RAND Europe, as a member of the DECIDE centre, is conducting a rapid evaluation to assess the feasibility of a full evaluation of the MHF@home programme, focusing on data collection, diversity of impl...

With a record number of young people seeking mental health support in the UK, NIHR has awarded £1.5m for digital projects aiming to improve access.

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.

Oracle Corp has launched a new electronic health records (EHR) system for ambulatory healthcare providers in the United States, the enterprise software giant says, marking its latest push into the com...

Two weeks with the Alpha-Stim NHS device: sleep, anxiety, pain, brain fog—what changed, what didn’t, and who it suits.

A biotech startup converted a failed clinical trial into an AI model with potential diagnostic value. This emerging strategy demonstrates h… #Artificialintelligence #Biotechinnovation #Clinicaltrials #Healthtechnology #Medicaldevices

A vendor demonstration of a kiosk that flags over‑the‑counter and prescription interactions prompted commissioners and community participants to press the company on whether kiosk receipts, central storage and "VIP" profiles could breach patient privacy or fall outside HIPAA protections.

Background: Standards for the secondary use of health data remain under development and do not comprehensively address specific categories of health data. Existing health data and interoperability standards are primarily designed for the primary use of health care, leaving gaps in their applicability for research, regulatory and decision-making. This research is part of the Integration of Heterogeneous Data and Evidence Towards Regulatory and Health Technology Assessment Acceptance (IDERHA) project, which creates a federated processing environment for secondary health care use cases. Objective: This study aims to recommend a set of standards and tools to support harmonization objectives in the secondary use of health data. The proposed standards establish a shared representation model for data providers and stakeholders, improving data adherence to findable, accessible, interoperable, and reusable (FAIR) principles and interoperability. Methods: In this exploratory assessment and #feasibility study, we performed a consensus-based evaluation of health data standards and harmonization tools across major health-data categories. State-of-the-art standards from different standard-producing organizations were evaluated based on community adoption, technical suitability, and alignment with European Health Data Space requirements. Data-harmonization tools were tested in a pilot experiment using synthetic Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)–based data to assess the quality and #feasibility of applying harmonization workflows within IDERHA. We also assessed the inherent adherence to FAIR principles of the OMOP-CDM, identifying its strengths in semantic interoperability and data reuse. Finally, we addressed the datasets handling challenges (metadata discoverability, global identifiers, access control, and licensing), which are not inherently solvable by bare data description standards, through complementary mechanisms implemented in IDERHA. Results: We recommend a set of standards to support the secondary use of heterogeneous health data across clinical, imaging, genomic, socioeconomic, environmental, and patient-reported outcome measure (PROM)/patient-reported experience measure (PREM) domains. This standards collection, centered on OMOP-CDM and complemented by established extensions, was paired with selected harmonization and quality-assessment tools. #feasibility testing on synthetic OMOP data confirmed the suitability of the tools for evaluating data quality, mapping accuracy, and extract, transform, and load. A lung-#Cancer use case demonstrated how diverse datasets can be harmonized and prepared for federated analysis. A FAIR-dataset maturity evaluation showed that integrating OMOP data into the IDERHA environment substantially improved adherence to FAIR principles—particularly in metadata completeness, interoperability, and hosting capabilities—supporting enhanced discoverability, accessibility, and reuse of standardized health data. Conclusions: By strengthening adherence to FAIR principles, this research facilitates the broader integration of standardized health data for regulatory processes and health technology assessment applications. The proposed standards and tools provide a comprehensive overview of existing approaches, while offering solutions that can be applied to other scenarios involving the secondary use of health data.

Hyper-personalized medicine represents the cutting edge of healthcare, which aims to tailor treatment and prevention strategies uniquely to each individual. ...

Background: Falls and fractures are a major clinical concern for older adults. International clinical guidelines recommend annual fall risk screening for all adults aged 60 years and older. However, existing falls risk–screening algorithms have shown limited sensitivity and specificity in detecting future falls. Emerging health technologies, including wearable sensors, in-silico finite element models (FEMs), and virtual reality (VR) technology, show promise in enhancing fall and fracture risk assessments by providing more personalized objective data to support targeted primary prevention. However, large-scale longitudinal studies are required to evaluate their predictive accuracy and cost-effectiveness for systematic community–based screening. Objective: The TARGET (Targeted Assessment and Recruitment of Geriatrics for Effective Fall Prevention Treatments) study is an ongoing national prospective cohort study in Singapore designed to develop cost-effective and scalable frameworks for the early detection and prevention of falls and fractures using novel health technologies. This paper describes the rationale, design, and baseline characteristics of TARGET, and provides preliminary insights into how these novel technologies perform in discriminating between older adults with and with no history of falls at study baseline. Methods: A total of 2291 community-dwelling Singapore residents aged ≥60 years were enrolled between 2022 and 2024. Participants underwent a comprehensive baseline fall risk assessment comprising (1) home-based interview to assess sociodemographic, anthropometric, cognitive, physical, functional, and psychosocial status; (2) gait assessment using wearable inertial measurement units (IMUs) motion sensors; (3) dual-energy X-ray absorptiometry and whole-body 3D scans to construct subject-specific FEMs; and (4) VR-based cognitive assessment to probe spatial navigation deficits. Prospective follow-up for 2 years is ongoing, with linkage to national electronic medical records (EMRs) to obtain detailed clinical, medication, and pathological data. Results: Of the total cohort, 58% (1327/2291) were female, with a mean age of 74.6 years at baseline. TARGET represents a relatively healthy older adult population, with 83% (1898/2291) remaining fully independent, 44% (1016/2290) with mild cognitive impairment, 50% (440/879) with osteopenia, and 15% (358/2291) reporting falls in the past year. Significant baseline functional, psychosocial, cognitive, and biomechanical differences were identified between fallers and nonfallers. Fallers had poorer physical and psychosocial health, greater gait variability, and a higher risk of osteoporotic fractures. Novel risk prediction models integrating epidemiological, IMUs, FEM, VR, and EMR data are in development and being validated against gold standard clinical assessments, with cost-effectiveness evaluations of the economic feasibility of adopting these screening measures at scale. Conclusions: Leveraging comprehensive epidemiological, EMR, and health technology data, TARGET is well positioned to identify novel biomarkers and systemic interactions that may predict falls and fracture risk, with potential application to other age-related diseases with shared pathophysiology. In particular, TARGET’s focus on community-based screening using technologies that can be administered by nonclinical personnel has the potential to reduce substantial burden and costs within the health care sector.