Biol Sport. 2026;43:949–970
1. World Health Organization. Physical Activity and Sedentary Behaviour: A Brief to Support People Living with Type 2 Diabetes. Geneva: World Health Organization; 2022.
2.
Pedersen BK, Saltin B. Exercise as medicine – evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand J Med Sci Sports. 2015; 25(suppl 3):1–72. doi: 10.1111/sms.12581.
3.
American College of Sports Medicine. Guidelines for Exercise Testing and Prescription. 4th ed. Philadelphia, PA: Lea & Febiger; 1991.
4.
Cerin E, Leslie E. How socio-economic status contributes to participation in leisure-time physical activity. Soc Sci Med. 2008; 66(12):2596–2609. doi: 10.1016/j.socscimed.2008.02.012.
5.
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Advances in Neural Information Processing Systems. Vol 30. Curran Associates, Inc.; 2017:6000–6010.
6.
Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med. 2023; 388(13):1233–1235. doi: 10.1056/ NEJMc2304040.
7.
Topol E. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. 1st ed. New York, NY: Basic Books; 2019.
8.
Zaleski A, Berkowsky RS, Craig K, Pescatello L. Comprehensiveness, accuracy, and readability of exercise recommendations provided by an AI-based chatbot: mixed methods study. JMIR Med Educ. 2024; 10(1):e51308. doi: 10.2196/51308.
9.
Dergaa I, Ben Saad H, 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. 2024; 41(2):221–241. doi: 10.5114/ biolsport.2024.133661.
10.
Nutbeam D, Lloyd JE. Understanding and responding to health literacy as a social determinant of health. Annu Rev Public Health. 2021; 42:159–173. doi: 10.11 46/annurev-publhealth-090419-102529.
11.
van Arum S, Genç HU. Selective trust: Understanding human-AI partnerships in personal health decision-making process. Int J Hum Comput Stud. 2025; 181:103152. doi: 10.1016/j.ijhcs .2024.103152.
12.
Düking P, Sperlich B, Voigt L, et al. 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):56–72. doi: 10.520 82/jssm.2024.56.
13.
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. doi: 10.5114/biolsport.2025.145911.
14.
Rahimi SA, Cwintal M, Huang Y, et al. Application of artificial intelligence in shared decision making: scoping review. JMIR Med Inform. 2022; 10(8):e36199. doi: 10.2196/36199.
15.
An R, Shen J, Wang J, Yang Y. A scoping review of methodologies for applying artificial intelligence to physical activity interventions. J Sport Health Sci. 2024; 13(3):428–441. doi: 10.1016 /j.jshs.2023.09.010.
16.
Lai X, Chen J, Lai Y, et al. Using large language models to enhance exercise recommendations and physical activity in clinical and healthy populations: scoping review. J Med Internet Res. 2025; 27:e59309. doi: 10.2196/59309.
17.
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. Appl Sci. 2025; 15(7):3497. doi: 10.3390/app15073497.
18.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372:n71. doi: 10.1136/bmj.n71.
19.
Popay J, Roberts H, Sowden A, et al. Guidance on the Conduct of Narrative Synthesis in Systematic Reviews: A Product from the ESRC Methods Programme Version 1. Lancaster: Lancaster University; 2006. doi: 10.13140/2.1.1018.4643.
20.
Braun V, Clarke V. One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qual Res Psychol. 2021; 18(3):328–352. doi: 10.1080 /14780887.2020.1769238.
21.
Pajo LS, Rabuya R, Andacao A, Tuano AMS, Lobo J. A 10-week large language model-generated (LLM) versus human-made volleyball training program on the jumping performance of collegiate volleyball athletes. J Phys Educ. 2025; 36:e3611. doi: 10.4025/jphyseduc .v36i1.3611.
22.
Genç A, Aydin R, Canuzakov K, et al. Digital coaches: an alternative to expert coaches for men’s fitness goals. Phys Act Rev. 2025; 13(2):35–44. doi: 10.16926/par.2025.13.18.
23.
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. 2025; 20(1):39–56. doi: 10.55860/13v7e679.
24.
Ebrahimi E, Rashidy P, Mohammadalinezhad SE, Hajizadeh R. The effect of a 6-week AI-generated core stability training program on balance and flatfoot in blind female students. J Asian Paralympic Movement. 2024; 4(2):83–91.
25.
Philuek P, Kusump S, Sathianpoonsook T, et al. The effects of Chat GPT generated exercise program in healthy overweight young adults: a pilot study. J Hum Sport Exerc. 2024; 20(1):169–179. doi: 10.55860/1epqgp77.
26.
Comendant C. Large Language Model-Based Sport Coaching System Using Retrieval-Augmented Generation and User Models [Bachelor’s thesis]. Enschede, Netherlands: University of Twente; 2024.
27.
Akrimi S, Schwensfeier L, Düking P, Kreutz T, Brinkmann C. ChatGPT-4o-generated exercise plans for patients with type 2 diabetes mellitus—assessment of their safety and other quality criteria by coaching experts. Sports. 2025; 13(4):92. doi: 10.3390/sports 13040092.
28.
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.
29.
Saraç H, Ulusoy İT, Alpay J, Ödemiş H, Söğüt M. Evaluating the potential role of AI chatbots in designing personalized exercise programs for weight management. Int J Hum Comput Interact. Published online February 27, 2025:1–8. doi: 10.1080/10447318 .2025.2462752.
30.
Erol E, Arıkan H. Does ChatGPT provide comprehensive and accurate information regarding the effects, types and programming of core exercises? Turk J Kinesiol. 2024; 10(3):178–182. doi: 10.31459/turkjkin.1516614.
31.
Wachholz F, Manno S, Schlachter D, Gamper N, Schnitzer M. Acceptance and trust in AI-generated exercise plans among recreational athletes and quality evaluation by experienced coaches: a pilot study. BMC Res Notes. 2025; 18(1):112. doi: 10.1186/s13104-025-07172-9.
32.
Shin D, Hsieh G, Kim YH. PlanFitting: personalized exercise planning with large language model-driven conversational agent. In: Proceedings of the 7th ACM Conference on Conversational User Interfaces. ACM; 2025:1–19. doi: 10.1145/3719160.3736607.
33.
Strömel KR, Henry S, Johansson T, Niess J, Woźniak PW. Narrating fitness: Leveraging large language models for reflective fitness tracker data interpretation. In: Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM; 2024:1–16. doi: 10.1145/3613904.3642032.
34.
Haag D, Kumar D, Gruber S, et al. The last JITAI? Exploring large language models for issuing just-in-time adaptive interventions: Fostering physical activity in a prospective cardiac rehabilitation setting. In: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. ACM; 2025:1–18. doi: 10.1145/3706598.3713307.
35.
Shneiderman B. Human-Centered AI. 1st ed. Oxford: Oxford University Press; 2022.
36.
Wang D, Churchill E, Maes P, et al. From human-human collaboration to human-AI collaboration: designing AI systems that can work together with people. In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. ACM; 2020:1–6. doi: 10.1145/3334480.3381069.
37.
Puce L, Ceylan Hİ, Trompetto C, et al. Optimizing athletic performance through advanced nutrition strategies: Can AI and digital platforms have a role in ultraendurance sports? Biol Sport. 2024; 41(4):305–313. doi: 10.5114/biolsport .2024.141063.
38.
Wackerhage H, Schoenfeld BJ, Hamilton DL, Lehti M, Hulmi JJ. Stimuli and sensors that initiate skeletal muscle hypertrophy following resistance exercise. J Appl Physiol. 2019; 126(1):30–43. doi: 10.1152/japplphysiol.00685.2018.
39.
Zourdos MC, Klemp A, Dolan C, et al. Novel resistance training–specific rating of perceived exertion scale measuring repetitions in reserve. J Strength Cond Res. 2016; 30(1):267–275. doi: 10.1519/JSC.000000000000 1049.
40.
Stone M, Plisk S, Collins D. Strength and conditioning: training principles: evaluation of modes and methods of resistance training – a coaching perspective. Sports Biomech. 2002; 1(1):79–103. doi: 10.1080 /14763140208522788.
41.
Roberts BM, Nuckols G, Krieger JW. Sex differences in resistance training: a systematic review and meta-analysis. J Strength Cond Res. 2020; 34(5):1448–1460. doi: 10.1519/JSC .0000000000003521.
42.
Gao Y, Xiong Y, Gao X, et al. Retrieval-augmented generation for large language models: a survey. arXiv. Preprint posted online March 27, 2024. doi: 10.48550/ arXiv.2312.10997.
43.
Riebe D, Franklin BA, Thompson PD, et al. Updating ACSM’s recommendations for exercise preparticipation health screening. Med Sci Sports Exerc. 2015; 47(11):2473–2479. doi: 10.1249/MSS .0000000000000664.
44.
Grote T, Berens P. On the ethics of algorithmic decision-making in healthcare. J Med Ethics. 2020; 46(3):205–211. doi: 10.1136 /medethics-2019-105586.
45.
Huang L, Yu W, Ma W, et al. A survey on hallucination in large language models: principles, taxonomy, challenges, and open questions. ACM Trans Inf Syst. 2025; 43(2):1–55. doi: 10.1145/3703155.
46.
Ryan GA, Snarr RL, Eisenman ML, Rossi SJ. Seasonal training load quantification and comparison in college male soccer players. J Strength Cond Res. 2022; 36(4):1038–1045. doi: 10.1519/JSC.000000000000 3589.
47.
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989; 13(3):319–340. doi: 10.2307/249008.
48.
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019; 366(6464):447–453. doi: 10.1126 /science.aax2342.
49.
Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019; 25(1):37–43. doi: 10.1038/s41591 -018-0272-7.
50.
Puce L, Żmijewski P, Cotellessa F, et al. The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming. Biol Sport. 2026; 43(1):355–367. doi: 10.5114/biolsport .2026.152352.
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.
