Investigating Factors of Active Aging Among Chinese Older Adults: A Machine Learning Approach

Abstract

Background and Objectives: With the extension of healthy life expectancy, promoting active aging has become a policy response to rapid population aging in China. Yet, it has been inconclusive about the relative importance of the determinants of active aging. By applying a machine learning approach, this study aims to identify the most important determinants of active aging in 3 domains, i.e., paid/unpaid work, caregiving, and social activities, among Chinese older adults. Research design and Methods: Data were drawn from the first wave of the China Health and Retirement Longitudinal Study, which surveys a nationally representative sample of adults aged 60 years and older (N = 7,503). We estimated Random Forest and the least absolute shrinkage and selection operator regression models (LASSO) to determine the most important factors related to active aging. Results: Health has a generic effect on all outcomes of active aging. Our findings also identified the domain-specific determinants of active aging. Urban/rural residency is among the most important factors determining the likelihood of engaging in paid/unpaid work. Living in a multigenerational household is especially important in predicting caregiving activities. Neighborhood infrastructure and facilities have the strongest influence on older adults’ participation in social activities. Discussion and Implications: The application of feature selection models provides a fruitful first step in identifying the most important determinants of active aging among Chinese older adults. These results provide evidence-based recommendations for policies and practices promoting active aging.