ArticleBMC research notes2025
Acceptance and trust in AI-generated exercise plans among recreational athletes and quality evaluation by experienced coaches: a pilot study.
Article in BMC research notes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Psychological Dimensions of AI Implementation in Taekwondo: A Qualitative Case Study of Coach and Athlete Perceptions.Behavioral sciences (Basel, Switzerland) · 2026Article
- The AI recommendation paradox: a systematic review evaluating the promise, peril, and path forward for large language models in exercise recommendation.Biology of sport · 2026Review
- Large language models for promoting physical activity: a review of experiential and behavioral outcomes, social roles, and human-likeness in persuasive LLMs.Frontiers in digital health · 2026Review
- Comparative evaluation of ChatGPT versions in training program design: scientific approach, accuracy, and practical applicability.BMC sports science, medicine & rehabilitation · 2025Article
- The associations of AI assisted training on sport performance among student athletes based on a dual path chain mediation model with the moderating role of psychological adaptability.Frontiers in psychology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesLarge language models are becoming increasingly significant tools in everyday life, including the context of training and sports. However, the extent to which recreational athletes actually rely on AI-generated training plans and the differences in trust towards these technologies between users and non-users have not yet been investigated. Furthermore, there is a lack of information regarding the current quality of such AI-generated training plans. The aim of this project was to examine how users and non-users differ in their trust towards these technologies and to assess the quality of AI-generated training plans.
resultsIn our sample, 54% of the participants trained using a structured training plan, with 25% of those utilizing AI-generated training plans. Users of these AI-based tools exhibited significantly (p = 0.030) higher levels of trust in these technologies compared to non-users. The quality of the output from large language models has now reached a level where even professional coaches are often unable to distinguish whether a training plan was AI-generated or created by a human expert. This suggests that AI-generated training plans could potentially match the standards of those developed by experienced coaches, making them a viable option for athletes seeking guidance in their training.
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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.