Outputs Show only Author
Title
Category
Date
Authors
Projects
Virtual Reality and Stress Management: A Systematic Review Amidst the growing prevalence of chronic stress and its potential negative impacts on mental health, this review explores the use of virtual reality (VR) as a stress management solution, aiming to assess its viability and effectiveness in this context. A comprehensive search was conducted on MEDLINE, PsycINFO, and Embase from inception until February 2024. Eligible studies were primary research papers that focused on the use of VR as an intervention to mitigate psychological stress and/or distress. We included studies where the assessment of stress levels primarily relied on self-report measures. A total of 50 studies involving 2885 participants were included in our systematic review. VR-based interventions varied across studies, implementing tools such as cognitive behavioural therapy, exposure therapy, mindfulness and relaxation, repetition tasks, and psychoeducation. The reviewed studies yielded mixed results; however, a strong indication was present in highlighting the promising potential of VR-based interventions. Many studies observed a decrease in psychiatric symptoms in participants and reported increased quality of life. Various studies also found VR to be a valuable tool in promoting stress reduction and relaxation. VR was proven useful in exposing participants to stressors in a safe, controlled way. These potential benefits appear to come with no risk of harm to the participants. Although the findings are heterogenous, there is sufficient evidence supporting the use of VR for stress management across a range of contexts and populations. Overall, VR appears to be a generally low-risk, feasible intervention for those struggling with stress. Toronto Metropolitan University, University of Toronto Publication 2024-07-15 Shakila Meshkat, Mahsa Edalatkhah, Corinna Di Luciano, Josh Martin, Gursharanjit Kaur, Gyu Hee Lee, Haley Park, Andrei Torres,
Mazalek, A. , Bill Kapralos, Adam Dubrowski,
Bhat, V. Leveraging large language models for automated depression screening Mental health diagnoses possess unique challenges that often lead to nuanced difficulties in managing an individual's well-being and daily functioning. Self-report questionnaires are a common practice in clinical settings to help mitigate the challenges involved in mental health disorder screening. However, these questionnaires rely on an individual's subjective response which can be influenced by various factors. Despite the advancements of Large Language Models (LLMs), quantifying self-reported experiences with natural language processing has resulted in imperfect accuracy. This project aims to demonstrate the effectiveness of zero-shot learning LLMs for screening and assessing item scales for depression using LLMs. The DAIC-WOZ is a publicly available mental health dataset that contains textual data from clinical interviews and self-report questionnaires with relevant mental health disorder labels. The RISEN prompt engineering framework was utilized to evaluate LLMs' effectiveness in predicting depression symptoms based on individual PHQ-8 items. Various LLMs, including GPT models, Llama3_8B, Cohere, and Gemini were assessed based on performance. The GPT models, especially GPT-4o, were consistently better than other LLMs (Llama3_8B, Cohere, Gemini) across all eight items of the PHQ-8 scale in accuracy (M = 75.9%), and F1 score (0.74). GPT models were able to predict PHQ-8 items related to emotional and cognitive states. Llama 3_8B demonstrated superior detection of anhedonia-related symptoms and the Cohere LLM's strength was identifying and predicting psychomotor activity symptoms. This study provides a novel outlook on the potential of LLMs for predicting self-reported questionnaire scores from textual interview data. The promising preliminary performance of the various models indicates there is potential that these models could effectively assist in the screening of depression. Further research is needed to establish a framework for which LLM can be used for specific mental health symptoms and other disorders. As well, analysis of additional datasets while fine-tuning models should be explored. Vector Institute, McMaster University, Toronto Metropolitan University, University of Toronto Publication 2025-07-01 Bazen Gashaw Teferra, Argyrios Perivolaris, Wei-Ni Hsiang, Christian Kevin Sidharta, Alice Rueda, Karisa Parkington, Yuqi Wu, Anuja Soni,
Samavi, R. , Rakesh Jetly, Yanbo Zhang, Bo Cao, Sirisha Rambhatla, Sridhar Krishnan,
Bhat, V.
YouTube
