Evidence map›Paper›PMID 41710442›Full record

ArticleJournal of sports science & medicine2026

ChatGPT Outperforms Personal Trainers in Answering Common Exercise Training Questions.

Brecht D'hoe, Daniel Kirk, Jan Boone, Alessandro Colosio

Abstract read
In one paragraph

Article in Journal of sports science & medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Brecht D'hoeDepartment of Movement and Sports Sciences, Ghent University, Ghent, Belgium.
Daniel KirkDepartment of Twin Research & Genetic Epidemiology, King's College London, St Thomas Hospital, Westminster Bridge Road, London SE1 7EH, UK.
Jan BooneDepartment of Movement and Sports Sciences, Ghent University, Ghent, Belgium.
Alessandro ColosioInter-University Laboratory of Human Movement Biology, Université Saint-Etienne, Saint-Etienne, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since its launch, the chatbot ChatGPT has gained significant popularity and may serve as a valuable resource for evidence-based exercise training advice. However, its capability to provide accurate and actionable exercise training information has not been systematically evaluated. This study assessed ChatGPT's proficiency by comparing its responses to those of human personal trainers. Nine currently active level 4 (European Qualification Framework (EQF)) personal trainers (PTs) submitted their most frequently asked exercise training questions along with their own answers to them, and these questions were then posed to ChatGPT (version 3.5). Responses from both sources were evaluated by 18 PTs and 9 topic experts, who rated them on scientific correctness, actionability, and comprehensibility. Scores for each criterion were averaged into an overall score, and group means were compared using permutation tests. ChatGPT outperformed PTs in six of nine questions overall, with higher ratings in scientific correctness (5/9), comprehensibility (6/9), and actionability (5/9). In contrast, none of the responses from PTs were higher than those from ChatGPT for any question or metric. Our results suggest that ChatGPT can be used as a tool to answer questions that are frequently asked to PTs, and that chatbots may be useful for delivering informational support relating to physical exercise.

Indexed as

ExerciseComprehensionGenerative Artificial IntelligenceHumansLarge Language ModelsArtificial Intelligenceexercisemachine learningnatural language processingtraining guidance

Identifiers

PMID41710442
PMCPMC12912680

What OpenQuestion holds

Textmetadata
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Registered trials

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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.