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Preventive Medicine 31, 15–22 (2000)doi:10.1006/pmed.2000.0661, available online at http://www.idealibrary.com on
Social–Cognitive and Perceived Environment InfluencesAssociated with Physical Activity in Older Australians
Michael L. Booth, M.P.H., Ph.D.,*,1 Neville Owen, Ph.D.,† Adrian Bauman, M.P.H., Ph.D.,‡Ornella Clavisi, M.P.H.,† and Eva Leslie M.H.N.†
*Department of Public Health and Community Medicine, A2
†School of Human Movement, Deakin University, 221 B‡School of Community Medicine, University of New Sou
Background. Regular physical activity in olderadults can facilitate healthy aging, improve functionalcapacity, and prevent disease. However, factors associ-ated with physical inactivity in older populations arepoorly understood. This study attempts to identify so-cial–cognitive and perceived environmental influ-ences associated with physical activity participationin older populations.
Methods. In a randomly selected sample of 449 Aus-tralian adults age 60 and older, we assessed self-re-ported physical activity and a range of social–cognitiveand perceived environmental factors. Respondentswere classified as sufficiently active and inactive basedon energy expenditure estimates (kcal/week) derivedfrom self-reported physical activity. Two logistic re-gression models, with and without self-efficacy in-cluded, were conducted to identify modifiable indepen-dent predictors of physical activity.
Results. Significantly more males than females werephysically active. Physical activity participation wasrelated to age with a greater proportion of those age65–69 being active than those age 60–64 or 70 or older.High self-efficacy, regular participation of friends andfamily, finding footpaths safe for walking, and accessto local facilities were significantly associated with be-ing active.
Conclusion. Identifying predictors of physical activ-ity in older populations, particularly social support,facility access, and neighbourhood safety, can informthe development of policy and intervention strategies
to promote the health of older people. q 2000 AmericanHealth Foundation and Academic Press
Key Words: aged; aging; physical activity; exercise;psychological theory.
1 To whom reprint requests should be addressed. Fax: 61-2-9845-0663. E-mail: [email protected].
1
7, University of Sydney, New South Wales 2006, Australia;urwood Road, Burwood, Victoria 3125, Australia; and
th Wales, Kensington, New South Wales 2031, Australia
INTRODUCTION
Regular, moderate-intensity physical activity (for ex-ample, brisk walking, cycling, and some forms of houseand garden work) is now seen to have a key role in thepromotion of good health and the prevention of disease[1,2]. For older adults, being regularly active has beenfound to be associated with better physical and psycho-logical health [3,4] and older adults who are physicallyactive also have improved functional capacity [5]. Re-cent evidence indicates that, among older adults, lowfitness is a risk factor for functional decline, and thatthere is a protective effect of physical activity and physi-cal fitness on functional limitations [6,7].
Following the release of the 1996 U.S. Surgeon Gen-eral’s Report [2] and numerous national- and state-levelphysical activity policy documents in industrializedcountries, there are now a growing number of practicalinitiatives associated with large-scale promotional cam-paigns [8]. In Australia, a national campaign targetingmiddle-age and older adults produced significant short-term changes in reported walking for exercise [9,10].Such campaigns tend to be broadly based on social–marketing and social–cognitive models of physical ac-tivity behavior. In order to fine-tune media messages,community programs, and resource materials, relevantattributes of campaign target groups need to be identi-fied more specifically and more-focused theoreticalmodels are required [11].
In the context of these new initiatives, it is particu-larly important to better understand the barriers andopportunities for physical activity among older adults.Descriptive studies using representative population
samples in Australia, for example, have found peopleover 55 years to be over four times more likely to bephysically inactive than young adults and to be signifi-cantly more likely to report that they were physically5 0091-7435/00 $35.00Copyright q 2000 by American Health Foundation and Academic Press
All rights of reproduction in any form reserved.
in scope for the interview was identified, and appoint-
16 BOOTH
unable to exercise [12]. In another Australian popula-tion study, those over 60 years most commonly reportedpoor health and injury as barriers to being more active[13]. Although such data from general population sur-veys are informative, they tend to be general in theirscope and tend not to identify specific attributes whichmight usefully be addressed in campaigns or programstailored to older adult groups.
Duda and Tappe [14] and Dzewaltowski [15] haveproposed social–cognitive models of physical activityinvolvement specifically for older adults. They have em-phasized the motivational orientations to exercise ofolder adults, within which an orientation toward healthbenefits has emerged as a strong influence [14]. Otheraspects of motivational orientation to exercise for adultsare self-efficacy, outcome expectations, and dissatisfac-tion [15]. These social–cognitive constructs are embed-ded in Bandura’s broader model [16], which emphasizesthe importance of social and environmental influencesproviding feedback about behaviors, opportunities, andconsequences. Thus, motivational orientation to exer-cise is likely to be shaped significantly by the socialinfluences to which older adults are exposed and theenvironmental settings in which they find themselves.
A theoretical framework which addresses explicitlythe social and environmental determinants of physicalactivity in older adults may assist in identifying morespecifically the opportunities to influence their physicalactivity habits. Sallis and Owen [17] review the findingsof studies on the variables that may determine the prob-ability of being active for a wide range of ages, settings,and program attributes. Self-motivational constructs,self-efficacy, expectations of health benefits, and per-sonal skills emerged as important. Social and environ-mental context factors also emerged as important influ-ences: spouse support, family and peer influences, andaccess to facilities. Drawing on these findings, and onconcepts from social–cognitive and environmental psy-chology theory, Sallis and Hovell [18] and Sallis andOwen [17,19] argue for a model of the determinants ofexercise behavior in which environmental and socialinfluences are ascribed a central role. Personal, cogni-tive, and physiological factors are seen as important,but these authors argue that proximal social promptsand other influences initiate activity. Environmentalsettings and supports and social and contextual conse-quences of activity are less well-understood, but areprobably very important determinants of whether peo-ple engage in physical activity. Importantly, the influ-ences of particular social and environmental settingsmay be addressed in the campaigns and policy initia-tives now being pursued in many countries.
Very few studies have attempted to identify the vari-ous factors which may influence physical activity par-ticipation among older people and no studies have ap-plied the broad range of variables represented by
ET AL.
Bandura’s social cognitive theory to understanding thephysical activity habits of older adults. The use of awell-established theory which has already contributedsubstantially to our knowledge of the determinants ofphysical activity participation among other populationgroups is a potentially fruitful approach. In this study,we interviewed a randomly selected sample of Austra-lian adults, 60 years old and over, whom we classifiedas physically active or inactive, using previously devel-oped self-report measures [20,21]. We also assessedsocial–cognitive variables, perceptions of social and
environmental influences, reported access to local com-munity and neighborhood facilities, and perceived rein-forcers for activity. The extent to which such variableswere associated with physical activity participationwas examined.METHODS
Sampling and Survey Methods
The Population Survey Monitor is a standard surveyconducted by the Australian Bureau of Statistics (ABS)four times per year. The questionnaire on physical ac-tivity was supplementary to the November 1995 Popu-lation Survey Monitor. In the southern hemisphere, No-vember is in late Spring and the weather in most ofthe populated areas of Australia is generally mild andconducive to being physically active. A randomly se-lected sample of community-dwelling adults was drawnfrom all, except sparsely populated, areas of Australia,using a three-stage systematic randomized samplingprocedure. Stage one was a computerized random selec-tion of Census Collectors’ Districts (CCD). Stage twocomprised random selection of 12 dwellings within eachcensus district with the starting point being selectedusing random number tables and a grid. After selectingthe street corner nearest to the grid reference, dwellings(no dwelling types were excluded) were selected bycounting to the left side of the road from a randomlyassigned start and using a predetermined skip (whichensures that the selected dwellings are evenly distrib-uted across the CCD) between selections. The thirdstage consisted of the selection of one individual withinthe selected household by identifying which eligiblehousehold member had most recently had a birthday.
Randomly selected households were approached bymail, informing them of their selection in the surveyand advising them that an interviewer would call toarrange a suitable time to conduct the survey interview.At the initial contact, basic demographic informationon the household was collected, the number of persons
ments were made to conduct the interviews. All inter-views were conducted face-to-face by trained ABS inter-viewers. The questionnaire was administered to allselected interviewees age 60 or older (N 5 449).
A
PHYSICAL ACTIVITYMeasures
Sociodemographic measures. Participants wereasked to report their age, sex, marital status, employ-ment status, living situation, and country of birth. Agewas categorized as 60–64 years, 65–69 years, and 70years or older. Those who were separated, divorced,widowed, or never married were classified as “not mar-ried” and the remainder of respondents were classifiedas “married.” Respondents living as a single personwere classified as “living alone”; all alternative livingsituations were classified as “other.” Those not in thelabor force were classified as “retired”; respondentsworking full time or part time or who were activelylooking for work were classified as “working.” Respon-dents were also classified as having been born in Aus-tralia or born overseas.
Attitudes. Expectations and expectancies weretreated as identical to the behavioral beliefs and out-come evaluations, respectively, which are the two com-ponents of Ajzen’s Theory of Planned Behaviour atti-tudes [22]. Twelve attitude attributes (outcomesassociated with being physically active) were identifiedfrom the literature and from focus group discussionswhich preceded the development of the questionnaire:stress relief; becoming too tired; aggravating a currentinjury or medical condition; improved sleep; take uptoo much time; greater ease in carrying out chores;improved health; risk of injury; weight control; beinguncomfortable while being active; opportunities to meetpeople and make friends; and having fun. Respondentswere asked, for each potential outcome, “Compared tobeing inactive, how likely is it that physical activitywould . . .”. Responses were made on a five-point unipo-lar Likert scale which ranged from “very likely” to “veryunlikely.” To assess expectancies, respondents wereasked, for each potential outcome, “How important isit for you to . . .”. Responses were made on a five-pointbipolar Likert scale which ranged from “very im-portant” (2) to “very unimportant” (22). Both responsescales included a “don’t know” option. As less than 6%of cases had missing values on each item, the missingvalues were replaced with the mean value for each item.For each of the 12 attributes, the values of the responsesto the expectation and expectancy items weremultiplied, yielding 12 values ranging from 210 to 10for each respondent. The attitude items were subjectedto a principal components factor analysis, with obliquerotation. In order to assess the factorability of the corre-lation matrix obtained from the attitude scale, Bart-lett’s test of sphericity and the Kaiser–Meyer–Olkin
(KMO) measure of sampling adequacy were calculated.Factors with eigenvalues greater than 1 were retained.The resulting attitude factor scores were split into ter-tiles, reflecting low, medium, and high strength of atti-tudes toward physical activity.ND OLDER PEOPLE 17
Environmental influences. Physical environmentswere assessed by asking if the respondent had any exer-cise equipment at home (e.g., exercise bike, swimmingpool, exercise video); about the safety or difficulty ofwalking in the neighborhood during the day; and aboutaccess to various facilities which might be used for phys-ical activity such as the local exercise hall, recreationcenter, cycle path(s), golf course, gym, park, swimmingpool, tennis court, or bowling green. Each item had adichotomous (yes/no) response format. Characteristicsof the social environment for physical activity were as-sessed by asking about the frequency, over the past 3months, with which friends or family offered to partici-pate in an activity with the respondent; gave helpfulreminders to be active; encouraged the respondent tobe active; or took over chores to allow the respondentto be active. The social environment items were allassessed on five-point Likert scales ranging from “veryoften” to “never.” These four items were summed toconstruct the social environment scale (Cronbach’s a 50.72) and dichotomized on the basis of a median split,dividing the variable into high and low socially support-ive environments.
Self-efficacy. Respondents were asked how confi-dent they were that they could exercise in the followingsituations: when they were tired; when they were in abad mood; when they did not have time; when theywere on holidays; when it was raining; or when it tooka lot of effort. The response scales were five-point Likertscales which ranged from “very confident” to “not at allconfident.” The five self-efficacy items extracted a singlefactor accounting for 46% of the variance and Cron-bach’s a for the self-efficacy scale was 0.76. The itemswere summed to form a single self-efficacy variablewhich was dichotomised into high and low self-efficacybased on a median split.
Social reinforcement. Respondents were askedabout the frequency, over the past 3 months, with whichfriends or family said that physical activity seemed tobe good for their appearance; got upset about the activi-ties the respondent did or wanted to do; or criticized theactivities in which the respondent participated. Theseitems were assessed on five-point Likert scales rangingfrom “very often” to “never.” The three reinforcementitems had a Cronbach’s a of 0.33 and so were analyzedas separate items. Those respondents who were toldthat their activity was good for their appearance “veryoften,” “often,” or “sometimes” were categorized as re-ceiving high positive reinforcement and those who weretold this “rarely” or “never” were categorized as receiv-ing low positive reinforcement. Those who reported that
their friends or family got upset about the activitiesthey did or that their friends or family criticized ormade fun of the activities in which they participated“very often,” “often,” or “sometimes” were categorized as
Table 1 shows the proportions of respondents in each
18 BOOTH
receiving high negative reinforcement on each variableand those who were exposed to this reinforcement“rarely” or “never” were categorized as receiving lownegative reinforcement.
Social modeling. Respondents were asked about thefrequency with which the respondent saw other peoplein the neighborhood involved in activities such as walk-ing or jogging and the second asked about the frequencywith which family or friends participated in some typeof physical activity. Responses to these two items wererecorded on five-point Likert scales ranging from “veryoften” to “never.” A third item asked about the numberof times per week the repondent’s partner participatedin some form of physical activity. The responses wererecorded on a scale with the response options: “.3 timesper week,” “1–3 times per week,” “,once per week,”“never/not applicable,” and “don’t know.” Those respon-dents who reported that their family or friends wereactive or who reported seeing others in the neighbor-hood being active “very often,” “often,” or “sometimes”were categorized as experiencing high social modelingon these variables. Those respondents who reportedthat their partner was physically active at least onceper week were also categorized as experiencing highsocial modeling.
Physical activity participation. This was assessedusing previously developed measures from Australia[20,21]. Respondents were asked to think about thepast 2 weeks and to report the frequency (for at least10 minutes consecutively) and the amount of time (ex-pressed in hours) spent, on average, per session partici-pating in vigorous activities which made them sweator puff and pant; walking for exercise, leisure, or recre-ation; and in moderate-intensity activities such as gar-dening. For each type of activity, the amount of timespent per session was multiplied by the frequency ofparticipation to give the total amount of time spent ineach activity. To estimate energy expenditure resultingfrom participation in each type of activity the time spentwas multiplied by the rate of energy expenditure as-cribed to that type of activity: 3.5 METS for walking andmoderate-intensity activity and 9.0 METS for vigorous-intensity activity. The three energy expenditure esti-mates were then summed to yield total leisure timeenergy expenditure which was multiplied by self-re-ported weight in kilograms to produce weight-correctedenergy expenditure expressed as kcals.kg21.fort-night21. A dichotomous physical activity variable wascreated by defining those who reported expending 800
kcals.kg21 or more per week as sufficiently active andthose who reported expending less than 800 kcals.kg21per week as inactive. This variable was used as theoutcome variable in the analyses.
ET AL.
Data Analysis
The bivariate relationships between sociodemo-graphic, social–cognitive, and perceived environmentalvariables and physical activity participation were ini-tially analysed by (x2) analysis. Forced entry logisticregression analysis (Statistical Package for the SocialSciences, Version 8.0) was used to identify independentpredictors of inactivity. Two analyses were conducted:one including self-efficacy and one excluding self-effi-cacy. Self-efficacy has repeatedly been shown to bestrongly associated with physical activity participationand its inclusion in the logistic regression model islikely to result in the exclusion of other relevant vari-ables. The logistic regression analysis which includedself-efficacy revealed that those respondents with lowself-efficacy were significantly less likely to be physi-cally active (OR 5 0.42, 95% CI 0.26–0.69) than thosewith high self-efficacy. Because we wished to exploremore fully the associations between physical activityand other social cognitive variables, only the resultsof the logistic regression analysis which excluded self-
efficacy are reported. The models were adjusted for age,sex, country of birth, marital status, employment sta-tus, and living situation. These variables were consid-ered potential confounders and were retained in themodels.RESULTS
Response Rate
A total of 3230 households were visited, resulting incompleted questionnaires from 2374 households (re-sponse rate 5 73.5%). Nonresponses were due to refusalto be interviewed (292), vacant dwellings (330), uncon-tactable after three to five call-backs during interviewweek; and death, illness, or language problems (60). Ofthose who responded to the survey, 449 were age 60 orolder. The interviewers did not interview those whowere clearly disabled (24 cases). A further 23 respon-dents did not provide sufficient information on theirphysical activities to allow estimation of energy expen-diture, leaving 402 cases for analysis. Fifty-five percentof the respondents were female. Thirty-three percentof the respondents were age 60–64, 29% were age 65–69, and 38% were 70 years or older. Sixty percent weremarried and 71% were born in Australia. Approxi-mately 84% of the respondents were voluntarily out ofwork or retired. One-third (35%) of the respondentsreported living alone and the remainder were in someother living arrangement.
sociodemographic category who were classified as ade-quately or inadequately active and the value of Pear-son’s x2 statistic for the cross-tabulations between ac-tivity category and demographic measure. Significantly
Living alone 143 (35.6) 59.4 40.6
Other 259 (64.4) 53.3 46.7 0.23Employment statusRetired 339 (84.3) 60.3 39.7Working 63 (15.7) 54.6 45.4 0.4
more males (55%) than females (38%) were adequatelyactive and the proportion who were physically activevaried with age. Those age 65–69 were the most active(54%), followed by those age 60–64 (44%) and thoseage 70 or older (38%). Overall, there were few othersignificant relationships between physical activity par-ticipation and the sociodemographic measures.
Make you uncomfortable 0.66Make present injury worse 0.81Help in making friends 0.87Good way to have fun 0.80Takes up too much time 0.79
AND OLDER PEOPLE 19
resulting from principal components analysis withoblique rotation. The four factors explained 59% of thevariance and none of the scale items loaded signifi-cantly on more than one factor. The first factor is relatedto improvements to health associated with physical ac-tivity participation (Health Benefits factor, Cronbach’sa 5 0.74), the second is related to concerns that beingactive may increase the risk of an injury or may exacer-bate an existing health problem (Risk of Harm factor,Cronbach’s a 5 0.64), the third factor is related to thepleasures associated with being active (Enjoyment fac-tor, Cronbach’s a 5 0.71), and the fourth, single-item,factor (Time factor) is related to time constraints associ-ated with physical activity participation.
Only the Health Benefits factor was significantly as-sociated with physical activity participation, with alarger proportion of active people expressing a positiveattitude to the health benefits of physical activity thana low or negative attitude to physical activity. Althoughthe remaining attitude variables all showed the expec-ted associations with physical activity, none of the asso-ciations were statistically significant (Table 3).
Environmental influences. Significantly greaterproportions of those who were adequately physicallyactive, compared with those who were inadequately ac-tive, reported that they had access to a recreation cen-ter; a cycle track; a golf course; a park; and a swimmingpool (Table 3). However, this was not significant for useof home exercise equipment or access to an exercisehall, gym, tennis courts, or bowls green. Although asignificantly greater proportion of active people re-ported that they found little difficulty using the foot-paths (uneven paths, hills, dogs), the difference in theproportion of people who considered the neighborhoodsafe for walking during the day was not statisticallysignificant. A significantly greater proportion of thosewho were physically active reported that they regularlyreceived support and encouragement from their friendsand family to be active.
Self efficacy. A significantly greater proportion ofactive respondents had high self-efficacy (48%) com-pared with inactive respondents (26.6%).
Social reinforcement. The only reinforcement vari-able which was significantly associated with physicalactivity was family and friends saying that physicalactivity was good for the respondents’ appearance, withthose in the active group being more likely to havefamily or friends offering this type of reinforcement.No significant differences were observed between thegroups for family and friends being critical of physical
PHYSICAL ACTIVITY
TABLE 1
Comparisons of Demographic Characteristics of Inactive andActive Respondents
Inactive ActiveVariable n (%) (%) (%) x2 P value
GenderMale 180 (44.8) 47.8 55.2Female 222 (55.2) 61.7 38.3 0.005
Age60–64 133 (33.1) 56.4 43.665–69 117 (29.1) 46.2 53.8701 yrs 152 (38.8) 61.8 38.2 0.04
Marital statusMarried 240 (59.7) 52.1 47.9Not married 162 (40.3) 60.5 39.5 0.10
Country of birthAustralia 284 (70.6) 58.1 41.9Overseas 118 (29.4) 49.2 50.8 0.10
Living situation
Attitudes. For the attitude scale correlation matrix,the Bartlett test of sphericity statistic was x2 5 974.83(66), P , 0.001, and the KMO measure of samplingadequacy was 0.76. Table 2 shows the factor loadings
TABLE 2
Factor Loadings (Based on Rotated Principal ComponentAnalysis) Greater than 0.4 for Attitude Items
Factor
1 2Health Risk of 3 4
Attitude items benefits harm Enjoyment Time
Relieve stress 0.71Make you too tired 0.71Maintain or improve health 0.69Control weight 0.59Help with sleep 0.53Help carry out reg. chores 0.51Cause injury 0.79
activity or being upset by respondents’ physical activityhabits (Table 4).
Social modeling. Both the frequency of the respon-dent’s partner being physically active and the frequency
analyses.
Self-efficacyHigh self-efficacy 26.6 48.0 0.001
a The questionnaire item asked about access to various facilitieswhich might be used for physical activity (e.g., golf course, exerciseclasses) and had a yes/no response format.
with which friends and family participated in physicalactivity were significantly associated with being ade-quately active. Observing others in the neighborhoodbeing physically active approached, but did not reach,statistical significance (Table 4).
Logistic Regression
The results of the logistic regression analyses areshown in Table 5. These indicate that both age andsex were significantly associated with physical activityparticipation. Having friends who participate regularlyin physical activity, finding footpaths safe for walking,
and having access to a park were significantly associ-ated with the outcome measure. That more of the vari-ables were significantly associated with physical activ-ity participation in the bivariate analyses than in theET AL.
adjusted logistic regression analysis suggests the exis-tence of confounding among the perceived physical envi-ronment variables.
Self-efficacy was found to be strongly associated withphysical activity in the initial logistic regression modelthat we tested. It was excluded from the logistic regres-sion model reported here, as it has been consistentlyassociated with higher levels of physical activity, and
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TABLE 3
Bivariate Relationships among Physical Activity Participation,Attitudes to Physical Activity, the Perceived Physical
Environment, and Self-Efficacy
Variable Inactive (%) Active (%) x2 P value
Attitude factorsHealth benefits
Low 38.6 24.6Medium 31.8 36.9High 29.6 38.5 0.01
Risk of harmLow 30.0 37.4Medium 31.8 34.6High 38.1 27.9 0.09
EnjoymentLow 35.4 30.7Medium 33.2 30.2High 31.4 39.1 0.27
TimeLow 36.3 31.8Medium 33.2 31.7High 30.5 37.4 0.33
Perceived physicalenvironment
Access toa 32.9 40.8 0.10Home equipment 30.5 34.6 0.38Local hall 26.9 38.5 0.01Recreation center 34.1 46.9 0.009Cycle track 37.2 46.9 0.05Golf course 27.8 35.2 0.11Gym 63.7 81.0 0.001Park 44.4 58.7 0.004Swimming pool 34.5 42.5 0.10Tennis court 47.5 54.7 0.15Bowling green
Feel safe walking 92.8 95.5 0.26Footpaths safe 75.3 85.5 0.01
would be expected to have particularly strong and directeffects. It can be considered a strong indicator of physi-cal activity and has been excluded in previous studies[23,24] from the type of multivariate analyses that wereport here.
DISCUSSION
This cross-sectional survey examined the relation-ships among physical activity participation, social–cognitive, and environmental influences in a randomsample of Australian adults age 60 or older. Thestrengths of the current study are that it is based on arandom sample of older adults and that it has applieda well-developed theory to understanding the influ-ences on physical activity participation. The main limi-tations of the study are that it is a cross-sectional survey(precluding conclusions as to causality) and that thesample size is not sufficiently large to allow stratified
Analyses of the bivariate relationships between socio-economic factors and physical activity found that ap-proximately 55% of men and 38% of women were ade-quately active and that a greater proportion of those
TABLE 4
Proportion of Active and Inactive Respondents Who ReportedHigh Social Support, High Positive or Negative Social
Reinforcement, and High Exposure to Social Reinforcement
Inactive Active x2
Variable (%) (%) P value
Social environmentHigh social support 42.7 55.6 0.010
Social reinforcementAppearance (others say activity is
good for appearance) 31.7 43.8 0.01Criticism (others criticize
respondent for being active) 7.3 7.9 0.84Family upset (family upset with
respondent’s activities) 10.6 12.4 0.57Social modeling
Others active (frequency ofobserving activity in theneighborhood) 81.2 87.7 0.075
Partner is active (frequency ofpartner being active) 64.5 74.9 0.014
Participation with friends or family(frequency of partner beingactive) 46.8 66.9 0.001
A
Yes 0.13 0.05 1.14 1.03–1.26Footpaths perceived as
safe for walkingYes 1.0No 20.56 0.57 0.57 0.34–0.97
age 65–69 were active than those age 60–64 or 70 orolder. The finding that a greater proportion of men thanwomen were physically active is generally consistentwith the literature [12] and suggests that a strongeremphasis on the needs and interests of women in physi-cal activity promotion strategies is appropriate. Thefinding that the prevalence of adequate physical activ-ity participation was higher among those age 65–69than the younger age group is probably related to thefact that most working adults in Australia retire atage 65, giving them more time to participate in leisureactivities. That physical activity declined among theolder age group is probably due to declining health.Actual or perceived poor health can be a major barrierto regular physical activity among the older population[25]. None of the remaining measures of socioeconomicstatus was significantly associated with physical activ-ity participation.
Of the attitude measures, only the attitudes to thehealth benefits of physical activity factor were signifi-cantly associated with physical activity participation,indicating that the active respondents believed morestrongly that physical activity would lead to health ben-efits than the inactive.
Many of the perceived physical environment vari-ables were significantly related to physical activity par-ticipation in the bivariate analyses. Those who were
physically active were significantly more likely thanthose who were inactive to report that a recreation cen-ter, a cycle path, a park, a golf course, and a swimmingpool were accessible to them. Although there was noND OLDER PEOPLE 21
difference in the proportions of active and inactive re-spondents who felt the neighborhood was safe for walk-ing, a greater proportion of the active respondents re-ported that the footpaths in their area presented fewerobstacles to safe and comfortable walking. Althoughthe latter finding is consistent with other Australianstudies, the former finding is not [26]. That only accessto a park and safe footpaths were significantly related tophysical activity participation in the logistic regressionmodels suggests that there was confounding among theperceived physical environment variables. Programs topromote physical activity participation among olderpeople should give attention to the particular aspectsof the neighborhood environment which our studyhas identified.
The associations between the perceived physical en-vironment and physical activity participation are gen-erally consistent with studies of the determinants ofphysical activity in younger population groups. For ex-ample, Sallis and his colleagues [27] found a significantcorrelation between the convenience of local facilitiesand participation in vigorous activity among adults age18–23 and Hovell and his colleagues [28] found thatthe convenience of local facilities was associated withincreases in walking in a large community sample.
Although these findings suggest that accessibility offacilities which support physical activity may have astrong influence over the proportion of older people whoare physically active, the results should be interpretedwith some caution. It may be that those who choose tobe more active seek out appropriate facilities, so regularparticipation in physical activity may result in greaterawareness of facilities rather than the presence of facili-ties “causing” greater physical activity participation.Mapping the presence of facilities in a geographicallydefined area against participation in physical activitymay be a useful approach to resolving this question [18].
A greater proportion of active respondents reportedreceiving support from family and friends to be activeand also reported being told that being active was goodfor their appearance. Other studies which have exam-ined the relationship between physical activity partici-pation and social support have found that support issignificant for women, but not for men [29,30]. Thepresent study does not have a sufficiently large samplesize to stratify the analyses by gender.
Having a partner who was physically active and hav-ing friends who were physically active were both signifi-cantly associated with physical activity participation,suggesting that a social environment in which physicalactivity is a common occurrence may be an importantinfluence. Finally, having high self-efficacy for physical
PHYSICAL ACTIVITY
TABLE 5
Variables Associated with Physical Activity in the LogisticRegression Analysis
Logistic regression model
Variable B SE OR 95% CI
GenderMale 1.0Female 20.53 0.21 0.59 0.39–0.89
Age60–64 1.065–69 0.56 0.27 1.75 1.03–2.97701 20.02 0.26 0.98 0.59–1.63
Participate regularly withfriends or family
Yes 1.0No 0.75 0.22 0.47 0.31–0.73
Access to a local parkNo 1.0
activity was strongly associated with being adequatelyphysically active. This finding is consistent with moststudies of the determinants of physical activity partici-pation with population groups of all ages [31].
E
22 BOOTHThe findings of this study will be useful in policydevelopment and intervention planning to promote thehealth of older people as participation in regular physi-cal activity is considered an effective intervention to
reduce/prevent a number of functional declines associ-prescribed exercise regimen. J Sport Exerc Psychol 1993;15:
ated with aging [5]. The two factors which stand outas being most strongly associated with physical activityin our analyses are local opportunities to walk and sup-port from family and friends. These factors could be afocus of campaigns targeting older people and couldalso inform community-based strategies to encourageand provide opportunities for older adults to be moreactive.
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