Evidence map›Paper›PMID 40177088›Full record

ArticleFrontiers in public health2025

Selection and connection: the impact of internet use on the depression of Chinese older adults.

Kaishan Jiao, Yue Sun

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 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

2 authors.

Kaishan JiaoSchool of Ethnology and Sociology, Minzu University of China, Beijing, China.
Yue SunSchool of Ethnology and Sociology, Minzu University of China, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Internet usage has significant effects on the mental health of older adults, particularly in reducing depression levels. However, its impact may differ based on factors such as internet usage frequency, purposes, gender, and urban-rural residence. This study aims to examine the differential effects of internet use on depression among older adults, exploring the mediating role of social networks in these relationships. Methods: Based on panel data from the 2018 and 2020 China Longitudinal Aging Social Survey, a fixed-effects model was employed to systematically analyze the impact of internet use (including whether they use the internet, frequency of use, and purposes of use) on the mental health of older adults. The mediating role of social networks (family networks and friend networks) was tested using the Bootstrap method. Results: The study found that internet use significantly reduces depression levels among older adults (coefficient = -0.440, Discussion: This study highlights the complex relationship between internet use, social networks, and mental health in older adults. Significant gender and urban-rural differences exist in the impact of internet use on depression among older adults, indicating the need for tailored interventions. The findings also emphasize the importance of promoting internet use for social purposes while being mindful of the potential negative impacts of excessive digital engagement among older adults, particularly in rural areas. Additionally, social networks, especially friend networks, play an important role in enhancing the mental health benefits of internet use for older adults.

Indexed as

DepressionInternetInternet UseSocial NetworkingAgedAged, 80 and overChinaEast Asian PeopleFemaleHumansLongitudinal StudiesMaleMental HealthMiddle AgedRural PopulationSocial SupportChinadepressioninternetmental healtholder adultssocial networks

Identifiers

PMID40177088
PMCPMC11961419

What OpenQuestion holds

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LicenceCC BY
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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.