Elsevier

Preventive Medicine

Volume 54, Issue 1, 1 January 2012, Pages 68-73
Preventive Medicine

Interactions between psychosocial and built environment factors in explaining older adults' physical activity

https://doi.org/10.1016/j.ypmed.2011.10.004Get rights and content

Abstract

Objective

To evaluate ecological model predictions of cross-level interactions among psychosocial and environmental correlates of physical activity in 719 community-dwelling older adults in the Baltimore, Maryland and Seattle, Washington areas during 2005–2008.

Method

Walkability, access to parks and recreation facilities and moderate-to-vigorous physical activity (MVPA) minutes per week (min/week) were measured objectively. Neighborhood aesthetics, walking facilities, social support, self-efficacy, barriers and transportation and leisure walking min/week were self-reported.

Results

Walkability interacted with social support in explaining total MVPA (B = 13.71) and with social support (B = 7.90), self-efficacy (B = 7.66) and barriers (B =  8.26) in explaining walking for transportation. Aesthetics interacted with barriers in explaining total MVPA (B =  12.20) and walking facilities interacted with self-efficacy in explaining walking for leisure (B =  10.88; Ps < .05). Summarizing across the interactions, living in a supportive environment (vs. unsupportive) was related to 30–59 more min/week of physical activity for participants with more positive psychosocial attributes, but only 0–28 more min/week for participants with less positive psychosocial attributes.

Conclusion

Results supported synergistic interactions between built environment and psychosocial factors in explaining physical activity among older adults. Findings suggest multilevel interventions may be most effective in increasing physical activity.

Highlights

► Built environment and psychosocial factors interact in influencing physical activity. ► Older adults are the least active age group. ► Environment and psychosocial factors together generally influenced activity the most. ► Interventions to increase activity should focus on both levels of factors.

Section snippets

Design

The present analyses used data from the Senior Neighborhood Quality of Life Study (SNQLS) that was conducted in the Baltimore, Maryland-Washington, DC and Seattle-King County, Washington regions during 2005–2008 (King et al., 2011). The primary aim of SNQLS was to investigate the relationship between built environment factors and physical activity in older adults. Census block groups (n = 216) were chosen to represent high and low walkability based on an index using parcel and street network data

Results

Participant demographic characteristics are presented in Table 1 and descriptive statistics of independent variables are presented in Table 2. Final sample size ranged from 687 to 709 due to missing data. Participants engaged in an average of 93.6 min/week of total MVPA, 40.9 min/week of walking for transportation, and 99.5 min/week of walking for leisure (see Table 3). Table 4 shows the relation of the environmental and psychosocial variables to min/week of physical activity.

Discussion

The present study provided some support for ecological model-predicted interactions between built environment and psychosocial factors in explaining physical activity among older adults. Six significant interactions were found (p < 0.05) and an additional 5 interaction terms displayed a trend for significance (p < 0.10) across the three physical activity metrics examined. For 5 of the 6 significant interactions, physical activity minutes were greater when both psychosocial and environmental factors

Conclusions

Experience with multilevel community interventions is accumulating (Bors et al., 2009), but evaluations have not been sufficient to understand their effects (Samuels et al., 2010). The present findings, if suitably replicated, imply that multilevel interventions that change both psychosocial and environmental variables may be most effective in increasing physical activity. The results also suggest considerable utility can be gained by targeting populations that rank high on one but not the

Conflict of interest

The authors do not have conflicts of interest to declare.

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