# Significant associations between multidimensional factors and physical activity among older adults: an application of Andersen’s behavioral model

## TL;DR

This study explores how factors like age, gender, and health influence physical activity in older adults using a multidimensional model.

## Contribution

The study applies Andersen’s behavioral model to identify multidimensional factors influencing physical activity in older adults.

## Key findings

- Predisposing, enabling, and need factors are all associated with physical activity levels in older adults.
- Gender, age, pension insurance, and community facilities significantly predict physical activity levels and insufficient activity.
- Self-rated health strongly correlates with physical activity, suggesting the need for health-focused interventions.

## Abstract

Physical activity plays a crucial role in promoting the health of the older adults. However, previous studies examined the influencing factors of physical activity among the older adults from a single-dimensional perspective, failing to adequately account for the interactions between various factors. Consequently, this study applied Andersen’s behavioral model to examine predisposing, enabling, and need factors associated with physical activity characteristics among the older adults.

Based on Andersen’s behavioral model, this cross-sectional study analyzes factors affecting physical activity among 586 randomly older selected adults (aged ≥60) in Taiyuan via questionnaires. Factors are categorized into predisposing, enabling, and need dimensions, and are analyzed using ordinal and binary logistic regression.

Predisposing, enabling, and need factors were all associated with physical activity levels and insufficient physical activity among older adults. Specifically, gender (OR = 1.893, 95%CI: 1.303–2.751), age (OR = 1.739, 95%CI: 1.034–2.927), pension insurance purchase (OR = 0.312, 95%CI: 0.194–0.501), community facilities convenience score (OR = 1.363, 95%CI: 1.076–1.727), self-rated unhealthy health (OR = 0.375, 95%CI: 0.167–0.845), general health (OR = 0.372, 95%CI: 0.212–0.653), and relatively healthy (OR = 0.472, 95%CI: 0.281–0.793) were identified as significant predictors of physical activity levels. For insufficient physical activity, the influencing factors included gender (OR = 0.470, 95%CI: 0.302–0.732), age (OR = 0.458, 95%CI: 0.250–0.840), pension insurance purchase (OR = 3.944, 95%CI: 2.322–6.698), convenience of community facilities (OR = 0.718, 95%CI: 0.543–0.948), poor self-rated health (OR = 4.555, 95%CI: 1.811–11.456), and fair self-rated health (OR = 2.533, 95%CI: 1.302–4.926).

Older adults’ physical activity levels needed improvement. Among these factors, age-related limitations can hinder participation in activities, while gender differences result in varying preferences and abilities regarding activities. Promoting pension insurance and improving community facilities could boost activity. Self-assessed health status is strongly associated with levels of physical activity. Governments should implement evidence-based health education, tailored incentives, strengthened community support networks, and upgraded age-friendly infrastructure to boost participation. These measures enhance public health and advance active aging through building age-friendly societies.