Activity Profiles among Older Adults: Latent Class Analysis Using the Korean Time Use Survey

Abstract

This study empirically explored the activity profiles of Korean older adults by considering a wide range of activities simultaneously and further investigated the socioeconomic factors associated with activity profiles. Gender differences in activity profiles were examined in-depth. Latent class analysis (LCA) was used to identify activity profiles based on a nationally representative sample of older adults from the most recent two waves of the Korean Time Use Survey (n = 3034 for 2014 and n = 3960 for 2019). Multinomial logistic regression analysis was employed to further examine the factors associated with the activity profiles. The findings revealed four distinct activity groups, although there were differences in activity profiles between the two waves. Several sociodemographic factors, such as gender, age, assets and income, were significantly associated with the activity profiles. Findings from this study can inform policy makers seeking interventions that enhance the overall well-being of older adults through activity engagement.