ASSESSMENT OF SLEEP HEALTH IN ELITE FEMALE BASKETBALL PLAYERS DURING INTERNATIONAL COMPETITION: A CASE STUDY
Bibliographic record
Abstract
INTRODUCTION & AIMS Sleep health is recognised as an essential component to optimise overall wellbeing and athletic performance. However, athletes frequently report sleep disturbance prior to and during competition, with nearly a quarter of athletes experiencing moderate to severe sleep problems international travel and subsequent competition. The Athlete Sleep Screening Questionnaire (ASSQ) was developed as a sleep screening tool to detect clinically significant sleep disturbances and daytime dysfunction in athletes. The purpose of this study was to assess sleep health (sleep disturbances, sleep difficulty and daytime dysfunction) via a sleep screening tool in elite female basketball players during international competition. METHODS Participants were 12 elite female basketball players (New Zealand Women’s Senior National Team, Tall Ferns) competing at the 2021 International Basketball Federation Women’s Asia Cup in Amman Jordan. The Athlete Sleep Screening Questionnaire (16 item) was used to assess sleep health and associated factors during the 7-day tournament to determine the impact of international travel. The ASSQ was scored according to previous validated methods to determine chronotype, sleep difficulty score, clinical sleep problem category (none, mild, moderate, or severe), and potential referral to a sleep physician for assessment due to poor sleep health. Categorical variables were summarised with frequency and percent. The study was approved by the University of Southern Queensland Human Research Ethics Committee (H21REA182). RESULTS Clinical sleep problems, as categorised by sleep difficulty score ≥ 8, were reported in 33% of the athletes, with 25% classified as moderate and 8% classified as severe. Fifty percent of the athletes reported sleep disturbance during travel, while 42% reported daytime dysfunction when travelling for competition. According to chronotype classification, 75% of athletes were categorised into evening type, 33% ‘definitely evening’ type sub-category, with 25% categorised into morning type. In total, 42.0% of athletes (5 players) were identified for further assessment. CONCLUSION This study highlights the effective use of the ASSQ as an easy to administer sleep screening tool to determine sleep health in elite female basketball players. The ASSQ provides a method of accurately identifying athletes that would benefit from sleep hygiene education, sleep travel management and subsequent preventative measures related to clinically significant sleep problems. In this case study, a substantial portion of elite female basketball players appear to suffer from suboptimal sleep and would likely benefit from interventions targeting sleep health. This is an important consideration and provides valuable insights into indicators of sleep health in the management of female athlete sleep health and wellbeing during international competition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".