Evidence map›Paper›PMID 41143868›Full record

ArticleJMIR aging2025

Digital Pathways to Reducing Depression Among Aging Populations Through the "Broadband China" Pilot Program: Quasi-Natural Experiment.

Zhiying Li

Abstract read
In one paragraph

Article in JMIR aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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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3 · Its place in the literature

Who cites it

5 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

1 author.

Zhiying LiSchool of Government, University of Chinese Academy of Social Sciences, Beijing, China.ORCID https://orcid.org/0000-0002-2219-5473

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the rise of digital technology, infrastructure development has become vital for social welfare and public health. However, evidence on its effects on depressive symptoms among middle-aged and older adults remains limited.

objectiveThis study evaluates the impact of digital infrastructure development on depressive symptoms among middle-aged and older adults, focusing on underlying mechanisms, heterogeneous effects, and health inequalities.

methodsWe use longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), 2011-2020 (N=56,211). Exploiting the quasi-natural experiment of the "Broadband China" pilot policy, we apply a difference-in-differences approach to estimate the effect on depressive symptoms. Mediation analysis follows the Baron-Kenny 3-step procedure, with bootstrap tests (95% CI) for robustness, and causal interpretation relies on standard assumptions for observational data. Subgroup analyses explore heterogeneity across age, education, and sex groups.

resultsOur findings indicate that the "Broadband China" pilot significantly reduces depressive symptoms among middle-aged and older adults (-0.33, P<.01). The positive effect is primarily mediated through strengthened social networks, including increased family connection, close social interactions, and greater social participation. Heterogeneity analysis shows that the benefits for depression reduction are more pronounced among women (-0.38, P<.01), middle-aged adults (-0.41, P<.01), and those with lower levels of education (-0.33, P<.01). Moreover, the results suggest that digital infrastructure plays a compensatory role in mitigating health disparities, thereby reducing inequalities in depression outcomes (-0.01, P<.01).

conclusionsDigital infrastructure reduces depressive symptoms among aging populations mainly by strengthening social networks. Embedding infrastructure into long-term strategies, enhancing digital literacy, and integrating digital health services are key to promoting healthy aging and reducing inequalities.

Indexed as

AgingDepressionDigital TechnologyAgedChinaFemaleHumansLongitudinal StudiesMaleMiddle AgedPilot Projectsdepressiondifference-in-differencesdigital infrastructurehealth inequalitymiddle-aged and older adultssocial networks

Identifiers

PMID41143868
PMCPMC12603584

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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.