Purpose in Life and Loneliness Among African American Women: An Application of Latent Growth Mixture Modeling

Abstract Purpose in life has been widely proposed as a significant predictor of positive health outcomes. However, few studies have employed longitudinal methods to address the effects of purpose in life on loneliness. Furthermore, studies have rarely addressed the role of purpose in life in levels of loneliness among people of color. This study investigated the extent to which purpose in life predicted African American women’s loneliness over time. Using data from 661 African American women (Mage = 44.92, SD = 8.10) across four waves of the Family and Community Health Study (FACHS), latent growth mixture modeling was employed to explore the trajectories of loneliness across approximately 10 years and whether level of purpose in life was a significant predictor of the trajectories. This study also accounted for potent influential factors such as positive and negative social support, religiosity, racial discrimination, and financial strain as covariates. Findings revealed significant between-individual differences in loneliness trajectories, with individuals reporting a higher initial level of purpose in life tending to have lower levels of loneliness over time. Thus, greater purpose in life predicted lower loneliness among African American women, highlighting the importance of these factors in their psychosocial well-being.

Authors

Institutions

Publication Details

Journal
Innovation in Aging
Published
2025-12-01
DOI
https://doi.org/10.1093/geroni/igaf122.1764
Primary Topic
Health disparities and outcomes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Purpose in Life and Loneliness Among African American Women: An Application of Latent Growth Mixture Modeling

Eunbea Kim, Carolyn E. Cutrona, Daniel Russell
Innovation in Aging
Health disparities and outcomes
article

Purpose in Life and Loneliness Among African American Women: An Application of Latent Growth Mixture Modeling

Eunbea Kim, Carolyn E. Cutrona, Daniel Russell
article en

Abstract

Abstract Purpose in life has been widely proposed as a significant predictor of positive health outcomes. However, few studies have employed longitudinal methods to address the effects of purpose in life on loneliness. Furthermore, studies have rarely addressed the role of purpose in life in levels of loneliness among people of color. This study investigated the extent to which purpose in life predicted African American women’s loneliness over time. Using data from 661 African American women (Mage = 44.92, SD = 8.10) across four waves of the Family and Community Health Study (FACHS), latent growth mixture modeling was employed to explore the trajectories of loneliness across approximately 10 years and whether level of purpose in life was a significant predictor of the trajectories. This study also accounted for potent influential factors such as positive and negative social support, religiosity, racial discrimination, and financial strain as covariates. Findings revealed significant between-individual differences in loneliness trajectories, with individuals reporting a higher initial level of purpose in life tending to have lower levels of loneliness over time. Thus, greater purpose in life predicted lower loneliness among African American women, highlighting the importance of these factors in their psychosocial well-being.

Innovation in AgingVol. 9(Supplement_2)
Iowa State University (US), Auburn University (US)
Gender equality
Openalex Percentile: Top 26%
Health disparities and outcomes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.