Journal of Sports Science and Medicine
Journal of Sports Science and Medicine
ISSN: 1303 - 2968   
Ios-APP Journal of Sports Science and Medicine
Androit-APP Journal of Sports Science and Medicine
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©Journal of Sports Science and Medicine (2024) 23, 56 - 72   DOI: https://doi.org/10.52082/jssm.2024.56

Research article
ChatGPT Generated Training Plans for Runners are not Rated Optimal by Coaching Experts, but Increase in Quality with Additional Input Information
Peter Düking1, , Billy Sperlich2, Laura Voigt3, Bas Van Hooren4, Michele Zanini5, Christoph Zinner6
Author Information
1 Department of Sports Science and Movement Pedagogy, Technische Universität Braunschweig, Braunschweig, Germany
2 Integrative and Experimental Exercise Science, Department of Sport Science, University of Würzburg, Würzburg, Germany
3 Institute of Psychology, German Sport University Cologne, Cologne, Germany
4 Department of Nutrition and Movement Sciences, School of Nutrition and Translational Research in Metabolism (NUTRIM), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
5 School of Sport, Exercise, and Health Sciences, Loughborough University, Loughborough, United Kingdom
6 Department of Sport, University of Applied Sciences for Police and Administration of Hesse, Wiesbaden, Germany

Peter Düking
✉ Department of Sports Science and Movement Pedagogy, Technische Universität Braunschweig, Braunschweig, Germany
Email: peter.dueking@tu-braunschweig.de
Publish Date
Received: 13-10-2023
Accepted: 19-12-2023
Published (online): 01-03-2024
 
 
ABSTRACT

ChatGPT may be used by runners to generate training plans to enhance performance or health aspects. However, the quality of ChatGPT generated training plans based on different input information is unknown. The objective of the study was to evaluate ChatGPT-generated six-week training plans for runners based on different input information granularity. Three training plans were generated by ChatGPT using different input information granularity. 22 quality criteria for training plans were drawn from the literature and used to evaluate training plans by coaching experts on a 1-5 Likert Scale. A Friedmann test assessed significant differences in quality between training plans. For training plans 1, 2 and 3, a median rating of <3 was given 19, 11, and 1 times, a median rating of 3 was given 3, 5, and 8 times and a median rating of >3 was given 0, 6, 13 times, respectively. Training plan 1 received significantly lower ratings compared to training plan 2 for 3 criteria, and 15 times significantly lower ratings compared to training plan 3 (p < 0.05). Training plan 2 received significantly lower ratings (p < 0.05) compared to plan 3 for 9 criteria. ChatGPT generated plans are ranked sub-optimally by coaching experts, although the quality increases when more input information are provided. An understanding of aspects relevant to programming distance running training is important, and we advise avoiding the use of ChatGPT generated training plans without an expert coach’s feedback.

Key words: Artificial intelligence, data-informed training, digital health, digital training, innovation, individualization, mHealth, technology


           Key Points
  • Artificial Intelligence such as “ChatGPT” may be used by (novice) runners to generate training plans e.g. due to a lack of access to highly qualified coaches, yet the quality of such training plans is currently unknown.
  • ChatGPT generated training plans increase in ratings by coaching experts if more input information is provided, yet are not rated optimal
  • ChatGPT can provide recommendations for training plans, but does currently not cover many aspects which are relevant in a coach-athlete relationship such as motivation, monitoring, and training plan adjustments
 
 
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