@article {12098, title = {The Association Between Patterns of Social Engagement and Conversion From Mild Cognitive Impairment to Dementia: Evidence From the Health and Retirement Study.}, journal = {Alzheimer Disease \& Associated Disorders}, volume = {36}, year = {2022}, pages = {7-14}, abstract = {

OBJECTIVES: This study examined the association between patterns of social engagement and conversion from cognitive impairment, no dementia (CIND) to dementia. It also tested whether social engagement is associated with conversion independently from physical and cognitive engagements.

METHOD: Data from 2 waves (2010 and 2014) of the Health and Retirement Study (HRS) were used. The sample consisted of 1227 people who had CIND in 2010. To identify the heterogeneity of social engagement, latent class analysis was utilized. Multinomial logistic regression analysis was utilized to investigate the association between patterns of social engagement and probability of conversion to dementia and death or dropout.

RESULTS: The result showed that patterns of social engagement that represent higher level and more variety of social engagement were associated with lower probabilities of conversion to dementia in 4 years but not with probabilities of death or dropout. The relationship held after controlling for physical and cognitive engagements.

DISCUSSION: Findings implied that promoting social engagement may be protective against developing dementia even for the high-risk group of people with CIND. Future studies should investigate the mechanism behind the relationship between patterns of social engagement and lower probabilities of conversion to dementia.

}, keywords = {cognitive impairment, environmental enrichment model, Latent Class Analysis}, issn = {1546-4156}, doi = {10.1097/WAD.0000000000000486}, author = {Amano, Takashi and Sojung Park and Morrow-Howell, Nancy and Carpenter, Brian} } @article {10074, title = {Consolidated measures of activity among older adults: results of a three data set comparison.}, journal = {Journal of Gerontological Social Work}, year = {2019}, abstract = {This study explores the potential to consolidate a broad range of activity items to create more manageable measures that could be used in statistical modeling of multi-activity engagement. We utilized three datasets in the United States: Panel Study of Income Dynamics, Health and Retirement Study, and Midlife in the United States. After identifying activity items, exploratory and confirmatory factor analysis were used to empirically explore composite activity measures. Findings suggest that discrete activity items can be consolidated into activity domains; however, activity domains differ across datasets depending on availability of activity items. Implications for research and practice are further discussed.}, keywords = {Physical activity}, issn = {1540-4048}, doi = {10.1080/01634372.2019.1582123}, author = {Lee, Yung Soo and Putnam, Michelle and Morrow-Howell, Nancy and Inoue, Megumi and Jennifer C. Greenfield and Chen, Huajuan} } @article {10446, title = {PATTERNS OF WEALTH TRAJECTORY IN LATER LIFE: CRITICAL PERIOD, ACCUMULATION, AND SOCIAL MOBILITY MODELS}, journal = {Innovation in Aging}, volume = {3}, year = {2019}, month = {2019/11/08}, pages = {S382 - S382}, abstract = {Wealth, an important financial cushion for older adults to buffer economic stress, requires a longer time to accumulate and develop in one{\textquoteright}s course of life. However, little is known about the trajectories of wealth in later life, and how the life course socioeconomic status (SES) may contribute to the development of wealth at old-age. This study investigated longitudinal patterns of wealth trajectory and whether SES across the life course affects these trajectories using critical period, accumulation, and social mobility models. Using data from 16,189 adults aged 51 and older from the 2004-2014 Health and Retirement Study, a growth mixture model was used to explore distinct wealth trajectories. Impacts of life course models were studied using multinomial logistic regression. Results showed that four heterogeneous latent classes of wealth were identified: Stable high (reference group), Low and increasing, Stable low, and High but decline. Disadvantaged adulthood SES, accumulated exposure to socioeconomic risks, and downward or persistent socioeconomic disadvantage over the life course were associated with Stable low, Low and increasing, and High but decline, supporting all three life course mechanisms on wealth development in later life. Evidence suggests that wealth development is heterogeneous across individuals, and a strong gradient effect of life-course SES on wealth trajectories are clearly observed. Programs and policies should address the effects of life course on wealth development to strengthen the economic well-being in later life.}, keywords = {modeling, social mobility, Wealth, wealth trajectory}, isbn = {2399-5300}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6840301/}, author = {Chen, Yu-Chih and So Jung Park and Morrow-Howell, Nancy} } @article {10.1093/geront/gny023, title = {Activity Patterns and Health Outcomes in Later Life: The Role of Nature of Engagement}, journal = {The Gerontologist}, volume = {59}, year = {2018}, month = {04}, pages = {698-708}, abstract = {The health benefit of activity participation at older ages is documented in the current literature. Many studies, however, only explored the health benefits of engaging in a few activities and did not examine mechanisms connecting activity participation to health. We investigated the pathway between activity and health by testing the mediation role of the nature of engagement (physical, cognitive, and social) on physical, mental, and cognitive health of older adults.We analyzed data of 6,044 older adults from the 2010 and 2012 Health and Retirement Study linked with 2011 Consumption and Activity Mail Survey. We used latent class analysis to identify the patterns of participating in 33 activities as well as patterns of nature of engagement, and examined how these patterns were associated with cognition, depressive symptoms, and self-rated health in later life.Meaningful patterns of activity (high, medium, low, passive leisure, and working) and the nature of activity engagement (full, partial, and minimal) were identified. High and working groups, compared to the passive leisure group, showed better health and cognition outcomes. The nature of engagement mediated the relationship between activity patterns and health, especially for older adults who were either full or partially engaged.The nature of engagement may play a more important role than the activity itself in relation to health. Identifying the heterogeneity in activity engagement in later life is critical for tailoring interventions and designing programs that can improve the health of older adults.}, keywords = {Activity engagement, Cognition, depression, Self-rated health}, issn = {0016-9013}, doi = {10.1093/geront/gny023}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29659800}, author = {Chen, Yu-Chih and Putnam, Michelle and Lee, Yung Soo and Morrow-Howell, Nancy} } @article {8028, title = {An investigation of activity profiles of older adults.}, journal = {J Gerontol B Psychol Sci Soc Sci}, volume = {69}, year = {2014}, month = {2014 Sep}, pages = {809-21}, publisher = {69}, 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.

}, keywords = {Aged, Aged, 80 and over, Aging, Black or African American, Cohort Studies, Cross-Sectional Studies, Employment, Female, Florida, Health Surveys, Hispanic or Latino, Human Activities, Humans, Male, Middle Aged, Models, Psychological, Motor Activity, Prospective Studies, Regression Analysis, United States}, issn = {1758-5368}, doi = {10.1093/geronb/gbu002}, url = {http://psychsocgerontology.oxfordjournals.org/content/early/2014/02/12/geronb.gbu002.abstract}, author = {Morrow-Howell, Nancy and Putnam, Michelle and Lee, Yung Soo and Jennifer C. Greenfield and Inoue, Megumi and Chen, Huajuan} }