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  1. Comparative Study of the Long-Term Impact of the COVID-19 Pandemic on Mental Health and Nutritional Practices Among International Elite and Sub-Elite Athletes: A Sample of 1420 Participants from 14 Countries
    Taheri M, Saad HB, Washif JA, Reynoso-Sánchez LF, Mirmoezzi M, Youzbashi L, et al.
    Sports Med Open, 2023 Nov 08;9(1):104.
    PMID: 37938473 DOI: 10.1186/s40798-023-00653-w
    BACKGROUND: Although several studies have shown that the Coronavirus Disease 2019 (COVID-19) lockdown has had negative impacts on mental health and eating behaviors among the general population and athletes, few studies have examined the long-term effects on elite and sub-elite athletes. The present study aimed to investigate the long-term impact of COVID-19 lockdown on mental health and eating behaviors in elite versus sub-elite athletes two years into the pandemic. A cross-sectional comparative study was conducted between March and April 2022, involving athletes from 14 countries, using a convenient non-probabilistic and snowball sampling method. A total of 1420 athletes (24.5 ± 7.9 years old, 569 elites, 35% women, and 851 sub-elites, 45% women) completed an online survey-based questionnaire. The questionnaire included a sociodemographic survey, information about the COVID-19 pandemic, the Depression, Anxiety and Stress Scale-21 Items (DASS-21) for mental health assessment, and the Rapid Eating Assessment for Participants (REAP-S) for assessing eating behavior.

    RESULTS: The results showed that compared to sub-elite athletes, elite athletes had lower scores on the DASS-21 (p = .001) and its subscales of depression (p = .003), anxiety (p = .007), and stress (p 

  2. Fulltext Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI's GPT-4 model
    Dergaa I, Saad HB, El Omri A, Glenn JM, Clark CCT, Washif JA, et al.
    Biol Sport, 2024 Mar;41(2):221-241.
    PMID: 38524814 DOI: 10.5114/biolsport.2024.133661
    The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI's Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model's ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model's potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition-specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback.
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