Evidence map›Paper›PMID 39871247›Full record

ArticleBMC health services research2025

How does digital literacy affect the health status of senior citizens? Micro-level evidence from the CFPS data.

Xiaoyi Chen, Nian Wang

Abstract read
In one paragraph

Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 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

2 authors.

Xiaoyi ChenCollege of Digital Economics, Nanning University, Nanning, 530000, China.
Nian WangChina-ASEAN Institute of Statistics, Guangxi University of Finance and Economics, Mingxiu West Road 100, Nanning, 530003, China. nianwang104@163.com.

Funding

Guangxi First-class Discipline Statistics Construction Project Fund GJKY[2022]1Project of National Social Science Fund of China 21CJY075Project of National Social Science Fund of China 22XTJ002the Major Project of National Statistical Science of China 2023LZ003
6 · The paper itself

Abstract

backgroundThe popularization of the Internet and digital technology has called for higher digital literacy among citizens, especially the elderly. However, most existing studies didn't measure digital literacy at the micro level, and the impact mechanism has rarely been discussed. The purpose of this study is to clarify whether and how digital literacy affects the health status of senior citizens.

methodsThe data used in this study are collected from the China Family Panel Studies (CFPS) from three periods: 2016, 2018, and 2020. The balanced panel data comprised 7836 samples, with 2612 samples per year. Based on the CFPS data, this study constructs a balanced panel and employs a two-way fixed-effects model for the estimation. The instrumental variable (IV) method is employed for tackling the endogenous problems. Next, the mediation effect model is applied to identify the influencing mechanism.

resultsFirst, digital literacy can improve the health status of senior citizens. This result remains valid after introducing lagged explanatory variables and addressing the endogeneity issues. Second, social support acts as a partial mediator in the relationship between digital literacy and the health status of senior citizens. Third, the heterogeneity analysis reveals that the effect of digital literacy on the health status of senior citizens varies across age groups, urban-rural types, and education levels.

conclusionsThis study examines the impact of digital literacy on the health status of senior citizens at the micro-level and identifies the mediation mechanism. The results enhance our understanding of the positive effects of digitalization on aging society and offer useful insights for the government in formulating more targeted active aging strategies.

Indexed as

Computer LiteracyDigital TechnologyHealth LiteracyHealth StatusAgedAged, 80 and overChinaFemaleHumansMaleMiddle AgedSocial SupportDigital literacyHealth statusMediation mechanismSenior citizensSocial support

Identifiers

PMID39871247
PMCPMC11773774

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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.