ArticleBiology of sport2024
Artificial intelligence in sport: Exploring the potential of using ChatGPT in resistance training prescription.
Article in Biology of sport, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Comparing human-led reflective dialogue and AI-based tactical guidance as innovative training approaches in soccer: effects on tactical performance and problem-solving.BMC sports science, medicine & rehabilitation · 2026Article
- AI-Generated Exercise Prescriptions for At-Risk Populations: Safety and Feasibility of a Large Language Model Assessed by Expert Evaluation.Journal of clinical medicine · 2026Article
- Large Language Model-Based Agents for Physical Activity and Cognitive Training: Scoping Review.JMIR AI · 2026Review
- ChatGPT Outperforms Personal Trainers in Answering Common Exercise Training Questions.Journal of sports science & medicine · 2026Article
- Toward autonomous artificial intelligence agents in sports science: a modular framework for development, validation, and implementation.Biology of sport · 2026Review
- Review
- Generative artificial intelligence and large language models in sports medicine: a scoping review of applications, accuracy, and ethical implications.Frontiers in public health · 2026Article
- A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification.Frontiers in systems biology · 2026Review
- ChatGPT-generated rehabilitation programs in sports physiotherapy: an expert evaluation and a mixed-methods study of clinical applicability.Frontiers in medicine · 2026Article
- More details, less variability? A crossover design study on the impact of information granularity on ChatGPT's training program stability.Biology of sport · 2026Article
- The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming.Biology of sport · 2026Article
- Changes in physical fitness and body composition of athletes after the COVID-19 lockdown: a systematic review, meta-analysis, and meta-regression, with assessment of the certainty of evidence.Biology of sport · 2026Review
- Longitudinal cohort study on subsequent injury risk in professional football players in the Qatar Stars League: a probabilistic approach using basic learning.Biology of sport · 2026Article
- Digital feedback via free ChatGPT within the reciprocal teaching style: improving fundamental handball skills and students' attitudes among university beginners.Frontiers in sports and active living · 2026Article
- Comparative evaluation of ChatGPT versions in training program design: scientific approach, accuracy, and practical applicability.BMC sports science, medicine & rehabilitation · 2025Article
- Evaluation Strategies for Large Language Model-Based Models in Exercise and Health Coaching: Scoping Review.Journal of medical Internet research · 2025Article
- A professional assessment of training plans for muscle hypertrophy and maximal strength developed by generative artificial intelligence.Biology of sport · 2025Article
- GPT- 4 as a virtual fitness coach: a case study assessing its effectiveness in providing weight loss and fitness guidance.BMC public health · 2025Article
- Assessment of Recommendations Provided to Athletes Regarding Sleep Education by GPT-4o and Google Gemini: Comparative Evaluation Study.JMIR formative research · 2025Article
- Physical Activity to Counter Age-Related Cognitive Decline: Benefits of Aerobic, Resistance, and Combined Training-A Narrative Review.Sports medicine - open · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
OpenAI's Chat Generative Pre-trained Transformer (ChatGPT) technology enables conversational interactions with applications across various fields, including sport. Here, ChatGPT's proficiency in designing a 12-week resistance training programme, following specific prompts, was investigated. GPT3.5 and GPT4.0 versions were requested to design 12-week resistance training programmes for male and female hypothetical subjects (20-years-old, no injury, and 'intermediate' resistance training experience). Subsequently, GPT4.0 was requested to design an 'advanced' training programme for the same profiles. The proposed training programmes were compared with established guidelines and literature (e.g., National Strength and Conditioning Association textbook), and discussed. ChatGPT suggested 12-week training programmes comprising three, 4-week phases, each with different objectives (e.g., hypertrophy/strength). GPT3.5 proposed a weekly frequency of ~3 sessions, load intensity of 70-85% of one repetition-maximum, repetition range of 4-8 (2-4 sets), and tempo of 2/0/2 (eccentric/pause/concentric/'pause'). GPT4.0 proposed intermediate- and advanced programme, with a frequency of 5 or 4 sessions, 60-90% or 70-95% intensity, 3-5 sets or 3-6 sets, 5-12 or 3-12 repetitions, respectively. GPT3.5 proposed rest intervals of 90-120 s, and exercise tempo of 2/0/2. GPT4.0 proposed 60-180 (intermediate) or 60-300 s (advanced), with exercise tempo of 2/1/2 for intermediates, and 3/0/1/0, 2/0/1/0, and 1/0/1/0 for advanced programmes. All derived programmes were objectively similar regardless of sex. ChatGPT generated training programmes which likely require additional fine-tuning before application. GPT4.0 synthesised more information than GPT3.5 in response to the prompt, and demonstrated recognition awareness of training experience (intermediate vs advanced). ChatGPT may serve as a complementary tool for writing 'draft' programme, but likely requires human expertise to maximise training programme effectiveness.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.