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

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 three domains, i.e., paid/unpaid work, caregiving, and social activities, among Chinese older adults. Research design and method Data were drawn from the first wave of the China Health and Retirement Longitudinal Study (CHARLS), which surveys a nationally representative sample of adults aged 60-year-old and above (N=7,503). We estimated Random Forest and the least absolute shrinkage and selection operator (LASSO) regression models 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 multi-generational 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.