Duo Yu

Population impact begins before an application is built. A lifestyle needs assessment in communities in Ireland and Romania shows that barriers, motivations, and preferred forms of digital coaching differ across local settings. This finding complements a survey-based model of digital-health intention, in which expectations, health beliefs, and perceptions of persuasive design shaped willingness to adopt digital services. Both studies caution against treating access to a smartphone as equivalent to readiness, trust, or sustained use.The qualitative contributions deepen this point. A meta-synthesis of cardiovascular patients' experiences identifies reassurance, human oversight, age-friendly design, and culturally responsive connections among families, hospitals, and communities as recurring needs. A protocol focused on young adults with cardiovascular disease brings a life-course perspective and asks how the possibilities offered by a technology are understood and used by a population with distinctive social roles and expectations. Although the latter does not yet report findings, it highlights a neglected issue: the same interface may enable independence for one group while creating surveillance, anxiety, or inconvenience for another.These contributions place co-design, usability, and social context within the causal pathway of effectiveness. An intervention that is not reached, trusted, understood, or sustained cannot improve population health. Evaluations should therefore report who participates, who declines, who disengages, and why not only outcomes among active users.Mobile and wearable technologies can make behavior and physiology visible in everyday life. In a community sample, wearable-derived sleep duration showed a nonlinear association with hypertension, illustrating how objective longitudinal measures can refine risk characterization beyond coarse self-report. The Terrapino application similarly demonstrates the reach of mobile cognitive-health assessment and identifies modifiable factors associated with an Alzheimer's risk score among thousands of users.These studies show the epidemiologic potential of devices that people already carry or wear, but they also mark some important boundaries. Cross-sectional associations cannot establish that changing sleep will reduce hypertension, and predicting a questionnaire-derived risk score is not the same as predicting incident dementia.Voluntary app users may also differ systematically from the wider population. Digital measurement is therefore most useful when it is validated against meaningful outcomes, stable across devices and generalizable to different demographic groups, and connected to an intervention that can change the measured risk.Two technically adjacent studies reinforce the importance of measurement quality. A pilot classification framework for voice disorders found that agreement was stronger for broad categories than for specific and perceptually ambiguous diagnoses. A YOLOv8 study achieved promising local performance in detecting and staging pressure injuries from clinical images. Although neither study directly evaluates population-level chronic disease prevention, both expose issues that apply throughout AI-enabled health research: algorithms inherit uncertainty in clinical labels, and impressive internal accuracy does not guarantee performance across patients, devices, or settings.Several contributions explore AI as a means of stratifying risk or anticipating change.An electronic medical record model identified patients at risk of exercise intolerance after percutaneous coronary intervention, with strong internal discrimination and potentially useful negative predictive value. The Sugar Slay ecosystem combines glucose forecasting, wearable data, gamification, personalized feedback, and caregiver support for type 1 diabetes. These systems illustrate complementary uses of prediction: prioritizing rehabilitation assessment at the service level and supporting near-term decisions in daily self-management.Yet prediction is an intermediate outcome, not a health benefit. A model may perform well in a local dataset but lose calibration when prevalence, workflow, coding, or patient mix changes. A forecast may be accurate but arrive too late, demand an action that is unavailable, or increase cognitive burden. Before deployment, prediction systems need temporal and geographic external validation, subgroup assessment, prospective monitoring, and comparison with simpler rules or existing care. More importantly, studies must evaluate the complete chain from prediction to response: whether an alert was seen, whether the recommended action was appropriate and completed, and whether outcomes improved.The broader reviews in the Topic place these examples in context. Reviews of AIsupported chronic disease self-management and AI in type 2 diabetes describe functions spanning monitoring, risk prediction, conversational support, multi-omics, continuous glucose monitoring, and personalized treatment. Their scope shows how quickly the field is expanding, while also revealing that technical development is advancing faster than evidence on long-term clinical effectiveness, affordability, and equitable implementation.The strongest practical message across the collection is that digital information produces value when it changes care. In type 2 diabetes, an AI-and big-datasupported mobile program translated risk estimates into individualized diet, exercise, and glucose-monitoring plans and reported modest improvements in glycemia, selfcare, and quality of life compared with conventional nurse-led management. Because allocation was not randomized, the results require confirmation, but the intervention illustrates a closed loop in which data inform action and subsequent data allow support to adapt.Other interventions show that useful digital transformation does not always require complex AI. App-based follow-up after arteriovenous-fistula...

Background: Intimate partner homicide is one of the leading causes of death for women, disproportionately affecting Black women. Purpose: To investigate the severity of lethality risk and its relationship with individual characteristics, experiences of discrimination, and mental health among urban Black women in Wisconsin since the start of COVID-19. Methods: This analysis was part of a larger mixed-methods study. Women were recruited from community agencies in Wisconsin. Eligible participants could participate either in person, by phone call, or via Teams. Lethality was assessed using the Lethality Assessment Protocol (LAP). The experiences of discrimination were measured by the Experiences of Everyday Discrimination (EDS). Mental health and all other demographic variables were self-reported. The data collection was conducted between 2023 and 2025. Analyses were conducted using descriptive analysis, simple linear regression, and multiple regression. All analyses were conducted in SPSS version 29. Results: Data from 185 urban Black women were analyzed, with an overall mean weighted LAP score of 17.29. The overall regression was significant, accounting for approximately 5% of the variance in LAP scores. Experiencing discrimination at least once per year was significantly associated with LAP score ( p < 0.05) when controlling for all other variables. Conclusion: These findings highlighted that the experiences of discrimination are a significant predictor of lethality risk among Black women experiencing IPV, underscoring the contribution of structural and social factors to health inequities. The study underscored the need to appraise victims’ experiences of discrimination within IPV risk-assessment policies and to integrate these policies into routine care, and it calls for further research on pathways to IPV homicide in marginalized communities. Further research is needed to better understand the pathways of IPV homicide, particularly among highly marginalized communities.