Estimating the Impact of Sustained Social Participation on Depressive Symptoms in Older Adults
Artikel i vetenskaplig tidskrift, 2021

Background: Social participation has been suggested as a means to prevent depressive symptoms. However, it remains unclear whether a one-time boost suffices or whether participation needs to be sustained over time for long-term prevention. We estimated the impacts of alternative hypothetical interventions in social participation on subsequent depressive symptoms among older adults. Methods: Data were from a nationwide prospective cohort study of Japanese older adults ≥65 years of age (n = 32,748). We analyzed social participation (1) as a baseline exposure from 2010 (approximating a one-time boost intervention) and (2) as a time-varying exposure from 2010 and 2013 (approximating a sustained intervention). We defined binary depressive symptoms in 2016 using the Geriatric Depression Scale. We used the doubly robust targeted maximum likelihood estimation to address time-dependent confounding. Results: The magnitude of the association between sustained participation and the lower prevalence of depressive symptoms was larger than the association observed for baseline participation only (e.g., prevalence ratio [PR] for participation in any activity = 0.83 [95% confidence interval = 0.79, 0.88] vs. 0.90 [0.87, 0.94]). For activities with a lower proportion of consistent participation over time (e.g., senior clubs), there was little evidence of an association between baseline participation and subsequent depressive symptoms, while an association for sustained participation was evident (e.g., PR for senior clubs = 0.96 [0.90, 1.02] vs. 0.88 [0.79, 0.97]). Participation at baseline but withholding participation in 2013 was not associated with subsequent depressive symptoms. Conclusions: Sustained social participation may be more strongly associated with fewer depressive symptoms among older adults.

Depressive symptoms

Social participation

Japan

Time-dependent confounding

Time-varying exposure

Older adults

Machine learning

Targeted maximum likelihood estimation

Författare

Koichiro Shiba

Harvard School of Public Health

Jacqueline M. Torres

University of California

Adel Daoud

Harvard University

Göteborgs universitet

Data Science och AI

Kosuke Inoue

University of California

Satoru Kanamori

Teikyo University

Taishi Tsuji

University of Tsukuba

Masamitsu Kamada

University of Tokyo

Katsunori Kondo

National Center for Geriatrics and Gerontology

Chiba University

Ichiro Kawachi

Harvard School of Public Health

Epidemiology

1044-3983 (ISSN) 1531-5487 (eISSN)

Vol. 32 886-895

Ämneskategorier (SSIF 2025)

Folkhälsovetenskap, global hälsa och socialmedicin

DOI

10.1097/EDE.0000000000001395

PubMed

34172690

Mer information

Senast uppdaterat

2025-11-18