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

Dergaa I 1 , Saad HB 2 , El Omri A 3 , Glenn JM 4 , Clark CCT 5 , Washif JA 6 Show all authors , Guelmami N 7 , Hammouda O 8 , Al-Horani RA 9 , Reynoso-Sánchez LF 10 , Romdhani M 8 , Paineiras-Domingos LL 11 , Vancini RL 12 , Taheri M 13 , Mataruna-Dos-Santos LJ 14 , Trabelsi K 15 , Chtourou H 15 , Zghibi M 7 , Eken Ö 16 , Swed S 17 , Aissa MB 18 , Shawki HH 19 , El-Seedi HR 20 , Mujika I 21 , Seiler S 22 , Zmijewski P 23 , Pyne DB 24 , Knechtle B 25 , Asif IM 26 , Drezner JA 27 , Sandbakk Ø 28 , Chamari K 29

Affiliations 

  • 1 Primary Health Care Corporation (PHCC), Doha, Qatar
  • 2 University of Sousse, Farhat HACHED hospital, Research Laboratory LR12SP09 «Heart Failure», Sousse, Tunisia
  • 3 Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha 3050, Qatar
  • 4 Neurotrack Technologies, Redwood City CA, USA
  • 5 College of Life Sciences, Birmingham City University, Birmingham, B15 3TN, UK
  • 6 Sports Performance Division, National Sports Institute of Malaysia, Kuala Lumpur, Malaysia
  • 7 High Institute of Sport and Physical Education of Kef, Jendouba, Kef, Tunisia
  • 8 Interdisciplinary Laboratory in Neurosciences, Physiology and Psychology: Physical Activity, Health and Learning (LINP2), UFR STAPS (Faculty of Sport Sciences), UPL, Paris Nanterre University, Nanterre, France
  • 9 Department of Exercise science, Yarmouk University, Irbid, Jordan
  • 10 Department of Social Sciences and Humanities, Autonomous University of Occident, Los Mochis, Mexico
  • 11 Departamento de Fisioterapia, Instituto Multidisciplinar de Reabilitação e Saúde, Universidade Federal da Bahia, Brazil
  • 12 Centro de Educação Física e Desportos, Universidade Federal do Espírito Santo, Vitória, Espírito Santo, Brazil
  • 13 Department of Motor Behavior, Faculty of Sport Sciences, University of Tehran, Tehran, Iran
  • 14 Department of Creative Industries, Faculty of Communication, Arts and Sciences, Canadian University of Dubai, Dubai, United Arab Emirates
  • 15 Research Laboratory Education, Motricité, Sport et Santé (EM2S) LR19JS01, High Institute of Sport and Physical Education of Sfax, University of Sfax, Sfax 3000, Tunisia
  • 16 Department of Physical Education and Sport Teaching, Inonu University, Malatya 44000, Turkey
  • 17 University of Aleppo Faculty of Medicine: Aleppo, Aleppo Governorate, Syria
  • 18 Postgraduate School of Public Health, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
  • 19 Department of Comparative and Experimental Medicine, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan
  • 20 Department of Chemistry, Faculty of Science, Islamic University of Madinah, Madinah, 42351, Saudi Arabia
  • 21 Department of Physiology, Faculty of Medicine and Nursing, University of the Basque Country, Leioa, Basque Country
  • 22 Department of Sport Science and Physical Education, University of Agder, Kristiansand, Norway
  • 23 Jozef Pilsudski University of Physical Education in Warsaw, Warsaw, Poland
  • 24 Research Institute for Sport and Exercise, University of Canberra, Canberra, ACT, Australia
  • 25 Institute of Primary Care, University of Zurich, Zurich, Switzerland
  • 26 Department of Family and Community Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
  • 27 Center for Sports Cardiology, University of Washington, Seattle, Washington, USA
  • 28 Center for Elite Sports Research, Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
  • 29 Higher institute of Sport and Physical Education, ISSEP Ksar Saïd, Manouba University, Tunisia
Biol Sport, 2024 Mar;41(2):221-241.
PMID: 38524814 DOI: 10.5114/biolsport.2024.133661

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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