JMIR Mental Health

The global shortage of mental health professionals, exacerbated by increasing mental health needs post-COVID-19, has stimulated growing interest in leveraging large language models (LLMs) to address these challenges.

Shame and stigma often prevent individuals with social anxiety disorder (SAD) from seeking and attending costly and time-intensive psychotherapies, highlighting the importance of brief, low-cost, and scalable treatments. Creating prescriptive outcome prediction models is thus crucial for identifying which clients with SAD might gain the most from a unique scalable treatment option. Nevertheless, widely used classical regression methods might not optimally capture complex nonlinear associations and interactions.

Integrated digital interventions for the treatment of comorbid depression and substance use disorder have been developed, and evidence of their effectiveness is mixed.

Background: People with past suicidal thoughts and behavior (STB) are often excluded from digital mental health intervention (DMHI) treatment trials. This may perpetuate barriers to care and reduce treatment generalizability, especially in populations with elevated rates of STB, like body dysmorphic disorder (BDD). We conducted a randomized controlled trial (RCT, N = 80) of a smartphone-based cognitive behavioral therapy (CBT) for BDD that allowed for most forms of past STB except for past-month active suicidal ideation.

The effects of traditional health-promoting and preventive interventions in mental health and mental health literacy are often attenuated by low adherence and user engagement. Gamified approaches such as serious games (SGs) may be useful to reach and engage youth for mental health prevention and promotion.

Numerous mental health applications (MHealth apps) purport to alleviate depressive symptoms. Strong evidence suggests that brief cognitive behavioral therapy (bCBT)-based MHealth apps can decrease depressive symptoms, yet there is limited research elucidating the specific features that may augment its therapeutic benefits. One potential design feature that may influence effectiveness and user experience is the inclusion of virtual agents that can mimic realistic, human face-to-face interactions.

Mental health issues like occupational stress and burnout, compounded with the after-effects of COVID-19, have affected healthcare professionals (HCPs) around the world. Digital mental health interventions (DMHIs) can be accessible and effective in supporting well-being among HCPs. However, low engagement rates of DMHIs are frequently reported, limiting the potential effectiveness. More evidence is needed to reveal the factors that impact HCPs’ decision to adopt and engage with DMHIs.

Digital mental health interventions (DMHIs) are a promising approach to reducing the public health burden of mental illness. DMHIs are efficacious, can provide evidence-based treatment with few resources, and are highly scalable relative to one-on-one face-to-face psychotherapy. There is potential for DMHIs to substantially reduce unmet treatment needs by circumventing structural barriers to treatment access (eg, cost, geography, and time). However, epidemiological research on perceived barriers to mental health care use demonstrates that attitudinal barriers, such as the lack of perceived need for treatment, are the most common self-reported reasons for not accessing care. Thus, the most important barriers to accessing traditional psychotherapy may also be barriers to accessing DMHIs.

Digital health technologies (DHTs) for psychosis have been developed and tested rapidly in recent years. However, research examining mental health professionals’ views on the use of DHTs for people with psychosis is limited. Given the increased accessibility and availability of DHTs for psychosis, an up-to-date understanding of staff perception of DHTs for psychosis is warranted.

Therapist guided internet-delivered cognitive behavioral therapy (guided ICBT) appears to be efficacious for depression, panic disorder (PD) and social anxiety disorder (SAD) in routine care clinical settings. However, implementation of guided ICBT in specialist mental health services is limited partly due to low referral rates from general practitioners (GP), which may stem from lack of awareness, limited knowledge of its effectiveness, or negative attitudes toward the treatment format. In response, self-referral systems were introduced in mental health care about a decade ago to improve access to care. Yet, little is known about how referral pathways may affect treatment outcomes in guided ICBT.

This scoping review examines the effects of various sound interventions, including music, natural sounds, and speech, on the stress response in adults.

Large language models (LLMs) are increasingly used in the mental health field, with promising results in assessing mental disorders. However, correctness, dependability, and equity of LLM-generated information have been questioned. Amongst other, societal biases and research underrepresentation of certain population strata may affect LLMs. Because LLMs are already used for clinical practice, including decision support, it is important to investigate potential biases to ensure a responsible use of LLMs. Anorexia nervosa (AN) and bulimia nervosa (BN) show a lifetime prevalence of 1–2%, affecting more women than men. For men, sexual orientation was identified as a risk factor, with homosexual men having a higher risk of developing an eating disorder (ED) than heterosexual men. However, men are underrepresented in ED research and research on the association between gender and sexual orientation with prevalence, symptoms, and treatment outcomes of AN and BN is scarce.