JMIR Formative Research

Digital mental health has become increasingly capable of representing and adapting to individuals in time. When support is delivered through another person, however, the relevant decision context includes conditions that cannot be specified from the primary user alone. This viewpoint proposes making selected conditions relevant to a support decision within a specified relationship explicit. We introduce relational ecological momentary assessment (REMA) as a conceptual method for representing these conditions in a relational decision space. In its initial dyadic implementation, REMA combines a low-burden report of the primary user’s emotional zone (Z) with supporter contact availability (Cp) and supporter capacity (Cs). The meaning of these reports depends on the relationship and the proposed form of support. Willingness, consent, and safety determine whether an action is permissible to consider, while data validity concerns whether the reports are current and interpretable. These conditions and rules delimit relational support eligibility by specifying which classes of support can still be considered. Coregulation as a reciprocal temporal process remains outside the scope of this implementation. We situate the proposal within research on ecological momentary assessment, just-in-time adaptive interventions, dyadic digital health, and systemic digital mental health; provide a hypothetical implementation example; and show how 1 Z report can inform separate decisions involving different supporters. REMA remains conceptual and requires empirical evaluation. Its broader proposition is that when digital health recruits another person, the decision-relevant conditions across that specified relationship should be made explicit.

Psychological assessment of adult neurodivergence relies on multi-informant data that frequently disagree; discrepancies may reflect masking, context-dependent expression, or limited self-insight rather than measurement error.

Internet-based cognitive behavioral therapy (ICBT) has shown promise for the treatment of panic disorder (PD), but little is known about its feasibility and use across cultures and languages.

Transitions of care, where patients move between clinical settings or providers, pose considerable risks to patient safety, situations exacerbated by the absence of adequate digital infrastructure that enables information exchange. The timeliness and accuracy of medication information transfer are central to ensuring patient safety during transitions of care, including post-discharge from the hospital. In designing a multifaceted digital intervention to support the transfer of medication information from hospital to community settings, it was necessary to gauge the views of all relevant stakeholders on the practical challenges involved in transferring medication information.

Mobile health is emerging as a ubiquitous paradigm for delivering health care interventions when self-management is feasible. However, interventions targeting older or frail participants should account for age-related cognitive, visual, and physical needs to optimize usability and long-term retention.

Virtual consultations (VCs), including telephone and video consultations, are increasingly central to primary care. However, evaluation often prioritizes patient satisfaction or technology over quality and safety. Existing frameworks incorporate limited input from general practitioners (GPs) and lack standardized quality and safety indicators.

Gambling disorder (GD) carries a disproportionate burden of suicide risk. Clinicians frequently encounter patients with suicidal ideation (SI) at treatment entry, yet no published study has examined whether SI at intake predicts treatment engagement or symptom change in GD care.

The Secondary Care Medicines Data (SCMD) makes detailed data on medicines issued by National Health Service (NHS) hospital trusts in England openly available for the first time. These data provide an opportunity to understand medicine use patterns, highlight variation, and drive improvements in evidence-based practice, prescribing safety, and cost-effectiveness. However, this dataset requires extensive domain and technical knowledge to access, clean, transform, and link to other external sources before it can be used effectively.

Health care public reporting (PR) systems are intended to reduce information asymmetry and support informed health care decision-making. However, their adoption remains limited, partly because health care information is complex, difficult to interpret, and may evoke cognitive and emotional responses. Existing research has focused largely on system design and development, with limited attention to how users’ central and peripheral-route information-processing mechanisms shape PR system adoption.

Informal caregivers play a central role in long-term care but experience elevated risks of stress, depressive symptoms, and caregiver burden, alongside barriers to accessing preventive support. Low-threshold digital public health interventions may offer scalable support; however, evidence on their feasibility, usability, and contextual fit remains limited, particularly for heterogeneous caregiver populations.

Depression is a prevalent psychiatric condition with functional and socioeconomic consequences. Understanding the barriers to professional help-seeking and treatment acceptance among individuals with depressive symptoms is important.

Wearable physiological sensing and mobile self-report methods offer new opportunities for monitoring occupational well-being in real-world settings. However, interpreting continuous physiological signals in relation to psychological states such as job burnout and work engagement remains challenging, particularly when using deployable systems that rely on proprietary signal processing.