AI Article Synopsis
- The study aimed to identify risk factors contributing to overweight/obesity in older adults by comparing various predictive models using survey data from 400 individuals in China.
- Six machine learning algorithms were tested, with the CatBoost model being the most effective; the key factors influencing obesity included gender, transportation-related physical activity, and road network density.
- Conclusions highlighted that lack of physical activity, poor neighborhood design, and incidents of falls are significant contributors to overweight/obesity among older adults, suggesting targeted prevention strategies are needed.
Article Abstract
Objective: To estimate the importance of risk factors on overweight/obesity among older adults by comparing different predictive model.
Methods: Survey data from 400 older individuals in China was employed to assess the impacts of four domains of risk factors (demographic, health status, physical activity and neighborhood environment) on overweight/obesity. Six machine learning algorithms were utilized for prediction, and SHapley Additive exPlanations (SHAP) was employed for model interpretation.
Results: The CatBoost model demonstrated the highest performance among the prediction models for overweight/obesity. Gender, transportation-related physical activity and road network density were top three important features. Other significant factors included falls, cardiovascular conditions, distance to the nearest bus stop and land use mixture.
Conclusion: Insufficient physical activity, denser road network and incidents of falls increased the likelihood of older adults being overweight/obese. Strategies for preventing overweight/obesity should target transportation-related physical activity, neighborhood environments, and fall prevention specifically.
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| http://dx.doi.org/10.1016/j.gerinurse.2024.12.038 | DOI Listing |
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