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The relationship between quality of life, sleep quality, mental health, and physical activity in an international sample of college students: a structural equation modeling approach

Moussa-Chamari I, Farooq A, Romdhani M, Washif JA, Bakare U, Helmy M, et al.

Front Public Health, 2024;12:1397924.
PMID: 39050600 DOI: 10.3389/fpubh.2024.1397924

OBJECTIVE: We assessed the direct and indirect relationships between sleep quality, mental health, and physical activity with quality of life (QOL) in college and university students.
METHODS: In a cross-sectional design, 3,380 college students (60% females; age = 22.7 ± 5.4) from four continents (Africa: 32%; America: 5%; Asia: 46%; and Europe: 15%; others: 2%) completed the Pittsburgh Sleep Quality Index (PSQI); Insomnia Severity Index (ISI); Epworth Sleepiness Scale (ESS); the Depression, Anxiety, and Stress Scale 21 (DASS); the International Physical Activity Questionnaire short-form (IPAQ); and the World Health Organization Quality of Life-BREF (WHOQOL-Brief).
RESULTS: We showed that sleep quality, insomnia, and depression had direct negative effects on the physical domain of QOL (β = -0.22, -0.19, -0.31, respectively, p
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.
Fulltext Training During the COVID-19 Lockdown: Knowledge, Beliefs, and Practices of 12,526 Athletes from 142 Countries and Six Continents

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,
Fulltext Correction to: Training During the COVID-19 Lockdown: Knowledge, Beliefs, and Practices of 12,526 Athletes from 142 Countries and Six Continents

Washif JA, Farooq A, Krug I, Pyne DB, Verhagen E, Taylor L, et al.

Sports Med, 2022 Oct 22.
PMID: 36272061 DOI: 10.1007/s40279-022-01776-y