JMIR Mental Health
Internet interventions, technologies, and digital innovations for mental health and behavior change.
JMIR Mental Health is the official journal of the Society of Digital Psychiatry.
Editor-in-Chief:
John Torous, MD, MBI, Harvard Medical School, USA
Impact Factor 4.8 CiteScore 10.8
Recent Articles
![Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: Benchmark Study Article Thumbnail](https://asset.jmir.pub/assets/9f6a46f33fb9f805274454de15e2065e.png 480w,https://asset.jmir.pub/assets/9f6a46f33fb9f805274454de15e2065e.png 960w,https://asset.jmir.pub/assets/9f6a46f33fb9f805274454de15e2065e.png 1920w,https://asset.jmir.pub/assets/9f6a46f33fb9f805274454de15e2065e.png 2500w)
Comprehensive session summaries enable effective continuity in mental health counseling, facilitating informed therapy planning. However, manual summarization presents a significant challenge, diverting experts’ attention from the core counseling process. Leveraging advances in automatic summarization to streamline the summarization process addresses this issue because this enables mental health professionals to access concise summaries of lengthy therapy sessions, thereby increasing their efficiency. However, existing approaches often overlook the nuanced intricacies inherent in counseling interactions.
![Reliability and Validity of Ecological Momentary Assessment Response Time–Based Measures of Emotional Clarity: Secondary Data Analysis Article Thumbnail](https://asset.jmir.pub/assets/f601f421ae6071eca2edce9fa00f4a50.png 480w,https://asset.jmir.pub/assets/f601f421ae6071eca2edce9fa00f4a50.png 960w,https://asset.jmir.pub/assets/f601f421ae6071eca2edce9fa00f4a50.png 1920w,https://asset.jmir.pub/assets/f601f421ae6071eca2edce9fa00f4a50.png 2500w)
Emotional clarity has often been assessed with self-report measures, but efforts have also been made to measure it passively, which has advantages such as avoiding potential inaccuracy in responses stemming from social desirability bias or poor insight into emotional clarity. Response times (RTs) to emotion items administered in ecological momentary assessments (EMAs) may be an indirect indicator of emotional clarity. Another proposed indicator is the drift rate parameter, which assumes that, aside from how fast a person responds to emotion items, the measurement of emotional clarity also requires the consideration of how careful participants were in providing responses.
![Technologies for Supporting Individuals and Caregivers Living With Fetal Alcohol Spectrum Disorder: Scoping Review Article Thumbnail](https://asset.jmir.pub/assets/84082447034c4c746f933104183edfa5.png 480w,https://asset.jmir.pub/assets/84082447034c4c746f933104183edfa5.png 960w,https://asset.jmir.pub/assets/84082447034c4c746f933104183edfa5.png 1920w,https://asset.jmir.pub/assets/84082447034c4c746f933104183edfa5.png 2500w)
Fetal alcohol spectrum disorder (FASD) is a common developmental disability that requires lifelong and ongoing support, but is often difficult to find due to a lack of trained professionals, limited funding and support available. Technology could provide cost-effective, accessible, and effective support to those living with FASD and their caregivers.
![In-Person and Teleconsultation Services at a National Hospital in Peru: Time Series Analysis of General and Psychiatric Care Amid the COVID-19 Pandemic Article Thumbnail](https://asset.jmir.pub/assets/d73519ed951138ce562edd845f849889.png 480w,https://asset.jmir.pub/assets/d73519ed951138ce562edd845f849889.png 960w,https://asset.jmir.pub/assets/d73519ed951138ce562edd845f849889.png 1920w,https://asset.jmir.pub/assets/d73519ed951138ce562edd845f849889.png 2500w)
The COVID-19 pandemic led to a global reduction in health care accessibility for both infected and noninfected patients, posing a particular burden on those with chronic conditions, including mental health issues. Peru experienced significant devastation from the pandemic, resulting in a collapsed health care system and leading to the world’s highest per capita mortality rate as a result of COVID-19. Understanding the trends in health care utilization, particularly in mental health care, is crucial for informing pandemic response efforts and guiding future recovery strategies.
![Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation Article Thumbnail](https://asset.jmir.pub/assets/599563ccd5bced40d98a8b8aabfedf4c.png 480w,https://asset.jmir.pub/assets/599563ccd5bced40d98a8b8aabfedf4c.png 960w,https://asset.jmir.pub/assets/599563ccd5bced40d98a8b8aabfedf4c.png 1920w,https://asset.jmir.pub/assets/599563ccd5bced40d98a8b8aabfedf4c.png 2500w)
Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications.
![News Media Framing of Suicide Circumstances and Gender: Mixed Methods Analysis Article Thumbnail](https://asset.jmir.pub/assets/2221ecbf8c0234ddda7deca0d6291c0a.png 480w,https://asset.jmir.pub/assets/2221ecbf8c0234ddda7deca0d6291c0a.png 960w,https://asset.jmir.pub/assets/2221ecbf8c0234ddda7deca0d6291c0a.png 1920w,https://asset.jmir.pub/assets/2221ecbf8c0234ddda7deca0d6291c0a.png 2500w)
![The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis Article Thumbnail](https://asset.jmir.pub/assets/c5a135a21a23692e7b6cb5f40e6b9dda.png 480w,https://asset.jmir.pub/assets/c5a135a21a23692e7b6cb5f40e6b9dda.png 960w,https://asset.jmir.pub/assets/c5a135a21a23692e7b6cb5f40e6b9dda.png 1920w,https://asset.jmir.pub/assets/c5a135a21a23692e7b6cb5f40e6b9dda.png 2500w)
Large language model (LLM)-powered services are gaining popularity in various applications due to their exceptional performance in many tasks, such as sentiment analysis and question answering. Recently, research has been exploring their potential use in digital health contexts, particularly in the mental health domain. However, implementing LLM-enhanced conversational artificial intelligence (CAI) presents significant ethical, technical, and clinical challenges. In this work, we discuss two challenges that affect the utilization of LLM-enhanced CAI for individuals with mental health issues, focusing on the use case of depressed patients: the tendency to humanize LLM-enhanced CAI and their lack of contextualized robustness. Our approach is interdisciplinary, relying on considerations from philosophy, psychology, and computer science. We argue that the humanization of LLM-enhanced CAI hinges on the reflection of what it means to simulate “human-like” features with LLMs and what role these systems should have in interactions with humans. Further, to ensure contextualizing robustness of LLMs requires considering the specificities of language production in depressed individuals, as well as its evolution over time. Finally, we provide a series of recommendations to foster the responsible design and deployment of LLM-enhanced CAI for the therapeutic support of individuals with depression.
![The Efficacy of Web-Based Cognitive Behavioral Therapy With a Shame-Specific Intervention for Social Anxiety Disorder: Randomized Controlled Trial Article Thumbnail](https://asset.jmir.pub/assets/bdde99524e8f8e2dbc88d1a1dd22dd40.png 480w,https://asset.jmir.pub/assets/bdde99524e8f8e2dbc88d1a1dd22dd40.png 960w,https://asset.jmir.pub/assets/bdde99524e8f8e2dbc88d1a1dd22dd40.png 1920w,https://asset.jmir.pub/assets/bdde99524e8f8e2dbc88d1a1dd22dd40.png 2500w)
Social Anxiety Disorder (SAD) is one of the most prevalent psychological disorders, and is generally co-occurring with elevated shame levels. Previous shame-specific interventions can significantly improve outcomes in social anxiety treatments. Recent review suggests that integrating a more direct shame intervention could potentially increase the effectiveness of Cognitive Behavioral Therapy. The Web-based CBT (WCBT) has proven efficacy, sustaining benefits for six months to four years. Previous evidence indicated that shame predicted the reduction of social anxiety and mediated between engagements in exposure and changes in social anxiety during WCBT.
![Insights Derived From Text-Based Digital Media, in Relation to Mental Health and Suicide Prevention, Using Data Analysis and Machine Learning: Systematic Review Article Thumbnail](https://asset.jmir.pub/assets/90d3edd66864db5688609559fb22ef8f.png 480w,https://asset.jmir.pub/assets/90d3edd66864db5688609559fb22ef8f.png 960w,https://asset.jmir.pub/assets/90d3edd66864db5688609559fb22ef8f.png 1920w,https://asset.jmir.pub/assets/90d3edd66864db5688609559fb22ef8f.png 2500w)
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