Observing Ramadan and sleep-wake patterns in athletes: a systematic review, meta-analysis and meta-regression
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of observing Ramadan on athletes' sleep patterns. DESIGN: Systematic review and meta-analysis. DATA SOURCES: The entire content of PubMed/MEDLINE and Web of Science. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Single-group, prepost and cross-over design studies conducted in athletes aged ≥18 years, training at least twice a week and published in English before 12 July 2018 were included. Studies assessing sleep quantity, quality, daytime sleepiness and/or daily naps based on objective or subjective methods were deemed eligible. STUDY APPRAISAL: The methodological quality was assessed using 'QualSyst'. RESULTS: Of 13 selected articles, 7 were of strong quality, 3 were moderate and 3 were weak. 11 studies evaluated total sleep time (TST); this decreased during Ramadan in 4 studies, increased in 1 and remained unchanged in 6. Pooled TST findings indicated a moderate effect size (- 0.97, SE=0.37, 95% CI -1.69 to -0.25, t=-2.64, p=0.01) with significant heterogeneity but no publication bias. Meta-regressions showed no effects of study year, age, sample size, type of sport or competition level, but there were effects of country (with France and Tunisia being the most affected countries and Turkey the least affected, Q=32.14, p<0.0001) and study design (Q=7.74, p=0.02). Four studies measured self-reported sleep quality and it decreased in three studies. One study of sleep architecture reported more frequent waking and more light sleep during Ramadan. Daily nap duration was increased in two studies, but daytime sleepiness remained unchanged in four studies. CONCLUSION: When athletes continue to train at least two times/week while observing Ramadan, TST is decreased compared with athletes' baseline levels.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".