The Effects of Travel on Sleep Quantity and Quality of Elite Junior Ice Hockey Players
Bibliographic record
Abstract
Sleep loss is associated with reduced alertness, slowed reaction time, and impaired recovery, and is thought to negatively affect athletic performance. A number of variables can influence the quantity and quality of sleep, including traveling overnight; a common practice in some sports. The degree to which overnight travel affects the sleep of adolescent athletes is not well known. PURPOSE: To examine the effect of travel and sleep locale on the quantity and quality of sleep in elite adolescent athletes. METHODS: Sleep quantity and quality of 20 players from a tier 1 junior ice hockey team (age range 16-21; mean 17.9 ± 1.3 y; 180.5 ± 4.3 cm, 83.5 ± 6.6 kg) were recorded on three occasions: at home (HOME), at a hotel the evening following a long duration of travel (∼9 hr.) (HOTEL), and on a bus during overnight travel (BUS). All travel occurred within the same time zone. Players were fitted with a combined motion sensor and heart rate monitor worn at the waist at least 1-hr before sleep. Heart rate (HR) and movement data were recorded during sleep and then averaged over the entire length of the sleep interval. Movement recordings were scaled between 0 (no movement) and 15 (continual movement). Daytime sleepiness, using the Epsworth Sleepiness Scale, was assessed the day after each sleep condition. RESULTS: The average time of sleep onset was 23:35 (Home), 23:50 (Hotel), and 01:07 (BUS). Sleep duration was significantly less in BUS (321.6 ± 34 min) than in HOME (520.8 ± 44 min) and HOTEL (558.9 ± 63 min). The duration of sleep in HOTEL was also greater than in HOME (p=0.019). The mean sleep HR was significantly higher in BUS (57.8 ± 5.7 beats.min-1) than in HOME (52.5 ± 7.7 beats.min-1;p=0.005) or HOTEL (52.1 ± 4.4 beats.min-1;p<0.001). Movement was greater during BUS (0.70 ± 0.6) than in HOME (0.15 ± 0.1;p=0.004) or HOTEL (0.14 ± 0.1;p=0.003). The players’ rating of daytime sleepiness was highest after BUS (12.2 ± 3.8) than HOME (8.3 ± 2.5;p=0.001) and HOTEL (6.1 ± 2.9;p<0.001). Sleepiness ratings were lower following HOTEL than HOME (p=0.03). CONCLUSION: Sleep quality and quantity were not adversely affected by sleeping in a hotel following a long day of travel. Sleep quality and quantity were significantly affected when players were required to sleep on a bus. Teams should carefully consider the effects of overnight travel on performance.
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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 | 0.000 | 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) | 0.001 | 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".