Evidence mapPaperPMID 39314813Full record

ArticleDigital health

Exploring observed and instructed mHealth use in the middle-aged and elderly people (MAEP): A social learning perspective.

Kai Zeng, Lucong Dong, Yujing Xu, Xiaofen Zheng

Abstract read
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Article in Digital health. 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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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Kai ZengSchool of Management, Zhejiang University of Technology, Hangzhou, China.
Lucong DongSchool of Management, Zhejiang University of Technology, Hangzhou, China.ORCID https://orcid.org/0009-0008-9896-9376
Yujing XuSchool of Management, Zhejiang University of Technology, Hangzhou, China.ORCID https://orcid.org/0000-0001-8900-4777
Xiaofen ZhengSchool of Management, Zhejiang University of Technology, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Based on social learning theory, this study aimed to explore the intention of middle-aged and elderly people (MAEP) to engage with mobile health (mHealth) and the underlying effects of usability and utility. The goal was to garner insights that could enhance the advancement of mHealth and improve the scope of benefits of mHealth use among MAEP in the future. Methods: We employed a survey-based approach to delve into the mHealth use intentions among MAEP individuals aged 45 and above. A total of 371 valid survey responses were collected and analyzed using SmartPLS 3.0 for statistical examination and model verification. Results: Our hypotheses tests revealed that vicarious utility fully mediated the relationship between observed use and direct use intention and both indirect use intentions. Instructed usability and instructed utility were found to fully and partially mediate the relationship between instructed use and indirect use intention, respectively. Conclusions: This study demonstrates that the observed and instructed use behaviors of MAEP can promote their eventual intention to adopt mHealth through processes of observational and reinforcement learning. These findings underscore the importance of understanding the underlying effects of MAEP's intention to use mHealth is critical to increasing their adoption of mHealth, and thereby potentially improving their health outcomes.

Indexed as

instructed usemHealthmiddle-aged and elderly peopleobserved usesocial learning theory

Identifiers

PMID39314813
PMCPMC11418324

What Socratic holds

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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 Socratic graph.