JMIR Mental Health

Telemental health services effectively address major challenges in mental healthcare delivery. To maximize the potential of the services, it is essential to facilitate patient use and reduce use disparities. Nevertheless, determinants of patient use of telemental health services have been scarcely investigated thus far.

People worldwide are confronted with environmental and sociopolitical stressors that act as potent sources of subjective uncertainty. The uncertainty arising in response to the volatility and unpredictability of adversities is amplified by their representation or misrepresentation in media news. While the causal effect of media news on vicarious traumatization has been well established, we argue that the impact of negative media news is principally related to distress and anxiety stemming from the uncertainty-inducing effect of media representations of the state of the world. As a growing body of research suggests, minimizing uncertainty related to global stressors is a significant driver of media news use. However, extensive media exposure perpetuates stress and is associated with symptoms of psychopathology. The self-perpetuating vicious circle of worry and excessive media consumption has been amply confirmed by new research related to the COVID-19 pandemic. Furthermore, attempts to alleviate stress and anxiety stemming from uncertainties often result in maladaptive strategies. In particular, the adoption of rigid behavioral patterns may prompt various forms of socially detrimental behavior. Critical factors in prevention and remediation include limiting media overexposure and implementing therapeutic interventions that focus on increasing tolerance to uncertainty.

Given the increasing prevalence of mental health problems among adolescents, early intervention and appropriate management are needed to decrease mortality and morbidity. Artificial Intelligence (AI) 's potential contributions, although significant in the field of medicine, have not been adequately studied in the context of adolescents’ mental health.

Over 80% of trials worldwide fail to complete patient recruitment within the initially planned time frame. Over the past decade, the use of social media for recruitment in medical research has become increasingly popular. While Google and Facebook are well established, newer social media channels such as Instagram and TikTok garner less research attention as recruitment tools. Although some studies have investigated the advantages and disadvantages of using social media for recruitment, a considerable gap still exists in understanding the precise mechanisms and factors that make different social media platforms most effective and cost-efficient for patient recruitment in mental health studies.

Increasing patient satisfaction with telemental health services is crucial for promoting widespread implementation and ensuring consistent utilization rates in the future, where the services could be a beneficial addition to routine mental healthcare. Nevertheless, knowledge regarding determinants of patient satisfaction with telemental health services is very limited.

Strong evidence suggests physical activity (PA) can ameliorate postpartum depression (PPD) symptoms; however, many postpartum individuals do not meet PA guidelines. Electronic health (eHealth) interventions are a promising approach to address common barriers to PA during postpartum.

Mental health disorders such as anxiety and depression are common among individuals of child-bearing age. Such disorders can affect pregnancy and postpartum well-being. This study aims to study the impact of prenatal mental health on the pregnancy journey, and highlights the utility of mobile health technologies like PowerMom for symptom tracking and screening.

Consumers are increasingly using large language model–based chatbots to seek mental health advice or intervention due to ease of access and limited availability of mental health professionals. However, their suitability and safety for mental health applications remain underexplored, particularly in comparison to professional therapeutic practices.

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.