Evidence map›Paper›PMID 42600074›Full record

Trial reportJournal of medical Internet research2026

Effects of a Safety User Interface Bundle on Verification Intentions in Generative AI Chat Use Among Older Chinese Adults: Randomized Vignette Survey.

Jun'an Yu, Jun Chen, Anjie Ren, Hui Duan, Hua Meng, Zhuo Gao

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 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
–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

6 authors.

Jun'an YuFaculty of Science, University of Auckland, Auckland, New Zealand.ORCID http://orcid.org/0009-0008-3174-3292
Jun ChenMedical Services Management Department, Peking University People's Hospital (PKUPH), Beijing, China.ORCID http://orcid.org/0009-0003-1494-1418
Anjie RenHealthcare & Education Research Center, Chengdu Gongyun Education & Management Research Institute, Chengdu, China.ORCID http://orcid.org/0009-0001-7701-4537
Hui DuanSchool of Public Administration and Policy, Renmin University of China, Beijing, China.ORCID http://orcid.org/0009-0006-6515-2357
Hua MengDepartment of Economics and Management, Sichuan University of Architectural Technology, Chengdu, China.ORCID http://orcid.org/0009-0002-1753-7894
Zhuo GaoDepartment of Human Resource Management, Beijing Geriatric Hospital, 118 Wenquan Road, Haidian District, Beijing, 100095, China, 86 15201400966.ORCID http://orcid.org/0009-0008-9020-6163

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Generative AI chat systems are increasingly used for everyday information seeking, but plausible errors and omissions can mislead users when outputs are accepted without scrutiny. Interface-level safety cues may help users calibrate trust and engage in verification; yet, evidence in older Chinese adults remains limited. Objective: This study aimed to test whether adding a safety user interface (UI) bundle to a generative AI chat interface increases verification intention among older Chinese adults and to examine selected secondary outcomes, including reliance intention, trust calibration, perceived trustworthiness, comprehension, usability/readability, cognitive load, and a behavioral proxy of verification. Methods: We conducted a cross-sectional survey with an embedded randomized UI vignette experiment between May 22, 2025, and September 3, 2025. Chinese adults aged ≥60 years were recruited through community sites, outpatient clinic waiting areas, and WeChat (Tencent Holdings Ltd) groups, and randomized 1:1 to view screenshots of a baseline chat UI or a safety UI bundle containing generic source-label cues, and an uncertainty and verification nudge. Each participant completed 2 scenarios (service/travel decision and general well-being related to sleep/fatigue), followed by measures of verification intention (primary), reliance intention, trust calibration index, comprehension (0-8), perceived trustworthiness, usability/readability, cognitive load (0-10), manipulation checks, and a behavioral proxy (expanding optional "source information"). Analyses used intention-to-treat regression models with covariate adjustment. Results: Of 214 consenting respondents who started the survey, 200 were included in the analysis (100 per arm). The safety UI bundle increased verification intention (mean 4.72, SD 0.63 vs 4.41, SD 0.59 on a 7-point scale; adjusted β=0.293, 95% CI 0.128-0.457; P<.001). Reliance intention did not increase (mean 4.97, SD 0.54 vs 5.03, SD 0.58; adjusted β=-0.105, 95% CI -0.239 to 0.029; P=.13). Trust calibration improved (trust calibration index: mean -0.29, SD 1.43 vs 0.29, SD 1.43; adjusted β=-0.567, 95% CI -1.005 to -0.129; P=.01). Expansion of optional source information was numerically higher, although the adjusted CI included the null (42% vs 27%; adjusted odds ratio [OR]=1.76, 95% CI 0.95-3.27; P=.07). Comprehension remained high and similar across arms (mean 6.33, SD 1.14 vs 6.32, SD 1.08; adjusted β=-0.132, 95% CI -0.428 to 0.163; P=.38). Perceived trustworthiness was modestly lower in the Safety UI arm (mean 5.20, SD 0.61 vs 5.39, SD 0.66; adjusted β=-0.199, 95% CI -0.382 to -0.016; P=.03). Usability/readability was unchanged, and cognitive load did not increase. Manipulation checks indicated higher cue recognition in the Safety UI arm. Conclusions: In a randomized static-vignette survey of older Chinese adults, a brief safety UI bundle was associated with higher verification intention and a trust calibration index consistent with lower overreliance risk, without detectable reductions in comprehension or usability/readability. Because the intervention was tested as a bundle using screenshots and generic source labels, findings should be interpreted as evidence for a practical interface-level strategy rather than proof that any single cue caused the observed effects.

Indexed as

Generative Artificial IntelligenceIntentionSafetyUser-Computer InterfaceAgedChinaCross-Sectional StudiesEast Asian PeopleFemaleHumansMaleMiddle AgedSurveys and QuestionnairesTrustChinagenerative AIhuman-computer interactionlarge language modelsolder adultsrandomized experimentsafety cuestrust calibrationuser interfaceverificationvignette survey

Identifiers

PMID42600074
PMCPMC13475784

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.