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Am J Public Health. 2010 April; 100(4): 654–660.
PMCID: PMC2836350
NIHMSID: NIHMS294507

Physical Activity Resources and Changes in Walking in a Cohort of Older Men

Yvonne L. Michael, ScD, MS,corresponding author Leslie A. Perdue, MPH, Eric S. Orwoll, MD, Marcia L. Stefanick, PhD, Lynn M. Marshall, ScD, and for the Osteoporotic Fractures in Men Study Group

Abstract

Objectives. We evaluated the influence of physical activity resources and neighborhood-level socioeconomic status (SES) on walking among community-dwelling older men.

Methods. Participants reported time walked per day at baseline (2000–2002) and follow-up. Residential addresses were linked to a geographic information system database to assess proximity to parks, trails, and recreational facilities. Log-binomial regression analyses were conducted to test the hypothesis that men living near physical activity resources were more likely to increase or maintain time walked.

Results. Average time walked per day declined by 6 minutes between baseline and follow-up (P < .05). There was a significant interaction of neighborhood SES and physical activity with walking time (P < .1). Proximity to parks and proximity to trails, respectively, were associated with a 22% (95% confidence interval [CI] = 1.01, 1.47) and 34% (95% CI = 1.16, 1.55) higher likelihood of maintaining or increasing walking time in high-SES neighborhoods, but there was no association in low-SES neighborhoods. Proximity to recreational facilities was not associated with walking.

Conclusions. Uncovering reasons that proximity to parks and trails is not associated with maintenance of walking activity among men in low-SES neighborhoods could provide new insight into ways to promote physical activity.

Walking is the most common form of exercise among older adults.1,2 Walking can be integrated into daily routines3,4 and can be a feature of neighborhood life.57 A physically active lifestyle is linked to disease prevention and risk reduction, as well as to physiological, functional, psychological, and social benefits.8,9 Despite this widespread knowledge, physical activity declines with age, and the majority of older adults do not meet physical activity recommendations to maintain good health.10,11 Therefore, identifying factors that encourage elderly adults to maintain physical activities, such as walking, are vital for health promotion.

The behavioral ecological model (BEM) of health promotion asserts that both the physical and social environments contribute to an individual's health behaviors.12 In the context of physical activity, the BEM theory posits that proximate physical activity resources—such as parks, trails, and recreational facilities—serve as cues for physical activity behaviors. Observing or interacting with people engaging in activities in locations where physical activity occurs provides social and environmental reinforcement for one to do the same.

The BEM underlies the hypothesis that physical activity will increase in populations that have access to nearby physical activity resources. To date, cross-sectional studies have generally shown positive associations between proximate physical activity resources and physical activity,1321 although some studies have reported no association.22,23 Despite promising results, cross-sectional studies provide no information on the effects that proximate physical activity resources have on maintenance of physical activities. In addition, few studies have focused specifically on older adults, a population that may be particularly sensitive to the influence of accessibility to physical activity resources.22,23 Finally, although neighborhood socioeconomic status (SES) affects the availability and quality of physical activity resources,13,2427 few studies have evaluated the extent to which neighborhood SES may affect the association between physical activity resources and level of activity.

To determine the effects of proximate physical activity resources on maintenance of walking activity, we conducted a prospective study among community-dwelling men 65 years or older from the Portland, Oregon, metropolitan area. Using a novel combination of individual-level and neighborhood-level data, we sought to determine whether older men who live within one eighth, one quarter, or one half mile of physical activity resources, including parks, trails, and recreational facilities, are more likely than men who live further from these resources to maintain or increase the amount of time they spend walking. We also assessed whether relationships between physical activity resources and maintenance of walking activity differ according to neighborhood SES.

METHODS

The design and participant recruitment procedures for the Osteoporotic Fractures in Men Study (MrOS) have been described elsewhere.28,29 Briefly, community-dwelling men aged 65 years or older were enrolled from March 2000 through April 2002 at 6 US clinical centers. Each clinical site attempted to enroll men who represented the racial/ethnic demographic profile of the area according to the 1990 US census.28 Men were eligible to participate if they could walk without assistance from another person; had at least 1 natural hip, to allow measurement of bone density; and were able to provide written informed consent. Men were reinterviewed during a follow-up visit in 2005 or 2006.

We used data from an ancillary study, Neighborhoods and Physical Activity in Elderly Men, which was initiated in 2006 among 513 participants enrolled at the MrOS Portland Clinical Center. ArcGIS (Environmental Systems Research Institute Inc, Redlands, CA) was used to geocode participants' residential addresses at baseline, and these addresses were linked to existing maps in the Regional Land Information System database maintained by Metro, the regional planning agency for the Portland metropolitan area.

Measures

Walking activity.

We used responses to 2 questions from the Physical Activity Scale for the Elderly30 to assess amount of time spent walking per week at baseline and at the follow-up visit. The first question was “Over the past 7 days, how often did you walk outside your home or yard for any reason? For example, for fun or exercise, walking to work, walking the dog, etc.?” Response options were never, seldom (1 to 2 days), sometimes (3 to 4 days), and often (5 to 7 days). The second question was “On average, how many hours per day did you spend walking?” Response options were less than 1 hour, between 1 and 2 hours, 2 to 4 hours, and more than 4 hours.

Responses to these questions were used to calculate an estimate of the average number of minutes spent walking per day. We calculated change in minutes of walking per day by subtracting the estimated number of minutes at baseline from the estimated number of minutes at follow-up. Men were categorized as having increased 30 minutes or more, decreased 30 minutes or more, or remained stable. In our cohort, 30 minutes represented approximately one half of a standard deviation of change in time walked. This cut point signified a meaningful change according to the distribution of change scores as well as public health recommendations to exercise at least 30 minutes on most days.

Physical activity resources.

We used data collected by Metro to assess the availability of proximate physical activity resources, specifically parks, trails, and recreational facilities. Distance to a walking or hiking trail that was not part of a park was quantified in Cartesian measurements (straight-line), and distance to a park was quantified in network distance (distance needed to travel to reach the park destination). We grouped park and trail distances into one eighth, one quarter, and one half mile categories.

Because the coverage of parks in Portland is relatively high, only one eighth and one quarter mile distances were used for parks. Trail coverage was lower, and thus only one quarter and one half mile distances were used for trails. Standard Industrial Classification codes (codes 7991, 7992, and 7997) were used to compile the number of local recreational facilities within one half mile and one quarter mile radii of a participant's home, and dichotomous variables were created representing the proximity of at least 1 recreational facility (versus none) at the one quarter and one half mile distances.

Neighborhood SES.

We used the census-block-group measure developed by Diez Roux et al.31 to establish neighborhood SES at baseline. We calculated z scores for 6 individual US census variables: median household income; percentage of households with interest, dividend, or rental income; median value of housing units; percentage of individuals aged 25 years or older who had completed high school; percentage of individuals aged 25 years or older who had completed college; and percentage of individuals in executive, managerial, or professional specialty occupations. We then summed these scores. High and low SES were based on a median split of the neighborhood SES score.

Other baseline data.

Comprehensive data collected from MrOS participants were categorized as follows. Demographic characteristics included age, race (White versus non-White), and marital status (married versus unmarried). Individual SES data included occupation (manual versus nonmanual), education, and score on the MacArthur Scale of Subjective Social Status community subjective social status subscale (high versus low from a median split).32 The following occupations were classified as manual: food preparation; building and grounds cleaning and maintenance; personal care and service; farming, fishing, and forestry; construction and extraction; installation, maintenance, and repair; production; and transportation and material.

An index was created to represent number of comorbid conditions, including diabetes, stroke, hypertension, heart attack, angina, congestive heart failure, chronic obstructive pulmonary lung disease, arthritis, and cancer (0 or 1 to 2 versus 3 or more). Each individual condition was also considered as a dummy variable (yes versus no). Health behaviors included lifetime tobacco smoking (ever versus never), body mass index ([BMI; defined as weight in kilograms divided by height in meters squared]; normal versus overweight or obese), and weekly alcohol consumption (quartiles). Participants with a BMI below 25 kg/m2 were considered to be of normal weight, and those with a BMI of 25 kg/m2 or above were classified as overweight or obese.

The physical and mental summary scales of the Medical Outcomes Study Short Form 12 (quartiles),33 self-rated health (good, very good, or excellent versus fair, poor, or very poor), and the Teng Mini-Mental State measure (quartiles)34 were used to assess physical and mental health. In addition, we used 3 performance-based measures to assess physical function at baseline: walking speed on a 6-meter walk, completion of 5 chair stands, and completion of a 20-centimeter narrow walk 6 meters in length.

Statistical Analysis

We used χ2 or Fisher exact tests (for categorical variables) and t tests (for continuous variables) to compare baseline characteristics between participants who were included and not included in the analytic sample. The mean number of participants per block group was 1.89 (SD = 1.60), and the median was 1 (range: 1–12). Because the number of participants in each block group was relatively low, with the majority of groups having only 1 participant, we did not evaluate within-neighborhood correlations.

The primary outcome measure was maintenance of or an increase in walking time. We used a log-binomial regression (through PROC GENMOD in SAS35) to model the probability that proximity to specific types of physical activity resources was associated with increased or maintained daily walking times. We evaluated confounding by adding sets of covariates to the models containing the physical activity resource. These covariate sets included demographic and socioeconomic characteristics, health behaviors, chronic conditions and self-reported health, and physical function. Covariate sets that changed the effect estimate for the physical activity resource by 10% or more, as well as any sets that were significantly associated with both the outcome and the exposure, were retained in the model. We did not adjust the models evaluating the association between physical activity resources and probability of maintaining or increasing walking because none of the covariate blocks met the criteria for confounding.

We assessed the modifying influence of neighborhood SES by testing the addition of an interaction term (high or low neighborhood SES by physical activity resource) to the models. Any interaction terms that were significant at P < .1 were stratified. To ensure that neighborhood-level SES was acting independently of individual-level SES, we forced the MacArthur Scale of Subjective Social Status subscale score into the models examining neighborhood SES.

RESULTS

Men were excluded from the Neighborhoods and Physical Activity in Elderly Men cohort (n = 513) if they resided outside Portland's urban growth boundary (which separates urban land from rural land; n = 37), were missing data on any of the primary exposure or modifying variables (n = 8), or reported not walking outside their home at baseline (n = 46); these exclusions left us with an analytic sample of 422 men. There were no significant differences between those included and not included in the analyses with respect to education, occupation, marital status, physical function, or BMI. At baseline, men who were included in the study were slightly older (median = 74 years versus 71 years) and were more likely to report excellent, very good, or good health (93% versus 86%) than were those excluded.

Baseline characteristics of the study sample by neighborhood SES are provided in Table 1. Relative to men living in high-SES neighborhoods, those living in low-SES neighborhoods were significantly (P < .05) more likely to have 2 or more comorbid conditions, less likely to be Caucasian, less likely to have more than a high school education, more likely to work in a manual occupation, and less likely to consume alcohol. Men living in low-SES neighborhoods were also significantly (P < .05) less likely to live near a trail (within one quarter or one half mile) than were men in higher SES neighborhoods.

TABLE 1
Baseline Sample Characteristics, by Neighborhood Socioeconomic Status: Neighborhoods and Physical Activity in Elderly Men Study, 2000–2002

At baseline, men walked an average of 1 hour per day (SD = 58 minutes); this declined to 54 minutes per day (SD = 51 minutes) during follow-up (which averaged 3.6 years). Although the average time spent walking declined during the study period, almost 22% (n = 92) of the men increased the amount of time they walked per day by 30 minutes or more, and 53% (n = 222) maintained baseline levels of time walked per day. Table 2 shows average number of minutes walked per day at baseline and follow-up by change in status.

TABLE 2
Mean Number of Minutes Participants Walked Per Day and Changes in Walking: Neighborhoods and Physical Activity in Elderly Men Study, 2000–2006

There was a nonsignificant positive association between proximity to recreation facilities and changes in walking. Distances to a park and a trail were positively associated with maintaining or increasing walking between baseline and follow-up, although neither reached statistical significance (P < .05). There were significant interactions between park (one eighth mile) and trail (one half mile) proximity and neighborhood SES in relation to changes in walking (P < .1).

Residence within one eighth of a mile of a park was associated with a 22% increased probability of maintenance of or increases in time spent walking per week during follow-up (relative risk [RR] = 1.22; 95% confidence interval [CI] = 1.01, 1.47) among men living in high-SES neighborhoods, whereas proximity to a park was not associated with changes in walking among men living in low-SES neighborhoods (RR = 0.89; 95% CI = 0.70, 1.13; Figure 1). Similarly, residence within one half mile of a trail was associated with a 34% increased probability of maintaining or increasing time spent walking per week during follow-up (RR = 1.34; 95% CI = 1.16, 1.55) among men living in high-SES neighborhoods, whereas proximity to a trail was not associated with changes in walking among men living in low-SES neighborhoods (RR = 0.93; 95% CI = 0.71, 1.23; Figure 2).

FIGURE 1
Risk ratio that time walked per day remained the same or increased between baseline and follow-up, by socioeconomic status and proximity to a park: Neighborhoods and Physical Activity in Elderly Men Study, 2000–2002.
FIGURE 2
Riskratio that time walked per day remained the same or increased between baseline and follow-up, by socioeconomic status and proximity to a trail: Neighborhoods and Physical Activity in Elderly Men Study, 2000–2002.

We conducted a sensitivity analysis to determine whether there were any changes in the associations after removal of men who had changed their addresses between baseline and follow-up (n = 46). There was no qualitative difference in our results when these 46 men were excluded from the analysis. Because these men were not driving the association between changes in walking and neighborhood physical activity resources, they were not removed from the sample.

DISCUSSION

The results of our prospective cohort study revealed a positive association among urban-dwelling older men between living within one eighth mile of parks and one half mile of trails and maintaining or increasing time spent walking, although the association was limited to men living in high-SES neighborhoods. Our observations are consistent with evidence from previous cross-sectional studies that adults living within walking distance of physical activity resources such as parks and trails are more likely to use these resources.13,14,16,18,21 For example, Duncan and Mummery reported that, after control for psychosocial variables, participants who lived farther than 400 meters from a footpath were 69% less likely than were participants who lived within 400 meters of a footpath to have walked in the previous week,16 suggesting that pathways near a person's home are important environmental cues for physical activity.

We did not find an association between private recreation facilities and activity, as has been reported in prior research.15,17,19 Unlike these previous studies, we assessed walking rather than overall physical activity. It is possible that private recreation facilities are more closely associated with activities other than walking, such as tennis or swimming. Hillsdon et al. reported no association between level of physical activity and access to green space; in fact, those with high levels of access to green spaces reported less physical activity than those with low levels of access.20 The reason for the inconsistency between our finding and that of Hillsdon et al. may be that green space, such as parks or trails, is more specific to walking behavior than to overall physical activity. Because our study was longitudinal, it was better suited than most prior research for discerning temporal relationships between physical activity resources and walking.

To our knowledge, only 1 other study has evaluated the role that access to physical activity resources plays in changes in physical activity among older adults.22 In that study, Li et al. reported a significant association between self-reported proximity to playgrounds, parks, and gyms and smaller rates of decline in walking over a 12-month period. Our findings not only confirm those of Li et al. but extend them given our use of objective measures of proximity to physical activity resources, a longer follow-up period, and consideration of neighborhood SES as an effect modifier.

Similar to previous studies in which physical activity resources were more prevalent in higher SES neighborhoods,13,2427 men in our study who lived in lower-SES neighborhoods were significantly less likely than men in higher-SES neighborhoods to have access to trails. The modifying effect of neighborhood SES on the relationship between physical activity resources may reflect a higher level of social and environmental reinforcement for physical activity in high-SES neighborhoods. Prior research has documented that physical activity resources in areas of higher deprivation are similar to resources in areas of lower deprivation in terms of number of features and amenities. However, those using resources in higher deprivation areas are more likely to encounter graffiti, trash, and evidence of drug use.36

Strengths and Limitations

This study involved significant strengths, especially the availability of high-quality geographic information system data to quantify access to physical activity resources and our ability to account for the influence of both neighborhood and individual SES. However, several limitations should be noted. Although it can be suggested that physical activity facilities are poor or unsafe in low-SES neighborhoods37 and that residents of those neighborhoods have inadequate time to use such facilities,38 we were not able to determine whether either of those hypothetical explanations pertained in our study. Also, we assessed walking via self-report, and the purpose of the walking activity was not specified (e.g., walking for transportation, errands, or exercise). Previous research has shown that different correlates are associated with walking for transportation than with walking for exercise or leisure.8,3944

Also, we assessed walking at only 2 time points. Future studies should attempt to collect longitudinal data at more regular intervals to determine trajectories and patterns of change. For example, the small average reduction in walking time observed in our cohort could have resulted from differences in the seasons in which study visits took place. Future investigations involving regular data collection in each season are necessary to address this issue.

Finally, our analyses were restricted to older men living in a single geographic area who reported regular walking outside the home, which may limit the generalizability of our results. However, it is possible that a study performed in a single geographic area can inform policy decisions in another locale. One good example in Portland is the overwhelming positive response to the “Sunday Parkways,” 7- to 8-mile “temporary parks” along city streets connecting neighborhoods and residents created by restricting selected streets to nonmotorized transportation. This initiative was modeled on a similar one in Bogotá, Colombia, in which every Sunday the city closes 70 miles of major roads so that people can walk, bike, and so forth without having to contend with traffic.45

Conclusions

The results of this study show that proximity to physical activity resources such as parks and trails may be important for maintaining moderate physical activity over time among older men residing in high-SES neighborhoods. These findings support an eco-social model of physical activity promotion incorporating neighborhood-level resources such as parks and trails. Uncovering the reasons that proximity to parks and trails is not associated with maintenance of walking activity among men living in low-SES neighborhoods could yield new insight into ways to promote the use of nearby physical activity resources.

Acknowledgments

This research was supported by a National Institute of Aging grant (AG026002) to Yvonne L. Michael. The Osteoporotic Fractures in Men Study (MrOS) is supported by National Institutes of Health (NIH) funding. The MrOS is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Institute on Aging, the National Center for Research Resources, and the NIH Roadmap for Medical Research (grants U01 AR45580, U01 AR45614, U01 AR45632, U01 AR45647, U01 AR45654, U01 AR45583, U01 AG18197, U01-AG027810, and UL1 RR024140).

Human Participant Protection

Secondary data from the Osteoporotic Fractures in Men Study (MrOS) were used in this investigation. The MrOS was approved by the institutional review boards of the participating clinics, and written informed consent was obtained from all study participants.

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