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The Effects of Travel on Sleep Quantity and Quality of Elite Junior Ice Hockey Players

2015· article· en· W2461152425 on OpenAlexaff
Jason P. Brandenburg, Michael Gaetz

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2015
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsAlertnessSleep (system call)Heart rateEveningPsychologyPhysical therapyMedicineAudiologyInternal medicineBlood pressureComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.329
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2015
Admission routes1
Has abstractyes

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