JMIR Mental Health

Blended care psychotherapy (BCP) combines face-to-face psychotherapy with digital interventions, such as videoconferencing, apps, or virtual reality, which can be incorporated in various ways and at different stages of the therapeutic process. Despite its increasing implementation, conceptual and terminological inconsistencies continue to impede a shared understanding of the competencies required for its effective delivery.

Auditory verbal hallucinations (AVH) are among the most disabling symptoms of schizophrenia and often persist despite treatment. Virtual reality–assisted therapies (VRTs) are a new generation of relational interventions for AVH, but comparative evidence against active interventions is currently lacking.

Large language models are increasingly used within and alongside therapy. As large language models perform more therapy functions, questions arise about what the future might hold for therapists and what they will do. We argue that the enduring therapist role in the age of AI-assisted care is currently best understood through relational, adaptive, and accountability functions. These functions include therapeutic challenge, use of the therapeutic relationship as a mechanism of change, rupture detection and repair, bearing witness to suffering, calibration of pace and treatment burden, and clinical judgment under uncertainty across the broader care pathway. Drawing on psychotherapy theory, digital mental health research, the declarative-procedural-reflective model by Bennett-Levy, and our clinical experience, we propose a clinically informed, hypothesis-generating, relational-adaptive-accountability framework. This framework is intended to support further empirical testing and may have implications for workforce development, supervision, training, and service design.

Meditation has become increasingly popular in recent decades. However, relatively little remains known about the prevalence of and risk factors for adverse experiences related to a single meditation practice.

People increasingly turn to conversational AI for companionship, emotional support, and well-being, using both purpose-built companion apps, such as Replika and Character.AI, and general-purpose assistants, such as ChatGPT and Claude. While some evidence suggests potential benefits, including short-term reductions in loneliness and mood improvement, several adverse outcomes have been reported in both clinical and nonclinical populations, including emotional dependence, exacerbation of symptoms, and self-harm. The fluent and apparently empathic responses from these models lead users to engage with them not only as tools but also as if they were social entities. This framing is conceptually misleading and may pose risks across different user profiles, particularly for vulnerable individuals. Drawing on research in AI, psychiatry, psychology, and network science, we highlight mechanisms through which emotional reliance develops and the boundary between tool and companion erodes. Design choices that evoke personality and warmth encourage users to anthropomorphize these systems. Simulated empathy, generated through probabilistic language patterns rather than genuine emotional experience, creates a structurally asymmetric interaction, in which the user discloses and the system responds, but without reciprocity, vulnerability, or accountability. Overvalidation and sycophancy can reinforce maladaptive cognitions, delusional ideation, and distorted perceptions of reality, as they tend to reinforce people’s beliefs, even at the expense of the accuracy of models’ responses. These mechanisms are not incidental: they emerge from alignment procedures that reward responses perceived as warm and empathic. The result is a self-reinforcing feedback loop between the model and the user that may amplify maladaptive beliefs, delusional ideation, and emotional distress, even in those who engage for largely functional purposes. Understanding these dynamics requires an examination of both what these agents can do—considering their technical limitations and implementations—and what humans believe they can do, including social and psychological impacts. We argue that conversational AI should be treated primarily as a tool supporting human systems rather than as a substitute for human relationships. Perhaps more importantly, reviewing the current hype surrounding AI interactions can help reformulate a paradigm that contributes to human well-being and societal value, while minimizing misconceptions, maladaptive interactions, or social disintegration.

Timely follow-up is crucial for youth whose first mental health contact is through a hospital; virtual care could facilitate this.

Emotion regulation (ER) is a transdiagnostic construct encompassing distinct strategies that can be used to encourage adaptive coping skills in support of youth’s (ages 10 to 24 years) mental health. ER is responsive to training and intervention, such as via video and app-based games. Previous literature shows that video and app-based games impact the psychological well-being of youth and may be an accessible way to help youth learn about and implement emotional regulation strategies in their lives.

Major depressive disorder affects over 280 million people worldwide, and access to effective treatment remains limited. Transcranial direct current stimulation (tDCS) is a noninvasive option, and portable devices now allow for home-based delivery under varying degrees of remote supervision.

Medication adherence is poor among individuals with serious mental illness (SMI). Few studies have demonstrated the effectiveness of remote medication dispensing and adherence monitoring interventions among individuals with SMI.

Mental health chatbots are increasingly used to support people with depressive symptoms, and large language models make these systems more flexible than rule-based chatbots. However, it remains unclear how well large language model–based chatbots deliver structured psychological interventions.

In recent years, innovations in generative AI, in particular large language models (LLMs) in the form of AI chatbots, have found their way to the general public. First studies indicate a growing prevalence of individuals talking to AI chatbots about mental health–related topics; yet, knowledge of how, why, and which individuals are using these AI chatbots for their mental health is limited.

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.