Evidence map›Paper›PMID 40606101›Full record

ArticleFrontiers in public health2025

The cumulative effect of various social activities and depression: a longitudinal study in Chinese older adults.

Sixiang Tao, Ming Huo, Hongzhao Wang

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sixiang TaoFaculty of Physical Education, Huainan Normal University, Huainan, China.
Ming HuoFaculty of Rehabilitation Engineering, University of Health and Rehabilitation Sciences, Qingdao, China.
Hongzhao WangFaculty of Physical Education, Huainan Normal University, Huainan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: While depression is linked to social activities, few studies comprehensively examine the cumulative and diverse effects of social activity trajectories. Study investigates the long-term relationship between the progression of depression and the patterns of changes in social activity participation. Method: This study adopted a follow-up research design and analyzed data from three periods of the China Health and Aged Care Tracking Survey (CHRLS) between 2015 and 2020. Data from 3,762 participants aged 60 and older who participated in CHARLS between 2015 and 2020 were included in the analysis. Depressive symptoms were measured by using the Epidemiological Studies Depression Scale (CES-D). A Kaplan-Meier survival analysis was performed, followed by a Cox regression analysis, to identify the interactions between depression and the trajectory of patterns of changes in social activity participation. Results: In the baseline survey of the third wave study (2015), the prevalence of depression was 24.9%. This increased significantly at each subsequent follow-up, reaching 28.7% at the fourth wave and 32.7% at the fifth wave. Survival analyses showed a statistically significant protective effect of Internet use and leisure-oriented social activities on the prevention of depression after adjusting for confounders such as demographic characteristics, health status, and health behaviors. Conclusion: Continued participation in recreational social activities and use of the Internet are strongly associated with effective resistance to depression.

Indexed as

DepressionSocial ParticipationAgedChinaEast Asian PeopleFemaleHumansLongitudinal StudiesMaleMiddle Ageddepressionlongitudinal studyolder adults in Chinaparticipationsocial activity

Identifiers

PMID40606101
PMCPMC12213448

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.