Michele Lastella

The impact of training schedules on the sleep and fatigue of elite athletes

In any sport, successful performance requires a planned approach to training and recovery. While sleep is recognized as an essential component of this approach, the amount and quality of sleep routinely obtained by elite athletes has not been systematically evaluated. Data were collected from 70 nationally ranked athletes from seven different sports. Athletes wore wrist activity monitors and completed self-report sleep/training diaries for 2 weeks during normal training. The athletes also recorded their fatigue level prior to each training session using a 7-point scale. On average, the athletes spent 08:18 ± 01:12 h in bed, fell asleep at 23:06 ± 01:12 h, woke at 6:48 ± 01:30 h and obtained 06:30 ± 01:24 h of sleep per night. There was a marked difference in the athletes’ sleep/wake behaviour on training days and rest days. Linear mixed model analyses revealed that on nights prior to training days, time spent in bed was significantly shorter (p = 0.001), sleep onset and offset times were significantly earlier (p < 0.001) and the amount of sleep obtained was significantly less (p = 0.001), than on nights prior to rest days. Moreover, there was a significant effect of sleep duration on pre-training fatigue levels (p ≤ 0.01). Specifically, shorter sleep durations were associated with higher levels of pre-training fatigue. Taken together, these findings suggest that the amount of sleep an elite athlete obtains is dictated by their training schedule. In particular, early morning starts reduce sleep duration and increase pre-training fatigue levels. When designing schedules, coaches should be aware of the implications of the timing of training sessions for sleep and fatigue. In cases where early morning starts are unavoidable, countermeasures for minimizing sleep loss – such as strategic napping during the day and correct sleep hygiene practices at night – should be considered.

Athletes' precompetitive sleep behaviour and its relationship with subsequent precompetitive mood and performance

Abstract This investigation examined precompetitive sleep behaviour of 103 athletes and how it relates to precompetitive mood and subsequent performance. Results revealed that on the night before competition athletes slept well under the recommended target of eight hours of sleep for healthy adults, with almost 70% of athletes experiencing poorer sleep than usual. It was found that anxiety, noise, the need to use the bathroom and early event times were amongst the most commonly reported causes of disrupted sleep in athletes on the night prior to competition. The negative moods of fatigue and tension were both significantly negatively correlated with precompetitive relative sleep quality (r = −0.28, P = 0.004, r = −0.21, P = 0.030, respectively) and total sleep time (r = −0.23, P = 0.023, r = −0.20, P = 0.044, respectively). Additionally, tension was positively correlated with number of awakenings (r = −0.20, P = 0.045). Vigour was seen to be significantly positively associated with relative sleep quality (r = 0.24, P=0.013). The relationships between relative sleep quality and fatigue, tension and vigour accounted for approximately 4 − 5% of the variance in mood scores. Disrupted sleep did not demonstrate any significant relationship with relative sporting performance. Conclusions from the present investigation are that athletes may be at particular risk of disrupted sleep on the night prior to competition, and this disruption can negatively relate to an athletes precompetitive mood states.

Sleep/wake behaviour of endurance cyclists before and during competition

Abstract Good sleep is critical for optimising recovery and athletic performance. Yet, few studies have investigated how athletes sleep before and during competition. The aim of this study was to determine whether such sleep is poorer than that before a usual training day. Twenty-one male endurance cyclists’ (age: 19.9 ± 1.7 years) sleep/wake behaviour was assessed using wrist activity monitors for 11 nights, including a six-night baseline training phase, three nights before competition and two nights during competition. Cyclists had less sleep on the night before competition (6.5 ± 0.9 h) and during the first night of competition (6.8 ± 0.8 h) than at baseline (7.4 ± 0.6 h). Cyclists also went to bed and woke up earlier during competition than at baseline. Competition schedules and competition itself can disrupt the sleep/wake behaviour of athletes during competition. Future investigations should examine sleep during three stages of competition (i.e. before, during and after competition). This will help coaches develop a greater understanding of how sleep changes during different phases of competition and enable them to plan post-competition training programmes to ensure appropriate rest and recovery is obtained.

The validity of activity monitors for measuring sleep in elite athletes.

OBJECTIVES There is a growing interest in monitoring the sleep of elite athletes. Polysomnography is considered the gold standard for measuring sleep, however this technique is impractical if the aim is to collect data simultaneously with multiple athletes over consecutive nights. Activity monitors may be a suitable alternative for monitoring sleep, but these devices have not been validated against polysomnography in a population of elite athletes. DESIGN Participants (n=16) were endurance-trained cyclists participating in a 6-week training camp. METHODS A total of 122 nights of sleep were recorded with polysomnography and activity monitors simultaneously. Agreement, sensitivity, and specificity were calculated from epoch-for-epoch comparisons of polysomnography and activity monitor data. Sleep variables derived from polysomnography and activity monitors were compared using paired t-tests. Activity monitor data were analysed using low, medium, and high sleep-wake thresholds. RESULTS Epoch-for-epoch comparisons showed good agreement between activity monitors and polysomnography for each sleep-wake threshold (81-90%). Activity monitors were sensitive to sleep (81-92%), but specificity differed depending on the threshold applied (67-82%). Activity monitors underestimated sleep duration (18-90min) and overestimated wake duration (4-77min) depending on the threshold applied. CONCLUSIONS Applying the correct sleep-wake threshold is important when using activity monitors to measure the sleep of elite athletes. For example, the default sleep-wake threshold (>40 activity counts=wake) underestimates sleep duration by ∼50min and overestimates wake duration by ∼40min. In contrast, sleep-wake thresholds that have a high sensitivity to sleep (>80 activity counts=wake) yield the best combination of agreement, sensitivity, and specificity.

Does Hydrotherapy Help or Hinder Adaptation to Training in Competitive Cyclists

PURPOSE Cold water immersion (CWI) may be beneficial for acute recovery from exercise, but it may impair long-term performance by attenuating the stimuli responsible for adaptation to training. We compared effects of CWI and passive rest on cycling performance during a simulated cycling grand tour. METHODS Thirty-four male endurance-trained competitive cyclists were randomized to CWI for four times per week for 15 min at 15°C or control (passive recovery) groups for 7 d of baseline training, 21 d of intensified training, and an 11-d taper. Criteria for completion of training and testing were satisfied by 10 cyclists in the CWI group (maximal aerobic power, 5.13 ± 0.21 W·kg; mean ± SD) and 11 in the control group (5.01 ± 0.41 W·kg). Each week, cyclists completed a high-intensity interval cycling test and two 4-min bouts separated by 30 min. CWI was performed four times per week for 15 min at 15°C. RESULTS Between baseline and taper, cyclists in the CWI group had an unclear change in overall 4-min power relative to control (2.7% ± 5.7%), although mean power in the second effort relative to the first was likely higher for the CWI group relative to control (3.0% ± 3.8%). The change in 1-s maximum mean sprint power in the CWI group was likely beneficial compared with control (4.4% ± 4.2%). Differences between groups for the 10-min time trial were unclear (-0.4% ± 4.3%). CONCLUSION Although some effects of CWI on performance were unclear, data from this study do not support recent speculation that CWI is detrimental to performance after increased training load in competitive cyclists.

The Chronotype of Elite Athletes

Abstract The aims of this study were (i) to compare the chronotype distribution of elite athletes to a young adult population and (ii) to determine if there was a tendency for athletes to select and/or participate in sports which suited their chronotype. A total of 114 elite athletes from five sports (cricket, cycling, hockey, soccer and triathlon) participated in this study. The participants’ chronotype, sleepiness, sleep satisfaction and sleep quality were determined using the Horne and Östberg Morningness and Eveningness questionnaire, the Epworth Sleepiness Scale and questions concerning their sleep satisfaction and quality. All questionnaires were administered during a typical training phase that was not in the lead up to competition and/or post competition. No differences between chronotype group for sleepiness, sleep satisfaction or sleep quality were found. There was a significantly higher proportion of triathletes that were morning and intermediate types compared to the control group χ2 (2) = 7.5, p = 0.02. A significant relationship between sport and chronotype group (χ2(4)=15.9, p = 0.04) was observed, with a higher frequency of morning types involved in sports that required morning training. There was a clear indication that athletes tended to select and pursue sports that suited their chronotype. This was evident by the amount of morning types involved in morning sports. Given that athletes are more likely to pursue and excel in sports which suit their chronotype, it is recommended that coaches consider the athlete’s chronotype during selection processes or if possible design and implement changes to training schedules to either suit the athletes’ chronotype or the timing of an upcoming competition.

Effects of traditional balance and slackline training on physical performance and perceived enjoyment in young soccer players

ABSTRACT The aim of this study was to evaluate the effects of 12-week balance and slackline training programs on physical performance and perceived enjoyment scale in young soccer players. Forty-one preadolescent soccer players were assigned to two experimental groups performing traditional balance (BLT) or slackline training (SLT), and a control group. Pre-post assessment encompassed Balance Error Scoring System (BESS), Star Excursion Balance test (SEBT), sprint with 90° turns (S90), and countermovement jump (CMJ). The rate of perceived enjoyment scale (PACES) was applied at the end of the experimental period. SLT and BLT improved similarly in BESS, SEBT and S90. No changes were detected in the CMJ. Regarding PACES score, SLT presented significantly higher values than BLT. Young athletes may benefit from a motivating training approach, thus, a designed program based on slackline drills should be preferable to improve physical performance in terms of balance and change of direction ability in preadolescent soccer players.

How well does a commercially available wearable device measure sleep in young athletes

ABSTRACT 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.

Sleep at the helm: A case study of how a head coach sleeps compared to his team

In recent years, research examining the sleep of elite athletes has increased. This is predominantly due to the importance sleep has on an athlete’s psychological and physiological well-being. Despite the growing importance of sleep in athletes, the amount and quality of sleep coaches obtain has been neglected. The aim of this study was to examine the sleep of a head coach and compare it to his team. The sleep of 16 members of the Australian U/20 men’s football team (age 18.8 ± 0.9 years) and the head coach (age 55 years) was monitored using wrist activity monitors and self-report sleep diaries. Sleep was examined for 15 nights in preparation for the 2011 U/20 FIFA World Cup. The head coach went to bed earlier (23:30 h ± 65 min vs. 23:36 h ± 30 min), spent less time in bed (8.4 ± 1.3 h vs. 8.6 ± 1.0 h), obtained less sleep per night (6.4 ± 1.5 h vs. 6.6 ± 0.8 h), and woke up earlier (07:54 h ± 46 min vs. 08:12 h ± 52 min) than his team. In general, the head coach obtained less sleep than his team and slept considerably poorer the night before important games. Future investigations need to examine the extent to which sleep impairs psychological state, decision-making and overall coaching performance.

A comparison of traditional and modified Summated-Heart-Rate-Zones models to measure internal training load in basketball players

ABSTRACT The Summated-Heart-Rate-Zones training load (SHRZ TL) model is used to measure internal loading; however, a major limitation of this approach is the use of broad heart rate (HR) zones to quantify exercise intensity. Therefore, this study aimed to compare SHRZ TL outcomes derived using the traditional model and modified approaches using smaller HR zones. HR responses were monitored in 15 semi-professional basketball players during preparatory training to calculate SHRZ TL using the traditional approach with 10%HRmax zones (SHRZ10) and modified approaches with 5%HRmax zones (SHRZ5) and 2.5%HRmax zones (SHRZ2.5). Significant (P < 0.001) differences were evident in SHRZ TL between SHRZ10 (254.2 ± 41.7 AU) and SHRZ5 (275.9 ± 43.3 AU, unclear, small) as well as SHRZ2.5 (286.7 ± 44.3 AU, very likely, moderate). Use of SHRZ2.5 provides novel insight regarding internal loading in basketball players and may carry greater sensitivity for detection of maladaptive and adaptive responses to training.