Search

The aim of the study was to compare the WHOOP strap - a wearable device that estimates sleep based on measures of movement and heart rate derived from actigraphy and photoplethysmography, respectively. Twelve healthy adults (6 females, 6 males, aged 22.9 ± 3.4 years) participated in a 10-day, laboratory-based protocol. A total of 86 sleeps were independently assessed in 30-s epochs using polysomnography and WHOOP. For WHOOP, bed times were entered by researchers and sleeps were scored by the company based on proprietary algorithms. WHOOP overestimated total sleep time by 8.2 ± 32.9 minutes compared to polysomnography, but this difference was non-significant. WHOOP was compared to polysomnography for 2-stage (i.e., wake, sleep) and 4-stage categorisation (i.e., wake, light sleep [N1 or N2], slow-wave sleep [N3], REM) of sleep periods. For 2-stage categorisation, the agreement, sensitivity to sleep, specificity for wake, and Cohen's kappa were 89%, 95%, 51%, and 0.49, respectively. For 4-stage categorisation, the agreement, sensitivity to light sleep, SWS, REM, and wake, and Cohen's kappa were 64%, 62%, 68%, 70%, 51%, and 0.47, respectively. In situations where polysomnography is impractical (e.g., field settings), WHOOP is a reasonable method for estimating sleep, particularly for 2-stage categorisation, if accurate bedtimes are manually entered.

Objectives: There is a growing interest in monitoring the sleep of elite athletes. Polysomnography is considered the gold standard for measuring sleep, however this technique is impractical if the aim is to collect data simultaneously with multiple athletes over consecutive nights. Activity monitors may be a suitable alternative for monitoring sleep, but these devices have not been validated against polysomnography in a population of elite athletes., Design: Participants (n=16) were endurance-trained cyclists participating in a 6-week training camp., Methods: A total of 122 nights of sleep were recorded with polysomnography and activity monitors simultaneously. Agreement, sensitivity, and specificity were calculated from epoch-for-epoch comparisons of polysomnography and activity monitor data. Sleep variables derived from polysomnography and activity monitors were compared using paired t-tests. Activity monitor data were analysed using low, medium, and high sleep-wake thresholds., Results: Epoch-for-epoch comparisons showed good agreement between activity monitors and polysomnography for each sleep-wake threshold (81-90%). Activity monitors were sensitive to sleep (81-92%), but specificity differed depending on the threshold applied (67-82%). Activity monitors underestimated sleep duration (18-90min) and overestimated wake duration (4-77min) depending on the threshold applied., Conclusions: Applying the correct sleep-wake threshold is important when using activity monitors to measure the sleep of elite athletes. For example, the default sleep-wake threshold (>40 activity counts=wake) underestimates sleep duration by ∼50min and overestimates wake duration by ∼40min. In contrast, sleep-wake thresholds that have a high sensitivity to sleep (>80 activity counts=wake) yield the best combination of agreement, sensitivity, and specificity., (Copyright © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

In recent years, research examining the sleep of elite athletes has increased. This is predominantly due to the importance sleep has on an athlete’s psychological and physiological well-being. Despite the growing importance of sleep in athletes, the amount and quality of sleep coaches obtain has been neglected. The aim of this study was to examine the sleep of a head coach and compare it to his team. The sleep of 16 members of the Australian U/20 men’s football team (age 18.8 ± 0.9 years) and the head coach (age 55 years) was monitored using wrist activity monitors and self-report sleep diaries. Sleep was examined for 15 nights in preparation for the 2011 U/20 FIFA World Cup. The head coach went to bed earlier (23:30 h ± 65 min vs. 23:36 h ± 30 min), spent less time in bed (8.4 ± 1.3 h vs. 8.6 ± 1.0 h), obtained less sleep per night (6.4 ± 1.5 h vs. 6.6 ± 0.8 h), and woke up earlier (07:54 h ± 46 min vs. 08:12 h ± 52 min) than his team. In general, the head coach obtained less sleep than his team and slept considerably poorer the night before important games. Future investigations need to examine the extent to which sleep impairs psychological state, decision-making and overall coaching performance. [ABSTRACT FROM AUTHOR]