Assessment of Recommendations Provided to Athletes Regarding Sleep Education by GPT-4o and Google Gemini: Comparative Evaluation Study
Bibliographic record
Abstract
Background: Inadequate sleep is prevalent among athletes, affecting adaptation to training and performance. While education on factors influencing sleep can improve sleep behaviors, large language models (LLMs) may offer a scalable approach to provide sleep education to athletes. Objective: This study aims (1) to investigate the quality of sleep recommendations generated by publicly available LLMs, as evaluated by experienced raters, and (2) to determine whether evaluation results vary with information input granularity. Methods: Two prompts with differing information input granularity (low and high) were created for 2 use cases and inserted into ChatGPT-4o (GPT-4o) and Google Gemini, resulting in 8 different recommendations. Experienced raters (n=13) evaluated the recommendations on a 1-5 Likert scale, based on 10 sleep criteria derived from recent literature. A Friedman test with Bonferroni correction was performed to test for significant differences in all rated items between the training plans. Significance level was set to P<.05. Fleiss κ was calculated to assess interrater reliability. Results: The overall interrater reliability using Fleiss κ indicated a fair agreement of 0.280 (range between 0.183 and 0.296). The highest summary rating was achieved by GPT-4o using high input information granularity, with 8 ratings >3 (tendency toward good), 3 ratings equal to 3 (neutral), and 2 ratings <3 (tendency toward bad). GPT-4o outperformed Google Gemini in 9 of 10 criteria (P<.001 to P=.04). Recommendations generated with high input granularity received significantly higher ratings than those with low granularity across both LLMs and use cases (P<.001 to P=.049). High input granularity leads to significantly higher ratings in items pertaining to the used scientific sources (P<.001), irrespective of the analyzed LLM. Conclusions: Both LLMs exhibit limitations, neglecting vital criteria of sleep education. Sleep recommendations by GPT-4o and Google Gemini were evaluated as suboptimal, with GPT-4o achieving higher overall ratings. However, both LLMs demonstrated improved recommendations with higher information input granularity, emphasizing the need for specificity and a thorough review of outputs to securely implement artificial intelligence technologies into sleep education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".