Social media is an integral part of adolescents' daily lives, but the significant time they invest in social media has raised concerns about the effect on their mental health. Bans and severe restrictions on social media use are quickly emerging as an attempt to regulate social media use; however, evidence supporting their effectiveness is limited. Adolescents experience several benefits from social media, including increased social connection, reduced loneliness, and a safe space for marginalized groups (eg, LGBTQ+) to interact. Rather than enforcing bans and severe restrictions, emotion regulation should be leveraged to help adolescents navigate the digital social environment. This viewpoint paper proposes a nuanced approach toward regulating adolescent social media use by (1) discontinuing the use of ineffective bans, (2) recognizing the benefits social media use can have, and (3) fostering emotion regulation skills in adolescents to encourage the development of self-regulation.

Despite the efficacy of digital mental health technologies (DMHTs) in clinical trials, low uptake and poor engagement are common in real-world settings. Accordingly, digital technology experts or "digital navigators" are increasingly being used to enhance engagement and shared decision-making between health professionals and clients. However, this area is relatively underexplored and there is a lack of data from naturalistic settings. In this paper, we report observational findings from the implementation of a digital navigator in a multidisciplinary mental health clinic in Sydney, Australia. The digital navigator supported clients and health professionals to use a measurement-based DMHT (the Innowell platform) for improved multidimensional outcome assessment and to guide personalized decision-making. Observational data are reported from implementation logs, platform usage statistics, and response rates to digital navigator emails and phone calls. Ultimately, support from the digital navigator led to improved data collection and clearer communications about goals for using the DMHT to track client outcomes; however, this required strong partnerships between health professionals, the digital navigator, and clients. The digital navigator helped to facilitate the integration of DMHT into care, rather than providing a stand-alone service. Thus, collaborations between health professionals and digital navigators are mutually beneficial and empower clients to be more engaged in their own care.

Virtual reality exposure therapy (VRET) is a promising treatment approach for anxiety disorders. However, while its efficacy has been demonstrated in adults, research on the efficacy of VRET in the treatment of adolescents with anxiety disorders is largely lacking.

Patients with cancer often face depression and anxiety, and mindfulness-based interventions, including internet-based versions, can effectively reduce these symptoms and improve their quality of life. This study aims to investigate the impact of internet-based mindfulness-based interventions (e-MBIs) on anxiety, depression, and stress symptoms in patients with prostate or breast cancer.

Novel technologies, such as ecological momentary assessment (EMA) and wearable biosensor wristwatches, are increasingly being used to assess outcomes and mechanisms of change in psychological treatments. However, there is still a dearth of information on the feasibility and acceptability of these technologies and whether they can be reliably used to measure variables of interest.

Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support.

[This corrects the article DOI: 10.2196/56650.].

Knowledge has become more open and accessible to a large audience with the "democratization of information" facilitated by technology. This paper provides a sociohistorical perspective for the theme issue "Responsible Design, Integration, and Use of Generative AI in Mental Health." It evaluates ethical considerations in using generative artificial intelligence (GenAI) for the democratization of mental health knowledge and practice. It explores the historical context of democratizing information, transitioning from restricted access to widespread availability due to the internet, open-source movements, and most recently, GenAI technologies such as large language models. The paper highlights why GenAI technologies represent a new phase in the democratization movement, offering unparalleled access to highly advanced technology as well as information. In the realm of mental health, this requires delicate and nuanced ethical deliberation. Including GenAI in mental health may allow, among other things, improved accessibility to mental health care, personalized responses, and conceptual flexibility, and could facilitate a flattening of traditional hierarchies between health care providers and patients. At the same time, it also entails significant risks and challenges that must be carefully addressed. To navigate these complexities, the paper proposes a strategic questionnaire for assessing artificial intelligence-based mental health applications. This tool evaluates both the benefits and the risks, emphasizing the need for a balanced and ethical approach to GenAI integration in mental health. The paper calls for a cautious yet positive approach to GenAI in mental health, advocating for the active engagement of mental health professionals in guiding GenAI development. It emphasizes the importance of ensuring that GenAI advancements are not only technologically sound but also ethically grounded and patient-centered.

The field of mental health technology presently has significant gaps that need addressing, particularly in the domain of daily monitoring and personalized assessments. Current noninvasive devices such as wristbands and smartphones are capable of collecting a wide range of data, which has not yet been fully used for mental health monitoring.

Large language models (LLMs) are advanced artificial neural networks trained on extensive datasets to accurately understand and generate natural language. While they have received much attention and demonstrated potential in digital health, their application in mental health, particularly in clinical settings, has generated considerable debate.

[This corrects the article DOI: 10.2196/55500.].

Anxiety disorders are among the most common mental health conditions in childhood, but most children with anxiety disorders do not access evidence-based interventions. The delivery of therapeutic interventions via digital technologies has been proposed to significantly increase timely access to evidence-based treatment. Lumi Nova (BfB Labs Limited) is a digital therapeutic intervention designed to deliver evidence-based anxiety treatment for those aged 7-12 years through a mobile app incorporating immersive gaming technology.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden.

Real-time monitoring captures information about suicidal thoughts and behaviors (STBs) as they occur and offers great promise to learn about STBs. However, this approach also introduces questions about how to monitor and respond to real-time information about STBs. Given the increasing use of real-time monitoring, there is a need for novel, effective, and scalable tools for responding to suicide risk in real time.

While the number of digital therapeutics (DTx) has proliferated, there is little real-world research on the characteristics of providers recommending DTx, their recommendation behaviors, or the characteristics of patients receiving recommendations in the clinical setting.

[This corrects the article DOI: 10.2196/58432.].

Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions.

Motivational interviewing (MI) is a therapeutic technique that has been successful in helping smokers reduce smoking but has limited accessibility due to the high cost and low availability of clinicians. To address this, the MIBot project has sought to develop a chatbot that emulates an MI session with a client with the specific goal of moving an ambivalent smoker toward the direction of quitting. One key element of an MI conversation is reflective listening, where a therapist expresses their understanding of what the client has said by uttering a reflection that encourages the client to continue their thought process. Complex reflections link the client's responses to relevant ideas and facts to enhance this contemplation. Backward-looking complex reflections (BLCRs) link the client's most recent response to a relevant selection of the client's previous statements. Our current chatbot can generate complex reflections-but not BLCRs-using large language models (LLMs) such as GPT-2, which allows the generation of unique, human-like messages customized to client responses. Recent advancements in these models, such as the introduction of GPT-4, provide a novel way to generate complex text by feeding the models instructions and conversational history directly, making this a promising approach to generate BLCRs.

Depression affects 5% of adults and it is a major cause of disability worldwide. Digital psychotherapies offer an accessible solution addressing this issue. This systematic review examines a spectrum of digital psychotherapies for depression, considering both their effectiveness and user perspectives.

For the provision of optimal care in a suicide prevention helpline, it is important to know what contributes to positive or negative effects on help seekers. Helplines can often be contacted through text-based chat services, which produce large amounts of text data for use in large-scale analysis.

Digital mental health is a rapidly growing field with an increasing evidence base due to its potential scalability and impacts on access to mental health care. Further, within underfunded service systems, leveraging personal technologies to deliver or support specialized service delivery has garnered attention as a feasible and cost-effective means of improving access. Digital health relevance has also improved as technology ownership in individuals with schizophrenia has improved and is comparable to that of the general population. However, less digital health research has been conducted in groups with schizophrenia spectrum disorders compared to other mental health conditions, and overall feasibility, efficacy, and clinical integration remain largely unknown.