JMIR Mental Health

First-episode psychosis (FEP) imposes a substantial burden not only on the individual affected but also on their families. Given that FEP usually occurs during adolescence, families overtake a big part of informal care. Early family interventions, especially psychoeducation, are crucial for informal family caregivers to best support the recovery of their loved one with FEP and to reduce the risk of a psychotic relapse as much as possible, but also to avoid chronic stress within the family due to the burden of care. Digital health interventions offer the possibility to access help quicker, use less resources, and improve informal family caregiver outcomes, for example, by reducing stress and improving caregiver quality of life.

The dual diagnosis of cannabis use disorder (CUD) and severe mental disorder (SMD) results in clinically complex individuals. Cannabis use is known to have negative consequences on psychiatric symptoms, medication compliance, and disease prognosis. Moreover, the effectiveness of currently available psychotherapeutic treatments is limited in this population.

The need for scalable solutions facilitating access to eating disorder (ED) treatment services that are efficient, effective, and inclusive is a major public health priority. Remote access to synchronous and asynchronous support delivered via health apps has shown promise, but results are so far mixed, and there are limited data on whether apps can enhance health care utilization.

Routine process and outcome monitoring interventions added to psychotherapy are known to improve treatment outcomes, although they vary in format and effectiveness.

Cancer patients often face depression and anxiety, and Mindfulness-Based Interventions (MBIs), 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 prostate and breast cancer patients.

Despite the efficacy of digital mental health technologies (DMHTs) in clinical trials, low uptake and poor engagement is 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. Yet 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 is reported from implementation logs, platform usage statistics, and response rates to Digital Navigator e-mails 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, digital navigator, and clients. The Digital Navigator helped to facilitate the integration of DMHT in to 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.

Social media is an integral part of adolescent’s daily lives, but the significant time adolescents invest in social media has raised concern 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 (e.g,. LGBTQ+) to interact. Rather than enforcing bans and severe restrictions, emotion regulation should be leveraged to help adolescents navigate the online social environment. This viewpoint paper proposes a nuanced approach towards 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.

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.

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 healthcare settings leveraging mobile technology to improve scale and lower latency, cost and burden.

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 is a digital therapeutic intervention designed to deliver evidence-based anxiety treatment for 7-12-year-olds through a mobile application incorporating immersive gaming technology.

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.