Evidence map›Paper›PMID 41092074›Full record

ArticleJMIR cardio2025

AI HeartBot to Increase Women's Awareness and Knowledge of Heart Attacks: Nonrandomized, Quasi-Experimental Study.

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

Abstract read
In one paragraph

Article in JMIR cardio, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

6 authors.

Yoshimi FukuokaDepartment of Physiological Nursing, University of California, San Francisco, 521 Parnassus Ave, San Francisco, CA, 94143, United States, 1 415-476-8419.ORCID http://orcid.org/0000-0002-2245-9264
Diane Dagyong KimDepartment of Communication, University of California, Davis, Davis, CA, United States.ORCID http://orcid.org/0009-0003-7174-9684
Jingwen ZhangDepartment of Communication, University of California, Davis, Davis, CA, United States.ORCID http://orcid.org/0000-0003-1733-6857
Thomas J HoffmannDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States.ORCID http://orcid.org/0000-0001-6893-4449
Holli A DeVonUniversity of California, Los Angeles, Los Angeles, CA, United States.ORCID http://orcid.org/0000-0002-4526-9631
Kenji SagaeDepartment of Linguistics, University of California, Davis, Davis, CA, United States.ORCID http://orcid.org/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, but awareness and knowledge about it are declining. Artificial intelligence (AI) chatbots have great potential to educate women. Objective: This study aimed to evaluate the potential efficacy of HeartBot to increase women's awareness and knowledge of heart attack symptoms and care-seeking behavior. Methods: In this nonrandomized pilot, quasi-experimental study, 92 women aged ≥25 years without a history of heart disease completed the HeartBot interaction via SMS text messaging. The study was remotely conducted from October 2023 to January 2024. HeartBot, a fully automated AI chatbot, covered 15 topics of heart attack awareness, knowledge, symptoms, and care seeking in a single session. The mean length of the HeartBot interaction was 13.0 (SD 7.80) minutes. The primary outcomes consist of four questions: (1) recognizing signs and symptoms of a heart attack, (2) telling the difference between the signs and symptoms of a heart attack, (3) calling an ambulance or dialing 911 when experiencing heart attack symptoms, and (4) getting to an emergency room within 60 minutes after the onset of symptoms of a heart attack. Women were asked to answer the 4 questions before and after the HeartBot interaction on a scale of 1 to 4, with a higher score indicating higher levels of awareness and knowledge of heart attack risks and symptoms. Results: The mean age of the sample was 45.9 (SD 11.9) years. In total, 59.8% (55/92) of the sample identified as belonging to racial or ethnic minority groups. The mean length of the HeartBot interaction was 13.0 (SD 7.80) minutes. In ordinal logistic regression models, women showed significant improvements across the 4 self-reported outcomes (ie, heart attack symptoms and calling 911) even after controlling for potential confounding factors (outcome 1: adjusted odds ratio [aOR] 7.10, 95% CI 3.52-13.16; outcome 2: aOR 5.47, 95% CI 2.77-10.78; outcome 3: aOR 5.75, 95% CI 2.86-11.59; and outcome 4: aOR 2.85, 95% CI 1.54-5.25; P<.001 for all 4 outcomes). Conclusions: HeartBot led to a substantial increase in awareness and knowledge of heart attack risks and symptoms in women. These findings suggest that HeartBot is a promising approach to improving heart health education. A randomized controlled trial of HeartBot is warranted to establish its efficacy and safety for the clinical setting.

Indexed as

Artificial IntelligenceAwarenessHealth Knowledge, Attitudes, PracticeMyocardial InfarctionAdultAgedFemaleHumansMiddle AgedPilot ProjectsText MessagingAIartificial intelligencechatbotconversational agentheart attack symptomsheart diseasenatural language processingwomen

Identifiers

PMID41092074
PMCPMC12526652

What OpenQuestion holds

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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 OpenQuestion graph.