The Athlete Sleep Screening Questionnaire: a new tool for assessing and managing sleep in elite athletes
Bibliographic record
Abstract
BACKGROUND/AIM: The purpose of this study was to develop a subjective, self-report, sleep-screening questionnaire for elite athletes. This paper describes the development of the Athlete Sleep Screening Questionnaire (ASSQ). METHODS: A convenience sample of 60 elite athletes was randomly distributed into two groups; 30 athletes completed a survey composed of current psychometric tools, and 30 athletes completed a revised survey and a sleep specialist structured clinical interview. An item analysis was performed on the revised survey with comparison to clinical decisions regarding appropriate intervention based on a sleep specialist assessment. RESULTS: A comparison of existing sleep-screening tools with determination of clinical need from a sleep specialist showed low consistency, indicating that current sleep-screening tools are unsuitable for assessing athlete sleep. A new 15-item tool was developed (ASSQ) by selecting items from existing tools that more closely associated with the sleep specialist's reviews. Based on test-retest percentage agreement and the κ-statistic, we found good internal consistency and reliability of the ASSQ. To date, 349 athletes have been screened, and 46 (13.2%) identified as requiring follow-up consultation with a sleep specialist. Results from the follow-up consultations demonstrated that those athletes identified by the ASSQ as abnormal sleepers have required intervention. CONCLUSIONS: The research developed a new athlete-specific sleep-screening questionnaire. Our findings suggest that existing sleep-screening tools are unsuitable for assessing sleep in elite athletes. The ASSQ appears to be more accurate in assessing athlete sleep (based on comparison with expert clinical assessment). The ASSQ can be deployed online and provides clinical cut-off scores associated with specific clinical interventions to guide management of athletes' sleep disturbance. The next phase of the research is to conduct a series of studies comparing results from the ASSQ to blinded clinical reviews and to data from objective sleep monitoring to further establish the validity of the ASSQ as a reliable sleep screening tool for elite athletes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".