Growth Mixture Modeling of Loneliness Trajectories in Older Adults: The Role of Cognitive Function
Elnaz Abaei, Peter Martin

TL;DR
This study finds that cognitive function can influence how loneliness changes over time in older adults, suggesting cognitive improvements might help reduce loneliness.
Contribution
The novel contribution is using growth mixture modeling to identify distinct loneliness trajectories and linking them to cognitive function in older adults.
Findings
Three loneliness trajectories were identified: low-stable, high-decreasing, and moderate-increasing.
Higher immediate word recall scores at time 4 were associated with a less steep increase in loneliness over time.
Cognitive function, particularly word recall, significantly predicted loneliness trajectories.
Abstract
Loneliness is a growing public health concern among older adults in the United States, with significant implications for cognitive function. This study utilized conditional growth mixture modeling (GMM-CV) to investigate the longitudinal trajectories of loneliness and how cognitive function (measured by immediate word recall, delayed word recall, and serial 7s) predicted these trajectories. Data were drawn from a sample of 3,944 older adults (Mage = 68.74) using eight waves of the Health and Retirement Study (HRS) data from 2006–2020. Data from every two years were pooled to increase the sample size and create four time points. GMM-CV in Mplus identified three distinct loneliness trajectories best fit the Data: LL= -25,495.269, BIC=51,214.097, SSABIC=51,128.303, Entropy=0.814, Adj. LMR-LRT (.52)=245.630, BLRT(.51)=255.518. The three identified classes were: Class 1: Low-Stable…
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Taxonomy
TopicsHealth disparities and outcomes · Aging and Gerontology Research · Dementia and Cognitive Impairment Research
