Objectives: In this study, we advance knowledge about activity engagement by considering many activities simultaneously to identify profiles of activity among older adults. Further, we use cross-sectional data to explore factors associated with activity profiles and prospective data to explore activity profiles and well-being outcomes.
Method: We used the core survey data from the years 2008 and 2010, as well as the 2009 Health and Retirement Study Consumption and Activities Mail Survey (HRS CAMS). The HRS CAMS includes information on types and amounts of activities. We used factor analysis and latent class analysis to identify activity profiles and regression analyses to assess antecedents and outcomes associated with activity profiles.
Results: We identified 5 activity profiles: Low Activity, Moderate Activity, High Activity, Working, and Physically Active. These profiles varied in amount and type of activities. Demographic and health factors were related to profiles. Activity profiles were subsequently associated with self-rated health and depression symptoms.
Discussion: The use of a 5-level categorical activity profile variable may allow more complex analyses of activity that capture the "whole person." There is clearly a vulnerable group of low-activity individuals as well as a High Activity group that may represent the "active ageing" vision.
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| http://dx.doi.org/10.1093/geronb/gbu002 | DOI Listing |
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