Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students' Perception, and Applications.

Background: Text-generating artificial intelligence (AI) such as ChatGPT offers many opportunities and challenges in medical education. Acquiring practical skills necessary for using AI in a clinical context is crucial, especially for medical education. Objective: This explorative study aimed to investigate the feasibility of integrating ChatGPT into teaching units and to evaluate the course and the importance of AI-related competencies for medical students. Since a possible application of ChatGPT in the medical field could be the generation of information for patients, we further investigated how such information is perceived by students in terms of persuasiveness and quality. Methods: ChatGPT was integrated into 3 different teaching units of a blended learning course for medical students. Using a mixed methods approach, quantitative and qualitative data were collected. As baseline data, we assessed students' characteristics, including their openness to digital innovation. The students evaluated the integration of ChatGPT into the course and shared their thoughts regarding the future of text-generating AI in medical education. The course was evaluated based on the Kirkpatrick Model, with satisfaction, learning progress, and applicable knowledge considered as key assessment levels. In ChatGPT-integrating teaching units, students evaluated videos featuring information for patients regarding their persuasiveness on treatment expectations in a self-experience experiment and critically reviewed information for patients written using ChatGPT 3.5 based on different prompts. Results: A total of 52 medical students participated in the study. The comprehensive evaluation of the course revealed elevated levels of satisfaction, learning progress, and applicability specifically in relation to the ChatGPT-integrating teaching units. Furthermore, all evaluation levels demonstrated an association with each other. Higher openness to digital innovation was associated with higher satisfaction and, to a lesser extent, with higher applicability. AI-related competencies in other courses of the medical curriculum were perceived as highly important by medical students. Qualitative analysis highlighted potential use cases of ChatGPT in teaching and learning. In ChatGPT-integrating teaching units, students rated information for patients generated using a basic ChatGPT prompt as "moderate" in terms of comprehensibility, patient safety, and the correct application of communication rules taught during the course. The students' ratings were considerably improved using an extended prompt. The same text, however, showed the smallest increase in treatment expectations when compared with information provided by humans (patient, clinician, and expert) via videos. Conclusions: This study offers valuable insights into integrating the development of AI competencies into a blended learning course. Integration of ChatGPT enhanced learning experiences for medical students.

Patient motivation as a predictor of digital health intervention effects: A meta-epidemiological study of cancer trials.

The objective of this meta-epidemiological study was to develop a rating that captures participants' motivation at the study level in digital health intervention (DHI) randomised controlled trials (RCTs). The rating was used to investigate whether participants' motivation is associated with the effect estimates in DHI RCTs for cancer patients. The development of the rating was based on a bottom-up approach involving the collection of information that captures participants' baseline motivation in empirical studies from the Smartphone-RCCT Database. We specified three indicators for rating: indicator 1 captures whether the study team actively selects or enhances the motivation of the potential study participants; indicator 2 captures the study participants' active engagement before the treatment allocation; and indicator 3 captures the potential bond and trust between the study participants and the person/institution referring to the study. The rating of each indicator and the overall rating varies between high motivation, moderate motivation, and low motivation. We applied the rating across 27 DHI RCTs with cancer patients. We performed meta-regression analysis to examine the effect of patient motivation on quality of life (QoL), psychological outcomes, and attrition. The intraclass correlation coefficient (ICC) indicated moderate to poor inter-rater reliability. The meta-regression showed that cancer patients' overall motivation before engaging in the intervention was associated with the treatment effect of QoL. Patient motivation was not found to be associated with psychological outcomes or attrition. Subgroup analyses revealed that the clinical effects of DHIs were more prevalent in the high-motivation subgroups, whereas the low-motivation subgroups were unlikely to show intervention benefits. The likelihood of dropouts from DHIs seems to be especially high among the low-bond (indicator 3) subgroup. We suggest using single indicators since they reflect specific content. Better reporting about baseline motivation is required to enable meaningful interpretations in not only primary studies but also in evidence syntheses.

Effects of app-based relaxation techniques on perceived momentary relaxation: Observational data analysis in people with cancer.

OBJECTIVE: To examine the effects of six relaxation techniques on perceived momentary relaxation and a possible association of relaxation effects with time and practice experience in people with cancer. METHODS: We used data from participants with cancer in a larger study practicing app-based relaxation techniques over 10 weeks, assessed momentary relaxation before and after every third relaxation practice, and analyzed momentary relaxation changes with a linear mixed-effects model. RESULTS: The sample included 611 before-after observations from 91 participants (70 females (76.9%)) with a mean age of 55.43 years (SD 10.88). We found moderate evidence for variations in momentary relaxation changes across different techniques (P = .026), with short meditation, mindfulness meditation, guided imagery, and progressive muscle relaxation more frequently observed and leading to more relaxation than body scan and walking meditation. Furthermore, we found moderate evidence for increasing momentary relaxation changes over time (P = .046), but no evidence for an association between momentary relaxation and the number of previous observations (proxy for practice experience; P = .47). CONCLUSION: We compared six app-based relaxation techniques in a real-life setting of people with cancer. The observed variations in perceived momentary relaxation appear to correspond with the popularity of the techniques used: The most popular relaxation techniques were the most effective and the least popular were the least effective. The effects increased over time, likely caused by dropout of individuals who gained no immediate benefit. Our findings open an interesting avenue for future research to better understand which relaxation techniques work best for whom in which situations. TRIAL REGISTRATION: DRKS00027546; https://drks.de/search/en/trial/DRKS00027546.

Engagement With a Relaxation and Mindfulness Mobile App Among People With Cancer: Exploratory Analysis of Use Data and Self-Reports From a Randomized Controlled Trial.

BACKGROUND: Mobile health (mHealth) apps offer unique opportunities to support self-care and behavior change, but poor user engagement limits their effectiveness. This is particularly true for fully automated mHealth apps without any human support. Human support in mHealth apps is associated with better engagement but at the cost of reduced scalability. OBJECTIVE: This work aimed to (1) describe the theory-informed development of a fully automated relaxation and mindfulness app to reduce distress in people with cancer (CanRelax app 2.0), (2) describe engagement with the app on multiple levels within a fully automated randomized controlled trial over 10 weeks, and (3) examine whether engagement was related to user characteristics. METHODS: The CanRelax app 2.0 was developed in iterative processes involving input from people with cancer and relevant experts. The app includes evidence-based relaxation exercises, personalized weekly coaching sessions with a rule-based conversational agent, 39 self-enactable behavior change techniques, a self-monitoring dashboard with gamification elements, highly tailored reminder notifications, an educational video clip, and personalized in-app letters. For the larger study, German-speaking adults diagnosed with cancer within the last 5 years were recruited via the web in Switzerland, Austria, and Germany. Engagement was analyzed in a sample of 100 study participants with multiple measures on a micro level (completed coaching sessions, relaxation exercises practiced with the app, and feedback on the app) and a macro level (relaxation exercises practiced without the app and self-efficacy toward self-set weekly relaxation goals). RESULTS: In week 10, a total of 62% (62/100) of the participants were actively using the CanRelax app 2.0. No associations were identified between engagement and level of distress at baseline, sex assigned at birth, educational attainment, or age. At the micro level, 71.88% (3520/4897) of all relaxation exercises and 714 coaching sessions were completed in the app, and all participants who provided feedback (52/100, 52%) expressed positive app experiences. At the macro level, 28.12% (1377/4897) of relaxation exercises were completed without the app, and participants' self-efficacy remained stable at a high level. At the same time, participants raised their weekly relaxation goals, which indicates a potential relative increase in self-efficacy. CONCLUSIONS: The CanRelax app 2.0 achieved promising engagement even though it provided no human support. Fully automated social components might have compensated for the lack of human involvement and should be investigated further. More than one-quarter (1377/4897, 28.12%) of all relaxation exercises were practiced without the app, highlighting the importance of assessing engagement on multiple levels.

Methodological challenges in systematic reviews of mHealth interventions: Survey and consensus-based recommendations.

OBJECTIVE: Mobile Health (mHealth) refers to using mobile devices to support health. This study aimed to identify specific methodological challenges in systematic reviews (SRs) of mHealth interventions and to develop guidance for addressing selected challenges. STUDY DESIGN AND SETTING: Two-phase participatory research project. First, we sent an online survey to corresponding authors of SRs of mHealth interventions. On a five-category scale, survey respondents rated how challenging they found 24 methodological aspects in SRs of mHealth interventions compared to non-mHealth intervention SRs. Second, a subset of survey respondents participated in an online workshop to discuss recommendations to address the most challenging methodological aspects identified in the survey. Finally, consensus-based recommendations were developed based on the workshop discussion and subsequent interaction via email with the workshop participants and two external mHealth SR authors. RESULTS: We contacted 953 corresponding authors of mHealth intervention SRs, of whom 50 (5 %) completed the survey. All the respondents identified at least one methodological aspect as more or much more challenging in mHealth intervention SRs than in non-mHealth SRs. A median of 11 (IQR 7.25-15) out of 24 aspects (46 %) were rated as more or much more challenging. Those most frequently reported were: defining intervention intensity and components (85 %), extracting mHealth intervention details (71 %), dealing with dynamic research with evolving interventions (70 %), assessing intervention integrity (69 %), defining the intervention (66 %) and maintaining an updated review (65 %). Eleven survey respondents participated in the workshop (five had authored more than three mHealth SRs). Eighteen consensus-based recommendations were developed to address issues related to mHealth intervention integrity and to keep mHealth SRs up to date. CONCLUSION: mHealth SRs present specific methodological challenges compared to non-mHealth interventions, particularly related to intervention integrity and keeping SRs current. Our recommendations for addressing these challenges can improve mHealth SRs.

Role of Heart Rate Variability in the Association between Myocardial Infarction Severity and Post-Myocardial Infarction Distress.

OBJECTIVE: Myocardial infarction (MI) results in mental health consequences, including depression and post-traumatic stress disorder (PTSD). The risk and protective factors of such mental consequences are not fully understood. This study examined the relation between MI severity and future mental health consequences and the moderating role of vagal nerve activity. METHODS: In a reanalysis of data from the Myocardial Infarction-Stress Prevention Intervention (MI-SPRINT) study, 154 post-MI patients participated. MI severity was measured by the Killip Scale and by troponin levels. Depression and PTSD symptoms were assessed with valid questionnaires, both at 3 and 12 months. Vagal nerve activity was indexed by the heart rate variability (HRV) parameter of the root-mean square of successive R-R differences (RMSSD). Following multivariate analyses, the association between MI severity and distress was examined in patients with low and high HRV (RMSSD = 30 ms). RESULTS: In the full sample, the Killip index predicted post-MI distress only at 3 months, while troponin predicted distress at 3- and 12-months post-MI. However, HRV moderated the effects of the Killip classification; Killip significantly predicted symptoms of depression and PTSD at 3- and 12-months post-MI, but only in patients with low HRV. Such moderation was absent for troponin. CONCLUSION: MI severity (Killip classification) predicted post-MI depression and PTSD symptoms, but only in patients with low HRV, suggesting that the vagal nerve is a partial protective (moderating) factor in the relation between Killip score and post-MI distress.

Positive psychosocial factors and the development of symptoms of depression and posttraumatic stress symptoms following acute myocardial infarction.

Introduction: Acute myocardial infarction (MI) is a potentially fatal condition, leading to high psychological distress and possibly resulting in the development of depressive symptoms and posttraumatic stress symptoms (PTSS). The aim of this study was to investigate the association of clusters of positive psychosocial factors (resilience, task-oriented coping, positive affect and social support) with both MI-induced depressive symptoms and PTSS, independent of demographic factors. Methods: We investigated 154 consecutive patients with MI, 3 and 12 months after hospital discharge. All patients completed the short version of the German Resilience Scale, the Coping Inventory for Stressful Situations (CISS), the Enriched Social Support Inventory (ESSI) and the Global Mood Scale (GMS). The level of interviewer-rated MI-induced posttraumatic stress disorder (PTSD) symptoms at 3- and 12-months follow-up was evaluated through the Clinician-Administered PTSD Scale (CAPS). Depressive symptoms were assessed at 3- and 12-month follow-up with the Beck Depression Inventory (BDI-II). Results: Three different clusters were revealed: (1) lonely cluster: lowest social support, resilience and average task-oriented coping and positive affect; (2) low risk cluster: highest resilience, task-oriented coping, positive affect and social support; (3) avoidant cluster: lowest task-oriented coping, positive affect, average resilience and social support. The clusters differed in depressive symptoms at 3 months (F = 5.10; p < 0.01) and 12 months follow-up (F = 7.56; p < 0.01). Cluster differences in PTSS were significant at 3 months (F = 4.78, p < 0.05) and 12 months (F = 5.57, p < 0.01) follow-up. Differences in PTSS subscales were found for avoidance (F = 4.8, p < 0.05) and hyperarousal (F = 5.63, p < 0.05), but not re-experiencing, at 3 months follow-up. At 12 months follow-up, cluster differences were significant for re-experiencing (F = 6.44, p < 0.01) and avoidance (F = 4.02, p < 0.05) but not hyperarousal. Discussion: The present study contributes to a better understanding of the relationships among different positive psychosocial factors, depressive symptoms and PTSS following acute MI. Future interventions may benefit from taking into account positive psychosocial factors to potentially reduce patients' depressive symptoms and PTSS after MI.