119 research outputs found
ChatGPT is not ready yet for use in providing mental health assessment and interventions
No full textBackground: Psychiatry is a specialized field of medicine that focuses on the diagnosis, treatment, and prevention of mental health disorders. With advancements in technology and the rise of artificial intelligence (AI), there has been a growing interest in exploring the potential of AI language models systems, such as Chat Generative Pre-training Transformer (ChatGPT), to assist in the field of psychiatry.
Objective: Our study aimed to evaluates the effectiveness, reliability and safeness of ChatGPT in assisting patients with mental health problems, and to assess its potential as a collaborative tool for mental health professionals through a simulated interaction with three distinct imaginary patients.
Methods: Three imaginary patient scenarios (cases A, B, and C) were created, representing different mental health problems. All three patients present with, and seek to eliminate, the same chief complaint (i.e., difficulty falling asleep and waking up frequently during the night in the last 2°weeks). ChatGPT was engaged as a virtual psychiatric assistant to provide responses and treatment recommendations.
Results: In case A, the recommendations were relatively appropriate (albeit non-specific), and could potentially be beneficial for both users and clinicians. However, as complexity of clinical cases increased (cases B and C), the information and recommendations generated by ChatGPT became inappropriate, even dangerous; and the limitations of the program became more glaring. The main strengths of ChatGPT lie in its ability to provide quick responses to user queries and to simulate empathy. One notable limitation is ChatGPT inability to interact with users to collect further information relevant to the diagnosis and management of a patient's clinical condition. Another serious limitation is ChatGPT inability to use critical thinking and clinical judgment to drive patient's management.
Conclusion: As for July 2023, ChatGPT failed to give the simple medical advice given certain clinical scenarios. This supports that the quality of ChatGPT-generated content is still far from being a guide for users and professionals to provide accurate mental health information. It remains, therefore, premature to conclude on the usefulness and safety of ChatGPT in mental health practice
sj-pdf-1-jmh-10.1177_15579883211040920 – Supplemental material for Biological Responses to Short-Term Maximal Exercise in Male Police Officers
No full textSupplemental material, sj-pdf-1-jmh-10.1177_15579883211040920 for Biological Responses to Short-Term Maximal Exercise in Male Police Officers by Ismail Dergaa, Helmi Ben Saad, Mohamed Romdhani, Amine Souissi, Mohamed Saifeddin Fessi, Narimen Yousfi, Tasnim Masmoudi, Nizar Souissi, Achraf Ammar and Omar Hammouda in American Journal of Men's Health</p
Tunis Med: Templates to assist authors in the process of responding to reviewers and/or the editorial team (English (Box 1) and French (Box 2) versions)
No full textDigital Micro-learning Interventions in Athletic Performance Enhancement: A Comprehensive Scoping Review of Technology-Enhanced Motor Skill Acquisition in Sports
No full textThis comprehensive scoping review investigates digital micro-learning interventions in the context of athletic performance enhancement, focusing on technology-enhanced motor skill acquisition. Aimed at addressing the unique learning preferences of Generation Z athletes, this study synthesizes existing research on digital micro-learning applications in sports training and identifies critical gaps in empirical evidence. Utilizing systematic methodologies aligned with PRISMA-ScR guidelines, the review encompasses various sources, including theoretical frameworks, empirical studies, and platform analyses. Key findings highlight the potential of short-form video content on platforms like TikTok and YouTube for skill acquisition, while also revealing significant gaps in sports-specific effectiveness research. The project aims to inform future research priorities and enhance the integration of micro-learning strategies in sports training
Study quality assessed by the quality assessment tool for observational cohort and cross-sectional studies.
No full textStudy quality assessed by the quality assessment tool for observational cohort and cross-sectional studies.</p
Global Implications of COVID-19 Pandemic on Adults' Lifestyle Behavior: The Invisible Pandemic of Noncommunicable Disease
No full textCOVID-19 pandemic, with its subsequent lockdown and mobility restriction is a public health emergency that has obliged substantial modifications in daily routines and lifestyle of people worldwide. The drastic measures of social isolation and home confinement has impacted to a great extent the physical and psychological health. The resultant abrupt in lifestyle-related behavior such as physical inactivity, unhealthy dietary habit, sleep disturbance, stress, tobacco use, and alcohol intake, is directly linked to the rising global burden of non-communicable disease. This review aims at gaining a rich and extensive understanding of the potential negative impact triggered by COVID-19 on lifestyle-related behaviors that will influence long-term physical and mental wellbeing. Electronic database search was conducted on PubMed, ScienceDirect, Google Scholar, and Scopus from January 1, 2020 to March 15, 2021. Data related to COVID-19 impact on lifestyle habits were extracted from these studies. Articles were included if meeting the inclusion criteria (i.e., assessed the impact of COVID-19 on physical inactivity and sedentary behavior, dietary habits, sleep, mental health, vitamin D, and substance use among adults. Further search was conducted to address these behavioral changes among athletes. While physical isolation is a necessary public health measure to protect the population, outcomes of this review indicate that in light of adverse lifestyle changes brought by the pandemic, noncommunicable disease remains a critical concern. Hence, adopting healthy lifestyle behavior is essentially important especially during the current time to boost immunity and reduce the risk of COVID-19 infection. Recognizing the pandemic collateral effects offers a forward-looking perspective to guide the government and health authorities in planning prevention and control programs that focus on resilient and sustainable behavioral change
Machine learning applications in the analysis of sedentary behavior and associated health risks
No full textBackgroundThe rapid advancement of technology has brought numerous benefits to public health but has also contributed to a rise in sedentary lifestyles, linked to various health issues. As prolonged inactivity becomes a growing public health concern, researchers are increasingly utilizing machine learning (ML) techniques to examine and understand these patterns. ML offers powerful tools for analyzing large datasets and identifying trends in physical activity and inactivity, generating insights that can support effective interventions.ObjectivesThis review aims to: (i) examine the role of ML in analyzing sedentary patterns, (ii) explore how different ML techniques can be optimized to improve the accuracy of predicting sedentary behavior, and (iii) assess strategies to enhance the effectiveness of ML algorithms.MethodsA comprehensive search was conducted in PubMed and Scopus, targeting peer-reviewed articles published between 2004 and 2024. The search included the subject terms “sedentary behavior,” “sedentary lifestyle health,” and “machine learning sedentary lifestyle,” combined with the keywords “physical inactivity” and “diseases” using Boolean operators (AND, OR). Articles were included if they addressed the health impacts of sedentary behavior or employed ML techniques for its analysis. Exclusion criteria involved studies older than 20 years or lacking direct relevance. After screening 33 core articles and identifying 13 more through citation tracking, 46 articles were included in the final review.ResultsThis narrative review describes the characteristics of sedentary behavior, associated health risks, and the applications of ML in this context. Based on the reviewed literature, sedentary behavior was consistently associated with cardiovascular disease, metabolic disorders, and mental health conditions. The review highlights the utility of various ML approaches in classifying activity levels and significantly improving the prediction of sedentary behavior, offering a promising approach to address this widespread health issue.ConclusionML algorithms, including supervised and unsupervised models, show great potential in accurately detecting and predicting sedentary behavior. When integrated with wearable sensor data and validated in real-world settings, these models can enhance the scalability and precision of AI-driven interventions. Such advancements support personalized health strategies and could help lower healthcare costs linked to physical inactivity, ultimately improving public health outcomes
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