Journal of Medical Internet Research

Hospital-at-home (HaH) care models are increasingly being adopted as a strategy to treat older adults with acute care needs and reduce strain on health care systems. Technological innovations, particularly digital communication platforms, have become essential in enabling care delivery beyond traditional hospital settings. Among these, asynchronous messaging tools have the potential to facilitate safe, timely, and coordinated interactions between health care providers, patients, and caregivers. Despite growing interest, little is known about how the content and relational dynamics of such exchanges influence care experiences in real-world HaH contexts.

The secondary use of health data is essential for advancing medical research and improving clinical practice. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) enables large-scale, multicenter studies but faces challenges related to consistency, completeness, and transparency during data mapping from original data sources.

Several artificial intelligence (AI) governance frameworks have emerged to help health systems (HS) address AI-related risks. However, most fail to capture the multidimensional and evolving nature of real-world governance.

Given the diversity of human characteristics and experiences, personalization in nudges, messages, choice presentations, interventions, and overall product design has been increasingly adopted in digital health to promote engagement. Past studies on moderators and personalization in digital health and mental health services generally focused on demographic and symptom variables, with generally inconsistent findings or null findings. Cognitive, motivational, and decisional psychological attributes are largely overlooked. Psychology often uses long self-report scales to measure various psychological attributes. Although they are useful in tapping into individuals’ psychological profiles, when applied in real-life, everyday settings to assess individual differences, people are most likely unwilling to complete them. With the pressing need to personalize digital health platforms to enhance uptake, retention, and engagement, ultrashort versions of these psychological scales may be considered to allow assessment of multiple attributes at the same time. Scale shortening can be achieved through regression analyses of each item, factor analyses, item response theory, ant colony optimization, and machine learning methods, with each method having advantages, disadvantages, and conditions required to make it suitable. To illustrate, we provided examples of regression analyses of each item and factor analyses, with potential implications for personalizing narrative versus research-based messages in digital mental health contexts. We present a 3-tiered decision framework for scale shortening method selection depending on goals and possible constraints, with guidelines on validation methods for ultrashort scales. Moving forward, more validation studies and field studies in digital health platforms are needed to evaluate the ecological validity, reliability, and generalizability of these methods, bearing in mind the limitations and conditions where such shortening methods may not work well. Researchers may compare the effectiveness and limitations of personalization using ultrashort scales with other commonly adopted personalization methods (eg, based on longer scales, behavioral data, and large language models). Ethical concerns need to be considered and mitigated carefully, respecting diverse preferences, informed choices, and the privacy of service users. Our viewpoint piece is primarily intended for digital mental health researchers and practitioners, but may also be informative for the fields of digital health and medicine as well as personalization (eg, personalized health care, personalized nudging, and message matching) more broadly, given the common goal of boosting uptake and engagement as well as improving service users’ experiences.

Medical real-world data (RWD) are often siloed across organizations, making them inaccessible for research. Unlocking these data could advance clinical research and patient care. The pan-European Data Nexus platform (DNP) links RWD, facilitating its use, for example by artificial intelligence (AI) tools, to support the generation of real-world evidence. In Europe, particularly Germany, the secondary use of health data is governed by stringent regulatory requirements, including informed consent.

Traditional in-person neuropsychological tests for Parkinson disease (PD) lack accessibility, scalability, and PD specificity. Mobility impairments hinder access to in-person assessments, and long waiting times for expert evaluation limit scalability. Common tools for cognitive screening, such as the Montreal Cognitive Assessment, are generic and not specific to PD.

Incentivization in clinical trial participation can be challenging, with many studies failing to meet recruitment or retention goals despite traditional compensation strategies. Digital health evolves, and with it, new approaches can emerge to engage participants meaningfully. We propose unique digital images as a novel, symbolic incentive for clinical trials. Digital images combine qualities such as personalization, ownership, and digital visibility, which may drive engagement more effectively than monetary rewards alone. In our illustrative study, participants complete artificial intelligence–personalized digital therapeutics training using CURATE.DTx, generating individualized learning trajectories. These are transformed into digital artworks and minted as nonfungible tokens given as a reward upon trial completion. This concept integrates gamification, personalization, and blockchain technology to support both intrinsic and extrinsic motivation. We explore the implications for decentralized health care, long-term behavior change, and participant recognition in the context of preventive medicine and longevity science. Our aim is to encourage research into the use of digital incentives to transform the trial participant experience and promote sustained engagement in health interventions.

Prior research on human-like virtual profiles (VPs)—computer-generated imagery (CGI; displaying legible artificiality) or artificial intelligence (AI)–generated personas on social media—is often based on small samples and limited data. Furthermore, prior studies group highly photorealistic virtual influencers with abstract virtual characters, complicating comparisons. Recent generative AI has enabled large-scale production of synthetic content. These advances have drastically increased the quality and volume of synthetic media. Much of the prior literature predates these developments, leaving open questions about how posting behavior and VP design now relate to reach and engagement outcomes.

Generalized anxiety disorder (GAD) is characterized by stress-anxiety reinforcement cycles. Evidence for brief, self-guided, internet-based stress management as an adjunct to pharmacotherapy remains limited.

Many unhealthy habits develop in early childhood and can lead to long-term health risks, which disproportionately affect children with low socioeconomic position (SEP). Digital and blended lifestyle interventions can promote healthier lifestyles, yet families with lower SEP remain underrepresented and face unique barriers to healthy behaviors and intervention access. As a result, it remains unclear which intervention characteristics are most effective for these populations.

Maris et al document important ethical challenges at the intersection of electronic health record data and artificial intelligence development, but existing governance frameworks designed for secondary data use are categorically insufficient for artificial intelligence model training, which creates persistent deployable artifacts that encode local clinical patterns as generalizable knowledge. Drawing on two decades of stewardship framework development, we propose extending governance from data stewardship to model stewardship.

Youth vaping has become a significant public health concern, with high rates of initiation, nicotine dependence, and a tendency to underestimate vaping-related harms. Although many youth attempt to quit, most do so without formal support, and few report leveraging cessation apps. Mobile health tools have potential as accessible youth-oriented supports; yet, little is known about youth experiences with these interventions.