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Repeated-sprint training in the fasted state during Ramadan: morning or evening training?

2018· article· en· W2981571164 on OpenAlexaff
Asma Aloui, Tarak Driss, Hana Baklouti, Hamdi Jaafar, Omar Hammouda, Karim Chamari, Nizar Souissi

Bibliographic record

VenueThe Journal of Sports Medicine and Physical Fitness · 2018
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsMontfort HospitalUniversity of Ottawa
Fundersnot available
KeywordsMorningEveningSprintMedicinePhysical therapyRepeated measures designMathematicsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The present study assessed the optimal moment of the day for repeated-sprint training in the fasted state during Ramadan. METHODS: Thirty amateur soccer players were randomly assigned to a morning training group (MTG, training at ~08:00 a.m., N.=10), an evening training group (ETG, training around 06:00 p.m., N.=10), and a control group (N.=10). Training sessions, conducted on alternate days, consisted of 3 sets of 6×40-m shuttle sprints (2×20 m with 180° direction changes). A 20-second passive recovery and a 4-minute passive recovery were allowed between repetitions and sets, respectively. Before and after Ramadan, performance was assessed at both 08:00 a.m. and 06:00 p.m. by Countermovement Jump (CMJ), Repeated-Sprint Test (RST), and Yo-Yo Intermittent Recovery Test Level 1 (YYIRT1). RESULTS: After Ramadan, YYIRT1 performances were enhanced for both groups in the morning (7.82% and 6.29% for MTG and ETG, respectively, P<0.05), and in the evening (6.61% and 11.20%, respectively, P<0.05). Relative changes in YYIRT1 (P=0.33) and RST (-2.13% and -3.44% for MTG and ETG, respectively, P=0.49) at the specific time of training were similar for both groups. No differences were observed in CMJ performances before and after Ramadan for any group (P>0.05). CONCLUSIONS: Morning or evening repeated-sprint training conducted in the fasted state during Ramadan enhanced soccer-specific endurance similarly.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.847
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.337
Teacher spread0.284 · 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 teacher head, 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

Citations14
Published2018
Admission routes1
Has abstractyes

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