The night before night shift: Chronotype impacts total sleep and rapid eye movement sleep during a strategically delayed sleep.

Transition to night shift may be improved by strategically delaying the main sleep preceding a first night shift. However, the effects of delayed timing on sleep may differ between chronotypes. Therefore, the study aim was to compare the impacts of chronotype on sleep quality and architecture during a normally timed sleep opportunity and a delayed sleep opportunity. Seventy-two (36 female, 36 male) healthy adults participated in a laboratory study. Participants were provided with a normally timed sleep opportunity (23:00-08:00) and a delayed sleep opportunity (03:00-12:00) over two consecutive nights in a sleep laboratory. Sleep was monitored by polysomnography (PSG), and chronotype was determined from dim light melatonin onset (DLMO). A tertile split of DLMO defined early (20:24 ± 0:42 h), intermediate (21:31 ± 0:12 h), and late chronotype (22:56 ± 0:54 h) categories. Although there was no main effect of chronotype on any sleep measure, early chronotypes obtained less total sleep with delayed sleep than with normally timed sleep (p = 0.044). Intermediate and late chronotypes obtained more rapid eye movement (REM) sleep with delayed sleep than with normally timed sleep (p = 0.013, p = 0.012 respectively). Wake was more elevated for all chronotypes in the later hours of the delayed sleep opportunity than at the start of the sleep opportunity. Strategically delaying the main sleep preceding a first night shift appears to benefit intermediate and late chronotypes (i.e., more REM sleep), but not early chronotypes (i.e., less total sleep). Circadian processes appear to elevate wakefulness for all chronotypes in the later stages of a delayed sleep opportunity.

The effect of mild to moderate sleep restriction on subjective hunger in healthy young men.

There is good evidence to indicate severe sleep restriction increases subjective feelings of hunger, but the impact of mild to moderate sleep restriction (i.e., 5-7 h) on hunger has not been systematically evaluated. Healthy male participants (n = 116; 22.8 ± 2.1 years; 22.9 ± 3.7 kg⋅m-2) were recruited to a ten-day laboratory study. In a between groups design, participants were allocated to one of five time in bed conditions (5 h, 6 h, 7 h, 8 h or 9 h) for seven consecutive nights. Participants were provided a eucaloric diet and ratings of hunger, nausea and desire to eat certain foods were collected using visual analogue scales prior to meals (breakfast, lunch, afternoon snack, dinner and evening snack) on four days during the study. Data were analysed using linear mixed models with time in bed, time of day and study day as fixed effects and participant as a random effect. There was no main effect of time in bed, and no interaction between time in bed and study day, on hunger, nausea, prospective hunger or desire to eat certain foods. However, post-hoc analyses indicated that participants in the 5-h condition had an elevated desire to consume sweet foods and fruit on the final morning of the protocol. There was a main effect of time of day and study day on hunger; participants were hungriest prior to lunch time and hunger decreased over consecutive days of the protocol. When provided with a eucaloric diet, only 5-h time in bed increased desire to consume sweet foods and fruit in healthy young men.

A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults.

The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was to examine the validity of the six devices for assessing heart rate and heart rate variability during, or just prior to, night-time sleep. Fifty-three adults (26 F, 27 M, aged 25.4 ± 5.9 years) spent a single night in a sleep laboratory with 9 h in bed (23:00-08:00 h). Participants were fitted with all six wearable devices-and with polysomnography and electrocardiography for gold-standard assessment of sleep and heart rate, respectively. Compared with polysomnography, agreement (and Cohen's kappa) for two-state categorisation of sleep periods (as sleep or wake) was 88% (κ = 0.30) for Apple Watch; 89% (κ = 0.35) for Garmin; 87% (κ = 0.44) for Polar; 89% (κ = 0.51) for Oura; 86% (κ = 0.44) for WHOOP and 87% (κ = 0.48) for Somfit. Compared with polysomnography, agreement (and Cohen's kappa) for multi-state categorisation of sleep periods (as a specific sleep stage or wake) was 53% (κ = 0.20) for Apple Watch; 50% (κ = 0.25) for Garmin; 51% (κ = 0.28) for Polar; 61% (κ = 0.43) for Oura; 60% (κ = 0.44) for WHOOP and 65% (κ = 0.52) for Somfit. Analyses regarding the two-state categorisation of sleep indicate that all six devices are valid for the field-based assessment of the timing and duration of sleep. However, analyses regarding the multi-state categorisation of sleep indicate that all six devices require improvement for the assessment of specific sleep stages. As the use of wearable devices that are valid for the assessment of sleep increases in the general community, so too does the potential to answer research questions that were previously impractical or impossible to address-in some way, we could consider that the whole world is becoming a sleep laboratory.

A Week of Sleep Restriction Does Not Affect Nighttime Glucose Concentration in Healthy Adult Males When Slow-Wave Sleep Is Maintained.

The aim of this laboratory-based study was to examine the effect of sleep restriction on glucose regulation during nighttime sleep. Healthy males were randomly assigned to one of two conditions: 9 h in bed (n = 23, age = 24.0 year) or 5 h in bed (n = 18, age = 21.9 year). Participants had a baseline night with 9 h in bed (23:00-08:00 h), then seven nights of 9 h (23:00-08:00 h) or 5 h (03:00-08:00 h) in bed. Participants were mostly seated during the daytime but had three bouts of treadmill walking (4 km·h-1 for 10 min) at ~14:40 h, ~17:40 h, and ~20:40 h each day. On the baseline night and night seven, glucose concentration in interstitial fluid was assessed by using continuous glucose monitors, and sleep was assessed by using polysomnography. On night seven, compared to the 9 h group, the 5 h group obtained less total sleep (292 min vs. 465 min) and less REM sleep (81 min vs. 118 min), but their slow-wave sleep did not differ (119 min vs. 120 min), and their glucose concentration during sleep did not differ (5.1 mmol·L-1 vs. 5.1 mmol·L-1). These data indicate that sleep restriction does not cause elevated levels of circulating glucose during nighttime sleep when slow-wave sleep is maintained. In the future, it will be important to determine whether increased insulin is required to maintain circulating glucose at a normal level when sleep is restricted.

Correction: Bellenger et al. Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP. Sensors 2021, 21, 3571.

The authors wish to correct the following errors in the original paper [...].

Evaluating the Typical Day-to-Day Variability of WHOOP-Derived Heart Rate Variability in Olympic Water Polo Athletes.

Heart rate (HR) and HR variability (HRV) can be used to infer readiness to perform exercise in athletic populations. Advancements in the photoplethysmography technology of wearable devices such as WHOOP allow for the frequent and convenient measurement of HR and HRV, and therefore enhanced application in athletes. However, it is important that the reliability of such technology is acceptable prior to its application in practical settings. Eleven elite male water polo players (age 28.8 ± 5.3 years [mean ± standard deviation]; height 190.3 ± 3.8 cm; body mass 95.0 ± 6.9 kg; international matches 117.9 ± 92.1) collected their HR and HRV daily via a WHOOP strap (WHOOP 3.0, CB Rank, Boston, MA, USA) over 16 weeks ahead of the 2021 Tokyo Olympic Games. The WHOOP strap quantified HR and HRV via wrist-based photoplethysmography during overnight sleep periods. The weekly (i.e., 7-day) coefficient of variation in lnRMSSD (lnRMSSDCV) and HR (HRCV) was calculated as a measure of day-to-day variability in lnRMSSD and HR, and presented as a mean of the entire recording period. The mean weekly lnRMSSDCV and HRCV over the 16-week period was 5.4 ± 0.7% (mean ± 95% confidence intervals) and 7.6 ± 1.3%, respectively. The day-to-day variability in WHOOP-derived lnRMSSD and HR is within or below the range of day-to-day variability in alternative lnRMSSD (~3-13%) and HR (~10-11%) assessment protocols, indicating that the assessment of HR and HRV by WHOOP does not introduce any more variability than that which is naturally present in these variables.

The Effect of Game-Related Contextual Factors on Sleep in Basketball Players.

ABSTRACT: Fox, JL, Scanlan, AT, Stanton, R, and Sargent, C. The effect of game-related contextual factors on sleep in basketball players. J Strength Cond Res 36(10): 2942-2945, 2022-The purpose of this study was to determine whether contextual factors including game location, game outcome, and score-line margin influence subsequent nightly sleep duration and quality in basketball players. Seven semiprofessional, male basketball players were monitored across one competitive season using self-reported sleep diaries and activity monitors. Linear mixed models and effect sizes (ESs) were used to compare sleep duration and quality following games based on game location (played at home or away venues), game outcome (win or loss), and score-line margin (balanced [≤ 8-point margin] or unbalanced [≥ 9-point margin]). Sleep onset was later following away games (ES = 0.79 ± 0.86 [ moderate ], p = 0.12), losses (ES = 0.63 ± 0.79 [ moderate ], p = 0.18), and unbalanced games (ES = 1.36 ± 0.90 [ large ], p = 0.01). Wake time was later following losses (ES = 1.14 ± 0.85 [ moderate ], p = 0.02). Sleep fragmentation was higher following wins (ES = 0.65 ± 0.74 [ moderate ], p = 0.17). Sleep efficiency was higher following losses (ES = 0.70 ± 0.80 [ moderate ], p = 0.14). All other differences in sleep variables yielded trivial - small ESs ( p > 0.05). Because of the later sleep onset times of players, basketball practitioners should consider avoiding early morning travel or training sessions following away games, losses, and unbalanced games, to maintain similar nightly sleep durations in light of the contextual factors encountered. Basketball practitioners may also need to ensure player sleep is optimized following wins compared with losses given the lower quality observed.

Are acute player workloads associated with in-game performance in basketball?

To investigate associations between acute workload and in-game performance in basketball. Eight semi-professional, male basketball players were monitored during all training sessions (N = 28) and games (N = 18) across the season. External workload was determined using absolute (arbitrary units[AU]) and relative (AU·min-1) PlayerLoadTM (PL), and absolute (count) and relative (count·min-1) low-intensity, medium-intensity, high-intensity, and total Inertial Movement Analysis (IMA) events (accelerations, decelerations, changes-of-direction, and jumps). Internal workload was determined using absolute and relative Summated-Heart-Rate-Zones workload, session-rating of perceived exertion, rating of perceived exertion, and time (min) spent working > 90% of maximal heart rate. In-game performance was indicated by the player efficiency statistic. Repeated measures correlations were used to determine associations between acute workload variables (across the previous 7 days) and player efficiency. Relative PL (r = 0.13, small) and high-intensity IMA events (r = 0.13, small) possessed the strongest associations with player efficiency of the investigated workload variables (P > 0.05). All other associations were trivial in magnitude (P > 0.05). Given the trivial-small associations between all external and internal workload variables and player efficiency, basketball practitioners should not rely solely on monitoring acute workloads to determine performance potential in players.

Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP.

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP's PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP and ECG over 15 opportunities. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP's proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10-11%) and SWC (5-5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP's proprietary filter, which approached or exceeded the CV (3-13%) and SWC (1.5-6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.

An Individualized Intervention Increases Sleep Duration in Professional Athletes.

ABSTRACT: Sargent, C, Lastella, M, Schwerdt, S, and Roach, GD. An individualized intervention increases sleep duration in professional athletes. J Strength Cond Res 35(12): 3407-3413, 2021-Athletes typically obtain less sleep than is generally recommended for healthy adults. The aim of this study was to determine whether individualized feedback could increase sleep duration in professional cricket players in the 3 weeks before the start of the domestic season. Players were randomly assigned to a control group (i.e., no individual feedback; n = 8) or an intervention group (i.e., individual feedback about bedtime, wake time, and sleep duration; n = 7). Night-time sleep and daytime naps were monitored using wrist activity monitors in conjunction with self-report sleep diaries for 1 week before, and 1 week after, the feedback intervention. Cumulative sleep duration was calculated as the sum of the sleep duration for a night-time sleep episode and any naps that occurred on the following day. Differences in cumulative sleep duration before and after the intervention were examined using a mixed-effects analysis of variance. There was an interaction between group and week for cumulative sleep duration (p = 0.039; η2 = 0.6; large). The average cumulative sleep duration was longer (+36 minutes) in the intervention group in week 2 compared with week 1. Individualized feedback can be used to increase sleep duration in professional cricket players. In future, it will be important to determine whether improvements in sleep duration can be maintained throughout the season.

Exercise before bed does not impact sleep inertia in young healthy males.

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.

How to manage travel fatigue and jet lag in athletes? A systematic review of interventions.

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).

Sleep and the athlete: narrative review and 2021 expert consensus recommendations.

Elite athletes are particularly susceptible to sleep inadequacies, characterised by habitual short sleep (<7 hours/night) and poor sleep quality (eg, sleep fragmentation). Athletic performance is reduced by a night or more without sleep, but the influence on performance of partial sleep restriction over 1-3 nights, a more real-world scenario, remains unclear. Studies investigating sleep in athletes often suffer from inadequate experimental control, a lack of females and questions concerning the validity of the chosen sleep assessment tools. Research only scratches the surface on how sleep influences athlete health. Studies in the wider population show that habitually sleeping <7 hours/night increases susceptibility to respiratory infection. Fortunately, much is known about the salient risk factors for sleep inadequacy in athletes, enabling targeted interventions. For example, athlete sleep is influenced by sport-specific factors (relating to training, travel and competition) and non-sport factors (eg, female gender, stress and anxiety). This expert consensus culminates with a sleep toolbox for practitioners (eg, covering sleep education and screening) to mitigate these risk factors and optimise athlete sleep. A one-size-fits-all approach to athlete sleep recommendations (eg, 7-9 hours/night) is unlikely ideal for health and performance. We recommend an individualised approach that should consider the athlete's perceived sleep needs. Research is needed into the benefits of napping and sleep extension (eg, banking sleep).

A validation study of the WHOOP strap against polysomnography to assess sleep.

The aim of the study was to compare the WHOOP strap - a wearable device that estimates sleep based on measures of movement and heart rate derived from actigraphy and photoplethysmography, respectively. Twelve healthy adults (6 females, 6 males, aged 22.9 ± 3.4 years) participated in a 10-day, laboratory-based protocol. A total of 86 sleeps were independently assessed in 30-s epochs using polysomnography and WHOOP. For WHOOP, bed times were entered by researchers and sleeps were scored by the company based on proprietary algorithms. WHOOP overestimated total sleep time by 8.2 ± 32.9 minutes compared to polysomnography, but this difference was non-significant. WHOOP was compared to polysomnography for 2-stage (i.e., wake, sleep) and 4-stage categorisation (i.e., wake, light sleep [N1 or N2], slow-wave sleep [N3], REM) of sleep periods. For 2-stage categorisation, the agreement, sensitivity to sleep, specificity for wake, and Cohen's kappa were 89%, 95%, 51%, and 0.49, respectively. For 4-stage categorisation, the agreement, sensitivity to light sleep, SWS, REM, and wake, and Cohen's kappa were 64%, 62%, 68%, 70%, 51%, and 0.47, respectively. In situations where polysomnography is impractical (e.g., field settings), WHOOP is a reasonable method for estimating sleep, particularly for 2-stage categorisation, if accurate bedtimes are manually entered.

Impact of short- compared to long-haul international travel on the sleep and wellbeing of national wheelchair basketball athletes.

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.

Sleep/wake behaviour of endurance cyclists before and during competition.

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.

Alternatives to polysomnography (PSG): a validation of wrist actigraphy and a partial-PSG system.

The objective of this study was to assess the validity of a sleep/wake activity monitor, an energy expenditure activity monitor, and a partial-polysomnography system at measuring sleep and wake under identical conditions. Secondary aims were to evaluate the sleep/wake thresholds for each activity monitor and to compare the three devices. To achieve these aims, two nights of sleep were recorded simultaneously with polysomnography (PSG), two activity monitors, and a partial-PSG system in a sleep laboratory. Agreement with PSG was evaluated epoch by epoch and with summary measures including total sleep time (TST) and wake after sleep onset (WASO). All of the devices had high agreement rates for identifying sleep and wake, but the partial-PSG system was the best, with an agreement of 91.6% ± 5.1%. At their best thresholds, the sleep/wake monitor (medium threshold, 87.7% ± 7.6%) and the energy expenditure monitor (very low threshold, 86.8% ± 8.6%) had similarly high rates of agreement. The summary measures were similar to those determined by PSG, but the partial-PSG system provided the most consistent estimates. Although the partial-PSG system was the most accurate device, both activity monitors were also valid for sleep estimation, provided that appropriate thresholds were selected. Each device has advantages, so the primary consideration for researchers will be to determine which best suits a given research design.

The Night-Time Sleep and Autonomic Activity of Male and Female Professional Road Cyclists Competing in the Tour de France and Tour de France Femmes.

BACKGROUND: Sleep is a critical component of recovery, but it can be disrupted following prolonged endurance exercise. The objective of this study was to examine the capacity of male and female professional cyclists to recover between daily race stages while competing in the 2022 Tour de France and the 2022 Tour de France Femmes, respectively. The 17 participating cyclists (8 males from a single team and 9 females from two teams) wore a fitness tracker (WHOOP 4.0) to capture recovery metrics related to night-time sleep and autonomic activity for the entirety of the events and for 7 days of baseline before the events. The primary analyses tested for a main effect of 'stage classification'-i.e., rest, flat, hilly, mountain or time trial for males and flat, hilly or mountain for females-on the various recovery metrics. RESULTS: During baseline, total sleep time was 7.2 ± 0.3 h for male cyclists (mean ± 95% confidence interval) and 7.7 ± 0.3 h for female cyclists, sleep efficiency was 87.0 ± 4.4% for males and 88.8 ± 2.6% for females, resting HR was 41.8 ± 4.5 beats·min-1 for males and 45.8 ± 4.9 beats·min-1 for females, and heart rate variability during sleep was 108.5 ± 17.0 ms for males and 119.8 ± 26.4 ms for females. During their respective events, total sleep time was 7.2 ± 0.1 h for males and 7.5 ± 0.3 h for females, sleep efficiency was 86.4 ± 1.2% for males and 89.6 ± 1.2% for females, resting HR was 44.5 ± 1.2 beats·min-1 for males and 50.2 ± 2.0 beats·min-1 for females, and heart rate variability during sleep was 99.1 ± 4.2 ms for males and 114.3 ± 11.2 ms for females. For male cyclists, there was a main effect of 'stage classification' on recovery, such that heart rate variability during sleep was lowest after mountain stages. For female cyclists, there was a main effect of 'stage classification' on recovery, such that the percentage of light sleep (i.e., lower-quality sleep) was highest after mountain stages. CONCLUSIONS: Some aspects of recovery were compromised after the most demanding days of racing, i.e., mountain stages. Overall however, the cyclists obtained a reasonable amount of good-quality sleep while competing in these physiologically demanding endurance events. This study demonstrates that it is now feasible to assess recovery in professional athletes during multiple-day endurance events using validated fitness trackers.

Yin and yang, or peas in a pod? Individual-sport versus team-sport athletes and altitude training.

The question of whether altitude training can enhance subsequent sea-level performance has been well investigated over many decades. However, research on this topic has focused on athletes from individual or endurance sports, with scant number of studies on team-sport athletes. Questions that need to be answered include whether this type of training may enhance team-sport athlete performance, when success in team-sport is often more based on technical and tactical ability rather than physical capacity per se. This review will contrast and compare athletes from two sports representative of endurance (cycling) and team-sports (soccer). Specifically, we draw on the respective competition schedules, physiological capacities, activity profiles and energetics of each sport to compare the similarities between athletes from these sports and discuss the relative merits of altitude training for these athletes. The application of conventional live-high, train-high; live-high, train-low; and intermittent hypoxic training for team-sport athletes in the context of the above will be presented. When the above points are considered, we will conclude that dependent on resources and training objectives, altitude training can be seen as an attractive proposition to enhance the physical performance of team-sport athletes without the need for an obvious increase in training load.

Changes in blood gas transport of altitude native soccer players near sea-level and sea-level native soccer players at altitude (ISA3600).

OBJECTIVES: The optimal strategy for soccer teams playing at altitude is not known, that is, 'fly-in, fly-out' versus short-term acclimatisation. Here, we document changes in blood gas and vascular volumes of sea-level (Australian, n=20) and altitude (Bolivian, n=19) native soccer players at 3600 m. METHODS: Haemoglobin-oxygen saturation (Hb-sO2), arterial oxygen content (CaO2), haemoglobin mass (Hbmass), blood volume (BV) and blood gas concentrations were measured before descent (Bolivians only), together with aerobic fitness (via Yo-YoIR1), near sea-level, after ascent and during 13 days at 3600 m. RESULTS: At baseline, haemoglobin concentration [Hb] and Hbmass were higher in Bolivians (mean ± SD; 18.2 ± 1.0 g/dL, 12.8 ± 0.8 g/kg) than Australians (15.0 ± 0.9 g/dL, 11.6 ± 0.7 g/kg; both p ≤ 0.001). Near sea-level, [Hb] of Bolivians decreased to 16.6 ± 0.9 g/dL, but normalised upon return to 3600 m; Hbmass was constant regardless of altitude. In Australians, [Hb] increased after 12 days at 3600 m to 17.3 ± 1.0 g/dL; Hbmass increased by 3.0 ± 2.7% (p ≤ 0.01). BV decreased in both teams at altitude by ∼400 mL. Arterial partial pressure for oxygen (PaO2), Hb-sO2 and CaO2 of both teams decreased within 2 h of arrival at 3600 m (p ≤ 0.001) but increased over the following days, with CaO2 overcompensated in Australians (+1.7 ± 1.2 mL/100 mL; p ≤ 0.001). Yo-YoIR1 was lower on the 3rd versus 10th day at altitude and was significantly related to CaO2. CONCLUSIONS: The marked drop in PaO2 and CaO2 observed after ascent does not support the 'fly-in, fly-out' approach for soccer teams to play immediately after arrival at altitude. Although short-term acclimatisation was sufficient for Australians to stabilise their CaO2 (mostly due to loss of plasma volume), 12 days appears insufficient to reach chronic levels of adaption.