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

IF 2.4 Q2 Medicine
JMIR Cardio Pub Date : 2025-10-15 DOI:10.2196/80407
Yoshimi Fukuoka, Diane Dagyong Kim, Jingwen Zhang, Thomas J Hoffmann, Holli A DeVon, Kenji Sagae
{"title":"AI HeartBot to Increase Women's Awareness and Knowledge of Heart Attacks: Nonrandomized, Quasi-Experimental Study.","authors":"Yoshimi Fukuoka, Diane Dagyong Kim, Jingwen Zhang, Thomas J Hoffmann, Holli A DeVon, Kenji Sagae","doi":"10.2196/80407","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"9 ","pages":"e80407"},"PeriodicalIF":2.4000,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12526652/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Cardio","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/80407","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

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.

Abstract Image

AI HeartBot提高女性对心脏病的认识:非随机、准实验研究
背景:心脏病仍然是美国妇女死亡的主要原因,但对它的认识和知识正在下降。人工智能(AI)聊天机器人在教育女性方面具有巨大潜力。目的:本研究旨在评估HeartBot在提高女性对心脏病发作症状和求医行为的认识和知识方面的潜在功效。方法:在这项非随机、准实验研究中,92名年龄≥25岁、无心脏病史的女性通过短信完成了HeartBot互动。这项研究是在2023年10月至2024年1月期间远程进行的。HeartBot是一个全自动的人工智能聊天机器人,它在一次会话中涵盖了心脏病发作意识、知识、症状和寻求护理的15个主题。HeartBot相互作用的平均时间为13.0 (SD 7.80)分钟。主要结果包括四个问题:(1)识别心脏病发作的体征和症状,(2)区分心脏病发作的体征和症状,(3)在出现心脏病发作症状时拨打救护车或拨打911,以及(4)在心脏病发作症状出现后60分钟内到达急诊室。研究人员要求女性在与HeartBot互动前后回答4个问题,分值从1到4分不等,分值越高表明她们对心脏病发作风险和症状的认识和了解程度越高。结果:患者平均年龄45.9岁(SD 11.9)。总共有59.8%(55/92)的样本被确定属于种族或少数民族群体。HeartBot相互作用的平均时间为13.0 (SD 7.80)分钟。在有序逻辑回归模型中,即使控制了潜在的混杂因素(结果1:调整优势比[aOR] 7.10, 95% CI 3.52-13.16;结果2:调整优势比[aOR] 5.47, 95% CI 2.77-10.78;结果3:调整优势比[aOR] 5.75, 95% CI 2.86-11.59;结果4:调整优势比[aOR] 2.85, 95% CI 1.54-5.25;结论:HeartBot使女性对心脏病发作风险和症状的认识和知识大幅提高。这些发现表明,HeartBot是一种很有希望改善心脏健康教育的方法。心脏机器人的随机对照试验是必要的,以确定其有效性和安全性的临床设置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
JMIR Cardio
JMIR Cardio Computer Science-Computer Science Applications
CiteScore
3.50
自引率
0.00%
发文量
25
审稿时长
12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书