Current sport-scientific studies mostly neglect the assessment of sleep architecture, although the distribution of different sleep stages is considered an essential component influencing an athlete's recovery and performance capabilities. A mobile, selfapplied tool like the SOMNOwatch plus EEG might serve as an economical and time-friendly alternative to activity-based devices. However, self-application of SOMNOwatch plus EEG has not been validated against conventional polysomnography (PSG) yet. For evaluation purposes, 25 participants (15 female, 10 male; M age = 22.92 ± 2.03 years) slept in a sleep laboratory on two consecutive nights wearing both, conventional PSG and SOMNOwatch plus EEG electrodes. Sleep parameters and sleep stages were compared using paired t-tests and Bland-Altman plots. No significant differences were found between the recordings for Sleep Onset Latency, stages N1 to N3 as well as Rapid Eye Movement stage. Significant differences (Bias [95%-confidence interval]) were present between Total Sleep Time (9.95 min [−29.18, 49.08], d = 0.14), Total Wake Time (−13.12 min [−47.25, 23.85], d = −0.28), Wake after Sleep Onset (−11.70 min [−47.25, 23.85], d = −0.34) and Sleep Efficiency (2.18% [−7.98, 12.34], d = 0.02) with small effect sizes. Overall, SOMNOwatch plus EEG can be considered a valid and practical self-applied method for the examination of sleep. In sport-scientific research, it is a promising tool to assess sleep architecture in athletes; nonetheless, it cannot replace in-lab PSG for all clinical or scientific purposes.

Both daily demands as well as training and competition characteristics in sports can result in a psychobiological state of mental fatigue leading to feelings of tiredness, lack of energy, an increased perception of effort, and performance decrements. Moreover, optimal performance will only be achievable if the balance between recovery and stress states is re-established. Consequently, recovery strategies are needed aiming at mental aspects of recovery. The aim of the study was to examine acute effects of potential mental recovery strategies (MR) on subjective-psychological and on cognitive performance outcomes after a mentally fatiguing task. A laboratory-based randomized cross-over study with twenty-four students (22.8 ± 3.6 years) was applied. Participants were run through a powernap intervention (PN), a systematic breathing intervention (SB), a systematic breathing plus mental imagery intervention (SB+), and a control condition (CC) with one trial a week over four consecutive weeks. Mental fatigue was induced by completion of the 60-min version of the AX-continuous performance test (AX-CPT). The Short Recovery and Stress Scale (SRSS) and Visual Analog Scales (VAS) were assessed to measure effects on perceptual outcomes. Cognitive performance was measured with a reaction time test of the Vienna Test System (VTS). During all three recovery interventions and CC portable polysomnography was applied. Results showed a significant increase from pre-AX-CPT to pre-MR on fatigue states and recovery-stress states indicating that the induction of mental fatigue was effective. Moreover, results underlined that analysis yielded no significant differences between recovery interventions and the control condition but they revealed significant time effects for VAS, SRSS items, and cognitive performance. However, it could be derived that the application of a rest break with 20 min of mental recovery strategies appears to enhance recovery on a mainly mental and emotional level and to reduce perceived mental fatigue.

Self-reports and actigraphy are common methods of sleep monitoring. Portable polysomnography (p-PSG) may serve as a screening tool in natural environments. Common concerns with its use are that sleep and compliance might be affected. Further, dysfunctional beliefs of the subjects may contribute to sleep disturbances, which might manifest throughout sleep monitoring. This study examined the effect of monitoring sleep patterns and attitudes among healthy individuals. Sixty-eight physically active university students (26.6 ± 2.5 years) were assigned to the intervention (n = 35) or the control group (n = 33). Sleep monitoring consisted of 2-week online sleep logs and a 1-week actigraphy. Portable PSG was applied for the final two nights. Objective and subjective sleep parameters and ratings were compared between the baseline measurements and the first two nig hts of actigraphy and the two nights of p-PSG. The participants answered the Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS), pre-and post-monitoring. The groups did not display any interactiontime effect (p = 0.187) for DBAS. Also, there were no subjective insomnia complaints. Following the nights with p-PSG application, perceived restfulness of sleep was reduced between baseline measurement and the second p-PSG night (p = 0.045). In contrast, the objective parameters showed an increased sleep-efficiency (p < 0.001) and reduced wake after sleep-onset (p = 0.002) after both p-PSG nights. All other sleep parameters revealed no significant differences between actigraphy-only and p-PSG nights. Two-week sleep monitoring had no negative effect on the objective sleep patterns and attitudes about sleep. Yet, sleep with p-PSG led to reduced subjective sleep quality, which was not reflected in the objective sleep parameters. Contrarily, participants showed higher sleep efficiency and shorter waking phases, possibly due to changed bedtime routine. Hence, p-PSG may be applicable for field studies in sport science, provided the participants receive detailed information.

Sleep is identified as a reoccurring behavioral state of reduced movement and responsiveness, allowing rest from prior periods of wakefulness, and is considered a precious resource for both, psychological and physiological well-being. 1,2 It follows a specific architecture within a circadian and ultra-circadian rhythm, and is divided into five stages, with three sleep stages (N1-N3), rapid eye movement (REM) sleep, and the waking state. Sleep stages split into light (N1-N2) and slow-wave (N3) sleep. A healthy sleeper starts a sleep cycle with N1, followed by more robust sleep (N2) and deep sleep (N3). REM as the last stage completes one sleep cycle. Ideally, a sleep cycle repeats three to seven