HEALTH EDUCATION RESEARCH
Vol.25 no.3 2010
Pages 478–488
Advance Access publication 26 March 2010
Individual, social environmental and physical
environmental barriers to achieving 10 000 steps per day
among older women
Katherine S. Hall1* and Edward McAuley2
1
*Correspondence to: K. S. Hall. E-mail: Katherine.hall3@va.gov
Received on November 1, 2009; accepted on February 27, 2010
Abstract
This study examined the determinants of
attaining/not attaining 10 000 steps per day
among older women. Methods: Daily step
counts over 7 days were measured using accelerometry. Self-reported environmental characteristics, self-efficacy, social support and
functional limitations were assessed in 128 older
women. The presence of areas for activity within
1 km of each participant’s residence was
assessed using Geographic Information Systems. Multivariate analysis of variances were
used to examine the degree to which these
groups differed on measured constructs, and
discriminant analysis was used to determine
the profiles that discriminate among those who
did not attain 10 000 steps per day and those
who did. Results: Participants who did not attain 10 000 steps per day reported lower selfefficacy (P < 0.05), greater functional limitations
(P < 0.05), had significantly fewer walking paths
(P < 0.05) within 1 km of their home and
reported significantly less street connectivity
(P < 0.05) and safety from traffic (P < 0.05) than
those who achieved 10 000 steps per day.
Conclusion: Lack of perceived and actual environmental supports for walking, more functional limitations and lower self-efficacy are
barriers to achieving 10 000 steps per day. The
absence of these individual and environmental
characteristics inhibits walking behavior in
older women and should be considered in campaigns to promote a physically active lifestyle.
Introduction
The benefits of a physically active lifestyle extend
to all segments of the population, including older
adults [1]. Despite these known benefits, older
adults continue to lead sedentary lifestyles, with
older women in particular being the most inactive
segment of the population [2]. Research has demonstrated that achieving 10 000 steps per day is
associated with important health outcomes and
congruous with public health recommendations
for physical activity [3]. Previous studies aiming
to increase physical activity behavior in older adults
have largely focused on individual-level factors and
have been met with limited success, particularly
with respect to maintaining changes in physical
activity following the cessation of the intervention
[4–6]. In light of these trends, Satariano and
McAuley [7] called for the implementation of
a comprehensive research agenda, informed by
social ecological models, and which examines the
degree to which other important correlates of physical activity behavior such as social and environmental factors influence walking behavior in older
adults.
Ó The Author 2010. Published by Oxford University Press. All rights reserved.
For permissions, please email: journals.permissions@oxfordjournals.org
doi:10.1093/her/cyq019
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Geriatric Research, Education, and Clinical Center, Veterans Affairs Medical Center, 508 Fulton Street, GRECC 182,
Durham, NC 27705, USA and 2Department of Kinesiology and Community Health, University of Illinois at UrbanaChampaign, 906 S. Goodwin Avenue, 336 Freer Hall, Urbana, IL 61801, USA.
Determinants of walking in older women
measures of perceived environmental attributes. Although such measures are clearly related to physical
activity behavior [15], objective measures of environmental attributes, such as those assessed using
Geographic Information Systems (GIS), have also
demonstrated effects on physical activity behavior
[16, 17], effects that differ from those reported
among self-report measures [18, 19]. Moreover,
there are some neighborhood attributes that are difficult to measure using only objective measures
(e.g. aesthetics), and the same is also true relative
to measures of individual perceptions. Indeed, an
objective of Healthy People 2010 [20] was to increase the prevalence of objective data collection
techniques such as GIS in studies of physical activity behavior determinants. The ability of GIS to
measure the spatial characteristics of facilities and
other resources for activity such as parks and walking paths and their proximity to individual homes
provides researchers with valuable information relative to neighborhood accessibility. Few studies
have assessed perceived and objective measures
of environmental attributes synchronously [21],
and fewer still have done so in older adults.
The purpose of this exploratory study was to use
a social ecological model of walking behavior to
examine the important question of why some older
adults are able to achieve high levels of physical
activity while others are not. Specifically, we
sought to determine the social-cognitive, functional
and environmental (both perceived and objective)
characteristics that differentiated those who did not
accumulate the recommended 10 000 steps per day
from those whose daily step counts were at, or
above, 10 000. Based upon previous research
[22], we hypothesized that the individual, social
and environmental factors specified in this study
would be significantly associated with walking in
this sample of older women. Specifically, we hypothesized that individual-level factors would be
more strongly associated with walking behavior
than would social and environmental factors. This
preliminary analysis of relative influence has the
potential to inform future efforts to bolster physical
activity among older adults. Indeed, identifying in
a hierarchical fashion those factors that discriminate
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Social ecological models of health behavior propose interplay between intraindividual, social and
environmental factors to influence behavior. Specifically, a social ecological framework posits that
efforts to promote physical activity targeted at the
community level rely heavily on individual-level
variables. Similarly, practices designed to encourage physical activity adoption and maintenance,
which focus on the individual, are influenced
by community-level variables, such as environmental characteristics. As Satariano and McAuley [7]
cogently point out, adopting a social ecological perspective, in which these two approaches to promoting physical activity are viewed as complementary,
holds great potential for translational research that
addresses a comprehensive set of factors.
Among older adults, intra-individual factors
including, though not limited to, functional limitations and self-efficacy have been consistently associated with physical activity [8–10]. Self-efficacy,
reflective of an individual’s belief in his/her ability
to successfully complete a course of action, is the
most consistent individual-level predictor of physical activity in older adults. Functional limitations,
which reflect an individual’s level of difficulty with
daily activities, are both an important barrier to
physical activity and a consequence of physical
inactivity. Examining the role of compromised
function on walking behavior in older women is
an important avenue of exploration. Within the social environment, the degree of social support an
individual receives has also demonstrated significant effects on activity behavior, with greater social
support associated with increased physical activity
participation [11, 12]. Importantly, the degree to
which these factors determine the attainment of
public health guidelines, as defined here relative
to step counts, has yet to be examined in older
adults. Moreover, the relative influence of each of
these factors in the presence of other important
social ecological factors remains to be examined.
An increasing number of studies have examined
the role of environmental attributes such as access
to services, street connectivity, safety and land use
mix as correlates of physical activity behavior [13,
14]. Such studies have largely relied on self-report
K. S. Hall and E. McAuley
individuals who are successful in meeting activity
guidelines from those who are not has implications
for future strategies adopted in individual- and
community-level research, practice and policies
aimed at promoting physical activity.
Methods
Participants
Measures
Steps per day
Steps per day were measured with Actigraph
accelerometers (Model 7165). This model of accelerometer assesses vertical acceleration to generate
two forms of activity data: activity counts and
step counts. The Actigraph’s pedometer function
records a step for any vertical acceleration of
0.30 g or above. For this analysis, only the step
count data were used, as valid activity count cutpoints to assess activity intensity have yet to be
identified for older adults [25]. Participants were
instructed to wear the monitor for 7 days to collect
objective physical activity data. The total number of
480
Perceived environmental attributes
Perceptions of the physical environment were
assessed using the Neighborhood Environment
Walkability Scale (NEWS) [26]. This measure
assesses perceptions of nine environmental characteristics: residential density, land use diversity
(presence and proximity of stores and facilities),
access to services, street connectivity, walking/
cycling areas, aesthetics, pedestrian safety from
traffic, safety from crime and overall neighborhood
satisfaction. All subscales, excluding the residential
density and land use diversity scales, used a fourpoint Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree). The total score for each
scale was calculated by summing across all items
and dividing by the number of scale items. High
scores reflect favorable perceptions, whereas low
scores reflect unfavorable perceptions of the environment. The residential density scale measures the
prevalence of different residence styles in the
neighborhood. The response items range from 1
(none) to 5 (all) and the mean value for all items
serves as the total score. The land use diversity
scale assesses the proximity of stores and facilities
from the place of residence. Proximity was estimated by walking distance measured in minutes,
using a scale from 1 (1–5 min) to 5 (31+ min);
the total score for walking proximity was calculated
as the mean across all items. High scores denote
a greater proximity to facilities. Internal consistencies for these scales ranged from 0.53 to 0.94.
Objectively assessed environmental attributes
We used GIS to geocode each participant’s address.
A circular buffer of 1 km, a distance associated with
foot travel, was created around each point. The
presence or absence of schools, recreational areas
(e.g. golf courses, recreation centers), parks, walking paths and exercise/gym facilities were identified, along with the prevalence of each variable
in the neighborhood (i.e. 1 km buffer) of each
participant.
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A convenience sample of older communitydwelling women (M age = 69.6, N = 128) were
recruited from a prospective study of women’s
health [23] via an announcement in the project
newsletter, which described this separate mailbased study. Inclusion criteria for the parent study
were as follows: (i) female, (ii) race being Black or
White, (iii) ethnicity being Non-Hispanic/Latina,
(iv) no history of neurological disorder, (v) no history of physical disability that would prohibit an
assessment of physical function and (vi) adequate
mental status, as assessed by the Pfeiffer Mental
Status Questionnaire [24]. Of the initial 249 women
who received the newsletter, 153 expressed interest
in participating in the study. Consent forms were
mailed to all 153 individuals, of which 16 were not
returned. Over the course of the study, nine participants withdrew from participation citing: (i) being
too busy, (ii) personal illness or ill spouse and
(iii) unwilling to complete the questionnaire packet
and/or wear the activity monitor for 7 days.
steps taken each day were summed and divided
by the total number of days worn to arrive at an
average of daily steps taken.
Determinants of walking in older women
Procedures
The Barriers Self-Efficacy Scale [27] was used to
assess an individual’s belief in their ability to exercise three times per week for 40 min over the next
2 months in the face of commonly identified barriers to exercise (e.g. inclement weather, schedule
conflicts). Confidence was rated on a scale of 0%
(not at all confident) to 100% (highly confident),
with 10-point increments. The total score was calculated as the average score across all items, resulting in a total score value of 0–100. Internal
consistency for this scale was excellent (a = 0.96).
All procedures were approved by the University of
Illinois Institutional Review Board and informed
consent was obtained from each participant prior
to enrollment in the study. All measures were distributed and collected through the mail with the use
of self-addressed pre-paid envelopes. Upon receipt
of the signed consent form, each participant was
mailed a questionnaire packet along with the accelerometer and instructions for wearing the monitor.
Each participant was assigned a start and end date
for wearing the monitor. Additionally, participants
were asked to indicate dates and times when
the monitor was on/off using the provided log. Approximately 3 days after the package was sent,
a reminder call was made to each participant to
ensure that all materials were received and to remind the participant when they were to start wearing their monitor. Any other questions were also
answered at this time.
A second phone call was made to each participant 1 day prior to their requested finish date for
wearing the monitor. The purpose of this call was to
remind participants that they were approaching
their final day and that they were to return the monitor, log and completed questionnaire packets to the
laboratory using the provided envelope and postage. At this time, if participants indicated that they
had omitted a day of wear, they were asked to wear
the monitor for an additional day(s) so as to provide
7 complete days of data. Participants were paid $15
for their participation.
Social support
Social support was assessed using the Social
Support and Exercise Survey [28]. This scale measures the degree to which friends and family demonstrate verbal and behavioral support for exercise
behaviors in the previous 3 months. Participants
rated the frequency for each of the 10 items on
a 5-point Likert scale, ranging from 1 (never) to 5
(very often). Example items include ‘exercised with
me’ and ‘gave me helpful reminders to exercise’.
Support scores are created separately for friends
and family. Internal consistencies for the friends
(a = 0.91) and family (a = 0.92) subscales were
excellent.
Functional limitations
Functional limitations were measured using the
‘Function’ component of the abbreviated Late Life
Function and Disability Instrument (LL-FDI)
[23]. This measure is comprised of 15 items that
examine upper extremity function, basic lower extremity function and advanced lower extremity
function in older adults. Participants indicated the
level of difficulty experienced while performing an
activity (e.g. walking a mile without stopping for
rest) on a Likert scale ranging from 1 (none) to 5
(cannot do). Items are summed to arrive at a total
score for function, resulting in a scale score ranging from 15 to 75. Higher scores indicate greater
functional limitations. Internal consistency for the
Function component of the LL-FDI was excellent
(a = 0.88).
Data analysis
T-tests and v2 tests were first run to examine
the between-group differences on the demographic
and health status factors. Next, we used a series
of multivariate analysis of variance (MANOVAs)
to examine the between-group differences among
those who did not accumulate 10 000 steps per
day and those who accumulated >10 000
steps per day on the nine NEWS subscales, objective environmental attributes, self-efficacy, social
support and functional limitations, controlling for
any demographic variables that demonstrated
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Self-efficacy
K. S. Hall and E. McAuley
Results
Table I shows the characteristics of the overall
sample, the proportions of <10 000 steps per day
and >10 000 steps per day, and the characteristics
of these two groups. Overall, the sample (N = 128)
482
was comprised predominantly of married (54.8%)
white women (84.4%%), and the majority of whom
was retired (75.0%). The sample represented
a broad spectrum of socioeconomic status (40.6%
annual income >$40,000) and education level
(44.5% >college degree). Although community
dwelling, many of these women reported medical
diagnoses of hypertension (37%), hyperlipidemia
(28%) and functional impairment of the musculoskeletal system (85%). Recorded average daily step
counts showed significant variation across the sample, ranging from 1616 to 21 919 steps per day (M
steps per day = 8675).
Mean scores for each of the independent variables
for the entire sample and by group assignment are
shown in Table II. Participants in this study were
moderately efficacious, reported few functional limitations and reported moderate levels of social support
from friends and family for exercise. Of the objectively assessed environmental attributes, parks were
the most prevalent, followed by schools and walking
paths. Very few recreation areas and exercise/gym
facilities were identified within 1 km of participants’
residences. Relative to perceived environmental
attributes, participants reported moderate/high levels
of each of the nine characteristics.
Results of the t-tests and v2 tests (see Table I)
indicated that individuals in the <10 000 steps per
day group (n = 93) and individuals in the >10 000
steps per day group (n = 35) did not differ significantly (P < 0.05) on any of the demographic variables except for age. Participants reporting <10 000
steps per day were significantly older than participants reporting >10 000 steps per day (t(126) =
2.03, P < 0.05).
Results of the MANOVAs (see Table II) suggest
that participants who recorded <10 000 steps per
day had significantly fewer paths within 1 km of
their home (F = 5.88, P = 0.02), more functional
limitations (F = 6.96, P = 0.01), lower barriers selfefficacy (F = 9.48, P = 0.003), less street connectivity (F = 6.04, P = 0.02) and lower pedestrian/
traffic safety ratings (F = 4.39, P = 0.04) than participants who recorded 10 000 steps per day or
greater. No significant group differences were
observed for any of the other study variables.
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significant between-group differences in previous
analyses. Those variables that demonstrated significant (P < 0.05) differences between the two groups
in the MANOVAs were retained for subsequent
analyses.
Given the exploratory nature of our primary
research question, we used discriminant function
analysis to examine the relative influence of each
of these continuous variables on group membership. This analysis determined (i) whether the participants classified as taking ‘<10 000 steps per
day’ or ‘>10 000 steps per day’ presented different
profiles on our set of variables and (ii) which combination of these independent variables (i.e. discriminant functions) best discriminated these
groups. Thus, the functions classify participants
as ‘<10 000 steps per day’ or ‘>10 000 steps per
day’ based solely on their continuous values of
the significant independent variables. As only two
groups were being compared, only one discriminant
score was derived; discriminating <10 000 steps
per day from the >10 000 steps per day.
The discriminant function was evaluated using
a number of statistical tests. Wilks’ Lambda was
used to determine whether the two groups had
any difference in their mean scores for each independent variable. The canonical coefficient was
used to measure the association between the discriminant score and the two groups, and the discriminant loadings were used to determine which
variable contributed more to the differences between the groups. Finally, to assess the predictive
ability of the function, the percentage of participants correctly classified into the two groups by
the discriminant function was compared with the
percentage of participants who could be classified
correctly by chance (i.e. without the discriminating
function). All analyses were performed using SPSS
version 17 (PASW Inc.).
Determinants of walking in older women
Table I. Sample characteristics
Entire sample
(N = 128)
<10 000 steps
per day (n = 93)
>10 000 steps
per day (n = 35)
Group differences
Age (years) M (SD)
Marital status n (%)
Married
Widowed
Single
Divorced/separated
Annual income n (%)
<$40 000
>$40 000
Missing data
Education n (%)
Partial high school
High school graduate
1–3 years college
College graduate
Masters degree
PhD or equivalent
Race n (%)
White
Black
Employment status n (%)
Retired
Part-time
Full-time (>30 hours per week)
69.8 (5.89)
70.5 (6.05)
68.1 (5.16)
t(126) = 2.03 P = 0.04*
69 (54.8)
27 (21.4)
4 (3.2)
26 (20.6)
48 (52.7)
21 (23.1)
3 (3.3)
19 (20.9)
21 (60.0)
6 (17.1)
1 (2.9)
7 (20.0)
v2(3) = 0.683 P = 0.88
64 (50.0)
52 (40.6)
12 (9.4)
50 (53.7)
34 (36.6)
9 (9.7)
14 (40.0)
18 (51.4)
3 (8.6)
v2(1) = 2.33 P = 0.09
2 (1.6)
34 (26.6)
35 (27.3)
26 (20.3)
25 (29.5)
6 (4.7)
2 (1.6)
31 (24.2)
21 (16.4)
16 (12.5)
19 (14.8)
4 (3.1)
0 (0.0)
3 (2.3)
14 (10.9)
10 (7.8)
6 (4.7)
12 (1.6)
v2(5) = 11.31 P = 0.06
108 (84.4)
20 (15.6)
78 (83.9)
15 (16.1)
30 (85.7)
5 (14.3)
v2(1) = 0.067 P = 0.52
96 (75.0)
20 (15.6)
12 (9.4)
71 (76.3)
12 (12.9)
10 (10.8)
25 (71.4)
8 (22.9)
2 (5.7)
v2(2) = 2.38 P = 0.30
*Significant at P < 0.05.
Next, a discriminant analysis was conducted
using the 10 000 steps classification as the dependent variable and those variables that demonstrated
significant between-group differences in the
MANOVAs as covariates (e.g. age, functional limitations, self-efficacy, number of paths within 1 km
of the residence, perceived pedestrian/traffic safety
and perceived street connectivity). Table III summarizes the estimation of the discriminant function,
showing how the inclusion of each variable
changed Wilks’ Lambda, the F-value and the significance level of the function. The discriminant
function was significantly different for those attaining 10 000 steps and those who did not (Wilks’
Lambda = 0.859, v2 = 18.92, df = 4 and P =
0.001), and all of the independent variables contributed significantly to the discriminant function (P <
0.05). According to the discriminant function coefficients (see Table III), barriers self-efficacy was the
strongest correlate of group assignment, followed
by (in descending order) functional limitations,
street connectivity, number of paths within 1 km,
pedestrian/traffic safety and age. Overall, the discriminant function successfully predicted step behavior for 74.2% of cases with accurate predictions
being made for 90.3% of those who failed to reach
10 000 steps and only 31.4% of those who reached
10 000 steps.
Discussion
Although much of the physical activity literature
has focused upon individual-level correlates, social
ecological model of physical activity behavior
which emphasizes physical and social environmental factors along with intrapersonal variables have
received increasing attention [6, 7]. The present
study examined the relative influence of such
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Characteristics
K. S. Hall and E. McAuley
Table II. Comparison of characteristics of <10 000 steps per day and >10 000 steps per day groups
Variable
Possible scale range
<10 000 steps
per day
>10 000 steps
per day
M (SD) reported
range
M (SD)
M (SD)
57.65 (26.70) 0–100
25.52 (8.02) 15–54
53.34 (26.31)
26.65 (8.25)
69.12 (24.56)
22.54 (6.60)
F = 9.48 P = 0.003*
F = 6.96 P = 0.01*
1.38 (1.49) 0–5
1.18 (1.40)
1.89 (1.62)
F = 5.88 P = 0.02*
2.73 (2.64) 0–12
2.53 (2.55)
3.29 (2.81)
F = 2.13 P = 0.15
0.73 (1.07) 0–4
0.74 (1.09)
0.71 (1.02)
F = 0.02 P = 0.90
0.38 (0.69) 0–3
0.35 (0.67)
0.43 (0.74)
F = 0.29 P = 0.59
1.59 (1.98) 0–7
1.57 (2.00)
1.66 (1.95)
F = 0.05 P = 0.83
23.14 (10.83) 10–50
23.05 (10.63)
23.37 (11.50)
F = 0.02 P = 0.88
23.57 (9.80) 10–48
23.65 (9.90)
23.37 (9.67)
F = 0.02 P = 0.89
187.48 (22.33)
193.06 (39.22)
F = 1.01 P = 0.32
2.83 (1.05) 1–5
2.84 (1.08)
2.82 (0.99)
F = 0.01 P = 0.93
2.57 (0.60) 1–4
2.54 (0.66) 1–4
2.40 (0.98) 1–4
2.53 (0.59)
2.45 (0.65)
2.30 (0.98)
2.66 (0.63)
2.77 (0.66)
2.66 (0.92)
F = 1.18 P = 0.28
F = 6.04 P = 0.02*
F = 3.53 P = 0.06
3.15 (0.61) 1–4
2.84 (0.67) 1–4
3.11 (0.60)
2.77 (0.60)
3.26 (0.64)
3.04 (0.78)
F = 1.41 P = 0.24
F = 4.39 P = 0.04*
3.28 (0.50) 2–4
3.80 (0.66) 1–5
3.26 (0.49)
3.75 (0.59)
3.33 (0.55)
3.91 (0.81)
F = 0.50 P = 0.48
F = 1.39 P = 0.24
189.01 (27.91) 173–390
Group differences
*Significant at P < 0.05.
factors on meeting public health recommendations
to accumulate 10 000 steps per day among older
community-dwelling women. Analyses indicated
that women who walked <10 000 steps per day
did indeed differ significantly from women who
walked >10 000 steps per day on factors such as
self-efficacy, functional limitations, access to walking paths within 1 km from the home, pedestrian/
traffic safety, street connectivity and age. The percentage of individuals correctly classified by these
characteristics was superior to those that could be
484
obtained by chance. Indeed, 90.3% of those not
meeting the public health guidelines of 10 000
steps per day were classified correctly using the
profile identified by the discriminant analysis.
As hypothesized, the individual-level variables
(i.e. self-efficacy to overcome barriers to physical
activity and functional limitations) had the highest
discriminatory power. Older women with low/
moderate self-efficacy and women with greater
functional limitations had greatest difficulty accumulating 10 000 steps per day. That self-efficacy to
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Barriers self-efficacy 0 (low)–100 (high)
LL-FDI: functional limitations 15 (worst)–75
(best)
Number of paths within 1 km of residence
N/A
Number of parks within 1 km of residence
N/A
Number of recreation areas within 1 km of
residence N/A
Number of exercise/gym facilities within
1 km of residence N/A
Number of schools within 1 km of residence
N/A
Social support for exercise: friend
participation 10 (low)–50 (high)
Social support for exercise: family
participation 10 (low)–50 (high)
NEWS: residential density 173 (low)–865
(high)
NEWS: land use mix-diversity 1 (low)–5
(high)
NEWS: land use mix-access 1 (low)–4 (high)
NEWS: street connectivity 1 (low)–4 (high)
NEWS: walking/cycling facilities 1 (low)–4
(high)
NEWS: aesthetics 1 (low)–4 (high)
NEWS: pedestrian/traffic safety 1 (low)–4
(high)
NEWS: crime safety 1 (low)–4 (high)
NEWS: neighborhood satisfaction 1 (low)–5
(high)
Entire sample
Determinants of walking in older women
Table III. Summary of the estimation of the discriminant
function for <10 000 steps per day versus >10 000 steps per
day
Wilks’ F-value P-valuea Standardized
canonical
lambdaa
discriminant
function
coefficients
Age
NEWS: pedestrian/
traffic safety
Number of paths
within 1 km of
residence
NEWS: street
connectivity
LL-FDI: functional
limitations
Barriers self-efficacy
0.968
0.966
4.14
4.39
0.04
0.04
0.166
0.002
0.955
5.88
0.02
0.435
0.954
6.04
0.02
0.382
0.948
6.96
0.01
0.307
0.930
9.48
0.00
0.480
a
Indicates if the independent variable significantly (P < 0.05)
contributed to the discriminant function; differentiating the
<10 000 steps per day and >10 000 steps per day. This is most
easily observed by the degree to which the variable ‘minimizes’
the Wilks’ lambda. The lower the Wilks’ lambda, the greater
that variable differentiates the <10 000 steps per day and
>10 000 steps per day groups.
overcome barriers, a reflection of personal control,
was the strongest factor is important, for it suggests
two potential pathways for intervention to promote
walking behavior: targeting the environment and
targeting the individual. First, social ecological
approaches may enhance personal control beliefs
by reducing environmental barriers and improving
accessibility of the environments and activity programs [7]. These adjustments to the environment
are expected to lead to greater efficacy, which in
turn would positively influence activity behavior.
Second, efforts may target the individual’s sense
of control in an effort to enhance perceptions of
the environment, thereby resulting in increased activity behavior. Individuals who are efficacious in
their ability to overcome barriers to physical activity
are more likely to take advantage of the opportunities that exist in the environment and are also less
likely to be discouraged in the face of obstacles [29].
Clearly, self-efficacy plays a significant role in
walking behavior in women, and interventions that
target the sources of efficacy beliefs such as mastery
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Independent
variables entered
experiences, vicarious experience of success among
similar others, verbal persuasion that one possesses
the necessary capabilities to succeed and judgments
of physiological and affective readiness to participate [29] are critical to promoting activity behavior
in this demographic. However, the extent to which
the environment can be altered to reduce perceived
barriers to physical activity and enhance efficacy
beliefs has yet to be examined.
That functional limitations were inversely associated with meeting public health guidelines is consistent with previous literature in older adults [30].
However, given the cross-sectional nature of this
study, we are unable to tease out what is largely
believed to be a cyclical relationship, such that
declines in one component perpetuate declines in
the other. Further investigation to determine
whether the relationship between functional limitations and walking behavior differs as a function of
the type of limitations (e.g. upper body and lower
body) or the presence of health comborbidities is
warranted. Additionally, studies that examine the
potential role for the environment to promote/
inhibit physical activity among those with compromised function are needed.
Both the perceived and objective environment
were also significantly associated with group membership. Specifically, having fewer paths within 1
km of one’s home and reporting less street connectivity and less pedestrian safety from traffic were
significantly associated with walking less than the
recommended 10 000 steps per day, results which
are consistent with previous research [31]. Previous
research, however, suggests that the implications of
these findings be considered carefully. Studies of
walking trail use and proximity have reported that
even when the objective distance to a walking trail
was consistent between individuals, the absence or
presence of psychosocial and neighborhood factors
determined perceptions of proximity and individual
use of the walking trail [32, 33]. Thus, although our
results demonstrate that having access to walking
trails is associated with obtaining 10 000 steps, increasing the prevalence of walking trails may not be
sufficient and thus unlikely to result in significant
increases in walking behavior. Instead, it may
K. S. Hall and E. McAuley
486
fewer functional limitations, access to walking
paths and greater street connectivity and safety
from traffic may be necessary to promote activity,
these characteristics, in and of themselves, do not
reliably predict higher activity levels. Indeed, physical activity is a complex behavior, influenced by
a series of interactions between individual- and
environment-level variables [6, 15, 17]. As such,
individual-level interventions to increase physical
activity are likely to be more effective when the
individual’s neighborhood environment is supportive, with few barriers. Conversely, environmentlevel interventions to increase physical activity will
likely be met with limited success when individuallevel factors such as self-efficacy, motivation or
perceived importance of physical activity are low.
The use of multi-level modeling techniques to
account for factors at the individual and environment levels appears to be the next step in furthering
our understanding of these associations and testing
a truly ecological model of activity behavior [36].
To our knowledge, few studies have employed such
a design to examine neighborhood-level effects on
physical activity in older adults [5, 31, 37, 38]. Such
research holds the potential to extend current physical activity research by adopting a comprehensive
approach to examining both the impact of changes
at the level of the individual as well as the effect of
changes within the individual who resides in that
neighborhood on physical activity.
Indeed, a fundamental characteristic of ecological models is using several measurement time
points to examine causal inferences and potential
mediators. Such research is imperative to further
inform social ecological models and the application
of these models to change physical activity behavior. Clearly, the cross-sectional design of the present study is a limitation, as we are unable to
determine whether there is a minimally significant
amount of change in these variables that is necessary to evoke change from the <10 000 steps
per day to the >10 000 steps per day group. Furthermore, we do not address here the postulated
interaction effects between these individual and environmental barriers; effects that are central to ecological models. Future research employing more
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be that the integration of pathways which are not
only accessible to older individuals but which also
allows the completion of daily tasks (e.g. street
connectivity for access to businesses) may be more
apt to increase walking behavior in older women.
Furthermore, our results underscore the importance
of minimizing the degree to which pedestrians and
motorized traffic coexist in areas designated for activity, an ideal perhaps best achieved through public
policy and urban planning guidelines.
Somewhat surprisingly, women who walked
<10 000 steps per day had similar access to parks,
recreation areas or exercise/gym facilities within 1 km
of their home as those women who walked >10 000
steps per day. These findings may be due, in part, to
our definition of neighborhood, defined here as 1 km.
Although previous research suggests that this
distance is consistent with perceptions of walkability,
a standardized definition of neighborhood as it relates
to physical activity remains to be identified [34].
Additionally, no significant between-group differences were observed for social support from friends
or family to exercise, with both groups reporting
low/moderate ratings of social support. It is conceivable that this pattern of results may stem from properties of the scale. Six of 10 items appear to infer that
the family/friends are also physically active (i.e.
‘offered to be physically active with me’ or
‘changed their schedule so we could be active together’), perhaps reflecting the frequency (i.e. ‘very
often’, ‘rarely’) by which individuals exercised with
friends and family as opposed to social support
per se, which includes other elements, such as verbal
persuasion and provision of resources. Contrary to
previous studies reporting positive associations with
physical activity [2, 14, 22, 35], perceptions of residential density, land use mix-diversity, land use
mix-access, walking/cycling facilities, aesthetics,
crime safety and neighborhood satisfaction did not
differ significantly between groups.
It is important to note that our discriminatory
function was markedly better at classifying those
women who walked <10 000 steps per day, correctly classifying only one-third of those who accumulated >10 000 steps per day. Our results suggest
that while greater efficacy to overcome barriers,
Determinants of walking in older women
these data suggest that restricted access to opportunities to be physically active, reflected here in low
street connectivity and pedestrian safety and no/few
proximal walking paths, is a risk factor for physical
inactivity. In the presence of individual characteristics such as low self-efficacy and greater functional limitations, the effects of a prohibitive
neighborhood environment on walking behavior in
older women are magnified. To our knowledge, this
is the first study to examine the relative influence of
individual, social and environmental variables on
walking behavior in older women. This study was
designed to serve as an empirical evaluation of a
social ecological model of physical activity behavior; the results of which have implications for both
theory development and systematic application.
Indeed, the results of this study suggest that some
variables may be more important than others when it
comes to walking behavior in older women and, as
such, are prime targets for future efforts to increase
activity behavior. Taken together, our results suggest that the absence of these specific individual and
environmental characteristics inhibits walking behavior in older women and consequently should
be considered by urban planners, traffic engineers
and public health advocates in campaigns to
promote physical activity among older adults.
Funding
National Institute on Aging (AG20188 to E.M.).
Acknowledgements
This material is based upon work K.S.H. conducted
at the University of Illinois.
Conflict of interest statement
None declared.
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