. Indeed, this premise appears to be the case for some
20
theorists (e.g. Marsh). However, the current thesis incorporates measures of selfesteem or worth. Therefore, self-esteem will be used for this positive psychological
state, with self-concept only referenced where theorists consistently use the
expression.
The positive psychological state of self-esteem refers to an individual’s feelings of his
or her worthiness and competence (Muris, Meesters, & Fijen, 2003), and can be
described as an overall judgement made by the self of how well the self is doing (Fox,
1997). Self-esteem is regarded as both an indicator of well-being and as a marker for
recovery from illness, and is suggested to be the core of mental health as it represents
our self-rating of overall worth (Fox, 1999). Furthermore, self-esteem is generally
regarded as an important index of children's well-being and mental health (Muris et
al., 2003). High self-esteem has been linked to positive qualities such as resilience to
stress, leadership, adaptability, independence, positive social adjustment, life
satisfaction, and high level of achievement in education and work (Fox, 2000).
Furthermore, higher self-esteem is associated with beneficial adolescent behaviours
such as participation in exercise and sport, not smoking, healthier eating patterns, and
a lower suicide risk (Torres & Fernandez, 1995). On the other hand, low self-esteem
is associated with depression and anxiety, maladjustment both in school and social
relationships, sense of hopelessness, a lack of assertiveness and control, and suicidal
ideation (Fox, 2000; Harter, 1993; Higgins, 1989).
2.2 Self-esteem Models
Early self-esteem research simply viewed the self as a unidimensional construct.
Measurement of self-esteem only provided a global self-worth judgement (Harter,
1982), which clouded rather than clarified matters of interest (Fox, 1997).
Progression in self-esteem theory during the 1970/80’s led to the acknowledgment of
multidimensional models of self-esteem (Fox & Corbin, 1989; Harter, 1985; Marsh,
Byrne, & Shavelson, 1988; Shavelson, Hubner, & Stanton, 1976).
Pioneering work by Shavelson et al. (1976) identified six critical features in their
definition of the self-concept construct: 1) it is multi-dimensional; 2) it is hierarchical,
with situation specific perceptions at the base, broader domains ( e.g. academic,
social, physical) at the middle, and global self-concept at the apex; 3) Global self21
concept is stable, but as one descends the hierarchy, self-concept becomes more
situation specific and less stable; 4) the multi-dimensional nature of self-concept
increases with age; 5) self-concept is both descriptive and evaluative; 6) self-concept
can be differentiated from other constructs. Shavelson et al. (1976) provided an
academic/non-academic representation of the self-concept model (Figure 2.1).
Academic self-concept was divided into subject areas, with non-academic
incorporating social, emotional and physical self-concepts. These self-concepts were
further sub-divided, with for example, peers and significant others making up social
self-concept. The lowest level represents the situation specific self-concepts which
were closely related to behaviours.
22
Global Selfconcept
General
Academic and
Non-academic
Self-concept
Sub-areas of
Self-concept
Academic Selfconcept
English
History
Math
Social Selfconcept
Science
Peers
Evaluation of
Behaviour in
Specific Situations
Figure 2.1: A Hierarchical Model of Self-concept (adapted from Shavelson et al., 1976 pp. 413)
23
Significant
Others
Emotional
Self-concept
Emotional
States
Physical Selfconcept
Physical
Ability
Physical
Appearance
This model provided the prototype for new multidimensional models of self-esteem
(Marsh, 1997). Further, it provides a comprehensive and flexible framework to allow
formation and testing of domain-specific hypotheses concerned with the structure of
self-esteem (Shavelson et al., 1976). The model also allows researchers to focus on a
single domain or a series of domains, and test how such aspects are organised within
the hierarchy (Fox, 1990). Accordingly, Marsh and colleagues (Marsh, 1990; Marsh et
al., 1988), when assessing an academic self-concept model, adopted the hierarchical
model, and subsequently expand the academic self-concept into math/academic selfconcept and verbal/academic self-concept. This academic self-concept model has
subsequently been employed when assessing the relationship between academic selfconcept and academic achievement (Marsh, 1990; Marsh et al., 1988; Marsh &
Yeung, 1997).
Harter, (1982) hypothesised that from age 8, children make discrete judgements about
their competence in different domains, but also construct a view of their global selfesteem, over and above the specific competence judgements. This underlines the
hierarchical nature of self-evaluation, where self-worth is viewed as a superordinate
construct
with
competence
judgements
representing lower-order
evaluative
dimensions (Harter, 1982). Through open-ended questionnaires and interview
techniques, Harter (1985) identified five esteem domains (scholastic & athletic
competence; social acceptance; physical appearance; behavioural conduct), these
domains are recognised as specific content areas, which relate to the global selfesteem of children, and are correlated and predictive of global self-esteem (Figure
2.2).
24
Global SelfEsteem
Scholastic
Competence
Social
Acceptance
Behavioural
Conduct
Physical
Appearance
Athletic
Competence
Figure 2.2: Harter’s (1985) Multidimensional Self-esteem Model for children.
From a life span perspective, young children (< 8y) can make judgements about
themselves in specific domains (e.g. physical competence, peer acceptance), but are
unable to make judgements about their global self-esteem (Harter, 1999). Moving
through childhood, into adolescence and adulthood these judgements become more
differentiated as further domains (e.g. scholastic competence, job competence, humor)
become incorporated, relevant to specific stages of life (Harter, 2003); see Table 2.1).
Table 2.1: Identified self-worth domains during stages of life.
Children <8a
Childrenb
Studentsc
Cognitive competence
Physical Competence
Peer Aceptance
Maternal acceptance
Global Self-worth
Scholastic competence
Social acceptance
Athletic competence
Physical appearance
Behavioural conduct
Global Self-worth
Creativity
Intellectual ability
Scholastic competence
Job competence
Athletic competence
Appearance
Romantic relationships
Social acceptance
Close friendships
Parent relationships
Humour
Morality
Adultsd
Global Self-worth
Sociability
Job Competence
Nurturance
Athletic abilities
Physical appearance
Adequate provider
Morality
Household management
Intimate relationships
Intelligence
Sense of humour
a (Harter & Pike, 1984)
b (Harter, 1985)
c (Neeman & Harter, 1986)
d (Messer & Harter, 1986)
25
2.3.2 The Development of Self-esteem in Children
Harter’s rationale for the development of self-esteem within children dates back to
early work by self-esteem pioneers James (1892) and Cooley (1902). For James
(1892), global self-esteem resulted when individuals were successful in areas where
they had “pretensions” (did well at things important to them). James (1892) position
was that individuals focus on ability in domains of importance, where one has
aspirations to succeed. Thus, if someone perceives themselves as competent in
domains where they aspire to excel, the result should be high self-esteem. Conversely,
if one falls short of their ideal by being unsuccessful in domains where they aspire to
be competent, this will result in low self-esteem. However, lack of competence in
domains judged as unimportant will not affect self-esteem.
For Harter (1985), the general premise of attached importance is that if a child
perceives themselves to be competent in areas judged important, then there will be
little discrepancy between competence and importance, with the child having an
accompanying self-esteem score that is high. In contrast, if the child deems certain
domains are very important, but their perceived competence levels are low in these
areas, there would be a discrepancy that should result in low self-esteem (Harter,
1985). Therefore, two children with seemingly identical competence scores, can have
very different global self-esteem scores depending on the importance they attach to
specific domains
Work by Harter (1987; 1990) provides evidence of this and James (1892) position.
Assessing domains rated as very or sort of important, provided a linear relationship
highlighting that relatively low self-esteem is reported for those recognising that they
lack competence in domains they aim to succeed in (see Figure 2.3). Further, the
relationship between competence in important domains and self-esteem, r = .70, was
substantially higher than the relationship between competence in unimportant
domains and self-esteem, r = .30 (Harter, 1990).
26
-2
Mean Discrepancy Score
-1.8
-1.6
-1.4
-1.2
-1
Series1
-0.8
-0.6
-0.4
-0.2
0
'1.0-1.5
'1.6-2.0
'2.1-2.5
'2.6-3.0
'3.1-3.5
'3.6-4.0
Self-esteem Scores
Figure 2.3: Example of the relationship between self-esteem and the
competence/importance discrepancy scores (adapted from Harter, 1999, pp.150).
To overcome these discrepancy occurrences, the concept of discounting can be
employed as a self-enhancement strategy. Individuals, who attach low importance to
those domains where low competence is perceived, would prevent shortfalls in
competence impacting on self-esteem. Those unable to discount domains in which
they exhibit low competence would be more liable to importance – competence
discrepancies leading to low self-esteem (Harter, 1993).
For Cooley (1902), self-esteem was formed by looking at one’s reflection in the
metaphorical mirror of social appraisal. Self-esteem was formed from interpretations
of the reactions of other people to one’s characteristics and behaviours. Social support
in the form of positive regard from significant others, was the critical determinant of
self-esteem. Approval or disapproval from others becomes incorporated into one’s
own esteem for the self.
Examining Cooley’s (1902) position, Harter (1987; 1990) identified correlations
ranging from .50 to .65 between perceived social support from significant others and
self-esteem in children and adolescents. Further in line with Cooley’s (1902) selfesteem model, those with the lowest levels of support, reported the lowest self-esteem.
Those with moderate support, reported moderate levels of self-esteem, and those with
the most support reported the highest self-esteem. For children and adolescents
support from perceived classmates and parents were the best predictors of self-esteem.
27
Harter suggested her research (1987; 1990), reveals both James’ and Cooley’s
formulations of self-esteem, provide a powerful explanation for the level of selfesteem displayed by children and adolescents. Harter’s (1985) model suggests change
in competence for domains judged important, and shifts in approval or disapproval
from significant others, should lead to corresponding changes in self-esteem. Further,
Harter (1993) suggests, ‘alterations in the identified determinants should produce
changes in one’s level of self-esteem’ (p. 108). Determinants include, support from
significant others (Harter, 1993), academic achievement (Marsh, 1990), and exercise
and physical activity (Ekeland et al., 2005).
One self-esteem domain that has been proposed to be strongly linked with self-esteem
development is physical appearance. Harter (2000), talks of the inextricable link
between physical appearance and self-esteem, suggesting there is a robust relationship
between a child’s perception of their physical appearance and their level of selfesteem. Early work (Harter, 1990) identified the relationship between physical
appearance and self-esteem to be high and robust across the life span. This strong
relationship between physical appearance and global self-esteem has also been
replicated in other children based studies (Granleese & Joseph, 1994b; Muris et al.,
2003; Van Dongen-Melman, Koot, & Verhulst, 1993). Harter (1999), further
investigated domain links with self-esteem. Child population sub-groups where it was
expected that athletic competence, scholastic competence, and behavioural conduct
respectively would have the strongest relationship with self-esteem were assessed.
However, it was physical appearance again that provided the strongest relationship
with self-esteem. Harter (2000) suggests the reason for this may be that physical
appearance is qualitatively different from other domains in that one’s appearance is
always on show for others and the self to evaluate. In comparison, other esteem
domains are not constantly on show and one has more control over when personal
adequacies in these domains will be displayed.
For girls, physical appearance may be particularly problematic for self-esteem
development, given unattainable cultural images of physical beauty (Harter, 1999).
Further, girls who explicitly base their self-esteem on perception of their appearance
(e.g. high appearance importance, low perception of appearance) may be more at risk
of lower self-esteem (Harter, 1999). Perceptions of physical attractiveness in girls
28
steadily decline from childhood through to late adolescence, whereas no similar drop
occurs for boys (Harter, 2000). Harter (2000) suggests the focus on physical features
as a criterion for attractiveness and the pathway to esteem is not held for males in the
same way females do. Alternatively intelligence, academic success, athletic ability,
power and status are routes for males to positive evaluations of appearance (Harter,
2000). However, as Harter (2000) notes, male standards may be changing. Boys are
becoming more fashion and appearance conscious. Indeed, currently for both men and
boys there is a myriad of health, style and fitness media (magazines, websites,
television programmes) dedicated to the way a man should look.
Harter (2000) sums up the situation particularly for girls suggesting,
‘basing one’s sense of self-worth as a person on how one looks is a
particularly pernicious orientation for girls. Sadly, this is the orientation
underscored by our society and the media’ (pp.136).
2.3.3 The Measurement of Self-esteem
With the models and development of self-esteem well established, researchers
(Harter, 1985; Marsh 1988; 1990) aimed to tap adult and children’s global and
domain specific self-perceptions and the importance attached to a domain, in order to
confirm the proposed models and developmental theories.
The Self-Perception Profile for Children (SPPC; Harter, 1985) was developed for the
assessment of children’s self-esteem, and initial analysis provided moderate to strong
correlations between self-esteem domains and global self-esteem. The psychometric
qualities (factor structure, internal/test-retest reliability, validity) of the SPPC have
since been supported (Granleese & Joseph, 1994a, 1994b; Van Dongen-Melman et al.,
1993) with Muris et al. (2003) confirming the SPPC as a reliable and valid self-report
measure for assessing children’s self-esteem. Accompanying the SPPC is a scale,
which assesses how important the domains of self-esteem are to an individual.
Importance and discrepancy score can be obtained from the values reported. The
SPPC is not the only inventory available for the assessment of children’s self-esteem.
A review paper, Butler (2005), provides an overview of available inventories. These
29
include, the Self-esteem Scale (Rosenberg, 1965), the Piers-Harris Children’s Selfconcept Scale (Piers, 1969), and the Self-description Questionnaire (Marsh, 1988).
However, due to its psychometric properties and previous utility in physical activity
and self-esteem research, the SPPC was selected to provide a valid measure of selfesteem.
2.3.4 Self-esteem and Physical Activity
Physical activity and global self-esteem has shown a positive relationship in adult
populations (Fox, 2000; Spence et al., 2005; Scully, Kremer, Meade, Graham, &
Dudgeon, 1998). However, these relationships were often inconsistent and weak. The
same inconsistent and weak relationship between physical activity and self-esteem
also extend to child populations (Calfas & Taylor, 1994; Strong et al., 2005). Ekeland
et al’s. (2005) systematic review supports the weak beneficial effects of exercise on
self-esteem in children and young people. However, Fox (1999) suggests this weak
relationship between physical activity and global self-esteem is in line with theoretical
reasoning. Global self-esteem is a stable construct, and not easily changed by prowess
or success in a single area of life. Therefore, the relationship between physical activity
and specific domains of self-esteem (e.g social acceptance, athletic competence,
physical appearance) warrants investigation, as these relationships may indirectly
extend to the global self (Fox, 1999).
2.3.5 Physical Self-worth
Focusing on a single domain, the role of the physical self has come under much
consideration (Fox, 1997). It is suggested that the body, through its appearance,
attributes and abilities provides a substantial interface between individuals and the
world (Fox, 2000). Using the Shavelson et al. (1976) model and the Harter (1985)
approach to domain identification, Fox and Corbin (1989) explored the structure of
physical self-worth. They identified four sub-domains (sports competence, physical
condition, body attractiveness and physical strength), which were subordinate to
global perceptions of physical self-worth and overall global self-esteem in a
hierarchical structure (Figure 2.4). As with the Shavelson et al. (1976) model, the
structure can extend further to a facet and subfacet level. As this, and previous models
(Shavelson et al., 1976) suggest, the subfacet and facet levels are more accessible and
specific to change, with global and domain levels relatively stable to change. If
30
improvements at the lower levels are repeated often enough or over a prolonged
period of time, improved physical self-worth will occur (Fox & Corbin, 1989; Fox,
2000).
Superordinate
Subdomain Competence
Subfacet
General,
enduring
Physical SelfWorth
Domain
Facet
Global SelfEsteem
Football
Shooting
Strength
Condition
Appearance
Lifting
Long Run
Physique
Bench Press
5 Mile
Slim waist
Specific,
changing
Figure 2.4: Structure of Physical Self-worth (Fox, 1990)
During a similar period to Fox and Corbin (1989), a further model incorporating the
physical self was proposed. For Sonstroem and Morgan (1989), the lowest level of
their hierarchical model was represented by perceived self-efficacy. Perceived selfefficacy refers to the level and strength of a belief that one can successfully perform a
given activity (Sonstroem & Morgan, 1989). As with the Fox and Corbin (1989)
model, the arrangement provides specific physical self-efficacies at the base with
global self-esteem at the top of the hierarchy (Figure 2.5). The lower self-efficacies
are conceived as components of the higher level domains, with changes in the lower
levels hypothesised as being influential to changes in higher level domains
(Sonstroem & Morgan, 1989). This model was later expanded (Sonstroem, Harlow, &
Josephs, 1994), and incorporated the Fox & Corbin (1989) model, with the physical
self-efficacies below the physical self-worth sub-domains.
31
Global Selfesteem
General
Physical
Competence
Specific
Physical
Acceptance
Physical
Self-efficacy
Figure 2.5: Physical self-worth model, adapted from (Sonstroem & Morgan, 1989).
Harter’s (1993) conception of self-esteem development can also be applied to the
physical self. Children should be attracted to physical activities that promote their
physical self-worth through skill mastery and optimal challenges (Whitehead &
Corbin, 1997). These attractions should especially occur with support from significant
others (peers, parents, coaches, teachers). Similarly to Harter (1985), Fox (1990),
suggests that if a physical self-worth sub-domain is regarded as unimportant, then a
low sub-domain score is unlikely to negatively impact on physical self-worth.
However, if a sub-domain is perceived as important, an accompanying low
competence score should impact negatively on physical self-worth. Further, this filter
could apply from the physical self-worth domain to the global level.
The school environment clearly has the potential to influence the development of
children’s physical self-worth and global self-esteem. Physical education can promote
social competence, moral development and emotional stability, with self-esteem
widely chosen as a worthy curricular end-product (Fox, 1992). Indeed, the current
national curriculum states,
‘Competence in physical activity and the sense of enjoyment brought about
by being active and successful engenders a sense of confidence and selfesteem in students and enables them to become increasingly independent.
32
This confidence encourages them to get involved in physical activity for its
own sake and as part of a healthy lifestyle choice’.
(http://curriculum.qcda.gov.uk/key-stages-3-and-4/subjects/physicaleducation/keystage3/PE_and_the_national_curriculum_aims.aspx?return=/s
earch/index.aspx%3FfldSiteSearch%3Dconfidence%26page%3D1)
From an early age, curricular experiences may be engineered that through encounters
with physical activities and contact with physical educators, sound physical selfworth, which contributes to global self-esteem, can develop (Fox, 1992).
Furthermore, the development of basic physical competencies through a quality
physical education programme, has a powerful effect upon self-esteem, confidence
and peer acceptance (Bailey, 2000).
Although physical education provides an arena to develop and enhance physical and
global self-esteem, physical education is a compulsory form of physical activity,
which may be torturous for some children (Lagerberg, 2005). Physical education has
the potential of a double edged sword in producing both positive and negative affects
(Whitehead & Corbin, 1997). For example, high importance of success may be placed
in the physical domain, where actual physical competences are low.
2.3.6 Measurement of Physical Self-worth
Fox and Corbin (1989) developed the Physical Self-Perception Profile for use as a
measurement tool and to provide validation of their physical self-worth hierarchical
model. Figures 2.6 & 2.7 highlight the associations between the three tier model,
when assessing college students. Associations between the sub-domains and global
self-esteem highlight that physical self-worth is a mediator of the relationships
between sub-domains and global self-esteem. Correlation analysis confirmed this,
with physical self-worth displaying the strongest association with global self-esteem
(.64 females; .61 males). Furthermore, the sub-domains showed stronger associations
with physical self-worth compared to global self-esteem. Partial correlations,
accounting for the effect of physical self-worth, extinguished all but one sub-domain
association with global self-esteem. It was suggested the relationships between subdomains, physical self-worth and global self-esteem were consistent with the
hypothesised three-tier hierarchical self-esteem structure. The PSPP has been further
33
validated, with the physical self-worth mediating role also supported for adult female
exercisers (Sonstroem et al, 1994) and middle aged adults (McAuley, Mihalko, &
Bane, 1997). Fox & Corbin (1990) also developed the Perceived Importance Profile
(PIP) to be measured along side self-perceptions, in order to assess the importance of
physical self-worth sub-domains.
Global SelfEsteem
.32
Competence
.24
.33
Strength
Condition
.48
Appearance
Figure 2.6: Associations between physical self-worth sub-domains and
global self-esteem (Adapted from Fox & Corbin, 1989; averaged male and
female scores)
Global SelfEsteem
.62
Physical SelfEsteem
.49
.72
.43
Competence
Strength
.65
Condition
Appearance
Figure 2.7: Associations between physical self-worth, sub-domains and
global self-esteem (Adapted from Fox & Corbin, 1989; averaged male
and female scores)
34
The PSPP was constructed from a sample of college students and validated with
various adult populations. In order to provide a measurement tool for a younger age
group an adapted version of the PSPP (including the PIP) was constructed, validated
and named the Children and Youth Physical Self-Perception Profile (CY-PSPP)
(Whitehead, 1995). Physical self-worth was shown to have the strongest relationship
with global self-esteem (.60 males; .62 females). Furthermore, the partial correlations
accounting for the effect of physical self-worth extinguished or greatly reduced the
sub-domain associations with global self-esteem. This supported the hierarchical
structure and mediating effect of physical self-worth, in a sample of children and
adolescents.
Factor structure (including the mediating role of physical self-worth) and internal/testretest reliability has been confirmed with children as young as nine (Eklund,
Whitehead, & Welk, 1997; Welk & Eklund, 2005) and across Dutch (Van DongenMelman et al., 1993) Russian and British (Hagger, Ashford, & Stambulova, 1998),
Finnish (Miller, 2000) and Chinese (Hagger, Biddle, Chow, Stambulova, &
Kavussanu, 2003) cultures.
Within some of these studies minor cross-loading between constructs did occur.
However, children have a less differentiated sense of their physical self and may not
be able to make a distinction among the sub-domains of the physical self as easily
(Fox, 1992; Harter, 1985). Therefore, any minor question towards factorial validity
may relate to the characteristics of a child population, rather than indicating problems
with the CY-PSPP scale (Eklund et al., 1997; Welk, Corbin, Dowell, & Harris, 1997).
The CY-PSPP provides a useful, valid and reliable measure of children’s physical
self-perceptions (Welk et al., 1997; Welk & Eklund, 2005). Again, other physical selfworth measures are available, for example the Physical Self-Description
Questionnaire (Marsh, Richards, Johnson, Roche, & Tremayne, 1994), but as noted,
the CY-PSPP also provides a measure of perceived importance for the assessment of
competence – importance discrepancies.
2.3.7 Physical Self-worth and Physical Activity
Previous research has shown the domain and sub-domains of physical self-worth to be
correlated with children’s physical activity. With the help of 446 North American
35
children, Crocker, Eklund, & Kowalski (2000) investigated children’s physical
activity and physical self-perceptions. Children aged 10-14 were administered the
PSPP, with physical activity measured using the Physical Activity Questionnaire for
Older Children (PAQ-C). Results showed physical self-worth and it’s sub-domains to
be correlated with physical activity (r = 0.27 to 0.48, p < 0.05), with physical selfperception models able to predict 27-29% of the variance of physical activity. Further,
it was indicated that physical condition and sports competence had the strongest
associations and prediction with physical activity. It was suggested that sports
competence is likely to influence a child’s activity due to involvement in both school
and outside organised sports. Further, many of these activities would require aerobic
fitness and conditioning components. The obvious limitations of the study, the use of
a self-report instrument and the use of the PSPP over the CY-PSPP, were
acknowledged by the authors.
In a similar study using 253 Eastern European children aged 11-14y, Raudsepp,
Liblik, & Hannus (2002) assessed the relationship between physical self-perceptions
and moderate to vigorous activity (MVPA). A 7-day physical activity questionnaire
(Sallis et al., 1985) and the CY-PSPP were used as measurement tools. Similar results
to Crocker et al., (2000) were found, with physical self-worth and sub-domains being
correlated with moderate to vigorous physical activity (r = 0.25 to 0.28, p < 0.01).
Further, multiple regression analysis revealed sport/athletic competence, physical selfworth and strength competence to be the best predictors of MVPA. It was suggested
that identifying the relationship between self-esteem and physical activity may
provide information for physical activity promotion and interventions. However, the
limitation of using a recall questionnaire was not acknowledged by the authors.
Raustorp, Stahle, Gudasic, Kinnunen, & Mattsson (2005) assessed physical activity
and physical self-worth in 501 Swedish children aged 10-14. Children completed the
CY-PSPP, with physical activity being measured by pedometry over four days. The
results indicated fair associations between physical activity and physical self-worth
for boys, but poor associations for girls. Multiple regression analysis highlighted
strength competence and sports competence the strongest predictors of physical
activity in boys, with strength competence and body attractiveness the strongest
predictors for girls. It was suggested that physical activity programs support and
36
develop physical self-worth sub-domains particularly sports competence and body
attractiveness. The use of pedometry provided an objective measure of total activity,
with the potential to overcome some of the limitations associated with recall
questionnaires. Although, the children wore the pedometers for four days, there is no
mention to whether this was only weekdays or included weekend days. Furthermore,
it is not clear whether all children in the study provided physical activity data for all
four days or whether children with fewer than four days were excluded (See section
2.6.8.3, p. 50, regarding measurement days).
Both physical fitness and physical activity can be theorised to be related to physical
self-perceptions. Physical fitness measures are likely to have a strong association with
specific physical self-worth sub-domains. For example, endurance-run scores should
correlate with the physical condition sub-domain; push-up scores should correlate
with the strength sub-domain; and body fat should correlate with the attractive body
sub-domain (Whitehead, 1995). Research with children has provided moderate
evidence of this, with fitness measures generally correlated with the related physical
self-worth sub-domains (Welk et al., 1997; Welk & Eklund, 2005; Whitehead, 1995).
Habitual physical activity is related to both physical self-worth and its sub-domains,
as opposed to association between specific physical fitness measures and physical
self-worth sub-domains. Research with children confirms this, with significant
correlations identified between self-reported moderate to vigorous physical activity
(Crocker et al., 2000; Raudsepp et al., 2002; Welk & Eklund, 2005) and all physical
self-worth sub-domains. Futhermore, total daily steps as measured by pedometry also
displaying significant correlations with all physical self-worth sub-domains (Raustorp
et al., 2005). This suggests habitual physical activity may be associated
simultaneously with all constructs of the physical self-worth hierarchical model.
Within the studies identified above, boys generally have higher physical selfperceptions compared to girls, with body attractiveness an exception. This gender
difference in physical self-perception is suggested to be robust (Raudsepp et al.,
2002). Furthermore, gender differences may be due to sociological factors with boys
possessing higher expectations for success in physical activities and sports due to a
more male dominated learning context in physical education (Lee, Fredenburg,
37
Belcher, & Cleveland, 1999; Welk & Eklund, 2005). However, it may be possible
that both genders have similar estimations of physical self-perceptions, with females
scoring more modestly and males scoring generously (Ladd & Price, 1986).
The relationship between self-esteem and physical activity has been highlighted. The
role of motivation may also play a reciprocating part within this self-esteem –
behaviour relationship. Theory proposes children high in self-esteem and physical
competencies will be more motivated to approach new, and maintain participation in
physical activity and sports (Self-efficacy: Sonstroem et al., 1989; 1994; Competence
Motivation Theory: Harter, 1990). Furthermore, positive experiences and events that
promote competence in specific domains will increase a child’s intrinsic motivation
(Cognitive Evaluation Theory: Deci & Ryan, 1985). Achieving competence in one’s
phenomenological environment through intrinsic self-determined efforts, should lead
to higher self-perceptions and self-esteem (Whitehead & Corbin, 1997).
The physical self-efficacy theory of Sonstroem et al.(1989, 1994) may be the most
applicable, given the integration of self-efficacy into the physical self-worth model.
An assumption for the Sonstroem (1989, 1994) models is that behavioural outcomes
influence self-efficacy, which in turn influence global constructs of competence and
self-esteem. Those high in self-esteem and physical self-worth are more motivated to
approach and maintain physical activity behaviours, in order to maintain or enhance
competence and self-esteem (Sonstroem, 1997). In contrast, self-esteem can be
changed through positive and negative experiences, termed ‘skill development’
hypothesis (Sonstroem, 1997). Positive experiences in specific physical domains will
enhance self-efficacy and over time will generalise to higher-order self-constructs
(Biddle, 1997).
However, the role of motivation is a large and diverse area, with greater detail beyond
the scope of this current thesis, which aims to assess self-esteem as part of
psychological well-being and the effect of objectively measured physical activity on
psychological health. It is noted however, that results from the study chapters of this
thesis may relate to, and be discussed in terms of theoretical links to motivation.
Furthermore, the measurement of the competence domains of self-esteem and
38
competence sub-domains of physical self-worth may provide a measure of motivation
as proposed by Harter (1990).
2.4 Children’s Physical Activity: Current Trends
Current trends in children’s physical activity behaviours will now be discussed as it
has so far been suggested physical activity can promote healthy well-being and
prevent a move towards psychological distress, with sedentary behaviours increasing
the risk of psychological distress in the general population of children.
Physical activity participation rates in Europe, Australia and North America during
the late 1980’s and early 90’s suggested that 50% of children and adolescents were
insufficiently active for health (Armstrong & Van Mechelen, 1998). In order to
promote health improvements, it is suggested that children should spend at least 60
minutes in moderate to vigorous physical activity each day (Department of Health,
2004; Strong et al., 2005). Accelerometry data from the European Youth Heart Study
(EYHS) suggested 97.4% of boys and 97.6% of girls achieved the current health
related guidelines (Riddoch et al., 2004). Current pedometry data for the UK suggests
only 30% of boys and 40% of girls meet current physical activity guidelines (Duncan,
Al-Nakeeb, Woodfield, & Lyons, 2007). Some suggest, using accelerometry, that the
rates may be even lower, 5.1% for boys and 0.4% for girls (Riddoch et al., 2007).
However, discrepancies in accelerometry data are a result of methodological
inconsistencies regarding the identification of MVPA (discussed in section 2.6.8.1, p.
47). Indeed, a lack of objectivity is apparent with the key measurement of children’s
physical activity. Measurement of children’s physical activity in the late 1980’s and
early 90’s would have occurred predominately through self-report and heart rate
measurements. Current physical activity measurement predominately utilise motions
sensors, e.g. accelerometers. The differences of the various physical activity measures
are discussed in section 2.6 (p. 42) and it can be seen that the inconsistencies between
and within different physical activity measures can affect the comparability between
studies.
It is suggested that the current increase in obesity may in part be related to a decrease
in physical activity and an increase in sedentary behaviours (Wareham, van Sluijs, &
Ekelund, 2007). The displacement hypothesis suggests sedentary behaviours, for
39
example television viewing, will displace physical activities (Biddle et al., 2004).
Other hypotheses of the increased obesity levels include, increased snacking and meal
portion sizes while watching television (Coon, Goldberg, Rogers, & Tucker, 2001)
and increased exposure to television adverts for energy dense snacks and foods (Gore,
Foster, DiLillo, Kirk, & Smith West, 2003).
Television and associated media (internet, games consoles etc.) are often blamed for
causing an increase in sedentary activities. However, literature suggests this not to be
the case. Evidence suggests that the amount of time children spend watching
television (and associated media) are similar to that of previous generations and are
unlikely to be displacing physical activity (Biddle et al., 2004). Indeed, current studies
have shown no relationship between physical activity and television viewing (Smith,
Rhodes, Naylor, & McKay, 2008; Taveras et al., 2007). Further, it is suggested that
sedentary behaviours are multiple and diverse (Gorely, Marshall, Biddle, & Cameron,
2007; Marshall, Biddle, Sallis, McKenzie, & Conway, 2002). This lack of support for
the displacement notion suggests that sedentary behaviours and physical activity are
not opposite sides of the same coin (Owen, Leslie, Salmon, & Fotheringham, 2000),
but can coexist and compete with each other (Atkin, Gorely, Biddle, Marshall, &
Cameron, 2008; Gorely et al., 2007; Owen et al., 2000)
This has led to research examining at which time during the day children are most
active. A typical week day for children involves the periods in school and the periods
out of school. This has led to research examining the accumulation of physical
activity within these two periods. Physical activity accumulated during the school day
has been shown to be lower than the out of school period, with data collected during
the Spring season (Cox, Schofield, Greasley, & Kolt, 2006; Gidlow, Cochrane,
Davey, & Smith, 2008). Conversely, the school day has been shown to contribute to
the majority of accumulated physical activity (Fairclough, Butcher, & Stratton, 2007).
However, the Fairclough et al. (2007) data collection occurred during November and
December, when there may have been less opportunity to be physically active out of
school time (e.g. weather conditions, available daylight hours). Regardless of these
mixed results, both periods in the day provide opportunities for the promotion of
physical activity.
40
A substantial proportion of children’s daily physical activity occurs out of school, and
the period just after school may be critical in the accumulation of daily physical
activity (Atkin et al., 2008). Trost, Rosenkranz, & Dzewaltowski (2008) has shown
that children who attend after school programmes accumulated approximately 20
minutes of moderate to vigorous physical activity during this period, with activity
levels greater during free play than organised activities. Further, Atkin et al. (2008)
suggest that 40% of non-school physical activity occurred during the three hours just
after school, with adolescent boys and girls spending 21 and 18 minutes being
physically active during this time.
Correlates of physical activity include intra-personal (e.g. biological), inter-personal
(e.g social) and environmental (Biddle et al., 2004) factors. Socio-economic status
(SES) and environmental factors in particular may affect physical activity. Those
from lower SES backgrounds may have limited access to facilities that enable and
promote physical activity (Hume, Salmon, & Ball, 2005; Powell, Slater, Chaloupka,
& Harper, 2006; Roemmich et al., 2006). However, the relationship between SES and
physical activity in children has provided mixed results, with some suggesting there is
no relationship with physical activity (Kelly et al., 2006; Kristensen, 2008; Thomas,
Cooper, Baker, & Davies, 2006), while others advocate a positive relationship
(Kantomaa, Tammelin, Näyhä, & Taanila, 2007; Mo, Turner, Krewski, & Mo, 2005;
Van Lenthe et al., 2001). The relationship between SES and psychological well-being
is more apparent, with measures of educational background, income, social class, and
neighbourhood quality having a negative relationship with children’s mental health
and well-being (Meltzer et al., 2003; Curtis et al., (2004). With SES potentially
confounding the relationship with both physical activity and psychological wellbeing, there may be a need to account for SES within the research chapters of this
thesis.
The social variables of parent and peer modelling, and support may also affect
physical activity participation. Sallis, Prochaska, & Taylor (2000) suggest there is no
clear association between these social variables and children’s physical activity.
However, McElroy (2002) suggests there are positive parental modelling links, with
parents instilling perceptions of competence. Furthermore, King, Tergerson, & Wilson
(2008) have shown adolescents who received parental encouragement and who had an
41
exercising friend engaged in significantly more physical activity compared to their
counterparts.
The effects of seasonality and the weather typically highlight that physical activity
participation is highest in the summer and lowest in the winter. Duncan, Hopkins,
Schofield, & Duncan (2008) suggest particularly that an increase in temperature (10
degrees celsius) was associated with an increase in children’s physical activity
participation. Conversely, moderate rainfall (≥ 1.1 millimetres) was associated with a
decrease in children’s physical activity participation. These factors should be
considered when comparing physical activity across different locations and time
periods (Duncan et al., 2008).
2.5 Gender Differences in Physical Activity
In studies measuring the physical activity of boys and girls, a consistent finding is that
boys are generally more active than girls. This has been shown for the psychological
based studies (Crocker et al., 2000; Raudsepp et al., 2002) and health based studies
(Hussey, Bell, Bennett, O'Dwyer, & Gormley, 2007; Ness et al., 2007; Rowlands,
Eston, & Ingledew, 1999); the patterning of children’s physical activity (Riddoch et
al., 2004; Rowlands, Pilgrim, & Eston, 2007); age and sex differences in children’s
physical activity (Trost et al., 2002): and those promoting guidelines beneficial to
health (Biddle et al., 1998; Tudor-Locke et al., 2004). Rowlands et al. (2007) suggest
gender differences in activity are largely due to the intensity of the most frequent
bouts and the frequency of the least intensive bouts. Although not all studies noted a
gender difference in activity (Parfitt & Eston, 2005).
2.6 Measurement of Physical Activity
Physical activity is defined as any bodily movement produced by skeletal muscles that
result in energy expenditure (Caspersen, Powell, & Christenson, 1985). It includes
occupational activity, household chores, leisure activity, playing sports, and exercise
that is planned for fitness or health purposes (Dishman, Washburn, & Schoeller,
2001). In order to identify the determinants and health related outcomes of physical
activity, valid methods of assessing physical activity are required that are reliable,
unobtrusive, and practical to administer (Dishman et al., 2001)
42
Furthermore, accurate knowledge of physical activity levels allows the development
of physical activity intervention programmes and assessment of their effectiveness
(Sirard & Pate, 2001). Indeed, to understand why some young people are more active
than others and how to encourage them to be more active, it is necessary to measure
physical activity accurately and reliably. Valid methods of estimating physical activity
in children and adolescents are critical to understanding the dose-response
relationship between physical activity and chronic diseases and associated risk factors
(Sirard & Pate, 2001).
Techniques for measuring physical activity can be grouped into two broad categories:
1) subjective, e.g. observation, self or proxy report; 2) objective, e.g. physiological
indices, such as heart rate, doubly-labelled water and electronic motion sensors.
Objective measures typically estimate energy expenditure, a physiological
consequence of physical activity (Sirard & Pate, 2001), or provide a direct measure of
movement. Literature reviews (Dishman et al., 2001; Livingstone et al., 2003; Sirard
& Pate, 2001) evaluating these various techniques, provide the general consensus on
the strengths and limitations of the measurement techniques, which are outlined in the
following sections.
2.6.1 Observation
Direct observation of physical activity can provide information about activity, type,
context and duration but cannot measure activity intensity, either in terms of energy
expenditure or relative physiological strain (Dishman et al., 2001). Further,
observation is usually impractical for population studies, with smaller samples less
likely to be representative of the population it is intended to represent (Dishman et al.,
2001; Rowlands, Ingledew, & Eston, 2000). Indeed, the process is usually expensive,
produces relatively high experimenter burden, and can alter spontaneous activity
patterns if people are aware they are being observed (Livingstone et al., 2003; Sirard
& Pate, 2001).
2.6.2 Questionnaires and Diaries
For reasons of feasibility and cost, the instruments of choice in both small-scale and
large-scale studies of habitual physical activity in youth are standardised
questionnaires or diaries (Livingstone et al., 2003). However if children are the area
43
of study, this introduces problems, as children have difficulty recalling activity
patterns (Wallace, McKenzie, & Nader, 1985). Also, if parents and /or teachers
answer the questionnaires, the validity is limited as they only see the children for
limited time periods (Rowlands et al., 2000). Indeed, diaries and recalls rely on
memory, are subject to misrepresentation, particularly socially desirable responding,
and are inconsistent in reliability and validity (Livingstone et al., 2003). Moreover,
because youth activity patterns tend to be sporadic in terms of intensity and duration,
this again impacts on the recall of activity patterns (Bailey et al., 1995). Therefore the
utility of this type of instrument is especially problematic in younger children
(Livingstone et al., 2003).
These limitations aside, physical activity questionnaires can assess multiple activity
dimensions: type, frequency, duration, context and intensity. However, the Sallis &
Saelens (2000) review identified that most existing children’s recall questionnaires
only assess two or three physical activity dimensions. Only the Child/Adolescent
Activity Log (CAAL: Garcia, George, Coviak, Antonakos, & Pender, 1997) was
validated as an instrument to assess the multiple physical activity dimensions. The
CAAL however is used as either a 1 or 3-day recall. This would produce the need for
multiple administrations in order to characterise habitual physical activity behaviour.
Although 1-day recall questionnaires provide the highest validity (Welk, Corbin, &
Dale, 2000), administering a questionnaire many times a week can be a burden to
young participants, and expensive for population studies. Therefore, when assessing
children’s physical activity, it is important that the instrument selected be reliable,
accurate and feasible (Telford, Salmon, Jolley, & Crawford, 2004).
2.6.3 Doubly-Labelled Water (DLW)
DLW is considered by some to be the ‘gold standard’ for free-living energy
expenditure and is currently the most socially acceptable and powerful technique for
providing an objective measure of total energy expenditure (TEE; Livingstone et al.,
2003). In this method, a person drinks a prescribed dose of water containing isotopes
of hydrogen and oxygen. These tracers in essence convert the body into a metabolic
recorder that integrates H2O output and CO2 production for 7-14 days. By collecting
urine during this period, these rates can be measured, with TEE being estimated from
CO2 production (Dishman et al., 2001).
44
Advantages of the DLW technique include its objectivity, non-invasive nature, length
of assessment period (7-14 days), and it can be used easily with participants. Distinct
disadvantages however include the low availability and high cost of the isotopes and
associated technical support; the need for accurate dietary records during the
measurement period; and most importantly, information is limited to TEE, with no
frequency, intensity or duration information available (Eston, Rowlands, & Ingledew,
1998). As a result of these disadvantages, the DLW technique functions primarily as a
validating tool for less costly field methods, for the assessment of energy expenditure
(e.g. Hoos, Plasqui, Gerver & Westerterp, 2003).
2.6.4 Direct and Indirect Calorimetry
Similarly to the DLW technique, direct calorimetry is a precise measure of energy
expenditure. The method involves a person remaining in an enclosed chamber while
being studied, with caloric expenditure being determined by the production of heat
(Dishman et al., 2001). Although highly accurate, direct calorimetry obviously limits
normal free-living activity, again its value lies as an accurate tool to validate other
physical activity assessment methods. Indirect calorimetry involves measuring the
consumption of oxygen by analysing expired air, and is considered an accurate and
valid measure of short term energy expenditure (Sirard & Pate, 2001). Again
however, due to the use of non-portable and portable gas analysers, normal activity
patterns are limited.
2.6.5 Heart Rate (HR)
HR is commonly employed as an objective method of estimating children’s physical
activity, and is based on the linear relationship between oxygen uptake and HR (Eston
et al, 1998). The use of HR monitoring is widespread due to its ease of measurement,
its ability to record values over time, its reflection of the relative stress placed on the
cardiopulmonary system due to physical activity, and is cost effective for use in small
to moderate size studies (Sirard & Pate, 2001; Welsman & Armstrong, 1992).
However HR can also be elevated by emotional stress and responses can also reflect
muscle activity, state of hydration, stroke volume, ambient temperature, and humidity
(Eston et al, 1998; Livingstone et al, 2003). Moreover, Rowlands et al., (1999)
suggest that body fat itself may confound the relationship between physical activity
intensity as measured by HR and body fat, with an increase in cardiovascular stress
45
and HR during normal activities. Hence, interpretation of HR data at lower levels of
physical activity is confounded, with the relationship between HR and physical
activity more reliable at moderate to high intensity activity (Livingstone et al, 2003).
2.6.6 Motion Sensors
As almost all forms of physical activity require movement of the trunk or limbs, the
measurement of activity with motion sensors is appealing (Armstrong & Welsman,
1997). Uniaxial and triaxial accelerometers facilitate temporal tracking of the
frequency, intensity, and the duration of activity (Eston et al, 1998), with pedometers
providing a reasonably accurate estimate of distance walked and number of steps
taken (Eston et al, 1998). However, motion sensors are unable to capture static and
resistance exercise, and activities such as cycling.
2.6.7 Pedometers
Although unable to provide intensity level data, the pedometer is suggested to show
great potential for assessing daily activity (Eston et al., 1998). Further, there is ample
support that the simple and inexpensive pedometer is a valid option for assessing
physical activity in larger research studies (Tudor-Locke, Williams, Reis, & Pluto,
2002). The Digiwalker SW-200 is a common make of pedometer used in research,
and has been shown to provide a valid and reliable measure of children’s habitual
physical activity (McKee, Boreham, Murphy, & Nevill, 2005; Rowe, Mahar,
Raedeke, & Lore, 2004).
Importantly the pedometer can be used to evaluate interventions, monitor behaviour
change goals, and can provide evidence of children and adolescents meeting
recommended activity guidelines (Tudor-Locke et al., 2004). It has been suggested to
achieve the recommended 60 minutes of moderate physical activity daily, girls should
achieve 12000 steps/day and boys between 13000-15000 steps/day (Rowlands &
Eston, 2005; Tudor-Locke et al., 2004). Because of the cheaper cost, pedometers have
been recommended for use with larger population studies when assessing habitual
physical activity.
46
2.6.8 Accelerometers
With minimal interference to daily life and the ability to measure frequency, intensity
and duration of activity, the accelerometer is the current choice of researchers
objectively assessing physical activity, in small to medium sized studies (Rowlands,
2007). Most accelerometers use piezoelectric or piezoresistive technology to measure
the acceleration of body parts to which it is attached. When the device is accelerated
the piezosensor emits a voltage signal proportional to the intensity of the acceleration
(Esliger, Copeland, Barnes, & Tremblay, 2005). The measured acceleration data is
converted into some form of proprietary activity count. The activity counts provide an
assessment of movement intensity; greater accelerations provide greater counts
(Esliger et al., 2005). Therefore, research has developed activity count ‘cut-points’
that equate to given activity intensities. Further, an epoch is a set period of time an
accelerometer will record and then store movement data. The length of an epoch
typically ranges from one second to one minute and is selected by the user.
Currently, there is much attention focusing on the measurement of time accumulated
in various physical activity intensities, via published intensity ‘cut-points’. The
optimal length of the epoch is also an area of discussion. Other areas of interest cover
the minimal number of days an accelerometer should be worn, and the number of
hours of each day the accelerometer should be worn, in order to provide a sufficient
measure of physical activity. The next sections (2.6.8.1 – 2.6.8.6) will cover these
topics and the use of triaxial and uniaxial accelerometry for measuring children’s
physical activity.
2.6.8.1 Accelerometer ‘Cut-points’
Time spent in certain activity intensities is of particular importance to researchers. For
example, this information is necessary to assess whether children are achieving
recommended time periods as suggested in published guidelines (Biddle et al., 1998),
or whether an intervention designed to increase time spent in moderate to vigorous
activity is actually working. In order to retrieve this information from an
accelerometer, researchers can use published cut-points to provide different intensity
levels.
47
Intensity cut-points are typically derived from calibrating accelerometer counts with
energy expenditure (Freedson et al., 2005). Calibration of accelerometer outputs with
energy expenditure usually occurs within a laboratory setting and can involve children
running and walking on a treadmill and free play or structured activities (Rowlands,
2007). Different analytical techniques have been used, but most frequently linear or
non-linear regression equations are then used to define the relationship between
accelerometer counts and energy expenditure (Trost, 2007), often in the form of
metabolic equivalent thresholds (METs). The activity count equating to a given METs
value is then reported (e.g., > 3 METs = moderate intensity , > 6 METS = vigorous
intensity).
However a ‘cut-point conundrum’ (Trost, 2007, pp. 309) has occurred with multiple
published cut-points available. For example, Table 2.2 displays five published studies,
which highlights the difference in moderate and vigorous intensity cut-points
available when monitoring children’s physical activity of various ages, using an
Actigraph model accelerometer. The approaches used to obtain the cut-points vary
from study to study. Some display a narrow age range, with others having a large age
range. One study (Trost et al., 2002), has cut-points based on walking and running on
a treadmill only, while the other studies have a variety of free-play activities. One
study is gender specific (Treuth et al., 2004), while the other studies contain both
genders. Further, the heavy use of ambulatory activities may underestimate moderate
activity (Mattocks et al., 2008). These inconsistencies during calibration processes
contribute to the discrepancies in cut-points between studies (Mattocks et al., 2008).
48
Table 2.2: Differences in published Actigraph moderate and vigorous cut-points for
children
Study
Sample
Size
Age
Range
(y)
Activities
Trost et al.
(2002)
80
6-18
Walking and running on treadmill
Puyau et al.
(2002)
26
6-16
Treuth et
al. (2004)
74
13-14
Sirard et al.
(2005)
Mattocks et
al. (2007)
16
3-5
83
11-12
Sedentary: Nintendo, arts and crafts,
playtime (cards, puzzles, lego).
Light: Aerobic warm-up, treadmill 2.5
mph.
Moderate: including, treadmill 3.5-4 mph,
Vigorous: Treadmill 4.5-6 mph, jump
rope, soccer, walk-skip-jog (own pace).
Rest (lying), watch tv/play computer
games (sitting), sweep floor, slow walk
(2.5 mph), brisk walk (3.5 mph), step
aerobics, cycling, shoot baskets, stair
walk, run (5 mph).
Sitting talking, sitting playing, slow
walking, fast walking, jogging
Lying, sitting, slow walking (own pace),
brisk walking (own pace), jogging (own
pace), hopscotch.
Criterion
Moderate
Intensity Cutpoints (counts
per minute)
Energy
Expenditure
(Vo2)
Energy
Expenditure
(Vo2)
≥ 1267
Vigorous
Intensity
Cut-points
(counts per
minute)
≥ 4057
≥ 3200
≥ 8200
Energy
Expenditure
(Vo2)
≥ 3000
≥ 5200
Observation
≥ 2460
≥ 4924
Energy
Expenditure
(Vo2)
≥ 3600
≥ 6130
Differing threshold counts become problematic in the assessment and agreement on
whether children are achieving current guidelines at the moderate/3METs and
vigorous/6 METs boundaries (Freedson, Pober and Janz 2005; Guinhouya et al.
2006). Guinhouya et al. (2006) assessed the daily activity of 45 children aged 8-11
years for three consecutive days using an Actigraph accelerometer. Time spent in
MVPA was then calculated using two published cut-point definitions (Puyau et al.,
2002; Trost et al., 2002). The results showed a difference of 113min/d spent in MVPA
between the two definitions (Puyau et al: 28 min/d; Trost et al: 141 min/d). This
equated to 34% (Puyau et al.) and 100% (Trost et al.) of children undertaking 30
minutes of MVPA/d. Further, Guinhouya and Hubert (2008) question the cut-point
choice by Sherar, Esliger, Baxter-Jones and Tremblay (2007), suggesting that the low
activity count threshold for moderate activity (provided by Trost et al. 2002) led to the
inflation of time spent in MVPA.
Riddoch et al. (2007) reported only 5.1% of boys and 0.4% of girls were achieving the
current physical activity guidelines. However, the published cut-points employed
were those of Mattocks et al. (2007). It can be seen in table 2.2 the Mattocks et al.
49
(2007) cut-points yield the highest counts per minute cut-point for MVPA. This
explains the discrepancy with the European Youth Heart Study (Riddoch et al., 2004),
where the Trost et al. (2002) cut-points were used and > 95% of boys and girls were
reported to achieve current physical activity guidelines. The debate is ongoing with
Guinhouya & Hubert (2008) recently recommending that MVPA thresholds should
not be under 3000 counts per minute for children. The current lack of consensus on
thresholds limits comparability between studies and caution is required when
interpreting results.
2.6.8.2 Epoch Length
In the majority of studies discussed, the accelerometer is set to record activity every
minute (i.e. a 60s epoch) as this allows collection of data for a longer period of time.
Therefore, all activity or accelerations recorded in one minute will be stored as a sum
value for that minute. This one minute epoch can be problematic, especially when
monitoring children’s activity. Children display sporadic bouts of activity (Bailey et
al., 1995), here in lies the problem of a one minute epoch for children. A child may be
performing a vigorous bout of activity for < 10 seconds of a given minute, but be
fairly inactive for the other 50 seconds. When the data is summed over the minute,
that minute may only be recorded as light or moderate intensity activity. These
sporadic movements throughout a day can lead to the underestimation of time spent in
moderate and vigorous activities (Trost, McIver, & Pate, 2005). Therefore, it is
recommended that epoch length should be reduced to ≤ 10 seconds in order to reduce
the underestimation of time spent in the more intensive activity levels, when
monitoring children’s activity (Rowlands 2007).
2.6.8.3 Accelerometer Use, How Many Days?
Another consideration is how many days worth of data are needed to provide an
adequate estimate of someone’s typical activity pattern. Within children and youth
studies, recommendations range from 4-9 days of monitoring, which includes the
assessment of week and weekend days. Trost et al. (2005) suggests a 7-day protocol is
a sensible choice for youth studies, which should include both week and weekend
days, due to difference in physical activity measured during the week and weekend.
50
Related to this, is what constitutes a day. This is important as typically an
accelerometer will not be worn through a whole day. For example, an accelerometer
will be taken off for washing and water based activities, it may be taken off for
contact sports (rugby, martial arts), or there may be periods where the accelerometer
has not immediately been reattached (e.g. after changing clothes). Approaches to
define a day include activity during the waking hours of a day, or for a set time
period, 12 -24 hours (Ward, Evenson, Vaughn, Rodgers, & Troiano, 2005). Another
approach used in studies is to have a set number of hours that the monitor should have
been worn. For example, there must be ten hours worth of wearing time in a day. This
can be achieved through scrutiny of activity graphs or software that attempts to detect
periods of likely monitor removal. This is frequently done by identifying consecutive
minutes of zeros. There is no consensus on how many minutes of consecutive zeros
should be used to indicate monitor removal, with periods ranging from 10 – 180
minutes being reported (Rowlands, 2007).
2.6.8.4 Accelerometer: Measurement of Physical Activity Limitations
A limitation of this type of motion sensor is the inability to measure static movements
or dynamic movements of the arms and legs. In general, the sensor is positioned at hip
level in line with the centre of the thigh. Therefore any movement involving just the
arms (lifting of objects) or the legs (cycling) will not be captured. Also, water based
activities cannot be measured, as accelerometers are generally not waterproof.
Further, during contact sports (e.g. rugby, martial arts) a child may be instructed to
take off an accelerometer for safety reasons. The inability to measure these types of
activities may reflect in the loss of total activity counts and particularly time spent in
moderate and vigorous intensity activities.
2.6.8.5 Measurement of Children’s Physical Activity
Work by Rowlands et al., (2004) has shown the triaxial RT3 to be a valid measure of
physical activity for men and boys. Physical activity was measured by accelerometry
and oxygen consumption relative to body mass, during four treadmill speeds and three
non-regulated activities. RT3 vector magnitude counts for all activities were
positively correlated with oxygen consumption for the whole group (.85)
Further,
RT3 counts for all activities were highly predictive of oxygen consumption (R2 =
.73). Technical research by Powell and colleagues (2003; 2004) showed the test-retest
51
reliability of the RT3 to be good. However, due to variability between accelerometers,
it was recommended that inter-unit variability be tested before use. More recently
Krasnoff et al. (2008) concurred with Powell et al. with good intra unit reliability for
their sample of 22 RT3 monitors, but also stressed the poor reliability between units.
Sun, Schmidt, & Teo-Koh (2008) conducted a validation study measuring physical
activity of children in both outdoor and inside simulated free-living conditions. RT3
activity counts (vector magnitude) had strong positive correlations with heart rate,
indirect calorimetry, and accelerometry estimated energy expenditure, .73, .77 and .97
respectively. The results provided further support for the use of the RT3 as a measure
of physical activity in children.
The Actigraph is one of the most common uniaxial accelerometers used in research,
with many examples of its use in the literature, dating back to the CSA monitor, the
MTI, and currently the GT1M. This model of accelerometer has been shown to be a
valid and reliable physical measurement tool for pre-school children (Kelly, Reilly,
Fairweather, Grant, & Paton, 2004; Pate, Almeida, McIver, Pfeiffer, & Dowda, 2006),
children (Eston et al., 1998; Metcalf, Curnow, Evans, Voss, & Wilkin, 2002; Treuth et
al., 2004), and adults (Metcalf et al., 2002; Welk, Schaben, & Morrow, 2004).
Eston et al. (1998), assessed the measurement potential of the CSA accelerometer in
children aged 8 - 11. The CSA was positively correlated with scaled oxygen uptake
(.78), with regression analysis producing an R2 of 0.61. Activities in the assessment
were walking (4/6 km/h), running (8/10 km/h), crayoning, catching, and hopscotch.
Puyau et al. (2002) also assessed the CSA across a range of activities, sedentary
through to vigorous, in 26 children aged 6 – 16. The CSA was positively correlated
(.66) with oxygen uptake (room respiratory calorimetery). It was concluded that the
monitor was a valid and useful device for the assessment of children’s physical
activity.
2.6.8.6 Accelerometer Conclusions
Despite the discussed limitations, the use of accelerometers to objectively measure
children’s physical activity accurately has been validated on numerous occasions.
Further, there are review articles available to help with best practice when using
accelerometers for research (e.g. Trost et al., 2005; Ward et al., 2005), and to
52
highlight current issues with accelerometer assessment of physical activity in children
(Rowlands, 2007; Mattocks et al., 2008). In terms of ease of use and feasibility it is
suggested that the accelerometer is currently the physical activity measurement tool of
choice, particularly for children.
2.6.9 Physical Activity Measurement Combinations
In order to enhance measurement of physical activity in children and adolescents, it
seems common sense to apply a combination of the measurement methods discussed.
Currently, there has been a move to combine heart rate and accelerometry for the
purpose of objective monitoring of children’s physical activity. Limitations with
accelerometers are mainly biomechanical, whereas with heart rate, limitations are
mainly due to biological variance (Brage et al., 2004). With the limitations of these
two methods of physical activity measurement independent of each other, the
combination of the two may provide a more accurate measure of physical activity than
either method alone (Rowlands & Eston, 2007). The Actiheart is a small heart rate
recorder with an integrated omnidirectional accelerometer, and is attached to the chest
via two electrocardiogram electrodes. The Actiheart is able to measure heart rate,
heart rate variability, and acceleration in epochs of 15, 30 and 60 seconds. Validation
studies (Corder et al., 2007; Corder, Brage, Wareham, & Ekelund, 2005) have shown
the Actiheart to more accurately predict energy expenditure compared to
accelerometry or heart rate individually. These results suggest the Actiheart has great
potential as a measure of physical activity and energy expenditure in free-living
children (Trost, 2007). Further, the inclusion of self-report measures would provide a
record of what type of physical activities are being performed. However, the current
cost of the Actiheart limits it use to small-scale studies, but potentially could provide a
criterion measure field-based physical activity (Rowlands & Eston, 2007).
2.7 Physical Activity and Psychological Well-being
The positive benefits of regular exercise and physical activity on adult psychological
health have been highlighted (Fox, Stathi, McKenna, & Davis, 2007; Penedo & Dahn,
2005; Saxena et al., 2005). For children, these positive benefits have also been
highlighted (Calfas & Taylor, 1994; Ekeland et al., 2005; Larun et al., 2006; Strong et
al., 2005). However, studies have focused on different variables, with few
conceptualising psychological well-being as a multidimensional construct (Parfitt &
53
Eston, 2005). Further, due to feasibility issues, many studies employ a self-report
measure of children’s physical activity.
To try and overcome limitations of previous studies, Parfitt and Eston (2005) carried
out the first study utilising pedometers as a physical activity measurement tool, when
investigating the relationship between children’s habitual activity levels and
psychological well-being. It was rationalised that few studies have measured the
positive and negative constructs of psychological well-being (anxiety, depression,
self-esteem), with a need to consider the influence of habitual physical activity on
these constructs.
Results supported the hypothesis that physical activity is negatively associated with
anxiety and depression and positively associated with global self-esteem.
Furthermore, it represented the first use of an objective measure of physical activity
over a seven day period when assessing the relationship between physical activity and
children’s psychological well-being. However, there were limitations in the Parfitt &
Eston (2005) study. There was an inability to measure and discuss the intensity of the
accrued physical activity. Many studies assessing relationships between psychological
well-being constructs and physical activity employ a measure of total/daily activity.
Given current health guidelines suggest children accrue time spent in MVPA and
decrease time spent in sedentary behaviour, time spent in different physical activity
intensities should be assessed in relation to psychological well-being. As discussed in
this literature review, total activity and time spent in MVPA have shown positive
associations with self-esteem and negative associations with anxiety and depression,
with relationships at the lower intensities overlooked. Potentially, the reverse
relationships may occur with time spent in lower intensities, which warrants
investigation.
Study 1 of this thesis replicates Parfitt & Eston’s (2005) study by assessing the
relationship between children’s psychological well-being (anxiety, depression, global
self-esteem) and habitual physical activity. In order to assess relationships with total
physical activity and time spent in different physical activity intensities,
accelerometry was used as the physical activity measure. Further investigation into
the relationship between children’s psychological well-being and physical activity
54
incorporates an expanded model of self-esteem. Given that the model of self-esteem is
multidimensional and hierarchical, Study 2 assesses whether the current sample of
children has an adequate fit with the proposed models of self-esteem. Study 3 assesses
the relationship between children’s psychological well-being and physical activity,
which includes the domains of self-esteem and the sub-domains of physical selfworth. Study 4 employs a longitudinal design and assesses, through the use of multilevel modelling, the direction of relationships between children’s psychological wellbeing and physical activity. Based on the results of the previous studies, Study 5
primarily aimed to change the time children spent in physical activity intensities, and
investigates whether this leads to improved psychological well-being.
55
Chapter 3: General Methods Section
The following describes the general methods and instruments utilised in studies 1 – 5
to reduce repetition.
3.1 Subject recruitment, sampling and power calculations
Cohort 1: 80
children
recruited from
three Schools
59 provided
data for
Study 1
59 provided
data for
Study 2
57 provided
data for
Study 3
59 provided
data for
Study 4
Cohort 2: 35
children
recruited from
two schools
16 provided
data for Study
5
Figure 3.1 Subject recruitment and sampling for study chapters.
For studies 1-3 (alpha 0.05, power .80) an n 160 would detect a small effect (r = 0.1),
an n of 80 would detect a medium effect (r = 0.3) and an n 27 would detect a large
effect (r = 0.5) (Cohen, 1988). (Study 4 multi-level modelling, no power calculation).
For Study 5 (alpha 0.05, power .80) an n of 138 would detect a small effect (f = 0.1),
an n of 22 would detect a medium effect (f = 0.25) and an n of 16 would detect a large
effect (f = 0.4) (Cohen, 1988).
56
3.2 Assessment of Psychological Well-being (Studies 1-5)
The State-Trait Anxiety Inventory for Children (Speilberger et al., 1973): The
inventory assesses state and trait anxiety in children. As the research question was
concerned with the chronic relationship between physical activity and psychological
well-being, only items for the trait scale were completed. The state measure is adopted
when acute relationships are of interest. The inventory required the children to read
twenty individual statements and then decide whether they ‘hardly ever’, ‘sometimes’
or ‘often’ feel this way. A cross is placed in a box next to the word that describes how
they usually feel. The scoring allocated to ‘hardly ever’, ‘sometimes’ and ‘often’ is 1,
2, and 3 respectively for each statement. Inventory scores range from 20-60, with a
higher score representing higher anxiety. Statements in the inventory include ‘I worry
about what others think of me’, ‘I feel troubled’, and ‘I worry about making
mistakes’. Published alpha coefficients for children range, from .78 to .81
(Speilberger et al., 1973) and for the age group 9-11, .76 (Parfitt & Eston, 2005). Testretest reliability of the trait scale has shown a moderate coefficient range .65-.71
(Speilberger et al., 1973).
Child Depression Inventory (Kovacs & Beck, 1977): The inventory is composed of 27
items. Each item has three statements that are categorized by severity and are assigned
a numerical value from 0 to 2. For each item the child has to select the statement that
best describes him/her. For example, ‘things bother me all the time’, ‘things bother
me many times’, ‘things bother me once in a while’. The total score for the inventory
ranges from 0-54. However, one item (item 9, suicide ideation item) was removed
from the inventory as it was deemed unnecessary, thereby reducing the range of
scoring to 0-52. This is recognised as an acceptable procedure (Kovacs & Beck,
1977). Higher scores represent less favourable levels of depression. Good reliability
data (Cronbach’s alpha) for children have been observed, from .83 to .89 (Smucker,
Craighead, Craighead, & Green, 1986) and .77 (Parfitt & Eston, 2005). Adequate testretest reliability has also been shown, .74 to .77 (Smucker et al., 1986).
Self-Perception Profile for Children and Children and Youth’s Physical SelfPerception: In order to get a more robust understanding and measure of the children’s
self-perception the SPPC (Harter, 1985) was amalgamated with the CY-PSPP
(Whitehead, 1995). The SPPC assesses self-perception globally, which includes
57
subscales referring to scholastic, social and behavioural perceptions. Whereas the CYPSPP fundamentally assesses physical and sporting perceptions, it is suggested that
the use of both inventories would give a more complete measure of a child’s selfperception.
Self-Perception Profile for Children: The inventory contains the global self-esteem
construct, along with five specific domains: scholastic competence; social acceptance;
athletic competence; physical appearance; and behavioural conduct. Each subscale
has six items, giving a total of 36 items. Using a structured alternative format, the
child has to decide which statement is true for him/her and then whether it is ‘really
true’ or ‘sort of true’. Items include: ‘Some kids are popular with others their age’
BUT ‘other kids are not very popular’; and ‘some kids are often unhappy with
themselves’ BUT other kids are pretty pleased with themselves’. Item scores range
from 1 – 4, with scale scores ranging from 6 – 24. Higher scores reflect more positive
perceptions. Adequate Cronbach’s alpha has been shown across the six subscales,
ranging from .71 to .86 (Harter, 1985).
Children and Youth’s Physical Self-Perception Profile: The inventory was
developed from Fox & Corbin (1989) physical self-perception profile. The inventory
includes the domains of global general self-esteem and global physical self-worth.
Four subscales represent the sub-domains of physical self-worth: sport/athletic
competence; physical condition; attractive body; and strength competence. Each
subscale has six items, giving a total of 36 items.
A structured alternative format is used, with the child deciding which statement is true
for him/her and then whether it is ‘really true’ or ‘sort of true’. Items include: Some
kids do very well at all kinds of sports BUT other kids don’t feel that they are very
good when it comes to sports; and some kids don’t feel very confident about
themselves physically BUT other kids feel really good about themselves physically.
Item scores range from 1 – 4, with scale scores ranging from 6 – 24. Higher scores
reflect more positive perceptions. Previous work provided good alpha reliability,
ranging from 0.77 to 0.91 (Welk et al., 1997). Similar results (0.77) were also found
by Parfitt and Eston (2005). Further, both the factorial and predictive validity of the
58
CY-PSPP, for use with young children (8 year olds), has been supported (Welk &
Eklund, 2005).
Importance Scales: Both the SPPC and CY-PSPP contain an importance scale,
which assess the importance attached to each self-esteem domain and physical selfworth sub-domain measure, respectively. For the SPPC, importance measures of
scholastic competence; social acceptance; athletic competence; physical appearance;
and behavioural conduct are assessed. For the CY-PSPP, importance measures of
sport/athletic competence; physical condition; attractive body; and strength
competence are assessed. Each subscale has two items, giving a total of 12 items for
the SPPC and 8 items for the CY-PSPP.
Again a structured alternative format is used, with the child deciding which statement
is true for him/her and then whether it is ‘really true’ or ‘sort of true’. Items include:
Some kids think it’s important to be popular BUT other kids don’t think being popular
is all that important to how they feel about themselves; and some kids think it’s
important to be good at sport BUT other kids don’t think how good you are at sport is
that important. Item scores range from 1 – 4, with scale scores ranging from 2 – 8.
Higher scores reflect more importance attached to a domain or sub-domain.
In order to reduce the size of the amalgamated questionnaire and therefore ease the
burden on the children, replicated items or scales were removed. This led to the
removal of one set of global self-worth scales. Also the SPPC’s athletic competence
subscale was removed as it was deemed identical to the CY-PSPP’s sport/athletics
subscale. The final self-perception questionnaire contained 60-items, with the
importance scale containing 16-items.
59
3.3 Internal Consistency
Tables 3.1-3.3 display recommended acceptable Cronbach’s alpha levels (Kline,
1999) for the psychological well-being constructs measured.
Table 3.1: Cronbach’s alpha figures for anxiety, depression and
self-esteem (data collection one, study 1 & 2)
Anxiety
Depression
Global Self-esteem
Scholastic Competence
Social Acceptance
Physical Appearance
Behavioural Conduct
Physical Self-worth
Sport/Athletic Competence
Stamina/Condition Competence
Attractive Body
Strength Competence
.89
.89
.73
.75
.80
.86
.71
.83
.78
.68
.83
.85
Table 3.2: Cronbach’s alpha figures for anxiety, depression and
self-esteem (data collection two, study 3)
Anxiety
Depression
Global Self-esteem
Scholastic Competence
Social Acceptance
Physical Appearance
Behavioural Conduct
Physical Self-worth
Sport/Athletic Competence
Stamina/Condition Competence
Attractive Body
Strength Competence
.90
.89
.80
.83
.89
.86
.82
.92
.88
.86
.91
.90
Table 3.3: Cronbach’s alpha figures for anxiety, depression and
self-esteem (data collection three, incorporated in study 4)
Anxiety
Depression
Global Self-esteem
Scholastic Competence
Social Acceptance
Physical Appearance
Behavioural Conduct
Physical Self-worth
Sport/Athletic Competence
Stamina/Condition Competence
Attractive Body
Strength Competence
.89
.85
.89
.82
.91
.86
.82
.93
.87
.92
.91
.90
60
3.4 Assessment of Physical Activity (Studies 1, 3, 4)
Each child wore an RT3 triaxial accelerometer (Stayhealthy Inc. Monrovia, CA). The
RT3 accelerometer measures activity in three dimensions. The vectors are as follows:
vertical (x), anteroposterior (y), and mediolateral (z). Size and mass (including
battery) of the RT3 are 7.1 X 5.6 X 2.8 cm, 65.2g. The epoch interval was set at 1
minute. Previous research (Powell & Rowlands, 2004) has shown no difference in hip
placement of RT3 (left vs right) on activity counts. The RT3 has also been found to be
a reliable (Powell, Jones, & Rowlands, 2003; Powell & Rowlands, 2004) and valid
(Rowlands, Thomas, Eston, & Topping, 2004) tool for the assessment of activity, for
both adults and children.
For the present study, inter and intra-unit variability was tested while walking at 5.5
kph on a treadmill over two ten minute trials. The inter-unit coefficient of variation
(CV) was 7%, the intra-unit CV averaged 2% (range = 0.1 – 5.1%).
The
accelerometer was worn on the hip (approximately mid-line to the thigh) from when
each child got up until s/he went to bed at night. The children were instructed to keep
the accelerometer in the attachment clip to minimise the risk of data loss due to the
opening of the battery compartment.
To be included in the analyses, at least four days of data, including at least one
weekend day, needed to be recorded. To be classified as a day, a weekday needed to
have at least ten hours of data, with a weekend day needing at least eight hours of
data. Data were analysed from 07:00am until 22:00pm. The above criteria were based
on the reviews of accelerometer use in physical activity (see Trost et al., 2005; Ward
et al., 2005). In order to provide estimates of time spent at different physical activity
intensities, minute by minute epochs were converted into METS (Rowlands et al.,
2004; see Table 3.4), with average daily accumulated minutes measured.
61
Table 3.4: RT3 activity counts and physical activity intensity
Intensity
METs Equivalent
Sample Activity
Very Light
Accelerometer
Counts/mins
100.0 to 470.1
up to 1.9 METs
Light
288.4 to 976.8
1.9 – 3 METs
Seated to Standing
(minimal movement)
Playing catch
Moderate
976.8 to 2337.2
3 – 6 METs
Walking
Vigorous
> 2337.2
> 6 METs
Running
3.5 Assessment of Socio-economic Status (Studies 1, 3, 4)
In order to control for socio-economic status, each child’s postcode was put into the
http://neighbourhood.statistics.gov.uk/dissemination/ web page. The postcode then
relates to a lower-layer super output area (LSOA), which contains approximately 500700 households. An Index of Multiple Deprivation score for the LSOA is then
provided, ranging from 1 - 32,482. The lower the score the more deprived the area.
The index of multiple deprivation score is produced from separate deprivation indices
for income; employment; education, skills and training; health; barriers to housing
and services; the living environment; and crime. All scores are from the year 2004.
3.6 Assessment of Anthropometric Data (Studies 1, 3, 4, 5)
The children’s height and sitting height were measured using a stadiometer (SECA,
UK). Mass and percent body fat were assessed using Tanita TBF-305 body
composition scales (Tanita UK Ltd., Middlesex, UK).
3.7 Normality of Data (Studies 1, 3, 4)
Tables 3.5 & 3.6 present the Z scores for skewness and kurtosis for chapter 4 – 7.
Tabachnick & Fidell (2001), state that alpha levels of .001 indicate significant
skewness and/or kurtosis for small to moderate sample sizes. There appears to be a
problem with particularly positive skewness for the depression measure. Where
skewness occurred log transformation was utilised to reduce the positive or negative
skew.
62
Table 3.5: Skewness and Kurtosis for psychological constructs
Data Collection One
(Studies 1 & 2)
Zskewness
Zkurtosis
Data Collection Two
(Study 3)
Zskewness
Zkurtosis
Data Collection Three
(Incorporated in Study 4)
Zskewness
Zkurtosis
Anxiety
2.38
-.439
1.36
-.900
1.74
-1.24
Depression
4.75
3.52
3.86
1.79
3.03
.507
Global
Self-esteem
Scholastic Competence
-.588
-1.63
-2.54
.748
-2.57
-.162
-.102
.055
-1.47
-.467
.061
-1.01
Social
Acceptance
-3.56
3.70
-2.96
.390
-2.52
.093
Physical Appearance
-2.02
-.142
-2.04
-.424
-2.08
.183
Behavioural Conduct
.878
-.065
-.415
-1.10
.636
-.914
Physical Self-worth
-.457
-.931
-1.69
-.496
-1.80
-.105
Sports/Athletic
Competence
Stamina/Condition
Competence
Attractive
Body
Strength Competence
1.27
.351
-1.60
-.838
-2.45
.564
-.653
-.393
-1.24
-.717
-2.27
.415
-.691
-.865
-1.46
-.334
-2.17
.415
-.459
.657
-.418
-.616
.315
-.997
Note: values greater than ±3.29 indicate significant skewness/kurtosis at the .001 level.
Table 3.6: Skewness and Kurtosis for physical activity measures
Data Collection One
(Studies 1 & 2)
Zskewness
Zkurtosis
Data Collection Two
(Study 3)
Zskewness
Zkurtosis
Data Collection Three
(Incorporated in Study 4)
Zskewness
Zkurtosis
Average Daily Activity
Time spent in Sedentary
Intensity
Time spent in Very
Light Intensity
Time spent in Light
Intensity
Time spent in Moderate
Intensity
2.14
-1.25
-.243
1.53
.788
-1.55
-1.49
-.133
1.74
-.429
-1.24
-1.19
.877
-1.07
1.09
-1.05
1.29
.246
.299
-.346
.753
-1.23
.761
-.329
2.70
.998
.972
-.801
2.30
2.16
Time spent in Vigorous
Intensity
3.32
1.19
2.12
1.00
.752
-.783
Note: values greater than ±3.29 indicate significant skewness/kurtosis at the .001 level.
63
Chapter 4
Study 1: The Relationship Between Children’s Psychological Well-being and
Time Spent In Physical Activity Intensities
4.1 Introduction
Research supports the hypothesis that physical activity is related to various individual
psychological well-being variables in both adults and children, including anxiety,
depression, (Larun et al., 2006; Mutrie & Parfitt, 1998) and self-esteem (Crocker et
al., 2000; Ekeland et al., 2005; Raudsepp et al., 2002). However, the relationships
between psychological well-being and physical activity in children are not clear, and
are affected by similar research problems as those which affect the relationships
between physical activity and physical health (for example, what is assessed and how
it is assessed; for reviews see (Dishman et al., 2001; Livingstone et al., 2003).
To fully reflect psychological well-being, both negative and positive psychological
states need to be assessed (Masse et al., 1998). In order to overcome the previous
physical activity and psychological well-being measurement limitations when
assessing children, Parfitt and Eston (2005) explored the relationship between positive
and negative well-being variables and habitual physical activity in children. Seventy
children aged 9-10 years wore pedometers above the hip, over a seven-day period.
The summed total provided the physical activity measure. Anxiety, depression and
global self-esteem inventories were also completed.
Correlation analyses revealed that habitual physical activity had a strong positive
association with global self-esteem (r=0.66, p<0.01), and negative associations with
depression (r=-0.60, p<0.01) and anxiety (r=-0.48, p<0.01). However, using partial
correlations to explore unique relationships, once the variance of each of the other two
variables was accounted for, the significant relationships for anxiety and depression
were removed, but remained for self-esteem (r=0.36, p<0.01). Further, when groups
were created based upon activity level, analysis of variance revealed children
achieving >12000 steps/day had more positive psychological profiles than children
achieving <9200 steps/day.
64
Based upon published physical activity guidelines (Biddle et al., 1998), only 25% of
boys and 30% of girls accumulated the recommended 60 minutes of moderate
intensity activity per day. Interestingly, in the Parfitt and Eston (2005) study those
children achieving (or close to) these guidelines had significantly more positive
psychological profiles (significantly lower anxiety and depression and higher selfesteem) than those children not achieving the guidelines. It was concluded that the
psychological well-being components were each related in the hypothesised direction
to habitual physical activity (anxiety and depression negative; self-esteem positive).
Limitations of Parfitt and Eston’s (2005) study included the inability to measure and
discuss the intensity of the accrued physical activity. Furthermore, socio-economic
status (SES) was not investigated as a potential confounding variable. As previously
discussed (p. 41) SES could affect the relationship between physical activity and
psychological well-being.
Uniaxial and triaxial accelerometers facilitate temporal tracking of the frequency,
intensity, and the duration of activity (Eston et al., 1998). Work by Rowlands and
colleagues (Rowlands & Eston, 2005; Rowlands et al., 1999; Rowlands et al., 2000)
show both the accelerometer and pedometer to be reliable and valid tools for the
measurement of physical activity in children. Furthermore, the accelerometer has the
potential to provide data relating to minutes accumulated in different activity levels
(sedentary through to vigorous).
The first study of this thesis replicates Parfitt and Eston’s (2005) study with
accelerometry used to measure physical activity. This allowed estimation of total
daily physical activity and time spent in various activity intensity levels (very light,
light, moderate, vigorous), to assess psychological well-being – physical activity
relationships beyond the measures of total activity and time spent in MVPA of
previous research. A measure of SES was also included to assess whether it had an
effect on any psychological well-being – physical activity relationships. It was
hypothesised that differing relationships would occur between the various intensity
levels and the psychological well-being constructs (anxiety, depression, global selfesteem). These exploratory hypotheses would provide an initial insight into the use of
activity intensity and the relationship with psychological well-being in children.
65
4.2 Method
General Procedures
Ethical clearance was granted by the School’s Ethics Committee (reference 13/12/05
#2) before commencement of the study. Participants were 82 children (32 boys and 50
girls) from three primary schools in the East Devon area. The children’s ages ranged
between 9 and 10 years. Information letters outlining the study, and what would be
required from the parents and children, were sent home with the children, along with
consent forms. A total of 82 consent forms, signed by a parent and the child
participant, were returned to the schools prior to the study commencing.
All data were collected on school premises in a classroom environment. Children
completed the three psychological well-being inventories. The State-Trait Anxiety
Inventory for Children (STAIC) (Speilberger et al., 1973), the Child Depression
Inventory (CDI) (Kovacs & Beck, 1977), and the global self-esteem construct of the
Self-Perception Profile for Children (SPPC) (Harter, 1985). These inventories all
provided trait measures of the variables being assessed. The researcher was present to
answer any questions the children had about the questionnaires. SES and
anthropometric data were also collected; the children’s age, height, sitting height,
mass and percent body fat were recorded. The children were then given the RT3
accelerometers, which were used for the assessment of the children’s physical
activity. The RT3 accelerometers were worn by the children for a seven-day period.
After the seven days period, the researcher returned to collect the RT3 accelerometers.
All data were collected within a two-month time period (mid-March – mid-May
2006), on four collated 7-day sessions (approximately 20 participants per sessions).
Details of the measures used are given in the General Methods section (Chapter 3).
Data Analysis
Descriptive data (mean and SD) were calculated for age, anthropometric measures,
accelerometer counts and minutes spent at each intensity level. Independent t-tests
were performed to identify any gender differences across the descriptive data
variables. Following the t-tests and scrutiny of scatter plots (to confirm relationships
were similar across gender), data for the boys and girls were collapsed and Pearson’s
correlations (r) were used to assess the relationships between the psychological well66
being constructs and physical activity. This was followed by partial correlations to
explore whether relationships persisted after controlling for socio-economic status. To
explore the quantity of activity associated with more positive psychological profiles,
gender specific tertile groups for habitual physical activity and physical activity
intensity were created when significant correlations with psychological well-being
constructs were present. The quantity of activity associated with positive
psychological well-being was explored using a series of 2 (gender) X 3
(activity/intensity groups) analyses of covariance (SES was the covariate). Tukey’s
post hoc tests were used to follow up significant effects. All data analyses were
performed using the SPSS (13.0) statistical package, with alpha set 0.05.
4.3 Results
A total of 59 participants (24 boys, 35 girls) provided adequate (≥ four days of
accelerometer measurement, including one weekend day) data for analysis. T-tests
revealed no difference in anthropometric measures, and no differences for average
daily activity or time spent in different physical activity intensities, between boys and
girls (Table 4.1).
Table 4.1: Descriptive data for boys and girls
Age (yr)
Height (cm)
Mass (kg)
Body Fat %
Overall Count
Sedentary (mins)
Very Light (mins)
Light (mins)
Moderate (mins)
Vigorous (mins)
Boys (n = 24)
Mean
SD
9.5
0.5
145.9
21.6
36.1
7.2
17.4
7.4
399734
89882
981.5
60.4
219.7
34.9
108.7
18.9
90.4
38.7
24.4
20.0
Mean
9.5
139.8
36.4
21.1
370106
976.6
229.0
116.3
88.9
29.4
Girls (n = 35)
SD
0.5
7.5
9.6
8.5
81160
56.1
30.2
19.9
18.2
15.7
Correlation analysis (Table 4.2) revealed no associations for anxiety, depression, and
global self-esteem with total daily physical activity. Only the time accumulated in
very light intensity showed an association with psychological well-being. The very
light intensity was negatively associated (r = -.262, p = 0.045) with measures of
67
global self-esteem. However, partial correlations controlling for socio-economic
status removed the global self-esteem (r = -.201, p = 0.131) association with very
light intensity activity.
Table 4.2: Correlation analyses for relationships between physical activity intensity
and psychological well-being
Total Daily
Very
Light
Moderate
Vigorous
Activity
-.198
Light
.052
-.050
-.145
-.212
Depression
-.022
.093
.081
.098
-.115
Global Self-esteem
-.014
-.262*
-.137
-.002
.057
Anxiety
* = p < 0.05
Although the global self-esteem correlation became non-significant after controlling
for socio-economic status, it was deemed prudent to run an ANCOVA (SES as the
covariate) to provide further insight into the physical activity/well-being relationship.
An ANCOVA 2 (gender) X 3 (time spent in very light intensity activity tertile groups)
revealed there was no gender by intensity group interaction. There was a main effect
for very light intensity on global self-esteem (F(2,52)=3.26, p<0.05). Post hoc tests
indicated that measures of global self-esteem were significantly higher in the low
minutes group (191 minutes) than the high minutes group (262 minutes). There was
no gender main effect.
4.4 Discussion
The purpose of this study was to assess the relationship between children’s
psychological well-being and physical activity, while expanding the physical activity
measure to incorporate physical activity intensities. It was hypothesised that differing
relationships would occur between the various intensity levels and the psychological
well-being constructs (anxiety, depression, global self-esteem). Only the hypothesis
for self-esteem was supported; in accordance with previous literature (Parfitt & Eston,
2005), global self-esteem is suggested to be the dominant construct of those
psychological well-being constructs assessed. Global self-esteem was negatively
associated with time spent in very light intensity activity, with anxiety and depression
having no relationships with physical activity. Unlike the Parfitt & Eston (2005)
68
study, there was no association between total physical activity tertile groups and
psychological well-being. However, children who spent less time in very light
intensity activities displayed higher global self-esteem scores, after controlling for
SES. This may suggest the promotion of less time in very light activities in order to
improve psychological well-being for this age group.
In contrast to previous literature (Rowlands et al., 1999; Rowlands & Eston, 2005)
boys were no more active than girls. This lack of difference was also found by Parfitt
and Eston (2005). One explanation for this could be the boys included in the analysis
are not as representative a sample compared to other literature. Furthermore, this
cross-sectional data does not take into account seasonal variation in physical activity
and provides a less valid measure of physical activity. This suggests seasonal
variation may need to be taken into account
With only an association between global self-esteem and time spent in the very light
intensity activity being observed, this suggests the measure of global self-esteem
should be expanded in future research. The mechanisms of the psychological wellbeing – physical activity relationship may occur below the apex of the hierarchical
self-esteem model, as proposed in various hierarchical models (Fox & Corbin, 1989;
Harter, 1985; Shavelson et al., 1976; Sonstroem et al., 1994). In particular, the subdomains of physical self-worth have been theorised (Fox & Corbin, 1989; Whitehead,
1995), and shown to be associated with children’s physical activity (Crocker et al.,
2000; Raudsepp et al., 2002; Raustorp et al., 2005). As previously discussed (p. 21),
self-esteem becomes more situation specific at lower levels of the self-esteem
hierarchy. Therefore, these sub-domain constructs may provide sub-domain by
intensity specific associations.
Before the expansion of self-esteem is incorporated into a study, it is necessary to
investigate the self-esteem hierarchical model of the current sample, in order to assess
the fit of the hierarchical model in accordance with previous theorised self-esteem
models (Harter, 1985; Fox & Corbin, 1989; Whitehead, 1995). This is warranted, as a
lack of a theorised model for the current sample, would affect or negate the selfesteem processes from the base to the apex of the hierarchy, if associated with
physical activity. Therefore, this is the basis of chapter 5 (Study 2).
69
Chapter 5
Study 2: Global Self-esteem and Physical Self-worth Models and Measurement
Introduction
The validity and reliability of the models and measurement of self-esteem and
physical self-worth have been routinely assessed and scrutinised over the last few
decades (Harter, 1982, 1985; Marsh et al., 1994; Muris et al., 2003; Welk & Eklund,
2005; Whitehead, 1995). Often of review is the relationships between and predictive
ability of domain and sub-domains to global and physical self-worth, and the
hierarchical framework of physical self-worth.
For Harter (1985) self-esteem is a multidimensional construct. Specifically with
children, five domains were identified (scholastic & athletic competence; social
acceptance; physical appearance; behavioural conduct), which have been shown to be
associated with, and predictive of global self-esteem (Harter, 1985; Muris et al.,
2003). The most commonly used measure of children’s self-esteem is the SelfPerception Profile for Children (SPPC: Harter, 1985).
Within the area of the physical self, Fox and Corbin (1989) devised the Physical SelfPerception Profile (PSPP), with physical self-worth cast in a hierarchical framework.
It was suggested that physical self-worth would function as a superordinate among the
four sub-domains (athletic/sports competence; physical condition; attractive body;
strength competence). Physical self-worth was shown to have the strongest
relationship with global self-esteem, and mediate between sub-domain scores and
global self-esteem (Fox & Corbin, 1989). The adapted version of the PSPP for use
with children, the Children and Youth Physical Self-Perception Profile (CY-PSPP:
Whitehead, 1995), displayed similar results to Fox and Corbin (1989). Again, physical
self-worth was shown to have the strongest relationship with global self-esteem, and
mediate between sub-domain scores and global self-esteem (Whitehead, 1995).
Attached to the self-esteem inventories discussed, are ratings of perceived importance.
The importance scales provide a measure of importance attached by individuals to
their respective levels of domain and sub-domain competence/adequacy. As
previously discussed (p. 26), discrepancies are formed when an individual places high
70
importance to a domain or sub-domain where competence is low. Harter (1985), Fox
(1990) and Whitehead (1995) have all displayed similar associations. As competenceimportance discrepancies scores increased, physical self-worth and global self-esteem
scores decreased, as represented in the Figure 5.1.
-2
Mean Discrepancy Score
-1.8
-1.6
-1.4
-1.2
-1
Series1
-0.8
-0.6
-0.4
-0.2
0
'1.0-1.5
'1.6-2.0 '2.1-2.5
'2.6-3.0 '3.1-3.5
'3.6-4.0
Self-esteem Scores
Figure 5.1: Normative data on the relationship between self-esteem and the
competence/importance discrepancy score (adapted from Harter, 1999. pp.150).
However, inadequacies in the measurement of importance have been noted.
Whitehead (1995) failed to find a factor structure for the Children’s Perceived
Importance Profile, suggesting items may have been poorly written, coupled with
reservations about the factorial validity of importance measures within the research
community. In fact, with importance measures containing only two items, high alphas
are rarely produced suggesting the items may be unable to sufficiently capture the
abstract and subtle nature of importance to self-esteem (Fox, 1997).
The primary purpose of this study using 9-10 year old British children was to assess
the applicability of the current data, in line with self-esteem theory and models. This
includes assessing the multidimensionality of self-esteem, and the three-tier
hierarchical structure of physical self-worth. A secondary purpose of the study was to
assess physical self-worth as a mediating variable between global self-esteem and the
physical self-worth sub-domains, and assess self-esteem importance discrepancies. It
71
was hypothesised that the domains of self-esteem would be associated with and
predictive of global self-esteem. Physical self-worth sub-domains would be associated
and predictive of physical self-worth. Further, physical self-worth would mediate the
relationship between sub-domains and global self-esteem.
5.2 Method
General Procedures
Ethical clearance was granted by the School’s Ethics Committee (reference 13/12/05
#2) before commencement of the study. Participants were 59 children (24 boys and 35
girls) from three primary schools in the South West of England. The children’s ages
ranged between 9 and 10 years. Children completed a combined version of the SelfPerception Profile for Children (SPPC; Harter, 1985) and the Children and Youth
Physical Self Perception Profile (CY-PSPP; Whitehead, 1995). These inventories all
provided trait measures of the variables being assessed. The researcher was present to
answer any questions the children had about the questionnaires.
Details of the measures used, are given in the General Methods section (Chapter 3).
Data Collection
The children completed a psychological well-being inventory to assess global selfesteem, physical self-worth and the associated domain and sub-domain measures. The
children also completed the self-esteem importance inventories, assessing the
importance attached to domain and sub-domain constructs. The researcher was
present to answer any questions the children had about the questionnaires.
Data Analysis: Descriptive data provided means and standard deviations for all
esteem, domain and sub-domain measures. Cronbach’s alpha’s were produced to
assess the internal consistency of each self-esteem and importance measurement scale.
As it is suggested that a ratio of 40 participants to one predictor variable be used for
stepwise regression, forced entry regression was used, as the suggested ratio 5:1 is
more applicable to the current data (Vincent, 1999). Forced entry multiple regression
was used to show the relationship of all domain constructs together, with part
correlations displaying the unique contribution of each domain with global selfesteem for the (Harter, 1985) self-esteem model. The same procedures were then used
72
for the relationships of the physical self-worth sub-domains with physical self-worth
for the model proposed by Fox and Corbin (1989).
However, as the athletic
competence domain functions at a sub-domain level in the physical self-worth model,
it was replaced with the physical self-worth domain, at the domain level for the Harter
(1985) self-esteem model.
In order to test for the mediation properties of the physical self-worth construct, the
Baron and Kenny (1986) approach was adopted. This approach does have its critics, it
is suggested that only the conditions for mediation occur, rather than a statistical test
of the indirect effect of the independent variable on the dependent variable through
the mediating variable (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002;
Preacher & Hayes, 2004). In figure 5.2, it is suggested that a test of ab should address
mediation more directly than a series of separate significance tests not directly
involving ab (Preacher & Hayes, 2004), with the Baron & Kenny (1986) method
involving the analysis of pathways a and b separately. Although, the Baron & Kenny
(1986) method of mediation is a commonly used within the field of psychology and
provides an adequate test of the hypothesised theory.
Mediating
Variable (M)
a
Independent
Variable (X)
b
c´
Dependent
Variable (Y)
Note: Path a is the effect of the independent variable on the proposed mediator. Path b
is the effect of the mediator on the dependent variable, controlling for the independent
variable. Path c´ is the direct effect of the independent variable on the dependent
variable, controlling for the mediator.
Figure 5.2: Path diagram of mediation model
Two-factor analysis of variance was used to assess the difference in discrepancy
scores between low, medium and high global self-esteem and physical self-worth, and
between genders, to examine the evidence of the discounting hypothesis. All data
analyses were performed using the SPSS (13.0) statistical package, with alpha set
0.05, and data presented by gender.
73
5.3 Results
Table 5.1 highlights the mean scores and standard deviations of the esteem, domain
and sub-domains scales for boys (n = 24) and girls (n = 35). Table 5.2 highlight
Cronbach’s alpha’s for competence and importance scales
Table 5.1: Esteem scale means and standard deviations for boys and girls
Scale
Boys
Global Self-esteem
Scholastic Competence
Social Acceptance
Physical Appearance
Behavioural Conduct
Physical Self-worth
Sport/athletic Competence
Physical Condition
Attractive Body
Strength Competence
Girls
Global Self-esteem
Scholastic Competence
Social Acceptance
Physical Appearance
Behavioural Conduct
Physical Self-worth
Sport/athletic Competence
Physical Condition
Attractive Body
Strength Competence
Mean
SD
19.08
16.75
18.08
19.33
17.17
18.88
18.38
19.33
17.83
17.42
3.54
3.40
4.90
3.97
2.12
3.49
4.08
3.09
3.36
4.25
18.94
15.81
18.27
17.14
18.45
17.07
16.29
18.50
15.87
16.80
3.10
3.50
2.77
4.17
2.68
3.33
3.19
2.80
3.74
3.31
Table 5.2: Cronbach’s alpha figures for competence and importance scales of selfesteem
Global Self-esteem
Scholastic Competence
Social Acceptance
Physical Appearance
Behavioural Conduct
Physical Self-worth
Sport/Athletic Competence
Physical Condition
Attractive Body
Strength Competence
Competence
.73
.75
.80
.86
.71
.83
.78
.68
.83
.85
Importance
N.A.
-.01
.26
.55
.30
N.A.
.60
.28
.55
.63
74
Table 5.3 Forced entry regression and part correlations between global self-esteem
and global self-esteem domains
Boys
Forced Entry
Domains of
Global Selfesteem
Unique
Contribution
Scholastic
Competence
R
.916
Girls
R2
.839**
Adjusted
R2
.794
R
.825
R2
.681**
Adjusted
R2
.626
-.111
sr2
(part correlation)
.007
.024
sr2
(part Correlation)
.0005
Social
Acceptance
.402
.213**
.011
.00006
Physical
Appearance
.329
.101**
.245
.060*
Behavioural
Conduct
.039
.0004
.339
.070*
Physical
Self-worth
.326
.061*
.418
.097**
Total
Unique
Variability
.375
Shared
Variability
Total
Unique
Variability
.227
Shared
Variability
** = p < 0.01
* = p < 0.05
Contribution to
model
Domains of
Global Selfesteem
B
.464
B
.454
Table 5.3 highlights the results of the forced entry regression and part correlations of
boys and girls for the Harter (1985) model of self-esteem (with physical self-worth
replacing athletic competence). The results of the forced entry multiple regression
showed that scholastic competence, social acceptance, physical appearance,
behavioural conduct, and physical self-worth collectively accounted for 84% of the
variance in global self-esteem for boys (R2 = .839, p < 0.01) and 68% of the variance
in global self-esteem for girls (R2 = .681, p < 0.01). For boys, social acceptance (.213),
physical appearance (.101) and physical self-worth (.061) accounted for .375 of
unique variability in global self-esteem, with .464 shared between domains. For girls,
physical appearance (.060), behavioural conduct (.070) and physical self-worth (.097)
75
accounted for .227 of unique variance in global self-esteem, with .454 shared between
domains.
Table 5.4 Forced entry regression and part correlations between physical self-worth
and physical self-worth sub-domains
Boys
Forced Entry
Sub-domains of
Physical Selfworth
Unique
Contribution
Sport/athletic
Competence
R
.818
Girls
R2
.669**
Adjusted
R2
.599
R
.763
R2
.582**
Adjusted
R2
.526
-.035
sr2
(part correlation)
.001
.019
sr2
(part Correlation)
.0002
Physical
Condition
.008
.00003
.354
.052
Attractive
Body
.620
.198**
.526
.266**
Strength
Competence
.257
.045
.005
.00002
Unique
Variability
Shared
Variability
Unique
Variability
Shared
Variability
.198
.471
.266
.316
** = p < 0.01
* = p < 0.05
Contribution to
model
Sub-domains of
Physical Selfworth
B
B
The results of forced entry multiple regression (Table 5.4) showed that sport/athletic
competence, physical condition, attractive body, and strength competence collectively
accounted for 67% of the variance in physical self-worth for boys (R2 = .669, p <
0.01) and 58% of the variance in physical self-worth for girls (R2 = .582, p < 0.01).
For boys, only attractive body (.198) provided unique variability in physical selfworth, with .471 shared between sub-domains. Similarly for girls, only attractive body
(.266) provided unique variability in physical self-worth, with .316 shared between
sub-domains.
76
Table 5.5: Baron & Kenny (1986) test for mediation between physical self-worth subdomains and global self-esteem with physical self-worth domain the mediating
variable.
Boys
Girls
B
sig
B
sig
Athletic
Competence
Step 1: b(YX)
Step 2: b(MX)
Step 3: b(YM.X)
Step 4: b(YX.M)
.36
.46
.19
.18
.034
.019
.349
.140
.23
.44
.67
.07
.117
.012
.000
.600
Physical Condition
Step 1: b(YX)
Step 2: b(MX)
Step 3: b(YM.X)
Step 4: b(YX.M)
.47
.55
.55
.16
.048
.017
.010
.470
.43
.62
.64
.03
.021
.001
.000
.845
Attractive Body
Step 1: b(YX)
Step 2: b(MX)
Step 3: b(YM.X)
Step 4: b(YX.M)
.43
.82
.78
-.21
.048
.000
.010
.479
.42
.63
.64
.02
.002
.000
.001
.873
Strength
Competence
Step 1: b(YX)
Step 2: b(MX)
Step 3: b(YM.X)
Step 4: b(YX.M)
.36
.55
.60
.03
.034
.000
.020
.860
.48
.37
.56
.28
.002
.032
.000
.022
Note: Y = the dependent variable (global self-esteem), M = the mediator variable
(Physical self-worth), X = the independent variable (stated in italic). Step 3 = path b
on Figure 5.2, Step 4 = path c´ on Figure 5.2. For mediation to occur steps 1-3 should
display a significant effect, with step 4 displaying a non-significant effect.
Table 5.5 displays that for boys; physical self-worth mediates the associations
between global self-esteem and physical condition, attractive body, and strength
competence. For girls, physical self-worth mediates the association between global
self-esteem and athletic competence, physical condition, and attractive body. Athletic
competence is included as it has been suggested that step 1 is not necessary as this
outcome is implied if the other steps are met (Kenny, Kashy, & Bolger, 1998). Further
it is suggested that physical self-worth partially mediates the association between
global self-esteem and strength competence for girls, as step 4 is only required for
complete mediation (Kenny et al., 1998). The reduction in the size of the beta
supports this notion.
77
-8
Mean Discrepancy Score
-6
-4
-2
boys
girls
0
2
4
6
low
mid
Global Self-worth
high
Figure 5.3 Discrepancy scores for Global self-esteem level
Figure 5.3 shows the discrepancy scores plotted against low, medium and high levels
of global self-esteem for boys and girls. A two-way ANOVA revealed no main effect
for gender (f(1,53) = .107, p > 0.05) and global self-esteem level (f(2,53) = 2.942, p >
0.05), or interactive effect of gender and global self-esteem (f(2,53) = .279, p > 0.05).
-8
Mean Discrepancy Score
-6
-4
-2
boys
0
girls
2
4
6
8
low
mid
Physical Self-worth
high
Figure 5.4 Discrepancy scores for Physical self-worth level
78
Figure 5.4 shows the discrepancy scores plotted against low, medium and high levels
of physical self-worth for boys and girls. A two-factor ANOVA revealed no main
effect for gender (f(1,53) = .548, p > 0.05). A significant main effect occurred for
physical self-worth level (f(2,53) = 8.25, p < 0.01), with Tukey’s post-hoc test
revealing those with low physical self-worth having significantly worse discrepancy
scores (mean = -2.22, S.D. = 2.66) compared to the middle (mean = .146, S.D. = 1.91)
and high (mean = 1.54, S.D. = 2.42) physical self-worth discrepancy scores. There
was no interactive effect for gender and physical self-worth.
5.4 Discussion
Previous research has shown the domains of global self-esteem to be positively
associated and predictive of global self-esteem (Harter, 1985). Further, the subdomains of physical self-worth have been shown to be positively associated and
predictive of physical self-worth (Fox & Corbin, 1989; Whitehead, 1995). This study
aimed to replicate the assessment of these associations using a sample of 9-10 year
old British children. It was hypothesised that the domains of self-esteem would be
associated with and predictive of global self-esteem. Physical self-worth sub-domains
would be associated and predictive of physical self-worth. Further, physical selfworth would mediate the relationship between sub-domains and global self-esteem.
All hypotheses were supported; in accordance with previous literature (Harter, 1985;
Fox & Corbin, 1989; Whitehead, 1995), it has been demonstrated that domain and
sub-domain associations and the predictive ability of these (sub)domains exist in this
sample of children. Further, the mediating properties of the physical self-worth
domain and the role of importance were supported.
5.4.1 Global self-esteem and global self-esteem domains
All self-esteem domain measurement scales displayed the recommended Cronbach’s
alpha value of .7 or above (Field, 2005) with the exception of the behavioural conduct
scale, which was slightly below this figure (.68). Collectively, the domains of global
self-esteem, as theorised (Harter, 1985; Fox & Corbin, 1989) were associated and
predictive of global self-esteem. The prediction of global self-esteem from the
physical appearance domain for boys and girls is not too surprising and quite normal.
As previously discussed (p.28) Harter (1993) suggests there is a link between physical
79
appearance and global self-esteem, which is robust across the life span. Further,
perceptions of a persons looks can take priority over other domains as the number one
predictor of self-esteem. From an early age the physical-self appears to be a highly
prominent dimension that triggers evaluative psychological reactions that may be
integrated into the sense of one’s inner self (Harter, 1993).
For boys, global self-esteem was predicted by social acceptance, as children develop
their ability to make higher-order generalisations, evaluation of their abilities and
attributes emerges (Harter, Neil, & Paul, 2001). For example, generalisations
concerning the ability to make friends may include accounts of having school friends
and developing friendships at sporting events (Harter et al., 2001). For this sample of
boys it may be important to make friends and appear popular. Their age group is
among the oldest at their current school, and they are soon to be in a bigger school
with more children and adolescents to interact with. The lack of association between
scholastic competence and global self-esteem is not unexpected, given similar
findings by Marsh, Byrne, & Yeung (1999). However, the association can become
more prevalent as children become older, where the expectation of academic
achievement becomes relevant (Harter, 1999).
5.4.2 Physical self-worth and physical self-worth sub-domains
Collectively, the domains of physical self-worth, as theorised by (Fox & Corbin,
1989; Whitehead, 1995) were associated and predictive of physical self-worth. Only
the attractive body sub-domain displayed a unique prediction of physical self-worth,
for both boys and girls. This is in line with previous research displaying the attractive
body sub-domains strong association with physical self-worth (Whitehead, 1995;
Welk & Eklund, 2005). Indeed, as with physical appearance, and compared to the
other sub-domains, the body is always on show for evaluation from others (Fox,
2000).
5.4.3 Physical self-worth and mediation
The mediating properties of the physical self-worth variable were supported for all but
one (athletic competence for boys) of the sub-domains, when using the Baron &
Kenny (1986) method for testing mediation. Compared to previous literature
(Whitehead, 1995; Welk & Eklund, 2005) there was a lack of association between
80
sport/athletic competence and physical self-worth for boys. However, the small
sample of boys may have contributed to a lack of power in the analysis to find a
significant association. Indeed, the mean score of the boys sport/athletic competence
and physical self-worth (sport/athletic competence mean = 18.38, S.D. = 4.08;
physical self-worth mean = 18.88, S.D. = 3.49) in this study, is similar to the
Whitehead (1995; sport/athletic competence mean = 18.06, S.D. = 3.9; physical selfworth mean = 18.42, S.D. = 3.66) and Welk and Eklund (2005; sport/athletic
competence mean = 19.14, S.D. = 3.72; global self-esteem mean = 19.98, S.D. = 4.02)
studies.
Replacing athletic competence with physical self-worth at the domain level suggests
Harter’s (1985) model needs revision. Indeed, Fox and Corbin (1989) placed athletic
competence at the sub-domain level with physical self-worth functioning at the
domain level for their model of physical self-worth. Both boys and girls displayed a
unique prediction of global self-esteem from the physical self-worth domain, Indeed,
athletic competence appears to function better at the sub-domain level with physical
self-worth mediating it’s associations with global self-esteem (for girls).
5.4.4 Self-esteem and discrepancy scores
Although there was no significant main effect between discrepancy scores for low
medium and high levels of global self-esteem, there does appear to be a trend similar
to previous research (Harter, 1985; Fox, 1990; Whitehead, 1995). This pattern is
reinforced for physical self-worth. Those in the low physical self-esteem group
displayed significantly larger discrepancy scores compared to the middle and high
physical self-worth groups. This suggests, where physical self-worth sub-domain
scores were low, participants were unable to discount the influence of high
importance placed on these physical self-worth sub-domains. This would result in
lower physical self-worth scores (Fox, 1990). It should be noted the low Cronbach’s
alphas produced for the current study highlights a problem with the importance
measurement. This suggests that two items per scale may indeed be insufficient at
capturing the true value of the importance scales for this age group.
81
5.4.5 Conclusions
The domains and sub-domains of global self-esteem and physical self-worth were
clearly associated with, and predictive of, global self-esteem and physical self-worth
in this sample of 9-10 year old children. Further, the three-tier hierarchical structure
and mediating function of physical self-worth was supported. However, there is a
question mark over the reliability and validity of importance/discrepancy measures,
which warrants further investigation.
The current sample of children displayed self-esteem associations in line with
theorised and tested models of self-esteem. This allows for the expansion of selfesteem when assessing the relationship between children’s psychological well-being
and physical activity, within the next study chapter. Further, the effect of seasonal
variation in children’s physical activity levels will be accounted for, as suggested in
chapter four. Evidence shows that dissatisfaction with appearance in general is
associated with lower self-esteem (Harter, 1993). Also, overweight and obesity have
been identified as risk factors in the development of body image problems in children
(Davidson & Birch, 2001). Furthermore, research shows children most dissatisfied
with their perceived body shape are generally classified as inactive (Duncan et al.,
2006). Thus, the need to promote a positive body image and physical activity habits is
of importance to children’s health (Duncan, Al-Nakeeb, Nevill, & Jones, 2004).
Therefore, body fatness will be accounted for, as it has been shown to affect both
physical
activity
and
body
image
levels
(Duncan
et
al.,
2006).
82
Chapter 6
Study 3
The Relationship Between Children’s Chronic Physical
Activity and Expanded Psychological Well-being
This study formed the basis of an empirical study published in Acta Pediatrica: Parfitt, C.G.,Pavey, T.,
Rowlands, A.V. (2009). Children’s Physical Activity and Psychological Health: The Relevance of
Intensity. Acta Pediatrica. 98(6), 1037-1043.
Pavey, T.G.; Parfitt, G.; Rowlands, A.V. (2008) Children’s Psychological Well-being, Habitual
Physical Activity & Sedentary Behaviour., In (Jurimae, T, Armstrong, N, Jurimae, J, Eds) The
Proceedings of the 24th Pediatric Work Physiology Meeting. 86-89
The Study was awarded ‘Best Oral Presentation by a Young Investigator’ at Children and Exercise
XXIV The 24th Pediatric Work Physiology Meeting.
83
The Relationship Between Children’s Chronic Physical Activity and Expanded
Psychological Well-being
6.1 Introduction
Global self-esteem is at the apex of the self-esteem hierarchical model. However, the
lack of associations found with total physical activity and activity intensities in study
one (chapter 4) suggest a more complex relationship may be occurring or no
relationship exists. It is possible that associations may occur at the domain and subdomains levels of self-esteem, which are lost when only global self-esteem is taken
into account. The identification and improvement of these sub-domains may then
affect and improve global self-esteem as a whole (Fox & Corbin, 1989; Harter, 1993).
As previously discussed (p.28), there is a link between appearance and self-esteem
(Harter, 1993) with correlations of .70 to .80 being demonstrated. It can be suggested
that those children who are physically inactive may perceive themselves of having a
poorer appearance. Indeed, there is the suggestion that fatter children have a poorer
body image (Duncan, Al-Nakeeb, Nevill, & Jones, 2006). Therefore body fatness may
explain relationships between physical activity and self-perceptions, which may
reflect on their psychological well-being. It would be prudent in research exploring
activity and self-perceptions to take into account the influence of body fat.
Study one used up to seven days measurement of physical activity. The inclusion of
another seven days of physical activity measurement at another time of year would
allow for seasonal variation to be accounted for. This would occur at two different
seasons during the year (spring and autumn). This presents a better estimate of
children’s overall physical activity levels, and may provide stronger or more
representative relationships between physical activity and psychological well-being
constructs.
This study aims to assess the relationship between children’s psychological wellbeing (incorporating the global self-esteem domains and the sub-domains of physical
self-worth) and physical activity. Further, the effects of the seasons and the influence
of body fat will be accounted for.
84
From the results of study one and previous literature (Parfitt & Eston, 2005; Raudsepp
et al., 2002; Raustorp et al., 2005), it is hypothesised that total daily physical activity
is positively associated with global self-esteem, physical self-worth and the subdomains of physical self-worth, and that anxiety and depression are negatively
associated with physical activity. There were no significant associations between
anxiety, depression and physical activity in Study 1. However, with a more valid
measure of physical activity and physical activity intensity, any significant
associations with anxiety and depression are expected to be inverse at the higher
intensities. It is further hypothesised that different physical activity intensity levels
will have positive or negative associations with the psychological well-being
variables. Participants who spend more time in high intensity activity and/or less time
in very light activity will have more positive psychological profiles.
6.2 Method
General Procedures
Ethical clearance was granted by the School’s Ethics Committee (reference 13/12/05
#2) before commencement of the study. Participants were 82 children (32 boys and 50
girls) from three primary schools in the South West of England. The children’s ages
ranged between 10 and 11 years.
During the first meeting with the children, anthropometric data were collected; the
children’s age, height, sitting height, mass and body fat % were recorded and the
children were each given an accelerometer and shown how to wear it. The
accelerometers were worn by the children for two seven-day periods (one during the
spring and one during the autumn). After each seven-day period, the researcher
collected the accelerometers. Following the second seven-day period, the children
completed three psychological well-being inventories to assess trait anxiety,
depression, global self-esteem, and the various domain and sub-domain measures. The
researcher was present to answer any questions the children had about the
questionnaires.
Details of the measures used, are given in the General Methods section (Chapter 3).
85
Data Analysis
Descriptive data (mean and SD) were calculated for age, anthropometric measures,
accelerometer counts and minutes spent at each intensity level. Independent t-tests
were performed to identify any gender differences across the descriptive data
variables. Following the t-tests and scrutiny of scatter plots (to confirm relationships
were similar across gender), data for the boys and girls were collapsed and Pearson’s
correlations (r) were used to assess the relationships between the psychological health
constructs and recorded physical activity/intensity. This was followed by partial
correlations to explore relationships after controlling for socio-economic status. As
body fatness is related to psychological health (Duncan et al., 2006), it was necessary
to assess if physical activity made a significant contribution to the psychological
variables after fatness had been accounted for. Therefore, where significant
correlations were present, forced entry hierarchical regression analysis was used to
investigate whether physical activity explained any variance in the psychological
variables over and above that explained by body fat.
To explore the quantity of activity associated with more positive psychological health
profiles, gender specific tertile groups for habitual physical activity and physical
activity intensity were created. A series of gender (2) X activity level (3) MANOVAs
were carried out, one for each intensity of activity. The multivariate variables were
anxiety, depression and global self-esteem in the first set of analyses, the domains of
self-worth in the second set of analyses and finally the sub-domains of physical selfworth in the final set of analyses. Univariate analyses were used to follow up
significant effects. All data analyses were performed using the SPSS (15.0) statistical
package, with alpha set 0.05.
6.3 Results
A total of 57 participants (23 boys, 34 girls) provided adequate data for analysis.
Descriptive data (means and standard deviations) for the children are presented in
Table 6.1. T-tests revealed no difference in anthropometric measures between boys
and girls. Girls accumulated significantly more minutes in light intensity activity
(t(55) = 2.678, p = 0.010) and significantly fewer minutes in vigorous intensity
activity (t(55) = 2.218, p = 0.031) than boys. No further gender differences were
present.
86
Table 6.1: Descriptive data for boys and girls
Boys (n = 23)
Girls (n = 23)
Age (yr)
Mean
10.1
SD
0.3
Mean
10.1
SD
0.3
Height (cm)
144.3
5.8
143.0
7.1
Mass (kg)
37.9
8.2
37.6
7.8
Body fat %
16.5
7.9
19.1
8.9
378972
57062
364388
70572
Very light (mins)
224.4
32.2
231.2
27.0
Light (mins)
107.9a
18.0
120.0a
15.6
Moderate (mins)
87.1
14.7
87.6
17.1
Vigorous (mins)
34.6a
13.8
27.0a
11.9
Average daily counts
Intensity:
a
= significant gender difference, p < 0.05
Correlation analyses were used to investigate the relationship between total daily
physical activity (accelerometer counts) and each measure of psychological wellbeing (trait anxiety, depression and global, domain and sub-domain self-esteem
variables). As indicated in Table 6.2, there were no significant relationships between
total daily physical activity and anxiety or depression. For self-esteem, behavioural
conduct was significantly correlated (negatively) with total daily physical activity (r =
-0.359, p < 0.01), with physical condition positively correlated (r = 0.258, p < 0.05).
Correlation analyses were also used to investigate the relationship between physical
activity intensity and each measure of well-being. As indicated in Table 6.2, time
spent in very light intensity activity was positively associated with measures of
anxiety and depression (r > 0.345, p < 0.05), and negatively associated with measures
of global self-esteem, scholastic competence, physical appearance, physical selfworth, and attractive body and (r > -0.297, p < 0.05). Time spent in light intensity
activity was negatively associated with global self-esteem, physical appearance,
physical self-worth, and attractive body (r > -0.297, p < 0.05). Time spent in
moderate intensity activity was negatively associated with behavioural conduct (r = 0.315, p = 0.05). Finally, time spent in vigorous intensity activity was negatively
87
associated with anxiety and behavioural conduct (r > -0.273, p = 0.05) and positively
associated with measures of scholastic and athletic competence, social acceptance and
physical condition (r > 0.281, p < 0.05). All significant relationships remained after
partial correlations controlling for socio-economic status were computed (see
appendix 5C pp. 216).
Table 6.2: Correlation analyses for combined gender: Physical activity intensity and
psychological well-being
Total daily
activity
-.195
Very
light
.384**
Light
Moderate
Vigorous
.173
-.110
-.310**
Depression
-.014
.345**
.202
.103
-.177
Global Self-esteem
-.032
-.385**
-.345*
-.132
.121
Scholastic Competence
.081
-.341**
-.225
-.119
.281*
Social Acceptance
.248
-.118
.002
.018
.338**
Physical Appearance
-.050
-.385**
-.345*
-.132
.121
Behavioural Conduct
-.359**
-.067
-.229
-.315*
-.273*
-.055
-.388**
-.301*
-.139
.115
Sport/Athletic Competence
.242
-.133
-.056
.090
.335*
Physical condition
.258*
-.184
-.053
.112
.324*
Attractive Body
-.103
-.297*
-.297*
-.188
.066
Strength competence
.025
-.085
-.103
.039
.066
Anxiety
Physical Self-worth
* = p < 0.05, ** = p < 0.01
Hierarchical regression analyses revealed that time in very light intensity activity
explained significant variance in anxiety, depression, global self-esteem, scholastic
competence, physical appearance and physical self-worth above that explained by
body fat (Table 6.3a). Time in vigorous intensity activity explained additional
variance in anxiety, social acceptance, sport/athletic competence and physical
condition (Table 6.3b). The other activity measures did not add significant variance.
88
Table 6.3a: Regression analyses showing relationship between
well-being and time spent in very light activity after controlling
for body fat
Table 6.3b: Regression analyses showing relationship between
well-being and time spent in vigorous activity after controlling
for body fat
R2 Change
.058
.075*
R2
.058
.133
p
.071
.035
Beta
.195
-.278
.068
.019
.068
.087
.077
.299
.238
-.138
Body fat
Vigorous
.200**
.008
.200
.208
.001
.465
-.432
.090
Scholastic:
Body fat
Vigorous
.054
.061
.054
.155
.081
.059
-.192
.250
-.260
-.030
Social:
Body fat
Vigorous
.073*
.089*
.073
.162
.042
.020
-.221
.302
.001
.035
-.336
-.271
Appearance: Body fat
Vigorous
.184**
.003
.184
.187
.001
.677
-.420
.052
.057
.057
.088
.912
-.244
.016
Behavioural: Body fat
Vigorous
.057*
.100*
.057
.157
.025
.014
-.291
-.321
R2 Change
.058
.103*
R2
.058
.161
p
.071
.013
Beta
.125
.341
Anxiety:
Depression: Body fat
Very light
.068
.074*
.068
.142
.051
.035
.162
.290
Depression: Body fat
Vigorous
GSE:
Body fat
Very light
.200**
.069*
.200
.269
.001
.028
-.352
-.280
GSE:
Scholastic:
Body fat
Very light
.054
.077*
.054
.132
.081
.033
-.133
-.296
Social:
Body fat
Very light
.073
.001
.073
.074
.067
.832
Appearance: Body fat
Very light
.184**
.065*
.184
.249
Behavioural: Body fat
Very light
.057
.000
Anxiety:
Body fat
Very light
Body fat
Vigorous
PSW:
Body fat
Very light
.257**
.053*
.257
.309
.001
.048
-.424
-.244
PSW:
Body fat
Vigorous
.257**
.001
.257
.258
.000
.783
-.501
-.033
Sports C:
Body fat
Very light
.140**
.000
.140
.140
.008
.958
-.371
-.007
Sports C:
Body fat
Vigorous
.140**
.077*
.140
.217
.004
.025
-.327
.282
Phys Con:
Body fat
Very light
.184**
.002
.184
.186
.002
.743
-.414
-.043
Phys Con:
Body fat
Vigorous
.184**
.066*
.184
.250
.001
.033
-.386
.261
Att body:
Body fat
Very light
.232**
.020
.232
.252
.001
.234
-.430
-.151
Att body:
Body fat
Vigorous
.232**
.000
.232
.232
.000
.914
-.483
-.013
Strength:
Body fat
Very light
.001
.006
.001
.007
.998
.561
.000
-.084
Strength:
Body fat
Vigorous
.001
.004
.001
.005
.891
.651
-.019
0.63
89
* = p < 0.05, ** = p < 0.01
Total Daily Physical Activity Tertile Groups Analyses: There were no significant main
effects or interactions recorded in each of the three MANOVAs conducted.
Physical Activity Intensity Tertile Groups Analyses: The MANOVAs conducted on
the very light activity intensity groups resulted in significant activity level main
effects for the first (anxiety, depression and global self-esteem) set (Wilks’ Lambda =
0.693, f(6, 98) = 3.28, P < 0.01) and second (scholastic competence, social
acceptance, behavioural conduct, physical appearance and physical self-worth) set
(Wilks’ Lambda = 0.619, f(10, 94) = 2.54, P < 0.01) of variables. As displayed in
Table 6.4, univariate follow-up tests indicated that anxiety and global-self-esteem
accounted for the first effect with the middle amount of very light intensity activity
group recording significantly lower anxiety scores (f(2,51) = 4.8, P<0.01) and higher
global self-esteem scores than the high amount of very light intensity activity group.
Physical appearance and physical self-worth accounted for the second significant
main effect, with the middle amount of very light intensity activity group different to
the high amount of very light intensity activity group (f(2,51) = 6.73, P< 0.01, and
f(2,51) = 6 .3, P<0.01, respectively). No other significant effects were recorded.
These data suggests that those boys and girls who spent more time in very light
activity ( between 240-290 mins) had higher anxiety and lower global self-esteem,
physical appearance, and physical self-worth perceptions than those who spent a
moderate amount of time (between 200-240 mins) in very light activity. There were
no significant MANOVA effects for light, moderate or vigorous intensity activity.
90
Table 6.4: Well-being scores by time spent in very light and vigorous activity.
Psychological Health
Very Light Activity
Low Group
Middle Group
Vigorous Activity
High Group
Low Group
Middle Group
High Group
boys 191 ± 25min
216 ± 23min
261 ± 23min
21 ± 14min
31 ± 13min
49 ± 13min
girls 202 ± 20min
MANOVA 1: anxiety
30.5 (1.7)
228 ± 19min
28.8a(1.6)
262 ± 20 min
35.7a (1.6)
15 ± 8min
35.7 (1.6)
25 ± 8min
29.9 (1.6)
41 ± 8min
29.8 (1.6)
depression
8.3 (1.9)
7.1 (1.8)
11.2 (1.9)
10.6 (1.9)
9.3 (1.9)
6.8 (1.0)
GSE
18.9 (0.8)
20.6a(0.8)
16.6a (0.8)
17.6 (0.9)
19.2 (0.9)
19.4 (0.9)
MANOVA 2: scholastic
18.2 (0.9)
16.4 (0.9)
15.9 (0.9)
16.2 (0.9)
15.9 (0.9)
18.2 (0.9)
social
18.3 (1.1)
19.1 (1.0)
18.5 (1.1)
16.9 (1.0)
18.1 (1.0)
20.8 (1.0)
behavioural
17.5 (0.8)
18.0 (0.8)
17.1 (0.8)
18.2 (0.8)
18.1 (0.8)
16.4 (0.8)
appearance
17.5 (1.0)
20.2a(1.0)
14.2 a(1.0)
15.4 (1.0)
18.7 (1.0)
17.4 (1.1)
PSW
18.0 (1.0)
20.2a(0.9)
15.0a (1.0)
16.7 (1.1)
18.5 (1.1)
18.0 (1.0)
MANOVA 3: athletic
17.5 (1.1)
18.6 (1.0)
16.5 (1.1)
15.7 (1.1)
18.0 (1.0)
18.8 (1.0)
condition
19.1 (0.9)
19.9 (0.8)
17.6 (0.9)
17.1 (0.9)
19.8 (0.8)
19.6 (0.9)
attractive
16.3 (1.0)
19.1 (1.0)
14.5 (1.0)
15.6 (1.1)
17.6 (1.0)
16.6 (1.1)
strength
16.1 (1.0)
17.0 (1.0)
16.4 (1.0)
16.6 (1.0)
17.0 (1.0)
15.9 (1.0)
a = significant difference
91
6.4 Discussion
It was hypothesised that total daily physical activity would be positively associated
with global self-esteem, physical self-worth and the sub-domains of physical selfworth, and that anxiety and depression would be negatively associated with physical
activity. These hypotheses were not supported. It was further hypothesised that
different physical activity intensity levels will have positive or negative associations
with the psychological well-being variables. The results support the hypothesis that
time accumulated at different intensities of physical activity are associated with
children’s psychological health. This extends previous cross-sectional research
concerning the relationship between total habitual physical activity and psychological
health in children, using an objective measure of physical activity (Parfitt & Eston,
2005). Associations between psychological well-being and physical activity,
demonstrated that children accumulating high levels of very light activity report
higher levels of anxiety and depression and lower levels of global self-esteem, while
children accumulating high levels of vigorous activity report lower levels of anxiety
and higher levels of global self-esteem. These relationships persisted after controlling
for body fatness. This represents the second investigation into the relationship
between children’s psychological health and time accumulated at different activity
intensities that utilises an objective measurement of physical activity intensity.
Anxiety displayed associations at both the very light (positive direction) and vigorous
(negative direction) physical activity intensities, which may be explained by the
proposed mechanisms of anxiety and physical activity (p. 13). At the vigorous
intensity, both the acute (biochemical) and chronic (psychological e.g. distraction,
improved social networks) may have provided the association in the desired direction.
Conversely, these mechanisms may not be taking place at the very light intensity and
thus providing undesired association. The inhibition hypothesis (p. 18) may explain
the association between depression and time spent in very light intensity activity
(positive direction). Those with a more depressed mood, may feel less inclined to be
physically inactive. However, causality of the associations between anxiety,
depression and physical activity are beyond the scope of this cross-sectional study.
92
6.4.1 Total daily physical activity
Contrary to previous literature (Crocker et al., 2000; Raustorp et al., 2005) total daily
physical activity, physical self-worth and sub-domain associations were not strong.
Although the weak positive association with physical condition is in line with Crocker
et al. (2000) and Raustorp et al. (2005), and supports the argument that children who
are generally more active perceive that they have higher levels physical condition
perceptions.
Unlike Parfitt and Eston’s (2005), there were no associations between total daily
physical activity and global self-esteem. Behavioural conduct was the only domain
significantly associated (negatively) with total daily physical activity. It could be
suggested that encompassed in everyday physical activities of a ten year old, would be
notions of ‘messing about’, being mischievous and general ‘Tom foolery’ which may
have provided the negative association.
6.4.2 Physical activity intensity and psychological well-being
The relationship identified between activity intensity and psychological health
suggests that the opposing ends of the intensity spectrum may play a vital role in the
relationship with psychological health. Total time accumulated in very light intensity
activity had positive correlations with both anxiety and depression, and negative
correlations with global self-esteem, physical self-worth and other domains. Time
spent in vigorous intensity activity was negatively correlated with anxiety and
positively correlated with in particular, sub-domains of physical self-worth. Although
these data are unique in objectively measuring the different intensities, they do lend
some support to Raudsepp et al. (2002), who showed that physical self-worth domains
were positively correlated with moderate to vigorous physical activity using a sevenday physical activity recall questionnaire.
6.4.3 Body fat
Hierarchical regression controlling for the potentially confounding influence of body
fatness removed any significant relationship with the attractive body construct. This is
not that surprising due to the documented associations between body dissatisfaction
and fatness (Duncan et al., 2006). Significant additional variance still remained at the
very light and vigorous intensities for several aspects of psychological well-being,
93
notably anxiety, which was related to both ends of the physical activity intensity
spectrum. Although cross-sectional data, this suggests more time spent in vigorous
intensity activity and less time spent in very light intensity activity, may be of
importance for the psychological health of children, regardless of the influence of
body fat.
6.4.4 Tertile analysis
However, given the current findings from the tertile group analyses, this is not as
simple as decreasing the amount of time in very light activity and promoting time in
vigorous activity. Children in the middle tertile group of very light activity had a more
positive psychological profile than those spending a little time in very light activity.
Further, the absolute scores on the psychological health variables of this middle group
were commensurate with the scores recorded by individuals who spent a high amount
of time in vigorous activity. Potentially, it is the pattern of the amount of time in each
intensity that is important. The children in this middle group were perhaps balancing
the amount of time in each intensity to confer positive benefits. A consideration of the
tertile group allocation would support this, with 85% of children in this group in the
middle and high tertile group of moderate and vigorous intensity activity, whereas,
only 59% of children in the high tertile group (those who spent the most amount of
time in very light activity) were in these middle and high tertile groups of moderate
and vigorous activity.
One theory which may explain this patterning is the behavioural choice theory
(Epstein, Smith, Vara, & Rodefer, 1991). For many children there are attractive
sedentary and very light intensity alternatives to physical activity (e.g. television
viewing, computer games). However it is suggested that youth sedentary behaviours
are complex and unlikely to be represented by a single behaviour. Indeed, television
viewing and physical activity are un-associated and separate constructs (Gorely et al.,
2007; Taveras et al., 2007). Through the use of cluster sub-groups, Gorely et al.
(2007) established that active adolescents spend more time outside. Being inside may
restrict physical activity and provide more opportunities to engage in sedentary or
very light intensity behaviours. The tertile analysis of the current study has provided a
form of clustering and it is suggested that understanding why children and adolescents
94
in less active clusters have these behaviours may be important in the facilitation of
more active lifestyles (Gorely et al., 2007).
Given the current findings from the tertile group analyses, data for the anxiety, global
self-esteem, physical self-worth and various sub-domains suggest that up to four
hours in very light intensity activity along with 30 minutes or more in vigorous
intensity activity are associated with the most positive psychological profiles. This
study was cross-sectional in nature and research is needed to establish the causal
direction of this relationship. Based on the available evidence, and as previous
intervention research has indicated, physical activity has a positive impact on
psychological health constructs (Ekeland et al., 2005; Larun et al., 2006). Children
spending more than four hours each day in very light activity should be encouraged to
reduce this time by increasing the amount of time in more intense activity. A
reduction of around 40 minutes a day of very light intensity activity, which is
displaced to higher intensity activities, may help to improve the psychological health
profiles for children of this age group. The valence of this suggestion could be
addressed with research that focuses on reducing overall time in very light intensity
activity.
In contrast to previous literature (Rowlands & Eston, 2005; Rowlands et al., 2000),
there was no gender difference in total daily activity counts. This lack of difference
was also found by Parfitt and Eston (2005). However, as with Raudsepp et al. (2002),
boys did spend significantly more time in vigorous intensity activities than girls. The
girls spent significantly more time in light intensity activities. It is noted that all
children averaged over 60 minutes of moderate intensity activity daily which fits with
the physical activity guidelines (Biddle et al., 1998).
6.4.5 Conclusions
Psychological well-being was associated with time accumulated at vigorous and very
light physical activity intensities. Specifically, children who accumulated less than
four hours of very light intensity activity had more positive psychological health
profiles than children who accumulated more than four hours. However, the cause of
these very light intensity behaviours may include other determinants of exercise.
Examples include perceived barriers, social support and self-efficacy (Sallis et al.,
95
2000; Strauss, Rodzilsky, Burack, & Colin, 2001), enjoyment and motivation (Sallis
et al., 2000). However, and concurring with Kelly et al. (2006), not socio-economic
status.
Further, results indicate that the balance of time spent at high and low intensities may
be more important than absolute time spent at each. Although this study was crosssectional in nature, previous intervention research has highlighted the positive effects
of physical activity on psychological health constructs (Ekeland et al., 2005). The
promotion of a balanced physical activity profile focusing on reducing time in very
light activity and increasing time in higher intensity activities may improve
psychological health for this age group. Longitudinal research may help to clarify the
nature of the optimal balance of activity and the causal direction of the relationships
identified. Therefore, the aim of Study 4 (chapter 7) was to assess the longitudinal
relationship between children’s psychological well-being and physical activity over a
12-month period. This would also provide assessment of the direction of identified
relationships.
96
Chapter 7
Study 4: The Direction of the Relationship Between Children’s Physical Activity
and Psychological Well-being
7.1 Introduction
The results of Study 3 (chapter 6) clearly highlight various relationships between
physical activity intensities (particularly the very light and vigorous intensity), and
psychological well-being constructs. However, the cross-sectional nature of Study 3
does not allow for causal pathways to be inferred. Therefore, we can only hypothesise
that reducing the time spent in very light intensity activity, or increasing the time
spent in the vigorous intensity activity, may improve psychological well-being for this
age group. These cross-sectional designs are common in this area of research (Crocker
et al., 2000; Parfitt & Eston, 2005; Raudsepp et al., 2002; Raustorp et al., 2005), and
are an important first step. If no relationship exists then the investigation of causal
pathways would be unwarranted. These studies and those of this thesis have
highlighted that a relationship exists between physical activity and in particular the
domain and sub-domains of physical self-worth.
In order to infer the direction of causality, a longitudinal study, with the necessary
accompanying analysis is usually adopted. Different methods can be used to examine
longitudinal data. Raudsepp, Neissaar, and Kull (2008) examined the stability of
sedentary behaviours and physical activity over a 22-month period, in 345 children
aged 11-12y. Using latent growth modelling, results indicated moderate differential
stability of sedentary behaviours and physical activity. Further, it was indicated that
increases in sedentary behaviours over the 22-month period were inversely related
with changes in physical activity. However, the use of a three-day physical activity
recall would bring into question the sensitivity of the measure to detect changes in
physical activity. It was suggested that due to the relative stability of their group,
interventions should be targeted at high risk groups to reduce sedentary behaviours.
Knowles, Niven, Fawkner, and Henretty (2009) examined the relationship of physical
self-perceptions with physical activity in 150 girls (mean age 12.97). Two
measurements 12 months apart were conducted using a children’s physical activity
questionnaire and the CY-PSPP. The analysis firstly involved determining, through
97
regression analysis, standardised residuals/change scores for all variables. CY-PSPP
subscale change scores were then entered using multiple regression analysis with the
physical activity change score as the dependent variable. Results indicated that CYPSPP subscales accounted for 9.9% of the explained variance in physical activity
change. Only the subscale of stamina/condition competence was a significant
individual predictor. Again, the use of self-reported physical activity questions the
sensitivity of the physical activity measurement. It was suggested that interventions
aimed at increasing physical activity in early adolescent girls should focus on the
aspects related to perceptions of stamina and condition. However, the study only
looked at the influence of variables one way, i.e. the influence of CY-PSPP subscale
on physical activity change. Examining the influence of physical activity on changes
in CY-PSPP subscales, may have given an insight into whether a mutual or stronger
variance and relationships exist between measures.
Work by Marsh, Papaioannou, and Theodorakis (2006) examines this mutual or
reciprocal effect between the physical self and aspects of physical activity, which has
previously been shown to exist with academic self-concept and academic achievement
(Marsh, 1990). Data from 2,786 Greek primary, high, and senior school children,
were used to examine whether there was a reciprocal effect between the domain of
physical self-concept and exercise behaviour. It was suggested that prior physical selfconcept would influence subsequent physical self-concept, but would also influence
subsequent exercise behaviour, with the opposite premise for prior exercise
behaviour. Multilevel modelling analysis was used to examine the data, in the form of
longitudinal path models e.g. prior measures influencing subsequent measures, thus
providing a causal ordering model of change. Results showed that prior physical selfconcept influenced both subsequent physical self-concept (β = .43, SE = .02) and
exercise behaviour (β = .17, SE = .02). Also as expected, prior exercise behaviour
influenced subsequent exercise behaviour (β = .33, SE = .02) and physical self
concept (β = .10, SE = .02). The results suggested that physical self-concept and
exercise behaviour were reciprocally related and mutually reinforcing; with the
modelling suggesting that teachers and health professionals should aim to improve
both physical self-concept and exercise behaviour simultaneously. This reciprocal
effect has also been found for the sub-domain of sport concept and sporting
performance (Marsh, Gerlach, Trautwein, Ludtke, & Brettschneider, 2007).
98
Typical repeated measures multi-level models, involve individuals being measured in
a panel design, with the behavioural measurement taken at different times forming
level 1, which is nested within the individual at level 2 (Duncan, Jones, & Moon,
1996: see Figure 7.1). The flexible nature of the multi-level model provides efficient
use of all available data, as both the number of observations and spacing among the
observations is allowed to vary (Duncan et al., 1996).
Level 2 Person
Level 1 Time
1
0
6
12
(Months)
2
3
0
12
(Months)
0
6
(Months)
Figure 7.1: Multi-level model panel design (adapted from Duncan et al., 1996, pp.
882).
Examples of this type of design can be seen in children’s physiology through the work
of Welsman and colleagues, when investigating the influences of anthropometric
measures (age, sex, maturity, body mass, and body fatness) on changes in physical
activity levels (Armstrong, Welsman, & Kirby, 2000), and leg strength (De Ste Croix,
Armstrong, Welsman, & Sharpe, 2002); the influence of anthropometric measures and
blood haemoglobin concentrations on changes in peak oxygen uptake (Armstrong &
Welsman, 2001); and the influence of anthropometric measures, muscle volume and
leg strength on changes in short term power output (De Ste Croix et al., 2001).
The aim of this study is to expand on the relationships found in the previous chapters
of this thesis and assesses the direction of causality between psychological well-being
and physical activity, through the use of multi-level modelling analysis. Data were
collected over a 12 month period and at three time-points. This time period and the
data collection waves are consistent with the recommendations of Marsh et al. (1999).
Further, three time-points over a 12-month period, will account for the variation in
physical activity across two seasons.
99
It is hypothesised that the domains of self-esteem will influence changes in anxiety,
depression and global self-esteem. Further, the sub-domains of physical self-worth
will influence changes in physical self-worth and may influence changes in scholastic
competence, social acceptance, physical appearance, behavioural conduct. Physical
activity is expected to influence the changes in physical self-worth and its subdomains, and may influence self-esteem domains, anxiety, depression and global selfesteem. It is expected that physical self-worth and its sub-domains will influence the
changes in physical activity, which may exhibit a reciprocal effect between physical
activity and physical self-worth. Self-esteem domains, anxiety, depression and global
self-esteem may influence the changes in physical activity.
7.2 Method
General Procedures
Ethical clearance was granted by the School’s Ethics Committee (reference 13/12/05
#2) before commencement of the study. Participants were 82 children (32 boys and 50
girls) from three primary schools in the South West of England. The children’s ages
ranged between 9 and 11 years.
Data were collected three times over a 12 month period. Each data collection was
approximately six months apart. During each of the three meetings with the children,
anthropometric data were collected; the children’s age, height, sitting height, mass
and body fat % were recorded and the children were each given an accelerometer and
shown how to wear it. The accelerometers were worn by the children for a seven-day
period. After each seven-day period, the researcher collected the accelerometers. At
each of the three meetings the children also completed three psychological well-being
inventories to assess trait anxiety, depression, global self-esteem, and the various
domain and sub-domain measures. The researcher was present to answer any
questions the children had about the questionnaires.
Details of the measures used, are given in the General Methods section (Chapter 3).
100
Data Analysis
Descriptive statistics (means and standard deviations) for anthropometric variables,
total activity, time spent in physical activity intensities, and psychological well-being
construct scores were computed for participants on each test occasion. The influences
of physical activity and psychological well-being upon longitudinal changes in
psychological well-being, and/or the influence of psychological well-being upon
longitudinal changes in physical activity were investigated using the multi-level
modelling programme MLwiN.
Multilevel modelling is an extension of multiple regression, which is appropriate for
analysing hierarchically structured data. In longitudinal data sets the hierarchy can be
defined as the repeated measurement occasions (the level one units), grouped within
the individual subjects (who represent the level 2 units). Multilevel modelling is
preferable to traditional analytical approaches for longitudinal data (e.g. repeated
measures analysis of variance) as, in addition to describing the population mean
response, this method recognizes and describes variation around the mean of both
levels. The data were modelled using an additive, polynomial model in which all
parameters were fixed with the exception of the constant (intercept term), which
varied randomly at level 2 (between subjects), and the additive error ratio, which
varied randomly at level 1 (within subjects). A third level incorporating the three
different schools was initially adopted, to check for variation in the data between
schools. This level was subsequently dismissed, as there was no effect for school
throughout the analyses.
Longitudinal changes in psychological well-being were assessed using a three tier
hierarchical model (in line with Fox & Corbin, 1989), with anxiety, depression and
self-esteem incorporated at the global level. At the global level (Figure 7.2), each of
anxiety, depression and global self-esteem were assessed separately as the dependent
variable. The self-esteem domains of scholastic competence, social acceptance,
physical appearance, behavioural conduct, and physical self-worth were entered as it
is theorised these domains would feed into and influence at the global level (Fox &
Corbin, 1989; Harter, 1985). The influence of total daily physical activity and the
time spent in physical activity intensities were also investigated as explanatory
variables.
101
Anxiety
Physical Activity
Total Activity
Time in sedentary
Time in very light
Time in Light
Time in moderate
Time in vigorous
Depression
Global Self-esteem
Self-esteem Domains
Scholastic
Social
Physical
Behavioural
Competence Acceptance Appearance
Conduct
Physical
Self-worth
Figure 7.2: The influence of self-esteem domains and physical activity on
psychological well-being.
At the domain level (Figure 7.2.1), with physical self-worth being the dependent
variable, the sub-domains, sport/athletic competence, physical condition, attractive
body, and strength competence were entered as it is theorised these domains would
feed into and influence at the domain level (Fox & Corbin, 1989). Although the subdomains of physical self-worth are not theoretically linked, it seemed prudent to run
the same analysis for the other four self-esteem domains (scholastic competence,
social acceptance, physical appearance, behavioural conduct). For example, those
children with high sport/athletic competence may have larger and more social groups,
or those with high perceptions of attractive body may have high perceptions of
physical appearance. Again, the influence of total daily activity and the time spent in
physical activity intensities were investigated as explanatory variables.
102
Scholastic
Social
Competence Acceptance
Physical Activity
Total Activity
Time in sedentary
Time in very light
Time in Light
Time in moderate
Time in vigorous
Physical Behavioural
Appearance Conduct
Physical
Self-worth
Physical Self-esteem Sub-domains
Sport
Physical Attractive Strength
Competence Condition
Body Competence
Figure 7.2.1: The influence of physical self-worth subdomains and physical activity on global self-esteem
domains.
As the sub-domains of physical self-worth were at the lowest level of the three tier
hierarchical model, only the influence of total daily activity and the time spent in
physical activity intensities were investigated as explanatory variables (Figure 7..2.2).
Sport
Competence
Physical
Condition
Attractive
Body
Strength
Competence
Physical Activity
Total Activity
Time in sedentary
Time in very light
Time in Light
Time in moderate
Time in vigorous
Figure 7.2.2: The influence of physical activity on physical self-worth sub-domains
103
Longitudinal changes in physical activity were assessed with each of total daily
activity, and time spent in sedentary, very light, light, moderate, and vigorous
intensity as the dependent variable (Figure 7.2.3). All psychological well-being
constructs were entered into the model as explanatory variables. In all analyses
(psychological and physical activity dependent variables), the influence of gender and
body fat were also assessed.
Total Activity
.
Sedentary
Very Light
Light
Moderate
Vigorous
Psychological Well-being
Anxiety
Depression
Global Self-esteem
Scholastic Competence
Social Acceptance
Physical Appearance
Behavioural Conduct
Physical Self-worth
Sport/Athletic Competence
Physical Condition
Attractive Body
Strength Competence
Figure 7.2.3: The influence of psychological well-being constructs on physical
activity.
7.3 Results
Gender specific descriptive data for all variables are available (Appendices 5B & 5C,
pp. 214 – 215). Figures 7.3; 7.3.1; and 7.3.2 summarise the results of the modelling of
the global psychological well-being constructs, anxiety, depression, and global selfesteem (n = 159). For anxiety (Figure 7.3), a significant negative coefficient was
identified for scholastic competence (-0.768, SE 0.136), social acceptance (-0.410, SE
0.125), physical self-worth (-0.392, SE 0.126) and time in moderate intensity activity
104
(-0.040, SE 0.017). No other entered psychological or physical activity variables
contributed to the model significantly.
Anxiety
-0.768
(0.136)
Scholastic
Competence
-0.410
(0.125)
-0.392
(0.126)
Social
Acceptance
-0.040
(0.017)
Physical
Self-worth
Fixed Constant: 62.99 (3.11)
Random Level 2 Constant: 11.38 (3.50)
Random Level 1 Constant: 17.66 (2.62)
Time in moderate
activity
Note: estimate values are
significant when double the
standard error.
Figure 7.3: Significant explanatory variables for changes in anxiety.
For depression (Figure 7.3.1), a significant negative coefficient was identified for
scholastic competence (-0.357, SE 0.109), social acceptance (-0.495, SE (0.101),
physical appearance (-0.250, SE 0.114), behavioural conduct (-0.289, SE 0.123), and
physical self-worth (-0.436, SE 0.127). No other entered psychological variable or any
physical activity variables contributed to the model significantly.
Depression
-0.357
(0.109)
Scholastic
Competence
-0.495
(0.101)
Social
Acceptance
-0.250
(0.114)
Physical
Appearance
Fixed Constant: 41.08 (2.94)
Random Level 2 Constant: 16.22 (3.44)
Random Level 1 Constant: 8.12 (1.22)
-0.289
(0.123)
-0.436
(0.127)
Behavioural
Conduct
Physical
Self-worth
Note: estimate values are
significant when double the
standard error.
Figure 7.3.1: Significant explanatory variables for changes in depression.
105
For global self-esteem (Figure 7.3.2), a significant positive coefficient was identified
for social acceptance (0.278, SE 0.042), physical appearance (0.317, SE 0.049),
behavioural conduct (0.279, SE 0.050) and physical self-worth (0.217, SE 0.055). No
other entered psychological variable or any physical activity variables contributed to
the model significantly. There was no effect from gender or body fat at the global
level.
Global Self-esteem
0.278
(0.042)
Social
Acceptance
0.317
(0.049)
Physical
Appearance
Fixed Constant: 4.57 (1.16)
Random Level 2 Constant: 0.862 (0.359)
Random Level 1 Constant: 2.39 (0.354)
0.279
(0.050)
Behavioural
Conduct
0.217
(0.055)
Physical
Self-worth
Note: estimate values are
significant when double the
standard error.
Figure 7.3.2: Significant explanatory variables for changes in global self-esteem.
Figures 7.4; 7.4.1; 7.4.2; 7.4.3; and 7.4.4 summarise the results of the modelling of
the domains of self-esteem. For scholastic competence (Figure 7.4), a significant
positive effect was identified for physical condition (0.317, SE 0.085), and strength
competence (0.235, SE 0.076). No other entered psychological variable or any
physical activity variables contributed to the model significantly. For social
acceptance (Figure 7.4.1), a significant positive effect was identified for sport/athletic
competence (0.422, SE 0.076), and strength competence (0.207, SE 0.077). No other
entered psychological variable or any physical activity variables contributed to the
model significantly.
106
Scholastic
Competence
0.317
(0.085)
Social
Acceptance
0.235
(0.076)
Physical
Condition
Strength
Competence
0.422
(0.076)
0.207
(0.077)
Sport/Athletic
Competence
Strength
Competence
Fixed Constant: 7.13 (1.49)
Fixed Constant: 7.69 (1.39)
Random Level 2 Constant: 5.22 (1.31)
Random Level 2 Constant: 4.33 (1.28)
Random Level 1 Constant: 4.89
Random Level 1 Constant: 6.18 (0.914)
(0.730)
Note: estimate values are significant when double the standard
error.
Figure 7.4 & 7.4.1: Significant explanatory variables for changes in scholastic
competence and social acceptance.
For physical appearance (Figure 7.4.2) and behavioural conduct (Figure 7.4.3), a
significant positive effect was identified for attractive body (0.653, SE 0.064; 0.119,
SE 0.059). No other entered psychological variable or any physical activity variables
contributed to the model significantly.
Physical
Appearance
0.653
(0.064)
Attractive
Body
Fixed Constant: 8.24 (1.50)
Random Level 2 Constant: 3.20 (1.00)
Random Level 1 Constant: 5.15 (0.766)
Behavioural
Conduct
0.119
(0.059)
Attractive
Body
Fixed Constant: 16.03 (1.04)
Random Level 2 Constant: 5.42 (1.27)
Random Level 1 Constant: 4.08 (0.610)
Note: estimate values are significant when double the standard error.
Figure 7.4.2 & 7.4.3: Significant explanatory variables for changes in physical
appearance and behavioural conduct.
107
For physical self-worth (Figure 7.4.4), a significant positive effect was identified for
physical condition (0.265, SE 0.057), and attractive body (0.652, SE 0.050), with a
negative effect for time in very light intensity (-0.016, 0.005). No other entered
psychological or physical activity variables contributed to the model significantly. At
the domain level, there was no effect from gender, but body fat negatively predicted
changes in the physical appearance domain.
Physical Self-worth
0.265
(0.057)
Physical
Condition
0.652
(0.050)
Attractive
Body
Fixed Constant: 4.57 (1.16)
Random Level 2 Constant: 0.862 (0.359)
Random Level 1 Constant: 2.39 (0.354)
-0.016
(0.005)
Time in very light
activity
Note: estimate values are
significant when double the
standard error.
Figure 7.4.4: Significant explanatory variables for changes in physical self-worth.
Figures 7.5; 7.5.1; and 7.5.2 summarise the results of the modelling of the subdomains of physical self-worth. For sport/athletic competence (Figure 7.5), a
significant positive effect was identified for time in the vigorous intensity (0.055, SE
0.017). No other entered physical activity variables contributed to the model
significantly. For attractive body (Figure 7.5.1), a significant negative effect was
identified for time in the very light intensity (-0.022, SE 0.009). No other entered
physical activity variables contributed to the model significantly. For strength
competence (Figure 7.5.2), a significant positive effect was identified for time in the
vigorous intensity (0.054, SE 0.017). No other entered physical activity variables
contributed to the model significantly. For physical condition, there were no
significant effects for any physical activity variables. At the sub-domain level, there
was a gender effect for strength competence with girls displaying lower values, with
108
no other gender effects occurring. Body fat was a negative predictor for sports/athletic
competence and attractive body.
Sport/Athletic
Competence
0.055
(0.017)
Time in vigorous
activity
Fixed Constant: 8.24 (1.50)
Random Level 2 Constant: 3.20 (1.00)
Random Level 1 Constant: 5.15 (0.766)
Attractive
Body
Strength
Competence
-0.022
(0.009)
Time in very
light activity
Fixed Constant: 8.24 (1.50)
Random Level 2 Constant: 3.20 (1.00)
Random Level 1 Constant: 5.15 (0.766)
0.054
(0.017)
Time in vigorous
activity
Fixed Constant: 8.24 (1.50)
Random Level 2 Constant: 3.20 (1.00)
Random Level 1 Constant: 5.15 (0.766)
Note: estimate values are significant when double the standard error.
Figures 7.5 7.5.1; & 7.5.2: Significant explanatory variables for changes in
sport/athletic competence, attractive body and strength competence
Figures 7.6; 7.6.1; 7.6.2; 7.6.3; and 7.6.4 summarise the results of the modelling of
the total daily activity and time spent in physical activity intensities. For total daily
activity (Figure 7.6), a significant positive effect was identified for sport/athletic
competence (5753.76, SE 1719.14). No other entered psychological variables
contributed to the model significantly. For time spent in the very light intensity
(Figure 7.6.1), a significant negative effect was identified for physical self-worth (2.672, SE 0.679). No other entered psychological variables contributed to the model
significantly.
109
Total Daily
Activity
Time in Very
Light Activity
5753.76
(1719.14)
-2.672
(0.609)
Sport/Athletic
Competence
Physical
Self-worth
Fixed Constant: 280805 (31206)
Random Level 2 Constant: 1531391744
(749898624)
Random Level 1 Constant: 5458368000
(702694464)
Fixed Constant: 274.28 (11.29)
Random Level 2 Constant: 338.01 (105.31)
Random Level 1 Constant: 541.71 (80.37)
Note: estimate values are significant
when double the standard error.
Figure 7.6 & 7.6.1: Significant explanatory variables for changes in average daily
activity and time spent in very light activity
For time spent in the light intensity (Figure 7.6.2), a significant positive effect was
identified for sport/athletic competence (1.230, SE 0.423), with a negative effect for
physical self-worth (-1.528, SE 0.417). No other entered psychological variables
contributed to the model significantly.
Time in Light
Activity
-1.528
(0.417)
Physical
Self-worth
Fixed Constant: 105.19 (9.79)
Random Level 2 Constant: 102.73 (34.10)
Random Level 1 Constant: 187.92 (27.85)
1.230
(0.423)
Sport/Athletic
Competence
Note: estimate values are
significant when double the
standard error.
Figure 7.6.2: Significant explanatory variables for changes in time spent in light
activity.
110
For time spent in the moderate intensity (Figure 7.6.3), a significant positive effect
was identified for sport/athletic competence (0.971, SE 0.443). No other entered
psychological variables contributed to the model significantly. For time spent in the
vigorous intensity (Figure 7.6.4), a significant positive effect was identified for
sport/athletic competence (1.123, SE 0.301). No other entered psychological variables
contributed to the model significantly. For physical activity, there was a gender effect
for time spent in the very light intensity, with girls displaying higher values. Also,
there was a gender effect for time spent in the vigorous intensity, with girls displaying
lower values. There was no effect from body fat.
Time in Moderate
Activity
0.971
(0.443)
Sport/Athletic
Competence
Fixed Constant: 74.47 (8.05)
Random Level 2 Constant: 45.44 (48.76)
Random Level 1 Constant: 453.09 (66.08)
Time in Vigorous
Activity
1.123
(0.301)
Sport/Athletic
Competence
Fixed Constant: 21.91 (7.46)
Random Level 2 Constant: 61.41 (23.28)
Random Level 1 Constant: 143.85 (21.23)
Note: estimate values are significant when double the standard error.
Figure 7.6.3 & 7.6.4: Significant explanatory variables for changes in time spent in
moderate intensity activity and vigorous intensity activity.
7.4 Discussion
The purpose of this study was to assess the direction of causality between children’s
psychological well-being and physical activity, via multi-level modelling analysis
over a 12 month period. It was hypothesised that the domains of self-esteem will
influence changes in anxiety, depression and global self-esteem. Further, the subdomains of physical self-worth will influence changes in physical self-worth and may
influence changes in scholastic competence, social acceptance, physical appearance,
behavioural conduct. Physical activity is expected to influence the changes in physical
self-worth and its sub-domains, and may influence self-esteem domains, anxiety,
111
depression and global self-esteem. It is expected that physical self-worth and its subdomains will influence the changes in physical activity, which may exhibit a
reciprocal effect between physical activity and physical self-worth. Self-esteem
domains, anxiety, depression and global self-esteem may influence the changes in
physical activity.
The main hypotheses were supported; the domains of self-esteem influenced changes
in psychological well-being (anxiety, depression, global self-esteem). Also, the subdomains of physical self-worth influenced changes in physical self-worth and the
other domains associated with children’s self esteem. Furthermore, time spent in very
light intensity activity influenced changes in the physical self-esteem domain, with
time spent in very light and vigorous intensity activities influencing changes in
physical self-worth sub-domains. In addition, the sub-domain of sport/athletic
competence and the domain physical self-worth influenced changes in physical
activity. This suggests a reciprocal effect is occurring between changes in physical
activity and physical self-worth in children over a 12 month period. This represents
the first longitudinal study assessing the relationship between children’s psychological
well-being and physical activity using accelerometry.
7.4.1 Global level influences
In accordance with previous literature (Harter, 1985; Whitehead, 1995) social
acceptance, physical appearance, behavioural conduct, and physical self-worth where
all significant predictors of global self-esteem in a positive direction. Only scholastic
competence did not significantly predict global self-esteem, however, this may not be
too unexpected given previous findings by Marsh et al. (1999) of a lack of association
between the two constructs.
Although previous research has shown an association between physical activity
intensities and global psychological well-being constructs (Study 3), this is not
supported for the current longitudinal analysis. However, the global constructs are
relatively stable construct (Fox, 1999), and any effect provided by physical activity,
particularly for self-esteem, is theorised to occur at the lower levels of the hierarchy
(Fox, 2000; Sonstroem & Morgan, 1989). However, time spent in moderate intensity
activity did have a negative influence on changes in anxiety. This may have occurred
112
as previous research suggest moderate intensity activities (e.g. walking) can be more
useful at reducing anxiety and stress, where vigorous activities can place more stress
on a person (Biddle & Ekkekakis, 2005; Petruzzello et al., 1991).
7.4.2 Domain level influences
At the domain level of self-esteem, attractive body was the main predictor of changes
in physical self-worth. This is in line with previous cross-sectional research, which
displayed attractive body having the largest relationship with physical self-worth (Fox
& Corbin, 1989; Welk & Eklund, 2005; Whitehead, 1995). However, the only other
significant predictor of physical self-worth was the physical condition sub-domain.
This suggests that for this group of children the sport/athletic and strength competence
sub-domains have no direct influence on changes in physical self-worth, which is
inline with Study 3 of this thesis.
The sub-domains of physical self-worth were also significant predictors of changes in
the other self-esteem domains. As one may expect, presumably due to the theoretical
link of always being on display for everyone to see and judge (Fox, 2000; Harter,
2000), attractive body was a significant predictor of changes in physical appearance.
Further, sport/athletic competence being a significant predictor of changes in social
acceptance, would not be beyond reason. Those with high or improving sport/athletic
competence are more likely to engage in sporting activities (motivational/skill
development hypothesis; Sonstroem, 1997: p. 38), where social interactions and
friendships are likely to occur. Although there were other sub-domain to domain
effects, these are hard to rationalise theoretically, for example, the positive effect of
the strength competence sub-domain on the scholastic competence domain.
7.4.3 Sub-domain level influences
Time in very light intensity activity was a significant negative predictor of changes in
physical self-worth, which is inline with the correlation analysis of Study 3. Spending
large amounts of time in very light intensity activity may inhibit the enhancement of
the sub-domains of physical self-worth. Further, a change to spending more time in
very light
intensity activity may impact
on sub-domain perceptions
as
competence/adequacy decreases. Both these scenarios may have an effect on the
physical self-worth domain directly. The lack of significant physical activity
113
predictors for the other self-esteem domains may be due to the constructs being
relatively stable, and theoretically physical activity would not be linked to these
domains.
As theorised (Fox, 2000; Sonstroem et al., 1994) physical activity effects occurred at
the lower level of the physical self-worth sub-domains. Both time spent in very light
and vigorous intensity activity were significant predictors of physical self-worth subdomains, which is inline with the cross-sectional data in Study 3. Further, time spent
in vigorous intensity activity positively predicted changes in sport/athletic competence
and physical condition, corresponding with the correlation analysis of Study 3. Time
spent in very light intensity activity negatively predicting changes in attractive body
may be expected, considering the strong association between attractive body and
physical self-worth (Fox & Corbin, 1989; Whitehead, 1995).
7.4.4 Reciprocal effects
In accordance with previous longitudinal studies (Marsh et al., 2007; Marsh et al.,
2006), a reciprocal effect between physical activity and physical self-worth has been
identified. The physical self-worth domain significantly and negatively predicted
changes in time spent in the very light and light intensities. The sport/athletic
competence sub-domain significantly and positively predicted changes in total daily
activity, and time spent in the light, moderate and vigorous intensities. This partially
supports the findings by Knowles et al. (2009), although physical condition was the
only significant predictor of physical activity in the study.
As previously suggested, improvement in, and/or the maintenance of sport/athletic
competence perceptions (motivational/skill development hypothesis; Sonstroem,
1997) would lead to the engagement in potentially more sporting activities. This in
turn would increase average daily activity and time spent in the light, moderate and
vigorous intensities. Furthermore, for this sample of children, an increase in time
spent in vigorous intensity activity, has a reciprocal positive effect on sport/athletic
competence. This same reciprocal effect is also evident for time spent in very light
intensity activity and the physical self-worth domain, with physical self-worth also
negatively predicting changes in time spent in light intensity activity.
114
7.4.5 Physical activity pathways
From the analyses conducted, a pattern starts to emerge where improvement in
competence/adequacy perceptions at the sub-domain level, coupled with a
decrease/increase in time spent in physical activity intensities, may indirectly filter up
to improvements at the global well-being level. It is speculated that for future
research, two activity intensity pathways could be adopted, one for time spent in the
very light intensity and one for time spent in the vigorous intensity. Figures 7.7
visually represent how improvement in psychological well-being may occur.
115
Global
Level
Anxiety
Depression
Global Selfesteem
Anxiety
Depression
Global Selfesteem
Domain
Level
Physical
Appearance
Behavioural
Conduct
Physical
Self-worth
Scholastic
Competence
Social
Acceptance
Physical
Self-worth
Sub-domain
Level
Physical
Activity
Attractive
Body
Time in Very Light
Intensity Activity
Figure 7.7: Time in very light intensity pathway and time in vigorous intensity pathway
Blue Arrow = inverse effect
Green Arrow = positive effect
116
Sport/athletic
Competence
Physical
Condition
Time in Vigorous
Intensity Activity
Strength
Competence
It can be seen that both pathways may indirectly lead to improved anxiety, depression
and global self-esteem levels. For the very light pathway, it is suggested that a
decrease in time spent in very light intensity activity coupled with the improvement of
physical self-worth perceptions may improve psychological well-being. For the
vigorous pathway, an increase in time spent in vigorous intensity activity coupled
with an improvement in sport/athletic competence perceptions may improve
psychological well-being for this sample of children. The development of the
sports/athletic competence sub-domain has previously been recommended (Raustorp
et al., 2005). Further, as sport/athletic competence appears to be a core component for
the vigorous pathway, physical educators may wish to adopt a skill development
programme approach of Sonstroem (1997). Positive sporting and athletic experiences
will improve the sport/athletic competence perceptions (and other physical self-worth
sub-domains). Potentially, if these positive experiences can be coupled during
vigorous activities, then this may provide the best scenario to indirectly improve
psychological well-being. However, activity would need to be monitored carefully to
minimise negative experiences, which may impact at the sub-domain level.
Girls may particularly benefit from the vigorous pathway given the lower time spent
in the vigorous intensity, compared to boys. The negative effect of body fat for
physical appearance and attractive body would be expected (cf. Duncan et al., 2006).
The effect of body fat occurred primarily at the sub-domain level. Those with greater
body fat may benefit from a physical activity and skill development programme, as it
would be expected that weight loss, coupled with a controlled diet, would occur as
part of a physical activity programme (energy in-energy out, see Strong et al., 2005
and Summerbell et al., 2005). This may improve perceptions of competence at the
sub-domain level and the appearance/attractive body constructs, which may then feed
up to the global level. Again, activities would need to be monitored, to ensure
maintenance and development of competencies and avoid being counter-productive,
(Lagerberg, 2005).
The previous cross-sectional and the longitudinal relationships suggest there is room
for change, with the pathway recommendations above potentially providing positive
psychological well-being effects for a normal population of children. Additionally, the
pathways may be more applicable to those with initially low psychological well-being
117
level or an obese population, where the effects could be even larger. However, it is
unknown by how many minutes to decrease/increase activity and how long a period is
needed to identify if any changes are occurring.
7.4.6 Conclusions
Psychological well-being was directly influenced by the domain level in a three-tier
hierarchy. Physical activity predominately influenced at the physical self-worth subdomain level, but also at the domain and global levels. The physical self-worth
domain and sport/athletic competence sub-domain indicated reciprocal effects with
time spent in very light and vigorous intensity activity respectively. It was suggested
that pathways involving very light and vigorous intensity activity may indirectly
effect and improve psychological well-being. Future research intervening at very light
and vigorous intensity activity may provide further evidence of the indirect effect on
psychological well-being.
Two indirect physical activity pathways have been proposed, which may improve the
psychological well-being of children. The next step would be to intervene at the very
light or the vigorous physical activity intensity, to ascertain if changes in physical
activity behaviour elicit a change in psychological well-being. Study 5 (chapter 8)
assesses the utility of an intervention designed to reduce the time children spend in
sedentary and very light intensity behaviours.
118
Chapter 8
Study 5: An Intervention to Investigate Whether Reducing Time in Sedentary
Behaviour and Very Light Intensity Activity Impacts on Children’s
Psychological Well-being
8.1 Introduction
The positive effects of physical activity interventions on anxiety, depression (Larun et
al., 2006) and self-esteem (Ekeland et al., 2005) in children and adolescents are well
documented. However, the majority of interventions assessed in the above review
articles aimed to increase total daily activity or time spent in moderate-vigorous
activities. Data from Studies 3 and 4 of this thesis have established associations and
the direction of causal effects between psychological well-being components and time
spent in varying physical activity intensities. Predominately, time spent in very light
and vigorous intensity activities were shown to have the strongest directional
associations with psychological well-being. This suggests, interventions designed at
reducing time spent in the sedentary and very light activities or increasing time spent
in vigorous activities may help to improve psychological well-being in children.
Indeed Lagerberg (2005) suggests “in general, physical activity is an excellent way of
enhancing mental well-being” (pp.1700).
Previous interventions designed to promote or increase children’s physical activity
levels appear to have methodological flaws including no baseline data or control
groups, unreliable or invalid measures of physical activity, and poor reporting of
results and details (Salmon, Booth, Phongsavan, Murphy, & Timperio, 2007). Further,
recent literature reviews (Salmon et al., 2007; van Sluijs, McMinn, & Griffin, 2008)
highlight weak and/or inconclusive evidence for the effectiveness of interventions and
trials promoting children’s physical activity. However, the most effective
interventions promoted physical activity in the school setting, and included family or
community involvement and educational information (Salmon et al., 2007; Van Sluijs
et al., 2007).
A large scale study (54 schools, primary and secondary) ‘Schools on the Move’
project (Stathi, Nordin, & Riddoch, 2006) aimed to increase children’s physical
activity levels over a 24 week period. A year group from each school was invited to
take part. Participants, teachers, and parents received an information pack detailing
119
the aims and procedures of the study. Children participating received a pedometer in
order to monitor their physical activity levels throughout the study period.
Participants, teachers, and parents were also directed to the ‘Schools on the Move’
website. Here participants were asked to enter their daily pedometer step counts.
Further, the website provided information regarding the importance of being
physically active (physiological/psychological), tips for increasing and maintaining
activity levels, information regarding physical activity events coming up in their area
or school, and recommended step targets for health. Children were encouraged to
continually explore the website, with information on the website frequently updated.
Results showed that average steps increased steadily from 8355/day at week one to
16436/day at week 28. This increase was significant throughout the 28 week period.
Further, boys were more active than girls throughout the study period. However, there
was a steady decrease in the number of participants entering pedometer data, 1469
provided data in week one, 1107 in week two, 1000 in week 3, <500 in week 8, <100
in week 24, and 31 in week 28. By week 28 only 2 % of the starting participants
completed the study. This was highlighted as a main weakness of the study as the
analyses were not representative of the whole population taking part in the ‘Schools
on the Move Project’. It was suggested that those least interested in the project did not
provide any data, and those highly active may not have been motivated to increase
daily steps as they would be already engaged in vigorous activities. Further, some
participants had poor access to computers or problems accessing and navigating the
website, leading to drop-out. Others dropped out due to loss or breakage of the
pedometer.
Positive outcomes of the ‘Schools and the Move’ project included evidence for the
use of pedometers as an educational and physical activity motivational tool.
Furthermore, students and teachers increased their knowledge of healthy physical
activity, and developed strategies regarding the accumulation of healthy physical
activity levels. The ‘Schools on the Move’ project did achieve its primary objective of
increasing physical activity levels of young people, with recommendations for further
project improvements. These included, as a result of the low response rates, the need
for more careful design and management of data collection procedures, (incorporating
computer access and provision of pedometers). In addition, it was recommended that
120
more support should be given in the initial stages of the project (individual and
schools) to help address issues and problems with the project. This could be achieved
through the use of trained project coordinators within each school. Step targets should
be individually based as opposed to generic targets which may de-motivate less active
students. The provision of more extra-curricular sports clubs may help increase
physical activity levels.
From a behaviour change perspective, Lowe, Horne, Tapper, Bowdery, and Egerton
(2004) identified taste exposure, modelling, and rewards to reliably influence
children’s eating behaviours. Horne, Hardman, Lowe, and Rowlands (2007) applied
the combination of peer modelling and rewards to an intervention study aimed to
increase children’s physical activity behaviour. Children aged 9 – 11 years from an
experimental (n = 47) and control (n = 53) school took part in the study. For the
experimental and control group, a baseline measure of activity was taken over eight
days. Pedometers were used to provide total steps per day. During the intervention
(eight school days), the experimental school received peer modelling via fictional
characters who provided encouragement to be physically active. Targets of an
increase of 1500 steps per day for each participant’s baseline measure were set. To
receive a daily reward, a participant needed to reach or exceed their individual target.
Children were free to check their pedometers, in order to receive feedback concerning
progression towards their targets. Researchers checked each participant’s pedometer
daily, and provided either verbal praise if targets were met or verbal encouragement if
targets were not met. For the control group another eight days of physical activity
measures were taken, with no intervention taking place. Follow-up measures of
physical activity (eight days) were taken for both groups 12 week after the
intervention period.
Results indicated that for girls from the experimental school, steps per day were
significantly higher during the intervention and at follow-up compared to baseline and
girls from the control school. For boys from the experimental school, daily steps were
higher during the intervention compared to baseline and boys from the control school,
but not compared to boys from the control school at follow-up. It was concluded that
peer modelling, rewards and pedometer feedback were effective in increasing levels
of habitual physical activity in 9 – 11 year old children. There was a suggestion that
121
with the boys being more active than girls at baseline, and with high targets being set
(≥ 16000 steps per day), an activity ceiling level may have been reached, which was
harder to maintain over time. Further, it was suggested that the involvement of parents
would be beneficial due to the strong influence of parental support and
encouragement. However, there is a suggestion that tangible rewards can be
detrimental to children’s intrinsic motivation (Deci, Koestner, & Ryan, 1999), which
may negate the true effect of an intervention. The use of interventions using only
feedback and reinforcement in reaching behaviour goals may be warranted.
Based on the results of this thesis so far and the above previous research, a small scale
intervention was designed that aimed to reduce the time children spend in low
intensity activity, through the use of physical activity education and pedometer
feedback. The objective was to increase the ambulatory activities of children, which
may promote improved psychological well-being.
An increase in daily steps (e.g. walking), will provide the means to increase physical
activity (Horne et al., 2007; Stathi et al., 2006) while reducing the time spent in the
lower physical activity intensities. Although both the very light and vigorous
intensities have shown associations with various psychological well-being constructs,
it is suggested that the use of pedometers provides a simple way to monitor one’s
activity levels, with the aim of reducing time in very light intensity activity. The use
of a vigorous intervention however, would need very careful planning so it does not
appear as a form of compulsory physical activity, which for some children may be
counterproductive (Lagerberg, 2005). Previous research (Barr-Anderson et al., 2007;
Murtagh, Dixey, & Rudolf, 2006) has shown that structured forms of physical activity
(e.g. sports) provide a lack of enjoyment or confidence for many children. Indeed,
vigorous interventions may be ineffective or counterproductive for those not good at
skill games and sport generally, or for overweight children with fears of being teased
(Lagerberg, 2005). Further, the use of an intervention aimed at increasing daily steps
would minimise the time researchers spend with the participants and thus reduce
researcher effects from increased physical activity by just being there (Hawthorne
effect).
122
The primary hypothesis is that the experimental group will increase total physical
activity, with reduced time spent in the very light intensity activity compared to the
control group. Further, the reduction of time in the lowest activity intensities will be
displaced into higher activity intensities for the experimental group. A secondary
hypothesis is that concurrent with the changes in physical activity, improved
psychological well-being, particularly at the physical self-worth sub-domain level,
may occur for the experimental group.
Any increases in physical activity are likely to impact primarily on physical selfworth and its sub-domains, as a result of the theoretical link between physical selfworth and physical activity (Fox & Corbin, 1989; Sonstroem et al., 1994; Study 4). It
is less likely that changes will occur directly at the domain and global level, due to the
stability of these constructs (Fox, 1999). However, behaviours and improvements at
the sub-domain level if repeated often enough can eventually filter up to the domain
and global levels (Fox & Corbin, 1989). Indeed, Study 4 displayed a direction of
causality between physical activity and physical self-worth sub-domains, which may
indirectly affect psychological well-being. With this in mind, psychological measures
will be taken for physical self-worth and its sub-domains. However, current with the
theme of this thesis, it would seem prudent to also examine if any effects occurred at
the global psychological well-being level.
8.2 Method
Participants
Ethical clearance was granted by the School’s Ethics Committee (reference 10/07/07
#8) before commencement of the study. Participants were 35 children from two
primary schools in the South West of England. The children’s ages ranged between 9
and 10 years. The two schools provided an experimental (n=20) and control (n=15)
group.
General Procedures
There were approximately 30 children across the year group in each school providing
a maximum sample of 60 children. Information letters outlining the study, and what
would be required from the parents and children, were sent home with the children,
along with consent forms. A total of 35 consent forms, signed by parent and child,
123
were returned to the schools prior to the study commencing. The researchers have no
details on the 25 children who did not participate in the study. All data were collected
within the term time.
Experimental group: The objective of the intervention was to reduce the amount of
time spent in sedentary and very light intensity activity by increasing the amount of
ambulatory activity over a six-week period. Following the baseline measure, the
intervention comprised an education and familiarisation session (one 30-minute
session). During the session children were introduced to the importance of being
physically active (via a powerpoint presentation) and completed a practical session
about wearing the pedometer, taking and recording a reading from the pedometer and
re-setting the pedometer. Each child in the pedometer intervention group received a
target to increase his/her pedometer counts by 10% each day, for that week. This
target change is in line with Croteau (2004) and Waine, Macey, and Parfitt (2006),
and provides a similar increase to that of Horne et al. (2007). The children were free
to monitor their progress at any time during each day. The research team recorded
pedometer counts each week day, with the children recording weekend counts. This
process continued each week during the period of the intervention (six-weeks).
In line with Horne et al. (2007), each weekday children received a standardised verbal
comment from the research team based on their count. ‘well done, same again
tomorrow’, if the target was achieved, and ‘that was close, maybe tomorrow’, if the
target was not achieved. Further, each week, the children received a bar chart
highlighting their counts for the previous week and how each day compared to their
target count (see Appendices 4D & 4E for example, pp. 210 – 211). To reinforce
understanding, the education session was presented a further time half way through
the intervention. This incorporated a mini-quiz (missing key words to assess if
learning had occurred) and additional tips for reducing sedentary activities. The
children were encouraged to show and discuss with their parents the weekly counts
chart and the education received (via a print version of the Powerpoint education, see
Appendices 4C, pp. 207 – 209) in order to encourage family involvement.
Control Group: The control group was provided with a basic education session at the
start of the project during which the importance of being physically active was
124
explained The group also received the education sessions half-way through the
intervention period. There was no further contact with the participants, other than
during the weeks of the data sampling.
Experimental Group
Control Group
Baseline measure &
Education
Baseline measure &
Education
Week 1
(pre-test 1)
Pedometer intervention and
Week 2-4
(data sample 1)
data sample at week 4
No Contact until week
4 for data sample
Christmas Holiday
Baseline measure &
Education
Pedometer intervention
Post data sample
measure
Week 5
(pre-test 2)
Baseline measure &
Education
Week 6-8
No Contact
Week 9
(post-test)
Post data sample
measure
Figure 8.1: Intervention procedures timeline.
As a result of negotiations with the participating schools and time constraints of the
research team, the nine-week data collection period was the maximum time available
for the study, which allowed for six weeks of intervention procedures.
Data Sampling
Accelerometers were used at baseline to estimate the children’s PA levels and time
spent in PA intensities for both experimental and control group. The accelerometer
data also provided the average daily steps of the experimental group, on which the
initial 10% increase was based. This was followed by three weeks of the intervention.
Another data sample of the PA levels (via accelerometers) for both groups was taken
during the third week of the intervention. Due to the Christmas break and
125
measurement timings, a four week gap occurred during the intervention period.
Therefore, a third data sample (post Christmas) was a repeat of the baseline measure
procedures. After a further three weeks of the intervention, final post measures of PA
levels for both groups were taken. At each of the four data sampling periods all
children (both groups) completed three psychological well-being questionnaires. A
number of initiatives (Table 8.1) to increase the compliance of accelerometer and
pedometer wearing were incorporated (Trost et al., 2005; conference call 2005).
Table 8.1: Recommended accelerometer compliance strategies
Compliance Strategy
Make personal connection with participants
and teachers
Make monitors socially desirable (cool)
Provide instruction sheets for (non)wearing
Activity logs (time and reason for non
wearing)
Show participants what data will look like
How Incorporated
Meetings were held with Head and class
teachers, teaching assistants and the
participants, prior to commencement of the
study.
At meetings it was suggested that their year
group was specially selected to wear the
motion sensors and nobody else in the school
would be wearing them.
Instruction sheets were provided at all four
data collections for accelerometer wearing.
These were provided for accelerometer
wearing at all four data collections periods.
Graphical data from a previous study was
shown at baseline measures highlighting
compliant and non-compliant wearing of
accelerometers (see Appendices 4F pp. 212)
Assessment of Psychological Well-being
Three questionnaires were used to assess the children’s psychological well-being. The
State-Trait Anxiety Inventory for Children (STAIC; Speilberger et al., 1973), the
Child Depression Inventory (CDI; Kovacs & Beck, 1977), and the Children and
Youth’s Physical Self-Perception Profile (CY-PSPP; Whitehead, 1995). These
inventories all provide trait measures of the constructs being assessed.
Details of the psychological measures used, are given in the General Methods section
(Chapter 3).
Assessment of Physical Activity
During data sampling periods the Actigraph GT1M (Monrovia, CA) uniaxial
accelerometer was used to measure PA levels of both experimental and control group.
126
The Actigraph is one of the most common uniaxial accelerometers used in research,
with many examples of its use in the literature, dating back to the CSA monitor, the
MTI, and currently the GT1M. This model of accelerometer has been shown to be a
valid and reliable physical measurement tool for pre-school children (Kelly et al.,
2004; Pate et al., 2006), children (Eston et al., 1998; Metcalf et al., 2002; Puyau et al.,
2002; Treuth et al., 2004), and adults (Metcalf et al., 2002; Welk et al., 2004).
The GT1M version weighs 27grams, with dimensions of 3.8 x 3.7 x 1.8 cm. Due to
the sporadic activity patterns of children; a 5-second epoch was used, in order to avoid
underestimation of time spent in vigorous intensities (Trost et al., 2005). To be
included in the analyses, at least four days of data, including at least one weekend
day, needed to be recorded at each of the four data collection time-points. To be
classified as a day, a weekday needed to have at least ten hours of data, with a
weekend day needing at least eight hours of data. In order to provide intensity levels,
5-second epochs were converted into published METs for the sedentary, moderate and
vigorous intensities (Esliger et al., 2005; Mattocks et al., 2007; see Table 8.2).
However, there are no current published data to provide a very light category, so the
very light and light categories where derived from the University of Exeter School of
Sport and Health Sciences unpublished lab data.
Table 8.2: Actigraph activity counts and physical activity intensity
a
Intensity
Sedentary
Accelerometer
0 to 4a
Mets Equivalent
0
Sample Activity
No Movement
Very Light
4.01 to87.5
Up to 1.9 METs
Light
87.5 to 192
1.9 to 3 METs
Seated to Standing
(minimal movement)
Playing Catch
Moderate
192.01 to 510b
3 to 6 METs
Walking/Hopscotch
Vigorous
>510b
>6 METs
Running
from Esliger et al. (2005); b from Mattocks et al. (2007)
The Digiwalker DW-200 (Yamax, Tokyo, Japan) electronic pedometer was used to
count daily steps during the intervention periods for the experimental group. The
pedometer measures vertical oscillations, and has been shown to be a valid and
127
reliable measure of assessing physical activity in children (Eston et al., 1998; McKee
et al., 2005). The step-function of the Actigraph was used to provide the baseline step
measurements, and has been validated as providing an equivalent step count during
free-living ambulatory activities as the Yamax model pedometer (Le Masurier &
Tudor-Locke, 2003).
Data Analysis
Descriptive data (mean and SD) were calculated for age, accelerometer counts,
minutes spent at each intensity level, and psychological well-being constructs across
the four time-points. 2 (group) X 4 (time; pre-test 1, data sample 1, pre-test 2, post
test) factor ANOVA’s were used to highlight main effects for group and time, and any
interactions effects of group and time for all physical activity and psychological wellbeing measures. Where Mauchly’s Test of Sphericity was significant a GreenhouseGeisser epsilon was adopted. Pairwise comparisons were used to follow-up any main
effects for time or group. Bonferroni corrected t-tests were used to follow-up
significant interactions. All data analyses were performed using the SPSS (15.0)
statistical package, with alpha set 0.05.
Mean (± 1 SD) Daily Physical Activity
(accelerometer counts)
8.3 Results
650000
a
600000
550000
a
500000
450000
400000
350000
300000
250000
200000
pre-test 1
data sample 1
pre-test 2
post-test
Time-points
a = significant difference
Figure 8.2: Total daily physical activity across the four time-points (combined groups)
128
2 (group) X 4 (time) Repeated Measures ANOVA’s
A total of 16 participants (eight experimental group, eight control group) provided
adequate physical activity data for analysis (mean age 9.31 ± 0.48y). Figure 8.2
displays a significant main effect for time on average daily activity (f(3,42) = 3.65, p
< 0.05). Pre-test 2 (mean = 377515, ± 110699 cts/day) displayed significantly lower
physical activity compared to post-test (mean = 451198, ± 117548 cts/day). There was
no main effect for group or interaction effect of time and group, on average daily
Mean (± 1 SD) Minutes Spent in the
Sedentary Activity
activity.
680
a, b, c
630
a
b
Experimental Group
580
Control Group
530
c
480
pre-test 1
data
sample 1
pre-test 2
post-test
Time-points
a & b = significant within group differences
c = significant between group differences
Figure 8.3: Average daily time spent in sedentary behaviour across the four timepoints for the experimental and control groups
Figure 8.3 displays a significant interaction effect for time and group on time spent in
sedentary behaviour (f(1.89, 26.54) = 12.46, p < 0.01). There was a significant
difference at pre-test 1 (t(14) = 5.32, p < 0.01), with the experimental group (mean =
639 minutes, S.D. = 30.01) spending more time in sedentary behaviour compared to
the control group (mean = 550 minutes, S.D. = 36.21). At data sample 1, there was no
significant difference (t(14) = 0.061, p > 0.05) between the experimental (mean = 573
minutes, S.D. = 44.55) and control group (mean = 570 minutes, S.D. = 43.09). There
were no significant differences between groups at pre-test 2 and post-test. For the
129
experimental group, there was a significant (t(7) = 9.961, p < 0.001) decrease in time
spent in sedentary behaviour from pre-test 1 (mean = 639 minutes, S.D. = 30.01) to
data sample 1 (mean = 571 minutes, S.D. = 44.55) and post-test (mean = 573, S.D. =
39.87) respectively. There were no significant differences between time-points for the
control group. A time main effect was recorded (f(3,42) = 5.76, p < 0.01), this is
explained by the interaction. There was no significant main effect for group, on time
spent in sedentary behaviour.
An ANCOVA was run to assess whether differences between groups were still
present after controlling for baseline differences. A significant main effect for group
(f(1,13) = 14.17, p < 0.01) demonstrated that the experimental group was significantly
higher than the control group. Therefore baseline group differences did not explain the
findings and the group difference can be attributed to the intervention. There were no
Mean (± 1 SD) Minutes Spent in Very
Light Intensity Activity
time or interaction effects.
210
190
c
170
150
Experimental Group
130
Control Group
110
a
b
90
70
a, b, c
50
pre-test 1
data
sample 1
pre-test 2
post-test
Time-points
a & b = significant within group differences
c = significant between group differences
Figure 8.4: Average daily time spent in very light intensity activity across the four
time-points for the experimental and control groups
Figure 8.4 displays a significant interaction effect for time and group, on time spent in
very light intensity activity (f(3,42) = 20.87, p < 0.01). There was a significant
difference at pre-test 1 (t(14) = 8.11, p < 0.001), with the experimental group (mean =
130
97 minutes, S.D. = 18.20) spending less time in very light intensity activity compared
to the control group (mean = 165 minutes, S.D. = 15.01). At data sample 1, there was
no significant difference (t(14) = 0.506, p > 0.05) between the experimental (mean =
158 minutes, S.D. = 38.97) and control group (mean = 150 minutes, S.D. = 21.75).
There were no significant differences between groups at pre-test 2 and post-test. For
the experimental group, there was a significant (t(7) = 7.12, p < 0.001) increase in
time spent in very light intensity activity from pre-test 1 (mean = 97 minutes, S.D. =
18.20) to data sample 1 (mean = 158 minutes, S.D. = 38.97) and post-test (mean =
150.58, S.D. = 24.88) respectively. There were no differences between time-points for
the control group. A time main effect was recorded (f(3,42) = 8.01, p < 0.01), this is
explained by the interaction. There was no significant main effect for group, on time
spent in very light intensity activity.
An ANCOVA was run to assess whether differences between groups were still
present after controlling for baseline differences. A significant main effect for group
(f(1,13) = 19.37, p < 0.01) demonstrated that the experimental group was significantly
lower than the control group. Therefore baseline group differences did not explain the
findings and the group difference can be attributed to the intervention. There were no
Mean (± 1 SD) Minutes Spent in
Light Intensity Activity
time or interaction effects.
75
c
65
Experimental Group
55
Control Group
45
a
35
b
a, b, c
25
pre-test 1
data
sample 1
pre-test 2
post-test
Time-points
a & b = significant within group differences
c = significant between group differences
Figure 8.5: Average daily time spent in light intensity activity across the four timepoints for the experimental and control groups
131
Figure 8.5 displays a significant interaction for time and group, on time spent in light
intensity activity (f(1.76,24.66) = 4.35, p < 0.05). There was a significant difference at
pre-test 1 (t(14) = 4.13, p < 0.001), with the experimental group (mean = 41 minutes,
S.D. = 6.75) spending significantly less time in light intensity activity compared to the
control group (mean = 59 minutes, S.D. = 9.94). At data sample 1, there was no
significant difference (t(14) = 0.906, p > 0.05) between the experimental (mean = 52
minutes, S.D. = 10.62) and control group (mean = 56 minutes, S.D. = 9.22). There
were no significant differences between groups at pre-test 2 and post-test. For the
experimental group, there was a significant (t(7) = 5.80, p < 0.001) increase in time
spent in light intensity activity from pre-test 1 (mean = 41 minutes, S.D. = 6.75) to
data sample 1 (mean = 52 minutes, S.D. = 10.62) and post-test (post-intervention;
mean = 51.64 minutes, S.D. = 8.71) respectively. There were no differences between
time-points for the control group. A time (f(1.76,24.66) = 8.01, p < 0.01) and group
(f(1,14) = 8.01, p < 0.01) main effect were recorded, these are explained by the
interaction.
An ANCOVA was run to assess whether differences between groups were still
present after controlling for baseline differences. A significant main effect for group
(f(1,13) = 4.79, p < 0.05) demonstrated that the experimental group was significantly
lower than the control group. Therefore baseline group differences did not explain the
findings and the group difference can be attributed to the intervention. There were no
time or interaction effects.
132
Mean (± 1 SD) Minutes Spent in
Moderate Intensity Activity
90
80
a
a, b
70
b
60
50
40
30
20
10
0
pre-test 1
data sample 1
pre-test 2
post-test
Time-points
a & b = significant differences
Figure 8.6: Average daily time spent in moderate intensity activity across the four
time-points (combined groups).
Figure 8.6 displays a significant main effect for time, on time spent in moderate
intensity activity (f(3,42) = 8.01, p < 0.01). Pre-test 1 (mean = 53 minutes, S.D. = 16)
and post-test displayed (mean = 54 minutes, S.D. = 15) significantly more time spent
in moderate intensity activity compared to pre-test 2 (mean = 47 minutes, S.D. = 16).
There was no significant main effect for group or interaction effect for time and
group, on time spent in moderate intensity activity. There was no significant main
effect for time or group, and no significant interaction effect for time and group, on
time spent in vigorous intensity activity.
133
Table 8.3: Descriptive data experimental group
Anxiety
Time-point 1
Mean
S.D.
33
6.78
Time-point 2
Mean
S.D.
30.88
6.24
Time-point 3
Mean
S.D.
30.38
8.67
Time-point 4
Mean
S.D.
23.13
11.02
Depression
7
3.20
8.63
5.90
10
9.36
8.63
8.47
Global
Self-esteem
19.25
2.05
18.25
5.28
19.25
3.33
18.38
3.46
Physical
Self-worth
17.25
3.62
18.75
4.37
18
4.72
18.25
1.90
Sport/Athletic
Competence
17.75
3.41
18.75
4.71
18.38
4.98
17.25
1.83
Physical
Condition
17.5
3.63
19.5
6.18
19.25
5.04
19.50
2.79
Attractive
Body
18.13
3.85
18.26
3.77
18.13
3.48
18.18
2.07
Strength
Competence
16.88
2.75
18
5.30
17.75
4.37
17
4.34
Table 8.4: Descriptive data control group
Anxiety
Time-point 1
Mean
S.D.
33.63
5.40
Time-point 2
Mean
S.D.
34.25
5.42
Time-point 3
Mean
S.D.
28.5
7.15
Time-point 4
Mean
S.D.
33.13 11.32
Depression
16.38
10.38
16.19
10.78
12
6.05
13.38
8.98
Global
Self-esteem
18.63
2.77
16.88
3.88
19.25
2.76
16.50
2.14
Physical
Self-worth
17.38
2.13
17.38
2.06
17.38
2.06
17.38
2.72
Sport/Athletic
Competence
17.38
5.34
18
4.99
17.13
5.87
15
2.56
Physical
Condition
19.5
3.07
19
3.34
18.50
2.27
17.38
2.62
Attractive
Body
16.13
2.75
16.38
3.29
15.63
3.81
15.63
2.97
Strength
Competence
17.88
3.72
15.88
3.83
16.63
3.16
16.50
2.93
Tables 8.3 & 8.4, display the psychological well-being descriptive data for the
experimental and control groups. For all psychological well-being variables, there
were no significant main effects for time or group, and no significant interaction
effect for time by group.
134
8.4 Discussion
The primary hypothesis was that the experimental group would increase total physical
activity, with reduced time spent in the very light intensity activity compared to the
control group. Further, the reduction of time in the lowest activity intensities will be
displaced into higher activity intensities for the experimental group. A secondary
hypothesis was that concurrent with the changes in physical activity, improved
psychological well-being, particularly at the physical self-worth sub-domain level,
may occur for the experimental group.
Only one primary hypothesis was supported with the experimental group reducing
time spent in sedentary behaviour following baseline and maintained this drop,
whereas no decrease was seen in the control group. However, total daily physical
activity did not significantly increase for the experimental group, compared to the
control group. The secondary hypothesis, that changes in physical activity would be
accompanied by an improvement in psychological well-being constructs, was not
supported.
8.4.1 Changes in time spent in physical activity intensities
The experimental group did significantly reduce time spent in sedentary behaviour
from baseline (pre-test 1) to data sample 1, whereas no decrease was seen in the
control group. This significant difference was also apparent from baseline to post-test,
whereas no decrease was seen in the control group during the study. The reduced
time appears to be displaced into very light and light intensity activities with a
significant increase in time spent in these intensities from baseline to data sample 1
for the experimental group compared to the control group. Further, these reductions
and increases remained stable between subsequent time-points, with no further
significant differences for the experimental group and a significant difference from
baseline to post intervention. This suggests the intervention was effective for the
reduction of sedentary behaviours.
A lack of interaction effects at the moderate and vigorous intensities is not
unexpected, as the intervention was designed to reduce time in the lower intensities.
There may have been an expected increase in time spent in moderate intensity
activity, considering walking is an example activity of the moderate cut-points used
135
(Mattocks et al., 2007), and the aim of other intervention studies (Horne et al., 2007;
Stathi et al., 2006). However, given the increase in very light and light activities, the
children may have been performing activities below the moderate threshold (e.g.
fidgeting, jiggling of the leg, walking at a slower pace), which were still being
registered as steps by the pedometer.
8.4.2 Lack of changes in psychological well-being constructs
The lack of significant difference within and between groups at the global
psychological well-being level was not totally unexpected, considering the relatively
stable nature of these constructs (Fox, 1999) and the short intervention period.
Previous models of self-esteem, suggest the sub-domain level being more susceptible
to change (Fox & Corbin, 1989; Shavelson et al., 1976), with changes over longer
periods potentially leading to improvements at higher esteem levels. It was at this subdomain level that any significant changes were expected to occur.
Study 4 suggested a potential positive impact on psychological well-being from
physical activity, for a normal population of children. However, an explanation for
this lack of sub-domain and indeed lack of change at the global level may be due to a
combination of a six-week intervention period not being long enough to promote
positive changes, and the normal population used. Previous research highlights the
normal scores for the current sample. Physical self-worth scores from previous studies
range from 17 – 19.3 (Crocker et al., 2000; Raudsepp et al., 2002; Raustorp et al.,
2005; Whitehead, 1995), with the current samples physical self-worth scores ranging
from 17.25 – 18.72. Physical self-worth sub-domain scores from previous research
range from 15.54 – 18.54 (Crocker et al., 2000; Raudsepp et al., 2002; Raustorp et al.,
2005; Whitehead, 1995), with the current samples physical self-worth sub-domain
scores ranging from 17.40 – 18.94. Self-esteem scores from previous studies range
from 16.92 – 20.04 (Harter, 1985; Muris et al., 2003; Parfitt & Eston, 2005; Welk &
Eklund, 2005; Whitehead, 1995), with the current samples self-esteem score 18.25 –
19.25. Indeed, previous review articles suggests those with initially low levels of selfesteem may particularly benefit from increased physical activity (Fox, 2000).
Therefore, alongside the short intervention period, the relative high esteem scores in
the present study may explain the lack of effect.
136
Another explanation for a lack of change in the psychological well-being constructs
may be due to no reduction in time spent in very light activity. The previous studies of
this thesis highlighted an association and causal direction between the very light
intensity and psychological well-being, and thus, the intervention aimed to reduce
time spent in very light intensity activity. However, the intervention reduced time in
sedentary behaviour, but this time was displaced into the very light intensity causing a
slight increase, as opposed to the desired reduction. Further, based on Study 4, effects
would be expected via the attractive body sub-domain and the physical self-worth
domain (see Figure 7.7). However, compared to Study 3 (physical self-worth: 17.65 ±
4.50; attractive body: 16.58 ± 4.62) and Study 4 (physical self-worth: 17.96 ± 2.64;
attractive body: 16.94 ± 2.82) these two constructs already displayed higher scores
(see Table 8.3), potentially providing less room for change.
8.4.3 Changes in total daily physical activity
The results of the repeated measures ANOVA’s highlighted that although the
experimental group did increase total daily physical activity, this was not a significant
increase compared to the control group. However, the intervention period was during
the winter months, where potentially relatively few daylight hours and poor weather
reduced the opportunities to be physically active. Also, as suggested by Horne et al.
(2007), a ceiling effect may have occurred, where for some boys the step counts were
exceeding 15000 steps per day. This high step count coupled with reduced
opportunities to be physically active may have provided unattainable targets. Another
potential explanation is the highly active students may not have been motivated
enough to merely increase daily steps, as they were already involved in more vigorous
activities (Stathi et al., 2006). Statistically, the small sample sizes and large standard
deviations at all time points may have provided the lack of significant differences.
8.4.4 Methodological Limitations
The break in the intervention due to the Christmas holiday may have been
problematic, as it led to the intervention running for three weeks before and three
weeks after the Christmas period. The break highlighted for both groups, that total
daily physical activity and the time spent in very light through to vigorous intensity
activity, reduced post Christmas. This may be in part due to the daily school schedule
and activities outside school (e.g. sports clubs) yet to return to a proper structured
137
format, post Christmas. However for the experimental group, time spent in sedentary
behaviour, very light and light intensity activity at post-test returned to approximately
the same level as data sample 1. Although it may seem beneficial to have one solid
intervention period, Christmas and other holidays are naturally occurring time periods
for children and may warrant inclusion in future studies.
Although measures were taken in order to increase compliance of motion sensor
wearing, the lack of adequate physical activity data provided by the participants was
problematic. The study initially started with 35 participants, however after four data
collections only 16 or 46% of the participants provided adequate data for all timepoints. However, a decreasing availability of adequate data was also experienced for
the ‘Schools on the Move’ project (Stathi et al., 2006), even though the study had a
large sample size (1966). This represents that attrition of participants is a possibility
and needs to be considered when designing a study of this nature. The current
intervention did involve children in the experimental group remembering to wear
accelerometers on four 7-day occasions and pedometers everyday over a six-week
period. Although the teachers and parents were involved, this may have been a lot to
ask considering the prospective memory of children, is still developing (Kerns, 2000).
Prospective memory refers to remembering to carry out an intended action at some
point in the future (Kvavilashvili, Messer, & Ebdon, 2001), for example,
remembering to put on a motion sensor. However, involving teachers and parents
more as role models, by giving them their own pedometers and step targets, may have
provided better compliance and motivation for the participants to wear their
accelerometers and pedometers (Horne et al. 2007; Stathi et al., 2006).
A strength of the ‘Schools on the Move’ project (Stathi et al., 2006) were the
evaluation procedures employed at the time of the study. These included
questionnaires assessing general opinions about the project, motivation inventories,
semi-structured interviews and focus groups. These procedures presented information
regarding the strengths and weaknesses of the study and participant motives for
initially taking part and continuing in the study. A weakness of the current study was
the lack of evaluation procedures, and the lack of knowledge about the strengths and
weaknesses of the study and potential motivation issues related to continued
138
participation in the study. The inclusion of evaluation procedures would be
imperative, as an ongoing process to improve and inform future intervention studies.
The sample size provides a key limitation with only eight participants in each group.
Due to the heterogeneity of previous studies, the comparability of effect sizes is
problematic (Salmon et al., 2007). However, a power analysis (see appendix 5B for
graph, pp. 215) highlights that the current sample of 16 participants would have the
potential to detect any large effects, with only a small increase in participants (from
16 to 22), potentially detecting medium sized effects (as defined by Cohen, 1988). In
order to detect any small sized effects the number rises considerably to approximately
130 participants. A number this size can be problematic as the recruitment of children
for research purposes is not always easy, particularly when the use of school time is
being asked for.
8.4.5 Conclusions
The current study was successful in reducing the time children spent in sedentary
behaviour level while displacing this time into very light and light intensity activity.
However, this change in physical activity did not result in an improvement in any
psychological well-being constructs. This may be partly explained by the small
population, or the need for a longer intervention period. The main weaknesses of the
study were highlighted, with the need for a larger sample size at commencement of a
study involving the use of motion sensors. Further, evaluation procedures need to be
implemented in order to assess participation motives and the strengths and
weaknesses of an intervention protocol.
139
Chapter 9: General Discussion
9.1 Main Findings
This series of studies aimed to overcome the problems of previous research and
investigated the relationship between children’s psychological well-being and habitual
physical activity. Accelerometry assessed children’s physical activity and provided
measures of total physical activity and time spent in physical activity intensities.
Psychological well-being was assessed through measures of anxiety, depression, and
global, domain, and sub-domain self-esteem. Three cross-sectional, one longitudinal
and one intervention study were used to assess the relationship between children’s
psychological well-being and habitual physical activity. Analyses used included
correlation, hierarchical regression, multi-level modelling and factorial ANOVA’s.
The main findings of this thesis are that a relationship exists between children’s
psychological well-being and habitual physical activity, primarily time accumulated
in very light and vigorous intensity activity. Furthermore, physical activity was shown
to indirectly influence psychological well-being, with a reciprocal influence occurring
between physical activity and the sub-domains of physical self-worth. More
specifically, the research addressed a) the identification of a relationship between
children’s chronic habitual physical activity and psychological well-being, b) the
validation of the hierarchical model of self-esteem and the role of importance, c) the
direction of causality between children’s psychological well-being and habitual
physical activity, and finally d) a small scale physical activity intervention to improve
the psychological well-being of children was conducted. Sections 9.1.1 – 9.1.6 will
give an overview of the findings of the above research areas. Section 9.2 will address
potential limitations of the research area, with section 9.3 detailing potential future
research directions.
9.1.1 The Relationship Between Children’s Psychological Well-being and
Habitual Physical Activity
The primary focus of Studies 1 and 3 (chapters 4 and 6) was the investigation of the
relationship between children’s psychological well-being and physical activity, using
an objective measure (accelerometry) of physical activity. The use of accelerometry
aimed to overcome the limitations of previous research (e.g. self-report physical
activity measures) by providing a more accurate measure of total activity and time
spent at different physical activity intensities. The first Study (chapter 4) identified a
140
negative relationship between global self-esteem and time spent in very light intensity
activity (r = .262, p < 0.05). Further analysis showed that those spending less time in
very light intensity activity had higher global self-esteem scores compared to those
spending the most time in very light intensity activity. The results lend some support
to Parfitt and Eston (2005) who reported a significant relationship between total
activity (measured by pedometery) and global self-esteem.
Study 3 (chapter 6) extended Study 1 by measuring physical activity across two
seasons and expanding self-esteem to include a hierarchical model of self-esteem (see
pages 24 and 30 for models) and account for the effect of body fat. The results
highlighted significant relationships for time spent in very light intensity activity with
anxiety (r = .384), depression (r = .345), global self-esteem (r = -.385), scholastic
competence (r = -.341), physical appearance (r = -.385), physical self-worth (r = .388), and attractive body (r = -.297). Significant relationships were also identified for
time spent in vigorous intensity activity with anxiety (r = -.310), scholastic
competence (r = .281), social acceptance (r = .338), sport/athletic competence (r =
.335), and physical condition (r = .324). Many of these relationships remained after
accounting for the influence of body fat. Further analysis showed that those children
spending the most time (> 261 minutes) in very light intensity activity had inferior
psychological well-being scores compared to those spending a middle amount of time
(< 228 minutes) in very light activity. From this cross-sectional analysis it was
hypothesised that a reduction of around 40 minutes of time spent in the very light
intensity may help to improve the psychological profiles of children.
Both of these studies have clearly highlighted that a relationship exists primarily
between time spent in very light and vigorous intensity activity, and psychological
well-being. These relationships occur at the global well-being level, the self-esteem
domain level, and the physical self-worth sub-domain level, which is in line with
previous research (Crocker et al., 2000; Parfitt & Eston, 2005; Raudsepp et al., 2002;
Raustorp et al., 2005) and theorised models of self-esteem (Fox & Corbin, 1989;
Shavelson et al., 1976; Sonstroem et al., 1994; Whitehead, 1995). Importantly the use
of accelerometry provided a more accurate measure of total activity and time spent at
different physical activity intensities. However, as this research was cross-sectional,
the direction of the associations between psychological well-being and physical
141
activity were unclear. Therefore, the focus of study 4 (chapter 7) was the direction of
the relationship between psychological well-being and physical activity, using a
longitudinal study design and multi-level modelling analysis. During the crosssectional studies, the application of the hierarchical model of self-esteem to the
current sample was examined (study 2). The next section addresses the findings from
this chapter.
9.1.2 The Hierarchical Model of Self-esteem and the Role of Importance
Study 2 (chapter 5) allowed the examination of whether the current data fitted with,
and further validated, the proposed hierarchical models of self-esteem (Shavelson et
al., 1976; Whitehead, 1995). The mediating properties of physical self-worth were
also assessed, along with the role of importance. The global self-esteem domains
displayed a strong prediction of global self-esteem for both boys and girls (Boys: R2 =
.839; Girls R2 = .681). The same strong predictions were also displayed for physical
self-worth sub-domains with physical self-worth for both boys and girls (Boys: R2 =
.669; Girls R2 = .582). These R2 values were in line with previous research (Fox &
Corbin, 1989; Whitehead, 1985). Similar R2 values were displayed for the data
contained within studies 3 and 4 (chapters 6 and 7). Further, physical self-worth was
shown to mediate the associations of it’s sub-domains with global self-esteem for all
but one sub-domain. These findings highlight that the data contained within this thesis
adequately capture and support the hierarchical model of children’s global self-esteem
and physical self-worth.
The role of importance and discrepancies was supported in line with proposed theory
(Fox, 1990; Harter, 1985). However, the role of importance and discrepancies is
currently open to debate. Marsh, (1993) and Marsh & Sonstroem, (1995), using
multiple regression models, suggest there is little support for importance discrepancy
models in the prediction of both global and physical self-esteem. However, Hardy and
Moriarty (2006) claim alternative regression models provide evidence of strong and
moderate discounting which challenges previous importance hypothesis thinking. The
ensuing argument (Hardy & Leone, 2008; Marsh, 2008) based on a nomothetic or
ideographic style of data analysis currently provides a much clouded picture of
importance theory.
142
Hardy and Moriarty (2006) argue that importance should be determined
idiographically. Nomothetic refers to the study of a cohort of individuals, subjects are
seen as representing a class or population. Idiographic refers to the study of the
individual, properties setting him/her apart from other individuals. In self-esteem
terms, for a nomothetic approach, one would be considered to have high importance
for a particular domain of esteem if that importance rating were higher than other
people’s importance rating for the same domain (Hardy & Moriarty, 2006). For an
idiographic approach, one would be considered to have high importance for a
particular domain if that importance rating were higher than the same participant’s
rating of importance for other domains (Hardy & Moriarty, 2006).
This idiographic approach was assessed by entering the three most important and
three least important domains as two separate blocks in hierarchical regression
analysis, with the order of entry then reversed and analysed. Hardy and Moriarty’s
(2006) results showed that the three most important domains explained more variance
in general self-esteem than the three least important domains for the total sample and
all sub-groups within the total sample. Furthermore, there was sufficient evidence to
dispute Marsh and colleagues conclusions. The more important domains of selfesteem had a stronger influence on global self-esteem than the less important
domains, at the idiographic level.
The argument for which method is best to assess the effect of importance on selfesteem continues (see Marsh, 2008 and Hardy & Leone, 2008). However, while
conceptually an ideographic approach is defendable, it is not currently feasible to
adopt it with children. Adult self-esteem scales assess approximately 12 self-esteem
domains. With only five self-esteem domains proposed for children (Harter, 1985)
ranking of the most important and least important may be impractical. Further, the
cognitive ability of children to differentiate across domains would need to be
considered.
In addition to these issues relating to the measurement of the importance construct in
children, is the problem of scale reliability. Low Cronbach’s alpha’s within previous
research and this thesis (-.01 to .6), questions whether any further analysis should be
applied if importance ratings are not being properly captured. A consensus on the
143
analysis of importance data and the investigation of a more reliable importance
measure are warranted.
9.1.3 The Direction of the Relationship Between Children’s Psychological Wellbeing and Habitual Physical Activity
With a relationship between psychological well-being and physical activity
established within studies 1 and 3, study 4 (chapter 7) allowed for the examination of
the direction of the relationship between psychological well-being and physical
activity. A 12-month longitudinal study design was employed with the use of multilevel modelling for analysis of directional relationships. As proposed, the domains of
global self-esteem significantly predicted changes in global self-esteem in a positive
direction. The global self-esteem domains also significantly predicted changes in
anxiety and depression in a negative direction (i.e. esteem increased, anxiety and
depression decreased). At the domain level, the sub-domains of physical self-worth
positively predicted changes in physical self-worth, and other global self-esteem
domains. Further, time spent in very light intensity activity negatively predicted
physical self-worth. At the sub-domain level, time spent in very light intensity activity
significantly predicted changes in attractive body in a negative direction, with time
spent in vigorous intensity activity positively predicting changes in sport/athletic
competence and strength competence. The identified directional relationships,
particularly between physical activity, physical self-worth and its sub-domains were
in line with the data from the cross-sectional study (Study 3, chapter 6) of this thesis.
With the psychological variables as predictors of total daily activity and time spent at
different physical activity intensities, physical self-worth significantly predicted
changes in time spent in very light and light intensity activity in a negative direction.
Perceptions of sport/athletic competence significantly predicted changes in average
daily and time spent in light, moderate and vigorous intensity activity. Further,
reciprocal effects were identified for physical self-worth and time spent in very light
intensity activity.
The causal direction and reciprocal effect identified were unique as this was the first
study to assess the causal relations between psychological well-being and physical
activity in children using accelerometry. The results did lend some support to previous
144
research, which identified effects for physical self-worth sub-domains and selfreported physical activity (Knowles et al., 2009), and reciprocal effects for physical
self-concept and exercise behaviour (Marsh et al., 2006). The results of these studies
and this thesis highlight that the direction of the relationship between psychological
well-being and physical activity is reciprocal. This potentially identifies both physical
activity and psychological well-being areas for researchers and physical activity
educators to address and intervene at, in order to maintain and improve children’s
psychological well-being.
Importantly, two physical activity pathways were identified, which could indirectly
affect psychological well-being. A reduction in time spent in very light intensity
activity may, through changes in sub-domain and domain perceptions, filter up to
improve psychological well-being. Conversely, an increase in time spent in vigorous
intensity activity may provide, although through different routes, the same
psychological well-being improvements. These proposed pathways are in line with
suggestions from study 3 (chapter 6). Further, incorporating the psychological
reciprocal components, physical self-worth and sport/athletic competence, through
potentially skill development hypothesis (Sonstroem, 1997) may further assist with
well-being improvements. Although physical activity provided a directional
relationship with psychological well-being, it is unknown how much time
(reduction/increase) is needed to produce potential psychological improvements.
However, the reduction of time spent in very light intensity activity (40 minutes) and
an increase to over 30 minutes of vigorous intensity activity, as suggested in Study 3,
may provide potential guidelines for physical activity interventions in future studies.
During the cross-sectional and longitudinal studies both anxiety and particularly
depression contributed very little to the associations and direction of associations
between psychological well-being and physical activity and is represented by a lack of
discussion in the study chapters. This may be primarily due to the role of self-esteem.
As previously discussed (p. 18 & 21) self-esteem is the core component of
psychological well-being, with low levels of self-esteem causing anxiety and
depression, with high levels of self-esteem providing resilience against anxiety and
depression. Therefore, self-esteem may subsume the relationships between anxiety,
145
depression and physical activity, with previous research highlighting this concept
(Parfitt & Eston, 2005). Further research is needed to investigate this notion.
9.1.4 The Lack of Associations Between Daily Total Daily Physical Activity and
Psychological Well-being: An Anomaly?
A continued theme throughout the data collection, culminating in study 4 (chapter 7)
was the lack of any relationships between total daily physical activity and any of the
psychological well-being constructs. At first this may seem somewhat of an anomaly.
Previous research investigating the relationship between psychological well-being
constructs and total daily physical activity have consistently found a significant
relationship when using self-report (Crocker et al., 2000; Knowles et al., 2009; Motl
et al., 2004; Raudsepp et al., 2002) and pedometry (Parfitt & Eston, 2005; Raustorp et
al., 2005) measures of physical activity. However, when assessing the physical
activity measurements used in the above studies, this may provide reasoning for the
current lack of relationships.
The use of a self-report measure with children is problematic, due to children having
difficulty in recalling their sporadic activity patterns (Armstrong & Bray, 1991).
Therefore, a child will not be able to remember every single movement that occurred
during a day, which would lead to an underestimation of total physical activity
(Esliger & Tremblay, 2007), with more moderate and vigorous activities likely to be
recalled. Further, many physical activity questionnaires only measure leisure-time
physical activity, which only accounts for a relatively small proportion of daily
physical activity (Tremblay, Esliger, Tremblay, & Colley, 2007).
Pedometry correlates well with accelerometry (Rowlands et al., 1999), however,
pedometers only measure steps. The light and particularly very light intensity
movements may not be registered by a pedometer, again leading to an
underestimation of total physical activity. Further, pedometry may underestimate
vigorous activity due to the inability to account for increased energy expenditure,
through increased stride length during running (Rowlands & Eston, 2005). This may
also lead to an underestimation in total activity. Therefore, it can be argued that selfreport and pedometry measures of physical activity only measure a proportion of total
physical activity, and this proportion of measured physical activity provides
146
significant relationships with the psychological well-being constructs of previous
research.
The use of accelerometry in this thesis provides a measurement of the sporadic
movements of children throughout a day, which may lead to activity counts
incorporating all movements and activities (bar static and water based activities).
Therefore, data in this thesis suggests, it may not be the total amount of physical
activity a child does which is important, but the balance of time spent across the
physical activity intensities, which contributes to the relationship with psychological
well-being. Further research is needed to examine this notion further.
Factors included in the analysis of the studies, which may have affected the activity –
well-being relationship were socio-economic status and body fat. SES removed the
significant relationship between self-esteem and time spent in very light intensity
activity in study 1 (chapter 4). However, SES did not remove any significant
relationships with a more chronic measure of physical activity accounting for seasonal
variation in physical activity (study 3, chapter 6), or for the longitudinal study (study
4, chapter 7). This lends support to previous studies suggesting no relationship of SES
with physical activity in children (Kelly et al., 2006; Kristensen et al., 2008; Thomas
et al., 2006).
In this group of children, SES did not affect the relationship between psychological
well-being and physical activity. However, the post-code based SES measure assumes
socio-economic homogeneity within an area (Demissie, Hanley, Menzies, Joseph, &
Ernst, 2000) and may not have been sensitive enough to fully reflect the SES status of
these children. In conjunction with an area-based measure of SES, future research
could include an individual-based measure of SES. Questions containing information
about, for example, parental education and income may provide a more sensitive
measure of SES.
The children used in this thesis were all from a similar fairly rural geographical
location, with access to school, and out of school facilities of similar availability. This
potentially may also have led to the lack of SES effect. Studies incorporating a wider
range of children from potentially different SES backgrounds may yield an effect for
147
SES. Different backgrounds may include children from inner-city locations and
children from private schooling.
The negative effect of body fat on psychological well-being was particularly evident
for the attractive body sub-domain in both the cross-sectional (Study 3) and
longitudinal (Study 4) studies. As suggested, this may not be too surprising due to the
documented associations between body dissatisfaction and fatness (Duncan et al.,
2006). The negative effect of body fat was also evident for the physical appearance
domain, and the sports/athletic competence and physical condition sub-domains of the
longitudinal study. The associated fat loss with an increase in physical activity and
sports participation may help to improve perceived competence in the physical selfworth sub-domains. Indeed, physical activity intervention research has highlighted
that increased physical activity leads to reduced body image dissatisfaction and
improved physical self-perceptions in adolescent girls with low perceptions of body
image (Burgess, Grogan, & Burwitz, 2006), and improved physical self-perceptions in
overweight and obese children (Goldfield et al., 2007).
9.1.5 An Intervention to Improve Children’s Psychological Well-being.
The previous studies within this thesis highlighted a relationship and the direction of
this relationship, primarily between children’s psychological well-being and time
spent in very light and vigorous intensity activities. Study 5 (chapter 8) aimed to
reduce the time children spent in the lower physical activity intensities, by increasing
children’s daily step count through the use of physical activity education and
pedometer feedback over a six-week period. It was proposed that an increase of time
spent in ambulatory activities, may have promoted an improvement in psychological
well-being.
Despite the inclusion of several strategies to improve the wearing of the motion
sensors, the data analysis was severely impaired by the lack of adequate physical
activity data provided by the children, with only 46 % (16 children) providing
complete sets. However, those children in the experimental group who adhered to the
intervention programme, did reduce time sedentary behaviours and increase time in
very light and light intensity activity, compared to a control group. This lent support
to previous research aiming to increase physical activity through the use of pedometry
148
(Horne et al., 2007; Stathi et al., 2006). In spite of this, there were no changes in time
spent in moderate or vigorous intensity activity or any of the psychological well-being
constructs measured. It is likely that the six-week period may not have been long
enough to improve particularly the physical self-worth of this normal population of
children. Further, the aim of reducing time spent in very light intensity activity was
not met. The use of a larger sample size, the inclusion of evaluation procedures, and
greater control over the timing of an intervention, may potential improve future
intervention studies by providing a more adequate amount of data.
9.1.6 Overall Summary and Conclusions
The main findings from the research studies, as documented in this thesis, were that
relationships between psychological well-being and habitual physical activity exist for
a normal population of children aged 9-11 years old. The directions of these
relationships were identified, with time spent in very light and vigorous intensity
activity highlighting the potential for focusing on specific activity intensities for
future intervention research. The relationships and direction of relationships identified
were unique as they represented the first use of accelerometers when assessing the
relationship between children’s psychological well-being and physical activity. The
thesis has also shown that while it is possible to reduce the time accumulated in lower
intensity activity during a six-week intervention, this did not translate into improved
psychological well-being. More stringent intervention protocols are warranted.
9.2 Limitations
9.2.1 Questionnaires
Although the reliability and validity of the psychological questionnaires used in this
thesis has been esablished, the use of questionnaires does have problematic issues.
Certain assumptions must be made with self-report use, for example, the language
competence of the respondents and that the constructs under investigation are
established (Davis-Kean & Sandler, 2001). Further, issues relating to socially
desirable responding can influence scoring, for example, the desire to appear
competent in a self-esteem domain, not to appear anxious in certain settings, or the
need for approval from peers (Butler & Gasson, 2005). Particularly for the self-esteem
measures, the wording may provide a potential obstacle. The similar wording of the
six items for each domain/sub-domain may be frustrating for some children (Butler &
149
Gasson, 2005). Also, it has been suggested that the alternative structured format of the
SPPC and CY-PSPP may be conceptually demanding for some children leading to
incomplete and incorrect filling-out of this type of inventory (Marsh & Holmes, 1990;
Shevlin, Adamson, & Collins, 2003). Further, it should be acknowledged that the
combining of the SPPC and the CY-PSPP into one larger measure may have been
taxing for some children, particularly those with shorter attention spans. It would be
recommended that administration over several sessions (within seven to ten days) may
be more effective in reducing cursory and socially desirable responding, and present a
less taxing environment for the children. However, the time constraints applied to data
collections in this thesis did not provide this opportunity.
Although HRQOL questionnaires were proposed to provide only a basic measure of
psychological well-being or mental health (see pp. 11), this may provide a
measurement avenue for future research and ease the burden of the larger
questionnaires used in this thesis. Recently, Ravens-Sieberer et al. (2008) has shown
evidence of convergent validity, with confirmatory factor analysis confirming the
validity of the dimensional structure of the KIDSCREEN-52 HRQOL questionnaire
for children and adolescents. Applicable dimensions within this questionnaire include
psychological well-being, self-perceptions, and mood and emotions, which provide a
measure of positive and negative affect. The utility of a HRQOL questionnaire within
the research area of this thesis may warrant further investigation.
9.2.2 Accelerometers
The use of the RT3 accelerometer allowed the measurement of physical activity in
three planes of motion. However, only a one-minute epoch could be applied over a
seven-day measurement period. As previously discussed (section 2.6.8.2, pp. 50) this
may have led to an underestimation of time spent in the moderate and vigorous
intensities. However, at the commencement of the thesis only the RT3 model
accelerometer was available to measure children’s physical activity. The Actigraph
model accelerometer is able to provide a five-second epoch over a seven-day
measurement period, and provide a better estimation of time spent in moderate and
vigorous intensity activity. The Actigraph model became available for use with the
intervention study (chapter 8). Although this represented a change in accelerometer
model, it did provide an epoch which is suggested to be better for capturing the
150
spontaneous nature of children’s physical activity. Also, the Actigraph model
provided a step-count function comparable to that of a pedometer, which provided a
blinded baseline measure in the intervention study. Further, this also provided an
accelerometer more widely used and extensively validated in children’s physical
activity research (McClain & Tudor-Locke, 2008).
The issue of intensity threshold cut-points has been previously discussed (section
2.6.8.1, pp. 47). With many published threshold available particularly for the
Actigraph model, physical activity data can be interpreted in different ways.
Estimation of time spent in different physical activity intensities varies according to
the thresholds employed. This may have affected the results for the current sample of
children. However current work (Stone, Rowlands, & Eston, 2009) suggests
relationships detected with health were consistent regardless of whether sample
specific thresholds, or published thresholds, were applied to accelerometer measured
children’s physical activity. It was concluded that researchers should employ
published thresholds to ensure greater comparability between studies.
Although there are potential limitations with the use of accelerometers, they do
currently offer one of the better feasibility/ validity trade-offs (Esliger & Tremblay,
2007), and are becoming a more widely used measure of physical activity, when
objective measurement is a feasible option. Further, reviews of accelerometers for
research purposes provide current information on the best procedures when using
accelerometers to measure physical activity (for reviews see McClain & Tudor-Locke,
2008; Rowlands, 2007; Trost et al., 2005; Ward et al., 2005).
9.2.3 Sample Size
It should be acknowledged that a larger sample size within the cross-sectional studies
would have provided more power to the data analysis and improved the
generalisability of results to the population under investigation. Cohen, (1988, 1992)
suggests a power of .8 should be achieved when detecting an effect. Previous crosssectional research in the area of this thesis has shown a participant number of 35
sufficient to provide a significant correlation of r = .46 (Parfitt & Eston, 2005).
Research with larger samples has shown a participant number of 119 providing
significant effects as low as r = .17 (Raudsepp et al., 2002). This suggests the number
151
of participants in the current research was adequate to detect effects of a moderate to
large size. However, the current cross-sectional studies were under powered for the
detection of small effects (as defined by Cohen, 1988), and therefore more susceptible
to type II error at the lower effect sizes. Indeed, power analysis highlights for this type
of research, a sample size of 57 (lowest sample size of cross-sectional studies) would
detects effects of approximately ≥ .35, with power at the .8 level, and a sample size of
> 100 to detect effect sizes < .3 (see appendix 5A for graph, pp. 214).
9.3 Future Directions
9.3.1 Further Validation of the Direction of the Relationship between Children’s
Psychological Well-being and Physical Activity
Further validation of the directional relationships and reciprocal effects identified in
this thesis may be warranted. If the direction of the relationship between time spent in
very light and vigorous intensity activity and psychological well-being constructs can
be consistently established, this would provide important information for intervention
studies aimed at psychological well-being improvement. Further, guidelines and
recommendations for the maintenance and improvement of children’s psychological
well-being can be provided. Other areas to investigate would be the changes in the
psychological well-being – physical activity relationships, from childhood to
adolescence, and the changes that occur during adolescence. This may be particularly
applied to the body and appearance constructs, which can be problematic for girls
(Harter, 2000). Also, adolescents can vary greatly, with regard to whether selfevaluations are positive or negative, as judgements about one’s attributes and abilities
become more abstract (Harter, 2003). These changes in self-evaluations and changes
in bodily appearance, coupled with the associated decline in physical activity (Pate et
al., 2002), may provide differing relationships as children move into adolescence. For
example, the Knowles et al., (2009; crossref
pp. 97) study used a sample of
adolescent girls (N = 150; mean age = 12.79 ± 0.31), and identified physical condition
to be an important predictor of physical activity change, which differs to the findings
in this thesis where sport/athletic competence was the main predictor of physical
activity.
152
9.3.2 Intervention Study
A replication of the study 5 (chapter eight) is certainly warranted, with an aim of
overcoming the previous limitations associated with study 5 (small sample size,
evaluation procedures, timing of the intervention). Further, only the effect on
psychological well-being, from a reduction in sedentary behaviours has been assessed.
The effect of time spent in vigorous intensity activity has also been highlighted in the
cross-sectional (chapter six) and longitudinal (chapter seven) studies of this thesis,
with a suggested increase in vigorous physical activity to promote psychological
health. A physical activity intervention could contain a group aiming to increase
vigorous activity, a group aiming to reduce very light behaviours, and a control group.
The effect of any changes in very light and vigorous intensity activity on
psychological well-being could then be assessed.
As previously discussed (p. 122), an intervention focusing on vigorous intensity
activity would need careful planning so not to be counter productive. However,
children enjoy different types of activity (Lagerberg, 2005). Therefore, variety in a
vigorous intervention would be of importance. Furthermore, having a choice of
activity in a vigorous intervention, and autonomy/control associated with it, will
theoretically enhance the motivation to be active (Ryan & Deci, 2000) and adhere to
it. As a consequence, interventions to increase or change a child’s physical activity
behaviour would be largely independent of an instructor. However, an intervention to
increase vigorous activity that is not instructor dependent would require that the child
was able to regulate and perceive when they were working at the appropriate
intensity. A valid and reliable child based perceived exertion scale could be used to
achieve this. Study 3 suggests an increase to over 30 minutes a day of vigorous
intensity activities is associated with a more positive psychological profile. As with
study 5, simply increasing the amount of ambulatory activity accumulated during the
day, would reduce the time spent in sedentary behaviour. Data from Parfitt and Eston
(2005) would suggest that a final target of over 12,000 steps per day in children, with
study 3 suggesting a reduction of around 40 minutes per day of very light intensity
activity, would be associated with improved well-being.
153
9.3.3 The Relationship between Children’s Psychological Well-being and
Patterns of Habitual Physical Activity
The relationships identified in this thesis have used a measure of time accumulated in
physical activity intensities. However, given the sporadic nature of children’s physical
activity, the importance of very short bouts of activity to aspects of health has been
questioned (Riddoch et al., 2007) and may provide an area for future reasearch.
Evidence suggests that physical activity accumulated in 10-minute bouts may be
beneficial to adult health (Hardman, 2001), with some guidelines for youth
recommending bouts lasting five to ten minutes (Health Canada, 2002). However,
this hypothesis of a minimum duration of activity was based on self-report data,
which is less likely to include sporadic or incidental physical activity (Esliger &
Tremblay, 2007).
Objective measurement of children’s physical activity suggests children’s activity
bouts rarely last beyond five to ten minutes (Trost, Pate, Sallis, Freedson, Taylor, &
Dowda, 2002). Indeed, previous observational research in children aged 6-10 years
demonstrated that the mean duration of activity bouts was approximately 20 seconds
(Bailey et al., 1995). Rowlands et al. (2007) identified that a large proportion of
children’s physical activity was much shorter than five minutes, particularly bouts
lasting ≥ 4 seconds for activity accumulated in the moderate and vigorous intensity.
Rowlands et al. (2007) suggests it is unclear whether it is necessary for children to
accumulate prolonged bouts of physical activity for health, or whether short bouts are
just as important, and how this differs depending on the area of health under
investigation.
Recent research (Stone, Rowlands, Middlebrooke, Jawis, & Eston, 2009) investigated
the relationship between short activity bouts (≥ 4 seconds) and various physiological
health variables (waist circumference, aerobic fitness, blood pressure, microvascular
function). It was shown that relationships between aspects (frequency, duration,
intensity) of short bouts of activity (≥ 4 seconds) and the health variables were just as
strong as relationships identified with longer bouts (> 5-minutes). Therefore, placing
emphasise on frequent short bouts of activity, may be as appropriate as longer bouts
of activity. It would be of interest to investigate whether these short bouts of activity
are also associated with psychological health, particularly at the vigorous intensity
154
where the majority of accumulated time is made up of short bouts of activity
(Rowlands et al., 2007; Stone et al., 2009). This may extend further to the application
of physical activity interventions, where activities can be more suited to what children
actually do, which may lead to greater adherence (Rowlands et al., 2007).
This programme of research has highlighted a relationship between children’s
psychological well-being and physical activity. At a cross-sectional level of research,
these relationships predominately occurred at very light and vigorous intensity
activity. Longitudinal research identified the direction of these relationships, with
changes in time spent in very light and vigorous intensity activity having the potential
to indirectly affect psychological well-being. It is suggested a reduction of around 40
minutes per day of time spent in very light intensity activity or an increase to over 30
minutes per day of vigorous intensity activity may be beneficial to children’s
psychological health. Intervention research achieved the aim of reducing the time
children spent in the lower physical activity intensities. However, this change in
physical activity did not affect psychological well-being. The promotion of physical
activity for the maintenance and potential improvement of children’s psychological
well-being is warranted.
Current guidelines suggest children should engage in 60 minutes of moderate intensity
activity each day in order to promote and maintain health. Indeed, some suggest that
90 minutes of moderate intensity physical activity is more applicable to physical
activity guidelines for children (Andersen et al., 2006). This current research suggests,
of the proposed 60 and 90 minutes and physical activity each day, 30 minutes of
physical activity should take place in the vigorous intensity to impact on children’s
psychological well-being. Further, if coupled with a reduction of around 40 minutes in
very light intensity activity, this may provide an optimal physical activity level to
impact on children’s psychological well-being.
155
References
Alonso, J., Angermeyer, M. C., Bernert, S., Bruffaerts, R., Brugha, T. S., Bryson, H.,
et al. (2004). Prevalence of mental disorders in Europe: results from the
European Study of the Epidemiology of Mental Disorders (ESEMeD) project.
Acta Psychiatrica Scandinavia Suppl(420), 21-27.
Andersen, L. B., Harro, M., Sardinha, L. B., Froberg, K., Ekelund, U., Brage, S., et al.
(2006). Physical activity and clustered cardiovascular risk in children: a crosssectional study (The European Youth Heart Study). The Lancet, 368(9532),
299-304.
Angold, A., Costello, E. J., Erkanli, A., & Worthman, C. M. (1999). Pubertal changes
in hormone levels and depression in girls. Psychological Medicine, 29(05),
1043-1053.
Armstrong, N., & Bray, S. (1991). Physical activity patterns defined by continuous
heart rate monitoring. Archives of Disease in Childhood, 66(2), 245-247.
Armstrong, N., & Mechelen, W. V. (1998). Are Young People Fit and Active? In S.
Biddle, J. F. Sallis & N. Cavill (Eds.), Young and Active? Young People and
Health-Enhancing Physical Activity - Evidence and Implications (pp. 69-97).
London: Health Education Authority.
Armstrong, N., & Welsman, J. (1997). Young People and Physical Activity. Oxford:
Oxford University Press.
Armstrong, N., & Welsman, J. R. (2001). Peak oxygen uptake in relation to growth
and maturation in 11- to 17-year-old humans. European Journal of Applied
Physiology, 85(6), 546-551.
Armstrong, N., Welsman, J. R., & Kirby, B. J. (2000). Longitudinal changes in 11-13year-olds' physical activity. Acta Paediatrica, 89(7), 775-780.
Atkin, A. J., Gorely, T., Biddle, S., Marshall, S. J., & Cameron, N. (2008). Critical
hours: physical activity and sedentary behavior of adolescents after school
Pediatric Exercise Science, 20(4), 446-456.
Bailey, R. C., Olson, J., Pepper, S. L., Porszasz, J., Barstow, T. J., & Cooper, D. M.
(1995). The level and tempo of children's physical activities: an observational
study. Medicine and Science in Sports and Exercise, 27(7), 1033-1041.
Bailey, R. P. (2000). Movement development and the primary school child. In R. P.
Bailey & T. M. Macfadyen (Eds.), Teaching physical education 5–11.
London: Continuum.
Balon, R. (2005). Measuring anxiety: are we getting what we need? Depression and
Anxiety, 22(1), 1-10.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in
social psychological research: conceptual, strategic, and statistical
considerations. Journal of Personality and Social Psychology 51(6), 11731182.
Barr-Anderson, D. J., Young, D. R., Sallis, J. F., Neumark-Sztainer, D. R., Gittelsohn,
J., Webber, L., et al. (2007). Structured physical activity and psychosocial
correlates in middle-school girls. Preventive Medicine, 44(5), 404-409.
Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., & Erbaugh, J. (1961). An
inventory for measuring depression. Archives of General Psychiatry, 4, 561571.
Bhatia, S. K., & Bhatia, S. C. (2007). Childhood and adolescent depression. American
Family Physician 75(1), 73-80.
156
Biddle, S., & Ekkekakis, P. (2005). Physicall active lifestyles and well-being. In F. A.
Huppert, N. Baylis & B. Keverne (Eds.), The Science of Well-being (pp. 141170). Oxford: Oxford Press University.
Biddle, S., & Mutrie, N. (2008). The Relationship Between Physical Activity and
Anxiety and Depression: Can Physical Activity Beat the blues and Help with
Your Nerves. In Psychology of Physical Activity: Determinants, Well-being
and Interventions. London: Routledge.
Biddle, S. J. H. (1997). Cognitive Theories of Motivation and the Physical Self. In K.
R. Fox (Ed.), The Physical Self: From Motivation to Well-being (pp. 59-82).
Leeds: Human Kinetics.
Biddle, S. J. H., Fox, K. R., & Boutcher, S. H. (2000). Physical Activity and
Psychological Well-being. London: Routledge.
Biddle, S. J. H., Gorely, T., & Stensel, D. J. (2004). Health-enhancing physical
activity and sedentary behaviour in children and adolescents. Journal of Sports
Sciences, 22(8), 679.
Biddle, S. J. H., Sallis, J. F., & Cavill, N. (1998). Young and Active? Young People
and Health-enhancing Physical Activity: Evidence and Implications. London:
Health Education Authority.
Bieling, P. J., Antony, M. M., & Swinson, R. P. (1998). The State-Trait Anxiety
Inventory, Trait version: structure and content re-examined. Behaviour
Research and Therapy, 36(7-8), 777-788.
Birkeland, M. S., Torsheim, T., & Wold, B. (2008). A longitudinal study of the
relationship between leisure-time physical activity and depressed mood among
adolescents. Psychology of Sport and Exercise, 10(1), 25-34.
Birleson, P., Hudson, I., Buchanan, D. G., & Wolff, S. (1987). Cinical evaluation of a
self-rating scale for depressive disorder in childhood (Depression Self-Rating
Scale). Journal of Child Psychology and Psychiatry, 28(1), 43-60.
Bonilla, J., Bernal, G., Santos, A., & Santos, D. (2004). A revised Spanish version of
the Beck Depression Inventory: psychometric properties with a Puerto Rican
sample of college students. Journal of Clinical Psychology, 60(1), 119-130.
Boreham, C., & Riddoch, C. (2001). The physical activity, fitness and health of
children. . Journal of Sports Sciences, 19(12), 915.
Brady, E. U., & Kendall, P. C. (1992). Comorbidity of anxiety and depression in
children and adolescents. Psychology Bulletin, 111(2), 244-255.
Brage, S., Brage, N., Franks, P. W., Ekelund, U., Wong, M.-Y., Andersen, L. B., et al.
(2004). Branched equation modeling of simultaneous accelerometry and heart
rate monitoring improves estimate of directly measured physical activity
energy expenditure. Journal of Applied Physiology, 96(1), 343-351.
Brent, D. A., & Birmaher, B. (2002). Adolescent Depression. The New England
Journal of Medicine 347(9), 667-671.
Burgess, G., Grogan, S., & Burwitz, L. (2006). Effects of a 6-week aerobic dance
intervention on body image and physical self-perceptions in adolescent girls.
Body Image, 3(1), 57-66.
Butler, R. J., & Gasson, S. L. (2005). Self esteem/self concept scales for children and
adolescents: a review. Child and Adolescent Mental Health, 10, 190-201.
Calfas, K. J., & Taylor, W. C. (1994). Effects of physical activity on psychological
variables in adolescents Pediatric Exercise Science, 6(4), 406-423.
157
Camacho, T. C., Roberts, R. E., Lazarus, N. B., Kaplan, G. A., & Cohen, R. D.
(1991). Physical activity and depression: evidence from the Alameda County
Study. American Journal of Epidemiology, 134(2), 220-231.
Carey, M. P., Faulstich, M. E., Gresham, F. M., Ruggiero, L., & Enyart, P. (1987).
Children's Depression Inventory: construct and discriminant validity across
clinical and nonreferred (control) populations. Journal of Consulting and
Clinical Psychology, 55(5), 755-761.
Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity,
exercise, and physical fitness: definitions and distinctions for health-related
research. Public Health Reports, 100(2), 126-131.
Chorpita, B. F., & Barlow, D. H. (1998). The development of anxiety: the role of
control in the early environment. Psychological Bulletin, 124(1), 3-21.
Cicchetti, D., & Toth, S. L. (1998). The development of depression in children and
adolescents. American Psychologist, 53(2), 221-241.
Cohen, J. (1988). Statisitcal power analysis for the behavioural sciences. New York:
Academic Press.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
Cole, D. A., & Turner, J. E. (1993). Models of cognitive mediation and moderation in
child depression. Journal of Abnormal Psychology, 102(2), 271-281.
Comer, J. S., & Kendall, P. C. (2005). High-end specificity of the children's
depression inventory in a sample of anxiety-disordered youth. Depression and
Anxiety, 22(1), 11-19.
Committee on Evaluation of Children’s Health NRC. Measuring Children’s Health.
(2004). Children’s health the nation’s wealth: assessing and improving child
health. Washington: The National Academic Press.
Cooley, C. H. (1902). Human nature and the social order. New York: Scribner's.
Coon, K. A., Goldberg, J., Rogers, B. L., & Tucker, K. L. (2001). Relationships
between use of television during meals and children's food consumption
patterns. Pediatrics, 107(1), e7-.
Corder, K., Brage, S., Mattocks, C., Ness, A., Riddoch, C., Wareham, N. J., et al.
(2007). Comparison of two methods to assess PAEE during six activities in
children. Med Sci Sports Exerc, 39(12), 2180-2188.
Corder, K., Brage, S., Wareham, N. J., & Ekelund, U. (2005). Comparison of PAEE
from combined and separate heart rate and movement models in children.
Medicine and Science in Sports and Exercise, 37(10), 1761-1767.
Cox, M., Schofield, G., Greasley, N., & Kolt, G. S. (2006). Pedometer steps in
primary school-aged children: A comparison of school-based and out-ofschool activity. Journal of Science and Medicine in Sport, 9(1-2), 91-97.
Cox, R. H. (1985). Sport Psychology: Concepts and Applications. Dubuque: W.C.
Brown.
Crocker, P. R. E., Eklund, R. C., & Kowalski, K. C. (2000). Children's physical
activity and physical self-perceptions. Journal of Sports Sciences, 18(6), 383.
Croteau, K. A. (2004). A preliminary study on the impact of a pedometer-based
intervention on daily steps. American Journal of Health Promotion, 18(3),
217-220.
Csoti, M. (2003). School Phobia, Panic Attacks and Anxiety in Children. London:
Jessica Kingsley Publishers.
158
Curtis, L. J., Dooley, M. D., & Phipps, S. A. (2004). Child well-being and
neighbourhood quality: evidence from the Canadian National Longitudinal
Survey of Children and Youth. Social Science & Medicine, 58(10), 19171927.
Davis-Kean, P. E., & Sandler, H. M. (2001). A Meta-Analysis of Measures of SelfEsteem for Young Children: A Framework for Future Measures. Child
Development, 72(3), 887-906.
De Moor, M. H. M., Beem, A. L., Stubbe, J. H., Boomsma, D. I., & De Geus, E. J. C.
(2006). Regular exercise, anxiety, depression and personality: A populationbased study. Preventive Medicine, 42(4), 273-279.
De Ste Croix, M. B., Armstrong, N., Chia, M. Y., Welsman, J. R., Parsons, G., &
Sharpe, P. (2001). Changes in short-term power output in 10- to 12-year-olds.
Journal of Sports Sciences, 19(2), 141-148.
De Ste Croix, M. B. A., Armstrong, N., Welsman, J. R., & Sharpe, P. (2002).
Longitudinal changes in isokinetic leg strength in 10-14-year-olds. Annals of
Human Biology, 29, 50-62.
Deci, E., & Ryan, R. (1985). Intrinsic Motivation and Self-determination in Human
Behaviour. London: Plenum Press.
Deci, E., & Ryan, R. (2004). Handbook of self-determination research Rochester:
University of Rochester Press.
Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of
experiments examining the effects of extrinsic rewards on intrinsic motivation.
Psychological Bulletin, 125(6), 627-668; discussion 692-700.
Demissie, K., Hanley, J. A., Menzies, D., Joseph, L., & Ernst, P. (2000). Agreement
in measuring socio-economic status: area-based versus individual measures.
Chronic Diseases in Canada, 21(1), 1-7.
Diener, E. (2000). Subjective well-being. The science of happiness and a proposal for
a national index. American Psychologist, 55(1), 34-43.
Dishman, R. K., Renner, K. J., Youngstedt, S. D., Reigle, T. G., Bunnell, B. N.,
Burke, K. A., et al. (1997). Activity wheel running reduces escape latency and
alters brain monoamine levels after footshock. Brain Research Bulletin, 42(5),
399-406.
Dishman, R. K., Washburn, R. A., & Schoeller, D. A. (2001). Measurement of
Physical Activity. Quest 53(3).
Duncan, C., Jones, K., & Moon, G. (1996). Health-related behaviour in context: A
multilevel modelling approach. Social Science & Medicine, 42(6), 817-830.
Duncan, J. S., Hopkins, W. G., Schofield, G., & Duncan, E. K. (2008). Effects of
weather on pedometer-determined physical activity in children. Medicine and
Science in Sports and Exercise, 40(8), 1432-1438.
Duncan, M. J., Al-Nakeeb, Y., Nevill, A. M., & Jones, M. V. (2006). Body
dissatisfaction, body fat and physical activity in British children. International
Journal of Pediatric Obesity, 1(2), 89 - 95.
Duncan, M. J., Al-Nakeeb, Y., Woodfield, L., & Lyons, M. (2007). Pedometer
determined physical activity levels in primary school children from central
England. Preventive Medicine, 44(5), 416-420.
Ekeland, E., Heian, F., & Hagen, K. B. (2005). Can exercise improve self esteem in
children and young people? A systematic review of randomised controlled
trials. British Journal of Sports Medicine, 39(11), 792.
159
Eklund, R. C., Whitehead, J. R., & Welk, G. J. (1997). Validity of the children and
youth Physical Self-Perception Profile: a confirmatory factor analysis.
Research Quarterly for Exercise & Sport, 68(3), 249.
Endler, N. S., & Kocovski, N. L. (2001). State and trait anxiety revisited. Journal of
Anxiety Disorders, 15(3), 231-245.
Epstein, L. H., Smith, J. A., Vara, L. S., & Rodefer, J. S. (1991). Behavioral economic
analysis of activity choice in obese children. Health Psychology, 10(5), 311316.
Esliger, D. W., Copeland, J. L., Barnes, J. D., & Tremblay, M. S. (2005).
Standardizing and optimizing the use of accelerometer data for free-living
physical activity monitoring Journal of Physical Activity and Health, 2(3),
366-383.
Esliger, D. W., & Tremblay, M. S. (2007). Physical activity and inactivity profiling:
the next generation. Canadian Journal of Public Health, 98 Suppl 2, S195207.
Eston, R. G., Rowlands, A. V., & Ingledew, D. K. (1998). Validity of heart rate,
pedometry, and accelerometry for predicting the energy cost of children's
activities. Journal of Applied Physiology, 84(1), 362.
Fairclough, S. J., Butcher, Z. H., & Stratton, G. (2007). Whole-day and segmentedday physical activity variability of northwest England school children.
Preventive Medicine, 44(5), 421-425.
Field, A. (2005). Discovering Statistics Using SPSS. London: Sage.
Fisher, A., Reilly, J. J., Montgomery, C., Kelly, L. A., Williamson, A., Jackson, D.
M., et al. (2005). Seasonality in Physical Activity and Sedentary Behavior in
Young Children. Pediatric Exercise Science, 17(1), 31-40.
Fox, J. E., & Houston, B. K. (1981). Efficacy of self-instructional training for
reducing children's anxiety in an evaluative situation. Behav Res Ther, 19(6),
509-515.
Fox, K., Stathi, A., McKenna, J., & Davis, M. (2007). Physical activity and mental
well-being in older people participating in the Better Ageing Project.
European Journal of Applied Physiology, 100(5), 591-602.
Fox, K. R. (1990). The Physical Self-Perception Profile Manual: Office fo Health
Promotion, Northern Illinois University.
Fox, K. R. (1992). Physical Education and the Development of Self-esteem in
Children. In N. Armstrong (Ed.), New Directions in Physical Education (Vol.
2, pp. 33-54). Leeds: Human Kinetics.
Fox, K. R. (1997). The Physical Self: From Motivation to Well-being. Leeds: Human
Kinetics.
Fox, K. R. (1999). The influence of physical activity on mental well-being. Public
Health Nutrition, 2(3A), 411-418.
Fox, K. R. (2000). The effects of exercise on self-perceptions and self-esteem. In S. J.
H. Biddle, K. R. Fox & S. H. Boutcher (Eds.), Physical Activity and
Psychological Well-being. London: Routledge.
Fox, K. R., & Corbin, C. B. (1989). The physical self-perception profile: development
and preliminary validation. Journal of sport & exercise psychology 11(4), 408.
Freedson, P., Pober, D., & Janz, K. F. (2005). Calibration of accelerometer output for
children. Medicine and Science in Sports and Exercise, 37(11 Suppl), S523530.
160
Fristad, M. A., Emery, B. L., & Beck, S. J. (1997). Use and abuse of the Children's
Depression Inventory. Journal of Consulting and Clinical Psychology, 65(4),
699-702.
Garcia, A. W., George, T. R., Coviak, C., Antonakos, C., & Pender, N. J. (1997).
Development of the Child/Adolescent Activity Log: a comprehensive and
feasible measure of leisure-time physical activity. International Journal of
Behavioral Medicine, 4(4), 323 - 338.
Georgiades, K., Lewinsohn, P. M., Monroe, S. M., & Seeley, J. R. (2006). Major
Depressive Disorder in Adolescence: The Role of Subthreshold Symptoms.
Journal of the American Academy of Child & Adolescent Psychiatry, 45(8),
936-944
Gidlow, C. J., Cochrane, T., Davey, R., & Smith, H. (2008). In-school and out-ofschool physical activity in primary and secondary school children. Journal of
Sports Sciences, 26(13), 1411-1419.
Glied, S., & Pine, D. S. (2002). Consequences and correlates of adolescent
depression. Archives of Pediatrics and Adolescent Medicine 156(10), 10091014.
Goldfield, G. S., Mallory, R., Parker, T., Cunningham, T., Legg, C., Lumb, A., et al.
(2007). Effects of modifying physical activity and sedentary behavior on
psychosocial adjustment in overweight/obese children. Journal of Pediatric
Psychology, jsm017.
Goodman, E., & Whitaker, R. C. (2002). A prospective study of the role of depression
in the development and persistence of adolescent obesity. Pediatrics, 110(3),
497-504.
Goodwin, R. D. (2003). Association between physical activity and mental disorders
among adults in the United States. Preventive Medicine, 36(6), 698-703.
Gore, S. A., Foster, J. A., DiLillo, V. G., Kirk, K., & Smith West, D. (2003).
Television viewing and snacking. Eating Behaviours, 4(4), 399-405.
Gorely, T., Marshall, S. J., Biddle, S. J., & Cameron, N. (2007). Patterns of sedentary
behaviour and physical activity among adolescents in the United Kingdom:
Project STIL. Journal of Behavioural Medicine, 30(6), 521-531.
Granleese, J., & Joseph, S. (1994a). Further psychometric validation of the SelfPerception Profile for Children. Personality and Individual Differences, 16(4),
649-651.
Granleese, J., & Joseph, S. (1994b). Reliability of the Harter Self-Perception Profile
for Children and predictors of global self-worth. Journal of Genetic
Psychology 155(4), 487-492.
Gray, J. A., & McNaughton, N. (2000). The Neuropsychology of Anxiety. Oxford:
Oxford University Press.
Greer, R. D., Dorow, L., Williams, G., McCorkle, N., & Asnes, R. (1991). Peermediated procedures to induce swallowing and food acceptance in young
children. Journal of Applied Behavior Analysis 24(4), 783-790.
Guinhouya, C. B., & Hubert, H. (2008). Incoherence with studies using actigraph mti
among children age 6-12 yr. Medicine and Science in Sports and Exercise,
40(5), 979.
Guinhouya, C. B., Hubert, H., Soubrier, S., Vilhelm, C., Lemdani, M., & Durocher,
A. (2006). Moderate-to-vigorous physical activity among children:
discrepancies in accelerometry-based cut-off points. Obesity, 14(5), 774-777.
161
Hagger, M., Ashford, B., & Stambulova, N. (1998). Russian and british children's
physical self-perceptions and physical activity participation. Pediatric
Exercise Science, 10(2), 137-152.
Hagger, M. S., Biddle, S. J. H., Chow, E. W., Stambulova, N., & Kavussanu, M.
(2003). Physical self-perceptions in adolescence: generalizability of a
hierarchical multidimensional model across three cultures. Journal of CrossCultural Psychology, 34(6), 611-628.
Hankin, B. L., Abramson, L. Y., Moffitt, T. E., Silva, P. A., McGee, R., & Angell, K.
E. (1998). Development of depression from preadolescence to young
adulthood: emerging gender differences in a 10-year longitudinal study.
Journal of Abnormal Psychology, 107(1), 128-140.
Hardman, A. E. (2001). Issues of fractionization of exercise (short vs long bouts).
Medicine and Science in Sports and Exercise, 33(6 Suppl), S421-427.
Hardy, L., & Leone, C. (2008). Real evidence for the failure of the Jamesian
perspective or more evidence in support of It? Journal of Personality, 76,
1123-1136.
Hardy, L., & Moriarty, T. (2006). Shaping self-concept: the elusive importance effect.
Journal of Personality, 74(2), 377-402.
Harter, S. (1982). The Perceived Competence Scale for Children. Child Development,
53, 87-97.
Harter, S. (1985). Manual for the Self-Perception Profile for Children. Denver CO:
University of Denver.
Harter, S. (1987). The determinants and mediational role of global self-worthin
children. In N. Eisenberg (Ed.), Contempory Issues in Developmental
Psychology (pp. 219-242). New York: John Wiley.
Harter, S. (1990). Adolescent self and identity development. In S. S. Feldman & G. R.
Elliot (Eds.), At the Threshold: The Developing Adolescent (pp. 352-387).
Cambridge: Harvard University Press.
Harter, S. (1990). Causes, correlates and the functional role of global self-worth: a
life-span perspective. In R. Sternberg & J. Kolligian (Eds.), Competence
Considered (pp. 67-98). New Haven Yale University Press.
Harter, S. (1993). Causes and consequences of low self-esteem in children and
adolescents. In R. F. Baumeister (Ed.), Self-Esteem: The Puzzle of Low SelfRegard (pp. 87-116). New York: Plenum Press.
Harter, S. (1999). The Construction of the Self: A Developmental Perspective. New
York: Guilford Press.
Harter, S. (2000). Is self-esteem only skin-deep? The inextricable link between
physical appearance and self-esteem. Reclaiming Children and Youth, 9(3),
133-138.
Harter, S. (2003). The development of self-representation during childhood and
adolescence. In S. D. Leary & J. P. Tangney (Eds.), Handbook of Self and
Identity (pp. 610-642). New York: Guilford Press.
Harter, S., Neil, J. S., & Paul, B. B. (2001). Self-development in Childhood. In
International Encyclopedia of the Social & Behavioral Sciences (pp. 1380713812). Oxford: Pergamon.
Harter, S., & Nowakowski, M. (1987). The dimensions of depression profile for
children and adolescents. Denver: University of Denver.
Harter, S., & Pike, R. (1984). The pictorial scale of perceived competence and social
acceptance for young children. Child Development, 55(6), 1969-1982.
162
Health Canada. (2002). Canada’s physical activity guide for youth. Ottawa: Minister
of Public Works and Government Services.
Hendy, H. M. (2002). Effectiveness of trained peer models to encourage food
acceptance in preschool children. Appetite, 39(3), 217-225.
Higgins, E. T. (1989). Self-discrepancy theory: What patterns of self-beliefs cause
people to suffer? In L. Berkowitz (Ed.), Advances in experimental social
psychology (Vol. 22). New York: Academic Press.
Hoos, M. B., Plasqui, G., Gerver, W. J., & Westerterp, K. R. (2003). Physical activity
level measured by doubly labeled water and accelerometry in children. Eur J
Appl Physiol, 89(6), 624-626.
Horne, P. J., Hardman, C. A., Lowe, C. F., & Rowlands, A. V. (2007). Increasing
children's physical activity: a peer modelling, rewards and pedometer-based
intervention. Eur J Clin Nutr, 63(2), 191-198.
Horne, P. J., Tapper, K., Lowe, C. F., Hardman, C. A., Jackson, M. C., & Woolner, J.
(2004). Increasing children's fruit and vegetable consumption: a peermodelling and rewards-based intervention. Eur J Clin Nutr, 58, 1649-1660.
Houston, B. K., Fox, J. E., & Forbes, L. (1984). Trait anxiety and children's state
anxiety, cognitive behaviors, and performance under stress. Cognitive Therapy
and Research, 8(6), 631-641.
Hume, C., Salmon, J., & Ball, K. (2005). Children's perceptions of their home and
neighborhood environments, and their association with objectively measured
physical activity: a qualitative and quantitative study. Health Educ. Res.,
20(1), 1-13.
Hussey, J., Bell, C., Bennett, K., O'Dwyer, J., & Gormley, J. (2007). Relationship
between the intensity of physical activity, inactivity, cardiorespiratory fitness
and body composition in 7-10-year-old Dublin children. Br J Sports Med,
41(5), 311-316.
Jacka, F. N., Pasco, J. A., Dodd, S., Williams, L. J., Nicholson, G. C., & Berk, M.
(2008). Lower levels of physical activity in childhood predict adult depression.
Journal of Affective Disorders, 107(Supplement 1), S58-S59.
James, W. (1892). Psychology: The briefer course. New York: Henry Holt.
John C. Spence, Kerry R. McGannon, & Poon, P. (2005). The Effect of Exercise on
Global Self-Esteem: A Quantitative Review. Journal of Sport & exercise
psychology, 27(3), 311-334.
Kantomaa, M. T., Tammelin, T. H., Näyhä, S., & Taanila, A. M. (2007). Adolescents'
physical activity in relation to family income and parents' education.
Preventive Medicine, 44(5), 410-415.
Kelly, L., Reilly, J. J., Fairweather, S. C., Grant, S. B. S., & Paton, J. Y. (2004).
Comparison of two Accelerometers for Assessment of Physical Activity in
Preschool Children. Pediatr Exerc Sci, 16(4), 324-333.
Kelly, L. A., Reilly, J. J., Fisher, A., Montgomery, C., Williamson, A., McColl, J. H.,
et al. (2006). Effect of socioeconomic status on objectively measured physical
activity. Arch Dis Child, 91(1), 35-38.
Kendall, P. C., Cantwell, D. P., & Kazdin, A. E. (1989). Depression in children and
adolescents: Assessment issues and recommendations. Cognitive Therapy and
Research, 13(2), 109-146.
Kendall, P. C., Hollon, S. D., Beck, A. T., Hammen, C. L., & Ingram, R. E. (1987).
Issues and recommendations regarding use of the Beck Depression Inventory.
Cognitive Therapy and Research, 11(3), 289-299.
163
Kenny, D. A., Kashy, D. A., & Bolger, N. (1998). Data analysis in social psychology.
In D. T. Gilbert, S. T. Fiske & G. Lindzey (Eds.), The Handbook of Social
Psychology (pp. 233-264). Boston: McGraw-Hill.
Kerns, K. A. (2000). The CyberCruiser: An investigation of development of
prospective memory in children. Journal of the International
Neuropsychological Society, 6(01), 62-70.
Kessler, R. C., Andrews, G., Colpe, L. J., Hiripi, E., Mroczek, D. K., Normand, S. L.,
et al. (2002). Short screening scales to monitor population prevalences and
trends in non-specific psychological distress. Psychol Med, 32(6), 959-976.
Keyes, C. L. (2005). Mental illness and/or mental health? Investigating axioms of the
complete state model of health. J Consult Clin Psychol, 73(3), 539-548.
King, K. A., Tergerson, J. L., & Wilson, B. R. (2008). Effect of social support on
adolescents' perceptions of and engagement in physical activity. J Phys Act
Health, 5(3), 374-384.
Kirisci, L., & et al. (1996). Reliability and Validity of the State-Trait Anxiety
Inventory for Children in Adolescent Substance Abusers: Confirmatory Factor
Analysis and Item Response Theory. Journal of Child and Adolescent
Substance Abuse, 5(3), 57-69.
Kline, P. (1999). The Handbook of Psychological Testing (Second ed.). London:
Routledge.
Knowles, A.-M., Niven, A. G., Fawkner, S. G., & Henretty, J. M. (2009). A
longitudinal examination of the influence of maturation on physical selfperceptions and the relationship with physical activity in early adolescent girls.
Journal of Adolescence, 32(3), 555-566.
Korkeila, J. (2000). Measuring aspects of mental health. Retrieved 11/02/06, 2008,
from
http://groups.stakes.fi/NR/rdonlyres/5DD0D44F-9B09-480E-A6684E9285C10976/0/measuringaspectsofmh.pdf
Korkeila, J., Lehtinen, V., Bijl, R., Dalgard, O.-S., Kovess, V., Morgan, A., et al.
(2003). Review Article: Establishing a set of mental health indicators for
Europe. Scand J Public Health, 31(6), 451-459.
Kovacs, M., & Beck, A. T. (1977). An emprical-clinical approach towards a definition
of childhood depression. In J. G. Schulterbrandt & A. Raskin (Eds.),
Depression in childhood: diagnosis, treatment, and conceptual models. (pp. 125). New York: Raven Press.
Kovacs, M., Gatsonis, C., Paulauskas, S. L., & Richards, C. (1989). Depressive
disorders in childhood. IV. A longitudinal study of comorbidity with and risk
for anxiety disorders. Arch Gen Psychiatry, 46(9), 776-782.
Krasnoff, J. B., Kohn, M. A., Choy, F. K. K., Doyle, J., Johansen, K., & Painter, P. L.
(2008). Interunit and Intraunit Reliability of the RT3 Triaxial Accelerometer.
Journal of Physical Activity and Health, 5(4), 527-538.
Kristensen, P. L., Korsholm, L., Møller, N. C., Wedderkopp, N., Andersen, L. B., &
Froberg, K. (2008). Sources of variation in habitual physical activity of
children and adolescents: the European youth heart study. Scandinavian
Journal of Medicine & Science in Sports, 18(3), 298-308.
Kvavilashvili, L., Messer, D. J., & Ebdon, P. (2001). Prospective memory in children:
the effects of age and task interruption. Dev Psychol, 37(3), 418-430.
Ladd, G. W., & Price, J. M. (1986). Promoting Children's Cognitive and Social
Competence: The Relation between Parents' Perceptions of Task Difficulty
and Children's Perceived and Actual Competence. Child Development, 57(2),
446-460.
164
Lagerberg, D. (2005). Physical activity and mental health in schoolchildren: A
complicated relationship. Acta Paediatrica, 94(12), 1699-1701.
Lakdawalla, Z., Hankin, B. L., & Mermelstein, R. (2007). Cognitive theories of
depression in children and adolescents: a conceptual and quantitative review.
Clin Child Fam Psychol Rev, 10(1), 1-24.
Landgraf, J. M., Abetz, L., & Ware, J. A. (1996). The CHQ User's Manual. Boston:
The Health Institute, New England Medical Centre.
Lang, M., & Tisher, M. (1978). Children's Depression Inventory. Victoria: Australian
Council for Educational Research.
Larun, L., Nordheim, L. V., Ekeland, E., Hagen, K. B., & Heian, F. (2006). Exercise
in prevention and treatment of anxiety and depression among children and
young people. Cochrane Database Syst Rev, 3, CD004691.
Lau, J., Eley, T., & Stevenson, J. (2006). Examining the State-Trait Anxiety
Relationship: A Behavioural Genetic Approach. Journal of Abnormal Child
Psychology, 34(1), 18-26.
Lawlor, D. A., & Hopker, S. W. (2001) The effectiveness of exercise as an
intervention in the management of depression: systematic review and metaregression analysis of randomised controlled trials. British Medical Journal,
332, 763-767.
Le Masurier, G., Tudor-Locke, C. (2003). Comparison of pedometer and
accelerometer accuracy under controlled conditions. Medicine and Science in
Sports and Exercise, 35 (5), 867-871.
Lee, A., Fredenburg, K., Belcher, D., & Cleveland, N. (1999). Gender differences in
children's conceptions of competence and motivation in physical education.
Sport, Education and Society, 4(2), 161-174.
Li, H. C., & Lopez, V. (2004). The reliability and validity of the Chinese version of
the Trait Anxiety Scale for Children. Res Nurs Health, 27(6), 426-434.
Liebert, R. M., & Morris, L. W. (1967). Cognitive and emotional components of test
anxiety: a distinction and some initial data. Psychol Rep, 20(3), 975-978.
Livingstone, M. B. E., Robson, P. J., Wallace, J. M. W., & McKinley, M. C. (2003).
How active are we? Levels of routine physical activity in children and adults.
Proceedings of the Nutrition Society, 62(03), 681-701.
Lowe, C. F., Horne, P. J., Tapper, K., Bowdery, M., & Egerton, C. (2004). Effects of
a peer modelling and rewards-based intervention to increase fruit and
vegetable consumption in children. Eur J Clin Nutr, 58, 510-522.
Lytle, L. A., & Perry, C. L. (2001). Applying Research and Theory in Program
Planning: An Example from a Nutrition Education Intervention. Health
Promot Pract, 2(1), 68-80.
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V.
(2002). A comparison of methods to test mediation and other intervening
variable effects. Psychol Methods, 7(1), 83-104.
March, J. S., Parker, J. D. A., Sullivan, K., Stallings, P., & Conners, C. K. (1997). The
Multidimensional Anxiety Scale for Children (MASC): Factor Structure,
Reliability, and Validity. Journal of Amer Academy of Child & Adolescent
Psychiatry, 36(4), 554-565.
Marsh, H. W. (1986). Global self-esteem: its relation to specific facets of self-concept
and their importance. Journal of personality and social psychology 51(6),
1224-1236.
Marsh, H.W. (1988). The Self-description questionnaire I. San Antonio: The
Psychological Corporation.
165
Marsh, H. W. (1990). Causal ordering of academic self-concept and academic
achievement: a multiwave, longitudinal panel analysis. Journal of Educational
Psychology, 82(4), 646-656.
Marsh, H. W. (1990a). Self Description Questionnaire (SDQ) III. A theoretical and
empirical basis for the measurement of multiple dimensions of late adolescent
self-concept: An interim test manual and a research monograph.
Campbelltown: University of Western Sydney.
Marsh, H. W. (1990b). The structure of academic self-concept: the Marsh/Shavelson
model. Journal of Educational Psychology, 82(4), 623-636.
Marsh, H. W. (1993). Relations between global and specific domains of self : the
importance of individual importance, certainty, and ideals. Journal of
personality and social psychology 65(5), 975-992.
Marsh, H. W. (1994). The importance of being important: theoretical models of
relations between specific and global components of physical self-concept.
Journal of Sport & exercise psychology, 16, 306-325.
Marsh, H. W. (1997). The measurement of self-concept: a construct validation
approach. In K. Fox (Ed.), The Physical Self: From Motivation to Well-being
(pp. 27-58). Leeds: Human Kinetics.
Marsh, H. W. (2008). The elusive importance effect: more failure for the Jamesian
perspective on the importance of importance in shaping self-esteem. Journal
of Personality, 76, 1081-1122.
Marsh, H. W., Byrne, B. M., & Shavelson, R. J. (1988). A multifaceted academic selfconcept: its hierarchical structure and its relation to academic achievement.
Journal of Educational Psychology, 80(3), 366-380.
Marsh, H. W., Byrne, B. M., & Yeung, A. S. (1999). Causal ordering of academic
self-concept and achievement: reanalysis of a pioneering study and revised
recommendations. Educational Psychologist, 34(3), 155 - 167.
Marsh, H. W., Gerlach, E., Trautwein, U., Ludtke, O., & Brettschneider, W. D.
(2007). Longitudinal study of preadolescent sport self-concept and
performance: reciprocal effects and causal ordering. Child Development,
78(6), 1640-1656.
Marsh, H. W., & Holmes, I. W. M. (1990). Multidimensional self-concepts: construct
validation of responses by children. American Educational Research Journal,
27(1), 89-117.
Marsh, H. W., Papaioannou, A., & Theodorakis, Y. (2006). Causal ordering of
physical self-concept and exercise behavior: reciprocal effects model and the
influence of physical education teachers. Health Psychology, 25(3), 316-328.
Marsh, H. W., Richards, G. E., Johnson, S., Roche, L., & Tremayne, P. (1994).
Physical Self-Description Questionnaire: psychometric properties and a
multitrait-multimethod analysis of relations to existing instruments. Journal of
Sport & Exercise Psychology, 16(3), 270-305.
Marsh, H. W., & Sonstroem, R. J. (1995). Importance ratings and specific
components of physical self-concept: relevance to predicting global
components of self-concept and exercise. Journal of Sport & Exercise
Psychology 17(1), 84-104.
Marsh, H. W., & Yeung, A. S. (1997). Causal effects of academic self-concept on
academic achievement: structural equation models of longitudinal data.
Journal of Educational Psychology, 89(1), 41-54.
166
Marshall, S. J., Biddle, S., Sallis, J. F., McKenzie, T. L., & Conway, T. L. (2002).
Clustering of sedentary behaviours and physical activity among youth: a crossnational study. . Medicine and Science in Sports and Exercise, 34(5,S1), 129.
Martinsen, E. (2008). Physical activity in the prevention and treatment of anxiety and
depression. Nordic Journal of Psychiatry, 62, 25-29.
Masse, R., Poulin, C., Dassa, C., Lambert, J., Belair, S., & Battaglini, M. A. (1998).
Elaboration and validation of a tool to measure psychological well-being:
WBMMS. Canadian Journal of Public Health, 89(5), 352-357.
Matthey, S., & Petrovski, P. (2002). The Children's Depression Inventory: error in
cutoff scores for screening purposes. Psychological Assessment, 14(2), 146149.
Mattocks, C., Leary, S., Ness, A., Deere, K., Saunders, J., Tilling, K., et al. (2007).
Calibration of an accelerometer during free-living activities in children.
International Journal of Pediatric Obesity, 2(4), 218 - 226.
Mattocks, C., Ness, A., Leary, S., Tilling, K., Blair, S. N., Shield, J., et al. (2008). Use
of accelerometers in a large field-based study of children: protocols, design
issues, and effects on precision. Journal of Physical Activity & Health, 5 Suppl
1, S98-111.
McAuley, E., Mihalko, S. L., & Bane, S. M. (1997). Exercise and self-esteem in
middle-aged adults: multidimensional relationships and physical fitness and
self-efficacy influences. Journal of Behavioural Medicine, 20(1), 67-83.
McClain, J. J., & Tudor-Locke, C. (2008). Objective monitoring of physical activity
in children: considerations for instrument selection. Journal of Sports Science
and Medicine
McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting
interactions and moderator effects. Psychological Bulletin, 114(2), 376-390.
McDowell, I., & Newell, C. (1996). Psychological well-being. In Measuring Health, a
guide to rating scales and questionnaires (pp. 177-237). Oxford: Oxford
University Press.
McElroy, M. (2002). Resistance to Exercise: A Social Analysis of Physical Inactivity.
Leeds: Human Kinetics.
McKee, D. P., Boreham, C. A. G., Murphy, M. H., & Nevill, A. M. (2005). Validation
of the Digiwalker(tm) pedometer for measuring physical activity in young
children. Pediatric Exercise Science, 17(4), 345.
Meltzer, H., Gatward, R., Goodman, R., & Ford, T. (2003). Mental health of children
and adolescents in Great Britain. International Review of Psychiatry, 15(1-2),
185-187.
Messer, B., & Harter, S. (1986). Manual for the Adult Self-perception Profile. Denver:
University of Denver.
Metcalf, B. S., Curnow, J. S., Evans, C., Voss, L. D., & Wilkin, T. J. (2002).
Technical reliability of the CSA activity monitor: The EarlyBird Study.
Medicine and Science in Sports and Exercise, 34(9), 1533-1537.
Miller. (2000). Cross-cultural validity of a model of self-worth: application to Finnish
children. Social Behavior and Personality, 28(2), 105-118.
Mirowski, J., & Ross, C. E. (1989). Social Causes of Psychological Distress. New
York: Aldyne de Gruyter.
Mo, F., Turner, M., Krewski, D., & Mo, F. D. (2005). Physical inactivity and
socioeconomic status in Canadian adolescents. The International Journal of
Adolescent Medicine and Health 17(1), 49-56.
167
Mooney, C. Z., & Duval, R. D. (1993). Bootstrapping: A Non-parametric Approach
to Statistical Inference. Newbury Park: Sage.
Morris, L. W., Davis, M. A., & Hutchings, C. H. (1981). Cognitive and emotional
components of anxiety: literature review and a revised worry-emotionality
scale. Journal of Educational Psychology, 73(4), 541-555.
Motl, R. W., Birnbaum, A. S., Kubik, M. Y., & Dishman, R. K. (2004). Naturally
occurring changes in physical activity are inversely related to depressive
symptoms during early adolescence. Psychosomatic Medicine, 66(3), 336-342.
Muris, P., Meesters, C., & Fijen, P. (2003). The Self-Perception Profile for Children:
further evidence for its factor structure, reliability, and validity. Personality
and Individual Differences, 35(8), 1791-1802.
Muris, P., Merckelbach, H., Ollendick, T., King, N., & Bogie, N. (2002). Three
traditional and three new childhood anxiety questionnaires: their reliability and
validity in a normal adolescent sample. Behaviour Research and Therapy
40(7), 753-772.
Murtagh, J., Dixey, R., & Rudolf, M. (2006). A qualitative investigation into the
levers and barriers to weight loss in children: opinions of obese children.
Archives of Disease in Childhood, 91(11), 920-923.
Mutrie, N., & Parfitt, G. (1998). Physical activity and its link with mental health,
social and moral and moral health in young people. In S. J. H. Biddle, N.
Cavill & J. F. Sallis (Eds.), Young and Active? Young People and Healthenhancing Physical Activity: Evidence and Implications. (pp. 49-68). London:
Health Education Authority.
National Health Advisory Service (1995). Child and Adolescent Mental Health
Services:Together We Stand. London: HMSO.
Neeman, J., & Harter, S. (1986). Manual for the Self-perception Profile for College
Students. Denver: University of Denver.
Ness, A. R., Leary, S. D., Mattocks, C., Blair, S. N., Reilly, J. J., Wells, J., et al.
(2007). Objectively measured physical activity and fat mass in a large cohort
of children. PLoS Medicine, 4(3), e97.
North, T. C., McCullagh, P., & Tran, Z. V. (1990). Effect of exercise on depression.
Exercise and Sport Sciences Reviews, 18, 379-415.
Organisation, W. H. (2004). Promoting Mental Health: Concepts, Emerging
Evidence, and Practice: Summary Report. Geneva: Author.
Oweis, P., & Spinks, W. (2001). Biopsychological, affective and cognitive responses
to acute physical activity. Journal of Sports Medicine and Physical Fitness,
41(4), 528-538.
Owen, N., Leslie, E., Salmon, J., & Fotheringham, M. J. (2000). Environmental
determinants of physical activity and sedentary behavior. Exercise and Sport
Sciences Reviews, 28(4), 153-158.
Paluska, S. A. & Schwenk. T. L. (2000). Physical activity and mental health: current
concepts. Sports Medicine, 29(3), 167-180.
Parfitt, G., & Eston, R. G. (2005). The relationship between children's habitual
activity level and psychological well-being. Acta Paediatrica, 94(12), 17911797.
Pate, R. R., Almeida, M. J., McIver, K. L., Pfeiffer, K. A., & Dowda, M. (2006).
Validation and calibration of an accelerometer in preschool children. Obesity
14(11), 2000-2006.
168
Pate, R. R., Freedson, P. S., Sallis, J. F., Taylor, W. C., Sirard, J., Trost, S. G., et al.
(2002). Compliance with physical activity guidelines: prevalence in a
population of children and youth. Annals of Epidemiology, 12(5), 303-308.
Penedo, F. J., & Dahn, J. R. (2005). Exercise and well-being: a review of mental and
physical health benefits associated with physical activity. Current Opinion in
Psychiatry, 18(2), 189-193.
Petruzzello, S. J., Landers, D. M., Hatfield, B. D., Kubitz, K. A., & Salazar, W.
(1991). A meta-analysis on the anxiety-reducing effects of acute and chronic
exercise. Outcomes and mechanisms. Sports Medicine, 11(3), 143-182.
Piers, E. V. (1969). Manual for the Piers-Harris Children’s Self-concept Scale.
Nashville: Counselor Recordings and Tests.
Poulin, C., Lemoine, O., Poirier, L.-R., & Lambert, J. (2005). Validation study of a
nonspecific psychological distress scale. Social Psychiatry and Psychiatric
Epidemiology, 40(12), 1019-1024.
Powell, L. M., Slater, S., Chaloupka, F. J., & Harper, D. (2006). Availability of
physical activity-related facilities and neighborhood demographic and
socioeconomic characteristics: a national study. American Journal of Public
Health, 96(9), 1676-1680.
Powell, S. M., Jones, D. I., & Rowlands, A. V. (2003). Technical variability of the
RT3 accelerometer. Medicine & Science in Sports & Exercise, 35(10), 1773.
Powell, S. M., & Rowlands, A. V. (2004). Intermonitor variability of the RT3
accelerometer during typical physical activities. Medicine and Science in
Sports and Exercise, 36(2), 324-330.
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating
indirect effects in simple mediation models. Behavior Research Methods,
Instruments, and Computers 36(4), 717-731.
Puyau, M. R., Adolph, A. L., Vohra, F. A., & Butte, N. F. (2002). Validation and
calibration of physical activity monitors in children. Obesity Research, 10(3),
150-157.
Radloff, L. S. (1977). The CES-D Scale: a self-report depression scale for research in
the general population. Applied Psychological Measurement, 1(3), 385-401.
Raudsepp, L., Liblik, R., & Hannus, A. (2002). Children's and adolescents' physical
self-perceptions as related to moderate to vigorous physical activity and
physical fitness. Pediatric Exercise Science, 14(1), 97-106.
Raudsepp, L., Neissaar, I., & Kull, M. (2008). Longitudinal stability of sedentary
behaviors and physical activity during early adolescence. Pediatric Exercise
Science, 20(3), 251-262.
Raustorp, A., Stahle, A., Gudasic, H., Kinnunen, A., & Mattsson, E. (2005). Physical
activity and self-perception in school children assessed with the Children and
Youth - Physical Self-Perception Profile. Scandinavian journal of medicine &
science in sports 15(2), 126-134.
Ravens-Sieberer, U., Erhart, M., Power, M., Auquier, P., Cloetta, B., Hagquist, C., et
al. (2003). Item-Response-Theory Analyses of child and adolescent self-report
quality of life data: The European Cross Cultural Research Instrument
Kidscreen. Quality of Life Research, 12(7), 722.
Ravens-Sieberer, U., Gosch, A., Rajmil, L., Erhart, M., Bruil, J., Power, M., et al.
(2008). The KIDSCREEN-52 Quality of Life Measure for Children and
Adolescents: Psychometric Results from a Cross-Cultural Survey in 13
European Countries. Value in Health, 11(4), 645-658.
169
Reynolds, C. R., & Richmond, B. O. (1978). What I think and feel: a revised measure
of children's manifest anxiety. Journal of Abnormal Child Psychology, 6(2),
271-280.
Richard J. Butler, S. L. G. (2005). Self Esteem/Self Concept Scales for Children and
Adolescents: A Review. Child and Adolescent Mental Health, 10(4), 190-201.
Richter, P., Werner, J., Heerlein, A., Kraus, A., & Sauer, H. (1998). On the Validity
of the Beck Depression Inventory. Psychopathology, 31, 160-168.
Riddoch, C. J., Bo Andersen, L., Wedderkopp, N., Harro, M., Klasson-Heggebo, L.,
Sardinha, L. B., et al. (2004). Physical activity levels and patterns of 9- and
15-yr-old European children. Medicine and Science in Sports and Exercise,
36(1), 86-92.
Riddoch, C. J., Mattocks, C., Deere, K., Saunders, J., Kirkby, J., Tilling, K., et al.
(2007). Objective measurement of levels and patterns of physical activity.
Archives of Disease in Childhood, 92(11), 963-969.
Riley, A. W., Forrest, C. B., Rebok, G. W., Starfield, B., Green, B. F., Robertson, J.
A., et al. (2004). The Child Report Form of the CHIP-Child Edition: reliability
and validity. Medical Care, 42(3), 221-231.
Roemmich, J. N., Epstein, L. H., Raja, S., Yin, L., Robinson, J., & Winiewicz, D.
(2006). Association of access to parks and recreational facilities with the
physical activity of young children. Preventive Medicine, 43(6), 437-441.
Rosenberg, M. (1965). Society and the adolescent self image. Princeton: Princeton
University Press.
Rowe, D., Mahar, M., Raedeke, T. D., & Lore, J. (2004). Measuring physical activity
in children with pedometers: reliability, reactivity, and replacement of missing
data. Pediatric Exercise Science, 16(4), 343-354.
Rowlands, A. V. (2007). Accelerometer assessment of physical activity in children: an
update. Pediatric Exercise Science, 19(3), 252-266.
Rowlands, A. V., & Eston, R. G. (2005). Comparison of accelerometer and pedometer
measures of physical activity in boy and girls, ages 8-10 years. Research
Quarterly for Exercise and Sport, 76(3), 251.
Rowlands, A. V., Eston, R. G., & Ingledew, D. K. (1999). Relationship between
activity levels, aerobic fitness, and body fat in 8- to 10-yr-old children.
Journal of Applied Physiology, 86(4), 1428.
Rowlands, A. V., Ingledew, D. K., & Eston, R. G. (2000). The effect of type of
physical activity measure on the relationship between body fatness and
habitual physical activity in children: a meta-analysis. Annals of Human
Biology, 27(5), 479-497.
Rowlands, A. V., Pilgrim, E. L., & Eston, R. G. (2007). Patterns of habitual activity
across weekdays and weekend days in 9-11-year-old children. Preventive
Medicine, 46(4), 317-324.
Rowlands, A. V., Thomas, P. W. M., Eston, R. G., & Topping, R. (2004). Validation
of the RT3 triaxial accelerometer for the assessment of physical activity.
Medicine & Science in Sports & Exercise, 36(3), 518.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: classic
definitions and new directions. Contemporary Educational Psychology, 25(1),
54-67.
Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of
psychological well-being. . Journal of Personality and Social Psychology,
57(6), 1069-1081.
170
Sallis, J. F., Haskell, W. L., Wood, P. D., Fortmann, S. P., Rogers, T., Blair, S. N., et
al. (1985). Physical activity assessment methodology in the Five-City Project
American Journal of Epidemiology, 121(1), 91-106.
Sallis, J. F., Prochaska, J. J., & Taylor, W. C. (2000). A review of correlates of
physical activity of children and adolescents. Medicine and Science in Sports
and Exercise, 32(5), 963-975.
Sallis, J. F., & Saelens, B. E. (2000). Assessment of physical activity by self-report:
status, limitations, and future directions. Research Quarterly for Exercise and
Sport, 71(2 Suppl), S1-14.
Salmon, J., Booth, M. L., Phongsavan, P., Murphy, N., & Timperio, A. (2007).
Promoting physical activity participation among children and adolescents.
Epidemiologic Reviews 29(1), 144-159.
Salmon, P. (2001). Effects of physical exercise on anxiety, depression, and sensitivity
to stress: a unifying theory. Clinical Psychology Review, 21(1), 33-61.
Saxena, S., Van Ommeren, M., Tang, K. C., & Armstrong, T. P. (2005). Mental
health benefits of physical activity. Journal of Mental Health, 14(5), 445 451.
Scully, D., Kremer, J., Meade, M. M., Graham, R., & Dudgeon, K. (1998). Physical
exercise and psychological well being: a critical review. British Journal of
Sports Medicine, 32(2), 111-120.
Shavelson, R. J., Hubner, J. J., & Stanton, G. C. (1976). Self-concept: validation of
construct interpretations. Review of Educational Research, 46(3), 407-441.
Sherar, L. B., Esliger, D. W., Baxter-Jones, A. D., & Tremblay, M. S. (2007). Age
and gender differences in youth physical activity: does physical maturity
matter? Medicine and Science in Sports and Exercise, 39(5), 830-835.
Shevlin, M., Adamson, G., & Collins, K. (2003). The Self-Perception Profile for
Children (SPPC): a multiple-indicator multiple-wave analysis using LISREL.
Personality and Individual Differences, 35(8), 1993-2005.
Sirard, J. R., & Pate, R. R. (2001). Physical activity assessment in children and
adolescents. Sports Medicine, 31(6), 439-454.
Slawson, D. (2005). Aerobic Exercise Effective for Mild to Moderate Depression.
American Family Physician, 71, 1769-1770.
Smith, N. E., Rhodes, R. E., Naylor, P. J., & McKay, H. A. (2008). Exploring
moderators of the relationship between physical activity behaviors and
television viewing in elementary school children. American Journal of Health
Promotion, 22(4), 231-236.
Smucker, M. R., Craighead, W. E., Craighead, L. W., & Green, B. J. (1986).
Normative and reliability data for the Children's Depression Inventory.
Journal of Abnormal Child Psychology, 14(1), 25-39.
Sobel, M. E. (1982). Asymptotic confidence intervals for indirect efffects in structural
equation models. In S. Leinhart (Ed.), Sociological Methodology (pp. 290312). San Francisco: Jossey-Bass.
Sonstroem, R. J. (1997). The physical self-system: a mediator of exercise and selfesteem. In K. R. Fox (Ed.), The Physical Self: From Motivation to Well-being
(pp. 3-26). Leeds: Human Kinetics.
Sonstroem, R. J., Harlow, L. L., & Josephs, L. (1994). Exercise and self-esteem:
validity of model expansion and exercise associations. Journal of Sport &
Exercise Psychology, 16(1), 29-42.
Sonstroem, R. J., & Morgan, W. P. (1989). Exercise and self-esteem: rationale and
model. Medicine and Science in Sports and Exercise, 21(3), 329-337.
171
Speilberger, C. D., Edwards, C. D., Lushene, R. E., Montuori, J., & Platzek, D.
(1973). Preliminary test manual for the State-trait Anxiety Inventory for
Children ("how i feel questionnaire"). Palo Alto, California.: Psychologists
Press.
Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour
Research and Therapy, 36(5), 545-566.
Spence, J. C., McGannon, K. R., & Poon, P. (2005). The Effect of Exercise on Global
Self-Esteem: A Quantitative Review. Journal of Sport and Exercise
Psychology 27(3), 311-334.
Stallings, P., & March, J. S. (1995). Assessment. In J. S. March (Ed.), Anxiety
Disorders in Children and Adolescents (pp. 125-147). New York: Guilford
Press.
Stathi, A., Nordin, S., & Riddoch, C. (2006). Evaluation of the "Schools on the Move"
Project. London Sport Institute: Youth Sport Trust, Department of Educational
Skills, & Department of Health.
Stone, M. R., Rowlands, A. V., & Eston, R. G. (2009). Relationships between
acceleromter-assessed physical activity and health in children: impact of the
activity-intensity classification method. Journal of Sports Science and
Medicine, 8, 136-143.
Stone, M. R., Rowlands, A. V., Middlebrooke, A. R., Jawis, M. N., & Eston, R. G.
(2009). The pattern of physical activity in relation to health outcomes in boys.
International Journal of Pediatric Obesity, 1-10.
Storch, E. A., Roberti, J. W., & Roth, D. A. (2004). Factor structure, concurrent
validity, and internal consistency of the beck depression inventory - second
edition in a sample of college students. Depression and Anxiety, 19(3), 187189.
Strauss, R. S., Rodzilsky, D., Burack, G., & Colin, M. (2001). Psychosocial correlates
of physical activity in healthy children. Archives of Pediatrics & Adolescent
Medicine 155(8), 897-902.
Strong, W. B., Malina, R. M., Blimkie, C. J. R., Daniels, S. R., Dishman, R. K.,
Gutin, B., et al. (2005). Evidence based physical activity for school-age youth.
The Journal of Pediatrics, 146(6), 732-737.
Summerbell, C. D., Waters, E., Edmunds, L. D., Kelly, S., Brown, T., & Campbell, K.
J. (2005). Interventions for preventing obesity in children. Cochrane Database
Syst Rev(3), CD001871.
Sun, D. X., Schmidt, G., & Teo-Koh, S. M. (2008). Validation of the RT3
accelerometer for measuring physical activity of children in simulated freeliving conditions. Pediatric Exercise Science, 20(2), 181-197.
Tabachnick, B. G., & Fidell, L. S. (2001). Using Multivariate Statistics (Fourth ed.).
London: Allyn and Bacon.
Taveras, E. M., Field, A. E., Berkey, C. S., Rifas-Shiman, S. L., Frazier, A. L.,
Colditz, G. A., et al. (2007). Longitudinal relationship between television
viewing and leisure-time physical activity during adolescence. Pediatrics,
119(2), e314-319.
Taylor, A. H. (2000). Physical activity, anxiety, and stress. In S. Biddle, K. Fox & S.
H. Boutcher (Eds.), Physical Activity and Psychological Well-being. London:
Routledge.
Telford, A., Salmon, J., Jolley, D., & Crawford, D. A. (2004). Reliability and validity
of physical activity questionnaires for children: The Children's Leisure
Activities Study Survey (CLASS). Pediatric Exercise Science, 16(1), 64-78.
172
Thomas, N. E., Cooper, S. M., Baker, J. S., & Davies, B. (2006). Physical activity and
diet relative to socio-economic status and gender in British young people.
Health Education Journal, 65(3), 223-235.
Tomson, L. M., Pangrazi, R. P., Friedman, G., & Hutchinson, N. (2003). Childhood
depressive symptoms, physical activity and health related fitness. Journal of
Sport & Exercise Psychology, 25(4), 419-439.
Torres, R., & Fernandez, F. (1995). Self-esteem and value of health as determinants of
adolescent health behavior. Journal of Adolescent Health, 16(1), 60-63.
Tremblay, M. S., Esliger, D. W., Tremblay, A., & Colley, R. (2007). Incidental
movement, lifestyle-embedded activity and sleep: new frontiers in physical
activity assessment. Canadian Journal of Public Health, 98 (S2), S208-217.
Treuth, M. S., Schmitz, K., Catellier, D. J., McMurray, R. G., Murray, D. M.,
Almeida, M. J., et al. (2004). Defining accelerometer thresholds for activity
intensities in adolescent girls. Medicine and Science in Sports and Exercise,
36(7), 1259-1266.
Trost, S. G. (2007). State of the art reviews: measurement of physical activity in
children and adolescents. American Journal of Lifestyle Medicine, 1(4), 299314.
Trost, S. G., McIver, K. L., & Pate, R. R. (2005). Conducting accelerometer-based
activity assessments in field-based research. Medicine and Science in Sports
and Exercise, 37(S11), S531-543.
Trost, S. G., Pate, R. R., Sallis, J. F., Freedson, P. S., Taylor, W. C., & Dowda, M.
(2002). Age and gender differences in objectively measured physical activity
in youth. Medicine and Science in Sports and Exercise, 34, 350-355.
Trost, S. G., Rosenkranz, R. R., & Dzewaltowski, D. (2008). Physical activity levels
among children attending after-school programs. Medicine and Science in
Sports and Exercise, 40(4), 622-629.
Tudor-Locke, C., Pangrazi, R. P., Corbin, C. B., Rutherford, W. J., Vincent, S. D.,
Raustorp, A., et al. (2004). BMI-referenced standards for recommended
pedometer-determined steps/day in children. Preventive Medicine, 38(6), 857864.
Tudor-Locke, C., Williams, J. E., Reis, J. P., & Pluto, D. (2002). Utility of
pedometers for assessing physical activity: convergent validity. Sports
Medicine, 32(12), 795-808.
Van Dongen-Melman, J., Koot, H. M., & Verhulst, F. C. (1993). Cross-cultural
validation of Harter's Self-Perception Profile for Children in a Dutch sample.
Educational and Psychological Measurement, 53(3), 739-753.
Van Lenthe, F. J., Boreham, C. A., Twisk, J. W. R., Strain, J. J., Savage, J. M., &
Smith, G. D. (2001). Socio-economic position and coronary heart disease risk
factors in youth: Findings from the Young Hearts Project in Northern Ireland.
European Journal of Public Health, 11(1), 43-50.
Van Sluijs, E. M., McMinn, A. M., & Griffin, S. J. (2008). Effectiveness of
interventions to promote physical activity in children and adolescents:
systematic review of controlled trials. British Journal of Sports Medicine,
42(8), 653-657.
Vincent, W. J. (1999). Statistics in Kinesiology, 2nd ed. Champaign, IL.: Human
Kinetics.
Waine, I., Macey, D., & Parfitt, G. (2006). The effectiveness of daily step count
targets as an alternative to traditional gym-based cardiac rehabilitation.
Paper presented at the British Association of Cardiac Rehabilitation.
173
Wallace, J. P., McKenzie, T. L., & Nader, P. R. (1985). Observed vs. recall exercise
behaviour: a validation of a seven day exercise recall for boys 11-13 years old.
Research Quarterly for Exercise and Sport, 56, 161-165.
Ward, D. S., Evenson, K. R., Vaughn, A., Rodgers, A. B., & Troiano, R. P. (2005).
Accelerometer use in physical activity: best practices and research
recommendations. Medicine and Science in Sports and Exercise, 37(S11),
S582-588.
Wareham, N. J., van Sluijs, E. M. F., & Ekelund, U. (2007). Physical activity and
obesity prevention: a review of the current evidence. Proceedings of the
Nutrition Society, 64(02), 229-247.
Watson, D., & Kendall, P. C. (1989). Understanding anxiety and depression: their
relation to negative and positive affective state. In P. C. Kendall & D. Watson
(Eds.), Anxiety and Depression: Distinctive and Overlapping Features (pp. 325). New York: Academic Press.
Weems, C. F., & Silverman, W. K. (2006). An integrative model of control:
Implications for understanding emotion regulation and dysregulation in
childhood anxiety. Journal of Affective Disorders, 91, 113-124.
Weissman, M. M., Orvaschel, H., & Padian, N. (1980). Children's symptom and
social functioning self-report scales. Comparison of mothers' and children's
reports. The Journal of Nervous and Mental Disease, 168(12), 736-740.
Welk, G. J., Corbin, C. B., & Dale, D. (2000). Measurement issues in the assessment
of physical activity in children. Research Quarterly for Exercise and Sport
71(S2), S59-73.
Welk, G. J., Corbin, C. B., Dowell, M. N., & Harris, H. (1997). The validity and
reliability of two different versions of the Children and Youth Physical SelfPerception Profile. Measurement in Physical Education & Exercise Science,
1(3), 163-177.
Welk, G. J., & Eklund, R. C. (2005). Validation of the Children and Youth Physical
Self-perceptions Profile for Young Children. Psychology of Sport and
Exercise, 7, 132-151.
Welk, G. J., Schaben, J. A., & Morrow, J. R. (2004). Reliability of accelerometrybased activity monitors: a generalizability study. Medicine and Science in
Sports and Exercise, 36(9), 1637-1645.
Welsman, J. R., & Armstrong, N. (1992). Daily physical activity and blood lactate
indices of aerobic fitness in children. British Journal of Sports Medicine,
26(4), 228-232.
Whitehead, J. R. (1995). A study of children's physical self-perceptions using an
adapted Physical Self-Perception Profile questionnaire. Pediatric Exercise
Science, 7(2), 132.
Whitehead, J. R., & Corbin, C. B. (1997). Self-esteem in children and youth: the role
of sport and physical education. In K. R. Fox (Ed.), The Physical Self: From
Motivation to Well-being (pp. 175-204). Leeds: Human Kinetics.
174
Appendix 1
Questionnaires used within the research
175
Code No.: - ……………….
Appendix 1A
SCHOOL OF SPORT AND HEALTH SCIENCES
STAIC
Name………………………………………………………………
Date of Birth………………………..
Male/Female (please circle)
Kids sometimes have different feelings and
ideas.
There is no right answer or wrong answer.
Read each sentence and decide if it is
hardly ever, or sometimes, or often that
you feel this way.
Put a mark like this X next to the word
that describes how you usually feel.
Example:
I like ice cream
□ hardly ever
□ sometimes
□ often
176
Code No.: - ……………….
1. I worry about making mistakes
□ hardly ever
□ sometimes
□ often
2. I feel like crying
□ hardly ever
□ sometimes
□ often
3. I feel unhappy
□ hardly ever
□ sometimes
□ often
4. I have trouble making up my mind
□ hardly ever
□ sometimes
□ often
5. It is difficult for me to face my problems
□ hardly ever
□ sometimes
□ often
6. I worry too much
□ hardly ever
□ sometimes
□ often
7. I get upset at home
□ hardly ever
□ sometimes
□ often
8. I am shy
□ hardly ever
□ sometimes
□ often
9. I feel troubled
□ hardly ever
□ sometimes
□ often
10. Unimportant thoughts run through my
□ hardly ever
□ sometimes
□ often
11. I worry about school
□ hardly ever
□ sometimes
□ often
12. I have trouble deciding what to do
□ hardly ever
□ sometimes
□ often
13. I notice my heart beats fast
□ hardly ever
□ sometimes
□ often
14. I am secretly afraid
□ hardly ever
□ sometimes
□ often
15. I worry about my parents
□ hardly ever
□ sometimes
□ often
16. My hands get sweaty
□ hardly ever
□ sometimes
□ often
17. I worry about things that may happen
□ hardly ever
□ sometimes
□ often
18. It is hard for me to fall asleep at night
□ hardly ever
□ sometimes
□ often
19. I get funny feelings in my stomach
□ hardly ever
□ sometimes
□ often
20. I worry about what others think of me
□ hardly ever
□ sometimes
□ often
mind and bother me
Thank-you. You have finished this questionnaire
Code No.: - ……………….
Appendix 1B
SCHOOL OF SPORT AND HEALTH SCIENCES
CDI
Name………………………………………………………………….
Date of Birth……………………
Male/Female (please circle)
Kids sometimes have different feelings and ideas.
This form lists the feelings and ideas in groups. From each
group of three sentences, pick one sentence that describes you
best for the past two weeks. After you pick a sentence from
the first group, go on to the next group.
There is no right answer or wrong answer. Just pick the
sentence that best describes the way you have been feeling
recently. Put a mark like this X next to your answer. Put the
mark in the box next to the sentence that you pick. Here is an
example of how the form works. Try it. Put a mark next to the
sentence that describes you best.
Example:
□ I read books all the time
□ I read books once in a while
□ I never read books
REMEMBER, PICK OUT THE SENTENCE
THAT DESCRIBES YOU BEST IN THE PAST
TWO WEEKS.
177
Code No.: - ……………….
1. 1
Item
□ I am sad once in a while
□ I am sad many times
□ I am sad all the time
Item 2
□ Nothing will ever work out for me
□ I am not sure if things will work out for me
□ Things will work out for me
Item 3
□ I do most things ok
□ I do many things wrong
□ I do everything wrong
Item 4
□ I have fun in many things
□ I have fun in some things
□ Nothing is fun at all
Item 5
□ I am bad all the time
□ I am bad many times
□ I am bad once in a while
Item 6
□ I think about bad things happening to me once in a while
□ I worry that bad things will happen to me
□ I am sure that terrible things will happen to me
Item 7
□ I hate myself
□ I do not like myself
□ I like myself
Item 8
□ All bad things are my fault
□ Many bad things are my fault
□ Bad things are not usually my fault
Item 9
□ I feel like crying every day
□ I feel like crying many days
□ I feel like crying once in a while
Please Leave
Blank
Code No.: - ……………….
Item 10
□ Things bother me all the time
□ Things bother me many times
□ Things bother me once in a while
Please Leave
Blank
Item 11
□ I like being with people
□ I do not like being with people many times
□ I do not want to be with people at all
Item 12
□ I cannot make up my mind about things
□ It is hard to make up my mind about things
□ I make up my mind about things easily
Item 13
□ I look ok
□ There are some bad things about my looks
□ I look ugly
Item 14
□ I have to push myself all the time to do school work
□ I have to push myself many times to do school work
□ Doing school work is not a big problem
Item 15
□ I have trouble sleeping every night
□ I have trouble sleeping many nights
□ I sleep pretty well
Item 16
□ I am tired once in a while
□ I am tired many days
□ I am tired all the time
Item 17
□ Most days I do not feel like eating
□ Many days I do not feel like eating
□ I eat pretty well
Item 18
□ I do not worry about aches and pains
□ I worry about aches and pains many times
□ I worry about aches and pains all the time
178
Code No.: - ……………….
O
f
f
i
c
e
u
s
e
o
n
l
y
Item 19
□ I do not feel alone
□ I feel alone many times
□ I feel alone all the time
Item 20
□ I never have fun at school
□ I have fun at school only once in a while
□ I have fun at school all the time
Item 21
□ I have plenty of friends
□ I have some friends but I wish I had more
□ I do not have any friends
Item 22
□ My school work is alright
□ My school work is not as good as before
□ I do badly in subjects I used to be good in
Item 23
□ I can never be as good as other kids
□ I can be as good as other kids if I want to
□ I am just as good as other kids
Item 24
□ Nobody really loves me
□ I am not sure if anybody loves me
□ I am sure that somebody loves me
Item 25
□ I usually do what I am told
□ I do not do what I am told most times
□ I never do what I am told
Item 26
□ I get along with people
□ I get into fights many times
□ I get into fights all the time
Thank-you. You have finished this
questionnaire
Please Leave
Blank
Appendix 1c
Code No.: - ……………….
SCHOOL OF SPORT AND HEALTH SCIENCES
SELF-PERCEPTION PROFILE FOR CHILDREN
Name
Date of Birth
Male/Female
Introduction
This survey is interested in what each of you is like, what kind of person
you are like. Remember this is a survey not a test. There are no right or
wrong answer. Since you are all different from one another, each of you
will be putting down something different.
How do the questions work?
The questions talk about two kinds of kids, and we want to know which
kids are most like you.
First, we want you to decide whether you are more like the kids on the
left side or whether you are more like the kids on the right side. Don’t
mark anything yet, but first decide which kind of kid is most like you, and
go to that side of the sentence.
Second, we want you to think about and decide whether that is only sort
of true for you, or really true for you. If it’s only sort of true, then put
an X in the box under sort of true. If it’s really true, then put and X in
the box under really true.
For each sentence you only cross one box. Sometimes it will be on one
side of the page, other times it will be on the other side of the page. Do
not put a cross on both sides, just the one side most like you.
Ok, lets have a go at the practice question together.
Really
True
for me
e.g.
Sort of
True
for me
Sort of
True
for me
Some kids would rather
play indoors in their
spare time
BUT
-
Other kids would rather
watch T.V.
179
Really
True
for me
Code No.: - …………….
Name……………………………………………………..
What I Am Like
Really
True
for me
Sort of
True
for me
Sort of
True
for me
e.g.
Some kids would rather
play indoors in their
spare time
1.
Some kids feel that they
BUT
are very good at their
school work
2.
Some kids feel that they
are very good at their
schoolkids
work
Some
find it hard to
make friends
BUT
BUT
Other kids would rather
watch T.V.
Other kids worry about
whether they can do the
school work assigned to
them
Other kids find it’s
pretty easy to make
friends
3.
Some kids do very well at
BUT
all kinds of sports
Others kids don’t feel
that they are very good
when it comes to sport
4.
Some kids are happy with
BUT
the way they look
Other kids are not happy
with the way they look
5.
Some kids often do not
like the way they behave
BUT
Other kids usually like
the way they behave
6.
Some kids are often
unhappy with themselves
BUT
Other kids are pretty
pleased with themselves
7.
Some kids feel like they
are just as smart as
other kids their age
BUT
Other kids aren’t so sure
and wonder if they are as
smart
Some kids have a lot of
friends
BUT
Other kids don’t have
very many friends
8.
-
-
Really
True
for me
Code No.: - …………….
Really
True
for me
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
Name……………………………………………………..
Sort of
True
for me
Sort of
True
for me
Some kids wish they
could be a lot better at
sports
BUT
Other kids feel that they
are good enough at sport
Some kids are happy with
BUT
their height and weight
Other kids wish their
height or weight were
different
Some kids usually do the
right thing
BUT
Other kids often don’t do
the right thing
Some kids don’t like the
way they are leading
their life
BUT
Other kids do like the
way they are leading
their life
Some kids are pretty
slow in finishing their
school work
BUT
Other kids can do their
school work quickly
Some kids would like to
have a lot more friends
BUT
Other kids have as many
friends as they want
Some kids think they
could do well at just
about any new sports
activity they haven’t
tried before
Some kids wish their
body was different
BUT
BUT
Other kids are afraid
they might not do well at
sports they haven’t ever
tried
Other kids like their
body the way it is
Some kids usually act the
BUT
way they know they are
supposed to
Other kids often don’t
act the way they are
supposed to
Some kids are happy with
BUT
themselves as a person
Other kids are often not
happy with themselves
-
-
180
Really
True
for me
Code No.: - …………….
Really
True
for me
Name……………………………………………………..
Sort of
True
for me
Sort of
True
for me
19.
Some kids often forget
what they learn
20.
Some kids are always
doing things with a lot of BUT
kids
Other kids usually do
things by themselves
21.
In games and sports
some kids usually watch
instead of play
BUT
Others kids usually play
rather than watch
22.
Some kids wish their
physical appearance (how BUT
they look) was different
Other kids like their
physical appearance the
way it is
23.
Some kids usually get in
trouble because of things BUT
they do
Other kids usually don’t
do things that get them
in trouble
24.
Some kids like the kind
of person they are
Other kids often wish
they were someone else
25.
Some kids do very well at
BUT
their class work
Other kids don’t do very
well at their class work
26.
Some kids wish that more
BUT
people their age liked
them
Other kids feel that
most people their age do
like them
27.
Some kids feel that they
BUT
are better than others
their age at sports
Other kids don’t feel
they can play as well
28.
Some kids wish
something about their
face or hair looked
different
BUT
BUT
BUT
Other kids can remember
things easily
Other kids like their face
and hair the way they
are
Really
True
for me
Code No.: - …………….
Really
True
for me
Name……………………………………………………..
Sort of
True
for me
Sort of
True
for me
29.
Some kids do things they
BUT
know they shouldn’t do
Other kids hardly ever
do things they know they
shouldn’t do
30.
Some kids are very happy
BUT
being the way they are
Other kids wish they
were different
31.
Some kids have trouble
figuring out the answers
in school
BUT
Other kids almost always
can figure out the
answers
32.
Some kids are popular
with others their age
BUT
Other kids are not very
popular
33.
Some kids don’t do well
at new outdoor games
BUT
Other kids are good at
new games right away
34.
Some kids think that
they are good looking
BUT
Other kids think that
they are not good looking
35.
Some kids behave
themselves very well
BUT
Other kids often find it
hard to behave
themselves
36.
Some kids are not very
happy with the way they
do a lot of things
BUT
Other kids think the way
they do things is fine
37.
Some kids feel uneasy
when it comes to doing
vigorous physical
exercise
BUT
Really
True
for me
Others kids feel
confident when it comes
to doing vigorous physical
exercise
181
Code No.: - …………….
Really
True
for me
Name……………………………………………………..
Sort of
True
for me
Sort of
True
for me
38.
Some kids are confident
about how their bodies
look physically
39.
Some kids feel that they
BUT
lack strength compared
to other kids their age
Others kids feel that
they are stronger than
other kids their age
40.
Some kids are proud of
themselves physically
BUT
Others kids don’t have
much to be proud of
physically
41.
Some kids have a lot of
stamina for vigorous
physical exercise
BUT
Other kids soon get out
of breath and have to
slow down or quit
42.
43.
BUT
Others kids feel uneasy
about how their bodies
look physically
Some kids feel that they
BUT
have a good-looking (fitlooking) body compared
to other kids
Other kids feel that
compared to most, their
body doesn’t look so good
Some kids think that
they have stronger
muscles than other kids
their age
Other kids feel that they
have weaker muscles than
other kids their age
BUT
Some kids are happy with
BUT
how they are and what
they can do physically
Other kids are unhappy
with how they are and
what they can do
physically
BUT
Some kids don’t have
much stamina and fitness
Other kids have lots of
stamina and fitness
46.
Some kids find it
difficult to keep their
bodies looking good
physically
Other kids find it easy to
keep their bodies looking
good physically
47.
Some kids lack
confidence when it comes BUT
to strength activities
44.
45.
BUT
Other kids are very
confident when it comes
to strength activities
Really
True
for me
Code No.: - …………….
Really
True
for me
Name……………………………………………………..
Sort of
True
for me
Sort of
True
for me
Some kids don’t feel very
BUT
confident about
themselves physically
Other kids feel really
good about themselves
physically
Some kids try to take
part in energetic physical BUT
exercise whenever they
can
Other kids try to avoid
doing energetic exercise
if they can
50.
Some kids are pleased
with the appearance of
their bodies
BUT
Other kids wish that
their bodies looked in
better shape physically
51.
When strong muscles are
needed, some kids are
BUT
the first to step
forwards
Other kids are the last
to step forward when
strong muscles are
needed
Some kids have a positive
feeling about themselves BUT
physically
Other kids feel
somewhat negative about
themselves physically
53.
Some kids soon have to
quit running and
exercising because they
get tired
Other kids can run and
do exercises for a long
time without getting
tired
54.
Some kids feel that they
BUT
are often admired for
their good-looking bodies
Other kids feel that they
are seldom admired for
the way their bodies look
Some kids feel that they
are not as good as others
BUT
when physical strength is
needed
Other kids feel that they
are among the best when
physical strength is
needed
48.
49.
52.
55.
BUT
Really
True
for me
182
Code No.: - …………….
Really
True
for me
56.
57.
58.
59.
60.
Name……………………………………………………..
Sort of
True
for me
Sort of
True
for me
Some kids wish that they
BUT
could feel better about
themselves physically
Other kids always seem
to feel good about
themselves physically
When it comes to
activities like running,
some kids are able to
keep going
BUT
Other kids soon have to
quit to take a rest
BUT
Other kids are pleased
with how their bodies
look physically
Some kids don’t like how
their bodies look
physically
Some kids think that
they are strong, and have BUT
good muscles compared
to other kids their age
Other kids think that
they are weaker, and
don’t have such good
muscles as other kids
their age
Some kids are very
satisfied with themselves BUT
physically
Other kids are often
dissatisfied with
themselves physically
Really
True
for me
Code No.: - …………….
Name……………………………………………………..
HOW IMPORTANT ARE THESE THINGS TO HOW YOU FEEL ABOUT YOURSELF AS A PERSON
Really
True
for me
1.
2.
3.
4.
5.
6.
7.
8.
9.
Sort of
True
for me
Sort of
True
for me
Some kids think it is
important to do well at
school work in order to
feel good as a person
Some kids don’t think
that having a lot of
friends is all that
important
Some kids think it’s
important to be good at
sports
Some kids think that it’s
important to be good
looking in order to feel
good about themselves
Some kids think that it’s
important to behave the
way they should
Some kids don’t think
that getting good grades
is all that important to
how they feel about
themselves
Some kids think it’s
important to be popular
Some kids don’t think
doing well at athletics is
that important to how
they feel about
themselves as a person
BUT
BUT
Other kids don’t think
how well they do at
schoolwork is that
important
Other kids think that
having a lot of friends is
important to how they
feel as a person
BUT
Others kids don’t think
how good you are at sport
is that important
BUT
Other kids don’t think
that’s very important at
all
BUT
Other kids don’t think
that how they behave is
that important
BUT
Other kids think that
getting good grades is
important
BUT
BUT
Some kids think that how
they look is important to BUT
how they feel about
themselves as a person
Other kids don’t think
that being popular is all
that important to how
they feel about
themselves
Other kids feel that
doing well at athletics is
important
Others kids don’t think
that how they look is
important at all
183
Really
True
for me
Code No.: - …………….
Really
True
for me
10.
11.
12.
13.
14.
15.
16.
Name……………………………………………………..
Sort of
True
for me
Sort of
True
for me
Some kids don’t think
that how they act is all
that important
Some kids don’t think
that having a lot of
stamina for energetic
exercise is very
important to how they
feel about themselves
Some kids think it’s very
important to have a good
looking body in order to
feel good about
themselves as a person
BUT
BUT
BUT
Some kids think that
being physically strong is BUT
not all that important to
how they feel about
themselves as a person
Some kids feel that
having the ability to do a
lot of running and
exercising is very
important to how they
feel about themselves as
a person
BUT
Some kids don’t think
that having a body that
BUT
looks in good physical
shape is important to how
they about themselves
Some kids think that
having strong muscles is
very important to how
they feel about
themselves
BUT
Other kids think it’s
important to act the way
you are supposed to
Others kids think that
having a lot of stamina
for vigorous exercise is
very important
Others kids don’t think
that having a good looking
body is important at all
Others kids feel that it’s
very important to be
physically strong
Others kids don’t feel
that it’s all that
important to have the
ability to do a lot of
running and exercising
Others kids feel that it’s
very important to have a
body that looks in good
physical shape
Others kids feel that it’s
not at all important to
have strong muscles
Thank-you. You have finished this questionnaire
Really
True
for me
Appendix 2
Information Sheets
184
Appendix 2A
SCHOOL OF SPORT AND
HEALTH SCIENCES
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
Information Sheet for Child Participants and Parents
The Relationship Between Habitual Physical Activity and Psychological Well-being in Children
Thank you for showing an interest in this project. Please read this information sheet carefully before deciding
whether or not to participate, it will tell you a bit more about the study and what we would like to do. If you
decide to participate we thank you. If you decide not to take part it is not a problem and we thank you for
considering our request.
What is the aim of the project?
The School of Sport and Health Sciences research team have been studying various aspects of health, fitness and
psychology for a long time. The aim of this project is to study the relationship between habitual physical activity,
sedentary behaviour and psychological well-being. We want to investigate whether the amount of physical activity
you do affects how you feel, or whether it’s the other way round, how you feel affects the amount of physical
activity you do. This is important because with this knowledge we can design ways to improve both physical activity
and the way you feel.
What type of participant is needed?
Healthy boys and girls aged nine to ten years old with no known medical conditions
What will the participants be asked to do?
The participants will be asked to complete a series of questionnaires that assess well-being and a written measure
of how much physical activity you have done over a 7-day period. Your parent/s will also be asked to write down
how much physical activity they think you have done, along with some demographic information (e.g. marital status,
occupation, and education). The information your parents supply, will be returned in a provided envelope to ensure
confidentiality. Over the same 7-day period you will also get to wear a fun device called an accelerometer, which
measures your movement throughout each day, and a pedometer, which counts the number of steps you take each
day. We will also record measurements about your height, weight, and sitting height so we can account for how
quickly you grow. Body fat will also be measured using Tanita scales, which will send a very small electric current
around the body, this is completely safe and the current cannot be felt. In order to get the best results we will
need to measure you twice a year for one week. This will be a total of up to four measurements over a two year
period.
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
185
SCHOOL OF SPORT AND
HEALTH SCIENCES
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
What will the questionnaires involve doing?
The questionnaires looking at psychological well-being, will involve you ticking a box, which best describes you. To
complete these questionnaires may take up to 40-minutes; this includes instructions and trial questions. The
written physical activity questionnaire will have a list of physical and leisure activities and will ask you to write
down how many times and for how long you do these activities in a typical school week. Your parents will also write
down how much time you spend on these activities, using a similar questionnaire. The physical activity questionnaire
should take around 10 – 15 minutes to complete. The questionnaires will be completed at your school and a member
of the research team will be available to assist with any problems.
What will the accelerometers and pedometers involve doing?
This is the fun part, you will get to wear an accelerometer and pedometer for each 7-day measurement. The
accelerometer and pedometer will clip onto your outer or under garments, and will be resting against your hip.
Every time you move, the accelerometer will record data about how much and for how long you are moving. The
pedometer will count every time you take a step. You will be asked to wear them from when you get up, and until
you go to bed.
When will this take place?
We would like to start collecting data from May 2006, with three other data collection periods in the following
eighteen months (around four to five months apart).
What if I want to drop out of the study?
You can drop out of the study at any time, we will not be upset or cross and neither will your teachers at school.
What will you do with the results?
All the results we collect will be stored as numbers on a computer. After each data collection we will send you a
brief summary of your personal data for your own information. No one else will be told your individual results. We
will write the study up as a paper, the group results may be presented to other researchers but your information
will remain confidential.
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
186
SCHOOL OF SPORT AND
HEALTH SCIENCES
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
What should I do if I want to participate?
If you would like to be involved in this project please make sure you do the following:
1.
Both you and your parents/guardians complete the participant consent form
2. Return the forms to your school teacher
What if I have a question?
If you have any questions regarding the study please contact Dr. Ann Rowlands on (01392)
262878 or Mr. Toby Pavey on (01392) 262883 and we will be happy to help.
We hope you would like to take part in the study, and look forward to seeing you in the
coming weeks.
Best wishes, Dr. Ann Rowlands and Mr. Toby Pavey
This study has been reviewed and approved by the School of Sport and Health Sciences
Ethics Committee
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
187
Appendix 2B
SCHOOL OF SPORT AND HEALTH SCIENCES
Data Collection Information Sheet for Parents/Guardians and Child Participants
Thank-you for showing an interest and taking part in the study ‘The Relationship Between
Habitual Physical Activity and Psychological Well-being in Children’. This information sheet
should answer any question and help with guidance with aspects of the study.
The RT3 Accelerometers and Pedometers Motion Sensors
The RT3 accelerometer and pedometer should be worn from when your child gets up in the
morning, and until they go to bed at night. The RT3 accelerometer runs all by itself and just
needs to be worn.
Each morning at school) your child will write down on a log sheet the pedometer step count
for the previous day and then reset the pedometer by simply pressing the button below the
display screen. At the weekend (Saturday and Sunday morning) this process will need be
completed by a parent or the child participant. Two log sheets will be provided, one to use at
‘school’ and one to use at ‘home’.
If the motion sensors are taken off for any period of time, then simply write down on the log
sheet the reason why and for how long (see example on log sheet).
‘At what other times should the sensors be taken off ‘
1. Both sensors should be taken off when showering/bathing, and when swimming (no
waterproof sensors yet).
2. When playing organised contact sports outside of school (e.g. rugby, football), consult with
referees and officials. If they say it’s ok, then continue to wear. If not, remove for the
duration of the activity.
Tips When Wearing Motion Sensors
1. You may find it more convenient to wear the motion sensors clipped to underwear,
especially if you do not want the sensors to be seen, or if you are possibly worried about the
sensors falling or being knocked off (this is very rare, due to the strength of the clips).
2. The sensors can be worn on either side and should be positioned at the waistline of the
clothing they are being clipped to, directly over the midline of the thigh.
3. Try to forget the motion sensors are there, and just carry on as normal
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
188
SCHOOL OF SPORT AND HEALTH SCIENCES
Important: The RT3 accelerometers have a battery compartment (bottom right), if
this is opened or knocked open then any data collected will be lost, and no more data
can be collected. It is therefore important to keep the RT3 accelerometers in their
clips to avoid this happening. Also, keep the pedometers closed at all times and only
open to record step counts.
Tips to Help with Remembering to Wear Motion Sensors
1. When you take the motion sensors off at night, place them somewhere in sight and
convenient for the next morning, e.g. next to an alarm clock or bedside table.
2. If you have a mobile phone, maybe program a reminder or alarm for a specific time e.g. 510 minutes after you get up.
CLASS Survey
I would ask a parent/guardian to complete the Children’s Leisure Activities Study Survey
(CLASS). This involves recording the leisure activities you think your child does in a typical
week. Read each activity and decide whether it’s a Yes or a No, if Yes write down how many
times and for how many minutes/hours total. There are two columns, one for weekdays and
one for weekends (please refer to examples).
The CLASS Survey can be completed at any time during the 7-day data collection period. This
information can be returned to your child’s school at any time during the 7-day data
collection period in the envelope the information came in. Returning the information in this
sealed envelope ensures confidentiality of all your information.
Please remember to bring in the ‘home’ log sheet on the final day (the following Tuesday) of
data collection
Once again Thank-you all for taking part in this study and I hope all of you enjoy
taking part.
If there are any questions, do not hesitate to contact me Mr. Toby Pavey on: 01392
262818 (office) or 07834731242 (mobile), or email T.Pavey@ex.ac.uk
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
189
Appendix 2C
SCHOOL OF SPORT AND
HEALTH SCIENCES,
HEALTH AND
PERFORMANCE
PSYCHOLOGY GROUP
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
Information Sheet for Child Participants and Parents
The impact of a physical activity intervention on children’s multidimensional psychological
well-being
Thank you for showing an interest in this project. Please read this information sheet carefully before deciding
whether or not to participate, it will tell you a bit more about the study and what we would like to do. If you
decide to participate we thank you. If you decide not to take part it is not a problem and we thank you for
considering our request.
What is the aim of the project?
The School of Sport and Health Sciences research team have been studying various aspects of health, fitness and
psychology for a long time. Through previous research it has been identified that time spent in sedentary
activities has a negative association with psychological well-being. This study aims to improve children’s
psychological well-being by reducing the amount of time children spend in sedentary activities.
What type of participant is needed?
Healthy boys and girls aged nine to ten years old with no known medical conditions.
What will the participants be asked to do?
The objective of this study is to reduce the amount of sedentary activity by increasing the amount of waking
activity. The study will comprise an education and familiarisation phase during which the children will learn about
the importance of being physically active, and will complete during the study booster practical sessions about
wearing the pedometer and being physically active. Following a baseline period, each child will be set a target to
increase his/her pedometer counts by 10% daily each week. Each school day morning the researchers will record
each child’s pedometer count. Each child will then receive feedback, new modified goals at the start of each week,
and encouragement to meet the new goal.
This process will continue during the period of the study.
The
intervention period will be five weeks, with a baseline and post data collection period either end of the five weeks.
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
190
Data Collection Periods
The study data will be collected before and after the five week intervention. Each measurement point will involve
two visits to the school, seven days apart. At the first visit, the children will complete three psychological wellbeing inventories. The participants will then be introduced to an accelerometer and shown how to wear it. The
children will wear the accelerometer for seven days. Anthropometric measures will also be completed i.e. height,
weight, and body fat. Sitting height will also be measured to estimate maturation. Seven days later the researcher
will return to the school to collect the accelerometers and administer the children’s version of the CLASS.
What will the questionnaires involve doing?
The questionnaires looking at psychological well-being, will involve the children ticking a box, which best describes
them. To complete these questionnaires may take up to 30 minutes; this includes instructions and trial questions.
The written physical activity questionnaire (CLASS) will have a list of physical and leisure activities and will ask
the children to write down how many times and for how long they do these activities in a typical school week. The
physical activity questionnaire should take around 10 minutes to complete. The questionnaires will be completed at
the children’s school and a member of the research team will be available to assist with any problems.
What will the accelerometers involve doing?
This is the fun part, each child will get to wear an accelerometer for each 7-day measurement. The accelerometer
is a small box like device and will clip onto the children’s outer or under garments, and will be resting against the
children’s hip. Every time the children move, the accelerometer will record data about how much and for how long
they are moving. The children will be asked to wear the accelerometer from when they get up, and until they go to
bed.
When will this take place?
We would like to start the study in October 2007
What if I want to drop out of the study?
Anyone can drop out of the study at any time, we will not be upset or cross and neither will the teachers at school.
What will we do with the results?
All the results we collect will be stored as numbers on a computer. We will write the study up as a paper, the
group results may be presented to other researchers but each child’s information will remain confidential.
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
191
What should I do if I want to participate?
If you would like to be involved in this project please make sure you do the following:
3.
Both you and your parents/guardians complete the participant consent form
4.
Return the forms to your school teacher
What if I have a question?
If you have any questions regarding the study please contact Associate Professor Gaynor
Parfitt on (01392) 262869, Dr. Ann Rowlands on (01392) 262878 or Mr. Toby Pavey on
(01392) 262818 and we will be happy to help.
We hope you would like to take part in the study, and look forward to seeing you in the
coming weeks.
Best wishes, Associate Professor Gaynor Parfitt, Dr. Ann Rowlands and Mr. Toby Pavey
This study has been reviewed and approved by the School of Sport and Health Sciences
Ethics Committee
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
192
Appendix 2D
SCHOOL OF SPORT AND
HEALTH SCIENCES,
HEALTH AND
PERFORMANCE
PSYCHOLOGY GROUP
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
Information Sheet for Child Participants and Parents
The impact of a physical activity education intervention on children’s multidimensional
psychological well-being
Thank you for showing an interest in this project. Please read this information sheet carefully before deciding
whether or not to participate, it will tell you a bit more about the study and what we would like to do. If you decide
to participate we thank you. If you decide not to take part it is not a problem and we thank you for considering our
request.
What is the aim of the project?
The School of Sport and Health Sciences research team have been studying various aspects of health, fitness and
psychology for a long time. The aim of this project is to study the relationship between habitual physical activity,
sedentary behaviour and psychological well-being. We want to investigate whether the amount of physical activity you
do affects how you feel, or whether it’s the other way round, how you feel affects the amount of physical activity
you do. This is important because with this knowledge we can design ways to improve both physical activity and the
way you feel.
What type of participant is needed?
Healthy boys and girls aged nine to ten years old with no known medical conditions.
What will the participants be asked to do?
The participants will be provided with an education session at the start of the project during which the importance
of being physically active will be explained. The intervention period will be five weeks, with a baseline and post data
collection period either end of the five weeks.
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Associate Professor
Gaynor Parfitt
PhD Supervisor
Tel: (01392) 262869
c.g.parfitt@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392) 262818
T.Pavey@ex.ac.uk
193
Data Collection Periods
In order to help analyse the study, data will be collected before and after the five week intervention. Each
measurement point will involve two visits to the school, seven days apart. At the first visit, the children will complete
three psychological well-being inventories. The participants will then be introduced to an accelerometer and shown
how to wear it. The children will wear the accelerometer for seven days. Anthropometric measures will also be
completed i.e. height, weight, skinfolds. Sitting height will also be measured to estimate maturation. Seven days later
the researcher will return to the school to collect the accelerometers and administer the children’s version of the
CLASS.
What will the questionnaires involve doing?
The questionnaires looking at psychological well-being, will involve the children ticking a box, which best describes
them. To complete these questionnaires may take up to 30 minutes; this includes instructions and trial questions. The
written physical activity questionnaire (CLASS) will have a list of physical and leisure activities and will ask the
children to write down how many times and for how long they do these activities in a typical school week. The physical
activity questionnaire should take around 10 minutes to complete. The questionnaires will be completed at the
children’s school and a member of the research team will be available to assist with any problems.
What will the accelerometers involve doing?
This is the fun part, you will get to wear an accelerometer for each 7-day measurement. The accelerometer is a small
box like device and will clip onto the children’s outer or under garments, and will be resting against the children’s hip.
Every time the children move, the accelerometer will record data about how much and for how long they are moving.
The children will be asked to wear the accelerometer from when they get up, and until they go to bed.
When will this take place?
We would like to start the study in October 2007.
What if I want to drop out of the study?
You can drop out of the study at any time, we will not be upset or cross and neither will your teachers at school.
What will you do with the results?
All the results we collect will be stored as numbers on a computer. After each data collection we will send you a brief
summary of your personal data for your own information. No one else will be told your individual results. We will write
the study up as a paper, the group results may be presented to other researchers but your information will remain
confidential.
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Associate Professor
Gaynor Parfitt
PhD Supervisor
Tel: (01392) 262869
c.g.parfitt@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392) 262818
T.Pavey@ex.ac.uk
194
What should I do if I want to participate?
If you would like to be involved in this project please make sure you do the following:
5. Both you and your parents/guardians complete the participant consent form
6. Return the forms to your school teacher
What if I have a question?
If you have any questions regarding the study please contact Associate Professor Gaynor
Parfitt on (01392) 262869, Dr. Ann Rowlands on (01392) 262878 or Mr. Toby Pavey on (01392)
262818 and we will be happy to help.
We hope you would like to take part in the study, and look forward to seeing you in the coming
weeks.
Best wishes, Associate Professor Gaynor Parfitt, Dr. Ann Rowlands and Mr. Toby Pavey
This study has been reviewed and approved by the School of Sport and Health Sciences
Ethics Committee
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Associate Professor
Gaynor Parfitt
PhD Supervisor
Tel: (01392) 262869
c.g.parfitt@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392) 262818
T.Pavey@ex.ac.uk
195
Appendix 3
Informed Consent
196
SCHOOL OF SPORT AND
HEALTH SCIENCES
Appendix 3A
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
FORM OF CONSENT:
The Relationship Between Habitual Physical Activity and Psychological Well-Being in
Children
Please complete sections 1 and 2 and return the form to your child’s school
Section 1: Parent / Guardian
I agree to my child ……………………………………………………….. participating in a research
project concerned with habitual physical activity, sedentary behaviour, and psychological wellbeing in children, the nature of which has been explained to me by letter.
I have had the opportunity to discuss the research procedures with the research group and I
understand that my child will have to complete a series of questionnaires concerned with
psychological well-being, which may take up to 30 minutes. Further, a self-report of physical
activity will also be completed by both my child and myself. I also agree to supply demographic
information about myself. Completion of the activity questionnaire will take no longer than 10-15
minutes. I also understand that my child’s height, weight, body fat % and sitting height will be
measured. Also my child will be asked to wear an accelerometer and a pedometer for a seven-day
measurement period. I also understand that testing will occur at regular intervals over a two year
period, or up to six occasions.
The results and information will be stored in a computer in coded form and will be completely
confidential to the researchers. The information will be used for a PhD research thesis. My child is
free to withdraw from the study at any stage without giving a reason and without affecting his or her
relationship with the researchers or the School. You and your Child are free to access their data at
any point in the study and after each data collection will be sent a summary of your child’s data.
______________________
Name of parent
____________
Date
_______________
Signature
__________________________________________________________________________
Address (including postcode)
_______________________________
Telephone number
______________________________
Email Address
___________________________________________________
Name of Child’s School
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
197
SCHOOL OF SPORT AND
HEALTH SCIENCES
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
Section 2: Child/Pupil
I, …………………………………………………. agree to take part in the research project described to
me in the information sheet, the nature of which has been explained to me. I understand the extent
of my involvement in the study and that I am free to withdraw from the project at any time without
affecting my relationship with the University or the School.
_____________________
Date of Birth
______________________
Name of Child
____________
_______________
Signature
____________
_______________
Signature
Date
Completed by Researcher:
______________________
Name of Researcher
Date
This project has been reviewed and approved by the Ethics Committee of the School of Sport
and Health Sciences
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
198
Appendix 3B
SCHOOL OF SPORT AND
HEALTH SCIENCES,
HEALTH AND
PERFROMANCE
PSYCHOLOGY GROUP
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
FORM OF CONSENT:
The impact of a physical activity intervention on children’s multidimensional psychological
well-being
Please complete sections 1 and 2 and return the form to your child’s school
Section 1: Parent / Guardian
I agree to my child ……………………………………………………….. participating in a research
project concerned with habitual physical activity, sedentary behaviour, and psychological wellbeing in children, the nature of which has been explained to me by letter.
I have had the opportunity to discuss the research procedures with the research group and I
understand that my child will complete an education phase and will be asked to wear a pedometer
for the duration of the study.
Also, my child will have to complete a series of questionnaires concerned with psychological wellbeing, which may take up to 30 minutes. Further, a self-report of physical activity will also be
completed by my child. I also agree to supply demographic information about myself. Completion
of the activity questionnaire will take no longer than 10 minutes. I also understand that my child’s
height, weight, body fat % and sitting height will be measured. Also my child will be asked to wear
an accelerometer for a two seven-day measurement periods.
The results and information will be stored in a computer in coded form and will be completely
confidential to the researchers. The information will be used for a PhD research thesis. My child is
free to withdraw from the study at any stage without giving a reason and without affecting his or her
relationship with the researchers or the School. You and your Child are free to access their data at
any point in the study and after each data collection will be sent a summary of your child’s data.
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
199
SCHOOL OF SPORT AND
HEALTH SCIENCES,
HEALTH AND
PERFROMANCE
PSYCHOLOGY GROUP
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
_____________________
Name of parent
____________
Date
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
_______________
Signature
__________________________________________________________________________
Address (including postcode)
_______________________________
Telephone number
_______________________________
Email Address
___________________________________________________
Name of Child’s School
Section 2: Child/Pupil
I, …………………………………………………. agree to take part in the research project described to
me in the information sheet, the nature of which has been explained to me. I understand the extent
of my involvement in the study and that I am free to withdraw from the project at any time without
affecting my relationship with the University or the School.
_____________________
Date of Birth
______________________
Name of Child
____________
_______________
Signature
____________
_______________
Signature
Date
Completed by Researcher:
______________________
Name of Researcher
Date
This project has been reviewed and approved by the Ethics Committee of the School of Sport
and Health Sciences
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
200
Appendix 3C
SCHOOL OF SPORT AND
HEALTH SCIENCES,
HEALTH AND
PERFROMANCE
PSYCHOLOGY GROUP
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
FORM OF CONSENT:
The impact of a physical activity education intervention on children’s multidimensional
psychological well-being
Please complete sections 1 and 2 and return the form to your child’s school
Section 1: Parent / Guardian
I agree to my child ……………………………………………………….. participating in a research
project concerned with habitual physical activity, sedentary behaviour, and psychological wellbeing in children, the nature of which has been explained to me by letter.
I have had the opportunity to discuss the research procedures with the research group and I
understand that my child will complete an education phase. Also, my child will have to complete a
series of questionnaires concerned with psychological well-being, which may take up to 30 minutes.
Further, a self-report of physical activity will also be completed by my child. I also agree to supply
demographic information about myself. Completion of the activity questionnaire will take no longer
than 10 minutes. I also understand that my child’s height, weight, body fat % and sitting height will
be measured. Also my child will be asked to wear an accelerometer for a two seven-day
measurement periods.
The results and information will be stored in a computer in coded form and will be completely
confidential to the researchers. The information will be used for a PhD research thesis. My child is
free to withdraw from the study at any stage without giving a reason and without affecting his or her
relationship with the researchers or the School. You and your Child are free to access their data at
any point in the study and after each data collection will be sent a summary of your child’s data.
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
201
SCHOOL OF SPORT AND
HEALTH SCIENCES,
HEALTH AND
PERFROMANCE
PSYCHOLOGY GROUP
St Luke's Campus
Heavitree Road
EXETER
EX1 2LU
U.K.
Telephone
Fax
Email
Web
_____________________
Name of parent
____________
Date
+44 (0)1392 262896
+44 (0)1392 264706
sshs-school-office@ex.ac.uk
www.ex.ac.uk/sshs
_______________
Signature
__________________________________________________________________________
Address (including postcode)
_______________________________
Telephone number
______________________________
Email Address
___________________________________________________
Name of Child’s School
Section 2: Child/Pupil
I, …………………………………………………. agree to take part in the research project described to
me in the information sheet, the nature of which has been explained to me. I understand the extent
of my involvement in the study and that I am free to withdraw from the project at any time without
affecting my relationship with the University or the School.
_____________________
Date of Birth
______________________
Name of Child
____________
_______________
Signature
____________
_______________
Signature
Date
Completed by Researcher:
______________________
Name of Researcher
Date
This project has been reviewed and approved by the Ethics Committee of the School of Sport
and Health Sciences
Head of School
Professor Roger Eston
School Administrator
Elaine Davies
School Office
Tel: (01392) 262896
Fax: (01392) 264726
E.M.Davies@exeter.ac.uk
Dr Ann Rowlands
PhD Supervisor
Tel: (01392 262878)
A.V.Rowlands@exeter.ac.uk
Mr. Toby Pavey MSc
Research Assistant
Tel: (01392 262883)
T.Pavey@ex.ac.uk
202
Appendix 4
Intervention Materials
203
IMPORTANT INFORMATION ABOUT THE ACTIVITY MONITOR
The activity monitor is a small, plastic box with a machine inside of it. When you wear the
activity monitor, it measures how much you are moving. We will use the information the activity
monitor records to find out more about the activity of kids like you!
You should remember a few important things about the activity monitor:
Appendix 4A
 Snap the belt around your waist.
 Place the monitor over your RIGHT HIP.
 Make sure IT IS THE RIGHT WAY UP (Note – you should be able to read the
“Actigraph” label on the device).
 Wear the monitor all day from when you get up, and until you go to bed, including during
school and while playing sports
 DO NOT GET THE MONITOR WET (Sweat is okay).
 Take off the monitor to shower, bathe or swim
Once again Thank-you all for taking part in this study and I hope all of you enjoy taking part.
If there are any questions, do not hesitate to contact me Mr. Toby Pavey on: 01392 262818
(office) or 07834731242 (mobile), or email T.Pavey@ex.ac.uk
204
ACCELEROMETER LOG SHEET
Subject Number:……………………………Date of Birth:……………………….School:…………………………………………
DAY
AND DATE
Example
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
Number of times taken off
2 or twice
Reason for taking off
1. Shower
2. Swimming
Amount of time NOT worn
20 minutes
45 minutes
1.
2.
3.
4.
1.
2.
3.
4.
1.
2.
3.
4.
1.
2.
3.
4.
1.
2.
3.
4.
1.
2.
3.
4.
1.
2.
3.
4.
205
Appendix 4B
PEDOMETER LOG SHEET (week 2)
Name:
Target Steps: 12147
DAY
Pedometer
Count
Saturday
(13:00 pm)
Sunday
(13:00)
206
Appendix 4D
Steps
Pedometer Steps Week 3
24000
22000
20000
18000
16000
14000
12000
10000
8000
6000
4000
2000
0
Target Steps
Wednesday
Thursday
Friday
Saturday
Sunday
Monday
Tuesday
Day
Pedometer Steps Week 3
210
Appendix 4E
Steps
Pedometer Steps Week 3
30000
28000
26000
24000
22000
20000
18000
16000
14000
12000
10000
8000
6000
4000
2000
0
Target Steps
Wednesday
Thursday
Friday
Saturday
Sunday
Monday
Tuesday
Day
211
0
:0
8
:4 00
22 6 :
:3 0
21 4 :0
:2 00
20 2 :
:1 00
19 0 :
:0 00
18 8 :
:4 00
16 6 :
:3 00
15 4 :
:2 00
14 2 :
:1 00
13 0 :
:0 00
12 8 :
:4
10 : 00
36
9: : 00
24
8: : 00
12 0
7: : 0
00
6: : 00
48 0
4: : 0
36
3: : 00
24 0
2: : 0
12 0
1: : 0
00
0:
0
In this one the
person has taken
off the RT3
accelerometer,
and not
remembered to
put it back on
3000
2000
1000
0
:0
8
:4 00
22 6 :
:3 0
21 4 :0
:2 00
20 2 :
:1 00
19 0 :
:0 00
18 8 :
:4 00
16 6 :
:3 00
15 4 :
:2 00
14 2 :
:1 00
13 0 :
:0 00
12 8 :
:4
10 : 00
36
9: : 00
24
8: : 00
12 0
7: : 0
00
6: : 00
48 0
4: : 0
36
3: : 00
24 0
2: : 0
12 0
1: : 0
00
0:
0
0
:0
8
:4 00
22 6 :
:3 0
21 4 :0
:2 00
20 2 :
:1 00
19 0 :
:0 00
18 8 :
:4 00
16 6 :
:3 00
15 4 :
:2 00
14 2 :
:1 00
13 0 :
:0 00
12 8 :
:4
10 : 00
36
9: : 00
24
8: : 00
12 0
7: : 0
00
6: : 00
48 0
4: : 0
36
3: : 00
24 0
2: : 0
12 0
1: : 0
00
0:
0
Appendix 4F
Examples of Accelerometer Output
3000
18:00
12:00
2000
10:00
1000
TIME
Here is a typical output over a 24 hour period. The higher count seen at around 10:00 and 12:00
probably represents break times at school, e.g. activities in the playground. The high count at
around 18:00 is after school activities, e.g. local park or a sports activity.
3000
2000
1000
TIME
In this output we can see where the RT3 accelerometer was taken off for a short period, e.g.
swimming.
4000
TIME
212
Appendix 5
Supplementary Data
213
Appendix 5A
214
Appendix 5B
215
Appendix 5C
Average
daily activity
-.192
Very
light
.371**
Light
Moderate
Vigorous
.180
-.106
-.314*
Depression
-.012
.338*
.203
.107
-.175
Global self-worth
-.035
-.395**
-.336*
-.140
.159
Scholastic Competence
.079
-.337*
-.225
-.121
.281*
Social Acceptance
.245
-.106
.001
.014
.337*
Physical Appearance
-.052
-.383**
-.335*
-.134
.120
Behavioural Conduct
-.357**
-.082
-.231
-.314*
-.272*
-.058
-.385**
-.302*
-.141
.113
Sport/Athletic Competence
.240
-.111
-.058
.085
.339*
Physical condition
.257*
-.164
-.055
.118
.327*
Attractive Body
-.103
-.299*
-.297*
-.189
.066
Strength competence
.023
-.079
-.103
.037
.064
Anxiety
Physical self-worth
Table: Correlation analyses for the relationship between physical activity intensity
and psychological well-being controlling for socio-economic status
216
Appendix 5D
Table: Well-being scores by total daily activity and time spent in light and moderate intensity activity.
Psychological
Health
Total Daily Activity
(accelerometer counts)
Low Group
Middle Group
Light Activity
Moderate Activity
High Group
Low Group
Middle
Group
High Group
Low Group
Middle
Group
High Group
boys
32186 ± 22902
363876 ± 11027
444033 ± 36104
90 ± 4min
103 ± 3min
127 ± 15min
72 ± 7min
84 ± 8min
102 ± 10min
girls
287141 ±17918
357970 ± 29055
448736 ± 27016
102 ± 9min
119 ± 5min
138 ± 4min
69 ± 6min
86 ± 6min
108 ± 8min
MANOVA 1: anxiety
33.6 (1.7)
31.6 (1.6)
31.5 (1.8)
30.5 (1.7)
33.1 (1.7)
33.1 (1.8)
30.7 (1.7)
31.3 (1.7)
depression
8.3 (2.0)
10.2 (1.9)
8.0 (1.9)
8.7 (2.0)
7.4 (1.9)
10.6 (1.9)
9.1 (2.0)
8.5 (1.9)
9.1 (1.9)
GSE
19.7 (.93)
17.9 (.86)
18.9 (.90)
19.3 (.91)
19.8 (.86)
17.2 (.87)
19.0 (.96)
18.9 (.90)
18.3 (.92)
MANOVA 2:
scholastic
16.1 (.94)
16.9 (.89)
17.3 (.90)
17.9 (.95)
16.9 (.90)
15.8 (.91)
16.9 (.96)
17.1 (.90)
16.4 (.92)
social
17.8 (1.1)
18.6 (.98)
20.1 (1.0)
18.0 (1.1)
19.1 (1.0)
18.8 (1.1)
17.8 (1.2)
18.9 (1.1)
19.1 (1.1)
appearance
18.1 (1.2)
16.6 (1.1)
17.1 (1.2)
17.4 (1.1)
19.4 (1.1
15.0 (1.1)
17.8 (1.2)
18.2 (1.1)
15.9 (1.1)
behavioural
18.1 (.74)
16.6 (.70)
16.3 (.71)
18.1 (.85)
18.5 (.80)
16.2 (.81)
18.6 (.86)
17.5 (.81)
16.8 (.82)
PSW
18.8 (1.2)
17.0 (1.1)
17.7 (1.1)
18.1 (1.1)
19.1 (1.0)
16.0 (1.0)
18.6 (1.1)
18.3 (1.0)
16.5 (1.1)
MANOVA 3: athletic
16.6 (1.1)
16.9 (1.1)
19.1 (1.0)
17.1 (1.1)
18.1 (1.1)
17.5 (1.1)
17.1 (1.1)
17.5 (1.1)
18.1 (1.1)
condition
17.6 (.91)
18.8 (.86)
20.2 (.87)
18.5 (.95)
19.4 (.89)
18.7 (.91)
18.2 (.92)
18.8 (.87)
19.6 (.89)
attractive
17.1 (1.2)
16.7 (1.0)
16.2 (1.1)
16.9 (1.1)
18.1 (1.0)
15.0 (1.1)
17.5 (1.1)
17.5 (1.0)
16.0 (1.1)
strength
15.7 (1.1)
17.2 (1.0)
16.6 (1.0)
16.3 (1.1)
16.1 (1.0)
17.1 (1.0)
15.8 (1.0)
17.7 (.97)
16.8 (1.0)
30.0
(1.7)
217
Appendix 5E
Table 1: Descriptive Data for Boys (Chapter 7)
Time-point One
Time-point Two
(0 Months, n = 24) (6 Months, n = 25)
Mean
S.D.
Mean
S.D.
Age
10.16
.37
Time-point Three
(12Months, n = 21)
Mean
S.D.
10.69
.46
Height (cm)
145.92
21.57
144.30
5.86
146.27
5.28
Mass (kg)
36.07
7.19
38.46
8.41
38.46
7.49
Bodyfat %
17.40
7.45
17.32
8.67
15.76
7.55
399734
89883
357019
57652
449012
112414
981.54
60.43
984.55
59.07
963.70
62.87
219.73
34.86
234.27
37.27
218.44
33.75
108.70
18.94
107.80
20.79
113.74
16.15
90.44
24.35
82.96
16.80
102.32
22.49
38.66
19.91
29.54
12.98
40.17
17.05
32.46
7.31
31.24
6.58
27.10
5.58
Depression
8.96
8.68
8.42
6.82
5.57
4.55
Global
Self-worth
Scholastic
Competence
Social
Acceptance
Physical
Appearance
Behavioural
Conduct
Physical
Self-worth
Sport/athletic
Competence
Stamina/condition
Competence
Attractive
Body
Strength
Competence
19.13
3.54
19.38
3.30
20.50
3.44
16.75
3.40
16.88
3.69
18.88
2.83
18.13
4.90
19.38
4.29
19.38
4.81
19.38
3.97
17.88
4.22
19.25
3.86
17.18
2.11
17.25
3.42
18.38
2.96
18.88
3.49
18.25
4.20
19.50
4.32
18.38
4.08
18.13
4.69
19.50
4.49
19.38
3.09
19.00
3.59
20.00
3.63
17.88
3.35
17.25
4.22
18.24
4.25
17.5
4.25
17.75
4.52
19.13
4.09
Daily Activity
(Counts)
Time in Sedentary
(Minutes)
Time in V. Light
(Minutes)
Time in Light
(Minutes)
Time in Moderate
(Minutes)
Time in Vigorous
(Minutes)
Anxiety
218
Appendix 5F
Table 2: Descriptive Data for Girls (Chapter 7)
Time-point One
Time-point Two
(0 Months, n = 35) (6 Months, n = 33)
Mean
S.D.
Mean
S.D.
Age
10.09
.29
Time-point Three
(12Months, n = 27)
Mean
S.D.
10.50
.50
Height (cm)
139.79
7.45
142.88
7.15
145.13
7.21
Mass (kg)
36.45
9.65
37.18
7.52
37.79
6.84
Bodyfat %
21.07
8.50
18.60
8.45
17.86
7.39
370106
81160
353540
72144
400435
84401
976.57
56.08
970.85
71.56
957.03
53.67
228.96
30.23
230.42
30.17
220.55
25.67
116.25
16.88
122.34
19.39
122.50
16.67
88.88
18.21
88.00
22.86
101.33
27.09
29.35
15.73
23.34
10.12
31.69
14.12
33.89
8.13
32.36
7.77
29.20
7.31
Depression
8.66
6.93
8.97
8.71
6.75
6.39
Global
Self-worth
Scholastic
Competence
Social
Acceptance
Physical
Appearance
Behavioural
Conduct
Physical
Self-worth
Sport/athletic
Competence
Stamina/condition
Competence
Attractive
Body
Strength
Competence
18.88
3.10
18.25
4.19
20.38
3.55
15.81
3.50
16.75
4.01
18.50
3.14
18.27
2.77
17.91
4.64
19.18
3.53
17.14
4.17
16.75
5.35
17.88
4.18
18.45
2.68
18.15
3.64
18.75
3.18
17.07
3.33
17.30
4.81
18.00
4.46
16.29
3.19
16.88
4.54
18.00
3.69
18.51
2.80
18.50
3.97
18.75
4.38
15.87
3.74
16.25
4.89
16.78
4.37
15.80
3.31
15.25
4.22
15.38
3.42
Daily Activity
(Counts)
Time in Sedentary
(Minutes)
Time in V. Light
(Minutes)
Time in Light
(Minutes)
Time in Moderate
(Minutes)
Time in Vigorous
(Minutes)
Anxiety
219