Journal of Medical Internet Research

Adolescence is a critical period for mental health prevention; yet, many young people face barriers to help-seeking, including stigma, limited mental health literacy, and restricted access to services. Preventive digital mental health interventions (DMHIs) offer a scalable, low-threshold approach to early support, but uptake and sustained engagement remain limited. Existing research has predominantly relied on quantitative methods and has only partly captured adolescents’ perspectives. Qualitative research is therefore needed to inform the design and implementation of preventive DMHIs.

To cope with the challenges of digital transformation in health and well-being, people need digital health literacy (DHL), namely the competencies to access, understand, appraise, and apply digital health–related information and services for their own and others’ health and well-being. So far, however, there have been no data on the level of DHL in Switzerland.

In health systems, AI is increasingly being implemented as a form of clinical decision support (CDS). Research on patient perspectives, however, lags behind clinician-focused studies. Before the introduction of AI-based tools, clinicians often used and continue to use rule-based guidelines and risk calculators (RCs) to inform decision-making. Prior research has shown that RCs for CDS have mixed effects on patient adherence to treatment recommendations, whereas for AI-based tools, the effect is still not clear.

Clinical AI governance has advanced rapidly in technical validation, regulatory compliance, risk management, human oversight, and postdeployment monitoring. Yet a more fundamental question often remains implicit: under what conditions are health institutions justified in exercising consequential authority through AI over patients, clinicians, and communities? In this Viewpoint, we argue that legitimacy should be recognized as an independent governance requirement, distinct from technical assurance, legal and ethical compliance, trust, acceptance, and institutional approval. Our aim is to distinguish institutional approval from public authorization and to propose a normative framework for authorizing consequential clinical AI deployments. We address health system leaders, clinical AI governance and procurement teams, frontline clinicians, and patient and community representatives. Drawing on theories of legitimacy and public value and published clinical AI governance research, we make the following contributions: (1) the concept of the unclaimed decision, defined as a consequential choice about purpose, threshold, population, or priority for which no actor clearly assumes responsibility for public justification; (2) a tripartite framework comprising procedural authorization, substantive justification, and relational renewal; and (3) practical governance tools to support public justification, contestation, and renewal. These tools include 3 minimum auditable conditions: visibility of deployment; affected-party input before governing conditions are fixed; and conditional reviewability, which requires a predefined trigger that can be invoked by a named actor outside the deploying team. A 5-question public authorization test examines purpose, distribution, exclusion, meaningful choice and contestability, and renewal. Governance intensity should increase with clinical consequence; distributive reach; difficulty of reversal; and the absence of genuine, informed choice. For smaller health care organizations, we propose ways to perform the same core governance functions through existing advisory bodies, targeted interviews, shared regional resources, and standardized documentation. We also outline a heuristic for identifying affected publics, procedures for specifying participation authority and resolving disagreement, and procurement provisions that preserve reviewability across vendor boundaries. These are normative proposals requiring evaluation. Public authorization requires continuing substantive justification; it cannot be inferred from compliance, popularity, or completion of an audit checklist.

Female university students encounter appearance ideals, comparison cues, and appearance-related pressure across traditional and digital media environments. These sociocultural influences may coexist with psychological resources such as body appreciation and weight-control self-efficacy. However, variable-centered analyses may obscure heterogeneity in how these characteristics are combined within individuals. The measures used in this study assessed broader sociocultural appearance-related attitudes rather than platform-specific digital exposure.

Bipolar disorder (BP) affects 1% to 3% of adults worldwide, with diagnostic delays averaging 6 years and high initial misdiagnosis rates. The absence of objective diagnostic measures contributes to clinical uncertainty and suboptimal outcomes.

The human-horse bond has played a role in human development throughout history and has more recently been applied to mental health promotion through equine-assisted learning (EAL) programs. In EAL settings, therapeutic documentation and assessment commonly rely on practitioner observations documented through behavioral checklists, structured interviews, and progress notes, which can pose challenges due to time demands, subjectivity, and inconsistent standardization across practitioners and contexts.

Parkinson disease (PD) impairs mobility and motor function, making accurate mobility assessment important for effective management and treatment planning. Traditional clinical assessments often rely on periodic clinical evaluations, which may not capture day-to-day symptom fluctuations. Wearable devices offer a promising alternative with continuous, objective tracking of mobility (eg, gait). However, although these tools provide valuable insights for patients and clinicians, their successful implementation depends on their acceptability to users.