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

Ismail Dergaa, Helmi Ben Saad, Abdelfatteh El Omri, Jordan M. Glenn, Cain C.T. Clark, Jad Adrian Washif, Noomen Guelmami, Omar Hammouda, Ramzi A. Al-Horani, Luis Felipe Reynoso-Sánchez, Mohamed Romdhani, Laisa Liane Paineiras-Domingos, Rodrigo L. Vancini, Morteza Taheri, Leonardo Jose Mataruna-Dos-Santos, Khaled Trabelsi, Hamdi Chtourou, Makram Zghibi, Özgür Eken, Sarya SwedMohamed Ben Aissa, Hossam H. Shawki, Hesham R. El-Seedi, Iñigo Mujika, Stephen Seiler, Piotr Zmijewski, David B. Pyne, Beat Knechtle, Irfan M. Asif, Jonathan A. Drezner, Øyvind Sandbakk, Karim Chamari

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)221-241
Number of pages21
JournalBiology of Sport
Volume42
Issue number2
DOIs
Publication statusPublished - Feb 2024

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