Journal of Medical Internet Research

Evaluating Nursing Work Systems and Identifying Barriers for Robotic Technology Integration: Observational Study

2026-06-01T16:45:14-04:00

Background: Robotic technology has the potential to assist nurses, but the complexity and unpredictability of health care environments cannot be replicated in a laboratory setting. Furthermore, there is a lack of experiential evidence that robotic technology will meaningfully impact nursing. Collaborative development of technology and real-world usability studies offers the ability to address problems early in the design process when functional changes can be implemented. Objective: The purpose of this study was to use an observational study and systematically evaluate the work system of inpatient nurses to identify barriers to the integration of robotic technology. The objectives are to use an observational study of active hospital units to gain a deeper understanding of nursing tasks, workflow, and the health care setting; identify barriers to the integration of robotic technology using the people, environment, tools, and tasks (PETT) scan from the Systems Engineering Initiative for Patient Safety framework; and synthesize the work system components of the PETT scan into themes. Methods: We used the practice-oriented model of the Systems Engineering Initiative for Patient Safety, the PETT scan, to identify barriers for robotic technology use and innovation. A convenience sample of nursing staff was observed as they worked. Units included the emergency department, medical and surgical intensive care unit, preop or postanesthesia care unit, and general medical-surgical floor. The total number of observation hours per unit was based on data saturation, which occurred at variable times during the day shift, and was arranged with unit management. A total of 53 hours across 16 sessions were recorded. Multiple rounds of inductive and deductive coding were conducted. Briefly, a 3-phase iterative data analysis process was used—initial inductive content analysis, a deductive phase to organize emergent categories into a PETT scan, and finalization of the PETT scan with the identification of overarching themes. Results: Observations across all units yielded a broad set of barriers to integrating robotic and other health care technologies. Using the PETT scan, 78 barriers were identified and were summarized into 20 themes with supporting subthemes and exemplars. Conclusions: By systematically observing nursing workflows and synthesizing barriers into themes, this study provides new insight into the conditions that enable or constrain robotic integration. Findings suggest that robotic technologies are presently best suited for auxiliary and background roles. Broader integration into patient care workflows will depend on designs that align with clinical workflows, support interoperability and robustness, and address ethical, accountability, and coordination challenges inherent in nursing care, as well as maintained organizational support.

Pediatric Clinical Images Without Consent: A Governance Gap in the Long-Term Reuse of Health Data in Digital Health Ecosystems

2026-06-01T16:31:34-04:00

Digital health governance frameworks have primarily focused on prospective safeguards, including informed consent at the point of data collection, lawful processing, and data security. Comparatively less attention has been devoted to the long-term circulation of legacy clinical materials, particularly pediatric clinical images reused across educational and digital infrastructures. This viewpoint examines governance challenges associated with the prolonged educational and digital reuse of pediatric clinical images without identifiable evidence of consent. Drawing on a longitudinal case spanning more than 3 decades (1991-2026), this article illustrates how clinical images may continue circulating across textbooks, educational repositories, conference materials, e-books, and online teaching platforms long after their original creation and publication context. The case is informed by archival educational materials, institutional correspondence, publisher communications, and formal regulatory findings, including a decision issued by the Polish Patient Rights Ombudsman confirming continuing violations related to dissemination of intimate pediatric clinical images without identifiable consent. This article argues that current digital health governance frameworks remain insufficiently equipped to address persistence, traceability, provenance, and coordinated withdrawal of legacy clinical materials once they enter distributed educational ecosystems. Fragmented accountability across health care institutions, publishers, educational systems, libraries, repositories, and digital platforms may allow sensitive clinical materials to remain accessible despite regulatory intervention or removal requests. The article further discusses how publicly accessible educational materials may become incorporated into downstream artificial intelligence and machine learning ecosystems through digitization, aggregation, web scraping, and secondary dataset reuse. In this context, unresolved historical consent deficiencies may become embedded within artificial intelligence–enabled infrastructures without effective provenance tracking or remediation mechanisms. To address these limitations, this viewpoint proposes a lifecycle-oriented governance framework emphasizing long-term consent traceability, provenance-aware dissemination systems, verification checkpoints before reuse or republication, periodic review of legacy educational archives, and coordinated cross-platform withdrawal procedures.

Smartphone Keystroke Biomarkers as Predictors of Adverse Neuropsychiatric Sequelae After Trauma in Trauma Survivors: Prospective Observational Cohort Study

2026-06-01T16:30:03-04:00

Background: Adverse posttraumatic neuropsychiatric sequelae are common after trauma. Early identification of individuals at risk for these outcomes could enable the deployment of preventive interventions to survivors at greatest risk. Smartphone keystroke biomarkers show promise in identifying individuals with neuropsychiatric symptoms; however, to our knowledge, no research has examined whether they can be used to identify symptoms in the aftermath of trauma. Objective: This study evaluates whether passively collected keystroke data from smartphone use in daily life could identify individuals with high symptom levels, as well as worsening or recovery of symptoms, after trauma exposure. Methods: Data from a diverse cohort of individuals presenting to 27 emergency departments after trauma were analyzed. Inclusion criteria were presenting to the emergency department within 72 hours of trauma, age 18-75, and the ability to speak and read English. Exclusion criteria were solid organ injury, significant hemorrhage, operative intervention, or likely admission for over 72 hours. Participants installed an app that passively collected keystroke data during use of any app on their smartphone, beginning in the emergency department. Participants also completed serial symptom assessments over 8 weeks after trauma exposure. Results: A total of 3445 patients met study criteria, provided informed consent, and completed assessments in the emergency department. Of these, 1072 (mean age 40, SD 13; 616/1072, 57.46%, women; 565/1072, 52.71% non–Hispanic Black) installed the app on their Android smartphone and completed the 8-week assessment and were therefore included in analyses. Keystroke biomarkers related to typing speed, identified using bivariate linear mixed models controlling for false discovery rates, were associated with elevated pain, reexperiencing, and mental fatigue (absolute values of rs=0.22-0.25, Ps=.02). Separate change-of-operation and scrolling keystroke biomarkers were associated with increased reexperiencing symptoms (r=0.18, P=.047) and mental fatigue (rs=0.18-0.19, Ps=.031-.047). Further, changes in specific keystroke biomarkers were associated with worsening or recovery of pain (rs=0.07-0.10, Ps=.02), somatic symptoms (rs=0.02, Ps=.02), mental fatigue (rs=0.02-0.04, Ps=.02), sleep disturbance (absolute rs=0.07-0.09, Ps=.02), reexperiencing (rs=0.02-0.04, Ps=.02), and hyperarousal (rs=0.02-0.04, Ps=.02). Conclusions: In general, slower typing and scrolling speeds were associated with higher symptom levels, with small to medium effect sizes. Keystroke data passively collected via smartphone use may help identify individuals with significant or changing posttraumatic symptoms. Future research should continue to explore these keystroke biomarkers and whether they can be leveraged to connect vulnerable trauma survivors to appropriate services. Overall, these results add to the literature, indicating that passively collected keystroke data may help identify individuals with neuropsychiatric symptoms or changes and are, to our knowledge, the first to test whether keystroke biomarkers are useful in the aftermath of trauma. This represents a critical period during which preventive interventions could be deployed to reduce the long-term burden of trauma-related sequelae.

Cognitive Dissonance–Based Priming Intervention: Randomized Encouragement With in-the-Wild Phishing Simulation Attack in Health Care

2026-06-01T16:00:26-04:00

Background: Phishing remains a dominant initial attack vector in health care, exploiting psychological factors such as urgency and authority. Despite extensive investment in technical controls and awareness training, health care staff remain highly susceptible in real operational conditions. Cognitive dissonance (CD), the discomfort arising from inconsistencies between beliefs and actions, has been proposed as a mechanism to disrupt unsafe rationalization at the moment of exposure, but has rarely been evaluated in live organizational settings using objective behavioral outcomes. Objective: This study examined whether a brief CD-based priming intervention, delivered immediately prior to a real-world phishing simulation, was associated with differences in phishing susceptibility among health care staff. Secondary objectives explored whether CD exposure was associated with directional differences in security-related perceptions and self-reported practices. Methods: A 2-stage hybrid randomized-encouragement experiment was conducted at a large Norwegian hospital. In Stage 1, staff were randomly assigned to a control or CD-primed condition and completed a survey assessing security perceptions and self-reported practices (n=62). In Stage 2, an in-the-wild phishing simulation was sent to all staff, enabling objective measurement of phishing susceptibility via observed link-click behavior. Behavioral outcomes were analyzed across 3 groups—control (n=34), CD-primed (n=32), and neutral nonresponders (n=753)—using a prespecified omnibus chi-square test as the sole confirmatory analysis. Survey-based multivariate and univariate analyses were treated as exploratory due to limited sample size and variable construct reliability. Results: Due to voluntary uptake, only a subset of randomized participants received the intervention. Observed phishing click rates were 65% (22/34) in the control group, 44% (14/32) in the CD-primed group, and 53% (396/753) in the neutral group. The omnibus chi-square test did not detect a statistically significant association between group membership and click behavior (²=3.00; n=819; =.22; Cramér V=0.06). Descriptive comparisons within the randomized subset suggested lower click rates in the CD-primed group, but effect estimates were imprecise and associated with wide CIs. Survey-based analyses indicated group differences across combined psychological constructs; however, several constructs exhibited low internal consistency, and follow-up analyses were underpowered. Conclusions: In a real-world hospital phishing simulation, pre-exposure CD priming was associated with a directional but statistically nonsignificant pattern of reduced phishing click behavior. This evidence does not establish a reliable behavioral effect, and construct-level findings are exploratory. CD-based prompts may serve as a lightweight behavioral signal in real-world conditions, but larger, fully randomized, and longitudinal studies with improved psychometric validation are needed before such interventions can be considered reliable complements to established cybersecurity controls.

Human and Robot Assistance for Cognitive Load in Younger and Older Adults: Multimodal Within-Subject Experimental Study

2026-06-01T14:00:27-04:00

Background: Maintaining cognitive efficiency and independence is a central goal of healthy aging. Socially assistive robots (SARs) are increasingly proposed as scalable digital health solutions to support daily activities in older adults and to facilitate aging-in-place. However, concerns remain regarding whether robot-mediated assistance reduces or inadvertently increases cognitive load, potentially undermining usability, user acceptance, and long-term real-world adoption, particularly in aging populations. Objective: This study aimed to examine how robot-assisted (human-robot interaction [HRI]) and human-assisted (human-human interaction [HHI]) support influences cognitive load during task performance in younger and older adults. A multimodal assessment framework integrating behavioral, subjective, and physiological measures was used to identify age-related differences in cognitive effort and stress associated with different forms of assistance. Methods: A total of 60 healthy adults (30 younger adults: mean age 34.8, SD 10.1 years; and 30 older adults: mean age 72.3, SD 5.5 years) completed a modified Trail Making Test under 7 within-subject conditions: independent performance (baseline), 3 robot-assisted conditions, and 3 human-assisted conditions, each corresponding to low, medium, and high cognitive load levels. Performance accuracy and completion time were recorded as behavioral indicators. Perceived cognitive load was assessed using the National Aeronautics and Space Administration Task Load Index, and physiological stress was evaluated via pre- and postcondition salivary cortisol concentrations. Linear mixed-effects models were applied to examine main effects and interactions of age group, assistance type, cognitive load level, and time. Results: Significant interactions between age group and assistance type were observed for accuracy (=6.50; =.01) and perceived cognitive load (=4.58; =.03). Older adults demonstrated lower accuracy and higher perceived cognitive load during robot-assisted conditions compared with human-assisted conditions, whereas no such differences were observed in younger adults. Across age groups, human assistance improved performance at low and medium cognitive load levels. Physiological analysis revealed a significant age×assistance× time interaction (=5.16; =.02), with older adults showing increased posttask cortisol concentrations during robot-assisted interaction, indicating higher physiological stress. Conclusions: While both human and robotic assistance enhanced task performance relative to independent completion, the type of support critically shaped cognitive load responses in older adults. Robot-assisted interaction was associated with increased behavioral errors, higher perceived workload, and elevated physiological stress, suggesting that current SAR implementations may impose additional extraneous cognitive load in older users. These findings highlight the importance of designing adaptive, age-sensitive digital assistive systems that minimize cognitive burden through simplified interaction, responsive pacing, and multimodal support. Multimodal cognitive load assessment provides a valuable framework for optimizing the usability and effectiveness of assistive digital health technologies for aging populations.

Hospitalists Are Already Using AI—Why Implementation Will Determine Its Impact

2026-06-01T13:45:14-04:00

The adoption of artificial intelligence (AI) into clinical practice is accelerating, outpacing the development of organizational guidance, training, and governance. A recent study indicated that two-thirds of hospitalists are using AI, particularly large language model (LLM)–based platforms, in their clinical work. However, as with prior disruptive health technologies, adoption alone does not ensure meaningful improvement in care. Drawing on lessons from electronic health record implementation, we argue that AI’s ultimate impact will be determined not by use rates, but by implementation quality and fit. Poorly implemented digital tools have been shown to increase clinician workload and burnout, despite their intended benefits. Early evidence on LLM-based diagnostic AI further underscores this risk: clinical-decision making supported by AI may be suboptimal when integration, training, and workflow design are inadequate. To provide value, AI tools must be thoughtfully embedded into clinical reasoning processes through evidence-informed training, intentional workflow design, and supportive organizational culture. As AI technologies are rapidly adopted, three priorities come into focus: training clinicians on AI inputs and interpreting outputs, applying implementation science frameworks for AI deployment in clinical environments, and establishing strategies for ongoing evaluation of the impact of AI tools over time. Implementation science frameworks offer practical guidance to assess workflow integration, training needs, infrastructure, and potential unintended consequences that can then inform adaptation of implementation strategies to enhance contextual fit. In parallel, learning health system infrastructure can enable continuous monitoring and iterative adaptation using routinely collected clinical and workflow data that reflect the value of the intervention across the quintuple aim of clinical outcomes, health equity, cost, and patient and clinician experience. AI adoption in hospital medicine is likely inevitable. Its ability to advance the quintuple aim will depend on how effectively these tools are implemented, supported, evaluated, and adapted in practice.

Affordable GLP-1? When Digital Platforms Meet Policy Reform

2026-06-01T11:30:18-04:00

Effects of Multicomponent Digital Health Interventions on Multidimensional Physical Activity in Older Adults: Systematic Review, Meta-Analysis, and Meta-Regression of Randomized Controlled Trials

2026-05-29T16:45:17-04:00

Background: The comprehensive effects of multicomponent digital health interventions (DHIs) on multidimensional physical activity indicators and sedentary behavior (SB) remain controversial. Objective: This systematic review aimed to evaluate the impact of multicomponent DHIs on daily steps, moderate-to-vigorous physical activity (MVPA), light physical activity, total physical activity, and SB in older adults. Methods: PubMed, Web of Science, Embase, The Cochrane Library, and CINAHL were searched up to February 20, 2026. Randomized controlled trials concerning multicomponent DHIs for promoting exercise behavior in older adults were included. RoB 2.0 was used to evaluate study quality. Meta-analyses were performed using the Hartung-Knapp-Sidik-Jonkman random-effects model, and 95% prediction intervals (PIs) were calculated via Nagashima adjustment to evaluate effect dispersion. The GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) system was used to evaluate evidence certainty. Results: A total of 26 randomized controlled trials (n=4129) were included. The results showed that multicomponent DHIs significantly improved daily steps (mean difference [MD] 822.8, 95% CI 198.3 to 1447.3 steps/d; 95% PI –1452.4 to 3098.0) and MVPA (MD 45.9, 95% CI 23.9 to 67.9 min/wk; 95% PI –9.4 to 101.2). However, the improvements in SB (MD –283.7, 95% CI –610.8 to 43.5 min/wk; 95% PI –984.5 to 417.1), total physical activity (MD 104.4; 95% CI –109.2 to 318.0 min/wk; 95% PI –444.4 to 653.2), and light physical activity (MD 39.3, 95% CI –96.2 to 174.7 min/wk; 95% PI –227.6 to 306.2) did not reach statistical significance. As some included studies combined digital tools with human support, the independent contribution of digital technology remains uncertain. PIs indicated a certain degree of dispersion across different clinical contexts. Subgroup analysis showed higher effect sizes for standalone wearables, human-assisted interventions, and populations with chronic disease risks. Meta-regression showed that effect sizes remained stable across different ages and durations. The trim-and-fill method confirmed the robustness of MVPA results. GRADE assessment indicated “moderate” certainty for MVPA and “low” for daily steps and other indicators. Conclusions: This systematic review suggests that multicomponent DHIs may serve as an effective means for enhancing daily steps and MVPA in older adults. The innovation lies in evaluating the true effect distribution of multicomponent DHIs through Hartung-Knapp-Sidik-Jonkman random-effects models and Nagashima PIs. Compared with previous studies, this review identified the impact of population characteristics and control group differences on effect estimates using PI and subgroup models, confirming that advanced age did not significantly diminish the good adaptability of older adults to DHIs. Evidence limitations include high heterogeneity, lack of long-term follow-up, and differences between objective and subjective measurement tools. In practice, priority should be given to hardware carriers with simplified interaction and integrated human support, with tailored strategies developed for different risk subgroups. Trial Registration: PROSPERO CRD420261323151; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261323151

Patient Experiences With Online Laboratory Test Presentations From Access to Activation: Systematic Review

2026-05-29T15:30:18-04:00

Background: Federal regulations require that laboratory test results be released to patients through online portals in near–real time, often before clinicians review or contextualize them. While these policies expand transparency and affirm patients’ rights to their health information, access alone does not ensure that patients can make sense of their results or determine when and how to act. How regulatory mandates for transparency translate into appropriate engagement remains poorly understood. Objective: We aim to synthesize evidence on how patients access, interpret, and act upon laboratory test results delivered online; identify factors associated with comprehension and engagement; and evaluate how display design, clinical workflow, and patient characteristics shape engagement across the access-to-activation continuum. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidance, we systematically searched 5 databases—Web of Science, Embase, PubMed, CINAHL, and Library, Information Science & Technology Abstracts—for English-language, peer-reviewed empirical studies published from January 2013 through April 2026, supplemented by hand searching and citation tracking. Eligible studies examined patient access to, interpretation of, or responses to laboratory results delivered through portals. Records were independently screened, appraised for methodological quality, and synthesized using thematic analysis across a 3-stage continuum: access, interpretation, and activation. The protocol was not registered. Results: Thirty-nine studies met the inclusion criteria; most were from the United States. Retrospective analyses documented increases in result viewing, secure messaging, and follow-up visits after immediate-release policies. Patients value timely access but often struggle to determine the significance of borderline values and when follow-up is warranted. Visual and textual cues can improve clarity but may also overemphasize minor deviations or inadequately signal clinically significant findings. Patients frequently monitor portals and initiate contact to confirm clinician review, indicating that increased portal activity often reflects efforts to resolve uncertainty rather than confident self-management. Disparities in access, comprehension, and confidence were observed among older adults, non–English-speaking patients, those with public insurance, and those with lower health or digital literacy. Measures of comprehension and engagement varied widely. Conclusions: Expanded online access improves transparency but does not guarantee accurate interpretation or appropriate follow-up. In contrast to prior reviews examining patient perceptions, comprehension, and presentation formats in isolation, this synthesis traces patient experiences across an access–interpretation–activation continuum, revealing how interpretive challenges and emotional responses compound across stages. Meaningful activation depends not only on display clarity but on how informational, emotional, and workflow factors shape patients’ decisions across stages. Designing for activation, therefore, requires a holistic, systems-level approach that aligns result presentation, interpretive support, and clinical workflow to signal the clinical significance of laboratory test results and guide appropriate follow-up. Such approaches must be responsive to the needs of patients across diverse literacy, language, and clinical contexts.

Acceptability of Technologies to Support Early Dementia Detection: Qualitative Study With the Boston University Alzheimer’s Disease Center Cohort

2026-05-29T15:00:09-04:00

Background: Dementia is on the rise globally due to increasing life expectancies and population growth. Digital technologies may help detect early signs, enabling timely interventions to slow or reverse cognitive decline. However, to support the successful implementation of these digital technologies into health care settings, they must be acceptable to target users. Older adults and those with mild cognitive impairment (MCI) are at risk of developing dementia in later life and need to be able to use these technologies in order for this intervention to be approved and implemented in clinical practice. Objective: This study explored the perspectives of older adults and those living with a clinical diagnosis of MCI on the acceptability of using various digital technologies that have the potential to support early dementia detection. Methods: Participants were recruited from Boston University’s Alzheimer’s Disease Research Center. Participants selected at least 2 technologies from 9 different wearables and software to use for 2 weeks, at 3-month intervals, over a total duration of 2 years. A subgroup of self-selecting participants was interviewed after the first 2 weeks of use to gather initial perspectives regarding the acceptability of using the digital technologies. An inductive framework thematic analysis approach was used, assisted by NVivo (version 14.23.2; QSR International). Results: In total, 13 individuals living with a clinical diagnosis of MCI and 11 adults aged 65 years and older were interviewed. Our analysis identified five key themes: (1) gamification, (2) wearability, (3) user guidance, (4) burden of use, and (5) usefulness. Gamified apps were generally liked, although users with little experience of digital games needed time to adjust. Wearables resembling everyday accessories (eg, watches) were preferred, but complaints about tight or uncomfortable straps were frequently reported. Clear instructions were critical to support correct use, but many participants would have liked more troubleshooting support when technical issues arose. The use of 5 or more devices led to a high burden, especially when devices had practicality issues such as not being waterproof. Devices offering personal feedback were perceived as useful to satisfy personal interests, though some questioned their usefulness within health care. Participants raised concerns about losing valued personal interactions with health care professionals and questioned how their existing health conditions and treatment for such conditions may affect the validity of the data collected by the devices. Conclusions: These findings can guide researchers in choosing appropriate devices and minimizing burden. Future work should explore the views of those experiencing digital exclusion to ensure equitable access to dementia-detection technologies.