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Record W2110697263 · doi:10.5604/20815735.1047112

Energy expenditure and intake in judo athletes during training camp

2011· article· en· W2110697263 on OpenAlexaff
Peter Clarys, Amelie Rosseneu, Dirk Aerenhouts, Ewert Zinzen

Bibliographic record

VenueJournal of Combat Sports and Martial Arts · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsAthletesTraining (meteorology)Energy expenditurePsychologyPhysical therapyPhysical medicine and rehabilitationEnergy (signal processing)MedicineStatisticsMathematicsInternal medicineGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.014
GPT teacher head0.208
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2011
Admission routes1
Has abstractyes

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