Evidence map›Paper›PMID 42706292›Full record

ArticleScientific reports2026

Latent profiles of social networks and their association with digital health readiness among rural older adults with hypertension.

Ershan Xu, Cuijuan Lin, Jiangnan He, Yingzi Tang, Ying Xiong, Yan Pu, Tingting Liu

Abstract read
In one paragraph

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

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

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

7 authors.

Ershan XuSchool of Nursing, Hunan University of Medicine, Huaihua, 418000, Hunan, China.ORCID 0000-0003-3148-0855
Cuijuan LinSchool of Nursing, Xiangtan Medicine & Health Vocational College, Xiangtan, 411104, Hunan, China. 45915384@qq.com.
Jiangnan HeDongyang Town Health Center, Liuyang, 410329, Hunan, China.
Yingzi TangSchool of Nursing, Xiangtan Medicine & Health Vocational College, Xiangtan, 411104, Hunan, China.
Ying XiongSchool of Nursing, Hunan University of Medicine, Huaihua, 418000, Hunan, China.
Yan PuSchool of Nursing, Hunan University of Medicine, Huaihua, 418000, Hunan, China.
Tingting LiuSchool of Nursing, Hunan University of Medicine, Huaihua, 418000, Hunan, China.

Funding

2026 General Project of the Hunan Provincial Philosophy and Social Science Achievement Review Committee XSP26YBC422Natural Science Foundation of Hunan Province 2025JJ70440
6 · The paper itself

Abstract

Person-centered approaches to social networks profiles and their association with digital health readiness (DHR) remain underexplored among rural older adults with hypertension. This study aimed to identify latent social networks profiles and examine their independent association with DHR in this population. A cross-sectional survey was conducted among 576 rural older adults with hypertension by using the Lubben Social Network Scale-6 and the Digital Health Readiness Questionnaire (DHRQ) in Hunan Province, China. Latent profile analysis identified social networks profiles. Multivariable linear regression with forced entry examined the independent association between profiles and DHRQ after controlling for demographic, socioeconomic, and health-related confounders. Three profiles emerged: socially restricted (16.5%), moderately connected (65.3%), and well-integrated (18.2%). Social networks profiles remained independently associated with DHRQ (F

Indexed as

Digital HealthHypertensionRural PopulationSocial NetworkingAgedChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedSocial SupportSurveys and QuestionnairesDigital health readinessHypertensionLatent profile analysisRural older adultsSocial network

Identifiers

PMID42706292
PMCPMC13550577

What OpenQuestion holds

Textmetadata
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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.