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  1. Galy O, Washif JA, Wattelez G, Farooq A, Hue O, Sandbakk Ø, et al.
    Sci Rep, 2024 Apr 17;14(1):8866.
    PMID: 38632327 DOI: 10.1038/s41598-024-59375-y
    The aim of this study was to investigate relationships between changes in training practices and human development index (HDI) levels, and identify strategies employed by athletes who consistently maintained their training quantity during the first 100 days of the COVID-19 pandemic. A total of 10,074 athletes (5290 amateur and 4787 professional athletes from 121 countries) completed an online survey between 17 May to 5 July 2020. We explored their training practices, including specific questions on training frequency, duration and quantity before and during lockdown (March-June 2020), stratified according to the human development index (HDI): low-medium, high, or very high HDI. During the COVID-19 lockdown, athletes in low-medium HDI countries focused on innovative training. Nevertheless, women and amateur athletes experienced a substantial reduction in training activity. Performance-driven athletes and athletes from higher HDI indexed countries, were likely to have more opportunities to diversify training activities during lockdowns, facilitated by the flexibility to perform training away from home. Factors such as lockdown rules, socioeconomic environment, and training education limited training diversification and approaches, particularly in low-medium and high HDI countries. Athletes (amateurs and professionals) who maintained the quantity of training during lockdown appeared to prioritize basic cardiovascular and strength training, irrespective of HDI level. Modifying training and fitness programs may help mitigate the decrease in training activities during lockdowns. Customized training prescriptions based on gender, performance, and HDI level will assist individuals to effectively perform and maintain training activities during lockdowns, or other challenging (lockdown-like) situations.
  2. 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.
  3. Washif JA, Farooq A, Krug I, Pyne DB, Verhagen E, Taylor L, et al.
    Sports Med, 2022 04;52(4):933-948.
    PMID: 34687439 DOI: 10.1007/s40279-021-01573-z
    OBJECTIVE: Our objective was to explore the training-related knowledge, beliefs, and practices of athletes and the influence of lockdowns in response to the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

    METHODS: Athletes (n = 12,526, comprising 13% world class, 21% international, 36% national, 24% state, and 6% recreational) completed an online survey that was available from 17 May to 5 July 2020 and explored their training behaviors (training knowledge, beliefs/attitudes, and practices), including specific questions on their training intensity, frequency, and session duration before and during lockdown (March-June 2020).

    RESULTS: Overall, 85% of athletes wanted to "maintain training," and 79% disagreed with the statement that it is "okay to not train during lockdown," with a greater prevalence for both in higher-level athletes. In total, 60% of athletes considered "coaching by correspondence (remote coaching)" to be sufficient (highest amongst world-class athletes). During lockdown, 

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