JMIR Formative Research

Abstract Background People with disabilities use technology, like search engines, to seek health information online. This health information includes information on coronavirus disease, or COVID-19. COVID-19 remains a public health concern. Research shows that people with disabilities encounter frustrations, or "pain points," when seeking online information, but little is known about these specific pain points and who encounters them. Objective The goals of this study are to determine pain points for people with disabilities who seek health information online, and to assess how pain points impact the experience of technology use and information seeking. Methods Ten participants recruited from a prior quantitative survey completed the concurrent think-aloud study over a month-long period. Participants completed four online search tasks and narrated their experiences in real-time while doing so. Transcripts were stored in Taguette; thematic analysis was performed on these transcripts. Findings Participants were predominantly white, with three identifying as Asian. All ten participants reported having disabilities. Participants with attention deficit hyperactivity disorder (ADHD) reported distracting webpage layout, whereas participants with physical disabilities reported physical fatigue while navigating online information. All participants encountered AI-generated information; only one participant indicated trust in the AI-generated information. Other common sources of information included hospital and governmental webpages, peer-reviewed articles, and news and advertising results. News and advertising results were especially common with respect to search results for "COVID-19 vaccine." Themes identified included the following: accessibility/usability, AI-generated information, government/hospital and related sources of information, peer-reviewed articles, news and advertising, and sentiment and trust. Conclusions Information can be fatiguing, distracting, or otherwise difficult to navigate for people with diverse disabilities searching for COVID-19 related information online. Further work should incorporate user feedback from people with disabilities when designing online content.

Background: Continuous adhesive patch electrocardiographic (ECG) wearables are increasingly prescribed. Patient experience with these devices can influence adherence, but research in this area is limited. This study aimed to explore the perceptions and experiences of patients receiving wearable cardiac monitoring technology as part of their routine care through the lens of treatment burden. Methods: This was a qualitative study with semi-structured phone interviews conducted between February and May 2024. We recruited participants from primary care and outpatient clinics using maximum variation sampling to ensure diversity in sex, ethnicity, and education levels. Interviews were audio-recorded, transcribed, and analysed using reflexive thematic analysis. Results: Sixteen participants (mean age 51 years, 63% female) were interviewed (average duration: 33 minutes). Three themes were developed: 1) ?Experience using the device: Burden vs Ease of Use?, which captured participants? perceptions of how easily they could integrate the device in their daily lives; 2) ?Individual variability in responses to ECG self-monitoring? covered participants? emotional and cognitive response to knowing their heart rhythm was monitored; and 3) ?The care process shapes patient experiences? reflected support preferences during the set-up and monitoring period and the uncertainty regarding timely clinical and device feedback. Conclusions: Patients valued cardiac wearables for facilitating diagnosis and felt reassured knowing they were clinically monitored. However, gaps in information provided to patients seemed to cause anxiety for some participants. These concerns could be mitigated through clearer clinician communication and patient education at the time of prescription.

Patient-generated streaming data from wearable and digital technologies is increasingly promoted as a means of supporting mental health monitoring and clinical decision-making. While patient acceptance of these technologies has been reported, clinician perspectives remain underexplored despite their central role in determining whether streaming data are meaningfully integrated into routine care. This study explored clinicians experiences, as well as perceived facilitators and barriers, related to integrating patient-generated streaming data into routine mental health practice. A qualitative, exploratory interview study was conducted to examine clinicians experiences and perspectives on integrating patient-generated streaming data into mental health care. Semi-structured interviews were conducted with 33 clinicians, including family physicians (n=11), psychiatrists (n=12), and psychologists (n=10). Data were analyzed using reflexive thematic analysis guided by Braun and Clarkes six-step approach. Six themes were identified. Clinicians described variable use of digital and streaming technologies, ranging from routine engagement to deliberate non-use. Streaming data were viewed as clinically valuable when they provided longitudinal and objective insights, identified physiological and behavioural pattern changes, and supported patient engagement. However, clinicians emphasized that clinical usefulness was contingent on interpretability, contextual information, and relevance to decision-making. Major barriers included poor integration with electronic medical records, time constraints, data volume, limited organizational support, and uncertainty regarding data reliability and validity. Clinicians also expressed persistent concerns about privacy, governance, and regulatory oversight, highlighting the need for clear safeguards and accountability structures. Clinicians view patient-generated streaming data as a promising adjunct to mental health care, particularly for capturing longitudinal change between visits. However, meaningful clinical integration remains constrained by usability, workflow, organizational, and regulatory challenges, as well as limited confidence in data interpretation. Addressing these barriers through improved system integration, interpretive support, validation, and governance will be essential for translating the potential of streaming data into routine clinical practice.

Purpose: To understand how the Philips PAP device recall affected patient experiences, clinical practice, and health system responses. Methods: From November 2022 to August 2023, we interviewed individuals with OSA, physicians, respiratory therapists and health system leaders. We also received emailed responses from Health Canada. Interviews explored participants' experiences with the recall announcement and communication, their own responses and perceptions of actions taken by others, the overall impact of the recall and suggestions for improving future recall processes. Interviews were analyzed using an inductive thematic approach. Results: We interviewed 47 participants (16 individuals with OSA, 10 physicians, 17 public or private respiratory therapists, five health system leaders). Themes were organized into four domains: recall communication, execution, participant experiences, and the policy and regulatory context. Participants were confused due to inadequate information from Philips throughout the process. The burden of notifying patients and tracing devices mostly fell to healthcare providers and vendors, while replacement efforts were disorganized and frustrating. Individuals with OSA experienced emotional distress over therapy decisions and difficulties navigating the recall. Healthcare providers described moral distress from being unable to support patients adequately, and vendors faced additional logistical and financial strain. While regulatory authorities reported that Philips followed standard procedures, participants expressed a loss of trust in both the manufacturer and oversight systems. Conclusions: Interviews revealed that poor communication and execution of the Philips recall caused confusion, frustration and significant emotional and financial burden. Collaborative, context-specific strategies are required to improve future recalls.

BackgroundHwa-byung (HB) is a Korean culture-bound syndrome characterised by prolonged suppression of anger and somatic complaints. No evidence-based digital therapeutic (DTx) has been developed for HB. We evaluated the feasibility, user experience (UX), and preliminary clinical effect of an acceptance and commitment therapy (ACT)-based DTx application, Hwa-free, for HB. MethodsAdults aged 19-80 years diagnosed with HB were enrolled in a four-week app-based intervention with assessment at baseline (Week 0), Week 2, Week 4, and Week 8 follow-up. The primary outcome was UX assessed via a 22-item survey at Week 4. Secondary outcomes included HB-related symptom and personality scales, depression, anxiety, anger expression, psychological flexibility, health-related quality of life, and heart rate variability. ResultsOf 45 screened, 30 were enrolled and 28 constituted the modified intention-to-treat population. Mean app use was 19.9 {+/-} 7.9 days (71.2% adherence over 28 days). Adverse events were infrequent and unrelated to the intervention. Positive response rates exceeded 80% for video content (items 2-4: 82.8-89.7%), HB self-assessment (86.2%), meditation therapy (86.2%), and in-app guidance (85.7%). Pre-post improvements from baseline to Week 4 were observed in 11 of 18 clinical scales, including HB Symptom Scale ({Delta} = -9.8, Cohens d = -0.92), Beck Depression Inventory-II ({Delta} = -13.3, d = -1.11), and state anger ({Delta} = -7.8, d = -0.96). The HB screening-positive rate declined from 100% at baseline to 55.6% at Week 8. ConclusionsHwa-free demonstrated adequate feasibility, acceptable UX, and preliminary evidence of clinically meaningful improvement in HB-related symptoms. Future randomised controlled trial is warranted. Trial registrationCRIS, KCT0011105

Background Comprehensive sexuality education (CSE) is essential to the health and well-being of young people. In the Democratic Republic of Congo (DRC), where more than 65% of the population is under the age of 25, access to interpersonal CSE remains limited owing to sociocultural and structural barriers. This exposes young people to persistent socio-sanitary vulnerabilities. In this context, mobile health apps (MHAs) constitute a promising solution, supported by the growing use of smartphones among young Congolese. However, this group's intention to use MHAs for CSE has been the subject of little research to date. Objective The aim of this study was to identify predictors of intention to use MHAs among young Congolese, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). Methods A predictive correlational study was conducted in eight public secondary schools in Bukavu (DRC) with a stratified random sample of 859 students. Predictors of intention to use--performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and perceived risk (PR)--and moderators--age, gender, and past MHA experience--were measured from data collected through a self-administered UTAUT questionnaire. Descriptive and multivariate analyses were run on SPSS version 28. Results Mean age of participants was 16.3 years (SD = 1.5). Boys made up 55.1% of the sample. Overall, 51.0% of the sample owned a smartphone, of which 62.3% reported having easy access to mobile data and 16.2% were already using MHAs to learn about sexual health. Intention to use MHAs was positively influenced by PE ({beta} = 0.523, p < 0.001), EE ({beta} = 0.115, p < 0.001), and SI ({beta} = 0.113, p < 0.001). FC (p = 0.260) and PR (p = 0.631), however, had no significant influence. Age moderated all of the relationships tested (F (1, 849-854) = 9.97-20.82; p [&le;] 0.002), with more marked effects observed among younger participants 14-15 years old. The final model explained 44% of the variance, indicating good predictive power. Conclusion Intention to use digital CSE was explained primarily by PE, EE, and SI and moderated by age. To strengthen this intention, stakeholders will need to promote e-interventions that are pertinent, easy to use, socially valorized, and tailored to young people's needs and to the local context.

Objectives: Heart failure is a leading cause of mortality, necessitating identification of patients at increased risk needing intervention. In this study, we investigated if Fitbit data can reveal physiological trends associated with hospital visit risk. Materials and methods: Individuals with heart failure (n=249) were randomized into three arms for prospective 180-day monitoring. All arms received a Fitbit and wireless weight scale. Arm 1 received devices only; Arm 2 received a mobile app with surveys; Arm 3 received the app plus financial incentives. Results: 51 participants had hospital visits during the study period. These individuals took fewer steps (p=.002) and reported increased symptom severity (p=.044). Resting heart rate increased three days prior to a visit (p=.022). Baseline steps revealed a higher visit probability for less active participants (p=.003). Discussion and conclusion: Passive physiological monitoring can effectively identify individuals at risk of health exacerbation, demonstrating the potential of wearable devices for timely clinical intervention.

Background/Objectives: This study aimed to evaluate the efficacy and outcomes of operative hysteroscopy for the removal of endometrial polyps and assess the procedure's impact on pain experienced by patients. The research was conducted to determine whether the minimally invasive nature of operative hysteroscopy compromises patient comfort when compared with diagnostic hysteroscopy. Methods: The study was conducted at the Gynecology and Obstetrics Clinic of Ba[g]clar Training and Research Hospital over a period of four months. It included 200 women over 18 years of age who were indicated for hysteroscopy. Operative hysteroscopy procedures were performed without the use of a speculum, cervical dilation, anesthesia, or analgesic agents, emphasizing the procedure's minimally invasive approach. Pain assessment utilized the Visual Analog Scale (VAS). Patients were stratified into two groups--those undergoing operative and those undergoing diagnostic hysteroscopy--to compare outcomes and pain scores. Results: The study found that operative hysteroscopy successfully removed 85.1% of the lesions, primarily polyps. There was no significant difference in pain scores between the operative and diagnostic hysteroscopy groups, indicating that the minimally invasive procedure does not increase patient discomfort. Conclusions: Operative hysteroscopy is an effective and tolerable procedure for the removal of endometrial polyps, with high success in complete lesion removal and without significantly impacting the pain experienced by patients. The findings support the use of operative hysteroscopy as a first-line treatment option for endometrial polyps, underscoring the importance of patient selection and the need for further studies on long-term outcomes related to fertility and recurrence.

Background: The urgent care departments in Europe face a structural paradox: accelerating digitalisation is accompanied by a patient population that is disproportionately unable to engage with standard digital tools. An internal analysis at the Emergency Department (Akutafdelingen) of Nordsjaellands Hospital in Hilleroed, Denmark found that 43% of emergency patients struggle with digital solutions - a figure that reflects the predictable composition of acute care populations rather than any individual failing. Objective: This paper presents the design, iterative development, and secondary validation of the ED Adaptive Interface (v5): a prototype adaptive patient terminal developed in response to this challenge. The system operationalises what the author terms impairment-first design - a methodology that treats the most constrained patient experience as the primary design problem and derives the standard experience as a subset. The interface configures itself in under ten seconds via nurse-led setup, adapting across four axes of impairment: visual, motor, speech, and cognitive. System: Version 4 supports five accessibility modes, a heatmap pain assessment grid, a Privacy and Dignity panel, a live workflow tracker with care notifications, structured dual-category help requests, and plain-language medical term definitions across four languages. Version 5, reported here for the first time, introduces a Condition Worsening Escalation button, a Referral Pathway Display, a "Why Am I Waiting?" triage explainer, a Symptom Progression Log, MinSP/Yellow Card Scan simulation, expanded language support (seven languages: English, Danish, Arabic with full RTL layout, Turkish, Romanian, Polish, and Somali), and an expanded ten-item Communication Board. The entire system runs as a single 79-kilobyte HTML file with zero infrastructure requirements. Methods: To base the design on patient-generated evidence, two independent social media threads were subjected to an inductive thematic analysis (Braun and Clarke, 2006): a primary corpus of 83 entries in the Facebook group Foreigners in Denmark (collected March 2026) and a corroborating corpus in an international community group in the Aarhus region (collected April 2026). All identifiers in both datasets were fully anonymised under GDPR Article 89 research provisions prior to analysis. No participants were contacted. Generative AI tools were used to assist with drafting, writing, and prototype code development; all scientific content, data collection, analysis, and conclusions are the sole responsibility of the authors. Results: The first discourse corpus produced five major themes corresponding to the five problem areas the prototype was designed to address: system navigation and triage literacy gaps (31 entries); language and cultural barriers (6 entries); communication failures during care (5 entries); staff overload and capacity constraints (8 entries); and pain and severity assessment failures (14 entries). The corroborating dataset supported all five themes and introduced two additional themes: differential treatment of international patients and medical gaslighting as a long-term pattern of patient advocacy failure. One structural finding - the five most-liked comments incorrectly criticised the original poster for self-referring when she had received explicit 1813 telephone triage approval - directly inspired the Referral Pathway Display and "Why Am I Waiting?" features in v5. Conclusions: The convergence of design rationale and independent social evidence across all five problem categories suggests that impairment-first design is not a niche accessibility concern but a structural approach to healthcare interface quality. The prototype is ready for a structured clinical pilot using the System Usability Scale (SUS) and semi-structured staff interviews. The long-term roadmap includes full MinSP integration, hospital PMS connectivity, and clinical validation.

Chronic low back pain (CLBP) is persistent and refractory, affecting 20-30% of population worldwide. Neurofeedback has been explored as a potential non-pharmacological intervention for chronic pain, although evidence in CLBP remains limited. This study evaluated PainWaive, a consumer-grade digitally-delivered neurofeedback intervention targeting multiple pain-related frequency bands recorded over the sensorimotor cortex in individuals with CLBP. In a multiple-baseline experimental design, four participants completed daily assessments of pain severity and pain interference during randomly-assigned baseline phases of 7, 10, 14, and 20 days, followed by 20 sessions of the PainWaive intervention over four weeks. Daily pain assessments continued during the post-intervention and follow-up phases. Participants rated PainWaive's usability and acceptability at post-intervention. Anxiety, depression, wellbeing, and sleep disturbance were assessed at three timepoints. Aggregated Tau-U analyses indicated a large effect (-0.67) on pain severity from baseline to intervention and very large from baseline to post-intervention (-0.92) and follow-up (-0.92) phases. Large effects (-0.63, -0.62, and -0.70) were also observed for pain interference. Individual-level analyses showed significant reductions across all participants, with visual inspection confirming progressive decreases over time. The intervention was rated usable and acceptable by all participants, while psychological outcomes were mixed and varied across participants. The findings provide promising evidence that the PainWaive neurofeedback intervention may reduce pain severity and pain interference in some individuals with CLBP. By prioritising accessibility, usability, and self-administration, PainWaive supports a foundation for more patient-centred, technology-enabled approaches to chronic pain management. Further evaluation of this approach in randomised trials is required to establish efficacy.

Background: Oral health remains inadequately integrated within the Ayushman Bharat Digital Mission (ABDM), particularly in terms of structured risk assessment and its linkage to insurance-based decision-making. There is a growing need for scalable models that can connect clinical oral health data with digital health systems and support future artificial intelligence (AI)-driven applications. Aim: To develop and pilot test the ABHA-O-SHINE framework for oral health risk prediction and insurance prioritization, with a future scope for AI integration within the Ayushman Bharat Health Account (ABHA) ecosystem. Materials and Methods: A cross-sectional pilot study was conducted among 126 participants attending the outpatient department of Swargiya Dadasaheb Kalmegh Smruti Dental College and Hospital, Nagpur. Participants were selected based on predefined inclusion and exclusion criteria. Data collection included a structured questionnaire and clinical examination using the WHO Oral Health Assessment Form (2013). A composite risk score (0 to 14) was developed incorporating behavioral and clinical parameters. Participants were categorized into low, moderate, and high-risk groups, and corresponding insurance priority levels were assigned. Statistical analysis included descriptive statistics, Chi-square test, Spearman correlation, and binary logistic regression. Results: The majority of participants were categorized under moderate to high-risk groups. Tobacco use showed a statistically significant association with higher risk levels (p less than 0.05). Positive correlations were observed between total risk score and clinical indicators such as DMFT and CPI. Logistic regression analysis identified tobacco use and clinical scores as significant predictors of high-risk categorization. Conclusion: The ABHA-O-SHINE framework demonstrates feasibility in integrating oral health risk assessment with an insurance prioritization model. The framework is designed to be AI-compatible, enabling future automation through machine learning and image-based analysis within the ABDM ecosystem. Keywords: ABHA, ABDM, Oral Health, Risk Assessment, Insurance, Artificial Intelligence.

Cardiovascular disease (CVD) remains the leading cause of mortality in the United States, despite being largely preventable through effective management of risk factors. This study evaluates the impact of Phase II cardiac rehabilitation (CR) on functional capacity and quality of life, using data from the Montana Outcomes Project Cardiac Rehabilitation Registry. Functional capacity improvements were assessed via the six-minute walk test (6MWT) and Dartmouth COOP questionnaire, with statistical analyses exploring the influence of CR session attendance, demographic factors, and referring diagnoses. Results demonstrated significant gains in 6MWT, with a mean improvement of 330.73 feet (p < .0001), and quality of life scores across all subgroups. A dose-response relationship was observed, indicating greater improvements with increased CR sessions (p < .0001), though diminishing returns were observed beyond 24-35 visits. Demographic factors and complex conditions influenced outcomes, underscoring the need for tailored strategies to enhance CR access and effectiveness. These findings highlight the critical role of CR in improving patient outcomes and emphasize the importance of addressing barriers to participation in underserved populations.

A Wearable Monitoring System (WMS), comprising a chest patch, wrist-worn pulse oximeter, and arm-worn blood pressure device, was developed in preparation for a pilot Randomised Controlled Trial (RCT) on a UK surgical ward. The system was designed to support continuous physiological monitoring and early detection of deterioration. An initial prototype user interface was developed by the research team based on prior clinical experience and engineering knowledge. To ensure suitability for clinical practice, iterative user-centred refinement was undertaken through a series of clinician focus groups and wearability assessments. Six focus groups were conducted between November 2019 and May 2021 involving multidisciplinary healthcare professionals. Feedback from these sessions informed successive interface and system modifications. System development spanned the COVID-19 pandemic, during which the WMS was rapidly adapted and deployed to support clinical care on isolation wards. Feedback obtained during this period was incorporated into later versions of the system and provided a unique opportunity to examine changes in clinician priorities under pandemic conditions. Clinicians consistently prioritised alert visibility, alarm fatigue mitigation, parameter flexibility, and centralised monitoring. Notably, preferences regarding alert modality and access mechanisms evolved over time: early enthusiasm for mobile or smartphone-type devices shifted towards a preference for fixed, ward-based displays and audible alerts at the nurses station following pandemic deployment. Building on previous wearability testing in healthy volunteers, wearability testing using a validated questionnaire was completed by 169 patient participants during the RCT. The chest patch and pulse oximeter demonstrated high tolerability, whereas the blood pressure cuff showed poor wearability and was removed from the final system. These findings demonstrate the importance of iterative, clinician-led design for wearable WMS and highlight how extreme clinical contexts such as the COVID-19 pandemic can significantly reshape perceived requirements for safety-critical monitoring technologies.

Despite the global prevalence of postpartum depression (PPD), current referral uptake rates are far from satisfactory. While some qualitative studies have investigated factors affecting PPD referrals, a gap in quantitative analysis remains. Addressing this, our study utilized a discrete choice experiment (DCE) to understand the procedural elements influencing PPD referral uptake among diagnosed women. The DCE was conducted via home visits by healthcare providers and a comprehensive mobile app questionnaire. We constructed seven distinct referral attributes to explore participants' preferences, analyzed using mixed logit models and latent class analysis. This analysis identified key determinants and revealed the heterogeneities in referral preferences. A total of 698 individuals completed the DCE questionnaire. All assessed attributes, except for Accompaniment (going to clinic with a family member), were important determinants of preference. Participants generally preferred referrals to psychiatric clinics, face-to-face consultations, lower costs, and shorter waiting times. Significantly, participants' personal and socio-demographic characteristics also played a critical role in their referral preferences. Latent class analysis categorized participants into four distinct groups based on their preferences, with treatment cost and waiting times being the most decisive factors. In conclusion, the preference for PPD referrals is predominantly driven by convenience and access to specialist care. To enhance referral uptake, developing flexible and personalized referral programs that cater to these preferences is crucial.

Introduction Large language models are increasingly being used in healthcare. In interventional pain medicine, clinical reasoning is essential for procedural planning. Prior studies show that simplified prompts reduce clinical detail in AI-generated responses. It remains unclear whether this reflects knowledge loss or simply prompt-driven suppression of information. Methods We performed a controlled comparative study using 15 standardized low back pain questions representing common interventional pain questions. Each question was submitted to ChatGPT under three conditions, professional-level prompt (DP), fourth-grade reading-level prompt (D4), and clinician-directed rewriting of the D4 response to a medical level (U4[-&gt;]MD). No follow-up prompting was allowed. Three physicians independently rated responses for accuracy using a 0-2 ordinal scale. Clinical completeness was determined by consensus. Word count and Flesch-Kincaid Grade Level (FKGL) were also measured. Paired t-tests compared conditions. Results Accuracy was highest with professional prompting (1.76). Accuracy declined with the fourth-grade prompt (1.33; p = 0.00086). When simplified responses were rewritten for clinicians, accuracy returned to baseline (1.76; p {approx} 1.00 vs DP). Clinical completeness followed the same pattern showing DP 80.0%, D4 6.7%, U4[-&gt;]MD 73.3%. Fourth-grade responses were shorter and less complex. Upscaled responses were more complex and similar in length to professional responses. Inter-rater reliability was low (Fleiss {kappa} = 0.17), but trends were consistent across conditions. Conclusions Reduced clinical detail under simplified prompts appears to reflect constrained output rather than loss of knowledge. Clinician-directed reframing restores omitted content. LLM performance in interventional pain depends strongly on prompt design and intended audience.

Objectives: Accurate classification of menopausal stage is fundamental to midlife health research. Although the Stages of Reproductive Ageing Workshop (STRAW+10) criteria provide gold-standard criteria, their application in research settings is inconsistent. Classification challenges are compounded in individuals without observable menstrual cycles due to surgical or contraceptive-induced amenorrhoea. The Reproductive Ageing in Women (RAW) Questionnaire and accompanying classification Framework was developed and validated to improve consistency and inclusivity when classifying menopausal stage. Study design: A multi-phase study was conducted between May 2022 and July 2025. Phase one involved questionnaire development based on STRAW+10 and the Menopause-Specific Quality of Life Questionnaire. Phase two assessed content validity via expert review (n=3). Phase three evaluated face validity using think-aloud interviews and focus groups (n=14). Phase four validated RAW within a cross-sectional cohort study (n=156), and assessed construct validity (n=30), test-retest reliability (6-21 days; n=128; Kendall's Tau-b and Cohen's kappa), and biological validity using follicle stimulating hormone (FSH). Results: Feedback supported clarity, relevance and usability, with refinements improving inclusivity for surgical and contraceptive-induced amenorrhoea. Construct validity demonstrated consistent application of classification criteria. Questionnaire classification showed 93% concordance with self-identified menopausal status in the construct sample and 87.8% agreement within the cohort sample. Test-retest reliability was excellent ({tau}, p=0.940, p<0.001). Follicle stimulating hormone levels differed across RAW-classified stages (p<0.001), with 96.1% concordance between RAW pre and postmenopausal classifications and FSH thresholds. Conclusions: Prioritising menstrual characteristics while incorporating age and symptom criteria improves methodological consistency and inclusivity in menopausal stage classification. Longitudinal validation is warranted to assess temporal sensitivity across the menopausal transition.

BackgroundRecruitment remains a major barrier to timely clinical trial completion. Trialshub is an LLM-powered, chat-based platform intended to help users identify relevant trials and connect with coordinators to streamline recruitment workflows. ObjectiveTo evaluate the perceived usability and operational value of Trialshub, and identify implementation considerations for real-world deployment. MethodsA usability test was conducted at Morehouse School of Medicine for the Trialshub application. Purposively selected participants included clinical research coordinators and individuals with and without clinical trial search experience. Participants completed a pre-test survey assessing demographics, digital health information behaviors, and familiarity with AI tools, followed by a moderated usability session using a Trialshub prototype. Users completed scenario-based tasks (locating a breast cancer trial, reviewing results, and initiating coordinator contact) using a think-aloud protocol. Task ratings, screen recordings, and transcribed feedback were analyzed descriptively and thematically, and reported. ResultsParticipants reported high comfort with using digital tools and moderate-to-high familiarity with AI. Trialshubs chat-first design, guided prompts, and checklist-style eligibility display were perceived as intuitive and reduced cognitive load. Fast access to trials and the coordinator-contact workflow were viewed positively. Key usability issues included uncertainty at step transitions, insufficient cues for selecting results and next actions, and inconsistent system reliability (loading delays, errors, and broken trial detail pages). Participants also noted redundant questioning due to limited conversational memory, requested improved filtering/sorting, and clearer calls-to-action. All participants indicated that Trialshub has strong potential to meaningfully improve clinical trial processes. ConclusionsTrialshub shows promise for improving trial discovery and recruitment workflows, with identified design implications for real-world deployment.

Background: Perioperative pain management modality potentially influences psychiatric morbidity and healthcare utilization. The opioids have been most commonly used for managing postoperative pain and carry a high degree of risk for creating mood disorders, anxiety, sleep disturbances, and healthcare burdens. A novel non-opioid analgesic, Suzetrigine, may be able to effectively manage postoperative pain without some of the psychological and economic risks that come from the use of opioids. In this study, we measured psychiatric outcomes and emergency department (ED) usage among postoperative patients who received either suzetrigine or opioids. Methods: This was a retrospective cohort study using the TriNetX US Collaborative Network, encompassing 64 healthcare organizations. Adult patients (> Age 18 years) who underwent surgery and received suzetrigine were compared with patients who underwent surgery and received opioids. Propensity score matching (1:1) performed to match cohorts based on demographic factors (age, gender, racial/ethnic status), social determinants of health (ICD-10 Z55-Z65), family histories of substance abuse and psychiatric disorders (Z81.x), surrogate measures of prior healthcare utilization, and pre-existing clinical severity using Elixhauser-Charlson comorbidity proxies (hypertensive diseases [I10-I15], diabetes mellitus [E08-E13], ischemic heart disease [I22-I25], and chronic pulmonary disease [J42-J47]). Matching also included behavioral risk factors (tobacco use and physical inactivity) and body mass index (BMI). Following matching, there were 2,221 patients in each cohort. The primary outcome assessed within one year after surgery was ED utilization, depression, anxiety, post-traumatic stress disorder (PTSD) and sleep disorders. Risk estimates and survival analyses were used to compare the outcomes. Results: In propensity-matched analyses, suzetrigine use was associated with a reduction in multiple psychiatric outcomes and healthcare utilization compared to opioid analgesics. There was less ED utilization in the suzetrigine cohort (5.9% v 13.1%, RR 0.45, p< .001). The psychiatric outcomes were also lower in the suzetrigine cohort than the opioid cohort, including depression (3.1% v 4.7%, RR 0.65, p= .005), anxiety (4.7% v 7.2%, RR 0.65, p< .001), PTSD (0.5% v 1.4%, RR 0.36, p= .002), and sleep disorders (4.2% v 6.0%, RR 0.71, p= .008). The survival analysis suggested an earlier onset of psychiatric diagnosis among the opioid recipients. Conclusion: In a matched real-world cohort of surgical patients, suzetrigine use was associated with lower short-term rates of selected postoperative outcomes compared with opioid analgesics. Keywords: Suzetrigine; Opioid-Sparing; Analgesia; Postoperative Outcomes; Cohort Study

Wearable-derived physiological features have been associated with disease risk, but most current studies focus on single conditions, limiting understanding of cross-disease patterns. This study adopts a trans-diagnostic approach to examine whether wearable data capture shared and condition-specific physiological signatures across multiple chronic conditions spanning physical and mental health, and then evaluates the utility of these features for disease classification. A total of 9,301 patients with at least 21 days of consecutive FitBit data from the All of Us Controlled Tier Dataset version 8 were analyzed. Disease subcohorts included cardiovascular disease (CVD), diabetes, obstructive sleep apnea (OSA), major depressive disorder (MDD), anxiety, bipolar disorder, and attention-deficit/ hyperactivity disorder (ADHD), chosen based on prevalence and relevance. Logistic regression and XGBoost models were fitted for each disease subcohort versus the control cohort. We found that compared to using just baseline demographic and lifestyle features, incorporating wearable-derived features enabled improved classification performance in all subcohorts for both models, except for ADHD where improvement was mainly observed for ROC-AUC in logistic regression model likely due to the smaller sample size in ADHD subcohort. The largest performance gains were observed in MDD (increase in ROC-AUC of 0.077 for Logistic regression, 0.071 for XGBoost; p < 0.001) and anxiety (increase in ROC-AUC of 0.077 for logistic regression, 0.108 for XGBoost; p < 0.001). This study provides one of the first comprehensive transdiagnostic evaluations of wearable-derived features for disease classification, highlighting their potential to enhance risk stratification in the real-world setting as a practical complement to clinical assessments and providing a foundation to explore more fine-grained wearable data. Author summaryWearable devices such as fitness trackers and smartwatches are becoming increasingly popular and affordable, providing continuous measurements of heart rate, physical activity, and sleep. Alongside the growing digitization of health records, this creates new opportunities for large-scale, real-world health studies. In this study, we analyzed wearable-derived physiological patterns across a range of chronic conditions spanning both physical and mental health to better understand how these signals relate to disease risk. We found that incorporating wearable-derived heart rate, activity and sleep features improved disease risk classification across several conditions, with particularly strong gains for major depressive disorder and anxiety. By examining how individual features contributed to model predictions, we also identified meaningful associations between physiological signals and disease risk. For example, both duration and day-to-day variation of deep and rapid eye movement (REM) sleep were associated with increased risk in certain conditions. Our study supports the development of real-time, automated tools to assess disease risk alongside clinical care.

Background: Safety is a critical concern in behavioral health crisis units (BHCUs), where environmental risks (e.g., ligature points) can lead to injury to self or others. However, limited research has examined how perceived safety influences facility selection among patients and care partners, or how these perceptions align with AI-driven safety risk assessments in such environments. Method: To address these gaps, a nationwide discrete choice online survey was conducted using image-based scenarios of BHCU environments, where participants selected preferred facilities based on a range of attributes, including environmental safety risks (e.g., ligature points). Additionally, participants identified safety risks in survey images, which were compared with outputs from an AI-driven tool developed and trained to detect environmental risks by experts. Quantitative analysis using conditional logit models examined the influence of attributes on facility choice, while spatial comparisons of annotated images and heatmaps assessed participant and AI-identified risk alignments. Results: Findings revealed that the higher frequency of safety risks in images significantly reduced the likelihood of facility selection (p < .001, OR {approx} 1.28), highlighting the importance of perceived safety in user decision-making. While there was notable alignment between heatmaps generated by participants and AI, key differences emerged, suggesting that participant safety perception was influenced by features not fully captured by AI, such as the type of materials or unknown, out-of-label safety risks in facility images. Conclusions: Despite these limitations, results highlighted the value of integrating AI-driven assistive tools for non-expert user safety risk assessment to support decision-making for safer BHCU environments.