Evidence map›Paper›PMID 41556560›Full record

ArticleJournal of medical Internet research2026

Digital Engagement and Cognitive Function Among Older Adults in China: Cross-Sectional Questionnaire Study and Moderated Mediation Model Analysis.

Yongqi Du, Qing Niu, Gangrui Tan, Jianqian Chao, Shengxuan Jin, Leixia Wang

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

3 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

6 authors.

Yongqi Du *Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China.ORCID https://orcid.org/0009-0007-0160-5190
Qing Niu *Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China.ORCID https://orcid.org/0009-0008-7149-4251
Gangrui TanHealth Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China.ORCID https://orcid.org/0009-0001-3490-6762
Jianqian ChaoHealth Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China.ORCID https://orcid.org/0009-0000-0268-1696
Shengxuan JinHealth Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China.ORCID https://orcid.org/0000-0001-7232-6040
Leixia WangHealth Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China.ORCID https://orcid.org/0009-0000-9964-4970

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGiven the global demographic shifts and rapid digitalization, digital engagement has emerged as a critical determinant of healthy aging. While previous research has linked digital engagement to cognitive outcomes, the underlying mechanisms remain underexplored among Chinese older adults.

objectiveThis study aimed to analyze the relationships between digital engagement and cognitive function among older adults in China through a moderated mediation model guided by the technological reserve hypothesis, with digital health literacy (DHL) and social support as mediators and living arrangements as a moderator.

methodsWe conducted a cross-sectional questionnaire survey using stratified multistage sampling from June to November 2024, including 8123 participants aged 55 years and older. Digital engagement, defined as older adults' use of contemporary digital technologies to support routine daily activities, autonomy, independence, and social inclusion, was assessed using a multidimensional questionnaire. The Chinese eHealth Literacy Scale, the 3-item short version of the Perceived Social Support Scale, and the Mini-Cog test were used to assess DHL, social support, and cognitive function. Guided by a directed acyclic graph based on the technological reserve hypothesis, mediation and moderated mediation analyses were performed using the PROCESS macro in SPSS (IBM Corp) with 5000 bootstrap resamples.

resultsDigital engagement was positively associated with cognitive function among older adults (β=0.241, 95% CI 0.216-0.265). This association was partially mediated by DHL (β=0.059, 95% CI 0.049-0.069) and social support (β=0.012, 95% CI 0.008-0.016), with the combined indirect effects accounting for 29.5% of the total effect (β=0.071, 95% CI 0.061-0.082). Additionally, living arrangements significantly moderated the associations between digital engagement and cognitive function (β=0.109, 95% CI 0.052-0.166), digital engagement and DHL (β=0.063, 95% CI 0.014-0.112), and digital engagement and social support (β=0.151, 95% CI 0.089-0.212). These effects were stronger among older adults living alone.

conclusionsThis study contributes to the understanding of cognitive aging in the digital environment from the perspective of the technological reserve hypothesis and digital engagement. Digital engagement influenced cognitive function via DHL and social support, and these associations of digital engagement with cognitive function, DHL, and social support were stronger among older adults living alone. Digital health interventions and public health policies should target both DHL and social support among older populations and prioritize older adults living alone.

Indexed as

CognitionHealth LiteracyAgedAged, 80 and overChinaCross-Sectional StudiesDigital HealthDigital MediaFemaleHumansMaleMediation AnalysisMiddle AgedSocial SupportSurveys and Questionnairescognitive functiondigital engagementdigital health literacyliving arrangementsmoderated mediation modelsocial supporttechnological reserve hypothesis

Identifiers

PMID41556560
PMCPMC12869153

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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.