Evidence map›Paper›PMID 35448984›Full record

ArticleBMC geriatrics2022

Association between using social media WeChat and depressive symptoms among middle-aged and older people: findings from a national survey.

Xing Qu, Shannon H Houser, Jian Zhang, Jin Wen, Wei Zhang

Abstract read
In one paragraph

Article in BMC geriatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

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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

18 citing papers in PubMed.

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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

5 authors.

Xing QuInstitute of Hospital Management, West China Hospital, Sichuan University, Chengdu, 610041, China.
Shannon H HouserDepartment of Health Services Administration, University of Alabama at Birmingham, Birmingham, AL, USA.
Jian ZhangWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Jin WenInstitute of Hospital Management, West China Hospital, Sichuan University, Chengdu, 610041, China.
Wei ZhangWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China. weizhang27@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe aimed to assess the characteristics and health status of a study sample using social media WeChat and to identify the association between social media usage and depressive symptoms among people aged 45 and older in China.

methodsData were drawn from the China Health and Retirement Longitudinal Study (CHARLS). Depressive symptoms were measured by the 10-item form of the Center for Epidemiologic Studies Depression Scale (CES-D-10). The propensity score matching method (PSM) was performed to balance the characteristics of WeChat users and non-WeChat users. Multilevel logistic regression was used to test the association between the incidence of depressive symptoms and WeChat usage by introducing covariates step by step. Sensitivity analysis was conducted to estimate the robustness of the primary findings.

resultsA total of 5415 matching cases out of 11,338 total sample were used in this study to generate the final analysis. A multilevel logistic regression model showed that a significantly lower incidence of depression was related to WeChat usage after adjusting for all possible covariates (OR: 0.76, 95% CI: 0.62-0.94). The most popular WeChat functions used by the study population were watching news (80.4%), posting Moment messages (75.5%), chatting with friends (66.0%), and watching videos (65.2%). The sensitivity analysis yielded similar findings to the primary analyses.

conclusionsUsing social media WeChat showed an association with lower depressive symptoms among people aged ≥45 and older in our study sample. Further studies need to be explored on the promotion and education of social media WeChat usage, targeting the improvement of mental health-related issues through social network connections.

Indexed as

Social MediaAgedChinaDepressionHumansLongitudinal StudiesMiddle AgedRetirementDepressive symptomsElderly adultsMental healthSocial mediaWeChat

Identifiers

PMID35448984
PMCPMC9023108

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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.