Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI's GPT-4 model

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dc.authoridTrabelsi, Khaled/0000-0003-2623-9557
dc.authoridZmijewski, Piotr/0000-0002-5570-9573
dc.authoridDergaa, Ismail/0000-0001-8091-1856
dc.authoridHAMMOUDA, Omar/0000-0002-5002-687X
dc.authoridWASHIF, Jad Adrian/0000-0001-8543-4489
dc.authoridRomdhani, Mohamed/0000-0002-1715-1863
dc.authoridChtourou, Hamdi/0000-0002-5482-9151
dc.authorwosidTrabelsi, Khaled/ABG-2717-2020
dc.authorwosidZmijewski, Piotr/AAR-4689-2020
dc.authorwosidDergaa, Ismail/AAB-8260-2021
dc.authorwosidHAMMOUDA, Omar/ADW-4182-2022
dc.authorwosidWASHIF, Jad Adrian/AAH-4912-2021
dc.authorwosidRomdhani, Mohamed/ABH-7773-2020
dc.authorwosidChtourou, Hamdi/AEZ-6215-2022
dc.contributor.authorDergaa, Ismail
dc.contributor.authorBen Saad, Helmi
dc.contributor.authorEl Omri, Abdelfatteh
dc.contributor.authorGlenn, Jordan M.
dc.contributor.authorClark, Cain C. T.
dc.contributor.authorWashif, Jad Adrian
dc.contributor.authorGuelmami, Noomen
dc.date.accessioned2024-08-04T20:55:10Z
dc.date.available2024-08-04T20:55:10Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe 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 conditionspecific 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.en_US
dc.identifier.doi10.5114/biolsport.2024.133661
dc.identifier.endpage241en_US
dc.identifier.issn0860-021X
dc.identifier.issn2083-1862
dc.identifier.issue2en_US
dc.identifier.pmid38524814en_US
dc.identifier.scopus2-s2.0-85188146191en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage221en_US
dc.identifier.urihttps://doi.org/10.5114/biolsport.2024.133661
dc.identifier.urihttps://hdl.handle.net/11616/101884
dc.identifier.volume41en_US
dc.identifier.wosWOS:001184338700026en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherTermedia Publishing House Ltden_US
dc.relation.ispartofBiology of Sporten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAI Challengesen_US
dc.subjectAI Evaluationen_US
dc.subjectChatboten_US
dc.subjectChatGPTen_US
dc.subjectDigital Healthen_US
dc.subjectExercise Optimizationen_US
dc.subjectFitness Algorithmsen_US
dc.subjectMachine Learningen_US
dc.subjectPersonalized Medicineen_US
dc.subjectReal-time Monitoringen_US
dc.titleUsing artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI's GPT-4 modelen_US
dc.typeArticleen_US

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