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

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Title: Factors contributing to differences in physical activity levels in (pre)frail older adults living in rural areas of China
Authors: Xin, Zhang, Xiaoping, Zheng, Hans, Hobbelen, Healthy Ageing, Allied Health Care and Nursing, Barbara, van Munster, Qian, Tong, Tianzhuo, Yu, Feng, Li, Claudine J C, Lamoth, Healthy Ageing, Allied Health Care and Nursing
Source: PLoS ONE. 20(11)
Publisher Information: Public Library of Science.
Publication Year: 2025
Subject Terms: Humans, Aged, Female, Male, China, Exercise/physiology, Rural Population, Cross-Sectional Studies, Frail Elderly/statistics & numerical data, Middle Aged, 80 and over, Surveys and Questionnaires, mensen, op leeftijd, vrouwelijk, mannelijk, beweging/fysiologie, plattelandsbevolking, cross-sectionele studies, kwetsbare ouderen/statistieken en numerieke gegevens, middelbare leeftijd, 80 en ouder, vragenlijsten en enquêtes, Healthy Ageing, Frailty and adequate care, SDG 03 - Good Health and Well-being, Other
Description: INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Document Type: article
Language: English
Access URL: https://research.hanze.nl/en/publications/541d6433-b9ef-410d-a48b-197dff027f00
Availability: http://www.hbo-kennisbank.nl/en/page/hborecord.view/?uploadId=hanzepure:oai:research.hanze.nl:publications/541d6433-b9ef-410d-a48b-197dff027f00
Accession Number: edshbo.hanzepure.oai.research.hanze.nl.publications.541d6433.b9ef.410d.a48b.197dff027f00
Database: HBO Kennisbank
Description
Abstract:INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.