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


Creative Commons License

Silva A. F., Oliveira R., Akyildiz Z., Yıldız M., Ocak Y., GÜNAY M., ...More

Applied Sciences (Switzerland), vol.12, no.6, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 6
  • Publication Date: 2022
  • Doi Number: 10.3390/app12062791
  • Journal Name: Applied Sciences (Switzerland)
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: sleep, exercise, youth sports, football, WELLNESS, RECOVERY, LOAD, PERFORMANCE, PRESEASON, QUANTITY, DIARY
  • Gazi University Affiliated: Yes

Abstract

© 2022 by the authors. Licensee MDPI, Basel, Switzerland.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.