Portable PSG for sleep stage monitoring in sports: Assessment of SOMNOwatch plus EEG

Sleep assessment using EEG-based wearables - A systematic review

Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.

Self-tracking via smartphone app: Potential tool for athletes’ recovery self-management?

Self-tracking technologies are possible approaches to support recovery self-management activities for athletes. These may have become even more appealing due to stay-at-home restrictions as part of the 2020 pandemic regulations. This study examined user behaviour of smartphone and wearable technologies among 217 athletes (29% women, M age = 26.9 ± 7 years). The online survey comprised demographic questions and standardised questionnaires to assess usage of technologies, sleep quality (Pittsburgh Sleep Quality Index), daytime sleepiness (Epworth Sleepiness Scale), attitudes about sleep (Dysfunctional Beliefs and Attitudes about Sleep Scale), bedtime procrastination (Bedtime Procrastination Scale), and self-control (Brief Self-Control Scale). Fitness apps (46.5%) were more popular than sleep apps (15.7%) followed by nutrition apps (12%). The correlation between sleep apps and the other two apps indicate that non-users of sleep apps are probably also non-users of fitness or nutrition apps. Wearables were more frequently used to track fitness activities (36.9%) than sleep (17.5%). Considering sex, type of sport, competition participation, and training volume, no remarkable characteristics among users versus non-users of sleep apps were identified. There were also no significant differences among sleep indices between sleep app users and non-users. However, self-control was highest among sleep app users compared to non-users (d = 0.58). Despite 34.1% being identified as poor sleepers, behavioural sleeping patterns were within normal range. The results imply that athletes are not as attracted to self-tracking technologies as expected, which makes them less vulnerable to unsubstantiated feedback and inappropriate interventions by those tools. This serves as a starting point to explore the potential of self-tracking ambulatory assessment for physical activity and sleep behaviour of athletes in the post-pandemic era.

Validierung des Fragebogens zum Schlafverhalten von Sportlerinnen und Sportlern (FSVS)

Zusammenfassung. Der Athlete Sleep Behavior Questionnaire ist ein englischsprachiger Fragebogen zur Erhebung des individuellen Schlafverhaltens von Sportler_innen. Ziel dieser Studie war die Übersetzung des Athlete Sleep Behavior Questionnaire ins Deutsche sowie die wissenschaftliche Validierung dieser deutschen 18-Item-Version, dem Fragebogen zum Schlafverhalten von Sportlerinnen und Sportlern (FSVS). Die Validierungsstichprobe umfasste 450 Teilnehmende. Mittels eines dichotomen Items zur regelmäßigen Wettkampfteilnahme (ja/nein) wurde in Wettkampfsportler_innen ( n = 209) und Kontrollgruppe ( n = 241) unterteilt. Cronbach-α des FSVS-Gesamtwerts ergab .66. Die Pearson-Produkt-Moment-Korrelationsanalysen ergaben moderate Korrelationen zwischen FSVS-Gesamtwert, der Epworth-Sleepiness-Scale ( r = .33) und dem Pittsburgh-Sleep-Quality-Index ( r = .30). Es wurden signifikante Gruppenunterschiede für den FSVS-Gesamtwert gefunden, t‍(448) = 4.47, p < .001, d = 0.42, bei deskriptiv höheren Werten der Gruppe der Wettkampfsportler_innen. In einer separaten Stichprobe, die 13 Ruderer_innen der deutschen Junioren-Nationalmannschaft umfasste, wurden die Fragebogenwerte anschließend mit objektiven Schlafparametern korreliert. Dabei ließ sich eine hohe Korrelation zwischen FSVS-Gesamtwert und Einschlaflatenz ( rs = .68, p = .011) erkennen. Zusammenfassend ist der FSVS ein schnelles und valides Werkzeug, um dysfunktionale Schlafverhaltensweisen bei Wettkampfsportler_innen zu identifizieren. In Anlehnung an die Originalversion wird die Ableitung von Interventionen anhand einzelner Items empfohlen.

The First-Night Effect in Elite Sports: An Initial Glance on Polysomnography in Home-Based Settings

Self-applied portable polysomnography is considered a promising tool to assess sleep architecture in field studies. However, no findings have been published regarding the appearance of a first-night effect within a sport-specific setting. Its absence, however, would allow for a single night sleep monitoring and hence minimize the burden on athletes while still obtaining the most important variables. For this reason, the aim of the study was to assess whether the effect appears in home-based sleep monitoring of elite athletes. The study sample included eight male and 12 female German elite athletes from five different sports. Participants slept with a portable polysomnography for two nights, which they self-applied at night before going to bed. Time in bed and wake-up time in the morning were freely chosen by each individual athlete without any restrictions regarding time or sleeping environment. Participants were asked to keep the same location and time frame during the two days of monitoring and stick to their usual sleeping schedules. Sleep stages were manually scored using 30-s epochs. Sleep parameters and stages were later compared with the help of linear mixed models to investigate the factor time. Significant differences between the two nights were found for percentage of Non-REM sleep [T(19) = -2,10, p < 0.05, d = -0.47, 95%-CI (-7.23, -0.01)] with small effect size, Total Wake Time [T(19) = 2.30, p = 0.03, d = 0.51, 95%-CI (1.66, 35.17)], Sleep Efficiency [T(19) = -2.48, p = 0.02, d = -0.55, 95%-CI (-7.43, -0.63)], and Wake percentage [T(19) = 2.47, p = 0.02, d = 0.55, 95%-CI (0.61, 7.43)] with moderate effect sizes, and N3 Sleep Onset Latency [T(19) = 3.37, p < 0.01, d = 0.75, 95%-CI (7.15, 30.54)] with large effect size. Confidence Intervals for all other indices range from negative to positive values and hence specify, that parameters were not systematically negatively affected in the first night. Findings suggest that some individuals are more affected by the first-night effect than others. Yet, in order to keep the measurement uncertainties to a minimum, a more conservative approach with at least two monitoring nights should be used whenever possible, if no other supporting information on the athletes says otherwise.

Assessment of sleep quality and daytime sleepiness in German national ice hockey players preparing for the world championship

Objectives

Objectives were to examine subjective sleep quality and daytime sleepiness of the German ice hockey junior national team prior to the world championship to identify athletes of concern and areas of optimization with the intention of equally preventing injury and enhancing performance.

Methods

Twenty-one athletes (Mage = 18.5 ± 0.6 years, Mheight = 181.7 ± 4.3 cm, Mweight = 81.4 ± 7.1 kg), playing for national (n = 13) and international (n = 8) home clubs, answered the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) before training camp (T1, day 1) and prior to tournament (T2, day 11).

Results

Overall, 9 players at T1 and 7 at T2 were identified as bad sleepers (PSQI > 5), while high sleepiness (ESS > 10) was found for 6 athletes at each measurement time. Group means and standard deviations reduced descriptively for PSQI (T1 = 5.38 ± 2.31, T2 = 4.57 ± 2.36) and ESS (T1 = 9.24 ± 3.74, T2 = 8.48 ± 3.28). Tendential differences were visible for PSQI in international-based players (Z = −1.7, p = 0.09) and ESS in first-national-league players (Z = −1.73, p = 0.08) over time. Higher PSQI values for international-based players (6.25 ± 2.6) were found compared to first-national-league (5.83 ± 1.60) and lower-league players (4.00 ± 2.08), with large effect sizes for lower-league compared to international (d = 0.95) and national players (d = 0.98) at T1 and small effect sizes compared to first-league players (d = 0.24) at T2.

Conclusion

Findings emphasize great vulnerability and individuality and underline the importance of intraindividual sleep monitoring to meet the requirements needed to equally obtain health and enhance overall performance.

Acute Effects of Mental Recovery Strategies After a Mentally Fatiguing Task

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.

Sleep quality and athletic performance according to chronotype

BACKGROUND

When studying the quality of sleep in relation to athletic performance, the athlete's chronotype and habitual time consider important factors. We aim to investigate the sleep quality and athletes' performance according to chronotype in elite athletes.

METHODS

Three hundred forty elite athletes (males = 261, females = 79) were recruited for the present study. All participants were screening for chronotype by the Korean versions of the Morningness - Eveningness Questionnaire (MEQ-K). The Pittsburgh Sleep Quality Index (PSQI) and Wingate Anaerobic Test (WAnT) were measurement after screening.

RESULTS

PSQI global score, PSQI sleep quality, PSQI sleep onset latency, PSQI sleep disturbance, and PSQI daytime dysfunction were significant differences among the groups. WAnT mean power (W), mean power (W/kg), peak power (W), and peak power (W/kg) were significant differences among the groups. A negative correlation coefficient was found between PSQI score and WAnT mean power (W) (r = - 0.256, p < 0.01), mean power (W/kg) (r = - 0.270, p < 0.01), peak power (W) (r = - 0.220, p < 0.01), and peak power (W/kg) (r = - 0.248, p < 0.01).

CONCLUSIONS

This study indicates that related poor sleep quality and late-type chronotype may reduce the athletes' performance in elite athletes. In addition, the sleep quality is much higher in the early-type chronotype than in the late-type chronotype. Moreover, it also the athletic performance was better in the early-type chronotype than in the late-type chronotype.