Evidence map›Paper›PMID 41145598›Full record

ArticleScientific reports2025

Predicting older adults smart healthcare adoption using an extended TAM.

Jinfang Zhang, Yi Ma, Hongchao Zhang, Yuan Chen, Jiani Yu

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

5 authors.

Jinfang ZhangDepartment of Nursing, Xinchang Hospital of Chinese Medicine, 188 Shijiufeng Road, Xinchang, Shaoxing, 312500, China. 19818261819@163.com.
Yi MaDepartment of Nursing, Yunnan University of Traditional Chinese Medicine, Kunming, 650000, Yunnan Province, China.
Hongchao Zhang1Department of Nursing, Xinchang Hospital of Chinese Medicine, Shaoxing, 312500, Zhejiang Province, China.
Yuan Chen1Department of Nursing, Xinchang Hospital of Chinese Medicine, Shaoxing, 312500, Zhejiang Province, China.
Jiani Yu1Department of Nursing, Xinchang Hospital of Chinese Medicine, Shaoxing, 312500, Zhejiang Province, China.

Funding

Shaoxing MunicipalHealth Commission 2023SKY149
6 · The paper itself

Abstract

This study utilizes the Technology Acceptance Model (TAM) as its theoretical foundation to examine factors influencing the adoption of smart healthcare among community-dwelling older adults. Focusing on elderly residents in Shaoxing, Zhejiang Province, China, the "Smart Healthcare Usage Intention Scale" was developed based on TAM constructs and validated through reliability and validity testing. A total of 403 participants were recruited via convenience sampling between August 2024 and January 2025. Data were analyzed using descriptive statistics, t-tests, Pearson correlation analyses in SPSS 27.0, and structural equation modeling (SEM) in Amos 28.0 for path and mediation analyses. The results indicated a behavioral intention (BI) score of 10.00 ± 3.26. The model exhibited good fit (CMIN/DF = 2.713), revealing that personal tendency, social support, and perceived value had significant positive effects on both perceived usefulness (PU) and perceived ease of use (PEOU). Furthermore, PU and PEOU were found to positively influence BI, and PEOU also had a significant positive effect on PU. Mediation analysis identified six parallel and three serial mediating pathways, underscoring the essential mediating roles of PU and PEOU. These findings provide both theoretical and practical implications for promoting smart healthcare adoption in older adult populations.

Indexed as

Patient Acceptance of Health CareAgedAged, 80 and overChinaFemaleHumansIndependent LivingMaleMiddle AgedSocial SupportSurveys and QuestionnairesBehavioral intentionMediation analysisOlder adultsSmart healthcareTAM

Identifiers

PMID41145598
PMCPMC12559754

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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.