Evidence map›Paper›PMID 40297363›Full record

ArticleDigital health

The intention to use short videos for health information among Chinese adults: Based on the technology acceptance model.

Junjiang Liu, Rui Huang, Jinluan Ren, Peifan Li, Pengfei Wang

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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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

8 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

5 authors.

Junjiang LiuSchool of Economics and Management, Communication University of China, Beijing, China.ORCID https://orcid.org/0009-0006-7962-3316
Rui HuangSchool of Economics and Management, Communication University of China, Beijing, China.
Jinluan RenSchool of Economics and Management, Communication University of China, Beijing, China.ORCID https://orcid.org/0000-0002-1005-8666
Peifan LiSchool of Economics and Management, Communication University of China, Beijing, China.
Pengfei WangDepartment of Health Sciences, University of York, York, UK.ORCID https://orcid.org/0000-0002-2123-1532

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The dissemination of health information plays a crucial role in health promotion. With the evolution of the Internet, short videos have become a significant medium for health information dissemination. This study aims to examine how short video features influence users' intentions for health information and provide actionable recommendations for improving health communication strategies. Methods: By integrating trust and perceived interactivity into the technology acceptance model, this study introduces a modified acceptance model for short videos. The model was evaluated through a survey conducted among 435 Chinese adults aged 18 and above, who had prior experience with short videos. The analysis was carried out using the partial least squares structural equation modeling technique for parameter estimation and model validation. Results: The analysis revealed that the model accounts for 18.3% of the variance in perceived usefulness, 34.9% in attitudes towards the videos, and 26.4% in the intention to use them. Significant positive associations on usage intention were observed from trust (β = 0.184, Conclusions: Perceived usefulness, ease of use, trust, and perceived interactivity emerged as strong predictors of usage intention, with attitude serving as a mediating factor in the relationship between trust, perceived interactivity, and usage intention. These findings highlight the importance of creating authoritative, user-friendly, and engaging content of short videos to enhance health information dissemination effectiveness and credibility.

Indexed as

health informationperceived interactivityShort videostechnology acceptance modeltrustusage intention

Identifiers

PMID40297363
PMCPMC12035001

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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.