# Factors contributing to differences in physical activity levels in (pre)frail older adults living in rural areas of China

## TL;DR

This study explores why some older adults in rural China are more physically active than others, focusing on factors like health and social support.

## Contribution

The study identifies key factors influencing physical activity levels in pre-frail older adults in rural China using machine learning and SHAP analysis.

## Key findings

- Self-reported social support and general health were top predictors of physical activity levels.
- High physical activity groups showed better physical performance than low-moderate groups.
- XGBoost model achieved 82.4% accuracy in classifying physical activity levels.

## Abstract

Physical Activity (PA) is essential for enhancing the physical function of pre-frail and frail older adults. However, among this group, PA-levels vary significantly. Identifying the factors contributing to these differences could support tailored PA interventions. This study aims to examine factors associated with physical activity levels among pre-frail and frail older adults in rural China.

This is a cross-sectional study. A total of 284 (pre)frail older adults (aged ≥60 years) were included from ten rural healthcare centers in Northeast China. Participants were categorized into low-moderate and high physical activity groups assessed using the Short Form International Physical Activity Questionnaire. Four-dimensional data were collected, including demographics, health behaviors, objective physical performance measures, and self-reported perceived health profiles. Extreme Gradient Boosting (XGBoost), a machine learning algorithm, was employed for binary classification (low-moderate vs. high physical activity). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and F1-score. To enhance interpretability, SHapley Additive exPlanations (SHAP) were utilized to identify key predictive variables.

Mean age of participants was 70 years (59% female, 86% farmers). The low-moderate group averaged 1,187 MET/week, while the high physical activity group reached 8,162 MET/week. Physical performance tests showed significantly better scores in the high PA group. The XGBoost model achieved 82.4% accuracy (AUC: 0.769, specificity: 90%, sensitivity: 63%). SHAP analysis revealed that self-reported social support, general health, ambulation, and physical performance measures were the most important factors.

The high physical activity group demonstrated better physical function than the low-moderate physical activity group; though, both groups showed poorer physical function compared to the general older population. Self-reported health perceptions and social support significantly correlated with physical activity levels. Addressing these factors through targeted interventions—including community-based social support programs and structured mobility-enhancing exercises—may contribute to improved health outcomes and enhanced quality of life in this population.