2021
DOI: 10.1007/s40279-021-01555-1
|View full text |Cite
|
Sign up to set email alerts
|

Sleep Quality in Elite Athletes: Normative Values, Reliability and Understanding Contributors to Poor Sleep

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

2
14
0
2

Year Published

2022
2022
2024
2024

Publication Types

Select...
8

Relationship

1
7

Authors

Journals

citations
Cited by 18 publications
(22 citation statements)
references
References 37 publications
2
14
0
2
Order By: Relevance
“… 54 SOL could be recalled easily in the morning, which might result in increment in the perception of daytime dysfunction as well as perceived poor sleep quality. 55 The current study found there was a trend of shortening SOL after acute evening exercise compared to no exercise, and LIE might be the best method for the improvement of SOL (- 1.02 min). Oda and Shirakawa found a delay in SOL (+ 14 min) after HIE in the evening, 15 which was contrary to the current study that acute evening HIE did not delay sleep onset (−0.54 min).…”
Section: Discussionmentioning
confidence: 48%
“… 54 SOL could be recalled easily in the morning, which might result in increment in the perception of daytime dysfunction as well as perceived poor sleep quality. 55 The current study found there was a trend of shortening SOL after acute evening exercise compared to no exercise, and LIE might be the best method for the improvement of SOL (- 1.02 min). Oda and Shirakawa found a delay in SOL (+ 14 min) after HIE in the evening, 15 which was contrary to the current study that acute evening HIE did not delay sleep onset (−0.54 min).…”
Section: Discussionmentioning
confidence: 48%
“…The importance of optimal sleep for athletes is becoming increasingly recognised; as such there is a concomitant increase in descriptive sleep data in athletes [ 1 , 2 ]. On average, elite athletes have a lower than recommended sleep duration and sleep quality [ 1 , 3 5 ], potentially due to training and competition times [ 6 , 7 ], travel [ 8 ], stress [ 9 ], caffeine [ 10 ] and/or social media use [ 11 ]. Given the factors mentioned above, sleep onset and offset times in athletes may show substantial variation over time.…”
Section: Introductionmentioning
confidence: 99%
“…Alternatively, machine learning variable importance methods can be used to identify key predictor variables by selecting only the variables which are relatively important to the target variable values (Thornton et al 2017). This approach has been implemented when establishing the important training load indicators to predict injury status (Thornton et al 2017) and in establishing the importance of seven sleep components to the Pittsburgh Sleep Quality Index score (Halson et al 2021). However, using machine learning variable importance methods is reported to be suboptimal in identifying key predictors to the target variable values (Williamson et al 2021) and it affects classification accuracy.…”
Section: Introductionmentioning
confidence: 99%