JMIR Mental Health

Psychiatric hospitalizations are a major driver of mental health–related health care costs, with readmission risk and service utilization being highest in the months following discharge. Scalable, low-cost postdischarge interventions that reduce inpatient utilization are therefore of high policy relevance.

Social media influencers occupy a pervasive role in billions of users’ daily digital lives, particularly among adolescents and young adults. Audiences develop parasocial engagement with these figures, including parasocial relationships (PSRs) and parasocial interactions (PSIs). Despite growing concern about their mental health implications, no prior meta-analysis has quantitatively synthesized this evidence.

Suicide remains a leading cause of death in the United States and is on the rise. Limited evidence describes the current burden of suicidal ideation (SI) among commercially insured outpatients, a population that represents a large and rapidly expanding segment of those seeking care.

Mental health apps are frequently used as platforms for delivering digital health interventions to young people. New technology such as generative AI enables a wider range of engaging and more personalized features that can be included in mental health apps. However, there is limited insight into the opportunities for integrating generative AI into such apps.

Depression is underdiagnosed worldwide, and clinicians rely on interpreting patients’ subjective speech. Qualitative analysis of patient language does not scale, and existing computational approaches describe topics with keyword lists that miss clinical nuance.

Persecutory delusions have long mirrored prevailing cultural and technological concerns. Beliefs involving implanted devices, internet surveillance, hacked smartphones, algorithmic targeting, and AI-mediated control are increasingly visible in contemporary psychosis. However, we identified no prior review dedicated specifically to synthesizing technology-themed delusional content across historical and contemporary clinical contexts.

Digital therapeutics (DTx) have become increasingly prominent in mental health care, offering scalable, evidence-based interventions. A common assumption underlying their design and evaluation is that greater usage time leads to superior therapeutic outcomes, reflecting an implicit linear dose-response model. However, accumulating evidence challenges this simplified perspective and suggests that the relationship between usage time and clinical benefit is substantially more complex. In this viewpoint article, we critically examine the assumption that higher usage time is a necessary prerequisite for therapeutic success in digital mental health interventions. Drawing on psychotherapy dose-effect research, findings from digital intervention trials, and our own empirical work, we highlight several factors that complicate linear exposure models. These include rapid early response and plateau effects, substantial heterogeneity in user trajectories, motivational and contextual determinants of use, episodic patterns of engagement, and the distinction between on-platform activity and off-platform skill application. The linear dose metaphor, implicitly inherited from pharmacotherapy models, appears conceptually mismatched with learning-based digital interventions. Importantly, equating greater use with greater effectiveness risks conflating exposure with therapeutic mechanism, and may inadvertently promote evaluation frameworks that prioritize duration over meaningful change. Together, these insights suggest that usage time is an imperfect proxy for therapeutic engagement, and that high or continuous use should not be treated as a universal indicator of intervention quality. We argue for a shift toward mechanism-informed and individualized evaluation frameworks that prioritize the quality, timing, and functional impact of intervention use over cumulative exposure.

Bipolar disorder (BD) features episodic shifts among mania, hypomania, depression, mixed states, and euthymia. Timely detection of mood transitions is difficult due to infrequent clinical touchpoints. Digital health technologies, including wearables and smartphones, offer a unique opportunity to passively and continuously monitor behavior and physiology that could reflect underlying mood dynamics in real-world settings.

Digital mental health interventions using conversational AI agents are increasingly being adopted as scalable alternatives to traditional care. Engagement is typically measured using volume-based metrics (eg, session counts and total time on a platform). However, these metrics overlook engagement patterns over time, which are not well understood in relation to mental health outcomes.

Two-fold increases in the prevalence of youth anxiety and depression over the last two decades have mirrored exponential growth in opportunities for adolescent online social interaction via social media, short messaging service (SMS), and internet text messaging apps on smartphones. However, studies to date of self-reported online social interaction time have produced conflicting results. Understanding the role of dispositional and developmental differences in individuals’ responses to online versus offline social interactions may help elucidate whether and how online social interaction is related to anxiety and depression.

The rapid evolution of AI, particularly large language models (LLMs), has renewed interest in their potential role in forensic psychiatry report writing. Recent evidence demonstrates that contemporary LLMs perform well in selected medical knowledge, documentation, and information management tasks and may reduce the administrative burden when deployed under appropriate clinical supervision. However, forensic psychiatric reports differ fundamentally from routine clinical documentation. They constitute expert evidence prepared for legal proceedings and therefore require transparent reasoning, explicit weighing of competing evidence, a robust factual foundation, and personal professional accountability. This viewpoint examines whether AI can and should be used in forensic psychiatry report writing by integrating recent empirical evidence, forensic psychiatry guidance, legal and regulatory frameworks, and emerging governance recommendations. Rather than comparing AI with an idealized human evaluator, the manuscript argues that the appropriate comparison is between 2 imperfect systems of reasoning. Human experts remain susceptible to cognitive biases, omission errors, and disagreement, whereas contemporary LLMs exhibit distinct vulnerabilities, including hallucinations, hidden omissions, probabilistic reasoning, and limited explainability. Although the mechanisms differ, both may ultimately compromise the reliability of expert evidence if left unchecked. Current evidence supports AI for bounded, reversible, and independently verifiable tasks, such as document organization, chronology construction, indexing, transcription, and structured summarization, particularly within secure and validated environments. By contrast, there remains insufficient evidence to support AI-assisted generation or material shaping of psycholegal reasoning, credibility assessments, or final forensic opinions. Because these activities require interpretation, accountability, and reasoning that can withstand judicial scrutiny, they remain fundamentally human responsibilities. The most defensible implementation model is, therefore, one of AI around the report rather than AI writing the report, in which AI serves as a supervised productivity tool while the forensic psychiatrist retains full authorship, accountability, and justification of all substantive conclusions.

The burden of mental disorders is high in conflict-affected populations. In Palestine, we piloted Inuka Coaching, a digital intervention adapted from the Friendship Bench delivered by trained and supervised lay coaches. This paper documents the implementation of the intervention in this highly volatile context after October 7, 2023.