ArticleSports medicine (Auckland, N.Z.)2026
From GPT-4 to Expert-Endorsed Athlete Guidance: A Delphi Consensus on Sleep and Jet Lag.
Article in Sports medicine (Auckland, N.Z.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
backgroundLarge language models (LLMs) including GPT-4 are increasingly used to generate health information, but concerns persist about their accuracy and relevance, particularly for elite athletes.
objectiveThis study used GPT-4-generated frequently asked question (FAQ) responses on sleep and jet lag as the starting material for expert evaluation and consensus development, with the goal of producing consensus-based, athlete-specific guidance while identifying the limitations of AI-generated content.
methodsBetween November 2024 and March 2025, n = 17 international sleep and circadian experts from the Athlete Travel & Sleep Interest Group (ATSIG) participated in a two-round Delphi process. Experts rated 20 GPT-4-generated FAQ responses (10 on sleep, 10 on jet lag) for appropriateness using a 6-point Likert scale and provided qualitative feedback. Items were revised after round 1 using inductive thematic coding. Consensus was defined as ≥ 70% of participants rating an item as appropriate (scores 5-6) and ≤ 15% as non-appropriate (scores 1-2). Statistical analyses included Wilcoxon signed-rank tests, convergence metrics and dissent detection (outlier and bipolarity analysis).
resultsIn round 1, 15 of 20 items (75%) met consensus; by round 2, 18 of 20 (90%) achieved consensus. For sleep items, 7 of 10 reached consensus in round 1 and 9 in round 2; for jet lag, 8 items reached consensus in round 1 and 9 in round 2. Sleep Q6 (sleep and injury risk) narrowly missed the consensus threshold with 64.7% agreement, while Jet Lag Q9 (melatonin and sleep aids) remained below the 70% threshold. No item showed bimodal score distributions, suggesting no polarized disagreement. Descriptive rating patterns, increased consensus and qualitative expert feedback indicated improved clarity, accuracy and athlete-specific relevance after the Delphi process, although item-level statistical comparisons did not remain significant after Bonferroni correction for multiple testing. Qualitative analysis identified common concerns: for sleep items-imprecise or misleading content (55%), lack of athlete-specific relevance (30%) and outdated evidence (11%); for jet lag-outdated evidence (36%), imprecise or misleading content (34%) and formatting issues (17%).
conclusionsGPT-4-derived content may serve as useful preliminary material for expert discussion but should not be used as standalone guidance. Expert evaluation improved the clarity, safety and athlete-specific relevance of most sleep and jet-lag responses, while the final outputs should be interpreted as consensus-based guidance rather than definitive proof of correctness.
Identifiers
42470603What OpenQuestion 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 OpenQuestion graph.