The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was to examine the validity of the six devices for assessing heart rate and heart rate variability during, or just prior to, night-time sleep. Fifty-three adults (26 F, 27 M, aged 25.4 ± 5.9 years) spent a single night in a sleep laboratory with 9 h in bed (23:00–08:00 h). Participants were fitted with all six wearable devices—and with polysomnography and electrocardiography for gold-standard assessment of sleep and heart rate, respectively. Compared with polysomnography, agreement (and Cohen’s kappa) for two-state categorisation of sleep periods (as sleep or wake) was 88% (κ = 0.30) for Apple Watch; 89% (κ = 0.35) for Garmin; 87% (κ = 0.44) for Polar; 89% (κ = 0.51) for Oura; 86% (κ = 0.44) for WHOOP and 87% (κ = 0.48) for Somfit. Compared with polysomnography, agreement (and Cohen’s kappa) for multi-state categorisation of sleep periods (as a specific sleep stage or wake) was 53% (κ = 0.20) for Apple Watch; 50% (κ = 0.25) for Garmin; 51% (κ = 0.28) for Polar; 61% (κ = 0.43) for Oura; 60% (κ = 0.44) for WHOOP and 65% (κ = 0.52) for Somfit. Analyses regarding the two-state categorisation of sleep indicate that all six devices are valid for the field-based assessment of the timing and duration of sleep. However, analyses regarding the multi-state categorisation of sleep indicate that all six devices require improvement for the assessment of specific sleep stages. As the use of wearable devices that are valid for the assessment of sleep increases in the general community, so too does the potential to answer research questions that were previously impractical or impossible to address—in some way, we could consider that the whole world is becoming a sleep laboratory.

In any sport, successful performance requires a planned approach to training and recovery. While sleep is recognized as an essential component of this approach, the amount and quality of sleep routinely obtained by elite athletes has not been systematically evaluated. Data were collected from 70 nationally ranked athletes from seven different sports. Athletes wore wrist activity monitors and completed self-report sleep/training diaries for 2 weeks during normal training. The athletes also recorded their fatigue level prior to each training session using a 7-point scale. On average, the athletes spent 08:18 ± 01:12 h in bed, fell asleep at 23:06 ± 01:12 h, woke at 6:48 ± 01:30 h and obtained 06:30 ± 01:24 h of sleep per night. There was a marked difference in the athletes' sleep/wake behaviour on training days and rest days. Linear mixed model analyses revealed that on nights prior to training days, time spent in bed was significantly shorter (p = 0.001), sleep onset and offset times were significantly earlier (p < 0.001) and the amount of sleep obtained was significantly less (p = 0.001), than on nights prior to rest days. Moreover, there was a significant effect of sleep duration on pre-training fatigue levels (p ≤ 0.01). Specifically, shorter sleep durations were associated with higher levels of pre-training fatigue. Taken together, these findings suggest that the amount of sleep an elite athlete obtains is dictated by their training schedule. In particular, early morning starts reduce sleep duration and increase pre-training fatigue levels. When designing schedules, coaches should be aware of the implications of the timing of training sessions for sleep and fatigue. In cases where early morning starts are unavoidable, countermeasures for minimizing sleep loss - such as strategic napping during the day and correct sleep hygiene practices at night - should be considered.

Good sleep is essential for optimal performance, yet few studies have examined the sleep/wake behaviour of elite athletes. The aim of this study was to assess the impact of early‐morning training on the amount of sleep obtained by world‐class swimmers. A squad of seven swimmers from the Australian Institute of Sport participated in this study during 14 days of high‐intensity training in preparation for the 2008 Olympic Games. During these 14 days, participants had 12 training days, each starting with a session at 06:00 h, and 2 rest days. For each day, the amount of sleep obtained by participants was determined using self‐report sleep diaries and wrist‐worn activity monitors. On nights that preceded training days, participants went to bed at 22:05 h (s=00:52), arose at 05:48 h (s=00:24) and obtained 5.4 h (s=1.3) of sleep. On nights that preceded rest days, participants went to bed at 00:32 h (s=01:29), arose at 09:47 h (s=01:47) and obtained 7.1 h (s=1.2) of sleep. Mixed model analyses revealed that on nights prior to training days, bedtimes and get‐up times were significantly earlier (p<0.001), time spent in bed was significantly shorter (p<0.001) and the amount of sleep obtained was significantly less (p<0.001), than on nights prior to rest days. These results indicate that early‐morning training sessions severely restrict the amount of sleep obtained by elite athletes. Given that chronic sleep restriction of <6 h per night can impair psychological and physiological functioning, it is possible that early‐morning schedules actually limit the effectiveness of training.

The 10 min psychomotor vigilance task (PVT) is commonly used in laboratory studies to assess the impact of sleep loss, sustained wakefulness, and/or time of day on neurobehavioral performance. In field settings, though, it may be impractical for participants to perform a test of this length. The aim of this study was to identify a performance measure that is sensitive to the effects of fatigue but less burdensome than a 10 min test. Sixteen participants (11 female, 5 male; mean age = 21.7 years) slept in the sleep laboratory overnight then remained awake for 28 h from 08:00 h. During every second hour, participants completed three PVTs of differing duration (10 min, 5 min, 90 sec). For the 5 min/10 min comparison, ANOVA indicated that response time was significantly affected by test length (F1,14 = 26.9, p < .001) and hours of wakefulness (F13,182 = 46.1, p < .001) but not by their interaction (F13,182 = 1.7, ns). There was a strong correlation between response time on the 5 and 10 min PVTs (r = .88, p < .001). For the 90 sec/10 min comparison, ANOVA indicated that response time was significantly affected by test length (F1,14 = 65.9, p < .001) and hours of wakefulness (F13,182 = 29.7, p < .001) as well as by their interaction (F13,182 = 6.0, p < .001). There was a strong correlation between response time on the 90 sec and 10 min PVTs (r = .77, p < .001). The effects of hours of wakefulness on neurobehavioral performance were similar for the 5 min and 10 min PVTs. In contrast, performance on the 90 sec PVT was less affected by hours of wakefulness than on the 10 min PVT. In addition, performance on the 10 min PVT was more highly correlated with the 5 min PVT than the 90 sec PVT. These data indicate that the 5 min PVT may provide a reasonable substitute for the 10 min PVT in circumstances where a test shorter than 10 min is required.