ECSS Paris 2023: OP-AP20
INTRODUCTION: Sleep is essential for the health and well-being of all individuals. However, the younger population appears more vulnerable to short sleep durations, most likely due to a combination of biological, psychosocial, and societal pressures that come together and compete with one another affecting sleep [1]. Thus, it is common for teenagers to adjust their sleep schedules during the weekends to “catch up” on their sleep debt from weekdays [2, 3]; however, this might not be possible for young football players since they play official matches on the weekends during the competitive period. METHODS: Twenty-three highly trained young football players (mean ± SD; age: 17.3 ± 0.6 yrs) from the same U19 team were recruited. Sleep was objectively monitored using actigraphy devices for eight weeks, comprising two types of microcycles: normal (i.e., with an official match on the weekend) and off (i.e., with a weekend off, without matches). Linear mixed models were used to analyze the differences in sleep variables between microcycle type (normal vs. off) and night-type (weekday vs. weekend). Significance was set at p<0.05. Effect sizes from post-hoc analysis were calculated using Cohen’s d (d). RESULTS: Throughout the eight weeks, average total time in bed (TIB) was 505.1 ± 65.3 min, total sleep time (TST) 399.0 ± 63.7 min, sleep efficiency 78.9 ± 7.9%, and wake after sleep onset 98.3 ± 41.4 min. Average bedtime was at 23:48 ± 01:03, and wake-up at 08:18 ± 01:24. Eighteen (80%) players slept less than 7h on weekdays, independent of microcycle type. Sixteen (70%) players slept less than 7h on regular weekends, and seven (30%) players on off weekends. A significant (p<.05) main effect of the microcycle type, night of the week, and an interaction between both were verified for TIB, TST, bedtime, and wake-up time, indicating that weekend sleep adjustments were dependent of playing a match or not. Weekends off presented higher TIB (+35.8 min, p=.0001, d= .32) and TST (+33.6 min, p<0.0001, d=.33), and later bedtimes and wake-up times (+48 min, p<.0001, d=.43; and +01.06h, p<.0001, d=.60; respectively) compared with regular weekends with a match. Similar TIB and TST were observed between weekday and weekend-nights of normal microcycles. CONCLUSION: Young football players adopted a compensatory sleep behavior on weekends without competitive matches. Despite this behavior, it is questionable whether the observed increase in TST is sufficient to compensate weekdays sleep debt. Also, considering the importance of maintaining a regular sleep schedule, it is arguable whether this behavior is healthy, given the regular changes on sleep schedules between weekday and weekend nights. Funding: This work was supported by the Portuguese Foundation for Science and Technology [2022.09446.BD] and [UIDB04045/2020]. REFERENCES: 1. Carskadon, M.A.: Pediatric Clinics of North America (2011) 2. Bartel, K.A. et al., Sleep Med Rev. (2015). 3. Fox, J.L. et al., Sports Med. (2020)
Read CV João BarreiraECSS Paris 2023: OP-AP20
INTRODUCTION: Training loads and daily schedules are referred to as relevant sports factors that influence athletes sleep [1]. Yet, data examining training loads or daily schedules in young football players is scarce. Therefore, this study aimed to describe the sleep habits and needs of young football players, analyzing the differences between microcycle days and the influence of training/matches workloads. METHODS: The study included 60 young male football players with a mean age of 16.3 ± 1.4 years old. For two weeks, the players wore a 3-axial accelerometer during sleep, filled out sleep diaries, and answered the question "how many hours of sleep do you need to feel rested?". A difference of 1h or more between sleep needs and sleep duration was considered sleep deficit [2]. Training/matches external workloads were monitored using 10-hz GPS devices. Linear mixed models were used to examine the differences between sleep duration and sleep efficiency between training (TD), match (MD), and rest days (RD). Within-subjects correlations were tested between sleep duration, sleep efficiency and training/matches external workload metrics (total distance, high-speed running distance, and sprinting distance). RESULTS: : On average, players obtained 8.1 ± 1.5 hours of sleep duration, had a sleep efficiency of 83 ± 7% and reported needing 8.8 ± 0.9 hours of sleep. Twenty-seven athletes (45%) slept less than 8 hours per night, thirty-six (60%) had a sleep efficiency below 85%, and fifteen (30%) had a sleep deficit. Regarding the differences on sleep between microcycle days, players slept 8.0 ± 1.5 hours and had 83 ± 7% sleep efficiency on training days, 7.6 ± 1.5 hours and 83 ± 9% on match days, and 8.5 ± 1.3 hours and 84 ± 6% on rest days. There was a trivial main effect for microcycle days on sleep duration (F=3.1, p=0.04, ηp2= 0.009), but not on sleep efficiency (p=0.78). No associations were found between sleep duration/efficiency and external workload metrics (p>0.05). CONCLUSION: The results of this study revealed that one-third of the players presented a sleep deficit, and two-thirds had a sleep efficiency below 85%. It appears there were trivial to no differences between the microcycle days on sleep duration and sleep efficiency. Also, external training/match workloads throughout the microcycle were unrelated to sleep duration and efficiency References: 1. Walsh, N.P. et al., British journal of sports medicine, (2020). 2. Sargent et al., Int J Sports Physiol Perform, (2021). Funding: This work was supported by the Portuguese Foundation for Science and Technology [UI/BD/151482/2021 and UIDB04045/2020].
Read CV Lúcio CunhaECSS Paris 2023: OP-AP20
INTRODUCTION: Sleep is recognised as an essential component of athletic performance and recovery. Research on the sleep requirements of football players is scarce (1,2), revealing that athletes typically obtain less sleep than their self-assessed needs. This highlights a need to research how daily objective sleep data, factors related to training demands and sleep patterns impact sleep. This study aimed to compare the self-assessed sleep needs of football players with daily objective measures of sleep duration and to explore the relationship between daily fatigue, external workloads and sleep habits with sleep insufficiency. METHODS: For 2 weeks, 72 football players (19 females; 19.5 ± 4.4 years old) wore an accelerometer to measure sleep duration and efficiency and reported daily pre- and post-sleep perceived fatigue and sleep quality using a Likert scale (1: very, very low/poor; to 7: very, very high/good). Total distance during training/matches (external workloads) was measured using GPS devices. The players answered the question, "how many hours of sleep do you need to feel rested?". A daily sleep index was calculated by subtracting sleep duration from individual self-assessed sleep needs. A difference of 1 hour or more indicated insufficient sleep (1). Sleep quality index was measured using the Pittsburgh Sleep Quality Index (PSQI), daytime sleepiness with the Epworth Sleepiness Score, and chronotype with the Morningness-Eveningness Questionnaire. Differences between sleep needs and sleep duration were analyzed using a linear mixed model. A generalized mixed-effects regression tree was applied using the daily sleep index (sufficient sleep vs insufficient sleep) as the outcome variable. RESULTS: Players reported an average subjective sleep need of 8.4 (min-max: 6-11) hours and slept an average sleep duration of 8.2 (min-max: 5.5-10.3) hours (p < 0.001). The prevalence of insufficient sleep was 25% (157 out of 619 recorded nights), with 48 (67%) of players having at least one night of insufficient sleep. The generalized mixed-effects regression tree revealed daily post-sleep fatigue and sleep quality (PSQI) as the significant factors associated with daily sleep insufficiency. Also, those with high to very, very high daily post-sleep fatigue (> 5) had a higher prevalence of insufficient sleep than those with average to very, very low daily post-sleep fatigue (≤ 5). Among those with average to very, very low post-sleep fatigue (≤ 5), having poor sleep quality (PSQI >5) was associated with a higher prevalence of insufficient sleep. CONCLUSION: Daily post-sleep fatigue and sleep quality (PSQI) were selected as explanatory variables of daily sleep insufficiency. These results highlight that monitoring post-sleep fatigue can be used to track athletes obtaining insufficient sleep, which can negatively affect performance, recovery, and health. ACKNOWLEDGEMENTS: FCT project UIDB04045/2020 REFERENCES: 1. Sargent et al., Int J Sports Physiol Perform, 2021 2. Figueiredo et al., 28th ECSS, 2023
Read CV Pedro FigueiredoECSS Paris 2023: OP-AP20