Identifying Important Leisure-time Living Activities for Healthy Aging in the Singapore Longitudinal Aging Cohort Using Machine Learning Techniques

Abstract

Singapore's aging population has led to a government commitment to promoting healthy aging through the construction of smart and resilient communities. However, the design of effective community services can be challenging due to a lack of understanding of important leisure-time daily living activities that promote healthy aging. To address this issue, we developed a novel learning-based computational workflow to identify important living activities correlated with both clinical and biological health for healthy aging. Our analysis of 1356 community-living Chinese elderly in the Singapore Longitudinal Aging Study (SLAS) II cohort revealed that 10 living activities were significantly associated with clinically healthy aging, while 9 were significantly associated with biologically healthy aging through the selection of minimum number of features by 7 algorithms (Decision Tree, Random Forest, Naïve Bayes, Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron and XGBoost). We compared two learning-based feature selection methods algorithms, Recursive Feature Elimination (RFE) and Sequential Forward Selection (SFS), and found that features selected by SFS method outperformed those by RFE method. Physical exercise and senior club activities were found to be important leisure-time daily-living activities. Further analysis indicated that the active group, composed of older adults who participated in these activities, had significantly longer survival times, a lower mortality rate (lifespan) and a lower frailty rate (healthspan) compared to the non-active group (p<0.001). The percentage of dead/frail people in the non-active group tripled. These findings demonstrate the potential impact of using machine learning techniques to assist healthy aging studies. This work links biological health (aging markers and biological age), clinical health and leisure-time daily living activities in SLAS cohort studies. By identifying and prioritizing these activities, policymakers and service providers can develop interventions that are evidence-based and culturally appropriate, maximizing their potential impact on the health and well-being of older adults in Singapore.