Tag: pubmed

JMIR Mhealth Uhealth. 2026 Apr 29;14:e71509. doi: 10.2196/71509.

ABSTRACT

BACKGROUND: Wearable devices with real-time feedback (WRFs) provide increasing opportunities to enhance physical activity and improve rehabilitation through collecting and processing health-related data. Real-time feedback (RTF) from the device is expected to result in a more dynamic, coordinated, and synchronous rhythmic activity, defined as step-by-step movements mediated by the real-time heart rate feedback. However, age-specific characteristics in the user engagement with WRFs integrating real-time audio feedback have largely remained unexplored.

OBJECTIVE: This study explores the lived experiences of older adults using wearables with RTF to uncover motivations, aspirations, and hindering factors in their engagement with WRFs in rhythmic activity. The study explores narratives that older adults articulate in their previous use of wearables for physical activity, their experiences with WRFs during rhythmic activity, and their meaning-making of the interactive features enhancing the synchronization of the movement during rhythmic activity.

METHODS: The study was conducted as a qualitative interview study with 18 older adults who used a WRF for rhythmic activity during a 3-week period in their home environment. The wearable used in the study is a chest-band sensor device that helps users to synchronize their steps with their heartbeat through the provision of real-time audio feedback. The material consists of semistructured interviews before and after using the device. Material from the semistructured interviews was analyzed with an interpretative phenomenological analysis method.

RESULTS: The study identified four main themes characterizing older adults’ lived experiences with wearables, which are (1) use of wearable technologies without RTF in daily life, (2) embodied rhythmic negotiation with RTF, (3) interpretation of health data with RTF, and (4) temporal trajectories of device engagement with RTF. Older adults demonstrated intentional distancing from wearable technologies rather than simple disuse, prioritizing authentic bodily experiences over external validation. Their engagement was fundamentally relational, mediated through trusted social networks, and required dialogical support for data interpretation. Device-guided movement synchronization created contextually situated challenges that varied significantly based on environmental demands, individual bodily capacity, and exercise routines. Extended temporal engagement transformed participants’ relationships with the technology from initial disruption to potential integration, with RTF serving as a bridge toward enhanced embodied awareness when carefully designed.

CONCLUSIONS: The study concludes that RTF from the device can enhance synchronization and bodily awareness, but meaningful engagement requires adaptive designs that respect older adults’ authentic movement practices, accommodate their relational approach to technology validation, and allow sufficient time for embodied competency development.

PMID:42054677 | DOI:10.2196/71509

JMIR Res Protoc. 2026 Apr 29;15:e73060. doi: 10.2196/73060.

ABSTRACT

BACKGROUND: Veterans face an increased risk of common mental disorders when compared to civilian groups. However, veteran disengagement from treatment is a concern among health care providers, resulting in a need to explore novel ways of managing veteran mental health. Wearable devices, such as fitness trackers and smartwatches, have been explored for their potential to assess, monitor, and predict mental health outcomes in the general population. Such devices provide continuous data on metrics including physical activity, heart rate, sleep quality, and stress levels, offering a comprehensive view of the lifestyle and physiological factors influencing mental health.

OBJECTIVE: This study aims to explore the feasibility of using wearable technology as a data collection and potential health monitoring tool among UK veterans. It also aims to explore the associations between mental health, physical activity, and functioning factors among UK veterans.

METHODS: This is an observational feasibility study measuring mental health via validated questionnaires completed at baseline (T0), day 28 (T1), day 56 (T2), and day 84 (T3), and physiological metrics measured continuously via wrist-worn fitness trackers (Garmin vívosmart-5 watches) over 3 months (84 days). UK veterans will be recruited through convenience sampling methods. Statistical analysis will be exploratory, and machine learning models will be trained to detect changes in mental health and well-being outcomes.

RESULTS: Data collection was conducted between February 2025 and October 2025, and data analysis is scheduled to begin in January 2026.

CONCLUSIONS: This study will provide information on the feasibility of using wearable technology devices within a UK veteran population and may inform potential future interventions seeking to integrate wearable-derived data alongside the management of common mental disorders in veterans experiencing mental health difficulties. Findings would also enhance understanding of the relationship between mental health and physiological factors (eg, physical activity and sleep) in UK veterans.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/73060.

PMID:42054675 | DOI:10.2196/73060

JMIR Cancer. 2026 Apr 29;12:e80909. doi: 10.2196/80909.

ABSTRACT

BACKGROUND: Myeloid neoplasms (MNs) are most frequently diagnosed among adults aged 60 years and older. Cancer and chemotherapy can cause gait disturbances and increase fall risk in older adults with MNs. Exercise may improve gait, but there is a lack of research among older adults with MNs undergoing active chemotherapy.

OBJECTIVE: We explored gait changes following a home-based mobile health exercise intervention during 2 cycles of outpatient chemotherapy (8-12 weeks).

METHODS: In a single-arm pilot study, we included adults aged 60 years and older with MNs undergoing chemotherapy. Geriatric Oncology-Exercise for Cancer Patients intervention integrates a progressive aerobic walking and resistance exercise program with a mobile app. We assessed gait by using a waist-worn G-Walk motion sensor during a 6-minute walk at the preintervention and postintervention time points. Spatiotemporal outcomes included cadence (steps per minute), velocity (meters per minute), normalized stride (stride length normalized over height), and swing duration (percentage of the gait cycle during which a foot is in the air when walking). Regularity outcomes that measure gait rhythm included variability of normalized stride and variability of swing duration. Variability for both outcomes was quantified as the SD across all gait cycles. We calculated Cohen d effect sizes (ESs) for change in gait outcomes and used the Spearman rank correlation to correlate changes in daily steps and resistance exercise duration with gait outcomes.

RESULTS: We included 13 patients (mean age 71, SD 4.8 years); most were male (n=8, 61.5%), White individuals (n=12, 92.3%), and non-Hispanic individuals (n=13, 100%). Average daily steps were 3084 (SD 1765.5) at the preintervention time point and 3757 (SD 2623.6) at the postintervention time point. Patients performed resistance exercises for 25 minutes per day, 4 days per week at low intensity (mean rating of perceived exertion 3/10, SD 1.3). At the postintervention time point, we observed numerical changes in gait outcomes, including increased cadence (mean +4.6, SD 14.6 steps per minute; P=.24; ES=0.38) and decreased variability in normalized stride (mean -1.4%, SD 8.5%; P=.34; ES=-0.18) and swing duration (mean -0.1%, SD 1.1%; P=.54; ES=-0.15), although these improvements were not statistically significant. Increased daily steps significantly correlated with decreased swing duration variability (r=-0.72; P=.01). Resistance exercise duration significantly correlated with increased cadence (r=0.54; P=.06) and velocity (r=0.56; P=.05).

CONCLUSIONS: In our exploratory analyses, better adherence to exercise correlated with improved gait outcomes. Our ongoing pilot randomized controlled trial (ClinicalTrials.gov identifier: NCT04981821) will further examine the effects of the Geriatric Oncology-Exercise for Cancer Patients intervention on gait outcomes in this population.

PMID:42054674 | DOI:10.2196/80909

JMIR Hum Factors. 2026 Apr 29;13:e80734. doi: 10.2196/80734.

ABSTRACT

BACKGROUND: The rising popularity of apps that sync with continuous glucose monitors (CGMs) reflects growing interest in on-demand, personalized care. These platforms combine real-time glucose biofeedback with self-monitored behaviors to optimize metabolic health among individuals with and without diabetes. However, little is known about user characteristics, engagement patterns, or factors associated with sustained use of CGM-integrated digital health apps in real-world settings.

OBJECTIVE: This study aimed to describe user demographics, CGM usage patterns, and food logging behaviors among Vively app users and to identify characteristics of sustained engagement with CGM wear and food tracking.

METHODS: We conducted a retrospective observational study of Vively app users between August 2021 and February 2025. Vively is a commercial digital health app that integrates with Abbott FreeStyle Libre CGMs to deliver personalized nutrition guidance. Users with at least 1 day of CGM wear were included. Primary outcomes were CGM wear duration (total days) and food logging engagement. Factors associated with engagement were identified using negative binomial regression for CGM wear and hurdle negative binomial models for food logging, adjusting for age, sex, BMI, baseline glucose, and device connectivity; the food logging model additionally adjusted for CGM wear category.

RESULTS: The analytical sample included 7647 users (4782/6905, 69.3% female, mean age 44.4, SD 10.9 years, mean BMI 27.8, SD 6.1 kg/m²). Users wore CGMs for a median of 15 (IQR 14-30) days, with 42.7% (3263/7647) completing one full wear period (13-15 days) and 30.3% (2315/7647) completing 2 or more wear periods (≥28 days). Most users (7013/7647, 91.7%) logged food at least once, with a median of 47 (IQR 18-91) food entries over 12 days. Food logging declined progressively during CGM wear (mean 63.2%, SD 8) and dropped sharply after sensor removal (mean 2.4%, SD 1.1). In multivariate models, higher baseline glucose was associated with longer CGM wear (incidence rate ratio [IRR] 1.15, 95% CI 1.13-1.17) but fewer food logging days (IRR 0.96, 95% CI 0.94-0.98). Connected device syncing showed the strongest association for both CGM wear (IRR 1.32, 95% CI 1.28-1.37) and food logging (IRR 1.45, 95% CI 1.39-1.51). Older age and female sex were associated with higher engagement in both behaviors.

CONCLUSIONS: This large-scale analysis reveals distinct engagement patterns with CGM-integrated digital health applications. Food logging was largely concurrent with active CGM wear, dropping dramatically in CGM-free periods. The divergent associations of baseline glucose levels, with longer CGM wear but reduced food logging, may reflect different motivational drivers for passive monitoring versus active behavior tracking. These findings have important implications for designing sustainable digital health interventions that maintain user engagement beyond periods of biological feedback, though replication in more diverse samples and studies accounting for diabetes status and socioeconomic factors is needed.

PMID:42054670 | DOI:10.2196/80734

J Med Internet Res. 2026 Apr 29;28:e86769. doi: 10.2196/86769.

ABSTRACT

This study explores the role of open-source large language models (LLMs) in promoting artificial intelligence (AI) health equity from the perspective of the health service triangle model. First, it defines AI health, categorizes AI-supported decision-making patterns, and assesses the status quo of AI health inequalities. Second, by comparing open-source and closed-source LLMs in terms of patient privacy, data security, accessibility, and use, it demonstrates the distinct advantages of open-source LLMs for AI-enabled health services. Finally, based on the health service triangle model, this study demonstrates how open-source LLMs drive the democratization of AI-enabled health services-particularly benefiting low-resource regions-by expanding service types, improving accessibility, enhancing quality, and reducing costs. This study concludes that, while open-source LLMs must address challenges such as hallucination risks and ethical responsibilities, they ultimately enable AI health equity through technological sharing.

PMID:42054668 | DOI:10.2196/86769

J Med Internet Res. 2026 Apr 29;28:e80330. doi: 10.2196/80330.

ABSTRACT

BACKGROUND: Human milk feeding and breastfeeding are the recommended standards for infant feeding. Nevertheless, breastfeeding rates in the United States remain below target levels, with disparities across racial, ethnic, and income groups. The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) plays a substantial role in reducing these disparities by providing lactation support to individuals with low income. With ongoing WIC modernization efforts, there is an opportunity to create and optimize technology solutions responsive to WIC participants’ and staff’s needs to increase access to the program and its services.

OBJECTIVE: This study aimed to describe the development and pilot testing of Daily Drop, a gamified, low-bandwidth mobile app to provide human milk feeding education and support for WIC participants.

METHODS: Guided by the 5-stage model for comprehensive research on telehealth, Daily Drop underwent 3 stages: concept development, service design, and preimplementation. Using a mixed methods approach, the project team sought feedback from WIC leadership and staff at the state and local levels, state IT staff, and WIC participants at each stage. Suggestions from stages 1 and 2 were incorporated into the testable app before field testing (stage 3). During field testing, participants and staff completed surveys across multiple time points and qualitative interviews to evaluate the app’s feasibility, usability, and acceptability. Quantitative data were analyzed using descriptive statistics, and qualitative data were thematically analyzed.

RESULTS: Key feedback from WIC participants and staff included providing flexibility for a variety of human milk feeding approaches (stage 1); and providing easily accessible educational information throughout gameplay, diversifying progress tracking to emphasize knowledge growth and expertise development, and including supportive or affirming messages for users (stage 2). During field testing (stage 3), >67% of WIC participants agreed with 7 out of 12 acceptability, satisfaction, and usability questions about the app, reiterated in interviews where they highlighted the simplicity of the app and how it increased their confidence to feed human milk. However, barriers to app use included a lack of reminders and repetitive information for parents with previous human milk feeding experience. Similarly, for WIC staff, mean scores for acceptability and feasibility were 3.8 (SD 1.0) and 4.4 (SD 0.6), respectively (max 5) at the early phase, but these scores declined over time. Staff recommendations included providing further, in-depth training to increase their familiarity with the app and reporting, and integrating the reports into WIC’s management information system.

CONCLUSIONS: The development of Daily Drop followed an agile development, co-design approach with the involvement of relevant key partners at all stages of development. Overall, Daily Drop was deemed acceptable, usable, and engaging by WIC participants and staff. Future research could focus on testing its effectiveness in improving human milk feeding behaviors and cost-effectiveness.

PMID:42054666 | DOI:10.2196/80330

Rambam Maimonides Med J. 2026 Apr 26;17(2). doi: 10.5041/RMMJ.10573.

ABSTRACT

BACKGROUND: Chronic hypoxemia in patients with COPD is associated with increased morbidity and mortality. Although arterial partial pressure of oxygen (PaO₂) is widely used, it does not adequately reflect systemic oxygen transport. Arterial oxygen content (CaO₂) may provide a more comprehensive assessment.

OBJECTIVE: This study aimed to evaluate whether or not CaO₂ is a better predictor of mortality than PaO₂ in patients with COPD.

METHODS: This retrospective observational cohort study included 147 COPD patients aged ≥45 years. Patients were categorized according to CaO₂ levels (low, normal, high). Mortality at 1, 3, and 5 years was analyzed. Statistical methods included ROC curves, Kaplan-Meier survival analysis, and Cox regression models.

RESULTS: A total of 66 deaths (45.2%) occurred in the study cohort. Mortality was highest in the low CaO₂ group. The CaO₂ demonstrated better predictive performance than PaO₂ (AUC 0.73 versus 0.53, respectively). Low CaO₂ was associated with a 2.5-fold increased risk of mortality. Despite improvements in PaO₂ after long-term oxygen therapy, CaO₂ did not significantly change.

CONCLUSIONS: The CaO₂ is a more informative marker of oxygen transport and mortality risk than PaO₂ in COPD patients. It should be considered a complementary parameter in clinical assessment.

PMID:42054663 | DOI:10.5041/RMMJ.10573

Rambam Maimonides Med J. 2026 Apr 26;17(2). doi: 10.5041/RMMJ.10572.

ABSTRACT

OBJECTIVES: This study explored the relationship between visual perceptual skills and participation performance in 4-6-year-old children diagnosed with autism spectrum disorder (ASD).

METHODS: A cross-sectional design was employed for children diagnosed with ASD. Visual perceptual abilities were assessed using the Motor-Free Visual Perception Test-Fourth Edition (MVPT-4), and participation levels were measured using the Children Participation Questionnaire (CPQ). Statistical analysis was done using SPSS version 26, employing one-sample t-tests and Pearson correlation analysis.

RESULTS: A total of 48 participants were included in the study: mean age, 4.98±0.82 years; 66.7% were male; 79.2% attended regular schools. One-sample t-tests indicated significant deficits across all CPQ dimensions (P<0.001). Visual perception was negatively correlated with autism severity (r=-0.429, P=0.002) and positively correlated with participation diversity (r=0.404, P=0.004). In the activities of daily living (ADL) and instrumental ADL (IADL) occupations, visual perception was significantly associated with all CPQ elements. Conversely, play and leisure occupations showed mostly negative correlations with the CPQ occupations, while social participation and education showed mixed results. Visual perception was positively correlated with most elements but negatively associated with enjoyment (r=-0.428; P=0.002).

CONCLUSIONS: Preschool children with ASD demonstrate significant participation restrictions. Visual perception emerged as a critical determinant of participation, particularly in ADL and educational contexts. Early interventions targeting visual perception skills may enhance independence and functional engagement, though interventions should also address the enjoyment and emotional experience occupations to ensure holistic participation outcomes.

PMID:42054662 | DOI:10.5041/RMMJ.10572

JMIR Hum Factors. 2026 Apr 29;13:e87522. doi: 10.2196/87522.

ABSTRACT

BACKGROUND: Computerized clinical decision support (CDS) has the potential to improve patient outcomes by offering evidence-based guidance at the point of care-enhancing guideline adherence and diagnostic accuracy-and supporting system-level outcomes by enabling predictive analytics for more efficient resource planning. Prior work has identified factors that affect adoption, such as clinicians’ expectations of usefulness, ease of use, alignment with workflows, and resources to support utilization. However, CDS adoption is not static and changes according to dynamic systems of behaviors and workflows, requiring a deeper understanding of how evolving conditions affect implementation and outcomes.

OBJECTIVE: To explore the dynamic factors influencing CDS adoption, we examined the implementation of the “Unplanned readmission model version 1,” developed by Epic Medical Records System, at Duke University Health System, using group model building and system dynamics modeling.

METHODS: We first conducted group model-building workshops with staff (case managers, physical and occupational therapists, hospitalist faculty physicians, and resident physicians) who participate in decisions about discharging patients. Study team members guided participants to identify and connect variables in causal loop diagrams. We coded workshop transcripts in software designed for system dynamics analysis to identify themes, aggregated them into a causal loop diagram, and reviewed them with participants to converge on a common model. A team member applied equations to the pathways and tested data to simulate conditions leading to full, limited, or no adoption of a tool.

RESULTS: We identified key balancing loops driven by external pressure (eg, Centers for Medicare & Medicaid Services penalties) that motivated initial adoption and reinforcing loops based on perceived internal benefits to sustain use. While institutional incentives led to early training and tool use, efforts declined due to staff turnover, competing priorities (eg, COVID-19), and workflow changes. Reinforcing loops emerged when staff described clinical utility, such as improved discharge planning and team communication. However, staff also suggested that these loops were often weak due to difficulty linking the use of the tool to outcomes in real time. Simulation modeling showed that while strong external pressure and rapid training led to initial success, interest in using the tool waned as workflows improved and readmission rates approached Centers for Medicare & Medicaid Services goals. When conflicting priorities were introduced, adoption stalled earlier, and fewer staff were trained. In contrast, when internal motivation was strengthened by reducing the amount of evidence needed to perceive success, individual interest remained high even as institutional attention declined, sustaining tool use and further reducing readmissions.

CONCLUSIONS: External pressure to improve can be a strong motivator for initial adoption, but in the face of conflicting demands for attention, it may fall short of sustained long-term tool use. Tools are more likely to have extensive and sustained use when those using the tools can perceive internal benefits.

PMID:42054653 | DOI:10.2196/87522

JMIR Med Inform. 2026 Apr 29;14:e84095. doi: 10.2196/84095.

ABSTRACT

BACKGROUND: Brain tumor is one of the most malignant diseases of the central nervous system, and early accurate detection is of great significance for improving patient survival rate. However, the heterogeneity of brain tumors in terms of morphology, size, and location on magnetic resonance imaging (MRI) image, as well as their similarity to surrounding normal brain tissue, poses significant challenges for tumor detection.

OBJECTIVE: This study aims to develop a high-performance brain tumor detection framework that integrates feature enhancement, channel attention, and progressive pruning, achieving an optimal balance between detection accuracy, model efficiency, and interpretability for slice-level MRI tumor localization tasks.

METHODS: This paper proposes a convolution Prewitt-and-pooling-based preprocessing (CSPP) approach, based on the “you only look once” version 11 (YOLOv11) framework, which highlights important structural detail more effectively than traditional statistics. A dynamic convolution-based C3k2 (DCC) module was integrated to more efficiently capture both local and global features. A channel prior convolutional attention (CPCA) module was introduced before the detection head, enabling the network to specifically focus on information-rich channels and key spatial regions. Through a progressive hybrid pruning strategy (PHPS), the model was optimized for efficient inference. Furthermore, Eigen-class activation mapping (Eigen-CAM) was used to interpret the prediction result, making them more transparent.

RESULTS: Extensive experiments on 3 brain tumor MRI datasets demonstrated the superior performance of CDCP-YOLO (CSPP-DCC-CPCA-PHPS-YOLO). On Br35H, the mean average precision (mAP) at an intersection-over-union (IoU) threshold of 0.5 (mAP0.5) increased by 2.6%, average mAP over several IoU thresholds (0.50-0.95; mAP0.5:0.95) increased by 5.9%, and number of floating-point operations (×10⁹; GFLOPs) decreased by 47.7%. On Roboflow, mAP0.5 increased by 19.5%, mAP0.5:0.95 increased by 7.7%, and GFLOPs decreased by 47.7%. On Capstone, mAP0.5 increased by 6.9%, mAP0.5:0.95 increased by 5.8%, and GFLOPs decreased by 47.7%.

CONCLUSIONS: The proposed CDCP-YOLO framework achieves an optimal balance between accuracy, efficiency, and interpretability, providing a lightweight and reliable solution for slice-level brain tumor detection in MRI images.

PMID:42054652 | DOI:10.2196/84095