Evidence map›Paper›PMID 42326938›Full record

ArticleFrontiers in public health2026

AI virtual digital human influencers vs. human influencers: the impact of health short videos on older adults' cognition and attitude.

Jinglun Zhang

Abstract read
In one paragraph

Article 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

1 author.

Jinglun ZhangSchool of Communication, Universiti Sains Malaysia, Gelugor, Penang, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study examines differences between AI Virtual Digital Human (AI-VDH) influencers and physician influencers in terms of recognizability, credibility assessment, and their influence on health cognition and attitudes among older adults in Chinese short-video contexts. Methods: A mixed-methods approach combining questionnaires ( Results: Older adults generally expressed confidence in distinguishing AI-generated figures from real persons; however, they often struggled to establish stable recognition criteria in highly realistic scenarios and relied on platform prompts to calibrate their judgments. Credibility assessments were influenced by both content accessibility and life experience, while medical credentials and institutional endorsements functioned as important authoritative cues. In AI-VDH scenarios, participants were more sensitive to presentation cues such as voice, facial expressions, and movements, which prompted greater caution. Both video types produced high self-reported improvements in health knowledge and attitudes, although physician-presented videos achieved slightly higher evaluations. Discussion: This study extends the dual-processing-three-level framework to embodied AI health video contexts by emphasizing the distinction between subjective confidence and actual recognition performance, while incorporating platform labeling into credibility calibration mechanisms. The findings reveal a calibration bias characterized by "high confidence but unstable recognition standards" among older adults in AI-VDH contexts. Platform prompts such as "suspected AI generation" play an important role in credibility calibration. The study provides practical implications for optimizing platform labeling systems, standardizing health science communication content, and developing AI and health literacy interventions for older adults.

Indexed as

Artificial IntelligenceCognitionHealth Knowledge, Attitudes, PracticeVideo RecordingAgedAged, 80 and overChinaDigital HealthDigital MediaFemaleHumansMaleMiddle AgedSurveys and Questionnairescredibility assessmentdual-processing modelhealth short videosolder adultsvirtual digital human

Identifiers

PMID42326938
PMCPMC13275421

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.