JMIR Mental Health

Suicide remains a leading cause of death among young adults aged 18 to 25 years. Young adults experiencing suicidal ideation (SI) are increasingly using crisis text services (CTSs), a free and accessible option for crisis intervention. Little is known about CTSs from the young adult perspective.

Internet-based cognitive behavioral therapy (iCBT) is an effective and scalable alternative to face-to-face psychotherapy, but its reach is constrained by the time therapists spend reviewing patient input and manually drafting written responses. Studies suggest that large language models (LLMs) may be capable of generating high-quality therapeutic text, with the potential to support therapists in delivering treatment. Their suitability as therapist-support tools in structured iCBT, however, remains insufficiently studied.

AI-enabled self-management health tools are increasingly promoted within health care policy as part of digital self-management models for mental health care. However, development is concentrated on scalable, low-intensity interventions for common conditions, such as anxiety and depression, rather than on populations with the greatest clinical need, such as those with serious mental illness (SMI). This includes schizophrenia-spectrum and bipolar disorders, which remain comparatively underserved, despite experiencing a disproportionate burden of morbidity and service use. However, the clinical features of SMI—comprising fluctuating symptoms, multimorbidity, and elevated risk—may limit the suitability of low-intensity AI-driven self-management tools designed for mild-to-moderate conditions. In this Viewpoint, we argue that the scarcity of AI-enabled self-management tools for SMI reflects a structural feature of current innovation systems rather than one of technical infeasibility alone. Market incentives, regulatory pathways, and fragmented research pipelines favor low-risk, high-volume populations, thereby limiting development for clinically complex groups. Addressing this unevenness is essential and will require upstream intervention, including targeted public funding, improved data infrastructure, and administrative frameworks that support safe innovation in high-risk populations. Embedding equity for SMI as a primary design requirement will be necessary to ensure that AI-driven mental health tools do not reinforce current inequalities.

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.