Evidence map›Paper›PMID 42898346›Full record

ArticleFrontiers in psychology2026

Individual digital literacy, area-level digital exclusion, and inequalities in exercise participation among middle-aged and older adults: evidence from China.

Xiaofan Hao, Meiling Duan, Chenming Zhu, Chuanwen Yu

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Xiaofan HaoFaculty of Arts and Sports, Huanghe University of Science and Technology, Zhengzhou, Henan, China.
Meiling DuanZhengzhou Preschool Education College, Zhengzhou, Henan, China.
Chenming ZhuDongying Vocational College, Dongying, Shandong, China.
Chuanwen YuSchool of Sports and Health, Heze University, Heze, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Although digital exclusion has been widely discussed in relation to healthcare access and digital service use, less is known about its implications for exercise participation among middle-aged and older adults. This study examined how individual digital literacy and area-level digital exclusion jointly shape exercise participation in China. Methods: Data were drawn from three waves of the China Health and Retirement Longitudinal Study (CHARLS; 2013, 2015, and 2018) and linked to city-year indicators from the China Statistical Yearbook. The analytic sample included 13,085 person-wave observations from 9,201 unique respondents across 111 cities. Exercise participation was measured as a binary indicator of engagement in exercise-oriented physical activity. Individual digital literacy was assessed using a composite index of digital access, internet use, device resources, and digital behaviors. Area-level digital exclusion was measured using a reverse-coded city-year indicator of contextual digital exclusion. Mixed-effects logistic regression models were used to estimate the associations of individual digital literacy and area-level digital exclusion with exercise participation and to test their cross-level interaction. Results: 37.7% of observations reported exercise participation, increasing from 27.5% in 2013 to 40.3% in 2018. In fully adjusted models, higher individual digital literacy was associated with greater odds of exercise participation (OR = 1.11, 95% CI: 1.022-1.208, Conclusion: Individual digital literacy was positively associated with exercise participation among middle-aged and older adults in China, whereas area-level digital exclusion weakened this beneficial association. Policies promoting healthy aging should address both older adults' digital literacy and the broader digital exclusion in which they live.

Indexed as

digital exclusiondigital literacyexercise participationhealth equitymultilevel analysis

Identifiers

PMID42898346
PMCPMC13677843

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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.