Nancy Morrow-Howell1, Michelle Putnam2, Yung Soo Lee3, Jennifer C Greenfield4, Megumi Inoue5, Huajuan Chen6. 1. Brown School of Social Work and Center for Aging, Washington University in St Louis, Missouri. morrow-howell@wustl.edu. 2. School of Social Work, Simmons College, Boston, Massachusetts. 3. Department of Social Welfare, Incheon National University, Yeonsu-gu, Incheon, Korea. 4. Graduate School of Social Work, University of Denver, Colorado. 5. Graduate School of Social Work, Boston College, Chestnut Hill, Massachusetts. 6. Brown School of Social Work and.
Abstract
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.
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.
Authors: Stephen J Mooney; Spruha Joshi; Magdalena Cerdá; James W Quinn; John R Beard; Gary J Kennedy; Ebele O Benjamin; Danielle C Ompad; Andrew G Rundle Journal: Am J Prev Med Date: 2015-06-16 Impact factor: 5.043