Elite soccer athlete's sleep: A literature review

Aspects of Elite Female Football Players' Training Loads and Sleep Variations

The current study investigated the associations between female football players' training loads and their sleep variations. The sample included 21 female elite football players from a Norwegian top-league club with a mean age of 24 years (±2.8). Sleep duration, sleep quality, and training load were monitored every day over 273 consecutive days with a Somnofy sleep monitor based on ultra-wideband (IR-UWB) pulse radar and Doppler technology, and a FIFA-approved STATSports APEX 10 Hz GPS tracking system monitoring players' training loads. A multivariate analysis of variance (MANOVA) was conducted to investigate the relationships between the players' training loads and sleep. It was revealed that very high training loads were associated with reduced time in bed (p = 0.005), total sleep time (p = 0.044)), and rapid eye movement (p < 0.001). The present findings show that the female football players' sleep was disrupted when the training load, based on total distance (TDI), was very high. It appears to be a point where their sleep is somewhat consistent through low, medium, and high training loads, but with disrupted sleep when the training load reaches a very high level. Considering the reduced TIB after a very high training load, there should be suggested strategies to improve their sleep, such as extended TIB, to aid in longer TST and improved recovery.

Sleep Quality and Training Intensity in Soccer Players: Exploring Weekly Variations and Relationships

The aim of this study was twofold: it (i) analyzed the weekly variations of sleep quality and training intensity of youth soccer players and (ii) analyzed the relationships between sleep quality and training intensity. This study followed an observational design. Twenty men youth players (age: 18.81 ± 0.41 years) were monitored daily over two weeks for sleep quality and training intensity. Sleep quality was measured daily using the 15-item consensus sleep diary. The training intensity was measured daily using the CR-10 Borg’s scale as a measure of rate of perceived exertion (RPE); a heart rate (HR) sensor was used to measure minimum, average and peak; a global positioning system (GPS) was used for measuring the total distance covered and distances covered at different speed thresholds. Repeated measures ANOVA was used to analyze the weekly variations of sleep quality and training intensity. The Pearson correlation test was executed to analyze the relationships between sleep quality and training intensity. Repeated measures ANOVA revealed significant within-week variations in sleep duration (hours) (p = 0.043), quality of sleep (p = 0.035), RPE (p = 0.007), session-RPE (p = 0.011), HRminimum (p = 0.027), HRpeak (p = 0.005), total distance (p < 0.001), pace (p < 0.002), distance covered at 3.00–6.99 km/h (p < 0.001), distance covered at 7.00–10.99 km/h (p < 0.001), distance covered at 11.00–14.99 km/h (p < 0.001), distance covered at 15.00–18.99 km/h (p < 0.001) and distance covered at >19.00 km/h (p < 0.001). Significant small correlations were found between sleep duration before training and session-RPE (r = 0.252), total distance (r = 0.205), distance covered at 3.00–6.99 km/h (r = 0.209) and distance covered at 7.00–10.99 km/h (r = 0.265). Significant small correlations were found between session-RPE and sleep duration after (r = 0.233), total distance and quality of sleep after (r = 0.198), distance at 3.00–6.99 km/h and quality of sleep after training (r = 0.220), distance covered at >19.00 km/h and quality of sleep after training (r = 0.286), session duration and rested feeling after training (r = 0.227), total distance and rested feeling after training (r = 0.202), distance covered at 11.00–14.99 km/h and rested feeling after training (r = 0.222) and distance covered at >19.00 km/h and rested feeling after training (r = 0.214). In conclusion, sleep duration was longer in the training sessions during the middle of the week; the training intensity was also greater (485.8 ± 56.8 A.U.). Moreover, sleep outcomes after training were slightly correlated with both physiological and locomotor demands.