JMIR Mental Health

The high number of mental disorders poses challenges for healthcare systems. In 2020, digital health applications (DHA) were introduced in Germany as a new form of healthcare financed by the statutory health insurance. They aim to detect, monitor, treat, or alleviate disease, injury, or disability. DHA for mental disorders (DHA-MD) intend to improve outpatient care for patients with mental disorders. However, evidence on general practitioners (GP) perspectives on DHA-MD and their prescribing behavior is limited.

Digital interventions play an innovative role in the treatment of mental health disorders, offering evidence-based solutions across a wide range of conditions. Blended therapy (BT) - which integrates digitally delivered interventions with face-to-face therapy - has shown promise. However, challenges such as low uptake hinder widespread implementation. Mental health professionals are key stakeholders for the adoption of BT in routine care settings.

The availability of telebehavioral health care dramatically increased in response to the COVID-19 pandemic among both civilian and military populations. After restrictions were lifted, telebehavioral health use decreased but remained elevated compared to before the pandemic. Examining the use of treatment modalities and how it relates to care metrics can inform the future delivery of behavioral health care.

Cyberchondria, a combination of the words “cyber” and “hypochondriasis”, is a condition that is receiving increasing attention by clinicians and researchers globally. Researchers are currently using multiple instruments to quantify it. Furthermore, the instruments have been translated into multiple languages.

The growing global burden of mental health disorders has intensified the search for scalable, accessible, and cost-effective interventions. Conversational agents in the form of digital humans have emerged as promising tools to deliver mental health support across diverse populations and settings.

Computer perception (CP) technologies—including digital phenotyping, affective computing, and related passive sensing approaches—offer unprecedented opportunities to personalize health care, especially mental health care, yet they also provoke concerns about privacy, bias, and the erosion of empathic, relationship-centered practice. At present, it remains elusive what stakeholders who design, deploy, and experience these tools in real-world settings perceive as the risks and benefits of CP technologies.

The therapeutic relationship is the professional partnership between clinicians and patients that supports open communication and clinical decision-making. This relationship is critical to the delivery of effective mental health care. Integration of artificial intelligence (AI) into mental health care has the potential to support accessibility and personalized care; however, less is known about how AI might affect the dynamic of the therapeutic relationship.

We propose the Stanford Brainstorm Social Media Safety Plan (SMS) as a user-friendly, collaborative, and effective tool to mitigate the imminent dangers and risks to mental health that are associated with social media use by children, adolescents, and young adults. This tool is informed and inspired by suicide safety plans as part of suicide safety planning (SSP), which have long shaped the standard of care for psychiatric discharges from inpatient units, emergency rooms, and comprehensive psychiatric emergency programs (CPEPs), as well as longitudinal outpatient care following occurrences of suicidal ideation or suicide attempts. In many systems including those of the Veterans Health Administration (VHA), they constitute an absolute requirement prior to discharge of the patient. This social media safety plan is to be used proactively, in times of normalcy as well as crisis. While parental controls for digital devices and online platforms, official legal age requirements for online accounts, and individual parenting approaches abound, there is a dearth of practical tools that youth, families, schools, and communities can use to shape and alter social media use parameters, rules, and habits. Furthermore, providers in psychiatry, child and adolescent psychiatry, and mental health at large are often confronted with behaviors and issues related to social media use during already time and resource-limited appointments, providing a massive opportunity for interventions that are harm reduction-oriented and easy to disseminate. While it has not been studied in a clinical trial, we have used it extensively with patients and families, and presented it to larger audiences at mental health and technology conferences over the past two years. The responses and feedback we have received, as well as reported anecdotal experiences with using it, have been overwhelmingly positive. An already unfolding child and adolescent mental health epidemic in the US has been aggravated and deepened partly by way of easy access to social media (and digital-screen time) with inadequate safeguards and monitoring in place. Social media’s impacts and related interventions require a multitiered biopsychosocial and cultural approach: at the level of the individual child, the family, the school, the state, the market, and the nation. At the level of youth and their parents or caregivers, practical tools are desperately needed. We propose the Stanford Brainstorm Social Media Safety Plan as one such significant tool.

As mental health challenges continue to rise globally, there is increasing interest in the use of generative pre-trained transformers (GPT) models, such as ChatGPT, in mental healthcare. A few months after its release, tens of thousands of users interacted with GPT-based therapy bots, with mental health support identified as the primary use case. ChatGPT offers scalable and immediate support through natural language processing capabilities, but their clinical applicability, safety, and effectiveness remain underexplored.

Generative AI is reshaping mental health, but the direction of that change remains unclear. In this commentary, we examine the recent evidence and trends in mental health AI to identify where AI can provide value for the field while avoiding the pitfalls that have challenged the smartphone app and VR space. While AI technology will continue to improve, those advances alone are not enough to move AI from mental wellness to psychiatric tools and a new generation of clinical investigation, integration, and leadership will unlock the full value of AI.

Digital phenotyping refers to the objective measurement of human behavior via devices such as smartphones or watches and constitutes a promising advancement in personalized medicine. Digital phenotypes derived from heart rate, mobility, or sleep schedule data have been utilized in psychiatry to either diagnose individuals with psychotic disorders, or to predict relapse as a binary outcome. Machine learning models so far have achieved predictive accuracies that are significant but not large enough for clinical applications. This could hinge on broad clinical definitions, which encompass heterogeneous symptom and sign ensembles, thus hindering accurate classification. The five-factor model for the Positive and Negative Symptom Scale (PANSS), which entails five independently varying dimensions, is thought to better capture symptom variability. Utilizing the specific definitions of this refined clinical taxonomy in combination with digital phenotypes could yield more precise results.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterised by difficulties in attention, impulsivity, and hyperactivity. These difficulties can result in pervasive and longstanding psychological distress and social, academic, and occupational impairments.