Evidence map›Paper›PMID 42877101›Full record

Trial reportFrontiers in public health2026

High- versus low-anthropomorphic-cue packages and health-information trust: social presence and source credibility under explicit AI disclosure.

Shaohua Huo, Zhenfen Li, Yuqi Lin

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Frontiers in public health, 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
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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

3 authors.

Shaohua HuoJournalism and Communication School, Qilu Institute of Technology, Jinan, China.
Zhenfen LiJournalism and Communication School, Qilu Institute of Technology, Jinan, China.
Yuqi LinJournalism and Communication School, Qilu Institute of Technology, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: AI virtual opinion leaders are increasingly used in health communication, but it remains unclear whether bundled anthropomorphic-cue packages foster initial trust and information-adoption intention when AI identity is explicitly disclosed. Methods: Guided by the Computers Are Social Actors paradigm, Social Presence Theory, Source Credibility Theory, and the Information Adoption Model, we conducted a single-factor between-subjects randomized experiment comparing overall high- and low-anthropomorphic-cue packages. The packages differed simultaneously in avatar realism, gesture behavior, interpersonal/linguistic framing, and video duration; AI disclosure and the response-button structure were held constant. A total of 536 valid responses were analyzed using partial least squares structural equation modeling. Social presence, source credibility, trust, and adoption intention were measured by participant self-report immediately after a single branched-video exposure. Results: The overall high-versus-low package contrast increased social presence. Social presence was positively associated with source credibility; source credibility was positively associated with trust in AI-generated health information; and information trust was positively associated with health information adoption intention. The direct effect of the package contrast on information trust was not significant, whereas the bootstrapped serial indirect effect through social presence and source credibility was significant. Conclusion: Within the present Chinese sample and single-exposure setting, the randomized contrast supports an effect of the overall cue package on initial social presence, while the mediation estimates are consistent with the hypothesized indirect pathway to information trust. The design cannot identify the independent contribution of any visual, gestural, linguistic, vocal, interactive, or duration component. The outcome is self-reported adoption intention, not observed health behavior.

Indexed as

Artificial IntelligenceCuesDisclosureHealth CommunicationTrustAdultAvatarChinaFemaleHumansIntentionMaleYoung AdultAI virtual opinion leadersbundled anthropomorphic-cue packagehealth information adoption intentionsocial presencesource credibility

Identifiers

PMID42877101
PMCPMC13662049

What OpenQuestion holds

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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.