https://www.jmir.org/issue/feed 2025-01-01T11:30:03-05:00 JMIR Publications editor@jmir.org Open Journal Systems The leading peer-reviewed journal for digital medicine and health and health care in the internet age.  https://www.jmir.org/2026/1/e86760 2026-05-11T17:30:14-04:00 Juan Pablo Tabja Bortesi Maria P Becerra Jonathan Ranisau Bonnie Wen Praveen Nadesan P J Devereaux Michael McGillion Jeremy Petch

Background: Medication reconciliation (MedRec) has the potential to improve patient safety by enhancing the continuity of medication information across settings. MedRec involves 3 core tasks: the creation of a best possible medication history, the identification of medication discrepancies among medication lists, and the resolution of medication discrepancies. While artificial intelligence (AI) has the potential to improve MedRec, existing reviews have not identified the ways in which researchers have used AI to facilitate MedRec tasks and their constituent subtasks or the level of automation achieved. Objective: This scoping review aimed to map how previous research has applied AI to MedRec tasks and subtasks and assess the extent of automation achieved. Methods: We searched MEDLINE, Embase, Web of Science, IEEE Xplore, and Compendex in June 2024 for studies that used AI to support a MedRec task or subtask, excluding entirely rule-based tools or studies focused on other aspects of medication management. After screening 2345 unique records, we conducted backward citation searching of studies included at the full-text stage, identifying an additional 795 unique records. We used a 4-stage model of human information processing as a structural lens to guide our considerations of automation, mapping the core tasks of MedRec onto this model. Results: A total of 94 studies met the inclusion criteria. All studies addressed subtasks related to the creation of a best possible medication history. Only 2.1% (n=2) of the studies also addressed the identification of discrepancies. Thus, the highest stage of automated information processing achieved was information analysis, although most studies (92/94, 97.9%) only automated information acquisition steps. Most studies (67/94, 71.3%) used free-text clinical notes from the electronic health record, although a significant proportion (21/94, 22.3%) used images of pills or images of other medication-related items. Studies using text-based data used a variety of machine learning methods (eg, recurrent neural networks, conditional random fields, support vector machines, and transformers), whereas those that leveraged images typically used convolutional neural networks. Most studies (61/94, 64.9%) used publicly available data from benchmarking datasets (eg, n2c2 2022) and were strictly model development studies, with only 1.1% (n=1) being usability studies. Conclusions: This is the first review to consider the role of AI in the automation of MedRec tasks, offering a basis for prioritizing future development efforts. Current applications of AI to automate MedRec tasks are preliminary, with most work focusing on the extraction of medication information and limited to proof-of-concept model development. Future work should consider addressing infrastructural barriers to the AI-based automation of MedRec tasks (eg, data incompleteness in sources of medication information) and exploring approaches to automate discrepancy resolution. Beyond developing models, there is also a need to implement them in tools and evaluate them in real-world contexts. Trial Registration: OSF Registries osf.io/n64u3; https://osf.io/n64u3

2026-05-11T17:30:14-04:00 https://www.jmir.org/2026/1/e88374 2026-05-11T16:30:13-04:00 Ting Dai Changsheng Guo Lingli Gao Jinyu Huang Yan Chen Yujie Zhang Jing Gao Xiaodong Feng

Background: Frailty and sarcopenia represent substantial global health challenges, frequently diminishing patients’ quality of life through impaired muscle function and physical performance. Digital health interventions (DHIs) have shown promise in mitigating these conditions among older adults. However, outcomes of such interventions in this demographic are inconsistent, and a thorough synthesis of existing evidence is lacking. Objective: This study aimed to evaluate the effectiveness of DHIs in older adults with frailty and sarcopenia. Methods: A comprehensive search of PubMed, Web of Science, MEDLINE, Embase, and Cochrane Library was conducted from their inception until January 2026 to identify randomized controlled trials. Meta-analyses were performed using R software (R Foundation for Statistical Computing). Study quality was evaluated using the revised Cochrane Risk of Bias Tool 2.0 (Cochrane Collaboration), and evidence certainty was assessed using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Results: From 3506 records, 16 studies were included. DHIs significantly improved total skeletal muscle mass (weighted mean difference [WMD] 1.01, 95% CI 0.08-1.94, 95% prediction interval [PI] −0.95 to 2.96), gait speed (WMD 0.09, 95% CI 0.03-0.15, 95% PI −0.1 to 0.26), Timed Up and Go Test (TUGT: WMD −0.52, 95% CI −1.02 to −0.03, 95% PI −1.93 to 0.85), 30-second Chair Stand Test (30CST: WMD 2.19, 95% CI 0.89-3.48, 95% PI −1.59 to 5.66), balance (standardized mean difference [SMD] 0.61, 95% CI 0-1.21, 95% PI −0.94 to 2.13), and quality of life (SMD 0.16, 95% CI 0.05-0.27, 95% PI 0.04-0.28). No significant improvements were observed in Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-minute walk test (6MWT), 2-minute walk test (2MWT), Short Physical Performance Battery (SPPB), or BMI. Although the pooled effect was favorable, the wide 95% PI suggests substantial between-study heterogeneity. Subgroup analysis stratified by intervention duration revealed significant intersubgroup differences in ASMI (²=9.93; =.0016), indicating interventions lasting ≥12 weeks were more effective for improving ASMI (WMD 0.28, 95% CI 0.06-0.50, 95% PI −0.30 to 0.83). Subgroup analysis stratified by intervention type showed significant intersubgroup differences in balance (²=9.89; =.0195), with exergame-based interventions showing significant effects (SMD 0.83, 95% CI 0.26-1.40). Conclusions: This systematic review is the first to quantify the disease-specific efficacy of DHIs in improving muscle function, physical performance, and quality of life among older adults with frailty and sarcopenia, demonstrating their unique value as a scalable complementary approach. By overcoming geographical and resource constraints, DHIs support underserved populations. However, low evidence quality and heterogeneity warrant cautious interpretation. The 95% PIs indicate that actual effects may vary with population characteristics and implementation contexts. Nonetheless, DHIs represent a promising and cost-effective strategy for service expansion. Future high-quality studies are needed to better understand their effectiveness and implementation across settings. Trial Registration: PROSPERO CRD420251135302; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251135302

2026-05-11T16:30:13-04:00 https://www.jmir.org/2026/1/e82669 2026-05-11T15:00:19-04:00 Novalene Alsenay Goklish Emily E Haroz Rohan R Dayal Valentín Q Sierra Roy Adams Francene Larzelere Sinquah Paul Rebman Jacob L Taylor

Background: American Indian and Alaska Native communities experience disproportionately high suicide rates. While machine learning (ML) models leveraging electronic health records have emerged as promising tools for suicide risk identification, the optimal integration of these models with existing screening practices remains unclear. Objective: The objective of this study was to compare parallel and serial testing strategies that combine an ML suicide risk model and the Ask Suicide-Screening Questions (ASQ) against using the ASQ alone. To achieve this, we conducted a retrospective secondary analysis of electronic health record data. The cohort consisted of adult emergency department visits at an Indian Health Service facility between October 1, 2019, and October 2, 2021. Methods: Sensitivity, specificity, predictive values, and 95% CIs were averaged across 10 cross-validated patient-level folds. The final sample included 7897 American Indian patients with 26,896 visits, 824 (3.1%) of which had a positive ASQ result and 102 (0.4%) of which had the outcome of suicide attempt or death within 90 days of the visit. The logistic regression ML model previously developed using Indian Health Service–specific data was operationalized at the 95th and 75th percentiles to evaluate high-risk and medium-risk thresholds, respectively. A sensitivity analysis was performed to evaluate identification approaches across all emergency department visits during this period. Results: The ML medium-risk threshold alone identified the most true positives (sensitivity: 0.782, 95% CI 0.648-0.915; specificity: 0.751, 95% CI 0.725-0.777; positive predictive value [PPV]: 0.012, 95% CI 0.009-0.014; negative predictive value [NPV]: 0.999, 95% CI 0.998-0.999) in comparison to the ML high-risk threshold alone (sensitivity: 0.429, 95% CI 0.287-0.572; specificity: 0.955, 95% CI 0.948-0.961; PPV: 0.035, 95% CI 0.022-0.048; NPV: 0.998, 95% CI 0.997-0.999) or the ASQ alone (sensitivity: 0.178, 95% CI 0.073-0.282; specificity: 0.970, 95% CI 0.968-0.971; PPV: 0.022, 95% CI 0.010-0.034; NPV: 0.997, 95% CI 0.996-0.998). Combining the ML high-risk threshold with the ASQ in series yielded the greatest positive predictive ability (PPV: 0.050, 95% CI 0.014-0.086) at the cost of reduced sensitivity (0.129, 95% CI 0.036-0.221). Finally, the parallel testing approach using the ML medium-risk threshold yielded the greatest sensitivity (0.795, 95% CI 0.671-0.920; specificity: 0.742, 95% CI 0.716-0.767; PPV: 0.012, 95% CI 0.009-0.014; NPV: 0.999, 95% CI 0.998-0.999) without missing any cases identified through screening. Conclusions: Unlike existing studies that evaluate ML and screening tools in isolation, this study innovates by assessing combined parallel and serial testing strategies in a real-world setting. We demonstrated that, while serial testing maximizes predictive accuracy, it is often infeasible. Instead, parallel testing brings value as a clinical “safety net” to catch at-risk patients missed by standard practices. Ultimately, integrating ML in suicide prevention requires balancing statistical accuracy with setting-specific, real-world workflows.

2026-05-11T15:00:19-04:00 https://www.jmir.org/2026/1/e90153 2026-05-08T15:00:24-04:00 Pascal Petit Nicolas Vuillerme

Digital technologies are reshaping human behavior, health care delivery, and population health; however, their cumulative effects across the lifespan remain underexplored. This viewpoint argues that exposures arising from interactions with digital technologies should be formally integrated into exposome science as a distinct, measurable component of the human environment. Our aims are to (1) redefine the digital component of the exposome (the digital exposome) within the broader exposome framework, (2) examine its life course implications for health and equity, and (3) outline a research and policy agenda to enable its systematic measurement and integration into clinical and public health practice. Digital technology–related exposures can confer benefits such as enhanced health monitoring, personalized interventions, improved access to care, and the promotion of healthy behaviors. However, they may also introduce potential risks, including mental health challenges, cognitive and circadian disruptions, sedentary lifestyles, exposure to misinformation, and widening inequities among vulnerable populations. Despite their ubiquity, digital technology–related exposures remain poorly integrated into clinical medicine, epidemiology, or public and global health policies. Drawing on interdisciplinary evidence from exposure science, epidemiology, and digital phenotyping research, we propose a refined conceptual definition of the digital exposome grounded in the classical exposome domains. We propose redefining the digital exposome as the full spectrum of exposures resulting from interactions or proximities with digital technologies and their combined influence on health across the lifespan. This framework conceptualizes digital technology–related exposures as a dynamic set of environmental influences operating through sociotechnical, behavioral, and biological pathways over the life course. To operationalize this framework, we discuss practical approaches using validated behavioral instruments, objective device use logs, ecological momentary assessments, smartphone-based digital phenotyping, and wearable sensing technologies. Systematic measurement, large-scale longitudinal studies, and harmonized exposure metrics are needed to characterize the cumulative health impacts of digital environments more accurately. Emerging tools such as digital markers or biomarkers and digital phenotypes offer promising opportunities to link real-world technology use with physiological and biological outcomes, thereby supporting precision medicine and population health strategies. Ethical governance, privacy safeguards, and equity considerations must be embedded from the start, drawing on emerging exposomethics frameworks. Recognizing the digital exposome as a modifiable determinant of health offers a foundation for evidence-based guidance, prevention strategies, and policy interventions suited to increasingly digital societies. By integrating digital technology–related exposures into exposome science, clinical practice, and public health research, this viewpoint seeks to foster interdisciplinary dialogue, guide future empirical work, and support the development of safer and more equitable digital environments across the lifespan.

2026-05-08T15:00:24-04:00 https://www.jmir.org/2026/1/e73614 2026-05-08T14:45:13-04:00 Brett N Hryciw Cecilia Tran Rashi Ramchandani Cameron Love Christine Caron Aimee Sarti Annelise Miller Suzanne Madore Michael Chasse Andy Pan Simon Didcote Scott Millington Heather Galley Kwadwo Kyeremanteng Andrew Seely

Health care innovation is essential for improving patient outcomes, enhancing system efficiency, and preparing for future challenges; however, meaningful progress is often hindered by entrenched barriers such as resistance to change, fragmented interdisciplinary collaboration, and constrained financial and human resources. These persistent obstacles make it difficult for health care systems to translate creative ideas into sustainable, real-world improvements, underscoring the need for structured approaches that support collaboration and reduce implementation friction. To address these challenges, we developed the Actionable Innovation Day (AID) approach, a structured, participatory model designed to generate consensus-based, low-cost recommendations that are feasible for system improvement. The first regional AID event in Eastern Ontario gathered 57 multidisciplinary participants, including clinicians, administrators, patient partners, and industry leaders, for a full-day series of presentations, facilitated discussions, and targeted breakout sessions focused on critical care. Through guided deliberation and collaborative analysis, participants synthesized diverse perspectives into a prioritized set of improvement opportunities. The process yielded 28 actionable recommendations across 4 domains: health care innovation, regionalized care, critical care practices, and the use of artificial intelligence. A postevent survey (86% response rate) showed strong agreement, with 23 recommendations rated above 4 on a 5-point scale. The highest-ranked proposals emphasized the value of strengthening research-industry-clinical partnerships, integrating families more intentionally into intensive care unit rehabilitation and recovery processes, and implementing centralized regional coordination to optimize critical care capacity. Together, these findings illustrate not only the feasibility of the AID model but also the AID model’s ability to surface strategic, context-appropriate solutions that resonate across stakeholder groups. The AID process offers a scalable and adaptable template for advancing health care innovation through collaborative, real-world problem-solving. While this initial event focused on critical care, the underlying principles of structured engagement, iterative consensus building, and interdisciplinary co-design are broadly applicable to many sectors of health care. We encourage institutions, regional networks, and health system leaders to adopt and tailor the AID framework to their own local priorities, recognizing that inclusive innovation processes can accelerate system improvement even in resource-limited settings. Ultimately, the AID approach serves as both a methodology and a call to action: by empowering teams to collectively identify, refine, and champion actionable ideas, health care organizations can build the capacity and culture necessary to drive meaningful and sustained innovation across diverse clinical and operational domains.

2026-05-08T14:45:13-04:00 https://www.jmir.org/2026/1/e80059 2026-05-08T13:00:23-04:00 Zdenek Meier Marie Buchtova Jan Sandora Lukas Novak Jakub Helvich Ondrej Buchta Jana Furstova Klara Malinakova Peter Tavel

Background: Loneliness is a widespread public health concern linked to increased risks of health problems. As populations age, the demand for effective interventions to mitigate loneliness continues to grow. Objective: This meta-analysis aimed to examine the effectiveness of technology-based interventions in reducing loneliness. Methods: A systematic literature search was conducted in Web of Science, PsycInfo, PubMed, Scopus, Google Scholar, Embase, and the Cochrane Library in August 2024. We included randomized controlled trials that examined the effectiveness of technology-based interventions compared with any control group in reducing loneliness across all age groups. Nonrandomized studies, qualitative research, and studies lacking sufficient statistical data for effect size calculation were excluded. After screening 1089 records, 7 studies involving 580 participants met the inclusion criteria. Data were extracted by 3 independent reviewers, with discrepancies resolved by a fourth reviewer. The risk of bias was assessed using the Cochrane risk-of-bias tool. A random-effects model was used to synthesize effect estimates, with standardized mean differences as the primary effect size metric. Heterogeneity was assessed using the Q statistic and ² index, and a prediction interval was calculated to estimate the expected range of true effects. Results: We found a small and statistically nonsignificant overall effect of technology-based interventions on loneliness (pooled standardized mean difference=–0.21, 95% CI –0.59 to 0.17; 95% prediction interval –1.14 to 0.63). Substantial between-study variability was present (²=57%; τ²=0.08; =0.28), and the prediction interval indicated that true effects in future studies may range from substantial reductions to moderate increases in loneliness. Differences between intervention types could not be examined due to the limited number of eligible studies. The Egger test showed significant funnel plot asymmetry (=–5.47; =.003). However, since the trim-and-fill method identified no missing studies, the asymmetry is unlikely to be fully explained by publication bias. Conclusions: Unlike previous reviews, which focused primarily on older populations or included nonrandomized studies, this meta-analysis provides a rigorous synthesis of only randomized controlled trials across the lifespan. Our findings do not provide evidence for a reliable reduction in loneliness following technology-based interventions. Moderate heterogeneity indicates that effects differ across studies, and the limited number of eligible trials prevented the analysis of potential moderators. By incorporating robust prediction intervals and a broader age demographic, this study offers a more comprehensive view of the variability of intervention outcomes compared with existing literature. The wide prediction interval suggests that intervention effects may vary across settings. In practice, technology-based approaches should be viewed as supportive tools that require careful tailoring rather than universal solutions to loneliness. Trial Registration: PROSPERO CRD42024505117; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024505117

2026-05-08T13:00:23-04:00 https://www.jmir.org/2026/1/e91940 2026-05-08T13:00:23-04:00 Yongzheng Hu Wei Jiang

In this Viewpoint, we argue that patient-facing high-fidelity artificial intelligence (AI)–generated video requires governance that is operational, life cycle based, and embedded in existing institutional review pathways rather than limited to predeployment checks alone. Patient-facing high-fidelity AI-generated video—synthetic or substantially AI-mediated video that presents realistic human likeness, voices, or clinical communication cues—is rapidly entering patient education and clinical communication. We propose a risk-and-ethics matrix that combines residual clinical risk (likelihood × severity after mitigations) with an ethical alignment score that operationalizes autonomy, beneficence, nonmaleficence, and justice to yield actionable dispositions (encourage, permit with oversight, restrict or redesign, or prohibit). The framework links each disposition to dossier-based review, minimum controls, and postdeployment monitoring triggers—focused on measurable outcomes (eg, comprehension, content-attributable follow-up burden, incidents and complaints, and equity gaps) as well as provenance and change control—to support auditable, revisitable decisions over the system life cycle.

2026-05-08T13:00:23-04:00 https://www.jmir.org/2026/1/e80581 2026-05-08T10:00:22-04:00 Huiqin Shi Jingying Huang Jin Yang Mengbo Han

Background: Artificial intelligence (AI) in the domain of health care is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. Objective: This study aimed to explore barriers and facilitators influencing patients’ acceptance of AI. Methods: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Nine databases, including PubMed, Web of Science, and Embase, were comprehensively searched from inception to December 23, 2025. We included qualitative, quantitative, and mixed methods studies investigating adult patients’ attitudes toward medical AI. Two researchers independently screened records, extracted data, and appraised methodological quality using the Mixed Methods Appraisal Tool. Following the Joanna Briggs Institute convergent integrated approach, data synthesis was guided by integrating the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Theoretical Domains Framework (TDF). Factors were mapped to behavior change techniques (BCTs) and evaluated for practical feasibility using the Affordability, Practicability, Effectiveness and cost-effectiveness, Acceptability, Side-effects/safety, and Equity criteria. Results: A total of 61 studies met the inclusion criteria out of 7452 search results. Study designs included qualitative (n=20), quantitative (n=35), and mixed methods (n=6). Performance and effort expectancies were the primary determinants of acceptance. Major barriers comprised perceived operational complexity, lack of algorithmic trust, reduced interpersonal interaction, privacy vulnerabilities, and high costs. Facilitators included transparent data governance, interpretability of AI decisions, improved clinician-patient communication, and human-centered design. Education level and disease severity emerged as key moderating variables. Through UTAUT2-TDF mapping, we identified 25 distinct BCTs (6 high, 14 medium, and 5 low feasibility) and formulated 40 actionable intervention strategies. Conclusions: This study innovatively integrates the UTAUT2 and TDF frameworks to evaluate patient acceptance of medical AI. Unlike existing reviews that predominantly evaluate isolated psychosocial factors or purely technical attributes, this transtheoretical approach differentiates itself by merging technology adoption mechanisms directly with behavioral drivers. Consequently, it contributes to the field by systematically identifying multilevel factors influencing acceptance, including performance expectancy, effort expectancy, and ethical security, and translating these into 40 actionable BCTs. In real-world clinical practice, these findings provide a feasible, prioritized blueprint for clinicians and administrators to design patient-centered interventions, enhancing the clinical integration and long-term effectiveness of medical AI. Trial Registration: PROSPERO CRD42024598884; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024598884

2026-05-08T10:00:22-04:00 https://www.jmir.org/2026/1/e82677 2026-05-08T10:00:22-04:00 Bingyan Gong Nisha Yao Hangxin Xie Chuncheng Huang Tomoko Kishimoto Howard Berenbaum Wenting Mu

Background: Cognitive behavioral therapy (CBT) is the most examined psychotherapy for depression and anxiety, but delivery faces significant barriers such as limited access, cost, and time constraints. CBT-oriented psychological chatbots offer a promising means of addressing these challenges. Yet, their overall efficacy, user engagement, and acceptability have not been systematically synthesized. Objective: This study aimed to evaluate the efficacy, user engagement, and acceptability of CBT-oriented chatbots for adults with depressive and/or anxiety symptoms. Methods: A systematic search of 9 databases, including PubMed, Cochrane Central Register of Controlled Trials, Embase, Web of Science, PsycINFO, CINAHL, China National Knowledge Infrastructure, WanFang, and VIP Databases, was conducted from inception to February 2026. Eligibility criteria included randomized controlled trials comparing CBT-oriented chatbots with control groups in adults with depressive and/or anxiety symptoms. Risk of bias (ROB) was assessed using the Cochrane ROB tool. Random-effects meta-analyses (Hartung-Knapp-Sidik-Jonkman adjustment) calculated pooled effect sizes (Hedges ), 95% CIs, and 95% prediction intervals (PIs). Heterogeneity was evaluated using the ² statistic, and Galbraith plots were used to identify outliers for subsequent sensitivity analyses. Subgroup and meta-regression analyses examined potential moderators. The certainty of evidence was evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. Data on user engagement and acceptability were extracted and synthesized using narrative and quantitative methods where available. Results: Twenty-nine eligible randomized controlled trials were included. CBT-oriented psychological chatbots produced a moderate reduction in depressive symptoms at postintervention (=−0.55, 95% CI −0.70 to −0.40, 95% PI −1.23 to 0.13) and a small reduction in anxiety symptoms (=−0.26, 95% CI −0.37 to −0.14, 95% PI −0.67 to 0.15). At follow-up, effects were small for depression (=−0.32, 95% CI −0.55 to −0.09, 95% PI −0.93 to 0.29) and nonsignificant for anxiety (=−0.19, 95% CI −0.43 to 0.04, 95% PI −0.84 to 0.46). Subgroup and meta-regression analyses revealed that anxiety outcomes were significantly moderated by clinical profiles—showing distinct advantages for comorbid symptoms—and the proportion of female participants. The CBT-oriented chatbots received an adequate level of engagement that complied with digital intervention standards. Although user satisfaction ratings were generally favorable, technical limitations and repetitive interaction patterns remain to be addressed to enhance overall acceptability. Regarding the limitations of evidence, the overall certainty was rated as very low to low, predominantly driven by high ROB and substantial heterogeneity. Conclusions: This study innovatively isolates CBT-oriented chatbots from broader digital interventions, providing a precise, methodology-driven evaluation of theoretically grounded therapeutics. This review brings critical evidence to the field that these tools yield significant short-term relief, particularly for comorbid anxiety profiles. In the real world, CBT chatbots offer profound potential as scalable, low-barrier first-line tools. To sustain engagement, future developments must evolve from rigid rule-based scripts toward adaptive, large language model–driven architectures while ensuring clinical safety. Trial Registration: PROSPERO CRD42024615506; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024615506

2026-05-08T10:00:22-04:00 https://www.jmir.org/2026/1/e86815 2026-05-08T09:30:13-04:00 Jean-Charles Vauthier Lucie Choley Delphine Arduini Patrick Mas Bernard Kabuth

Background: Diabetes technologies—including continuous glucose monitoring (CGM), insulin pumps, and hybrid closed-loop systems—have profoundly transformed self-management in type 1 diabetes (T1D). While these technologies offer improved glycemic control and safety, their use in ultraendurance sports introduces specific cognitive, material, and organizational challenges that remain underexplored in digital health research. Objective: This study aimed to explore how adults living with T1D experience and use diabetes technologies in ultraendurance sports, with particular attention to tensions between autonomy, mental load, and vulnerability. Methods: We conducted semistructured interviews with 13 French-speaking adults with T1D who had completed at least one marathon or ultra-endurance event within the last 5 years and used ≥1 diabetes technology (CGM, pump, or hybrid closed loop). We adopted constructivist grounded theory (Charmaz), using iterative cycles of line-by-line and focused coding, constant comparison, and memo-writing to build and refine analytic categories. Sampling combined purposive strategies through associations and online communities with theoretical orientation (additional participants sought to elaborate emergent categories). Data collection ceased upon theoretical sufficiency, when further interviews no longer yielded substantively new insights for core categories. Two patient partners contributed to question framing, interim sense-checking, and manuscript review. Reporting followed the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist. Results: Five interrelated categories described how athletes negotiated technology in practice: (1) From episodic control to continuous anticipation (reframing glucose management through real-time visibility); (2) Gains in safety and performance (perceived benefits and expanded possibilities); (3) Redistributed mental work (hyper-vigilance, logistics, device management); (4) Keeping things working when they break (fragility in extreme conditions, redundancy, improvisation, and experiential expertise); and (5) Making diabetes visible (technologies mediating identity, solidarity, and stigma). Across categories, participants articulated a tension between optimization-oriented performance and a user-constructed robustness—the capacity to maintain function under uncertainty through redundancy and adaptive know-how. Conclusions: In ultraendurance contexts, diabetes technologies act as both enablers and obligations: they open participation while shifting and sometimes intensifying cognitive and organizational work. A grounded account centered on robustness-in-use highlights practical implications for clinicians (pre-event routines, redundancy planning), designers (context-aware algorithms; improved physical durability), and policy makers (equitable access and exercise-specific education). These findings underscore the value of constructivist, practice-oriented inquiry to inform digital health tool design and support for people living with chronic illness.

2026-05-08T09:30:13-04:00