Evidence map›Paper›PMID 42585572›Full record

ArticleJMIR research protocols2026

Short-Term Efficacy of the Artificial Intelligence HeartBot II in Increasing Awareness and Knowledge of Heart Attack in Women: Protocol for a Randomized Controlled Trial With a Waitlist Control.

Yoshimi Fukuoka, Diane Dagyong Kim, Jingwen Zhang, Thomas J Hoffmann, Kenji Sagae

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in JMIR research protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07416734 (Efficacy of the Artificial Intelligence HeartBot II in Increasing Awareness and Knowledge of Heart Attack in Women), which is not on this 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.

NCT07416734 nanot yet recruitingnot on this map

Efficacy of the Artificial Intelligence HeartBot II in Increasing Awareness and Knowledge of Heart Attack in Women: Study Protocol for a Randomized Controlled Trial With a Waitlist Control

TypeinterventionalSponsorUniversity of California, San FranciscoRan2026 to 2027Enrolled200ConditionsParticipants Must be Women Aged 25 Years or Older, Participants Should Have no Self-reported History of Heart Disease or Stroke, Participants Should Have no Terminal Illness or Diagnosed Cognitive Impairment, Including Alzheimer's Disease, Participants Should Not be a Healthcare Professionals or Healthcare TraineesArmsHeartBot II Program, Waitlist Control
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

5 authors.

Yoshimi FukuokaDepartment of Physiological Nursing, School of Nursing, University of California, San Francisco, 521 Parnassus Ave, San Francisco, CA, 94143, United States, 1 415476-8419.ORCID 0000-0002-2245-9264
Diane Dagyong KimDepartment of Communication, University of California, Davis, Davis, CA, United States.ORCID 0009-0003-7174-9684
Jingwen ZhangDepartment of Communication, University of California, Davis, Davis, CA, United States.ORCID 0000-0003-1733-6857
Thomas J HoffmannDepartment of Epidemiology & Biostatistics, and Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, United States.ORCID 0000-0001-6893-4449
Kenji SagaeDepartment of Linguistics, University of California, Davis, Davis, CA, United States.ORCID 0000-0003-3371-0618

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Heart disease remains a leading cause of death for women in the United States. Despite this burden, awareness that heart disease is the leading cause of death among women declined from 65% in 2009 to 44% in 2019, with the largest declines observed among Hispanic, Black, and younger women. Thus, innovative, scalable, and cost-effective educational strategies are needed to improve women's awareness of heart attack symptoms and appropriate care-seeking behaviors. Objective: This study aims to evaluate the short-term efficacy of the artificial intelligence (AI) HeartBot II, a chatbot-based educational intervention, in improving women's awareness and knowledge of heart attack symptoms and care-seeking behavior compared with a waitlist control group. Methods: This randomized controlled clinical trial (RCT) with a waitlist control will enroll 200 women aged 25 or older, who will be randomized using a 1:1 allocation ratio. The intervention group will download the AI HeartBot II app and complete the 4 modules (including information on heart attack symptoms, risk factors, and calling 911) over 12 weeks. The waitlist control group will start receiving an identical intervention at 12 weeks. The primary outcomes will be change from baseline to 12 weeks in a 4-item heart attack response preparedness score, calculated as the mean of 4 self-reported items assessing confidence in recognizing signs and symptoms of a heart attack, distinguishing heart attack symptoms from other medical problems, calling 911 or an ambulance if a heart attack is suspected, and reaching an emergency room within 60 minutes of symptom onset. The primary analysis will estimate the intervention effect using constrained longitudinal data analysis implemented with linear mixed models, including fixed effects for time and time-by-treatment group interaction. Sensitivity analyses for the individual ordinal items will use ordinal logistic mixed-effects models. Results: We received approval from the University of California, San Francisco, Institutional Review Board (No. 25-44825) on January 9, 2026, and this trial was registered on ClinicalTrials.gov (NCT07416734) on February 11, 2026, prior to enrollment of the first participant. Recruitment began in April 2026. As of manuscript submission, 86 participants were enrolled. Enrollment is expected to be completed by September 2026, and all follow-up assessments are anticipated to be completed by March 2027. Data analysis is expected to begin in spring 2027, with study results anticipated for publication later in 2027. Conclusions: To the best of our knowledge, this is the first RCT to rigorously evaluate the efficacy of the AI HeartBot II intervention. If effective, AI HeartBot II could provide a scalable, accessible, and cost-effective public health communication strategy to improve women's awareness of heart attack symptoms and promote timely care-seeking behaviors in the United States.

Indexed as

Artificial IntelligenceAwarenessHealth Knowledge, Attitudes, PracticeAdultFemaleHumansRandomized Controlled Trials as Topicartificial intelligencechatbotheart diseaseintelligent systemslarge language modelsmachine learningmobile appsnatural language processingrandomized controlled trialwomen

Identifiers

PMID42585572
PMCPMC13465624

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

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.