ArticleBMC sports science, medicine & rehabilitation2025
Comparative evaluation of ChatGPT versions in training program design: scientific approach, accuracy, and practical applicability.
Article in BMC sports science, medicine & rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A systematic review of the limitations of large language models in generating healthcare content.PLOS digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to comparatively evaluate the scientific approach, accuracy, and practical applicability of different versions of ChatGPT in generating training programs.
methodAdopting a mixed-methods design, the study employed seven distinct instruction sets, each developed with input from seven experts possessing a minimum of 10 years of professional experience (Certified Strength and Conditioning Specialists (CSCS), academicians holding PhDs in Sports Sciences with research focus on exercise physiology and training methodology) in their respective fields. Using these instruction sets, three versions of ChatGPT (ChatGPT-3.5, ChatGPT-4o, and ChatGPT-4.1) were tasked with generating 12-week resistance training programs for a hypothetical, healthy young adult male with a moderate training background. Using a rubric scoring scale, the generated programs were systematically evaluated and scored separately based on the following criteria: compliance with the initial program request, inclusion of literature references, adherence to exercise variety and progressive loading principles, individualization of progression and justification, program modifications, inclusion of warm-up and cool-down components, injury risk considerations, presence of incorrect recommendations and practical applicability, and accessibility.To determine whether differences in mean scores were statistically significant, the Friedman non-parametric test was applied. When significant differences were identified, pairwise comparisons were conducted using the Wilcoxon signed-rank test to determine which groups accounted for these differences. In addition, qualitative data analysis was performed to explore expert evaluations in depth, employing both content analysis and descriptive analysis techniques.
resultsStatistically significant differences were identified among the ChatGPT versions examined in this study: between ChatGPT-4o and ChatGPT-3.5 (p = .018), ChatGPT-4.1 and ChatGPT-3.5 (p = .018), and ChatGPT-4.1 and ChatGPT-4o (p = .018). Expert content analysis further indicated that ChatGPT-4.1 produced responses that were more detailed, internally consistent, and better supported by scientific literature compared to the other versions.
conclusionAlthough the ChatGPT versions examined in this study exhibited certain limitations, they demonstrated the potential to deliver structured exercise programs aligned with established training principles and relevant scientific literature. Nonetheless, to ensure that AI-assisted training plans provide safe, evidence-based, and individualized content, the involvement of qualified human expertise remains essential.
trial registrationNo official trial registration number was assigned.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.