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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). [ABSTRACT FROM AUTHOR]

Sleep inertia is the transitional state marked by impaired cognitive performance and reduced vigilance upon waking. Exercising before bed may increase the amount of slow‐wave sleep within the sleep period, which has previously been associated with increased sleep inertia. Healthy males (n = 12) spent 3 nights in a sleep laboratory (1‐night washout period between each night) and completed one of the three conditions on each visit – no exercise, aerobic exercise (30 min cycling at 75% heart rate), and resistance exercise (six resistance exercises, three sets of 10 repetitions). The exercise conditions were completed 90 min prior to bed. Sleep was measured using polysomnography. Upon waking, participants completed five test batteries every 15 min, including the Karolinska Sleepiness Scale, a Psychomotor Vigilance Task, and the Spatial Configuration Task. Two separate linear mixed‐effects models were used to assess: (a) the impact of condition; and (b) the amount of slow‐wave sleep, on sleep inertia. There were no significant differences in sleep inertia between conditions, likely as a result of the similar sleep amount, sleep structure and time of awakening between conditions. The amount of slow‐wave sleep impacted fastest 10% reciprocal reaction time on the Psychomotor Vigilance Task only, whereby more slow‐wave sleep improved performance; however, the magnitude of this relationship was small. Results from this study suggest that exercise performed 90 min before bed does not negatively impact on sleep inertia. Future studies should investigate the impact of exercise intensity, duration and timing on sleep and subsequent sleep inertia. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine player monitoring approaches used by basketball practitioners with a specific focus on the use of microsensors. An online survey was disseminated to basketball practitioners via international basketball-related organisations and social media channels. Multiple response, Likert-scale level of agreement, and open-ended questions captured data regarding if, and how player monitoring was performed, as well as barriers and facilitators to player monitoring, with an emphasis on the use of microsensors. Forty-four basketball practitioners completed the survey. Twenty-seven respondents (61%) implement player monitoring and thirteen (30%) use microsensors. Despite implementing player monitoring, over 85% of practitioners modify training based on their own observation. Respondents not currently monitoring players (39%) would commence monitoring if the tools or equipment were provided. 74% of respondents agree that microsensors are expensive. Only 56% of practitioners who use microsensors feel they have support for using the technology and analysing/interpreting the data. These findings suggest a low uptake of microsensors for player monitoring in basketball. Coaches and practitioners perceive player monitoring approaches to be cost-prohibitive and appear unsure of how player monitoring data should be used to optimise training outcomes for players. [ABSTRACT FROM AUTHOR]

Background: Adequate training loads promote favorable physical and physiological adaptations, reduce the likelihood of illness and injury, and, therefore, increase the possibility of success during competition.Objectives: Our objective was to systematically examine the association between training load and performance outcomes in team sports.Methods: We systematically searched the PubMed, SPORTDiscus, and PsycINFO databases for original research published before July 2018. The search included terms relevant to training load, performance, and team sports. Articles were screened using pre-defined selection criteria, and methodological quality was assessed independently by two authors before data were extracted by the lead author.Results: The electronic search yielded 5848 articles, 2373 of which were duplicates. A further 17 articles were retrieved from additional sources. In total, 26 articles met the inclusion criteria for this review, with quality scores ranging from 6 to 10 out of 11. Training exposure was more strongly associated with aerobic performance than other external training load measures. High-intensity activity (≥ 90% of maximum heart rate) was strongly associated with aerobic performance. The individualized training impulse model was strongly associated with aerobic performance, whereas various other training impulse models and perceptual training load measures showed weak associations with aerobic performance. There were no clear associations between training load and neuromuscular variables or game-related statistics.Conclusion: We found no consistent associations between external training load measures and performance. High-intensity internal training load appears to be the most prominent indicator of aerobic performance. [ABSTRACT FROM AUTHOR]

Currently, very little is known about the impact of short- or long-haul air travel on the sleep and wellbeing of wheelchair basketball athletes. Eleven national wheelchair basketball athletes wore actigraphy monitors prior, during, and after air travel to the United Kingdom. Upon arrival, participants rated their subjective jet-lag, fatigue, and vigor. Individuals traveled to the United Kingdom from different locations in Australia, the United States, and Europe and were categorised according to travel length [LONG (up to 30.2 h) or SHORT (up to 6.5 h)]. Linear mixed models determined effects of travel length on sleep and subjective ratings of jet-lag, fatigue, and vigor. During competition, subjective fatigue and jet-lag were substantially higher (ES = 0.73; ±0.77) and (ES = 0.57; ±0.60), subjective vigor was lower (ES = 1.94; ±0.72), and get-up time was earlier (ES = 0.57; ±0.60) for LONG when compared to SHORT. Travelling greater distances by airplane had a larger effect on subjective ratings of jet-lag, fatigue and vigor, rather than sleep. Irrespective of travel group, sleep and subjective responses were compromised, reflecting the travel requirements, competition-mediated influences, and/or due to a change in environment. [ABSTRACT FROM AUTHOR]

Fatigue is a significant contributor to motor-vehicle accidents and fatalities. Shift workers are particularly susceptible to fatigue-related risks as they are often sleep-restricted and required to commute around the clock. Simple assays of performance could provide useful indications of risk in fatigue management, but their effectiveness may be influenced by changes in their sensitivity to sleep loss across the day. The aim of this study was to evaluate the sensitivity of several neurobehavioral and subjective tasks to sleep restriction (SR) at different circadian phases and their efficacy as predictors of performance during a simulated driving task. Thirty-two volunteers (M ± SD; 22.8 ± 2.9 years) were time-isolated for 13-days and participated in one of two 14-h forced desynchrony protocols with sleep opportunities equivalent to 8 h/24 h (control) or 4 h/24 h (SR). At regular intervals during wake periods, participants completed a simulated driving task, several neurobehavioral tasks, including the psychomotor vigilance task (PVT), and subjective ratings, including a self-assessment measure of ability to perform. Scores transformed into standardized units relative to baseline were folded into circadian phase bins based on core body temperature. Sleep dose and circadian phase effect sizes were derived via mixed models analyses. Predictors of driving were identified with regressions. Performance was most sensitive to sleep restriction around the circadian nadir. The effects of sleep restriction around the circadian nadir were larger for simulated driving and neurobehavioral tasks than for subjective ratings. Tasks did not significantly predict driving performance during the control condition or around the acrophase during the SR condition. The PVT and self-assessed ability were the best predictors of simulated driving across circadian phases during SR. These results show that simple performance measures and self-monitoring explain a large proportion of the variance in driving when fatigue-risk is high. [ABSTRACT FROM AUTHOR]

This study compared the impact of split and consolidated sleep/wake schedules on subjective sleepiness during the biological day and biological night. This was achieved using a between-group design involving two forced desynchrony protocols: consolidated sleep/wake and split sleep/wake. Both protocols included 7 × 28-h days with 9.33 h in bed and 18.67 h of wake each day. While the consolidated sleep/wake protocol had 1 × 9.33-h sleep opportunity and 1 × 18.67-h wake period each day, the split sleep/wake protocol had 2 × 4.67-h sleep opportunities and 2 × 9.33-h wake periods each day. For both protocols, subjective sleepiness was measured using the Karolinska Sleepiness Scale every 2.5 h during wake. A total of 29 healthy adult males participated, with 13 in the consolidated sleep/wake group (mean age = 22.5 yrs) and 16 in the split sleep/wake group (mean age = 22.6 yrs). On average, subjective sleepiness during wake periods of the split condition was significantly higher than that during the first half of wake periods of the consolidated condition, but was similar to the level during the second half. These findings were observed for wake periods that occurred during both the biological day and biological night. Previous data have shown that cognitive impairment at night is lower for split schedules than consolidated schedules, but the current data indicate that feelings of sleepiness are greater for split schedules than consolidated schedules for at least half of the time awake. Thus, it should be explained to people operating split sleep/wake schedules that although they may perform well, they are likely to feel sleepy. [ABSTRACT FROM AUTHOR]

The consumption of caffeine in response to insufficient sleep may impair the onset and maintenance of subsequent sleep. This systematic review and meta-analysis investigated the effect of caffeine on the characteristics of night-time sleep, with the intent to identify the time after which caffeine should not be consumed prior to bedtime. A systematic search of the literature was undertaken with 24 studies included in the analysis. Caffeine consumption reduced total sleep time by 45 min and sleep efficiency by 7%, with an increase in sleep onset latency of 9 min and wake after sleep onset of 12 min. Duration (+6.1 min) and proportion (+1.7%) of light sleep (N1) increased with caffeine intake and the duration (−11.4 min) and proportion (−1.4%) of deep sleep (N3 and N4) decreased with caffeine intake. To avoid reductions in total sleep time, coffee (107 mg per 250 mL) should be consumed at least 8.8 h prior to bedtime and a standard serve of pre-workout supplement (217.5 mg) should be consumed at least 13.2 h prior to bedtime. The results of the present study provide evidence-based guidance for the appropriate consumption of caffeine to mitigate the deleterious effects on sleep. [ABSTRACT FROM AUTHOR]