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The Effect of a Low‐Glycemic Index Pulse‐Based Diet on Performance and Body Composition in Soccer Players

2016· article· en· W2921818426 on OpenAlexaffabout
Eliran Mizelman, Philip D. Chilibeck, Abdullah Hanifi, Mojtaba Kaviani, Eric Brenna, Gordon A. Zello

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGlycemic indexGlycemicPulse (music)MealMedicineGlycemic loadAnimal scienceBody mass indexFood scienceInsulinChemistryEndocrinologyInternal medicineBiologyPhysics

Abstract

fetched live from OpenAlex

Low‐glycemic index foods, such as pulses (i.e. lentils, peas, chickpeas, beans), have potential to improve endurance exercise performance because they do not induce a large release of insulin; this allows greater fat oxidation and preservation of muscle glycogen during endurance exercise. In addition to containing low‐glycemic index carbohydrate, pulses are a good source of protein and contain low levels of fat; therefore, they have potential to improve body composition. The objective of our study was to determine the effect of a one‐month pulse‐based diet on endurance performance and body composition in soccer players. Seventeen soccer players (11 females) who were university or city‐league players were randomized to consume their regular diet or were given a pulse‐based diet (i.e. 25% of daily caloric intake, as calculated from 3‐d food logs, was given as pulses, including beans, lentils, chickpeas, or peas) for one month, followed by a wash‐out of 2 weeks, and then the opposite diet for another month. Before and after each one‐month phase body composition was assessed by dual energy X‐ray absorptiometry. During each diet phase soccer performance was assessed as distance the players were able to cover during games by a global positioning system (Catapult Sports Optimeye X5). An hour before each game, players consumed 1.0 g available carbohydrate per kg of body mass, from either a pulse‐based sport nutrition bar (i.e. during the pulse‐diet phase) or a commercially‐available high‐glycemic index sport nutrition bar (i.e. during the regular diet phase). Distance covered during games was analyzed by magnitude‐based inferences, assuming a smallest substantial change in distance of 102 meters, based on game‐to‐game variability in distance covered. Results Male soccer players significantly decreased percent body fat during the pulse‐based diet phase (14.3 SD 2.2 to 13.4 SD 2.2%) compared to the regular‐diet phase (13.7 SD 2.1 to 14.1 SD 2.2%) (p=0.01); whereas, change in percent body fat was not different for females between the two diet phases (i.e. 24.5 SD 3.4 to 23.2 SD 4.3% during the pulse diet phase vs. 24.0 SD 3.8 to 23.1 SD 4.6% during the regular diet phase). During the pulse diet phase the soccer players covered an average of 9590 SD 1450 m during 90‐minutes of match time compared to 9360 SD 1471 m during the regular diet phase. The mean difference in distance covered between the pulse diet and regular diet phase (230 m, 90% CI −206 to 667 m) represents a 70% chance that the pulse diet was superior to the regular diet for increasing distance covered per game. We conclude that a pulse‐based diet improves body composition in male, but not female soccer players and that a pulse‐based diet may allow players to run a greater distance during soccer matches. Support or Funding Information Supported by the Saskatchewan Pulse Growers and the Western Grains Research Foundation

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.223
Teacher spread0.218 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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