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 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.