Background:Despite increasing recognition of human factors in technologically advanced manufacturing, psychological resilience remains an underexplored dimension. Existing studies often rely on generic instruments that fail to capture the specific adaptive challenges faced by workers in high-tech environments. There is a growing need for context-sensitive tools capable of assessing resilience in line with the human-centric vision of Industry 5.0. Objective:This study aimed to develop and conduct a preliminary evaluation of the Change Adaptation Resilience Evaluation Questionnaire for Industry 5.0 (CARE-QI 5.0), a multidimensional instrument designed to assess individual and contextual factors contributing to psychological resilience among workers in high-tech manufacturing settings. Methods:The questionnaire was developed through a 4-phase process: literature review, expert evaluation, focus groups with operators, and pilot testing within 2 manufacturing companies. CARE-QI 5.0 conceptualizes resilience as a higher-order construct comprising 2 second-order dimensions-individual resilience and contextual resilience-further articulated into 18 first-order subscales. Internal consistency and intersubscale correlations were assessed using data from a sample of workers employed in the mechanical and packaging industries in Northeastern Italy. Results:Findings on the 84 items showed good internal consistency across all subscales and meaningful patterns of intercorrelation. External validity showed that problem-solving self-efficacy, cognitive flexibility, and problem-oriented coping were positively associated with both individual-based resilience and a wide range of contextual resources, including support given by organizations, colleagues, and family, as well as openness to change. In contrast, perseverance in the face of difficulties was linked to cognitive inflexibility, avoidance, and seeking change, suggesting the presence of rigid or less adaptive coping functioning. Conclusions:CARE-QI 5.0 provides a theoretically grounded and context-sensitive tool for assessing psychological resilience as a multidimensional construct in technologically evolving manufacturing environments. By integrating both individual and contextual protective resources, this instrument captures key adaptive processes relevant to Industry 5.0 scenarios, where human-technology interaction plays a central role. While further psychometric validation is needed, including confirmatory factor analysis and measurement invariance testing, CARE-QI 5.0 shows promise for both theoretical and research use. It can support organizations in monitoring workers' adaptability and well-being, guiding targeted interventions and training strategies that align with workers' resilience profiles and their technological experience.
Background:Virtual clinics allow doctors to connect to patients in difficult-to-reach locations. While this can result in improved access to care, implementing existing virtual clinics in these locations is difficult due to their contextual constraints. To address these challenges, a virtual clinic system for remote, rural, and underserved areas was developed based on user-centered design principles. To complete the user-centered design cycle, the developed system was then implemented and evaluated in the target contexts. Objective:This study aimed to conduct a pilot study with the developed user-centered virtual clinic system to evaluate its performance in resource-limited settings. Patient, doctor, and nurse feedback was collected to understand how the virtual clinic system and implementation strategy might be improved before long-term implementation. Methods:A pilot study was conducted at 2 health care facilities in South Africa-1 underserved public primary health clinic in the city of Cape Town, and 1 remote occupational health clinic in the Northern Cape Province. Doctor and nurse dyads participated in using the system to consult with real patients in each clinic. Patients received both virtual and physical examinations by the same doctor, and these 2 consultations were compared with one another. Surveys were conducted to gather patient feedback pre- and post-virtual clinic consult. Observations of system usage were collected, and doctor and nurse participant interviews were conducted at the conclusion of each day. Qualitative data were thematically analyzed to understand barriers and facilitators of virtual consultations, as well as patient satisfaction. Results:A total of 12 patient consultations were conducted using the virtual clinic system across 2 health care facilities. Two doctors and 2 nurses participated in the study as users. Doctor and nurse confidence in using the system increased over time. Doctors were confident that the virtual consultation format could be used to diagnose patients remotely. Key indicators of patient satisfaction were being included in consultation communication and understanding the benefits virtual care could offer. Potential barriers to virtual consultations were infrastructure offered by the implementation environment and medical device limitations. Conclusions:The results from this pilot study indicate that virtual clinic consultation is possible in low-resource settings using the developed virtual clinic system. This system can support patients in remote, rural, and underserved areas to receive certain health care services from a doctor without the doctor having to be physically present in the facility. The results encourage further scaling of the system to support long-term implementation in low-resource health clinics in South Africa and other sub-Saharan African countries.
Patient safety classification systems are fundamental to surveillance, organizational learning, research, and governance because they enable adverse events and near misses to be organized into standardized categories for comparison and analysis. However, the increasing complexity of health information technology (HIT)-related patient safety incidents challenges the assumptions underpinning conventional classification approaches, as these incidents often emerge from dynamic, distributed, and evolving sociotechnical interactions rather than discrete, time-bounded events. In this Viewpoint, I argue that many of the challenges associated with classifying HIT-related patient safety incidents arise not simply from limitations of individual classification systems but from the inherent representational logic of classification itself. By viewing classification as a knowledge practice rather than merely a technical tool for organizing incident data, I contend that abstraction, boundary-setting, and standardization inevitably simplify complex sociotechnical processes and constrain how safety problems are represented, interpreted, and acted upon. I discuss 4 recurring representational limitations that characterize the application of patient safety classification systems to HIT-related incidents: fragmentation of sociotechnical interactions, loss of temporality and evolving processes, inadequate representation of scale and propagation across systems, and normalization of "use error" through simplified attribution of responsibility. These limitations can contribute to incomplete organizational learning, misaligned safety interventions, and challenges in interpreting and comparing classified patient safety data across health care settings. Rather than arguing against the continued use of patient safety classification systems, I propose that their strengths and limitations should be recognized simultaneously. Classification remains indispensable for surveillance, learning, and governance, but it should be interpreted as one component of a broader sociotechnical understanding of patient safety. Recognizing the representational limits of classification can support more reflexive interpretation of classification-based evidence and encourage complementary approaches that better capture the complexity of HIT-related patient safety.
BackgroundHealth-related technology use among nondigitally native adults is becoming widespread. Nevertheless, digital health interventions are typically not designed with the unique usability needs and preferences of this population in mind. Furthermore, most of these middle-aged and older adults are managing multiple medical conditions, which can also impact their preferences related to digital health interventions. A prime opportunity to address multimorbidity in nondigital natives using a digital health intervention is the intersection between mental health and chronic pain. ObjectiveThe goal of this study was to use established frameworks and end-user feedback to identify actionable, usability-related features that can be incorporated into existing digital health interventions and that are preferred among nondigitally native adults. We aimed to identify general usability adaptations, as well as just-in-time adaptive interventions (JITAIs). MethodsWe conducted a qualitative usability study of a convenience sample using a hybrid inductive-deductive content analysis to evaluate an existing mental health app (Wysa for Chronic Pain). Participants were 45 years or older; reported at least moderate symptoms of depression or anxiety (Patient Health Questionnaire -9 ≥10 or Generalized Anxiety Disorder -7 score ≥10); and endorsed having pain at least most days in the past 3 months. The Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies was used to identify potential usability-related adaptations for the target population. Development of usability-related JITAIs was guided by Nahum-Shani’s pragmatic framework for JITAI development and the Behavioral Integration Technology model. ResultsForty-two participants completed usability testing (mean age 57, SD 8 years; women n=32, 76%). Participants identified numerous opportunities to optimize their user experience, primarily by ensuring the app clearly describes how all its features are intended to be used and by minimizing navigation burden. Specifically, participants requested clear, step-by-step orientation and navigation instructions, rather than a brief onboarding experience that relies on user-led exploration and familiarity with conventional app symbols. Participants also recommended a customized experience based on their unique usage patterns. The most common JITAI opportunity identified was to strategically reduce user notifications in order to reduce the risk of notification fatigue and subsequent complete disengagement with the app. ConclusionsIdentifying actionable opportunities to improve usability and prompt engagement with “the right tool at the right time for the right person” holds promise to improve the effectiveness of digital health interventions across the age span. The usability-related refinement opportunities identified in this study are generalizable to other digital health interventions that are relevant to older users who are likely to be managing multimorbidity, are not digital natives, and are more likely than younger users to have physical or cognitive challenges. The integrated framework-based process described in this article can also serve as a model for optimizing other existing digital health interventions.
BackgroundRemoval platforms for nonconsensual intimate images (NCIIs) are essential, as they have become the recourse for victims of such abuse; yet their interfaces are not designed around the cognitive and emotional realities of trauma. Safety by Design directs platforms to protect vulnerable users but not how; trauma-informed care defines what that protection requires, while usability heuristics keep interfaces usable but are not themselves trauma-sensitive. However, no framework integrates them into design guidance that is both usable and protective for NCII removal platforms. ObjectiveThis study develops trauma-informed design, a cohesive framework that integrates trauma-informed care with established usability heuristics under Safety by Design, so that NCII removal platforms protect trauma-affected users while remaining realistically usable; a preliminary, domain-informed evaluation provides initial supporting evidence. MethodsWe used a 2-phase sequential exploratory design, drawing on a globally accessible NCII removal platform as an illustrative case. Phase 1 developed trauma-differentiated personas and mapped each persona’s user journey to surface pain points that varied by level of trauma. Phase 2 analyzed a focus group of 8 female Counseling-UX (User Experience) Psychology students who had completed coursework in user psychological safety with consensual qualitative research methodology. Participants assessed the plausibility of the personas and the proposed framework with respect to perceived emotional safety and perceived usability. ResultsMapping each persona’s journey surfaced pain points that differed by level of trauma, which were synthesized into a 6-guideline trauma-informed design framework: emotional scaffolding, process transparency, self-pacing mechanisms, institutional trust signals, context-aware support, and grounding tools. In phase 2, domain-informed evaluators judged the personas plausible, and the framework directions emotionally safer, and potentially more usable. ConclusionsIntegrating trauma principles into user experience evaluation can help identify and address psychological barriers in NCII removal platforms. The framework provides the preliminary, domain-informed foundation that trauma-sensitive design of such platforms requires. Because the framework was not tested with NCII victim-survivors or users actively seeking image-removal support, findings represent preliminary plausibility and acceptability evidence rather than ecological validation or real-world effectiveness.
Background:AI tools have the potential to enhance personalized clinical care, particularly in radiology. However, their integration into clinical workflows remains complex, especially in pediatric oncology, where early cancer detection is critical. Children with Li-Fraumeni syndrome (LFS), a rare cancer predisposition disorder, undergo regular surveillance whole-body magnetic resonance imaging (wbMRI), which presents an opportunity for AI-assisted tumor detection. Objective:We evaluated the feasibility of an AI-assisted overlay for highlighting tumor-like regions in pediatric surveillance wbMRI and explored how access to the overlay influenced radiologist workflow, candidate-lesion marking behavior, follow-up recommendations, and perceived workload. Methods:We developed a patch-based AI segmentation model trained on augmented 2D slices from 675 surveillance wbMRI volumes of pediatric patients with LFS. The model was designed to highlight regions with high tumor probability. A reader study was conducted with 2 radiologists who independently reviewed wbMRI cases both with and without AI assistance. We measured evaluation time, number and location of reader-marked candidate lesions, type of follow-up recommendation, and subjective feedback using structured questionnaires. Results:AI assistance altered interpretation workflows for both radiologists, with mixed effects. On average, the time required to evaluate each case increased when using the AI tool for both radiologists. However, one radiologist had an increase in the number of candidate lesion locations selected with the tool, and one had a decrease in the number of candidate lesion locations selected with the tool. Subjective feedback indicated that one of the radiologists reported lower mental demand with the AI tool, while both radiologists reported lower stress with the AI tool. Interrater variability was evident, underscoring the need for personalized calibration of AI tools. Conclusions:AI-assisted wbMRI interpretation can improve tumor detection in pediatric cancer surveillance by reducing false negatives. However, its influence on workflow efficiency and interradiologist variability highlights the importance of careful implementation. Successful integration requires addressing challenges such as improving the predictive precision of AI models, offering intuitive end-user designs and instructions, and building trust in AI outputs. AI outputs can influence workflow and behavior in reader-specific ways. Clinical translation will require larger, randomized, multireader studies and model refinement to reduce false positives and quantify lesion-level reader performance. This can help ensure better patient outcomes in addition to reduced clinician burnout.
BackgroundEpilepsy is a serious chronic neurological condition with no permanent cure. Continual self-management is important to mitigate seizure frequency and optimize quality of life in people with epilepsy who have greater disparities in accessing epilepsy care. The Management Information & Decision Support Epilepsy Tool (MINDSET [UTHealth, University of Arizona, and Radiant Digital]) was developed to enhance accessibility to epilepsy self-management (ESM) assessment and treatment. The purpose of this formative usability pilot study was to assess the user experience and functionality of MINDSET 2.0, an enhanced cross-platform online version of MINDSET, among a sample of patients with epilepsy prior to feasibility testing within neurology clinic settings. MethodsMINDSET 2.0 comprised an updated cross-platform architecture for easier accessibility and added quality of life, cognitive function, and social determinants assessments. User experience and functionality were assessed in January 2022. Six patients with epilepsy in Texas (n=4) and Arizona (n=2) participated in individual online usability sessions, completing a sociodemographic survey, accessing all components of MINDSET, and then completing usability rating scales and an exit interview. Logical inconsistencies in embedded algorithms were examined for the usability sample and in user case challenges to ensure functional fidelity. ResultsPatients reported low adherence to ESM behaviors in each of the management domains. More than 80% of patients agreed that MINDSET 2.0 was acceptable, easy to use, likable, credible, of appropriate duration, and motivationally appealing. Patients agreed that the program helped them think about and manage their epilepsy more carefully, and that it improved decision-making between them and their health care providers (100%). Patients provided lower ratings (≤50%) and reported the greatest number of difficulties with their understanding of how to select goals and strategies, and develop an action plan due to constraints of item response options leading to user confusion. An inconsistency in algorithm branched logic was identified that related to translating depression scores into recommendations for depression self-management programming. ConclusionsThe results replicated usability findings from earlier versions of MINDSET but also catalyzed adjustments to user survey response options and algorithm repair. The value of the formative user experience functionality assessment was demonstrated to ensure a high-fidelity program prior to feasibility testing in neurology clinic settings.
Background:Despite the growing importance of social media in mobile health (mHealth) communication, we lack a clear understanding of how emotional elements like emojis shape message effectiveness. Furthermore, since emojis are inherently tied to text, their impact may be highly dependent on the relevance and context of the accompanying written content. Objective:This study aimed to investigate how the valence of emojis affects the effectiveness of mHealth messages and to determine the role of emoji-text congruence in shaping message outcomes. Methods:A mixed-method approach was used, encompassing 3 complementary studies. First, an analysis of real-world health-related tweets (N1=257,648) quantified social media engagement in relation to positive, negative, and absent emojis. Building on these insights, 2 controlled experiments (N2=220; N3=190) further explored how negative emojis influence preventative health behaviors under varying levels of text congruence. Results:The automatic content analysis revealed that messages containing negative emojis generated significantly higher social media engagement than those with positive emojis (β=0.24, SE=0.03, t257, 593=9.59; P<.001). Subsequently, experimental findings indicated that negative emojis can effectively promote preventative health behaviors (t218=-4.15; P<.001). However, messages with high negative emoji-text congruence are more persuasive in promoting preventive behavior than those with low congruence (F1, 186=5.46, η2=.028; P<.05), highlighting the complex interplay between emotional signaling and message consistency. Conclusions:These findings advance our theoretical understanding of emoji-based health communication and provide practical guidelines for public health organizations seeking to optimize their mHealth messaging strategies. Our results highlight the need for careful consideration of both emotional valence and message coherence when designing health communications in the digital age.
Background Digital applications based on health care conversational agents (HCAs) are increasingly being developed to support health care provision. The usability and user experience of these solutions are critical determinants of their acceptability and, consequently, their impact on health-related outcomes. Objective This systematic review aims to synthesize current evidence on the use of valid and reliable subjective instruments for assessing the usability and user experience of HCAs and to examine whether assessment outcomes vary according to their technical characteristics. Methods A systematic search was conducted in PubMed, Web of Science, and Scopus from inception to February 2026. Studies were included if they used subjective instruments to evaluate the usability or user experience of HCAs. Results A total of 127 studies met the inclusion criteria. The studies examined 3 categories of HCAs—text-based, voice-based, and embodied—applied to patient care, health education and prevention, health data collection, and support for daily activities among older adults. The System Usability Scale (SUS) was the most frequently used assessment instrument. Comparative analysis of SUS scores indicated higher usability ratings for text-based HCAs relative to voice-based and embodied systems. However, SUS and other subjective instruments used in the included studies may not fully capture key dimensions of usability and user experience of HCAs. Additionally, substantial heterogeneity was observed in assessment methodologies across studies. Conclusions Comparative analysis suggested that text-based HCAs were associated with significantly higher SUS scores than voice-based and embodied HCAs. However, this finding should be interpreted with caution given the substantial heterogeneity across the included studies in health care application domains, study designs, evaluation contexts, participant populations, and HCAs’ implementation and use characteristics, as well as the limitations of the SUS in evaluating the usability of modern HCAs. The variability in assessment approaches underscores the need for standardized protocols and the development of more context-specific evaluation frameworks to enhance methodological consistency and comparability across studies.
Background:In the early stage of human-centered design (HCD), qualitative and generative methods are commonly used to explore patients' contexts and needs, emphasizing active patient involvement to ensure that design insights accurately reflect real experiences and enhance both design effectiveness and patient empowerment. However, certain challenges arise in the early stage of the HCD process, including (1) high vulnerability of patient participants, (2) less diverse and representative patient groups due to recruitment challenges, and (3) insufficient problem framing across diverse patient experiences. Objective:To address these challenges while embracing the system-level HCD perspective, we propose a data-enabled mixed method combining large-scale patient digital research (module A) and in-depth patient engagement research (module B). This paper explores the feasibility and potential value of this mixed method in addressing the identified challenges through a case study. Methods:In module A, we analyzed a large-scale dataset of online forum posts and validated the extracted topics with medical experts to create a patient community journey map through cocreation sessions. Guided by these findings, module B involved a diary study using a sensitizing paper prototype and semistructured follow-up interviews with 4 patients. Results:In module A, 37 topics and 10 upper clusters were summarized from 212,107 online posts, revealing that topics associated with the home context exhibited a higher density of emotional content than those related to the hospital context. Patients placed more emphasis on social and mental health during the follow-up stage than in the diagnosis and treatment phases. This shift reveals a gap in current remote monitoring systems, which focus mainly on physical health. Addressing this identified gap, we developed a prototype for use in the module B diary study involving 4 patients. Patients responded positively to the prototype, noting that remote monitoring incorporating social and mental well-being could help them better understand themselves, enhance self-awareness, and improve communication with their physicians. Overall, this study explores the preliminary value of this mixed method in effectively reframing design problems and deeply contextualizing patient needs. Conclusions:This study provides preliminary evidence supporting the potential of integrating large-scale digital patient research (module A) with in-depth patient engagement (module B) during the early stages of human-centered health care design. The core strength of this mixed method approach lies in its ability to facilitate problem reframing and cultivate a deeper sense of empathy and understanding of patient vulnerability prior to direct engagement. Simultaneously, it captures both the breadth and depth of patient perspectives, offering evidence-based insights that enhance the overall efficacy of user research. Further research across diverse medical contexts is essential to establish the generalizability of these findings.
BackgroundOverdose fatality reviews (OFRs) are an important public health tool for developing local overdose prevention strategies by reviewing individual overdose cases. While this approach offers a rich, contextual understanding of drug overdose factors in communities, it examines a small number of cases, providing limited insight into broader population-level risk patterns. To complement OFRs, we developed a real-time dashboard that visualizes trends about 5 key “touchpoints” (ie, interactions with medical and justice services preceding overdose). We then trained local OFR teams to use this dashboard to identify prevention opportunities. ObjectiveThis study examines the integration of population-level data into OFR practices, as well as the tensions that emerge between OFRs’ traditional case-driven review processes and the statistical, population-level analysis typical in public health. We analyze how and when the dashboard was used in meetings, determine the extent to which the data informed recommendations, and identify barriers that prevented the broad uptake of this intervention. MethodsWe observed 26 OFR meetings across 11 counties in Indiana over 10 months, from November 2024 to September 2025, during which teams conducted case reviews and developed recommendations. We documented instances of dashboard use as well as “missed opportunities,” in which relevant population-level data were available but not incorporated into the discussion. We also conducted semistructured interviews with OFR team members (n=7) to understand their perceptions of the dashboard, including its usefulness, usability, and adoption barriers. ResultsDespite its intended role, the dashboard was rarely integrated into OFR meetings; it was used only 10 times, compared to 114 missed opportunities in which relevant data could have informed discussions. Interviews revealed that this limited uptake was not solely due to usability barriers but reflected a deeper tension between 2 distinct analytic approaches. OFR teams prioritized narrative-driven case reviews that were grounded in empathy, local knowledge, and lived experience. This approach seemed at odds with the population-level visualizations shown in the dashboard, which required statistical abstraction and interpretation. Other barriers identified included limited time and resources, staff turnover, and varying levels of data fluency, which made it difficult for teams to confidently interpret the dashboard despite training. ConclusionsThe results highlight a tension between case-based and data-driven approaches to overdose prevention. These approaches are grounded in different workflows, values, and motivations, making it challenging for OFR teams to maintain their traditional, empathetic review practices while incorporating population-level trends. Our findings indicate the need for data tools that bridge these approaches, such as visualizations that connect aggregate patterns to individual cases. The results also underscore the need for additional support and training for teams, such as dedicated data specialists who interpret population-level trends and provide insights to augment team discussions and inform prevention strategies.
Artificial intelligence (AI) is now embedded infrastructure in perioperative care. Risk stratification algorithms, hemodynamic prediction tools, and clinical decision support systems are active in operating rooms at major health systems, and their adoption is accelerating. Yet the field has studied model performance and organizational implementation while largely bypassing the moment between them: the real-time encounter in which an anesthesia provider must decide, under active case conditions, what to do with an AI-generated output. We term this the cognitive transaction and argue it is the fundamental unit of perioperative AI implementation. The perioperative environment presents a specific constellation of conditions that existing human-AI interaction research was not designed to address. Continuous real-time decision demands, extreme time compression, high cognitive load, and consequences that unfold in seconds distinguish the operating room from the clinical contexts where most provider-AI interaction research has been conducted. What we know about AI adoption in radiology, oncology, or ambulatory care does not translate cleanly to this setting. The cognitive moment in anesthesia has its own structure, its own failure modes, and its own research requirements. This paper examines what those requirements are. We analyze how the operating room functions as a pre-existing human-machine cognitive system into which AI is now being inserted, and why the conditions of that system generate predictable vulnerabilities: miscalibrated trust, automation bias, and cognitive friction produced by interfaces optimized for technical accuracy rather than clinical usability. We argue that these failure modes are not incidental but structural, and that they will persist regardless of model performance until the provider-AI interaction is itself treated as a research object. We identify four priority research domains. The first concerns the structure of provider-AI disagreement and the methods needed to distinguish automation bias from legitimate clinical insight. The second concerns the longitudinal dynamics of trust calibration across repeated clinical encounters rather than single-session experimental designs. The third concerns interface design for high-acuity workflows, specifically what constitutes a usable AI output for a provider managing a patient in real time. The fourth concerns the need for ecologically valid study designs capable of capturing provider reasoning under actual intraoperative conditions rather than retrospective or survey-based proxies. The anesthesia and perioperative research community is positioned to lead this work. The clinical specificity, domain knowledge, and professional stake required to design meaningful studies are all present within the field. Evaluating the provider-AI dyad under intraoperative conditions, rather than the computational model in isolation, is both a methodological imperative and a patient safety priority.
Background:The risk of rehospitalization in patients with heart failure (HF) has initiated various efforts to prevent and simultaneously improve quality of life. Self-monitoring at home is one option, and technology is increasingly being used for this purpose. Objective:This pilot study aimed to evaluate the feasibility and preliminary effects of a digital home monitoring intervention on patient-reported outcomes and 30-day readmissions among patients with HF in Indonesia. Methods:A mixed methods pilot study was conducted, combining qualitative system development and quantitative evaluation. Patients were assigned to an intervention group (digital monitoring) or control group (standard care). Readmission rates were compared using chi-square tests and odds ratios. Changes in Kansas City Cardiomyopathy Questionnaire scores were analyzed using linear mixed-effects models. Results:A total of 60 patients were included (n=30, 50% in the intervention group; n=30, 50% in the control group). Readmission occurred in 20% (6/30) of patients in the intervention group and 43.3% (13/30) of patients in the control group (odds ratio 0.33, 95% CI 0.10-1.09; P=.10). Linear mixed-effects analysis showed greater improvement in Kansas City Cardiomyopathy Questionnaire overall summary score in the intervention group (P=.02). Improvements were observed in the physical limitation, symptom frequency, symptom burden, quality of life, and social limitation domains. During follow-up, 3.3% (1/30) of the patients in the intervention group died of non-HF-related causes, and 10% (3/30) of the patients in the control group died due to HF. Conclusions:This pilot study suggests that digital home monitoring is feasible and associated with improvements in patient-reported outcomes, with a potential signal toward reduced readmission. Larger studies are needed to confirm effectiveness.
BackgroundImplementing digital mental health interventions (DMHI) for those with psychosis is a persistent challenge. A process evaluation, or studies conducted alongside trials, is one research method that may address this issue. However, a synthesis of process evaluation data in this area is missing. ObjectiveThis study aimed to understand what is known about context, implementation, and mechanisms of impact by synthesizing process evaluation data from trials evaluating DMHIs used by people with psychosis. MethodsA scoping review using a 2-phase search strategy underpinned by the Medical Research Council (MRC) process evaluation framework was conducted. Database searches of Cochrane Central Register of Controlled Trials and PsycInfo in 2024 and 2025 first identified an index sample of peer-reviewed trials predominantly conducted in the United Kingdom (≥50% of samples from the United Kingdom in multicountry studies). Next, papers linked to the index sample were retrieved and included if they reported process evaluation data as operationalized in the MRC framework. Two authors independently screened references, extracted summary data, and assessed the quality of index trials. One author qualitatively synthesized process evaluation data using a deductive framework synthesis approach using the MRC framework. Findings were triangulated with senior authors and presented as a narrative synthesis. ResultsSearches identified 14 DMHIs and 45 papers reporting process evaluation data, though only 2 were labeled as such. Qualitative syntheses of process evaluation data generated five themes aligned with the MRC framework: (1) enhancing fit and supporting delivery (implementation strategies); (2) DMHI implementation varied across users, staff, and delivery settings (implementation outcomes); (3) helping users to respond in more helpful ways (mechanisms); (4) addressing perceived and actual implementation factors (context); and (5) limited impact of user characteristics on DMHI outcomes (context). ConclusionsThere is preliminary evidence that DMHIs can be delivered to people experiencing psychosis within trial settings, although use varied between individuals. Future implementation efforts may benefit from addressing contextual factors influencing DMHI use, including users’ treatment needs and preferences, everyday demands, and staff availability for blended interventions. Future research could evaluate implementation strategies, validate how and for whom DMHIs work, and embed process evaluation in trials. Trial RegistrationPROSPERO CRD42024439117; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024439117
Background:The rapid expansion of mobile technology has accelerated the integration of health applications and conversational AI into clinical and public health practices. To ensure these tools are effective and sustainable, usability evaluations and early user engagement during development are essential. The Health Information Technology Usability Evaluation Scale (Health-ITUES) is a validated and flexible usability assessment instrument that is available in multiple languages and applicable across diverse contexts. However, a Japanese version of this scale has not yet been developed. Objective:This study aimed to translate and validate a Japanese version of the Health-ITUES, customized for a sexually transmitted infection (STI)-related chatbot, and to support the usability assessment of emerging mobile health tools in Japan. Methods:We developed a Japanese version of the Health-ITUES using a chatbot under development as a consultation tool for young women regarding STIs. First, the original scale was customized to reflect the chatbot's specific purpose and intended usage context. Following established translation guidelines, we conducted forward translation from English to Japanese, back translation, expert review, and reconciliation. We then evaluated the reliability and validity of the Japanese version in a sample of 301 young women. Results:The Japanese version of the Health-ITUES demonstrated high internal consistency (Cronbach α=0.85-0.98). Confirmatory factor analysis supported acceptable construct validity (root mean square error of approximation is 0.10, comparative fit index>0.90). Additionally, the Health-ITUES scores showed strong correlations with satisfaction and usage intention for the tool (r=0.779 and 0.797, respectively). Conclusions:The Japanese version of the Health-ITUES provides initial evidence of reliability and validity in an STI-related scenario among young women and may facilitate more rigorous usability evaluations of mHealth and conversational AI tools in Japan.
BackgroundVideo-algorithmic patient monitoring (VAPM) combines remote, noncontact sensors and algorithmic analysis and is increasingly trialed in acute psychiatric and other care settings. While promoted for improving safety and reducing risk, it raises ethical concerns regarding safety, privacy and surveillance. Little is known about how those encountering VAPM in mental health care contexts anticipate its use and potential impacts, including where it has not yet been implemented. ObjectiveThis study aimed to explore the views of patients or mental health consumers, specialized mental health nurses and nurse academics, hospital managers, and technology vendors regarding the appropriateness and anticipated implications of VAPM in mental health inpatient care. MethodsThis qualitative study identified key stakeholders in Australia via networking techniques for participation in a deliberative workshop. A deliberative workshop was held, and the workshop discussion was audio-recorded, transcribed, and thematically analyzed, consistent with methods in health technology research, which enable exploration of different viewpoints, including convergences and divergences across stakeholder groups. ResultsIn total, 16 stakeholders participated, exploring themes concerning (1) contestation over the rationale for VAPM in mental health settings, (2) VAPM reshaping care and relationships, (3) perceived harms of VAPM, (4) perceived observational support for safety and reduced disruption, (5) serious privacy implications of VAPM, (6) the need for appropriate governance, and (7) the potential for VAPM to transform, not augment, service delivery. General views differed across groups. Patients or service users expressed concerns about privacy, coercion, and the potential to intensify stigma. Mental health nurses were cautious but interested in possible benefits for safety and suicide prevention. Hospital managers and technology vendors largely emphasized safety gains. ConclusionsThe findings suggest that the anticipated risks of VAPM are primarily experienced subjectively, as infringements on privacy, dignity, and trust, while purported benefits remain largely untested and unquantified. From a utilitarian perspective, direct comparison is therefore difficult—the risks are set out in the anticipated experiences of those with lived experience, and the benefits remain hypothetical. From this view, robust, independent evidence of real-world outcomes is required. Yet, for some participants, the very premise of such calculation was rejected, with privacy, dignity, and trust regarded as nonnegotiable, rather than items for trade-off. If VAPM is to be pursued at all, it should proceed only with extreme caution, with transparent evidence of outcomes, and with meaningful participation from those whose lives and care are most directly impacted.
Background:Body image dissatisfaction, disordered eating, and eating disorders represent significant public health concerns; however, many affected individuals never access evidence-based support. We co-designed and developed a rule-based chatbot, JEM, which conducts conversations addressing evidence-based psychoeducation and psychotherapeutic microinterventions. We previously demonstrated the feasibility, acceptability, and preliminary satisfaction of the JEM chatbot in a research setting. However, broader satisfaction, experiences, and user-reported outcomes in real-world settings have not yet been investigated. Objective:This study aims to conduct a real-world evaluation of the JEM chatbot in Australia and Canada, the two countries that have hosted a deployment of the chatbot to date. Specifically, we aim to explore user satisfaction and experiences with the chatbot and within-session differences in user mood and body image satisfaction when completing the chatbot's microinterventions. Methods:Respondents were users of the JEM chatbot aged 13 to 64 years who self-selected to complete a web-based overall evaluation survey (N=230; n=122 in Australia and n=108 in Canada) over a 6-month period. This evaluation survey included user demographic characteristics, satisfaction measures, and the System Usability Scale. Respondents for the within-session pre-post analyses were JEM chatbot users who chose to complete brief web-based surveys immediately before and after completing one of the chatbot's microinterventions during the same 6-month period. Sample sizes varied across microinterventions, ranging from 75 to 276 respondents overall (Australia: n=34-146; Canada: n=39-130). These surveys included validated visual analog scales (VAS) measuring mood (anxiety, depression, happiness, confidence) and body image satisfaction (body size satisfaction, body shape satisfaction, physical attractiveness). Results:Demographic characteristics showed that survey respondents were commonly young adult cisgender women and nonbinary individuals across Australia and Canada. Respondent satisfaction with the chatbot was high in both countries (Australia: mean 76.1, SD 22.7; Canada: mean 78.8, SD 14.3), and the usability of the chatbot was rated as "excellent" in both countries (Australia: mean 86.5, SD 16.9; Canada: mean 89.5, SD 11.6) according to the System Usability Scale. Across completed microintervention surveys, patterns of within-session pre-post ratings were broadly similar in Australia and Canada, with effect sizes generally ranging from very small to large across VAS-measured mood and body image outcomes. Conclusions:The JEM chatbot achieved high satisfaction and usability ratings. Among respondents who completed pre-post surveys, immediate within-session differences in mood and body image ratings were observed following the completion of chatbot microinterventions. The study findings were broadly similar across Australia and Canada. These results provide evidence of user experience and within-session differences following engagement with JEM and support continued evaluation in future studies.
