Holli A. DeVon

Aim: One in ten patients present to the emergency department (ED) with symptoms of acute coronary syndrome (ACS). The 13-item ACS Symptom Checklist is a validated tool for rapid ACS symptom assessment. We aimed to evaluate the effectiveness of the 13-item ACS Symptom Checklist in distinguishing NSTEMI patients with and without an occluded artery using natural language processing (NLP). Methods and results: We retrospectively extracted the 13 symptoms from the 13-item ACS Symptom Checklist for all patients admitted with NSTEMI. The outcome was an occluding coronary artery defined as one requiring revascularization. We applied Chi-square tests to assess the sensitivity and specificity of each symptom for an acutely occluded coronary artery. We used logistic regression models stratified by sex to measure the odds of an occluded artery after controlling for age, obesity, and diabetes. The majority of the 1905 patients were male (63.1%), older (66±12 years), and White (84%). Twenty-three percent of patients require revascularization. Common comorbidities included diabetes (17%) and obesity (42%). Symptoms differentiating patients with and without an occluded artery included palpitations (22.4% vs. 29.0%), arm pain (23.5% vs. 16.5%), unusual fatigue (19.1% vs. 3.3%), and lightheadedness (21.1% vs. 26.5%). Arm pain was associated with 1.532 (95% CI 1.155-2.033) increased odds of an occluded artery, with similar odds in men and women. Conclusion: Arm pain was the primary symptom predicting an occluded coronary artery in NSTEMI patients; of note, there were minimal sex differences. NLP was a useful tool for identifying arm pain and other symptoms from clinical notes.

Background: Artificial intelligence (AI) chatbots have become prominent tools in health care to enhance health knowledge and promote healthy behaviors across diverse populations. However, factors influencing the perception of AI chatbots and human-AI interaction are largely unknown. Objective: This study aimed to identify interaction characteristics associated with the perception of an AI chatbot identity as a human versus an artificial agent, adjusting for sociodemographic status and previous chatbot use in a diverse sample of women. Methods: This study was a secondary analysis of data from the HeartBot trial in women aged 25 years or older who were recruited through social media from October 2023 to January 2024. The original goal of the HeartBot trial was to evaluate the change in awareness and knowledge of heart attack after interacting with a fully automated AI HeartBot chatbot. All participants interacted with HeartBot once. At the beginning of the conversation, the chatbot introduced itself as HeartBot. However, it did not explicitly indicate that participants would be interacting with an AI system. The perceived chatbot identity (human vs artificial agent), conversation length with HeartBot, message humanness, message effectiveness, and attitude toward AI were measured at the postchatbot survey. Multivariable logistic regression was conducted to explore factors predicting women’s perception of a chatbot’s identity as a human, adjusting for age, race or ethnicity, education, previous AI chatbot use, message humanness, message effectiveness, and attitude toward AI. Results: Among 92 women (mean age 45.9, SD 11.9; range 26-70 y), the chatbot identity was correctly identified by two-thirds (n=61, 66%) of the sample, while one-third (n=31, 34%) misidentified the chatbot as a human. Over half (n=53, 58%) had previous AI chatbot experience. On average, participants interacted with the HeartBot for 13.0 (SD 7.8) minutes and entered 82.5 (SD 61.9) words. In multivariable analysis, only message humanness was significantly associated with the perception of chatbot identity as a human compared with an artificial agent (adjusted odds ratio 2.37, 95% CI 1.26-4.48; P=.007). Conclusions: To the best of our knowledge, this is the first study to explicitly ask participants whether they perceive an interaction as human or from a chatbot (HeartBot) in the health care field. This study’s findings (role and importance of message humanness) provide new insights into designing chatbots. However, the current evidence remains preliminary. Future research is warranted to understand the relationship between chatbot identity, message humanness, and health outcomes in a larger-scale study.

On behalf of the Joint European Society of Cardiology (ESC)/American College of Cardiology (ACC)/American Heart Association (AHA)/World Heart Federation (WHF) Task Force for the Universal Definition of Myocardial Infarction Endorsed by the European Association for Cardio-Thoracic Surgery (EACTS) and the Society of Thoracic Surgeons (STS) Affirmation of Value by the Society for Cardiovascular Angiography and Interventions (SCAI)

Hope and hopelessness are sometimes considered to be two ends of a spectrum. Hope is associated with positive health outcomes in healthy and ill populations. Conversely, hopelessness has been associated with increased adverse clinical events and death in patients with ischemic heart disease (IHD). The aim of the study was to determine the discriminant validity of the Dunn State‐Trait Hopelessness Scale and the Snyder Adult State Hope and Adult Trait Hope Scales. A total of 156 participants were enrolled in a randomized controlled trial testing a motivational intervention to promote physical activity and reduce hopelessness in adults hospitalized with ischemic heart disease. Data were collected 2 weeks after hospital discharge. Participants were male (68%), white (88.2%), married (60.8%), with a mean age of 62 years. There were moderately strong negative correlations ( r = −0.54 to −0.66) between total Dunn State‐Trait Hopelessness Scale and Snyder Adult State and Trait Hope scales. Correlations were virtually unchanged adjusting for age, sex, diagnosis, and marital status ( r = −0.5 to −0.63, p < 0.001) indicating that as hopelessness increases, hope decreases. Principal components analysis revealed four factors that explained 64.7% of the variance in scores while demonstrating conceptual distinctions between the Dunn State‐Trait Hopelessness Scale and Snyder Hope scales. We found a proportion of patients with hopelessness who still expressed hope for the future. Discriminant validity was supported by inverse correlations and factor analyses indicating conceptual distinctions between the two instruments. Findings suggest important implications for assessment of both hope and hopelessness in patients recovering from an IHD event since the two are conceptually distinct yet can occur simultaneously, and state hopelessness may improve through recovery.

Background: Artificial intelligence (AI) chatbots, driven by advances in natural language processing, can analyze and generate human language through computational linguistics and machine learning. Despite the rapid development of large language models, little investigation has been conducted to assess whether AI chatbot-delivered educational conversations can achieve a similar level of efficacy as human-delivered conversations. Objective: This study aims to evaluate and explore the potential efficacy of human-delivered conversations versus AI chatbot conversations in increasing women’s knowledge and awareness of symptoms and response to a heart attack in the United States. Methods: This is a secondary analysis of 2 datasets collected from the AI Chatbot Development Project. Women aged 25 years or older were recruited through flyers and social media. The first dataset contained conversational data where a research interventionist engaged in educational conversations with participants (human dataset), whereas the second dataset contained conversational data where an AI chatbot named HeartBot engaged in the same educational conversations with participants (HeartBot dataset). Knowledge and awareness of symptoms and response to a heart attack were measured at the pre- and post-interaction with either the human or HeartBot. Perceived message effectiveness and conversational quality were measured at the post-survey. Ordinal logistic regression analyses were conducted to explore factors predicting participants’ knowledge, adjusting for age, race or ethnicity, intervention group type, education, word count, message effectiveness, and message humanness. Results: A total of 171 participants (mean age=41.06 y, SD=12.08) in the Human dataset and 92 participants (mean age=45.85 y, SD=11.94) in the HeartBot dataset completed the study. Both human-delivered conversations and HeartBot conversations were associated with significant improvements in participants’ ability to recognize heart attack symptoms (adjusted odds ratio [AOR] 15.19, 95% CI 8.46‐27.25, P<.001; AOR 7.18, 95% CI 3.59‐14.36, P<.001), differentiate between symptoms (AOR 9.44, 95% CI 5.60‐15.91, P<.001; AOR 5.44, 95% CI 2.76‐10.74, P<.001), call emergency services (AOR 6.87, 95% CI 4.09‐11.55, P<.001; AOR 5.74, 95% CI 2.84‐11.60, P<.001), and seek emergency care within 60 minutes of symptom onset (AOR 8.68, 95% CI 4.98‐15.15, P<.001; AOR 2.86, 95% CI 1.55‐5.28, P<.001), even after adjusting for covariates. Comparing the 2 datasets via interaction tests showed a statistically significant improvement in human-delivered conversations versus HeartBot conversation for all but the calling an ambulance question (P=.09). Conclusions: The study’s findings provide new insights into the fully automated AI HeartBot, compared to the human-driven text message conversations, and suggest that it has potential in improving women’s knowledge and awareness of heart attack symptoms and appropriate response behaviors. Nevertheless, the current evidence remains preliminary. A randomized controlled trial is warranted to validate this study’s findings.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Heart disease remains a leading cause of death for women in the United States, yet awareness and knowledge are declining. Artificial intelligence (AI) chatbots have great potential to educate women. Objective: 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 pilot, quasi-experimental study, 92 women aged 25 years or older without history of heart disease completed the HeartBot interaction via Short Message Service. 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 (SD) length of the HeartBot interaction was 13.0 (7.80) minutes. The primary outcomes consist of 4 questions on (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 1-4, with higher score indicating higher levels of awareness and knowledge of heart attack risks and symptoms. Results: The sample mean (SD) age was 45.9 (11.9) years. 55 (59.8%) of the sample represented racial/ethnic minorities. The mean (SD) length of the HeartBot interaction was 13.0 (7.80) minutes. In ordinal logistic regression models, women significantly increased in the 4 self-reported outcomes (i.e., heart attack symptoms, calling 911) even after controlling for potential confounding factors (adjusted odds ratio (AOR) = 7.10, 95% CI: 3.52-13.16 for outcome 1; AOR=5.47, 95% CI: 2.77-10.78 for outcome 2; AOR=5.75, 95% CI: 2.86-11.59 for outcome 3; AOR=2.85, 95% CI: 1.54-5.25 for outcome 4; p < 0.001 for all 4 outcomes). Conclusions: HeartBot led to a significant increase in awareness and knowledge of heart attack risks and symptoms in women. These findings suggest that HeartBot is a promising approach to improve heart health education. A randomized controlled trial of HeartBot is warranted to establish its efficacy and safety for the clinical setting.

BACKGROUND Heart disease remains a leading cause of death for women in the United States, yet awareness and knowledge are declining. Artificial intelligence (AI) chatbots have great potential to educate women. OBJECTIVE 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 pilot, quasi-experimental study, 92 women aged 25 years or older without history of heart disease completed the HeartBot interaction via Short Message Service. 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 (SD) length of the HeartBot interaction was 13.0 (7.80) minutes. The primary outcomes consist of 4 questions on (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 1-4, with higher score indicating higher levels of awareness and knowledge of heart attack risks and symptoms. RESULTS The sample mean (SD) age was 45.9 (11.9) years. 55 (59.8%) of the sample represented racial/ethnic minorities. The mean (SD) length of the HeartBot interaction was 13.0 (7.80) minutes. In ordinal logistic regression models, women significantly increased in the 4 self-reported outcomes (i.e., heart attack symptoms, calling 911) even after controlling for potential confounding factors (adjusted odds ratio (AOR) = 7.10, 95% CI: 3.52-13.16 for outcome 1; AOR=5.47, 95% CI: 2.77-10.78 for outcome 2; AOR=5.75, 95% CI: 2.86-11.59 for outcome 3; AOR=2.85, 95% CI: 1.54-5.25 for outcome 4; p < 0.001 for all 4 outcomes). CONCLUSIONS HeartBot led to a significant increase in awareness and knowledge of heart attack risks and symptoms in women. These findings suggest that HeartBot is a promising approach to improve heart health education. A randomized controlled trial of HeartBot is warranted to establish its efficacy and safety for the clinical setting.

Background: Attention control is an important part of clinical trials, allowing the influence of attention to be parsed from the effects of the intervention. Poor control group design can lead to unintended outcomes, overestimate the effectiveness of an intervention, or threaten a potentially beneficial treatment. The aims of this paper were to describe the development of a novel attention control using TED Talks and provide recommendations for the design of an attention control condition. Methods: Twenty-four TED Talks were selected for inclusion in the control condition. The talks ranged from 6.0 to 19.1 minutes for a total length of 4 hours and 12 minutes (mean = 10 minutes, 30 seconds). Topics included education, technology, health, space, human behavior, sanitation, parenting, cliff climbing, public speaking, happiness, obituaries, procrastination, and age diversity. We then developed 3 to 4 simple questions reflecting the content of the Talk. Responses ranged from true/false to 4 multiple-choice answers. Four to five videos were selected for each attention control session and bundled to create an approximately 90-minute attention control condition to match the intervention (acupuncture) session (45 minutes × 10 sessions). Discussion: We developed an attention control condition that met crucial requirements of (1) equivalent attention to that of the experimental group, (2) a design that can hold the participant’s interest, (3) standardization to avoid potentially confounding outcomes, and (4) the evaluation of expectancy. Conclusion: Investigators can develop a rigorous attention control condition using TED Talks guided by our protocol.