JMIR Human Factors

Mode Effects on Self-Reported Health Across Web-Based, Interviewer-Administered, and Nurse-Administered Modes: Population-Based Study

2026-10-16T13:45:19-04:00

Background: Probability-based surveys remain essential to epidemiology, but expanding mixed-mode data collection, which combines web self-completion and interviewer-administered surveys, raises concerns that administration mode and interviewer presence may influence health reporting. These concerns warrant further investigation of interviewer-related biases and mode differences in health reporting. Objective: This study assessed whether responses to the 12-item General Health Questionnaire (GHQ-12) and 12-item Short Form Health Survey (SF-12), both self-completed, differed by interviewer presence and role: online without an interviewer, during a social interviewer visit, or during a nurse visit. It also assessed mode effects on doctor-diagnosed conditions recorded by interviewers in computer-assisted personal interviewing (CAPI) and when self-completed online. We further examined possible variation by sex and age. Methods: The analysis used randomized mode allocation embedded within wave 12 (2019) of the UK Household Longitudinal Study Innovation Panel. After applying relevant exclusions, the final analytic sample comprised 1111 households (1779 participants) across the 3 randomly assigned survey modes: web-based self-completion, interviewer-administered CAPI, or nurse-administered CAPI. The sample was predominantly White British, with a broadly balanced sex distribution and participants from diverse age, employment, education, and household income groups. Participants did not always complete the survey in their randomly assigned mode. Effects were estimated using a two-stage residual inclusion instrumental variable approach, with randomized mode as an instrument for actual mode, to address confounding arising from nonrandom selection into actual mode. Results: No statistically significant differences were observed across self-completion contexts for the GHQ-12 or the SF-12 mental and physical component scores. We found no statistically significant evidence of mode effects on doctor-diagnosed conditions. For example, no statistically significant mode effects in psychiatric conditions were identified across the comparisons: nurse relative to social interviewer (odds ratio [OR] 0.72, 95% CI 0.40‐1.29; =.53), web relative to social interviewer (OR 0.45, 95% CI 0.17‐1.18; =.14), and web relative to nurse (OR 0.63, 95% CI 0.27‐1.49; =.59). There was little evidence overall that mode-related differences varied by age or sex. Conclusions: Our findings may support embedding self-completion modules within interviewer-administered surveys for the collection of SF-12 and GHQ-12. Meanwhile, potential mode-related differences should be considered when designing mixed-mode surveys and interpreting self-reported health data.

Anticipated Barriers and Facilitators to the Implementation of an AI Tool Advising on the Placement of Mitigation Strategies to Improve Air Quality in the Hospital Setting: Qualitative Study

2026-10-13T15:15:19-04:00

Background: AI is increasingly prevalent within health care; however, its implementation within the hospital setting may come with its own limitations, given the high-pressure and sensitive environment. Barriers and facilitators to the implementation of AI within hospitals have been largely explored quantitatively, and there is a scarcity of qualitative research on the implementation of AI tools to improve air quality. Objective: This study aims to qualitatively explore, through a normalization process theory (NPT) lens, the anticipated barriers and facilitators to the successful implementation of an AI tool to guide the placement of mitigation strategies to reduce airborne transmission of infections within the hospital setting. Specifically, we sought to (1) understand the day-to-day work of key hospital staff involved in reducing airborne transmission of infections, (2) identify potential barriers to the implementation of an AI tool to guide the placement of mitigation strategies in the hospital setting, and (3) recognize necessary conditions for the successful implementation of the AI tool within the hospital setting. Methods: We conducted ethnographic observations (n=4) and semistructured interviews (n=18) with hospital staff following preliminary one-on-one meetings and group discussions involving patients, members of the public, national leaders in ventilation working within academic and industry environments, and hospital staff (n=70). Data were collected between October 2025 and May 2026 in the United Kingdom. Interview transcripts, field notes, and summaries from preliminary wider engagement work activities were analyzed in NVivo (Lumivero; version 12) following codebook thematic analysis to facilitate confirmatory triangulation. Findings were organized according to the core constructs of NPT. Results: Hospital staff understanding of the AI tool and their preparedness to invest time in its implementation varied across those consulted. The anticipated work required by hospital staff to successfully implement the AI tool and its recommended mitigation strategies was identified as a significant challenge for its scalability. Differences in the ways of working among trusts and the competition for limited resources (collective action), and issues associated with hospitals’ physical estate (coherence) were seen as particularly problematic. The need for local champions (cognitive participation) was raised as a facilitator, as well as expert validation and the assessment of the AI tool’s effectiveness (reflexive monitoring). Conclusions: This preimplementation study suggests that the successful implementation of an AI tool to improve air quality in the hospital setting depends not only on the effectiveness demonstrated by the AI tool but also on the way trial outcomes are conveyed to hospital staff. Hospital staff anticipate an initial resistance to the AI tool’s implementation. Further, the impact of the AI tool and suggested mitigation strategies in workflows, costs, and staff and patient outcomes may be decisive.

Risk Visualization With Petal-X and Continuous Glucose Monitoring to Support Person-Centered Care and Lifestyle Change in Cardiovascular Prevention: Qualitative Descriptive Study

2026-10-13T11:30:22-04:00

Background: Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality. In primary prevention, effective communication of individual risk and support for behavior change are crucial. Interactive risk visualizations can enhance understanding and engagement, while continuous glucose monitoring (CGM) offers immediate feedback on the physiological effects of daily choices. Objective: This study aimed to explore how patients at increased CVD risk and family medicine nurses perceive CVD risk visualization (Petal-X) and the integration of real-time glucose monitoring, with a focus on perceived person-centered care (PCC), motivation, and lifestyle change. This manuscript reports the qualitative phase of a larger sequential mixed methods study; the quantitative randomized controlled trial is reported separately. Methods: Using total population (census) sampling within a closed, predefined trial cohort, we invited every eligible patient who had completed the intervention arm of a parallel randomized controlled trial and every eligible registered nurse providing preventive consultations at the same single community health center in central Slovenia who had been familiarized with the intervention and with the Petal-X dashboard; 6 patients at increased CVD risk who had used the Petal-X dashboard in combination with CGM, and 6 nurses familiar with the intervention, took part, and interviews were conducted between September and December 2024. Data were collected through individual, face-to-face semistructured interviews that explored perceptions of the visualization, real-time glucose monitoring, and lifestyle change. Interviews were audio-recorded, transcribed verbatim, and analyzed by inductive qualitative content analysis within a qualitative descriptive design; coding continued until code saturation and data adequacy were judged to have been reached. Results: The analysis produced 11 subcategories, which were grouped into 4 categories, with an overarching theme running across them: visualization and digital tools as support for PCC to enhance motivation and lifestyle change in individuals at risk of CVD. Patients and nurses reported that personalized visual displays improved understanding of risk factors, facilitated dialogue and shared decision-making, and increased patients’ sense of involvement, responsibility, and control, although the 2 groups differed in emphasis: patients foregrounded personal meaning and self-regulation, whereas nurses foregrounded communication and fit with the consultation workflow. CGM was perceived as particularly motivating by linking behavior (food, activity, and stress) to real-time glucose changes, encouraging dietary and activity adjustments that some participants maintained after the intervention. Key barriers included variable digital literacy (especially among older users), fear or mistrust of technology, the attentional burden of continuous monitoring, potential information overload, workflow integration needs, and affordability or accessibility of CGM. Conclusions: Interactive CVD risk visualization Petal-X, especially when complemented by CGM, was perceived as a meaningful support for PCC and a driver of motivation and lifestyle change in primary prevention. Implementation should address training, usability, integration into routine workflows, and equity of access. Trial Registration: ClinicalTrials.gov NCT06226948; https://clinicaltrials.gov/study/NCT06226948

What Users Say About Reimbursable Digital Therapeutics in Germany: Large-Scale App Store Review Analysis Using a Large Language Model

2026-10-08T17:15:20-04:00

Background: In 2019, Germany introduced a unique regulatory framework for digital therapeutics (DTx), termed digital health applications (DiGAs) in Germany, with the goal of integrating evidence-based DTx into statutory health care. DTx are eligible for reimbursement by statutory health insurance if manufacturers demonstrate positive health care effects, such as improved health status or health literacy, in a controlled study setting. Although regulatory evaluation primarily relies on manufacturer-conducted studies, these studies do not fully capture how users experience and use DiGAs in everyday life. Objective: The study aims to systematically characterize user experiences with reimbursable DiGAs by analyzing the sentiment and thematic content in publicly available app store reviews at scale to identify recurring patterns of positive and negative feedback across regulatory-relevant quality dimensions. Methods: All DiGAs with publicly available mobile app reviews were identified via the official German Federal Institute for Drugs and Medical Devices (BfArM) directory. User reviews were extracted from the German Apple App Store and Google Play Store using a tailored Python script. Reviews were then processed using GPT-4o, which extracted up to 5 core statements per review and assigned each a sentiment label (positive, neutral, or negative) and one of ten predefined thematic categories to each statement: content, technology, cost and reimbursement, login and registration, prescription and approval, user experience and design, support, tracking and documentation, effectiveness, and overall impression, all derived from the regulatory and quality criteria applicable to DiGA certification. Classification quality was assessed through manual validation, in which each of the 3 authors independently reviewed one-third of all model outputs. Results: In total, 44 mobile DiGAs were included and analyzed. After data extraction and cleansing, the final dataset comprised 4328 reviews containing at least one interpretable statement, resulting in 9439 interpretable statements. A systematic validation of the automated classification demonstrated exceptionally high model performance, with 99% accuracy for sentiment classification (-scores of 1.00 for positive and 0.99 for negative categories) and 95% accuracy for category classification (average -score of 0.95). While the categories overall impression (1233/1431, 86.2% positive) and effectiveness (1440/1609, 89.5% positive) received particularly positive feedback, users commented most negatively on the login and registration process (263/280, 93.9% negative) and technology-related aspects (563/668, 84.3% negative). Conclusions: User feedback on reimbursable DiGAs is predominantly positive, particularly regarding perceived effectiveness, overall impression, and content. However, recurring criticism of login and registration processes, technical reliability, and prescription and approval procedures reveals persistent barriers to access and use. As many of these aspects are prerequisites rather than peripheral convenience issues, they should be treated as essential implementation requirements. Analyzed at scale with large language model-based methods, publicly available app store reviews can complement formal evaluation by providing user-centered real-world evidence for postmarket monitoring and regulatory quality improvement.

Clinicians’ Trust in AI-Based Clinical Recommendations Across Controlled Primary Care–Style Clinical Vignettes: Nurse-Dominant Experimental Pilot Study

2026-10-08T16:30:02-04:00

Background: Safe integration of AI-enabled clinical decision support requires understanding whether users’ trust and behavioral reliance are appropriately calibrated to recommendation quality. Objective: This pilot study examined a 3-item vignette-level trust construct and accept or reject behavior across sequential primary care–style clinical vignettes. Methods: A total of 68 health care professionals, including 59 (86.8%) registered nurses and 9 (13.2%) physicians, each evaluated 21 clinical vignettes from 1 of 2 series. Each series contained 10 correct and 11 intentionally incorrect AI recommendations. Participants accepted or rejected each recommendation and responded to 3 questionnaire items measuring trust, perceived transparency, and likelihood of acting on the recommendation before receiving correctness feedback and points. The unrounded item mean formed the trust composite. In the behavioral analysis, 50 unrecorded accept or reject responses were classified as rejections. A pooled cross-classified linear mixed-effects model was constructed to assess associations between the trust composite, recommendation correctness, vignette position, and participant and vignette characteristics. It included random intercepts for participant and vignette item, with case series included as a nuisance adjustment. Results: Participants accepted 374 of 748 (50%) incorrect recommendations and rejected 112 of 680 (16.47%) correct recommendations. Incorrect recommendations received lower prefeedback trust than correct recommendations (β=−0.772, 95% CI −1.036 to −0.507; standardized effect=−0.419). Trust declined modestly across vignette positions (β=−0.027, 95% CI −0.048 to −0.005; standardized effect=−0.088), with a decline in case series A but not case series B. Baseline intention to use AI was positively associated with trust (β=0.391, 95% CI 0.110-0.672; standardized effect=0.243), whereas higher perceived diagnostic difficulty was negatively associated with trust (β=−0.474, 95% CI −0.648 to −0.301; standardized effect=−0.257). In the exploratory lagged model, trust in the preceding recommendation was associated with trust in the current recommendation (β=0.266, 95% CI 0.210-0.322; standardized effect=0.264). Conclusions: Self-reported trust differentiated correct from incorrect recommendations in aggregate, while acceptance and rejection were not fully aligned with recommendation correctness. These preliminary findings identify a potential evaluation problem: lower trust in incorrect advice does not necessarily imply that users will reject it. The findings do not establish real-world clinical effects.

Determinants of Adoption of a Mechanical Ventilation Dashboard in Intensive Care: Measurement Instrument for Determinants of Innovations–Based, Single-Center, Cross-Sectional Evaluation

2026-10-07T15:30:20-04:00

Background: Lung-protective ventilation (LPV) reduces complications of mechanical ventilation, yet adherence in intensive care units (ICUs) remains inconsistent. Digital dashboards may support LPV by improving situational awareness and supporting protocol adherence. However, adoption of such tools in high-acuity clinical environments depends on a range of cognitive, professional, and contextual determinants. The Measurement Instrument for Determinants of Innovations (MIDI) provides a validated framework to systematically assess these factors. Objective: This study aims to identify determinants influencing the adoption of a newly piloted mechanical ventilation dashboard in the ICU using the MIDI framework. Methods: We conducted a single-center, cross-sectional evaluation among ICU health care professionals during a dedicated survey period within a pilot introduction of a mechanical ventilation dashboard at Amsterdam University Medical Center. Participants completed a structured questionnaire consisting of 24 MIDI items adapted to the ICU context rated on a 5-point Likert scale (“completely disagree” to “completely agree”), supplemented by open-ended questions on perceived barriers and facilitators to its use. Determinants were classified as facilitators when ≥80% of respondents selected “agree” or “completely agree” and as barriers when ≥20% selected “disagree” or “completely disagree.” Open-ended responses were analyzed using a general inductive thematic approach. Results: A total of 71 completed questionnaires were analyzed, including responses from nurses, physicians, intensivists, ventilation specialists, and researchers in mechanical ventilation. Six determinants met the criteria for facilitators: outcome expectations, self-efficacy, procedural clarity, low complexity, correctness, and observability. Two determinants met the criteria for barriers: relevance for client and professional obligation. Analysis of open-ended responses highlighted perceived barriers such as additional workload, the need for an extra device, overlap with existing systems, and limited role-specific relevance. Facilitators included improved situational overview, educational value, easier trend monitoring, and increased efficiency. Conclusions: This evaluation identified key determinants influencing the adoption of a mechanical ventilation dashboard in the ICU. While the dashboard was generally perceived as useful and easy to understand, adoption was shaped by determinants related to workflow integration, role-specific relevance, and professional responsibility. These findings suggest that successful introduction of digital clinical support tools in intensive care requires attention not only to technical design, but also to how such tools align with users’ roles, daily work processes, and shared clinical responsibilities. Systematic assessment of determinants provides actionable insights into the adoption of digital decision-support tools in high-acuity care settings.

Development and User Testing of an Online Platform to Deliver School-Based Inclusive Physical Education Solutions: Mixed Methods Study

2026-10-07T15:30:20-04:00

Background: Inclusive physical education (PE) plays a vital role in promoting participation and development among students with different abilities. However, many teachers do not have adequate tools to modify PE activities to meet these diverse needs. In addition, parents are essential partners, as their involvement helps reinforce strategies and provide useful information about their children. While online platforms provide a practical way to deliver such solutions, only a few have been intentionally created to support both teachers and parents in implementing inclusive PE learning. To address this gap, we codeveloped an online platform with adapted PE experts that digitizes an inclusion screening tool and delivers personalized, evidence-based inclusion strategies and resources to teachers and parents. Objective: This study aimed to develop an online platform that provides inclusion strategies for PE teachers and to examine how teachers and parents perceived its usability, acceptability, and overall usefulness using a mixed methods approach. Methods: A mixed methods design was used to evaluate the platform between September 2024 and March 2025. PE teachers (n=8) and parents of children with disabilities (n=8) were recruited through convenience sampling via national social media outreach and completed individual usability testing sessions via Zoom. Usability was assessed using the System Usability Scale (SUS), the Questionnaire for User Interaction Satisfaction (QUIS), and task performance metrics. Semistructured interviews were conducted to explore user experiences. Quantitative data were analyzed descriptively, while qualitative data were analyzed using codebook thematic analysis, with findings integrated using joint display analysis. Results: All participants successfully completed the assigned tasks, with a few instances of minor difficulty (14 total errors across 136 task attempts). Participants completed both usability testing and semistructured interviews. Usability and satisfaction scores were high across both groups (SUS: mean 85.63, SD 9.14 for teachers and mean 84.69, SD 12.50 for parents; QUIS: mean 8.52, SD 0.82 for teachers and mean 8.00, SD 1.31 for parents, out of 9). Mixed methods integration showed convergence between positive usability ratings and perceived professional value, while also identifying challenges related to navigation, terminology, learning for first-time users, and mobile interface design. Conclusions: The online platform provides strong usability and high satisfaction among PE teachers and parents. Future work will involve improving implementation and evaluating its impact on students’ participation outcomes.

Using Theory-Based Frameworks to Identify Barriers, Facilitators, Expectations, and Willingness to Pay for Online Medical Consultation Services in China: Qualitative Study

2026-10-06T17:30:15-04:00

Background: Online medical consultation (OMC) services have gained considerable attention as an integral component of telemedicine. Recently, AI has been increasingly integrated into OMC platforms, facilitating more efficient consultations and clinical decision-making. AI-driven OMC services can provide preliminary triage, medication guidance, and diagnostics for multiple medical conditions. Despite the availability and potential benefits of AI-driven OMC services, public acceptance and willingness to pay (WTP) for these services remain low. Objective: This study aimed to identify perceived barriers, facilitators, expectations, and factors shaping public acceptance of and stated WTP for AI-driven OMC services. Methods: We conducted semistructured qualitative interviews with patients, caregivers, and health care professionals to explore barriers, facilitators, expectations, and factors shaping public acceptance of and stated WTP for AI-driven OMC services. The study was informed by the theories of perceived risk and perceived benefit, which guided the development of the interview guide. All interviews were audio-recorded and transcribed verbatim. Data were analyzed using NVivo (version 15; Lumivero) with deductive thematic analysis guided by these theories. Coding was conducted independently and cross-checked by 2 researchers to ensure credibility and consistency. Results: Thematic analysis of 20 in-depth interviews identified 2 main themes and 11 subthemes. Perceived risks and perceived benefits emerged as 2 key perspectives influencing participants’ acceptance and WTP. Psychological, governance, social, functional, health, and financial risks reduced acceptance, whereas convenience, diversity, reliability, efficiency, and educational benefits promoted it. Participants’ self-reported WTP ranged from RMB 0 to RMB 200 (US $0-$27.28; RMB 1=US $0.1364 as of January 15, 2025), with participants who had prior experience with OMC generally reporting higher values than those without prior OMC experience. Conclusions: This study identified facilitators and barriers influencing public acceptance of and WTP for AI-driven OMC services using theoretical constructs. Our findings offer valuable insights into the development and refinement of AI-driven OMC services, enabling more targeted pricing strategies and tailored services that address public preferences and concerns, as well as supporting the development of standardized regulatory governance for digital medical consultation platforms.

Evaluating Acceptance and Use of Digital Health Platforms for Consultation and Medication Delivery Among Men Who Have Sex With Men and Transgender People Living With HIV: Cross-Sectional Survey Informed by an Extended UTAUT Approach

2026-10-06T15:00:18-04:00

Background: Digital health platforms can expand access to HIV care, but among men who have sex with men (MSM) and transgender people living with HIV and AIDS in Nigeria, adoption is shaped by structural stigma, criminalization, and fear of disclosure as much as by system functionality. Teleconsultation and medication-delivery platforms offer alternative pathways to care, but their acceptance within marginalized populations cannot be assumed and requires empirical investigation. Objective: This study aimed to examine the factors influencing behavioral intention (BI) to adopt TechAids, a confidentiality-oriented digital health platform designed to support HIV consultation and service access among MSM and transgender individuals in Nigeria, using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Methods: A cross-sectional survey was conducted in Lagos, Nigeria, through HIV service delivery and outreach settings facilitated by partnering nongovernmental organizations, supplemented by online LGBTQ+ (lesbian, gay, bisexual, transgender, queer, and other sexual and gender minorities) networks. The study used nonprobability convenience sampling with prespecified eligibility criteria. Data collection was conducted from January 2025 to May 2025. The sample comprised 141 platform users and 27 health care providers (N=168). An extended UTAUT model incorporated performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), trust, and perceived stigma (PS). Data were analyzed using correlation analysis and ordinary least squares (OLS) regression. Age and educational attainment were examined as potential moderating variables. Qualitative feedback was also collected to contextualize quantitative findings. Results: FC emerged as the strongest predictor of BI to use the platform (β=.813; <.001), explaining a substantial proportion of variance in adoption intention (²=0.652). Age demonstrated a modest but statistically significant positive effect on BI (β=.105; =.03). Contrary to theoretical expectations, PE, EE, SI, trust, and PS did not significantly predict intention to adopt the platform. Qualitative feedback highlighted practical infrastructure-related concerns, including reliable internet access, offline functionality, and availability of technical support, as more salient than psychological or feature-based considerations. Conclusions: In resource-constrained, highly stigmatized contexts, enabling infrastructure and practical support may outweigh cognitive, social, and attitudinal determinants of technology acceptance. Successful digital health interventions for marginalized populations therefore require privacy-conscious, user-centered design alongside sustained investment in the infrastructure that supports real-world use.

Telemedicine Adoption Among Health Care Professionals in Israeli Geriatric Medical Centers: Moderated Mediation Cross-Sectional Questionnaire Study

2026-10-05T15:45:03-04:00

Background: Telemedicine adoption among health care professionals is hindered by resistance to change (RTC), comprising routine-seeking, emotional reactivity, short-term focus, and cognitive rigidity, which interacts with perceived ease of use (PEU) and anxiety within the Technology Acceptance Model framework. Objective: This study aimed to test a moderated mediation model in which PEU mediates the association between RTC, assessed both overall and by dimension, and intention to use telemedicine, while telemedicine-related anxiety moderates the association between PEU and intention to use. Participants were recruited from 4 governmental geriatric medical centers in Israel. Methods: A cross-sectional study was conducted among 377 health care professionals from 4 Israeli governmental geriatric medical centers. Telemedicine use in the participating centers was limited, optional, and consisted mainly of remote patient monitoring. Data collection was conducted between March and June 2024. Data were collected using validated self-administered questionnaires measuring telemedicine-related anxiety, RTC with 4 dimensions (routine seeking, emotional reactivity, short-term focus, and cognitive rigidity), PEU, and intention to use telemedicine. Statistical analyses were performed using SPSS (version 28) and PROCESS Model 14 for moderated mediation analysis. Results: The moderated mediation analysis revealed that RTC was negatively associated with the intention to use telemedicine through the lower PEU, with this indirect pathway being attenuated but not eliminated by high telemedicine-related anxiety. The overall indirect effect remained negative and statistically significant across all anxiety levels. Conclusions: Health care professionals’ RTC reduced their intention to use telemedicine by lowering PEU, with telemedicine-related anxiety moderating but never eliminating this relationship. These findings suggest that telemedicine implementation in geriatric settings should consider both RTC and PEU. The positive indirect association observed for cognitive rigidity was unexpected and should be replicated before specific practical recommendations are made.