Comparing Sleep in Shared and Individual Rooms During Training Camps in Elite Youth Soccer Players: A Short Report

The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional Soccer

The main aim of the present study was to uncover multivariate relationships between sleep quantity and quality using principal component analysis (PCA) in professional female soccer players. A second aim was to examine the extent to which objective sleep quantity and quality variables can discriminate between perceived sleep. Ten objective sleep variables from the multisensory sleep-tracker were analyzed. PCA was conducted on the sleep variables, and meaningful principal components (PCs) were identified (eigenvalue > 2). Two sleep PCs were identified, representing the 'quantity of sleep' (quantity PC: eigenvalue = 4.1 and variance explained = 45.1%) and the 'quality of sleep' (quality PC: eigenvalue = 2.4 and variance explained = 24.1%). Cluster analysis grouped the players' sleep into three types: long and efficient, short and efficient, and long and inefficient; however, no association was found between the perceived sleep and the sleep clusters. In conclusion, a combination of both quantity and quality sleep metrics is recommended for sleep monitoring of professional female soccer players. Players should undergo a training process to improve self-assessment of sleep quality recorded from a subjective questionnaire, contrasting the perceived information with the sleep quality recorded objectively during a defined period in order to optimize the validity of their perceptions. The aim is to optimize the validity of their perceptions of sleep quality.

Monitoring load, wellness, and psychological variables in female and male youth national team football players during international and domestic playing periods

Aim

To study differences in total load exposure, wellness, and psychological variables in youth female (N = 19) and male (N = 20) national team football players during domestic and international playing periods, respectively.

Procedures

The players filled out questionnaires on well-being, stress, and resilience before and after both playing periods lasting 8 days each. The Hooper index was used to monitor daily wellness levels during both playing periods. The number of training sessions and matches were recorded, and the session rating of perceived exertion was collected. Training load, monotony, and strain were calculated. Daily measurements were used to evaluate in-period changes, and composite scores were used to describe differences between periods.

Results

The international compared to the domestic playing period was for both groups characterized by more matches played, longer field training session durations, and of fewer gym-based sessions (P < 0.05). The male players increased total exposure time (25%; P < 0.05), monotony (P < 0.001), and strain (P < 0.001), which was not changed in the female players. Well-being decreased (P < 0.05) during the international playing period in male players. Stress levels were higher (P < 0.05) for both genders during the international compared to the domestic playing period. During the international playing period, positive correlations were found between the initial levels of stress, and the change in stress (P = 0.03; r2 = 0.12), and between the changes in total load and changes in well-being (P = 0.02; r2 = 0.12), whereas a negative correlation was found between the changes in wellness and stress (P = 0.03; r2 = 0.14).

Conclusion

A playing period characterized by increased match focus, longer field training sessions, and fewer gym-based training activities may lead to changes in the physical and mental profiles of youth national team football players. Alterations to load exposure and wellness may influence mental health. Players with high initial stress levels may be subjected to greater changes compared to other players. Sports scientists and medical staff may benefit from initiating structured monitoring systems to track alterations in physical load and mental health in youth national team players.

To Sleep Dreaming Medals: Sleep Characteristics, Napping Behavior, and Sleep-Hygiene Strategies in Elite Track-and-Field Athletes Facing the Olympic Games of Tokyo 2021

PURPOSE

Few data are available on sleep characteristics of elite track-and-field athletes. Our study aimed to assess (1) differences in sleep between sexes and among different track-and-field disciplines, (2) the effect of individualized sleep-hygiene strategies on athletes' sleep parameters, and (3) daytime nap characteristics in track-and-field athletes.

METHODS

Sleep characteristics of 16 elite Olympic-level track-and-field athletes (male: n = 8; female: n = 8) were assessed during the preseason period, at baseline (T0), and during the in-season period, after the adoption of individualized sleep-hygiene strategies (T1). Sleep parameters were objectively monitored by actigraphy for a minimum of 10 days, for each athlete, at both T0 and T1. A total of 702 nights were analyzed (T0 = 425; T1 = 277).

RESULTS

Female athletes displayed better sleep efficiency (88.69 [87.69-89.68] vs 91.72 [90.99-92.45]; P = .003, effect size [ES]: 0.44), lower sleep latency (18.99 [15.97-22.00] vs 6.99 [5.65-8.32]; P < .001, ES: 0.65), higher total sleep time (07:03 [06:56-07:11] vs 07:18 [07:10-07:26]; P = .030, ES: 0.26), earlier bedtime (00:24 [00:16-00:32] vs 00:13 [00:04-00:22]; P = .027, ES: 0.18), and lower nap frequency (P < .001) than male athletes. Long-distance runners had earlier bedtime (00:10 [00:03-00:38] vs 00:36 [00:26-00:46]; P < .001, ES: 0.41) and wake-up time (07:41 [07:36-07:46] vs 08:18 [08:07-08:30]; P < .001, ES: 0.61), higher nap frequency, but lower sleep efficiency (88.79 [87.80-89.77] vs 91.67 [90.95-92.38]; P = .013, ES: 0.44), and longer sleep latency (18.89 [15.94-21.84] vs 6.69 [5.33-8.06]; P < .001, ES: 0.67) than athletes of short-term disciplines. Furthermore, sleep-hygiene strategies had a positive impact on athletes' total sleep time (429.2 [423.5-434.8] vs 451.4 [444.2-458.6]; P < .001, ES: 0.37) and sleep latency (14.33 [12.34-16.32] vs 10.67 [8.66-12.68]; P = .017, ES: 0.19).

CONCLUSIONS

Sleep quality and quantity were suboptimal at baseline in Olympic-level track-and-field athletes. Large differences were observed in sleep characteristics between sexes and among different track-and-field disciplines. Given the positive effect of individualized sleep-hygiene strategies on athlete's sleep, coaches should implement sleep education sessions in the daily routine of top-level athletes.