Energy expenditure and intake in judo athletes during training camp
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".