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Background: Obesity affects more than one-quarter of adults in the United Kingdom and is a leading cause of preventable disease and health care costs. Digital behavior change programs can provide scalable weight management support, but maintaining engagement is challenging, and engagement is strongly associated with weight loss success. Tailoring interventions to cognitive-behavioral phenotypes, distinct patterns of thinking and behavior, offers one strategy to improve adherence. Although such approaches show promise in controlled settings, evidence from real-world digital programs is limited. Objective: This study evaluated whether phenotype-tailored weekly advice improved engagement and weight loss in a national digital weight management program. Secondary aims were to assess correlations between advice use and outcomes, explore moderation by socioeconomic status, and capture participants’ perceptions of the advice. Methods: We conducted a quasi-experimental study among UK adults enrolled in a free 12-week program commissioned by the National Health Service. Eligible participants were aged 18-80 years with a BMI greater than 25 kg/m². The phenotype group (n=148; mean age 48 years; 127/148, 86% female; mean BMI 39 kg/m²) completed a 17-item questionnaire, were classified into one of 4 phenotypes, and received weekly tailored advice for 7 weeks. Comparators included a historical cohort enrolled 1 year earlier without phenotype advice (n=241; mean age 44 years; 171/241, 71% female) and nonresponders who did not complete the questionnaire (n=394; mean age 44 years; 299/394, 76% female). Primary outcomes were program engagement (any in-app activity such as meal logging, activity tracking, content reading, or coach messaging) and self-reported weight. Results: The phenotype group recorded a mean of 257 (SD 232) engagements over 7 weeks, significantly higher than both the historical cohort (mean 159, SD 187; P

Background: Family caregivers of persons living with dementia experience caregiver burden, depression, and poor health-related quality of life. Caregiving preparedness (perceived confidence in caregiving abilities) and caregiving balance (perceived confidence in finding equilibrium among overlapping demands of caregiving and personal needs) can reduce caregiver burden and depression and improve health-related quality of life. Smart-clothes technology for monitoring persons living with dementia may help reduce caregiver burden and improve quality of care, but empirical evidence remains limited. Objective: To examine preliminary effects of a smart-clothes assisted care system that monitors daily activity of persons living with dementia on outcomes for family caregivers. Methods: This non-randomized, quasi-experimental study recruited dyads of family caregivers and persons living with dementia by convenience sampling from dementia-care centers in northern Taiwan. Sixty dyads agreed to participate in the 6-month study. Persons living with dementia received either usual care (control group, n=30) or usual care in addition to smart-clothes assisted care (intervention group, n=30), which required wearing a smart-clothes vest 24 hours/day. The nurse-led intervention was conducted in the homes of the persons living with dementia from August 2020 to November 2023. Sensors in the smart-clothes vest and home monitored activity of persons living with dementia, which was transmitted via a smartphone app to a nurse who provided caregivers with real-time feedback and individualized care plans. Caregivers completed self-report structured questionnaires for outcomes of caregiving preparedness, balance, depressive symptoms, and health-related quality of life at baseline, 2-, 4-, and 6-months. Effects of the intervention were assessed with by comparing scores and changes in scores between the intervention and control group. Intention-to-treat analysis was employed; data were analyzed using generalized estimating equations. Results: Mean age of the 60 caregivers was 59.32 years, (SD=11.46); most were female, married, college-educated, and co-residing with their relative. Mean age of the 60 persons living with dementia was 79.95 years (SD=7.05); most were female, widowed and diagnosed with Alzheimer’s disease; 53.5% (32/60) had a Clinical Dementia Rating of 1.0. Compared with controls, caregiving balance was significantly higher for the intervention group at 2-, 4-, and 6-months (P

npj Mental Health Research - Unexpected events and prosocial behavior: the Batman effect

npj Mental Health Research - Unexpected events and prosocial behavior: the Batman effect

Background: Falls are a leading cause of injury, disability, and mortality among older adults, imposing significant burdens on individuals and healthcare systems worldwide. Effective fall risk assessment and intervention strategies are critical for improving mobility and preventing adverse health outcomes in aging populations. Traditional assessment methods often require specialized equipment or trained personnel, limiting accessibility and scalability. Objective: This study investigates the effectiveness of an artificial intelligence (AI)-assisted dynamic postural control screening system in predicting and mitigating fall risks among older adults. By integrating AI-driven image analysis with standardized mobility assessments, the study aims to enhance early detection and personalized intervention strategies for fall prevention. Methods: A cohort of elderly participants underwent AI-assisted postural control screening, which incorporated convolutional neural networks (CNNs) with the Short Physical Performance Battery (SPPB) to evaluate balance, gait, and lower limb strength. Participants were divided into an experimental group receiving AI-driven personalized exercise interventions and a control group following standard care. Mobility outcomes, including SPPB scores, gait speed, and sit-to-stand performance, were measured pre- and post-intervention. Results: Our findings suggest that AI-assisted postural control screening effectively enhances mobility in elderly individuals. The experimental group exhibited a 7% increase in SPPB scores, alongside notable improvements in gait speed (+13%) and sit-to-stand performance (-13%). These results indicate that real-time AI feedback may play a critical role in optimizing exercise interventions, as personalized recommendations can be dynamically adjusted based on individual progress. Notably, participants with higher adherence to AI-driven programs showed greater functional gains, underscoring the importance of continued engagement in technology-assisted fall prevention strategies. Conclusions: AI-driven dynamic postural control screening presents a scalable, cost-effective solution for fall risk assessment and prevention in older adults. While the technology shows promising outcomes, challenges such as accessibility, validation across diverse populations, and implementation in low-resource settings remain. Future research should explore long-term effects, broader demographic inclusion, and strategies to overcome technological barriers to enhance widespread adoption in geriatric care.

Publication date: Available online 26 November 2025 Source: Teaching and Learning in Nursing Author(s): Li Liu, Sichen Yi, Xiaomei Li, Yongjie Li, Qi Zhang

To evaluate the effectiveness of a structured handover tool implemented in paediatric inpatient care. Poor communication during clinical handovers can…

Policy Points In 2021, young adults without children became (temporarily) newly eligible for the earned income tax credit, the largest US poverty alleviation program. This 2021 expansion was asso...

Date Submitted: Nov 20, 2025. Open Peer Review Period: Nov 20, 2025 - Jan 15, 2026.