Journal of Science and Medicine in Sport (2006) 9, 319—326
ORIGINAL PAPER
Measurement properties of the CHAMPS physical
activity questionnaire in a sample of older
Australians
E.V. Cyarto ∗, A.L. Marshall, R.K. Dickinson, W.J. Brown
School of Human Movement Studies, The University of Queensland, St. Lucia, Brisbane, Qld. 4072,
Australia
Received 15 October 2005 ; received in revised form 17 February 2006; accepted 5 March 2006
KEYWORDS
Physical activity
measurement;
Elderly;
Reliability;
Predictive validity
Summary
Background: The effective evaluation of physical activity interventions for older
adults requires measurement instruments with acceptable psychometric properties
that are sufficiently sensitive to detect changes in this population.
Aim: To assess the measurement properties (reliability and validity) of the Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire in a
sample of older Australians.
Methods: CHAMPS data were collected from 167 older adults (mean age 79.1 S.D.
6.3 years) and validated with tests of physical ability and the SF-12 measures of
physical and mental health. Responses from a sub-sample of 43 older adults were
used to assess 1-week test—retest reliability.
Results: Approximately 25% of participants needed assistance to complete the
CHAMPS questionnaire. There were low but significant correlations between the
CHAMPS scores and the physical performance measures (rho = 0.14—0.32) and the
physical health scale of the SF-12 (rho = 0.12—0.24). Reliability coefficients were
highest for moderate-intensity (ICC = 0.81—0.88) and lowest for vigorous-intensity
physical activity (ICC = 0.34—0.45). Agreement between test-retest estimates of sufficient physical activity for health benefits (≥150 min and ≥5 sessions per week) was
high (percent agreement = 88% and Cohen’s kappa = 0.68).
Conclusion: These findings suggest that the CHAMPS questionnaire has acceptable
measurement properties, and is therefore suitable for use among older Australian
adults, as long as adequate assistance is provided during administration.
© 2006 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Introduction
∗
Corresponding author.
E-mail address: ecyarto@hms.uq.edu.au (E.V. Cyarto).
The number of Australians aged over 65 years
and, consequently, the number of people with
chronic disease and/or age-related disorders is set
1440-2440/$ — see front matter © 2006 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.jsams.2006.03.001
320
to rapidly increase over the next 20 years.1 Regular physical activity (PA) can prevent or postpone
the development of many chronic health conditions, and in older adults it can ameliorate the
declines in physical function commonly associated
with ageing.2—4 To prevent the course of unhealthy
ageing, research attention is beginning to focus on
the development and implementation of strategies
for increasing PA among older adults. To enhance
the evidence base, programs must be rigorously
evaluated. Measurement tools that provide valid
and reliable estimates of PA in this population need
to be identified.
In applied research settings, PA is usually
assessed using self-report methods (e.g. survey and
diaries). Population-based data collected using the
Active Australia (AA) questionnaire suggest that
only 44% of Australian adults aged 60—75 years
are sufficiently active for health benefit and that
almost one in five older Australians are completely
sedentary.5 The reliability and validity of the AA
questionnaire have been established6,7 but were
found to be lowest among those aged over 60
years.6 Whilst the AA questionnaire is the preferred
instrument for monitoring national population levels of PA over time, it may not be sensitive enough
to detect small but significant changes in PA following planned interventions in smaller samples of
older Australians.
Self-report data are limited by a number of
factors. Among older adults in particular, selfreport data may be plagued by recall bias8,9 and
by the ‘floor’ effects resulting from low levels
of PA in this population.8,10 To address these
issues, several PA questionnaires have been developed overseas to assist older adults recall and
report PA.11—15 Typically, these surveys emphasise
leisure and household activities, although the Physical Activity Scale for the Elderly (PASE)14 and
the Community Healthy Activities Model Program
for Seniors (CHAMPS) questionnaire15 also measure time spent in paid or volunteer work. Within
leisure and household domains, the PASE, the Yale
Physical Activity Survey (YPAS),13 and the CHAMPS
questionnaire15 cover the widest spectrum of activities. In terms of capturing small changes in PA over
time, the CHAMPS questionnaire has been found to
be more sensitive to change than the YPAS or the
PASE.8
Stewart and colleagues15 developed the CHAMPS
questionnaire as an outcome measure for a PA intervention study, because, at that time there were no
accurate self-report instruments for assessing PA in
under-active older adults. The questionnaire was
specifically designed to minimise social desirability
(by including non-physical activities such as hob-
E.V. Cyarto et al.
bies) and recall bias (use of recognition memory).
The CHAMPS questionnaire assesses the weekly frequency and duration of activities typically undertaken by older adults at light (e.g. leisurely walking, light gardening), moderate (e.g. cycling, heavy
housework) and vigorous (e.g. singles tennis, jogging) intensities. A MET value is assigned to each
activity, using the compendium,16 but with adjustment for the likely intensity of each activity among
older adults.15
To date, two studies have evaluated the psychometric properties of the CHAMPS questionnaire
among community-dwelling older adults in the
United States. They reported Intraclass Correlation
Coefficients (ICC) with moderate 6-month stability
(ICC = 0.58—0.67)15 and good test-retest reliability
over a 2-week period (ICC = 0.62—0.76).8 In these
studies, the CHAMPS data demonstrated modest
but acceptable predictive validity, including associations between CHAMPS scores and both physical functioning and health-related quality of life
measures.8 CHAMPS data also showed moderate
correlations with data from the PASE and the YPAS,8
and were able to discriminate between less active
and more active older adults.8,15
To date, no studies have explored the measurement properties of the CHAMPS survey in older Australian adults. The aim of the present study was
therefore to examine the predictive validity and
test-retest reliability of the CHAMPS questionnaire
in a sample of older Australians.
Methods
Participants
A sample of 167 older adults (132 women and 35
men) aged over 60 years was recruited from nine
independent-living retirement villages located in
the Brisbane metropolitan area. These residents
had volunteered to participate in an exercise program, due to commence at the completion of
this study. Participants were provided with verbal
and written information about the project, and
informed consent forms were signed prior to participation. Approval for this study was obtained from
the Medical Research Ethics Committee at The University of Queensland.
Procedures
Standard demographic and health-related questions and the short self-report measure of health
status (SF-12)17 were completed up to 1 week in
advance of the first functional assessment session
Measurement properties of the CHAMPS physical activity questionnaire
(T1). At T1 participants completed three components of the Senior Fitness Test18 (chair stand for
lower body strength, 8-ft ‘up and go’ for agility,
and step test for aerobic endurance) and a balance test.19 Immediately following these tests,
they completed the CHAMPS questionnaire in small
groups.
A research assistant was available to assist
participants who experienced difficulty reading
or understanding the written instructions. The
CHAMPS questionnaire asked participants to recall
the weekly frequency and duration of each activity included in a comprehensive list of light-,
moderate- and vigorous-intensity activities. Participants were asked to recall their activities in ‘a
typical week over the last 4 weeks’.
Consistent with the methods used by Harada
et al.8 and Stewart et al.15 , data from the four
physical performance tests and SF-12 were compared with the self-reported CHAMPS data to assess
the predictive validity of the survey. The specific
hypotheses were: (1) the CHAMPS scores would positively correlate with the physical performance test
Table 1
321
scores; and (2) higher correlations would be found
between CHAMPS scores and the physical component summary score of the SF-12 than between
CHAMPS scores and the mental component summary
score. To assess test-retest reliability, participants
from three of the villages (43 women and 13 men)
volunteered to complete a second questionnaire 1
week later (T2) and return it to the investigators via
a dedicated ‘post box’ located in a common area of
their village.
Data analysis
The sum of the products of weekly frequency and
duration (hours) of PA were computed to determine
the ‘volume’ (MET.hours) of activities categorised
as light- (≥2 MET but <3 MET), moderate- (≥3 MET
but <6 MET) and vigorous-intensity (≥6 MET). The
MET classifications were the same as those used
by Stewart and colleagues.15 Spearman’s rank correlation coefficients were calculated to examine
predictive validity (comparing the CHAMPS scores
with the physical performance tests and with the
Demographic, health and physical activity characteristics of the participants
Mean (S.D.)
Age (years)
Prescribed medications
Validity study (N = 167)
Reliability study (N = 43)
79.1 (6.3)
3.9 (2.7)
77.4 (6.6)*
3.8 (2.5)
Validity study (N = 167)
Reliability study (N = 43)
79.0
98.2
34.7
72.1
100.0
41.9
Education
Higher school or less
Trade or diploma
University degree
Employment status (retired)
77.8
17.4
4.8
85.0
74.4
21.0
4.7
83.7
Self-reported medical conditions
Arthritis
Hypertension
Elevated cholesterol level
Cardiovascular problems
Diabetes
Osteoporosis
55.1
46.1
36.5
22.8
11.4
33.5
51.2
46.5
32.6
25.6
11.6
20.9*
Physical activity
Sufficient activity times and sessionsa
Insufficient activity times and sessions
Sedentary
(N = 164)
15.9
82.3
1.8
Percentage
Gender (female)
Ethnicity (Caucasian)
Marital status (married)
(N = 41)
26.8*
73.2*
0
a Sufficient activity time and sessions is defined as at least 150 min in at least five sessions of moderate-intensity activity per
week.3
* Significant difference between validity study and reliability study participants (p < 0.05).
322
E.V. Cyarto et al.
SF-12 scores). After performing logarithmic and
square root transformations to normalise skewed
data, ICCs were used to assess test-retest reliability (comparing scores at T1 and T2).
Measures of weekly frequency and volume of
PA were used to categorise participants’ activity
level based on current Australian guidelines.3 Participants were classified as ‘sufficiently active’ if
they reported ≥150 min and ≥5 sessions of weekly
PA of at least moderate-intensity and ‘insufficiently
active’ if they did not. Percent agreement and
Cohen’s kappa20 were used to assess the reliability of the classification between T1 and T2.
Correlation coefficients ≥0.75 indicated excellent reliability and the magnitude of the kappa
value was classified as poor to slight (0—0.20),
fair (0.21—0.40), moderate (0.41—0.60), substantial (0.61—0.80), or almost perfect (0.81—1.00).21
For all analyses, significance was set at an alpha
level of p < 0.05. Data were analysed using the Statistical Package for the Social Sciences (SPSS), version 13.0.
Results
Complete predictive validity and test-retest reliability data were received from 167 and 43
participants, respectively. Participants were aged
between 65 and 96 years. The majority of participants were retired women, and 72% reported
three or more chronic health problems. The reliability sample was significantly younger, included
fewer participants with osteoporosis, and included
more participants classified as sufficiently active
than the validity sample (see Table 1). Thirteen of
those who volunteered to participate in the reliability study did not complete the CHAMPS questionnaire at T2. There were no significant differences
in demographic or health characteristics between
those who did and those who did not complete the
survey a second time.
Despite providing detailed written instructions
for completion and an example to follow, approximately 25% of participants needed assistance to
complete the CHAMPS survey at T1. The most common survey completion error was that participants
(n = 7) circled the total hours spent in a particular activity but did not write a response for the
frequency of participation. Although most participants took at least 15 min to complete the CHAMPS
questionnaire, some took considerably longer (up
to 30 min). The last item in the CHAMPS questionnaire allowed respondents to specify additional
type(s) of PA (not included in the list). The additional activities reported were: table tennis (n = 5),
carpet/indoor bowls (n = 4), lawn bowls (n = 3), and
pushing a wheelchair (n = 1). Using the compendium
as a guide,16 these activities were assigned a MET
value of 3.0, with the exception of carpet/indoor
bowls which was categorised as a light-intensity
activity (2.5 MET).
The CHAMPS data were significantly skewed,
thus Medians and Inter Quartile Ranges (IQR) are
reported in Tables 2 and 3. The numbers included
in each analysis vary because not all participants
provided complete frequency data for all the items
in the validity (n = 7) and reliability analyses (n = 4).
In a typical week over the previous 4 weeks, participants in both sets of analyses engaged most
frequently in light-intensity activities. In the pre-
Table 2 CHAMPS physical activity measures: descriptive statistics and correlations (Spearman’s Rho) with physical
performance tests and self reported physical and mental well-being (N = 167)
CHAMPS output
Median
IQR
a
Spearman’s Rho
Chair
stand
Step
test
8-ft up
& go
Tandem
balance
SF-12
physical
SF-12
mental
Moderate + vigorous-intensity PA (MET ≥ 3.0)
MET.hours per week
3.0
10.7
Frequency per weekb
2.0
5.0
0.19*
0.16*
0.32***
0.31***
0.31***
0.29***
0.31***
0.28***
0.18*
0.12
−0.14
−0.11
All activities
MET.hours per week
Frequency per weekc
0.21**
0.14
0.28***
0.26**
0.28***
0.28***
0.29***
0.23**
0.24**
0.15
−0.14
−0.09
a
b
c
*
**
***
IQR—–interquartile range.
N = 164.
N = 160.
p ≤ 0.05.
p ≤ 0.01.
p ≤ 0.001.
19.8
14.0
23.4
13.8
Descriptive profile and 1-week test-retest reliability of the physical activity outcomes derived from the CHAMPS questionnaire (N = 43)
CHAMPS outcomes (Time 1)
CHAMPS outcomes (Time 2)
ICCc
95% CI
p<
Mean (S.D.)a
Median
IQRb
Hours per week
Light-intensity
Moderate-intensity PA
Vigorous-intensity PA
Moderate + vigorous-intensity PA
All activities
8.3
3.2
0.8
4.0
12.3
(5.9)
(5.3)
(1.6)
(6.0)
(10.1)
7.0
0.5
0
1.5
10.5
8.0
5.0
0.5
5.0
11.0
7.1
2.0
0.3
2.3
9.4
(4.3)
(2.5)
(0.5)
(2.7)
(5.7)
6.0
0.5
0
1.0
8.5
6.0
3.5
0.5
4.0
7.5
0.67
0.85
0.34
0.78
0.76
0.47—0.81
0.74—0.92
0.05—0.58
0.63—0.87
0.60—0.86
0.0001
0.0001
0.05
0.0001
0.0001
Frequency per week
Light-intensityd
Moderate-intensity PAe
Vigorous-intensity PA
Moderate + vigorous-intensity PAe
All activitiesd
11.9
2.9
2.1
5.1
17.2
(7.1)
(4.0)
(2.7)
(5.2)
(10.3)
10.0
1.0
0
4.0
16.0
10.0
5.5
4.0
8.0
13.0
11.5
2.6
1.2
3.7
15.4
(6.4)
(3.5)
(2.2)
(4.5)
(9.8)
11.0
1.0
0
3.0
13.0
9.0
4.0
2.0
6.0
10.0
0.65
0.81
0.45
0.76
0.79
0.43—0.90
0.68—0.90
0.18—0.66
0.59—0.86
0.64—0.87
0.0001
0.0001
0.001
0.0001
0.0001
Volume (MET.hours per week)
Light-intensity
Moderate-intensity PA
Vigorous-intensity PA
Moderate + vigorous-intensity PA
All activities
19.8
11.4
5.0
16.4
36.3
(14.2)
(18.8)
(9.7)
(23.4)
(32.3)
15.1
2.0
0
6.3
29.6
18.9
17.3
3.0
23.0
33.0
17.1
7.1
1.7
8.9
26.0
(10.3)
(9.3)
(3.2)
(10.4)
(16.9)
14.1
1.8
0
4.8
24.4
13.3
12.3
3.0
15.3
19.0
0.66
0.88
0.44
0.76
0.75
0.46—0.80
0.79—0.93
0.17—0.65
0.61—0.87
0.58—0.86
0.0001
0.0001
0.001
0.0001
0.0001
a
b
c
d
e
Mean (S.D.)
Median
IQR
Measurement properties of the CHAMPS physical activity questionnaire
Table 3
S.D.—–standard deviation.
IQR—–interquartile range.
ICC—–intraclass correlation coefficient.
N = 39.
N = 41.
323
324
E.V. Cyarto et al.
Table 4 Proportion of participants meeting the Australian physical activity guidelines (at least 150 min in at least
five sessions of moderate-intensity physical activity per week) and consistency of classification between T1 and T2
(N = 41)
T1
T2
Sufficiently active (%)
Insufficiently active (%)
Sufficiently active
Insufficiently active
8 (19.5)
2 (4.9)
3 (7.3)
28 (68.3)
dictive validity sample, median measures of total
frequency and volume of weekly activities were
approximately seven times greater than those for
moderate- and vigorous-intensity PA. As hypothesised, significant, positive correlations were found
between the CHAMPS scores and the performancebased functional measures, although the magnitude
of the rho values was only fair (see Table 2). Also as
anticipated, there were stronger correlations with
the physical component summary score of the SF-12
than with the mental component summary score.
The sub-sample of participants who provided
test-retest reliability data also reported predominantly light-intensity activities and very few
reported any vigorous-intensity PA (see Table 3).
The median values at T1 were slightly higher than
at T2 for most measures. For hours per week, frequency per week, and MET.hours per week, 1-week
test-retest reliability was highest for the moderateintensity measures, ranging from ICC = 0.81 to 0.88,
and lowest for the vigorous-intensity measures
(ICC = 0.34—0.45). All ICCs, except those for the
vigorous-intensity measures, could be considered
moderate to excellent.
Only 11 (26%) of the 41 participants who provided
complete data at T1, and 10 (24%) at T2, were classified as sufficiently active. The percent agreement
and Cohen’s kappa statistics indicate a substantial
level of agreement in categorisation of participants
between T1 and T2 (see Table 4).
Discussion
This study examined the measurement properties
(predictive validity and test-retest reliability) of
the CHAMPS questionnaire, to determine its suitability for use with older Australians. To date,
the psychometric properties of this questionnaire
have only been evaluated with samples of older
Americans.8,15
In this study, the length of time required to complete the CHAMPS questionnaire was comparable to
previous reports involving samples of communitydwelling older adults.8,15 However, approximately
Percent agreement
Kappa
p
87.8
0.68
<0.001
one quarter of the participants required interviewer assistance and several provided incomplete
data. As previous researchers have not noted this
problem, we assume that it reflects the lower education levels of this Australian sample, 80% of whom
had no more than a high school education, compared with only 15% in the study by Stewart et
al.15 . Moreover, 12 of the 13 non-completers in the
reliability study had no post-school qualifications.
Thus, in its present form, the length and complexity of the CHAMPS questionnaire may have made
completing the questionnaire too cumbersome for
some individuals in this study. Further exploration
of ways to enhance the use of this questionnaire for
self-reporting PA in Australian samples is therefore
warranted.
For the most part, the activities included in the
CHAMPS questionnaire are meaningful and appropriate for Australian older adults. However, due
to differences in climate between North America and Australia, many of the Australians commented that they had never skated, and several
reported regularly playing lawn or carpet bowls in
the ‘other activity’ question. As up to 9% of Australians over the age of 65 years participate in
lawn or carpet bowls (only walking had a higher
rate of participation in a recent national survey22 ),
it will be important to include this activity if the
CHAMPS questionnaire is to be used more widely in
Australia.
This study provides additional support for the
predictive validity of the CHAMPS questionnaire
and both hypotheses were confirmed. Harada and
colleagues8 and Stewart and colleagues15 used the
Short Physical Performance Battery23 to provide a
measure of lower body functioning (summary score
comprising strength, balance and walking) and the
6-min walk to determine aerobic endurance. The
correlations reported in our study (rho = 0.19—0.32)
were more closely aligned with those reported by
Stewart et al.15 (r = 0.22—0.28) than with those
reported by Harada et al.8 (r = 0.39—0.54).
The self-report SF-3624 was used to measure
health-related quality of life in the two previous assessments of the CHAMPS questionnaire.8,15
In contrast, participants in this study completed
Measurement properties of the CHAMPS physical activity questionnaire
the SF-12,17 which has been shown to have good
validity against the SF-36.25 Across all three studies, as expected, the relationship between CHAMPS
outcomes and physical functioning measures was
stronger than with the mental health measures.
In the present study, the reliability coefficients
were higher than those reported in previous studies. For example, in this study the ICC was 0.75
for total volume of all activities compared with
0.62 and 0.66, respectively, in the studies reported
by Harada et al.8 and Stewart et al.15 . One likely
explanation is that the test-retest timeframe was
1 week in this study compared with 2 weeks8 and
6 months15 in the previous studies. In the design of
contemporary reliability studies,6,7,26 an attempt is
made to match the two recall periods as closely as
possible without allowing the possibility of recall
of responses. Unlike other measurement research
studies,6,26 participants in this study tended to
report less activity at T2 compared with T1. This
may be partly explained by the fact that at T1 some
participants may have been helped by the research
assistant, but when completing the CHAMPS questionnaire independently at T2, these participants
may have under-reported their PA.
Unique to this study, the CHAMPS data were used
to determine whether participants were meeting
the current Australian PA guidelines.3 Surprisingly,
only one in four participants in the reliability sample were categorised as sufficiently active (meeting
guidelines) at T1. However, the reliability statistics
indicate substantial agreement over the test-retest
period suggesting that CHAMPS can consistently
identify participants who are meeting the guidelines. These results compare favourably with the
degree of consistency found in reliability studies of
other PA questionnaires. For example, the percent
agreement score for this study is well within the
range reported by Craig and colleagues26 for the
International Physical Activity Questionnaire (IPAQ,
77—100%). In an evaluation of four PA measures
(AA survey, the short IPAQ and the physical activity
items in the Behavioral Risk Factor Surveillance System and in the Australian National Health Survey),
Brown and colleagues6 reported percent agreement
and kappa statistics ranging from 60% to 79% and
0.40 to 0.52, respectively, which are lower than the
corresponding values reported here for the CHAMPS
questionnaire.
There are two main limitations which should be
considered when interpreting the findings of this
study. Firstly, the participants were a convenience
sample of older adults living in retirement villages
who had volunteered to participate in an exercise intervention study. They may not therefore be
representative of all older Australian adults. Sec-
325
ondly, the MET values assigned to each activity were
obtained from Stewart and colleagues15 who modified the MET values identified in the compendium16
which were based on assessments of younger individuals. Until specific MET values are available
for older adults, it will not be possible to accurately define what constitutes light-, moderate- or
vigorous-intensity activity in this age group.
Based on the findings of this study, the recommendation made by Stewart et al.15 about having someone available to provide assistance to
respondents completing the CHAMPS questionnaire,
is strongly advised. In addition, completed questionnaires should be thoroughly reviewed at the
time of administration to minimise the amount of
missing data. It may also be easier for participants to follow the instructions if a simple, daily
activity (such as reading) is used as an example
to follow, instead of the one used by Stewart et
al.15 (visiting with friends or family). If problems
with self-completion are anticipated in the target
population and it is not feasible to have on-site
support for respondents, telephone-administration
of the CHAMPS survey may be a viable alternative.
In summary, the findings of this study are consistent with those reported previously and support
the use of CHAMPS as a measure of PA among older
Australian adults. In particular, CHAMPS data may
be used to identify those older adults who are insufficiently active for health benefit and are most in
need of PA intervention. Further examination of the
measurement properties of the CHAMPS questionnaire with more diverse samples of older adults is
recommended.
Practical implications
• The Community Healthy Activity Model Programs for Seniors (CHAMPS) questionnaire can
reliably measure the physical activity level of
older Australians.
• CHAMPS can be used to identify older adults
who do not meet the national physical activity
guidelines.
• It is strongly recommended to have someone
available to help respondents complete the
questionnaire and check for missing data.
Acknowledgements
This project was funded by the Australian Government (Department of Health and Ageing—–Office
for an Ageing Australia) and Blue Care (Uniting
326
Care Queensland). This research was carried out
whilst Ms Cyarto was holding an International Postgraduate Research Scholarship at The University of
Queensland. The authors wish to acknowledge the
individuals who took part in this study.
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