A mobile CT screening unit in a Banbury car park detected early-stage lung cancer in an asymptomatic patient—highlighting both the promise and ch… #Asymptomaticdisease #CTscreening #Earlydetection #Lungcancer #Mobilehealth

IntroductionOverweight and obesity are among the most prevalent health problems worldwide leading to various diseases and having a significant impact on the ...

Background: Perinatal #MentalHealth disorders affect #Approximately 20% of pregnant and postpartum individuals, and are associated with substantial maternal and infant morbidity. Traditional assessment relies on infrequent, subjective self-reports. Mobile devices, including smartphones and wearables, offer opportunities for continuous and objective measurement, but evidence on their assessment utility in perinatal populations remains fragmented. Objective: This review aimed to examine the #Application of wearable devices and smartphones for detecting and predicting perinatal #MentalHealth outcomes, with emphasis on predictive performance, informative features, and methodological rigor. Methods: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (PROSPERO CRD420251249218). Six databases (PubMed, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ACM #Digital Library) were searched initially in January 2026 and supplemented by an amended search in June 2026, with no publication date restrictions. Evidence was synthesized narratively, and the risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool With AI extension). Results: The initial and supplementary searches yielded 1952 unique records after deduplication, of which 10 studies met the inclusion criteria. The included studies covered postpartum #depression, prenatal stress, discrete emotions during pregnancy (eg, h#Appiness, #anxiety, and sadness), and maternal loneliness. High discrimination metrics were reported for postpartum #depression in individual studies, including a multiclass area under the curve of 0.85, a binary area under the curve of 0.871, and an -score of 0.9872. Heart rate variability, GPS-derived mobility, physical activity, and sleep features were most frequently reported as useful, and their interpretation requires perinatal-specific contextualization. Methodological quality was limited, with 80% (12/15) of PROBAST+AI assessment units rated as having high overall quality concern or risk of bias, mainly due to small samples, limited validation, inadequate handling of missing data, and potential overfitting in the analysis domain. Conclusions: Mobile sensing shows preliminary potential for perinatal #MentalHealth assessment, but current evidence does not yet support clinical screening or decision-making, and independent external validation in perinatal populations is currently lacking. Progress toward clinical utility requires broader #MentalHealth outcome coverage, larger longitudinal cohorts, standardized analytical and reporting practices, adoption of modeling #Approaches better suited to perinatal trajectories, independent external validation, and human-centered monitoring designs.

Background: The prevalence of knee osteoarthritis (KOA) in the adult population is high and patients profit from individualized therapy approaches. Just-in-time adaptive interventions (JITAIs) are upc...

Health AI Developer Foundations is a suite of open-weight models publicly available to help developers build and implement AI models for health applications.

Background: Mobile health technologies offer scalable opportunities to support home-based exercise programs. However, most commercially available apps are not tailored to individuals with spinal cord injury (SCI) and have rarely been evaluated for #usability and implementation outcomes in this population. Objective: This study aims to assess the acceptability, appropriateness, #feasibility, and quality of a mobile app–based intervention designed to support exercise adherence in individuals with SCI. Methods: We conducted a convergent parallel mixed methods study using quantitative data from standardized measures and qualitative feedback from focus groups. Twelve individuals with SCI (mean age 50.5, SD 17 years; n=6, 50% male; n=8, 67% White; n=6, 50% with thoracic injuries; n=6, 50% ambulators) engaged in a customized exercise regimen using the app for 2 weeks. Acceptability, appropriateness, and #feasibility of this mobile intervention were assessed using the Acceptability of Intervention Measure, Intervention Appropriateness Measure, and #feasibility of Intervention Measure. The objective and subjective qualities, and the perceived impact, of the app-based intervention were evaluated using the User Version of the Mobile Application Rating Scale. Results: Using a cutoff score of >4 out of 5, 67% (8/12) of participants rated the app-based intervention as highly acceptable and feasible, and 50% (6/12) rated it as highly appropriate. The objective quality of the app was rated 3.57 out of 5 on the User Version of the Mobile Application Rating Scale. At the scale level, information received the highest score (mean 4.10, SD 0.52), with functionality (mean 3.51, SD 0.66) and aesthetics (mean 3.50, SD 0.70) closely following. Engagement scored lowest (mean 3.18, SD 0.72). The app’s subjective quality averaged 3.27 (SD 0.88), and its perceived impact on exercise adherence was rated 3.79 (SD 0.92). Qualitative feedback indicated that customization, access to professional support, and SCI-specific exercises were positive features. Areas for improvement included bug fixes and enhanced features to boost engagement. Conclusions: The smartphone-based intervention demonstrates adequate acceptability and #feasibility, along with high information quality and favorable #usability, for supporting exercise adherence in individuals with SCI. Improvements in engagement features are recommended to enhance sustainable implementation.