Energy expenditure and intake in judo athletes during training camp
Notice bibliographique
Résumé
For optimal athletic performance, recovery and body composition, athletes need to synchronize diet and physical activi ties. Especially in judo athletes, where competition is organiz ed in weight categories, the balance between energy intake and energy expenditure is of uttermost importance [1]. Data concerning energy intake during training and competition preparation – often a weight reduction periodare available [2,3,4]. However, the literature lacks data on the energy balance during the training period of judo athletes. When not in weight reduction period, special attention needs to be given to energy intake, providing sufficient energy for basal metabolism, physical activities, and recovery without caloric overshoot [5]. A highly positive energy balance during training periods may result in excessive weight gain with negative consequences for the following weight reduction period. In order to properly harmonize an athlete’s dietary intake and training program, assessing the energy balance and physical activity status of the athlete is required [6,7,8]. Energy expenditure during training in judo athletes is not well described. The training program of a judo athlete is diverse, and consists of judo specific training (technique and randori) and non-specific training (resistance training, endurance training), [9,10]. Randori, which is a type of fight training, can be categorized as a high intensity activity with an intermittent pattern of activity and relative rest more or less comparable with a competition fight [10]. The diversity of the training program, the unstructured activities during the randori training, and the contact with the opponent, make it very difficult to estimate energy expenditure with most of the available methods. Monitoring should be done in the athlete’s normal environment enabling maintenance of habitual activity participation and dietary intake. Therefore, the methods used to determine activity pattern and dietary intake should preferably be as accurate as possible and at the same time easy in use with a minimal burden on the athlete. An activity diary (AD) is considered to be one of the most accurate subjective techniques, despite the high participant burden [8]. Though self-report methods can be a principal source of information, other approaches or the use of combined measu res may be needed to characterize better an athlete‘s activity level. Reporting the results with different instruments provides a more complete description of activity levels and permits triangulation of outcomes [11]. The SenseWear Armband (SWA) combines five different sensors into one device attached as an armband around the upper arm. The SWA has shown to give reliable estimates of TEE in healthy free living adults [12,13,14]. Johannsen et al. [14] found a significant agreement between the SWA and doubly labeled water estimates of TEE. Fruin and Rankin [15] found the SWA to provide valid and reliable estimates of energy expenditure at rest and on an ergometer as compared to indirect calorimetry.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».