Evening consumption of a whey protein rich in the amino acid tryptophan, alpha-lactalbumin (ALAC), has previously shown to benefit sleep—particularly among poor sleepers. Given trained populations often experience sleep difficulty, this study investigated whether evening supplementation of ALAC would influence sleep outcomes, mood, and next-day cognitive performance within a trained population with sleep difficulties. Nineteen trained participants (females, n = 11) with sleep difficulties (Athlete Sleep Screening Questionnaire: 8.1 ± 3.1; Pittsburgh Sleep Quality Index: 10.5 ± 4.1) completed this double-blinded, counterbalanced, randomized, crossover trial. Forty grams of ALAC or control were supplemented 2 hr presleep for three consecutive nights in a controlled environment, with sleep measured using dry electroencephalography. Blood samples were taken on the first evening of each experimental trial, with mood, sleepiness, and recovery assessed across the evening and morning. A cognitive testing battery was also completed each morning. During the ALAC condition, the primary findings were that participants had raised plasma tryptophan levels ( p < .01), increased nonrapid eye movement Stage 2 sleep duration (CON: 205.9 ± 33.3; ALAC: 216.5 ± 33.1 min), reduced rapid eye movement duration (CON: 110.8 ± 27.9; ALAC: 99.7 ± 23.1 min), and improved reaction time in cognitive tests involving sensory motor speed, spatial orientation, and vigilant attention ( p < .05). Data suggest evening supplementation of 40 g ALAC alters sleep architecture and improves next-morning reaction time in trained populations with sleep difficulties. Therefore, trained individuals experiencing sleep difficulty may benefit from acute ALAC supplementation to assist next-day performance. Future research should investigate this effect within habitual environments, outside of a tightly controlled setting.
Purpose: The current study examined the possible relationships between one-off single night sleep metrics and subsequent kicking performance in a youth soccer context.Methods: Twenty-eight under-17 academy players (15.9 ± 0.8 years-old) completed a kick testing protocol consisting in 20 attempts, 18 m from the goal and against a goalkeeper. Four digital video cameras (240 Hz) allowed to determine 3-D approach run, lower limb and ball velocities. Two additional cameras (60 Hz) were used to calculate 2-D mean radial error, bivariate variable error and accuracy. Over 24 h prior to testing, players were monitored by wrist actigraphy to determine their sleep indices. Self-reported sleep quality, sleepiness and chronotype scale scores (Horne and Östberg morningness-eveningness questionnaire) were also collected immediately before kicking experiment.Results: Multiple linear regressions indicated that wake up time and chronotype contributed to 40% of mean radial error. Self-reported sleep quality influenced respectively on 19% and 24% of accuracy and bivariate variable error variances. Taken together self-reported sleep quality and wake up time explained 33% of accuracy (all p < 0.05). Indicators of kicking velocity were non-significantly correlated with sleep (r = −0.30–0.29; p > 0.05).Conclusion: One-off sleep measures showed some sensitivity to acutely detect inter-individual oscillations in kicking performance. Low perceived sleep quality, later wake up time and a chronotype toward evening preference seem either related to immediately subsequent worst ability of ball placement when kicking. Monitoring sleep-wake transition and perceived sleep quality may be important to help prevent acute performance declines in targeting the goal during kick attempts from the edge of penalty area.
The validity of a commercially available wearable device for measuring total sleep time was examined in a sample of well-trained young athletes during night-time sleep periods and daytime naps. Participants wore a FitBit HR Charge on their non-dominant wrist and had electrodes attached to their face and scalp to enable polysomnographic recordings of sleep in the laboratory. The FitBit automatically detected 24/30 night-time sleep periods but only 6/20 daytime naps. Compared with polysomnography, the FitBit overestimated total sleep time by an average of 52 ± 152 min for night-time sleep periods, and by 4 ± 8 min for daytime naps. It is important for athletes and practitioners to be aware of the limitations of wearable devices that automatically detect sleep duration.
Objective: The main aim of this study was to explore the perceived relationship between sexual activities, sleep quality, and sleep latency in the general adult population and identify whether any gender differences exist. Participants/methods: We used a cross-sectional survey to examine the perceived relationship between sexual activity and subsequent sleep in the general adult population. Seven-hundred and seventy-eight participants (442 females, 336 males; mean age 34.5 ± 11.4 years) volunteered to complete an online anonymous survey at their convenience. Statistical Analyses: Chi square analyses were conducted to examine if there were any gender differences between sexual activities [i.e., masturbation (self-stimulation), sex with a partner without orgasm, and sex with a partner with orgasm] and self-reported sleep. Results: There were no gender differences in sleep (quality and onset) between males and females when reporting sex with a partner [ χ(2)2 = 2.20, p = 0.332; χ(2)2= 5.73, p = 0.057] or masturbation (self-stimulation) [ χ(2)2 = 1.34, p = 0.513; χ(2)2 = 0.89, p = 0.640] involved an orgasm. Conclusions: Orgasms with a partner were associated with the perception of favorable sleep outcomes, however, orgasms achieved through masturbation (self-stimulation) were associated with the perception of better sleep quality and latency. These findings indicate that the public perceive sexual activity with orgasm precedes improved sleep outcomes. Promoting safe sexual activity before bed may offer a novel behavioral strategy for promoting sleep.
Objectives We investigated the management of travel fatigue and jet lag in athlete populations by evaluating studies that have applied non-pharmacological interventions (exercise, sleep, light and nutrition), and pharmacological interventions (melatonin, sedatives, stimulants, melatonin analogues, glucocorticoids and antihistamines) following long-haul transmeridian travel-based, or laboratory-based circadian system phase-shifts. Design Systematic review Eligibility criteria Randomised controlled trials (RCTs), and non-RCTs including experimental studies and observational studies, exploring interventions to manage travel fatigue and jet lag involving actual travel-based or laboratory-based phase-shifts. Studies included participants who were athletes, except for interventions rendering no athlete studies, then the search was expanded to include studies on healthy populations. Data sources Electronic searches in PubMed, MEDLINE, CINAHL, Google Scholar and SPORTDiscus from inception to March 2019. We assessed included articles for risk of bias, methodological quality, level of evidence and quality of evidence. Results Twenty-two articles were included: 8 non-RCTs and 14 RCTs. No relevant travel fatigue papers were found. For jet lag, only 12 athlete-specific studies were available (six non-RCTs, six RCTs). In total (athletes and healthy populations), 11 non-pharmacological studies (participants 600; intervention group 290; four non-RCTs, seven RCTs) and 11 pharmacological studies (participants 1202; intervention group 870; four non-RCTs, seven RCTs) were included. For non-pharmacological interventions, seven studies across interventions related to actual travel and four to simulated travel. For pharmacological interventions, eight studies were based on actual travel and three on simulated travel. Conclusions We found no literature pertaining to the management of travel fatigue. Evidence for the successful management of jet lag in athletes was of low quality. More field-based studies specifically on athlete populations are required with a multifaceted approach, better design and implementation to draw valid conclusions. PROSPERO registration number The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO: CRD42019126852).
The aim of this study was to examine sleep/wake behaviour and sleep strategies before, during and after ultra-marathon running events exceeding 100 miles (161 km). A total of 119 athletes completed a web-based questionnaire regarding their habitual sleep/wake behaviour before, during, and after ultra-marathon participation. Event-specific data were grouped by race distance categories; 100-149 miles (161-240 km), 150-199 miles (241-321 km), and ≥200 miles (322 km). Athletes commonly reported not sleeping throughout the duration of their races (74%). However, for events that were ≥200 miles, athletes reported more sleep opportunities, longer sleep duration, and more total sleep when compared to events that were 100-149 miles in distance (p ≤ 0.001). This suggests that for races of shorter distances, the benefit of continuous racing outweighs the negative impact of continuous wakefulness/sleep deprivation. However, for longer races (≥200 miles), there is an apparent tradeoff between sleep deprivation and race strategy, whereby athletes cannot sustain a desired level of performance without obtaining sleep. This is consistent with established sleep/wake behaviour models suggesting that sleep need increases as wakefulness increases, or in this case, as race duration increases. For athletes participating in ultra-marathons, sleep management education and/or consultation with a sleep scientist prior to racing may be beneficial. Future research should examine the optimal strategies concerning the frequency and duration of sleep during ultra-marathons and the subsequent impact on performance.
Introduction: Recent sleep guidelines regarding evening exercise have shifted from a conservative (i.e., do not exercise in the evening) to a more nuanced approach (i.e., exercise may not be detrimental to sleep in circumstances). With the increasing popularity of wearable technology, information regarding exercise and sleep are readily available to the general public. There is potential for these data to aid sleep recommendations within and across different population cohorts. Therefore, the aim of this study was to examine if sleep, exercise, and individual characteristics can be used to predict whether evening exercise will compromise sleep. Methods: Data regarding evening exercise and the subsequent night's sleep were obtained from 5,250 participants (1,321F, 3,929M, aged 30.1 ± 5.2 yrs) using a wearable device (WHOOP 3.0). Data for females and males were analysed separately. The female and male datasets were both randomly split into subsets of training and testing data (training:testing = 75:25). Algorithms were trained to identify compromised sleep (i.e., sleep efficiency <90%) for females and males based on factors including the intensity, duration and timing of evening exercise. Results: When subsequently evaluated using the independent testing datasets, the algorithms had sensitivity for compromised sleep of 87% for females and 90% for males, specificity of 29% for females and 20% for males, positive predictive value of 32% for females and 36% for males, and negative predictive value of 85% for females and 79% for males. If these results generalise, applying the current algorithms would allow females to exercise on ~ 25% of evenings with ~ 15% of those sleeps being compromised and allow males to exercise on ~ 17% of evenings with ~ 21% of those sleeps being compromised. Discussion: The main finding of this study was that the models were able to predict a high percentage of nights with compromised sleep based on individual characteristics, exercise characteristics and habitual sleep characteristics. If the benefits of exercising in the evening outweigh the costs of compromising sleep on some of the nights when exercise is undertaken, then the application of the current algorithms could be considered a viable alternative to generalised sleep hygiene guidelines.
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