Journal of Medical Internet Research

Large language models (LLMs) are increasingly used to quantify psychological constructs from natural language, yet their outputs are typically evaluated only as predictions of an external label. When that label is itself imperfect, optimizing prediction conflates label agreement with construct measurement. No study has applied a multidomain measurement-oriented evaluation to LLM-derived psychological outputs.

The COVID-19 pandemic necessitated innovative approaches to health communication, particularly for non–English-speaking communities. Vietnamese Americans, who report lower rates of English proficiency, faced unique challenges accessing facts and subsequent “infodemic” of rapidly spreading information.

Musculoskeletal (MSK) conditions account for one in seven general practice consultations in the United Kingdom. Despite national guidelines, suboptimal care remains common. Digital clinical decision support systems (CDSSs) offer a promising route to improve care quality but have not been robustly evaluated in MSK primary care.

The growing impact of generative AI (GenAI) on medical education has given rise to many reviews discussing AI literacy, trustworthiness, potential uses, and institutional or faculty benefits. However, there has been surprisingly little review-level attention to its impact on students and learning.

Health care workers (HCWs) experienced significant mental health challenges, particularly anxiety, during the COVID-19 pandemic. Although social media became a primary source of information and connection, it was also a potential source of stress. The influence of social media on HCWs’ anxiety is not well understood.

Socially assistive robots (SARs) have the potential to support pediatric care; yet, routine clinical adoption remains limited. Because SARs interact directly with children, health care professionals (HCPs) may consider not only clinical utility and usability but also child-robot interaction, trust, and implementation concerns that may not be fully represented by conventional technology acceptance frameworks.

Digital behavior change interventions (DBCIs), including smartphone apps, wearable devices, and AI-driven adaptive systems, have become a dominant approach for promoting changes in health-related behavior. Although meta-analyses have consistently demonstrated short-term effectiveness, a recurring finding is that these effects are attenuated once user engagement declines or the intervention ends. We describe this phenomenon as the sustainability paradox: the same design features that maximize behavioral success during use, such as automated guidance, real-time feedback, and gamified rewards, may undermine the conditions necessary for the behavior to persist once the intervention is terminated. This viewpoint introduces narrative ownership as a conceptual framework for understanding and addressing this paradox. Narrative ownership refers to the extent to which individuals experience behavior change as an ongoing, interpretable, and self-authored process and comprises 3 components: process framing, narrative repair, and authorship. We argue that many contemporary DBCI architectures unintentionally displace these experiential processes by automating lapse correction, redirecting behaviors without interpretation, and externalizing the attribution of success. We map these tendencies onto 3 structural mechanisms—the erosion of process framing, the foreclosure of narrative repair, and the hollowing of authorship—and distinguish narrative ownership from adjacent constructs such as engagement, self-efficacy, and autonomy support. We also note that narrative ownership is double-edged: the same reflective processes can consolidate negative self-attributions, and its expression varies across cultural contexts, taking a familial or communal rather than strictly individual form in relational settings. Therefore, to translate this framework into practice, we outline 3 design principles for sustainable digital interventions: reflective pauses that support process framing, lapse reinterpretation features that enable narrative repair, and meaning-linking interfaces that strengthen authorship. For each principle, we derive testable propositions—for example, that reflective pauses will improve behavioral maintenance after the intervention ends and that meaning-linking interfaces will sustain behavior after app use is discontinued—along with candidate outcome measures and directions for empirical validation. Together, these principles reposition the user not merely as the recipient of behavioral guidance but also as the author of their own story of change in digital health interventions.

Artificial intelligence (AI)–driven lifestyle interventions are increasingly used for reducing diabetes risk. However, whether they produce clinical benefit beyond standard Diabetes Prevention Programs (DPPs) and whether effects vary by AI type, population, or delivery setting, remains uncertain.

The COVID-19 pandemic has been a critical external driver of telemedicine adoption. As the pandemic entered its later stages, countries worldwide transitioned from stringent containment measures to routine management. This policy shift may in turn have shaped patients’ willingness to use (WTU) telemedicine.

AI scribes are emerging tools designed to reduce clinician documentation burden and improve clinical quality and efficiency by transcribing and documenting patient interactions in real-time. Despite growing evidence of their efficacy and impact across medical disciplines, there remains limited research on factors that influence successful implementation, particularly in allied health practice.