Improper Assessment of the Effect of Ad Libitum Drinking on Cycling Performance
Notice bibliographique
Résumé
Dear Editor-in-Chief, In a recent article in Medicine & Science in Sports & Exercise, Bardis et al. (1) evaluated the effect of prescribed drinking (PD) versus ad libitum drinking (ALD) during what they called a simulated 30-km criterium-like performance in the heat. On the basis of their data, we believe that the authors incorrectly concluded that PD sufficient to match sweat losses provides a performance advantage during cycling in the heat compared with ALD. Bardis et al. (1) used a performance test consisting of three sets of a 5-km bout at 50% of maximal power output, followed by a 5-km all-out hill climb at 3% grade. The authors analyzed and interpreted their results by isolating and dichotomizing the performance times achieved during each hill climb without consideration of the fact that the overall performance during a cycling competition is a representation of the sum of each race segment, and not of any section in particular without taking into account the others. The authors report that cycling speeds were not significantly different between ALD and PD during the first (30.3 ± 2.3 vs 29.2 ± 2.7 km·h−1) and second (29.8 ± 2.1 vs 29.2 ± 2.4 km·h−1) 5-km hill climb. However, the subjects completed the third 5-km hill climb faster with PD than ALD (30.2 ± 2.4 vs 28.8 ± 2.6 km·h−1, P < 0.05). The conclusions of Bardis et al. were based on those observations. A computation taking into account all three performance bouts reveals that the ALD group completed the simulated race at a faster mean speed (29.6 vs 29.5 km·h−1) and time (30.4 vs 30.5 min) than the PD group. More striking is to consider the fact that at the start of the third hill climb, the ALD group was 282 m ahead of the PD group, and that by the time the ALD group had reached the finish line, the PD group was trailing by an impressive and practically relevant distance of 50 m. Clearly, the conclusion of Bardis et al. is not only erroneous but also misleading. Likely, the appropriate statistical analysis taking into account the overall mean speed will show that there was no performance difference between drinking strategies, which would be in line with results of Dugas et al. (3) who demonstrated no difference in performance between PD and ALD during an 80-km cycling time trial in the heat. Recently, Bardis et al. (2) evaluated the effect of mild dehydration on cycling performance using a similar research protocol consisting of three consecutive sets of a low-intensity, fixed-power output 5-km ride, an all-out 5-km hill climb, and a 5-min rest period. Ironically, in that work, the authors report the analysis of mean speed maintained by their subjects throughout the testing protocol. We find it interesting that the authors reported that analysis in their earlier work but not in the present paper and would appreciate comment on this change in methods.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».