JMIR Mental Health

Telemedicine has emerged as a promising tool to enhance adherence and monitoring in patients with eating disorders (EDs). Traditional face-to-face cognitive therapies remain the gold standard; however, integrating telemedicine may provide additional support and improve patient engagement and retention. Given the increasing use of digital health interventions, it is crucial to assess their safety and effectiveness in complementing conventional treatments.

Alexithymia, defined as difficulty identifying and describing one’s emotions, has been identified as a transdiagnostic emotional process that impacts the course, severity, and treatment outcomes of psychiatric conditions such as posttraumatic stress disorder (PTSD). As such, alexithymia is an important process to accurately measure and identify in clinical contexts. However, research identifying the association between the experience of alexithymia and psychopathology has been limited by an overreliance on self-report scales, which have restricted use for measuring constructs that involve deficits in self-awareness, such as alexithymia. Hence, more suitable and effective methods of measuring and identifying those experiencing alexithymia in clinical samples are needed.

Mental health researchers are increasingly using large language models (LLMs) to improve efficiency, yet these tools can generate fabricated but plausible-sounding content (hallucinations). A notable form of hallucination involves fabricated bibliographic citations that cannot be traced to real publications. Although previous studies have explored citation fabrication across disciplines, it remains unclear whether citation accuracy in LLM output systematically varies across topics within the same field that differ in public visibility, scientific maturity, and specialization.

Digital social activity, defined as interactions on social media and electronic communication platforms, has become increasingly important. Social factors impact mental health and can contribute to depression and anxiety. Therefore, incorporating digital social activity into routine mental health care has the potential to improve outcomes.

Artificial intelligence (AI), particularly large language models (LLMs), presents a significant opportunity to transform mental healthcare through scalable, on-demand support. While LLM-powered chatbots may help reduce barriers to care, their integration into clinical settings raises critical concerns regarding safety, reliability, and ethical oversight. A structured framework is needed to capture their benefits while addressing inherent risks. This paper introduces a conceptual model for prompt engineering, outlining core design principles for the responsible development of LLM-based mental health chatbots.

Digital conversational agents (or “chatbots”) that can generate human-like conversations have recently been adapted as a means of administering mental health interventions. However, their development for youth seeking mental health services requires further investigation.

Population ageing intensifies the global burden of dementia, creating significant challenges for patients, caregivers, and healthcare systems. While traditional in-person dementia care faces barriers, digital health technologies offer potential to enhance accessibility, efficiency, and patient-centered care. However, evidence on telemedicine and telehealth's applicability, safety, and effectiveness remains fragmented, underscoring systematic evaluation.

Although web–based mental health resources have the potential to assist millions, particularly those who face barriers to treatment, most mental health website visitors disengage before accessing resources that can help improve their mental health.

Mental health care systems worldwide face critical challenges, including limited access, shortages of clinicians, and stigma-related barriers. In parallel, large language models (LLMs) have emerged as powerful tools capable of supporting therapeutic processes through natural language understanding and generation. While previous research has explored their potential, a comprehensive review assessing how LLMs are integrated into mental health care, particularly beyond technical feasibility, is still lacking.

Background: Social anxiety disorder (SAD) and agoraphobia are common, impairing conditions often treated with cognitive behavioral therapy (CBT) conducted in groups. In CBT, exposure therapy is a core element. However, in-vivo exposure therapy is logistically challenging and aversive for both patient and therapist, especially in a group context, often leading to exposure being skipped all together in clinical practice. Virtual reality exposure (VRE), in which phobic stimuli is presented through immersive virtual reality technology, has shown promise as a flexible alternative to in-vivo exposure. We thus hypothesized that using VRE would result in more overall exposure and more individualized exposure, resulting in statistically significant symptom reduction compared with a group using in-vivo exposure.

Mental health-related artificial intelligence (MH-AI) systems are proliferating across consumer and clinical contexts, outpacing regulatory frameworks and raising urgent questions about safety, accountability, and clinical integration. Reports of adverse events, including instances of self-harm and harmful clinical advice, highlight the risks of deploying such tools without clear standards and oversight. Federal authority over MH-AI is fragmented, leaving state legislatures to serve as de facto laboratories for MH-AI policy. Some states have been highly active in this area during recent legislative sessions. Yet clinicians and professional organizations have mainly remained absent or sidelined from public commentary and policymaking bodies, raising concerns that new laws may diverge from the realities of mental healthcare.

Digital well-being encourages balanced mobile use. The Perceived Digital Well-Being in Adolescence Scale (PDWBA) scale measures this in adolescents but was validated only in Slovenia, raising questions about its relevance for other age groups and cultural contexts.