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Purpose: To determine whether menstrual-cycle symptoms are associated with sleep in elite female athletes. Methods: Sleep was assessed for a minimum of 25 nights (range = 25–31) using activity monitoring and sleep diaries. Menstrual-cycle symptoms were collected over the same duration in 12 elite female professional soccer players. Generalized estimating equations were used to examine the relationship between the day of the menstrual cycle (from day 1) and total menstrual-cycle symptoms on sleep characteristics. Results: There was a significant relationship between sleep duration and the day of the menstrual cycle (P =.042) and total symptoms reported that day (P <.001), with sleep duration increasing by 21 minutes for every symptom reported. There was a negative day × symptom interaction on sleep duration (P =.004), indicating that with increased symptoms, the day of the menstrual cycle had a smaller relationship with sleep duration. Sleep efficiency (P =.950), wake after sleep onset (P =.217), and subjective sleep quality (P =.080) were not related to the day of the menstrual cycle. The total symptoms reported had no relationship with sleep efficiency (P =.220), subjective sleep quality (P =.502), or sleep latency (P =.740) but did significantly relate to wake after sleep onset (P <.001), with a significant day × symptom interaction (P <.001). Conclusions: Sleep duration increased from day 1 of the menstrual cycle and was associated with the number of menstrual-cycle symptoms reported. All other sleep metrics remained unchanged; however, total symptoms reported were related to wake after sleep onset. Monitoring and managing menstrual-cycle symptoms should be encouraged due to a potential relationship with sleep characteristics. [ABSTRACT FROM AUTHOR]

BackgroundDisturbed sleep may negatively influence physical health, cognitive performance, metabolism, and general wellbeing. Nutritional interventions represent a potential non-pharmacological means to increase sleep quality and quantity.Objective(1) Identify an optimal suite of nutritional ingredients and (2) validate the effects of this suite utilising polysomnography, and cognitive and balance tests.MethodsThe optimal and least optimal combinations of six ingredients were identified utilising 55 male participants and a Box-Behnken predictive model. To validate the model, 18 healthy, male, normal sleepers underwent three trials in a randomised, counterbalanced design: (1) optimal drink, (2) least optimal drink, or (3) placebo were provided before bed in a double-blinded manner. Polysomnography was utilised to measure sleep architecture. Cognitive performance, postural sway, and subjective sleep quality, were assessed 30 min after waking.ResultsThe optimal drink resulted in a significantly shorter sleep onset latency (9.9 ± 12.3 min) when compared to both the least optimal drink (26.1 ± 37.4 min) and the placebo drink (19.6 ± 32.0 min). No other measures of sleep, cognitive performance, postural sway, and subjective sleep quality were different between trials.ConclusionA combination of ingredients, optimised to enhance sleep, significantly reduced sleep onset latency. No detrimental effects on sleep architecture, subjective sleep quality or next day performance were observed.

Purpose: Mental fatigue causes decreases in aspects of athletes' performance. Elite coaches commonly undertake cognitively demanding tasks and are seemingly at similar risk of subsequent performance impairment. However, elite sport coaches' experiences of mental fatigue, alongside other markers of psychobiological stress, have yet to be quantified. Methods: Three elite coaching and performance staff (2 women and 1 man) provided 100-mm visual analog scale ratings of mental fatigue, physical fatigue, readiness to perform, and salivary samples for later cortisol (sCort) and alpha-amylase (sAA) analysis. Data were obtained on the same morning each week across a 16-week preseason. Data were subset by individual coach for descriptive and repeated-measures correlational analyses. Results: Fluctuating mental fatigue was observed over the 16 weeks (min–max; coach 1 = 25–86 AU; coach 2 = 0–51 AU; and coach 3 = 15 − 76 AU). Elevated levels of mental fatigue were reported at multiple time points, with individual variability observed. sCort (in nanomoles per liter), sAA (in micromoles per liter), and sAA:sCort indicated that coaches experienced psychophysiological stress (min–max; coach 1 sCort = 8.42–17.31, sAA = 52.40–113.06, sAA:sCort = 3.20–12.80; coach 2 sCort = 4.20–9.70, sAA = 158.80–307.20, sAA:sCort = 21.10–61.70; and coach 3 sCort = 6.81−19.66, sAA = 86.55–495.85, sAA:sCort = 4.90–35.50). A significant inverse relationship between mental fatigue and readiness to perform (r = −.44 [−.64 to −.17], P =.002) was identified. Conclusions: Elite sport coaches report elevated instances of mental fatigue during a preseason training period. Those involved in elite sports should act to understand the presence and potential subsequent impacts of staff mental fatigue and consider management or mitigation strategies. Optimization of the cognitive performance of coaches and performance staff presents as a potential source of competitive advantage. [ABSTRACT FROM AUTHOR]

Context: To understand overtraining syndrome (OTS), it is important to detail the physiological and psychological changes that occur in athletes. Objectives: To systematically establish and detail the physiological and psychological changes that occur as a result of OTS in athletes. Methods: Databases were searched for studies that were (1) original investigations; (2) English, full-text articles; (3) published in peer-reviewed journals; (4) investigations into adult humans and provided (5) objective evidence that detailed changes in performance from prior to the onset of OTS diagnosis and that performance was suppressed for more than 4 weeks and (6) objective evidence of psychological symptoms. Results: Zero studies provided objective evidence of detailed changes in performance from prior to the onset of OTS diagnosis and demonstrated suppressed performance for more than 4 weeks accompanied by changes in psychological symptoms. Conclusions: All studies failed to provide evidence of changes in performance and mood from "healthy" to an overtrained state with evidence of prolonged suppression of performance. While OTS may be observed in the field, little data is available describing how physiological and psychological symptoms manifest. This stems from vague terminology, difficulties in monitoring for prolonged periods of time, and the need for prospective testing. Real-world settings may facilitate the collection of such data, but the ideal testing battery that can easily be conducted on a regular basis does not yet exist. Consequently, it must be concluded that an evidence base of sufficient scientific quality for understanding OTS in athletes is lacking. [ABSTRACT FROM AUTHOR]

Purpose: Professional road cycling races are physiologically demanding, involving successive days of racing over 1 to 3 weeks of competition. Anecdotal evidence indicates that cyclists' sleep duration either increases or deteriorates during these competitions. However, sleep duration in professional cyclists during stage races has not been assessed. This study examined the amount/quality of sleep obtained by 14 professional cyclists competing in the Australian Tour Down Under. Methods: Sleep was assessed using wrist activity monitors and self-report sleep diaries on the night prior to start of the race and on each night during the race. The impact of each day of the race on sleep onset, sleep offset, time in bed, sleep duration, and wake duration was assessed using separate linear mixed effects models. Results: During the race, cyclists obtained an average of 6.8 (0.9) hours of sleep between 23:30 and 07:27 hours and spent 13.9% (4.7%) of time in bed awake. Minor differences in sleep onset (P =.023) and offset times (P ≤.001) were observed during the week of racing, but these did not affect the amount of sleep obtained by cyclists. Interestingly, the 3 best finishers in the general classification obtained more sleep than the 3 worst finishers (7.2 [0.3] vs 6.7 [0.3] h; P =.049). Conclusions: Contrary to anecdotal reports, the amount of sleep obtained by cyclists did not change over the course of the 1-week race and was just below the recommended target of 7 to 9 hours for adults. [ABSTRACT FROM AUTHOR]

Purpose: Psychological stress is reported to be an important contributor to reduced sleep quality and quantity observed in elite athletes. The purpose of this study was to explore the association between psychological stress and sleep and to identify if specific aspects of sleep are disturbed. Methods: One hundred thirty-one elite athletes (mean [SD], male: n = 46, age 25.8 [4.1] y; female: n = 85, age 24.3 [3.9] y) from a range of sports completed a series of questionnaires in a 1-month period approximately 4 months before the 2016 Rio Olympic Games. Questionnaires included the Pittsburgh Sleep Quality Index; Recovery-Stress Questionnaire; Depression, Anxiety, and Stress Scale (DASS 21); and Perceived Stress Scale (PSS). Results: Regression analysis identified the PSS and DASS stress as the main variables associated with sleep. A PSS score of 6.5 or higher was associated with poor sleep. In addition, a PSS score lower than 6.5 combined with a DASS stress score higher than 4.5 was also associated with poor sleep. Univariate analyses on subcomponents of the Pittsburgh Sleep Quality Index confirmed that PSS is associated with lower sleep quality (t99 = 2.40, P =.018), increased sleep disturbances (t99 = 3.37, P =.001), and increased daytime dysfunction (t99 = 2.93, P =.004). DASS stress was associated with increased sleep latency (t94 = 2.73, P =.008), increased sleep disturbances (t94 = 2.25, P =.027), and increased daytime dysfunction (t94 = 3.58, P =.001). Conclusions: A higher stress state and higher perceived stress were associated with poorer sleep, in particular increased sleep disturbances and increased daytime dysfunction. Data suggest that relatively low levels of psychological stress are associated with poor sleep in elite athletes. [ABSTRACT FROM AUTHOR]

Purpose: Mental fatigue is emerging as an important consideration for elite sporting performance, yet it is rarely monitored. The present study assessed changes in mental fatigue in professional team-sport athletes across 2 seasons and examined the relationship between mental fatigue and other athlete self-report measures of well-being. Methods: Elite netballers contracted to all teams competing in Australia's premier professional netball competition during the 2018 and 2019 seasons (N = 154) participated. Using 5-point Likert scales, mental fatigue, fatigue (physical), tiredness, sleep quality, stress, mood, and motivation were assessed daily across 2 seasons composed of 14 round and finals series. Results: The ratings of mental fatigue significantly changed during both seasons. In 2018, lower ratings of mental fatigue were reported in round 1 versus 3, 4, 6, 8, and 14; round 7 versus 6; and round 6 versus 10 (P <.05). In 2019, lower ratings of mental fatigue were identified for round 1 versus 3, 9, 10 to 14, and semifinal; round 2 versus 10 to 13; and 5 versus 10 to 12 (P <.05). Ordinal regression revealed significant differences between mental fatigue and physical fatigue (P <.001), tiredness (P <.001), stress (P <.001), mood (P <.001), and motivation (P <.05). Conclusions: The present study found mental fatigue to significantly fluctuate across a season in elite netballers. Moreover, perceived mental fatigue differed from physical fatigue, tiredness, stress, mood, and motivation. The data impress the need for mental fatigue to be included as an independent measure of athlete well-being. Monitoring of mental fatigue can allow practitioners to implement strategies to manage its influence on performance. [ABSTRACT FROM AUTHOR]

Purpose: To examine the sleep of rugby league athletes the night before and following an evening match and to investigate the association between caffeine intake and sleep on the night of competition. Methods: On the night prior to, night of, and night after a professional rugby league match, 15 athletes had their sleep monitored using wrist activity monitors. Additionally, saliva samples were collected 60 minutes before and 30 minutes after the competition to assess salivary caffeine concentration. Differences in sleep across the 3 nights were examined using linear mixed models, and changes were assessed using effect size (ES). Pearson correlation (r) assessed the relationship between salivary caffeine levels and sleep indices. Results: On the night of competition, athletes went to bed later than the night before (P =.00002, ES = 1.84) and night after (P =.0003, ES = 1.49) competition. Consequently, their sleep duration was reduced on the night of competition compared with the previous night (P <.0000003, ES = 2.36) and night after competition (P =.001, ES = 1.53). Postcompetition salivary caffeine concentration was substantially elevated in athletes when compared with precompetition measures (P <.00000001, ES = 4.44), and moderate, nonsignificant correlations were observed between changes in salivary caffeine concentration and delayed bedtime (r =.48, P =.07), increased sleep latency (r =.45, P =.09), decreased sleep duration (r = −.30, P =.28), and reduced sleep efficiency (r = −.34, P =.22). Conclusions: These results demonstrate that evening competition results in sleep disturbance in rugby league athletes, and caffeine supplementation prior to and during competition leads to substantial increases in postcompetition salivary caffeine concentration. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to independently validate a wearable inertial sensor designed to monitor training and performance metrics in swimmers. Methods: A total of 4 male (21 [4] y, 1 national and 3 international) and 6 female (22 [3] y, 1 national and 5 international) swimmers completed 15 training sessions in an outdoor 50-m pool. Swimmers were fitted with a wearable device (TritonWear, 9-axis inertial measurement unit with triaxial accelerometer, gyroscope, and magnetometer), placed under the swim cap on top of the occipital protuberance. Video footage was captured for each session to establish criterion values. Absolute error, standardized effect, and Pearson correlation coefficient were used to determine the validity of the wearable device against video footage for total swim distance, total stroke count, mean stroke count, and mean velocity. A Fisher exact test was used to analyze the accuracy of stroke-type identification. Results: Total swim distance was underestimated by the device relative to video analysis. Absolute error was consistently higher for total and mean stroke count, and mean velocity, relative to video analysis. Across all sessions, the device incorrectly detected total time spent in backstroke, breaststroke, butterfly, and freestyle by 51% (15%). The device did not detect time spent in drill. Intraclass correlation coefficient results demonstrated excellent intrarater reliability between repeated measures across all swimming metrics. Conclusions: The wearable device investigated in this study does not accurately measure distance, stroke count, and velocity swimming metrics or detect stroke type. Its use as a training monitoring tool in swimming is limited. [ABSTRACT FROM AUTHOR]

Purpose: Anecdotal reports indicate that many elite athletes are dissatisfied with their sleep, but little is known about their actual sleep requirements. Therefore, the aim of this study was to compare the self-assessed sleep need of elite athletes with an objective measure of their habitual sleep duration. Methods: Participants were 175 elite athletes (n = 30 females), age 22.2 (3.8) years (mean [SD]) from 12 individual and team sports. The athletes answered the question "how many hours of sleep do you need to feel rested?" and they kept a self-report sleep diary and wore a wrist activity monitor for ∼12 nights during a normal phase of training. For each athlete, a sleep deficit index was calculated by subtracting their average sleep duration from their self-assessed sleep need. Results: The athletes needed 8.3 (0.9) hours of sleep to feel rested, their average sleep duration was 6.7 (0.8) hours, and they had a sleep deficit index of 96.0 (60.6) minutes. Only 3% of athletes obtained enough sleep to satisfy their self-assessed sleep need, and 71% of athletes fell short by an hour or more. Specifically, habitual sleep duration was shorter in athletes from individual sports than in athletes from team sports (F1,173 = 13.1, P <.001; d = 0.6, medium), despite their similar sleep need (F1,173 = 1.40, P =.24; d = 0.2, small). Conclusions: The majority of elite athletes obtain substantially less than their self-assessed sleep need. This is a critical finding, given that insufficient sleep may compromise an athlete's capacity to train effectively and/or compete optimally. [ABSTRACT FROM AUTHOR]

Horsley, BJ, Tofari, PJ, Halson, SL, Kemp, JG, Chalkley, D, Cole, MH, Johnston, RD, and Cormack, SJ. Validity and reliability of thoracic-mounted inertial measurement units to derive gait characteristics during running. J Strength Cond Res 38(2): 274-282, 2024--Inertial measurement units (IMUs) attached to the tibia or lumbar spine can be used to analyze running gait but, with teamsports, are often contained in global navigation satellite system (GNSS) units worn on the thoracic spine. We assessed the validity and reliability of thoracic-mounted IMUs to derive gait characteristics, including peak vertical ground reaction force (vGRFpeak) and vertical stiffness (Kvert). Sixteen recreationally active subjects performed 40 m run throughs at 3-4, 5-6, and 7-8 m·s21. Inertial measurement units were attached to the tibia, lumbar, and thoracic spine, whereas 2 GNSS units were also worn on the thoracic spine. Initial contact (IC) from a validated algorithm was evaluated with F1 score and agreement (mean difference 6SD) of gait data with the tibia and lumbar spine using nonparametric limits of agreement (LoA). Test-retest error {coefficient of variation, CV (95% confidence interval [CI])} established reliability. Thoracic IMUs detected a nearly perfect proportion (F1 $ 0.95) of IC events compared with tibia and lumbar sites. Step length had the strongest agreement (0 6 0.04 m) at 3-4 m·s21, whereas contact time improved from 3 to 4 (20.02860.018 second) to 7-8m·s21 (20.00460.013 second). All values for Kvert fell within the LoA at 7-8 m·s21. Test-retest error was #12.8% for all gait characteristics obtained from GNSS units, where Kvert was most reliable at 3-4 m·s21 (6.8% [5.2, 9.6]) and vGRFpeak at 7-8m·s21 (3.7% [2.5, 5.2]). The thoracic-spine site is suitable to derive gait characteristics, including Kvert, from IMUs within GNSS units, eliminating the need for additional sensors to analyze running gait. [ABSTRACT FROM AUTHOR]

Horsley, BJ, Tofari, PJ, Halson, SL, Kemp, JG, Johnston, RD, and Cormack, SJ. Thoracic-worn accelerometers detect fatiguerelated changes in vertical stiffness during sprinting. J Strength Cond Res 38(2): 283-289, 2024--Thoracic-mounted accelerometers are valid and reliable for analyzing gait characteristics and may provide the opportunity to assess running-related neuromuscular fatigue (NMF) during training and competition without the need for additional tests, such as a countermovement jump (CMJ). However, their sensitivity for detecting fatigue-related changes in gait across different speeds is unclear. We, therefore, assessed the changes in accelerometer-derived gait characteristics, including vertical stiffness (Kvert), following a repeated sprint protocol (RSP). Sixteen recreationally active subjects performed single and repeated CMJs on a force plate and 40 m run throughs overground at 3-4, 5-6, and 7-8 m·s21 pre-post a 123 40 mRSP. Gait characteristics (contact time, step frequency, step length, Kvert, etc.) were derived from an accelerometer contained within a global navigation satellite system unit on the thoracic spine using a validated algorithm. Changes in running gait and CMJ performance were assessed using a linear mixed-effects model (95% confidence interval [95% CI]; effect size [ES]). Significance was set at p < 0.05. A significant reduction in Kvert occurred at 7-8m·s21 following the RSP (28.51 kN·m21 [213.9, 23.11]; p 5 0.007; ES [95% CI] 5 20.39 [20.62, 20.15]) which coincided with a decreased jump height (20.03 m [20.04, 20.01]; p 5 0.002; ES [95% CI] 5 20.87 [21.41, 20.30]). However, all other gait characteristics were not significantly different irrespective of speed. Thoracic-worn accelerometers can detect changes in Kvert at 7-8 m·s21 which may be useful for monitoring NMF during sprinting. However, a RSP does not result in altered gait mechanics in subsequent running at lower speeds. [ABSTRACT FROM AUTHOR]

Purpose: To determine the impact of the quality and quantity of sleep during an international flight on subsequent objective sleep characteristics, training and match-day load, self-reported well-being, and perceptions of jet lag of elite female cricketers during an International Cricket Council Women's T20 World Cup. Methods: In-flight and tournament objective sleep characteristics of 11 elite female cricketers were assessed using activity monitors. Seated in business class, players traveled west from Melbourne, Australia, to Chennai, India. The outbound flight departed Melbourne at 3:30 AM with a stopover in Dubai for 2 hours. The arrival time in Chennai was 8:10 PM local time (1:40 AM in Melbourne). The total travel time was 19 hours 35 minutes. Perceptual ratings of jet lag, well-being, and training and competition load were collected. To determine the impact of in-flight sleep on tournament measures, a median split was used to create subsamples based on (1) in-flight sleep quantity and (2) in-flight sleep quality (2 groups: higher vs lower). Spearman correlation coefficients were calculated to assess the bivariate associations between sleep measures, self-reported well-being, perceptual measures of jet lag, and internal training and match-day load. Results: Mean duration and efficiency of in-flight sleep bouts were 4.72 hours and 87.45%, respectively. Aggregated in-flight sleep duration was 14.64 + 3.56 hours. Players with higher in-flight sleep efficiency reported higher ratings for fatigue (ie, lower perceived fatigue) during the tournament period. Tournament sleep duration was longer, and bed and wake times were earlier compared with habitual. Compared with other nights during the tournament, sleep duration was shorter following matches. Conclusions: Maximizing in-flight sleep quality and quantity appears to have implications for recovery and sleep exhibited during competition. Sleep duration was longer than habitual except for the night of a match, which suggests that T20 matches may disrupt sleep duration. [ABSTRACT FROM AUTHOR]