JMIR Mental Health

Aerobic exercise (AE) training has been shown to enhance aerobic fitness in people with schizophrenia. Traditionally, such training has been administered in-person at gyms or other communal exercise spaces. However, following the advent of the COVID-19 pandemic, many clinics transitioned their services to telehealth-based delivery. Yet, at present there is scarce information about the feasibility, safety, and efficacy of telehealth-based AE in this population.

Large Language Models (LLMs) have shown promise in transforming how complex scientific data is analyzed and communicated, yet their application to scientific domains remains challenged by issues of factual accuracy and domain-specific precision. The LIBR-TU Research Agent (LITURAt) leverages a sophisticated agent-based architecture to mitigate these limitations, using external data retrieval and analysis tools to ensure reliable, context-aware outputs that make scientific information accessible to both experts and non-experts.

The prevalence of adolescent mental health conditions, like depression and anxiety, has significantly increased. Despite the potential of machine learning (ML), there is a shortage of models that utilize real-world data (RWD) to enhance early detection and intervention for these conditions.

The integration of information technology into health care has created opportunities to address diagnostic challenges. Internet searches, representing a vast source of health-related data, hold promise for improving early disease detection. Studies suggest that patterns in search behavior can reveal symptoms before clinical diagnosis, offering potential for innovative diagnostic tools. Leveraging advancements in machine learning, researchers have explored linking search data with health records to enhance screening and outcomes. However, challenges like privacy, bias, and scalability remain critical to its widespread adoption.

Dementia is a syndrome that compromises neurocognitive functions of the individual and that is affecting 55 million individuals globally, as well as global health care systems, national economic systems, and family members.

As artificial intelligence (AI) tools are integrated more widely in psychiatric medicine, it is important to consider the impact these tools will have on clinical practice.

Mental health disorders significantly impact global populations, prompting the rise of digital mental health interventions like artificial intelligence-powered chatbots to address gaps in access to care. This review explores the potential for a "digital therapeutic alliance," emphasizing empathy, engagement, and alignment with traditional therapeutic principles to enhance user outcomes.

The increasing deployment of conversational artificial intelligence (AI) in mental health interventions necessitates an evaluation of their efficacy in rectifying cognitive biases and recognizing affect in human-AI interactions. These biases are particularly relevant in mental health contexts as they can exacerbate conditions such as depression and anxiety by reinforcing maladaptive thought patterns or unrealistic expectations in human-AI interactions.

Depression and anxiety are a leading cause of disability worldwide and often start during adolescence and young adulthood. The majority of young people live in low- and middle-income countries where there is a lack of mental health services. The World Health Organization (WHO) developed a guided, nonartificial intelligence chatbot intervention called Scalable Technology for Adolescents and youth to Reduce Stress (STARS) to reduce symptoms of depression and anxiety among young people affected by adversity.

Anxiety disorders are among the most prevalent mental disorders, and stress plays a significant role in their development. Ecological momentary interventions (EMIs) hold great potential to help people manage stress and anxiety by training emotion regulation and coping skills in real-life settings. InsightApp is a gamified EMI and research tool that incorporates elements from evidence-based therapeutic approaches. It is designed to strengthen people’s metacognitive skills for coping with challenging real-life situations and embracing anxiety and other emotions.

Increasing observation and evidence suggest that the process of digitalization could have profound impact to the development of human mind and self, with potential mental health consequences. Self-differentiation is important in human identity and self-concept formation, which is believed to be involved in the process of digitalization.

Mental health concerns have become increasingly prevalent; however, care remains inaccessible to many. While digital mental health interventions offer a promising solution, self-help and even coached apps have not fully addressed the challenge. There is now a growing interest in hybrid, or blended, care approaches that use apps as tools to augment, rather than to entirely guide, care. The Digital Clinic is one such model, designed to increase access to high-quality mental health services.