Biology of Sport
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Original paper

More details, less variability? A crossover design study on the impact of information granularity on ChatGPT’s training program stability

Zhangyu Yang
1, 2
,
Xing Zhang
3
,
Hansen Li
4
,
Jianfei Ye
5

  1. Department of Didactics of Body Expression, Faculty of Educational Sciences, University of Granada, Granada, Spain
  2. College of Physical Education, Fuyang Normal University, Anhui, China
  3. Department of Physical Education and Sport, Faculty of Sport Sciences, University of Granada, Granada, Spain
  4. School of Physical Education, Sichuan Agricultural University, Ya’an, China
  5. Institute of Physical Education, Huangshan University, Huangshan, China
Biol Sport. 2026;43:379–392
DOI: https://doi.org/10.5114/biolsport.2026.154148
Online publish date: 2025/09/29
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1. Cai M, Bai D, Hou D, et al. Effectiveness of nonpharmacological multi-component intervention on depressive symptoms in patients with mild cognitive impairment and dementia: A systematic review and meta-analysis. Int J Ment Health Nurs. Apr 2024; 33(2):297–308. doi: 10.1111/inm.13247.
2. Garcia-Hermoso A, Ramirez-Velez R, Lubans DR, Izquierdo M. Effects of physical education interventions on cognition and academic performance outcomes in children and adolescents: a systematic review and meta-analysis. Br J Sports Med. Nov 2021; 55(21):1224–1232. doi: 10.1136 /bjsports-2021-104112.
3. Guo L, Liang L. Physical activity as a causal variable for adolescent resilience levels: A cross-lagged analysis. Front Psychol. 2023; 14:1095999.
4. Wright LJ, Veldhuijzen van Zanten JJ, Williams SE. Examining the associations between physical activity, self-esteem, perceived stress, and internalizing symptoms among older adolescents. J Adolesc. 2023; 95(6):1274–1287.
5. Philippot A, Dubois V, Lambrechts K, et al. Impact of physical exercise on depression and anxiety in adolescent inpatients: A randomized controlled trial. J Affect Disord. Mar 15 2022; 301:145–153. doi: 10.1016/j.jad .2022.01.011.
6. Wang X, Cai ZD, Jiang WT, Fang YY, Sun WX, Wang X. Systematic review and meta-analysis of the effects of exercise on depression in adolescents. Child Adolesc Psychiatry Ment Health. Feb 28 2022; 16(1):16. doi: 10.1186/s13034-022 -00453-2.
7. De Visser HS, Fast I, Brunton N, et al. Cardiorespiratory fitness and physical activity in pediatric diabetes: a systemic review and meta-analysis. JAMA Network Open. 2024; 7(2):e240235–e240235.
8. Sandbakk SB, Walther J, Solli GS, Tønnessen E, Haugen T. Training quality—what is it and how can we improve it? Int J Sports Physiol Perform. 2023; 18(5):557–560.
9. Xu Y, Liu Q, Pang J, et al. Assessment of Personalized Exercise Prescriptions Issued by ChatGPT 4.0 and Intelligent Health Promotion Systems for Patients with Hypertension Comorbidities Based on the Transtheoretical Model: A Comparative Analysis. J Multidiscip Healthc. 2024; 17:5063–5078. doi: 10.2147/JMDH.S477452.
10. Zhang X, Weakley J, Li H, Li Z, García-Ramos A. Superset versus traditional resistance training prescriptions: a systematic review and meta-analysis exploring acute and chronic effects on mechanical, metabolic, and perceptual variables. Sports Med. 2025; 55(4):953–975.
11. Zhang X, Li H, Feng S, Su S. The effect of various training variables on developing muscle strength in velocity-based training: a systematic review and meta-analysis. Int J Sports Med. 2023; 44(12):857–864.
12. Brenner JS, Watson A, Council On Sports M, Fitness. Overuse Injuries, Overtraining, and Burnout in Young Athletes. Pediatrics. Jan 1 2024; 153(2) doi: 10.1542/peds.2023-065129.
13. Wang C, Stokes T, Steele R, Wedderkopp N, Shrier I. Injury risk increases minimally over a large range of changes in activity level in children. arXiv preprint arXiv:201002952. 2020.
14. Morrow JR Jr., Defina LF, Leonard D, Trudelle-Jackson E, Custodio MA. Meeting physical activity guidelines and musculoskeletal injury: the WIN study. Med Sci Sports Exerc. Oct 2012; 44(10):1986–92. doi: 10.1249 /MSS.0b013e31825a36c6.
15. Gabbett TJ, Oetter E. From tissue to system: What constitutes an appropriate response to loading? Sports Med. 2025; 55(1):17–35.
16. Mittal K, Dhar M. Use of ChatGPT by physicians to build rehabilitation plans for the elderly: a mini-review of case studies. JIAG. 2023; 19(2):86–93.
17. Zhang G, Li G, Li H, Su Y, Li Y. GPT-4 as a Virtual Fitness Coach: An Evaluation of Its Effectiveness in Providing Weight Loss and Fitness Guidance. BMC Public Health. 2025; 25(1):2466. doi: 10.1186/s12889-025-22739-8.
18. Bayles MP. ACSM’s exercise testing and prescription. Lippincott williams & wilkins; 2023.
19. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW. Correlates of physical activity: why are some people physically active and others not? The lancet. 2012; 380(9838):258–271.
20. Dergaa I, Chamari K, Zmijewski P, Ben Saad H. From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing. Biol Sport. Apr 2023; 40(2):615–622. doi: 10.5114/biolsport.2023.125623.
21. Zhang X, Yin M, Zhang M, Li Z, Li H. The Development and Validation of an Artificial Intelligence Chatbot Dependence Scale. Cyberpsychol Behav Soc Netw. 2025; 28(2):126–131.
22. Zhang X, Li Z, Zhang M, et al. Exploring artificial intelligence (AI) Chatbot usage behaviors and their association with mental health outcomes in Chinese university students. J Affect Disord. 2025; 380:394–400.
23. Raiaan MAK, Mukta MSH, Fatema K, et al. A review on large language models: Architectures, applications, taxonomies, open issues and challenges. IEEE access. 2024; 12:26839–26874.
24. van Dis EAM, Bollen J, Zuidema W, van Rooij R, Bockting CL. ChatGPT: five priorities for research. Nature. Feb 2023; 614(7947):224–226. doi: 10.1038 /d41586-023-00288-7.
25. Schneider K, Tomchuk D, Snyder B, Bisch T, Koch G. Incorporating artificial intelligence into athletic training education: developing case-based scenarios using ChatGPT. Athl Train Educ J. 2024; 19(1):42–50.
26. Onan D, Arıkan H, Can İ, Güven Ş, Işıkay L, Ozge A. Examining the ability of artificial intelligence with ChatGPT-4.0 to create an exercise program: Case scenario examples” lumbar disc herniation, chronic migraine, and urge urinary incontinence”. Turk J Kinesiol. 2025; 11(1):28–44.
27. Düking P, Sperlich B, Voigt L, Van Hooren B, Zanini M, Zinner C. ChatGPT generated training plans for runners are not rated optimal by coaching experts, but increase in quality with additional input information. J Sports Sci Med. 2024; 23(1):56.
28. Arslan S. Exploring the Potential of Chat GPT in Personalized Obesity Treatment. Ann Biomed Eng. Sep 2023; 51(9):1887–1888. doi: 10.1007/ s10439-023-03227-9.
29. Puce L, Bragazzi NL, Currà A, Trompetto C. Harnessing Generative Artificial Intelligence for Exercise and Training Prescription: Applications and Implications in Sports and Physical Activity—A Systematic Literature Review. Applied Sciences. 2025; 15(7). doi: 10.3390/app15073497.
30. Brown T, Mann B, Ryder N, et al. Language models are few-shot learners advances in neural information processing systems 33. arXiv. 2005; 14165.doi: 10.48550/ arXiv.2005.14165.
31. Ji Z, Lee N, Frieske R, et al. Survey of hallucination in natural language generation. ACM computing surveys. 2023; 55(12):1–38.
32. Wang C, Liu X, Awadallah AH. Cost-effective hyperparameter optimization for large language model generation inference. PMLR; 2023:21/1–17.
33. Stavrinou PS, Astorino TA, Giannaki CD, Aphamis G, Bogdanis GC. Customizing intense interval exercise training prescription using the “frequency, intensity, time, and type of exercise”(FITT) principle. Front Physiol. 2025; 16:1553846.
34. Havers T, Masur L, Isenmann E, et al. Reproducibility and quality of hypertrophy-related training plans generated by GPT-4 and Google Gemini as evaluated by coaching experts. Biol Sport. 2025; 42(2):289–329.
35. Dergaa I, Fekih-Romdhane F, Hallit S, et al. ChatGPT is not ready yet for use in providing mental health assessment and interventions. Front Psychiatry. 2024; 14:1277756.
36. Verloigne M, Veitch J, Carver A, et al. Exploring associations between parental and peer variables, personal variables and physical activity among adolescents: a mediation analysis. BMC public health. 2014; 14:1–11.
37. Masagca RC. The AI coach: A 5-week AI-generated calisthenics training program on health-related physical fitness components of untrained collegiate students. J Hum Sport Exerc. 2024; 20(1):39–56.
38. Rocha-Silva R, Rodrigues MAM, Viana RB, et al. Critical analysis of information provided by ChatGPT on lactate, exercise, fatigue, and muscle pain: current insights and future prospects for enhancement. Adv Physiol Educ. Dec 1 2024; 48(4):898–903. doi: 10.1152/advan.00073.2024.
39. Schoenfeld BJ, Grgic J, Ogborn D, Krieger JW. Strength and hypertrophy adaptations between low-vs. high-load resistance training: a systematic review and meta-analysis. J Strength Cond Res. 2017; 31(12):3508–3523.
40. Dergaa I, Saad HB, El Omri A, et al. Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI’s GPT-4 model. Biol Sport. Mar 2024; 41(2):221–241. doi: 10.5114 /biolsport.2024.133661.
41. WHO. Physical activity. Accessed 31/03, 2025. https://www.who.int/news-room /fact-sheets/detail/physical-activity.
42. Bhattacharya P, Prasad VK, Verma A, et al. Demystifying ChatGPT: An in-depth survey of OpenAI’s robust large language models. Arch Comput Methods Eng. 2024:1–44.
43. Chen J, Liu Z, Huang X, et al. When large language models meet personalization: Perspectives of challenges and opportunities. World Wide Web. 2024; 27(4):42.
44. Washif JA, Pagaduan J, James C, Dergaa I, Beaven CM. Artificial intelligence in sport: Exploring the potential of using ChatGPT in resistance training prescription. Biol Sport. 2024; 41(2):209–220. doi: 10.5114 /biolsport.2024.132987
Copyright: Institute of Sport. This is an Open Access article distributed under the terms of the Creative Commons CC BY License (https://creativecommons.org/licenses/by/4.0/). This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
  
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