Individual and environmental variables related to outdoor walking among older adults: Verifying a model to guide the design of interventions targeting outdoor walking

PLoS One. 2024 Jan 10;19(1):e0296216. doi: 10.1371/journal.pone.0296216. eCollection 2024.

Abstract

Objective: To estimate the relationships between individual and environmental variables and outdoor walking (OW) in older adults with OW limitations through verifying a conceptual model.

Methods: Baseline data from 205 older adults participating in a randomized trial of a park-based OW program were analyzed using structural equation modeling. We evaluated a three latent factor model: OW (accelerometry and self-report); individual factors (balance; leg strength; walking self-confidence, speed and endurance; mental health; education; income; car access); and environmental factors (neighbourhood walkability components).

Results: Mean age was 75 years; 73% were women. Individual factors was significantly associated with OW (β = 0.39, p < .01). Environmental factors was not directly associated with OW but was indirectly linked to OW through its significant covariance with the individual factors (β = 0.22, p < .01). The standardized factor loadings from the individual factors on walking self-confidence and walking capacity measures exceeded 0.65.

Conclusions: Better walking capacity and more confidence in the ability to walk outdoors are associated with higher OW in older adults. Better neighbourhood walkability is indirectly associated with more OW. The conceptual model demonstrates an individual and environment association; if the capacity of the individual is increased (potentially through walking interventions), they may be able to better navigate environmental challenges.

MeSH terms

  • Accelerometry*
  • Aged
  • Educational Status
  • Female
  • Humans
  • Income*
  • Latent Class Analysis
  • Male
  • Walking

Grants and funding

The authors disclose receipt of the following financial support for the research and authorship of this article: This work was supported by the Visual and Automated Disease Analytics (VADA) Graduate Training Program of Natural Science and Engineering Research https://vada.cs.umanitoba.ca/ (YL) and Canadian Institutes of Health Research (grant number 376439) https://cihr-irsc.gc.ca/e/193.html (NS,RB,SW).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.