Importance:Guideline-adherent management of pediatric in-hospital cardiac arrest (IHCA) remains challenging, and deviations from best practices are common. Augmented-reality (AR)-enabled, role-specific decision support may improve adherence to American Heart Association (AHA) Pediatric Advanced Life Support (PALS) guidance and key performance metrics. Objective:To determine whether an AR-enhanced, role-specific decision support system improves resuscitation performance and adherence to AHA PALS guidelines during simulated pediatric IHCA. Design, Setting, and Participants:This open-label, multicenter, simulation-based randomized clinical trial was conducted from April to May 2025 at 2 tertiary pediatric emergency centers (Geneva, Switzerland and Alberta, Canada). Participants included teams of pediatric nurses and physicians. Intervention:Teams managed a standardized scenario of a 12-minute IHCA due to hyperkalemia (progressing from nonshockable to shockable rhythms) using the AR-enhanced, role-specific decision support system (intervention) or AHA PALS pocket cards (control). Main Outcomes and Measures:The primary outcome was time from recognition of loss of pulse to first epinephrine. Secondary outcomes included adherence to 3- to 5-minute epinephrine dosing intervals, time to first defibrillation, adherence to 2-minute shock and rhythm-check cycles, chest compression fraction, peri-shock pause, medication-dosing accuracy, and user experience and technology acceptance. Results:A total of 54 participants were randomized into 18 teams (18 team leaders [12 female [71%] and 36 nurses [33 female [87%]), with 9 teams (27 participants) in each group. Mean (SD) time to first epinephrine was shorter in the intervention group (97.2 [38.5] vs 113.8 [44.5] seconds; mean difference, -16.6 seconds; 95% CI, -51.3 to 17.0 seconds; P = .40), but this difference was not significant. For subsequent epinephrine, the intervention group improved consistency: mean (SD) deviation from the 4-minute target was 17.2 (32.5) vs 49.7 (40.3) seconds (mean difference, -32.4 seconds; 95% CI, -58.8 to -5.8 seconds; P = .03), with fewer guideline violations (2 of 19 participants [11%] vs 9 of 21 participants [43%]; risk difference, -0.32; 95% CI, -0.55 to -0.05; risk ratio, 0.25; 95% CI, 0.06 to 0.996; P = .03). Time to first defibrillation and adherence to 2-minute cycles were similar between groups. Estimates for chest compression fraction, peri-shock pauses, and medication-dosing accuracy did not suggest meaningful between-group difference. User experience and technology acceptance were favorable. Conclusions and Relevance:In this randomized clinical trial, AR support did not clearly improve time to first epinephrine in simulated pediatric cardiac arrest, with estimates compatible with both benefit and little or no effect. It improved adherence to epinephrine dosing intervals without impairing other performance domains. Trial Registration:ClinicalTrials.gov Identifier: NCT06376643.

This randomized clinical trial evaluates the effect of an augmented-reality decision support tool on time to epinephrine and adherence to pediatric advanced life support guidelines during simulated in-hospital pediatric cardiac arrest. QuestionDoes a multifaceted, augmented reality (AR)-enhanced, role-specific clinical decision support system improve adherence to American Heart Association (AHA) Pediatric Advanced Life Support (PALS) guidelines and key performance metrics during simulated pediatric in-hospital cardiopulmonary arrest compared with AHA PALS pocket cards?FindingsIn this randomized clinical trial of 18 teams of pediatric nurses and physicians (54 participants) there was no statistically significant difference in time to first epinephrine between groups, while adherence to epinephrine dosing intervals improved with AR support.MeaningWhile AR support did not clearly improve time to first epinephrine in this study, findings suggest it may improve adherence to resuscitation guidelines without impairing other performance metrics. ImportanceGuideline-adherent management of pediatric in-hospital cardiac arrest (IHCA) remains challenging, and deviations from best practices are common. Augmented-reality (AR)-enabled, role-specific decision support may improve adherence to American Heart Association (AHA) Pediatric Advanced Life Support (PALS) guidance and key performance metrics.ObjectiveTo determine whether an AR-enhanced, role-specific decision support system improves resuscitation performance and adherence to AHA PALS guidelines during simulated pediatric IHCA.Design, Setting, and ParticipantsThis open-label, multicenter, simulation-based randomized clinical trial was conducted from April to May 2025 at 2 tertiary pediatric emergency centers (Geneva, Switzerland and Alberta, Canada). Participants included teams of pediatric nurses and physicians.InterventionTeams managed a standardized scenario of a 12-minute IHCA due to hyperkalemia (progressing from nonshockable to shockable rhythms) using the AR-enhanced, role-specific decision support system (intervention) or AHA PALS pocket cards (control).Main Outcomes and MeasuresThe primary outcome was time from recognition of loss of pulse to first epinephrine. Secondary outcomes included adherence to 3- to 5-minute epinephrine dosing intervals, time to first defibrillation, adherence to 2-minute shock and rhythm-check cycles, chest compression fraction, peri-shock pause, medication-dosing accuracy, and user experience and technology acceptance.ResultsA total of 54 participants were randomized into 18 teams (18 team leaders [12 female [71%] and 36 nurses [33 female [87%]), with 9 teams (27 participants) in each group. Mean (SD) time to first epinephrine was shorter in the intervention group (97.2 [38.5] vs 113.8 [44.5] seconds; mean difference, -16.6 seconds; 95% CI, -51.3 to 17.0 seconds; P = .40), but this difference was not significant. For subsequent epinephrine, the intervention group improved consistency: mean (SD) deviation from the 4-minute target was 17.2 (32.5) vs 49.7 (40.3) seconds (mean difference, -32.4 seconds; 95% CI, -58.8 to -5.8 seconds; P = .03), with fewer guideline violations (2 of 19 participants [11%] vs 9 of 21 participants [43%]; risk difference, -0.32; 95% CI, -0.55 to -0.05; risk ratio, 0.25; 95% CI, 0.06 to 0.996; P = .03). Time to first defibrillation and adherence to 2-minute cycles were similar between groups. Estimates for chest compression fraction, peri-shock pauses, and medication-dosing accuracy did not suggest meaningful between-group difference. User experience and technology acceptance were favorable.Conclusions and RelevanceIn this randomized clinical trial, AR support did not clearly improve time to first epinephrine in simulated pediatric cardiac arrest, with estimates compatible with both benefit and little or no effect. It improved adherence to epinephrine dosing intervals without impairing other performance domains.Trial RegistrationClinicalTrials.gov Identifier: NCT06376643

BackgroundEffective team communication is critical in pediatric cardiopulmonary arrest management, where delays or miscommunication can jeopardize survival. TeamScreen, a web-based interface displayed on a large screen, was developed to enhance cardiopulmonary resuscitation (CPR) by providing real-time visualization of clinical data and resuscitation steps aligned with the American Heart Association pediatric advanced life support algorithms. ObjectiveThis study evaluated the usability of the TeamScreen Figma prototype, evaluating how efficiently and accurately experienced emergency physicians and nurses retrieved critical information during a simulated pediatric in-hospital cardiac arrest scenario. Although no strict time constraints were imposed, participants were instructed to perform the tasks as spontaneously and as quickly as possible. MethodsUsability testing involved 20 pediatric emergency physicians and nurses with varied CPR experience. Participants performed 21 information retrieval tasks within a simulated pediatric cardiac arrest scenario (shockable rhythm). The data collected included audio-video recordings via the think-aloud method and participant responses to the Post-Study System Usability Questionnaire (PSSUQ) version 3 and a posttest survey. Effectiveness, efficiency, and satisfaction were measured by task completion rates, time-on-task metrics, and PSSUQ scores, respectively. Think-aloud data were analyzed for usability issues using Nielsen Norman Group’s rating scale and Bastien and Scapin’s ergonomic criteria. ResultsFive physicians and 15 nurses achieved a mean task success rate of 81.19% (SD 16.87%), with a mean completion time of 8.13 (SD 7.07) seconds, calculated across all 21 tasks and all participants. PSSUQ scores reflected high satisfaction (mean 2.40 [SD 1.24] of 7.00; the lower the better), notably for information clarity and system utility. Qualitative analyses identified 16 usability issues, of which 5 were deemed major, primarily involving information visibility, navigation, and density, highlighting areas for interface and workflow enhancement. ConclusionsThe usability evaluation confirmed TeamScreen’s potential to improve real-time information access during pediatric CPR, with high task success and satisfaction scores supporting its role in aiding decision-making. Challenges with visibility, navigation, and information density require further refinement. These findings will guide improvements and inform the design of multicenter trials to assess TeamScreen’s efficacy in simulation-based resuscitation settings.

Background: Heart failure (HF) is a prevalent chronic condition for which optimal management depends not only on guideline-directed medical therapy but also on patients' understanding of their disease, recognition of warning signs, and sustained medication adherence, which remains challenging in routine care. Mobile health interventions may support therapeutic education and self-management; however, many available apps lack validated content and local relevance. Cardio-Meds is a mobile app developed at Geneva University Hospitals to support HF self-management through structured educational content, interactive quizzes, medication lists with reminders, and tools for monitoring weight and vital signs. Objective: This study aims to evaluate the impact of a 30-day Cardio-Meds intervention on HF knowledge and medication adherence in patients with HF with reduced or mildly reduced ejection fraction. Methods: We conducted a single-center, pilot randomized controlled trial in patients followed at the outpatient HF clinic or enrolled in cardiac rehabilitation at Geneva University Hospitals in 2024. Eligible participants had HF with a left ventricular ejection fraction less than 50%, were receiving HF-specific pharmacotherapy, speak French, and owned a smartphone. Participants were recruited by phone and randomized to Cardio-Meds use for 30 days, a self-guided intervention with a single standardized technical support call. Outcomes were self-assessed using standardized questionnaires: HF knowledge and self-management using the Dutch Heart Failure Knowledge Scale (DHFKS; score range 0-15); medication adherence using the Basel Assessment of Adherence to Immunosuppressive Medication Scale, covering initiation, implementation, and persistence; and usability in the intervention group using the System Usability Scale (score range 0-100). Between-group differences in DHFKS scores were analyzed using analysis of covariance adjusted for baseline values. Results: A total of 49 participants were included (25 intervention, 24 control); 78% (n=38) were male, and the mean age was 62 (SD 11.4) years. In the intervention group, median app usage was 123 (IQR 74-273) minutes, with a median of 43 (IQR 19-85) logins. Mean baseline DHFKS scores were similar between groups (intervention 11.1, SD 2.4 vs control 10.5, SD 2.9). At 30 days, mean scores increased significantly in the intervention group (12.4, SD 2.4; mean change +1.3; P<.001) and remained stable in the control group (10.4, SD 3; mean change -0.1; P=.82), with a significant adjusted between-group difference of +1.3 points (P<.001). No significant between-group differences were observed for medication adherence. Usability was high, with a mean score of 84.3 (SD 15), and 64% (16/25) of intervention participants reported that they would continue using the app. Conclusions: In a stable ambulatory HF population, the Cardio-Meds intervention demonstrated short-term improvement in HF knowledge, while no effect was observed on medication adherence within the 30-day follow-up period. The app showed high usability and acceptability. Larger multicenter studies with longer follow-up are needed to assess clinical impact.

Abstract Background In cardiac arrest management, cognitive aids provide prompts to encourage recall of critical information, which may improve clinical performance. Whether cognitive aids influence provider workload, cognitive load, teamwork dynamics, or leadership during cardiac arrest remains unknown. In this study, we evaluated the effect of using a multi-faceted decision support system with augmented reality-based cognitive aids (i.e. InterFACE-AR) vs. the American Heart Association (AHA) Pediatric Advanced Life Support (PALS) pocket card on provider workload and cognitive load, teamwork, and leadership during simulated pediatric cardiac arrest. Methods We conducted secondary analysis of data collected from a prospective, randomized controlled trial comparing the use of the InterFACE-AR system to the AHA PALS pocket card during simulated pediatric cardiac arrest. Participants were recruited in groups of 3 to perform the roles of team leader, medication nurse, and documenting nurse. All teams completed a 12-min simulated cardiac arrest scenario. Provider workload (NASA-RTLX) and cognitive load (Paas score) were captured from participants after the scenario. Teamwork (TEAM score) and leadership performance (CALM score) were assessed via video review. Results A total of 18 simulation sessions were analyzed (Control: n = 9; InterFACE-AR: n = 9), involving 54 participants in total. Team leaders using the InterFACE-AR system had lower RTLX (mean difference [MD]: -15.0; 95% confidence interval [CI]: -27.0 to -4.6, p = 0.022) and Paas score (MD: -2.4; 95%CI: -3.6 to -1.4, p < 0.001), while documenting nurses showed similar reductions (RTLX -13.7, 95%CI: -26.7 to -0.4, p = 0.049; Paas -1.6, 95%CI: -2.8 to -0.1, p = 0.046) compared with those using PALS pocket card. Medication nurses demonstrated no statistically significant differences in RTLX (p = 0.098) or Paas score (p = 0.194). Teams using the InterFACE-AR system achieved significantly higher TEAM scores compared to those using PALS pocket card only (39.2 vs 35.8, MD: 3.4, 95%CI: 0.8 – 5.9, p = 0.030). CALM scores did not differ significantly between groups. Conclusion Use of an AR-based decision support system during simulated pediatric cardiac arrest reduces workload and cognitive load for the team leader and documenting nurse, but does not affect workload or cognitive load of medication nurses. Use of the InterFACE-AR system seems to improve teamwork performance but does not influence leadership performance of team leaders. Trial registration ClinicalTrials.gov. Identifier: NCT06376643 .

BACKGROUND:Mainstream social networks only partly meet the needs of people with chronic conditions, exposing users to fragmented features, limited moderation, and unreliable content. OBJECTIVE:To design and prototype a patient-dedicated social network "Dubble" aligned with explicit and implicit user needs. METHODS:We applied user-centered design: interviews (n=5), focus group (n=7), and card sorting (2x4) to derive personas, an experience map, the information architecture, and a Figma prototype. RESULTS:Three primary needs emerged: emotional support (100%), experience sharing (80%), belonging (60%), plus non-social requirements: confidentiality (40%) and information reliability (100%). Participants were reluctant to use public platforms due to misinformation, fake accounts, and hostile behavior, and wanted relationships that can extend to in-person meetings (60%). The prototype comprises four sections: Home (experience feed), Community (thematic groups and activities), Dubble (one-to-one pairing to mitigate "mass effect"), and Resources (vetted information). CONCLUSIONS:Early findings translate user priorities into features that support sharing, belonging, close-knit peer support, and trustworthy information. A larger evaluation will assess usability, acceptability, and psychosocial impact.

Pediatric cardiac arrests are time-sensitive events requiring effective team communication, situational awareness, and rapid decision-making. To support resuscitation teams, we developed InterFACE-AR, a digital system designed to improve adherence to American Heart Association’s Pediatric Advanced Life Support guidelines. One key component, the Guiding Pad tablet app, is a bedside cognitive aid that enables nurses to document team actions and resuscitation events in real time while providing algorithm-based prompts to guide the team’s next step in the care pathway. This study aimed to evaluate the usability of the Guiding Pad app by assessing its effectiveness, efficiency, and user satisfaction (aesthetic, ease of use, and clarity of content). We also identified usability problems and proposed design improvements. Usability tests were conducted among pediatric emergency nurses who completed a simulated pediatric cardiac arrest scenario using the Guiding Pad. Participants were asked to perform 27 predefined tasks while verbalizing their thought process using the think-aloud method. Following the scenario, they completed the Post-Study System Usability Questionnaire (PSSUQ) and participated in a semi-structured interview. Data sources included audio and screen recordings, questionnaire responses, and interview notes. Quantitative outcomes were task completion and success rates, task duration, number of clicks, and PSSUQ scores. Qualitative outcomes were usability problems identified during task performance and feedback from interviews. On average, tasks were completed in 11.75 seconds (SD 8.35) and 2.06 clicks (median 1.36). Of the 27 predefined tasks, 21 (77.78%) were fully completed, while completion rates for the remaining tasks ranged from 67% to 93%. The mean overall PSSUQ score was 2.38 out of 7, with lower values reflecting higher satisfaction. A total of 20 usability problems were identified, most of which related to guidance issues such as insufficient prompting, suboptimal grouping of items, and legibility concerns. The Guiding Pad was generally well received and demonstrated overall acceptability and supported task performance in a pediatric resuscitation scenario. However, several usability issues were identified that warrant further refinement. Addressing these limitations may enhance the app’s effectiveness, efficiency, and user satisfaction, thereby strengthening its suitability for real-world clinical use.

BackgroundPediatric emergency departments (PEDs) often face high volumes of low-acuity visits, reflecting gaps in primary care access and socio-economic disparities. We investigated how neighborhood socio-economic vulnerability, pediatrician availability, and proximity to the PED jointly influence PED utilization in Geneva, Switzerland.MethodsIn this retrospective ecological study (Jan 2023-Dec 2024), we aggregated all PED visits for children aged 0-16 years by neighborhood and Canadian Triage Acuity Scale (CTAS) level. Neighborhood visit incidence (unique patients per child population) was modeled using mixed-effects regression against a composite socio-economic vulnerability index (NSVI), pediatrician density within a 2 km radius, and distance to the PED, incorporating an exponential decay function for distance and postal code as a random intercept.ResultsThere were 68,482 PED visits by 35,994 children (35.1% of Geneva under-16 population). Low-acuity visits (CTAS 4-5) comprised ~50% of encounters. Both distance and socio-economic vulnerability showed clear dose-response relationships, with stronger effects observed for lower-acuity visits, and no interaction effect between them. Overall, proximity accounted for up to 20.8% of non-urgent PED use, while neighborhood socio-economic vulnerability explained up to 19.7% of low acuity visits across Geneva. Pediatrician density showed a modest inverse association for low-acuity visits only.ConclusionsBoth proximity and socio-economic vulnerability are independent determinants of non-urgent PED use. Policies focusing only on primary care access risk missing key drivers of PED use, highlighting the need for locally tailored strategies such as community outreach near hospitals or programs to strengthen health literacy among families.

BACKGROUND:Approximately 19% of adults in Europe are affected by chronic pain, which reduces the quality of life. Pain-management mobile health (mHealth) apps offer a promising solution for self-management, but user engagement and adherence can limit their clinical impact. User experience design and research play a crucial role in optimizing usability and long-term adoption of digital health interventions. OBJECTIVE:This study aims to evaluate the user experience of Dolodoc, a mobile app for chronic pain self-management, using a mixed methods approach that assesses acceptability through a content quality survey and examines use by analyzing overall use patterns. METHODS:A cross-sectional acceptability study of the main content of Dolodoc was conducted among patients with chronic pain recruited from the Geneva University Hospitals pain center and through snowball sampling. Participants rated 84 evidence-based self-management strategies by using a 5-point Likert scale based on 5 acceptability criteria: understandability, motivation, feasibility, relevance, and alignment with the related quality-of-life dimension. To reduce participant fatigue and avoid random responses, the 84 strategies were randomly divided across survey versions. Use was assessed through metrics collected over 6 months with Piwik PRO Analytics to observe real-world use behaviors among Dolodoc users. RESULTS:In the acceptability study, 33 participants rated the self-management strategies positively across all dimensions. On a scale from -2 to 2, the strategies were well understood (mean 1.47, SD 0.76), motivational (mean 1.12, SD 0.96), feasible (mean 1.01, SD 1.05), relevant (mean 0.99, SD 1.09), and aligned with the dimensions (mean 1.33, SD 0.89). The use study demonstrated that 60% (486/802) of the patients used Dolodoc only once, indicating that long-term adherence remains a challenge. Within Dolodoc, pain tracking, useful links, and medication logging were the most actively used features. CONCLUSIONS:This study highlights the gap between acceptability and long-term adherence to mHealth solutions. Improving personalization and accessibility could increase user engagement and long-term adherence. Future iterations of the app should incorporate tailored interventions and real-time feedback mechanisms. In addition, leveraging a digital navigation follow-up could facilitate user adoption and sustained engagement.

BackgroundMobile health (mHealth) apps are increasingly used to support healthy lifestyle behaviors through features such as health tracking and personalized reminders. Personalized messaging, tailored to users’ profiles, has been shown to improve engagement and retention in health-related contexts. Prior research has linked personality traits, based on the Big Five model, to preferences for specific app mechanisms, leading to the development of a preference matrix for personalizing mHealth apps. This matrix comprises 15 mechanisms derived from behavior change techniques and gamification elements, intended to guide developers in optimizing engagement according to user profiles. ObjectiveThis study aimed to validate this preference matrix by examining whether the associations between mechanisms and Big Five personality traits reported in the literature align with user preferences observed in an experimental setting. MethodsA cross-sectional study was conducted using an online survey that collected demographic data, mHealth app usage, and personality traits. Participants were presented with mockups illustrating 15 mechanisms and were asked to select their preferred options. Logistic regression and ordinal logistic regression analyses were performed to examine associations between personality traits, mechanism selection, and motivation scores. All analyses were adjusted using the Bonferroni correction to account for multiple comparisons. ResultsA total of 214 participants completed the survey (mean age 29.42, SD 10.41 y; n=118, 55.1% women; n=89, 41.6% men; n=5, 2% identifying as other; and n=2, 1% nonrespondents). Higher conscientiousness significantly increased the likelihood of selecting the collection mechanism (eg, collecting badges or points; odds ratio [OR] 1.87, 95% CI 1.27-2.75). For competition (eg, competing with other users), conscientiousness (OR 3.22, 95% CI 1.73-6.00) and agreeableness (OR 1.93, 95% CI 1.08-3.45) were significant predictors. Preferences for rewards (eg, virtual incentives such as points or virtual currency) were associated with conscientiousness (OR 2.36, 95% CI 1.53-3.63) and neuroticism (OR 1.97, 95% CI 1.36-2.86). Additionally, 4 mechanisms—self-monitoring, progression, challenge, and quest—were selected by more than half of the participants, independent of personality traits. ConclusionsThe findings partially validate the proposed preference matrix. Conscientiousness consistently emerged as a key predictor of preference across multiple mechanisms, highlighting its central role in engagement with gamified mHealth features. While some mechanisms appear to have universal appeal, others show personality-specific preferences, underscoring the value of combining baseline mechanisms with targeted personalization strategies in mHealth app design. International Registered Report Identifier (IRRID)RR2-10.2196/38603

Background: Augmented reality (AR)-based cognitive aids provide real-time guidance during resuscitation, but their impact on clinicians’ visual attention is not well understood. This study aimed to describe visual attention distribution among clinicians using an AR-based decision support system during simulated pediatric cardiac arrest. Methods: This descriptive study was a secondary analysis of data from the intervention arm of a randomized clinical trial. Pediatric resuscitation teams from two centers participated in a standardized cardiac arrest simulation. The AR-based decision support system provided role-specific, real-time visual guidance, including task prompts, timers, and medication information displayed within the clinician’s field of view. Visual attention data from team leaders and medication nurses were collected using AR devices (HoloLens 2) with built-in eye tracking. Predefined areas of interest (AOIs) were established, and visual attention was quantified using fixation percentage, fixation count, and fixation duration. Results:Nine simulation sessions were analyzed. AR components accounted for a substantial proportion of visual attention (Mean percentage (SD) team leaders: 41.9% (14.9); medication nurses: 59.0% (14.6)). Team leaders demonstrated distributed attention across multiple AOIs, whereas medication nurses showed concentrated attention on medication-related tasks. Distinct fixation patterns were observed, with some AOIs characterized by frequent, brief fixations and others by less frequent, longer fixations. A substantial proportion of attention occurred outside predefined AOIs (team leaders: 46.0% (15.2); medication nurses: 22.9% (10.7)). Conclusion:Visual attention during use of the AR-based decision support system by both team leaders and medication nurses was characterized by substantial engagement and distinct role-specific attention patterns.

Artificial intelligence systems that record voice and video during pediatric emergencies are emerging as human-computer interaction (HCI) technologies with direct implications for clinical work, promising improvements in documentation, team performance, and post-event debriefing. Yet the perspectives of those most affected, including clinicians, parents, and child patients, remain largely absent from the design and governance of these technologies. This position paper argues that this has direct consequences for the legitimacy and effectiveness of these systems. We examine four areas where these missing perspectives prove consequential (consent, emotional impact, surveillance dynamics, and participatory governance) and propose four positions for reorienting AI recording in pediatric emergency care toward stakeholder-centered HCI inquiry.

Background: Cardiac arrest is a critical medical emergency that requires strict adherence to clinical guidelines to achieve optimal outcomes. Deviations from these guidelines, often due to task complexity, can adversely affect patient outcomes. Augmented reality (AR) offers a way to deliver role-specific, in-view guidance, but evidence on its perceived usability, user experience, and acceptability in cardiac arrest resuscitation remains limited. Objective: This study aimed to design, develop, and evaluate a role-specific AR decision support system for resuscitation team leaders and medication nurses. In this observational study, we assessed clinicians' perceived usability, user experience, and technology acceptance of the new AR system in a high-fidelity simulated cardiac arrest scenario. Methods: We conducted a prospective observational pilot study using a high-fidelity simulated pediatric cardiac arrest scenario. A total of 10 clinicians were recruited from Alberta Children's Hospital, including 5 (50%) of 10 pediatric emergency physicians serving as team leaders (men: 3/5, 60%, and women: 2/5, 40%; median age 41, IQR: 40-42 y) and 5 (50%) of 10 emergency nurses serving as medication nurses (men: 1/5, 20%, and women: 4/5, 80%; median age 45, IQR: 42-46 y). Participants used role-specific AR decision support interfaces deployed on HoloLens 2 head-mounted displays. Following the simulation, perceived usability, user experience, and technology acceptance were assessed using validated questionnaires: the System Usability Scale, User Experience Questionnaire, and Technology Acceptance Model. Data were collected via postsimulation surveys and analyzed descriptively. Results: Descriptive analyses were performed without inferential statistical testing. The mean System Usability Scale scores were 75.5 (SD 9.25, 95% CI 64.0-87.0) for team leaders and 82.0 (SD 11.20, 95% CI 68.0-96.0) for medication nurses. User experience was positive across roles, with mean User Experience Questionnaire scores indicating favorable attractiveness (team leaders: 1.87, SD 1.14, 95% CI 0.45-3.28; medication nurses: 2.43, SD 0.52, 95% CI 1.79-3.08), pragmatic quality (team leaders: 1.88, SD 0.87, 95% CI 0.80-2.97; medication nurses: 1.80, SD 0.69, 95% CI 0.94-2.66), and hedonic quality (team leaders: 2.40, SD 0.89, 95% CI 1.30-3.50; medication nurses: 2.28, SD 0.69, 95% CI 1.42-3.13). Technology acceptance was high, with mean combined Technology Acceptance Model scores of 5.92 (SD 0.46, 95% CI 5.35-6.49) for team leaders and 6.02 (SD 0.56, 95% CI 5.32-6.71) for medication nurses. Conclusions: This study introduces a novel role-specific AR decision support system that delivers tailored, in-view guidance to resuscitation team leaders and medication nurses during cardiac arrest. Unlike prior cognitive aids that present uniform or device-agnostic information, this system explicitly adapts interface content and structure to distinct clinical roles and workflows. The findings contribute early empirical evidence on the perceived usability, user experience, and acceptability of role-tailored AR support in high-acuity team settings and yield transferable design principles for developing role-aware AR interfaces. In real-world contexts, such systems may support protocol adherence and team coordination during resuscitation training and early-stage clinical deployment, informing future evaluations that incorporate objective performance and workflow outcomes.

Background: Pediatric cardiopulmonary resuscitation (CPR) is a highly complex and time-critical process that demands precise team coordination and strict adherence to pediatric advanced life support (PALS) guidelines. In real-world practice, adherence often deteriorates due to cognitive overload, fragmented communication, and disruption of information flow under stress. Although digital cognitive aids have shown potential to improve adherence, existing tools are often limited to single tasks, lack team-wide integration, or fail to adapt in real time to dynamic clinical environments. Objective: This study aimed to design and evaluate InterFACE (Interconnected and Focused Mobile Applications on Patient Care Environment), an integrated, augmented reality (AR)-enabled digital health system developed to support real-time PALS adherence and enhance team coordination during pediatric resuscitation. Methods: A structured, mixed methods, user-centered design process was used. Persona development and spatial analysis characterized the needs and positions of key resuscitation roles. A 3-round Delphi process with experts identified critical information elements for display. Iterative user experience (UX) prototyping was performed, followed by simulation-based evaluations of three system components: (1) TeamScreen, a wall-mounted team display providing a shared overview of the resuscitation process; (2) Guiding Pad (developed by Pierre Louis Rebours and Marc Ibrahim), a tablet-based app for documentation and algorithm navigation; and (3) AR head-mounted displays (HMDs) for team leaders and medication nurses, delivering role-specific, context-aware guidance. Usability was assessed with standardized instruments, including the System Usability Scale (SUS), Technology Acceptance Model (TAM), and User Experience Questionnaire (UEQ). Results: The Delphi study achieved consensus on 20 core information elements, distributed across the 3 interfaces. Usability testing demonstrated high acceptance across all modalities. The Guiding Pad supported effective navigation of resuscitation algorithms with a 78%-100% task completion rate. The TeamScreen achieved an overall task success rate of 81%, improving situational awareness despite some confusion in high-density regions. AR HMDs received favorable evaluations, with SUS scores rated "Good" to "Excellent," and UEQ ratings indicating high intuitiveness, stimulation, and attractiveness. Participants consistently described InterFACE as intuitive, useful for real-time decision-making, and supportive of team synchronization. Reported challenges included interface complexity, incomplete integration with patient monitors, and potential cognitive load from simultaneous information streams. Conclusions: InterFACE represents a significant advancement in digital cognitive aids by combining shared displays, tablets, and AR guidance into a synchronized, role-specific ecosystem. The system shows promise in enhancing adherence to PALS, reducing cognitive load, and improving team coordination in simulated pediatric resuscitations. While results demonstrate strong usability and acceptance, further research is needed to evaluate clinical effectiveness in real-world settings, including randomized controlled trials, integration with hospital information systems via Fast Healthcare Interoperability Resources (FHIR) standards, and potential artificial intelligence-driven decision support to optimize adaptability and long-term skill retention.

Mobile health technologies are increasingly important for behavior change in chronic disease's prevention and management. Yet, user diversity limits the efficiency of one-size-fits-all solutions. This study examines how age and gender influence preferences for 14 mechanisms for behavior change. In a cross-sectional online survey (N=214, M_age=29.4), logistic regressions tested these effects. Four mechanisms, self-monitoring, progression, challenge, and quests were universally preferred. Participants aged over 35 years disliked rewards (OR= 0.24, p<.01) but preferred prompts and cues (OR= 1.52, p=.05); women preferred avatars (OR= 2.39, p<.01) and were less attracted to social comparison (OR=-.83, p<.05) and competition (OR= 0.34, p<.01). These findings highlight the need for personalized mHealth designs to optimize engagement and effectiveness.

The integration of diverse healthcare data into unified data lake infrastructures is a promising way to support research and clinical decision-making. Yet, querying these data lakes, such as those based on MongoDB, remains complex, often requiring specialized expertise and custom scripts. Large language models (LLMs) offer new opportunities by translating natural language queries into executable commands, but direct access raises major privacy concerns. We present a method for indirect LLM-assisted querying of a healthcare data lake that preserves data confidentiality by avoiding any direct model access to real data. The multistep workflow includes user intent clarification, schema and example guided query generation, and expert validation before execution. In a preliminary evaluation using five representative test queries, the pipeline consistently produced syntactically valid code aligned with user intent. Qualitative assessment showed its potential for reducing technical barriers while also highlighting current limitations, including hallucinated fields and missing lookups. These findings suggest that indirect LLM-assisted querying represents a promising step toward more secure and usable access to healthcare data lakes, though further large-scale validation is required.

Digital tools offer novel opportunities to support healthcare skill acquisition, particularly through serious games designed to train caregivers. In this article, we present a hybrid narrative engine architecture that combines symbolic rules, tree structures, and the generative capabilities of large language models (LLMs). This approach addresses the need to create interactive scenarios that focus on the specific challenges of care management. At the core of the system, rules and story trees ensure narrative coherence, while LLMs generate contextualized text, enhancing interaction with more credible non-player characters (NPCs). The engine is designed to provide a customisable user experience, with real-time feedback and scenario unfolding that adapts to the player’s choices and progress. Future developments include extending the approach to other mental health disorders, improving authoring tools, and establishing a clinical evaluation framework to accurately assess the therapeutic impact.

Background:Adopting healthy habits improves longevity and well-being. Mobile health (mHealth) apps support such behaviors, with over 35,000 available as of 2018. Personalization and gamification are recognized as effective strategies to enhance user engagement and behavioral outcomes in mHealth applications. Objectives:This cross-sectional study explores links between user typologies (Hexad Scale) and preferences for 15 game and behavior change mechanisms. Methods:A preference matrix, derived from the literature, was tested on data from 214 respondents (M age = 29.42; 118 women, 89 men, 5 other). Demographics and Hexad-based user typologies were recorded. Participants selected their top five mechanisms from 15 randomized mockups with brief descriptions. Logistic and ordinal logistic regressions, with Bonferroni correction, were used to assess associations. Results:Significant associations were observed for five mechanisms. Philanthropists were less likely to prefer collection (OR = 0.77, p < 0.01), whereas players favored it (OR = 1.11, p < 0.05) and showed strong preferences for rewards (OR = 1.39, p < 0.01) and, to a lesser extent, self-monitoring (OR = 0.88, p < 0.05). Socializers preferred cooperation (OR = 1.14, p < 0.01) but were less inclined toward demonstration of behavior (OR = 0.92, p < 0.05). Free spirits favored demonstration of behavior (OR = 1.25, p < 0.01), while achievers were less likely to prefer it (OR = 0.86, p < 0.05). Four mechanisms, self-monitoring, progression, challenge, and quest were selected by over 50% of participants. Conclusion:This study validated the preference matrix, highlighting four mechanisms, self-monitoring, progression, challenge, and quest, as broadly appealing across user profiles for mHealth design. Three novel profile-mechanism associations were identified, refining the model and underscoring the need for replication with a more diverse sample.

BackgroundMobile health apps have shown promising results in improving self-management of several chronic diseases in patients. We have developed a mobile health app (Cardiomeds) dedicated to patients with heart failure (HF). This app includes an interactive medication list; daily self-monitoring of symptoms, weight, blood pressure, and heart rate; and educational information on HF delivered through various formats. ObjectiveThis study aimed to perform a mixed methods usability study of Cardiomeds. MethodsSmartphone users with HF were recruited from the HF outpatient clinic at the University Hospital of Geneva. The usability test was conducted in 2 stages, with modifications made to the app after the first stage to address major usability issues. Each stage required 10 participants to perform 14 tasks, such as entering vital signs, entering a new medication and time of intake, or finding information about HF. Each task was timed, sessions were recorded, and all data were anonymized. After completing the tasks, patients completed the System Usability Scale 10-item questionnaire and answered 5 open questions about their perceptions of Cardiomeds. ResultsTwenty patients with HF, 75% (15/20) of whom were men, with a mean age of 55 years, were included in this study. The average time to complete all 14 tasks was 18 (SD 5.7) minutes. Manual medication entry was the most time-consuming task, taking an average of 154.40 (SD 68.08) seconds in the first stage, 103.10 (SD 42.76) seconds in the second stage, and 128 (SD 63) seconds overall. The mean overall success rate was 77% (SD 0.23%) for the first stage and 94% (SD 0.07%) for the second stage. A total of 30% (3/10) of participants in the first stage completed all tasks without any help compared with 50% (5/10) of participants during the second stage. The average System Usability Scale score was 80% (SD 17%), showing a slight increase from 79% (SD 16%) in the first stage to 80% (SD 28%) in the second stage, which qualifies the app as “good” in terms of usability. Between the 2 stages, part of the app interface was redesigned to address the key issues identified in the first stage. Despite these improvements, problems related to guidance were frequent and comprised 36% (8/22) of the problems in the first stage and 40% (6/15) in the second stage. In response to open questions, 85% (17/20) of the participants responded that they would like to use the app when it became available. ConclusionsThe usability test indicated that Cardiomeds is a suitable and user-friendly app for patients with HF. The app will be further tested in a randomized clinical trial (2022-00731) after acute HF hospitalization to assess its impact on patients’ knowledge about HF, self-care, and quality of life.

Anorexia Nervosa (AN) is a severe eating disorder requiring interventions that effectively involve the entire family. Family-Based Therapy (FBT) has shown promise, yet traditional training methods for caregivers can be time-consuming and challenging. We present a serious game leveraging large language models (LLMs) that generate realistic, scenario-based dialogues between caregivers and teens with AN, offering a safe space to practice supportive communication strategies. To enhance the credibility and therapeutic relevance of these generated dialogues, we utilize a few-shot learning approach informed by domain expert feedback, enabling the model to produce contextually accurate and empathetic exchanges. By iteratively refining the system prompt with expert-validated examples, we substantially improve dialogue authenticity without the need for extensive model retraining. This approach provides a scalable, flexible solution that can be adapted to various therapeutic scenarios, ultimately broadening the reach and efficacy of digital health interventions.