Student-athletes are subject to significant demands due to their concurrent sporting and academic commitments, which may affect their sleep. This study aimed to compare the self-reported sleep quality, quantity, and intraindividual variability (IIV) of students and student-athletes through an online survey. Student-athletes will have a poorer sleep quality and quantity and experience more IIV. Case-control study. Level 4. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while sleep quantity and IIV were assessed using the Consensus Sleep Diary. Initially, the PSQI and additional questions regarding sport participation habits were completed by 138 participants (65 students, 73 student-athletes). From within this sample, 44 participants were recruited to complete the sleep diary for a period of 14 days. The mean PSQI score was 6.89 ± 3.03, with 65% of the sample identified as poor sleepers, but no difference was observed between students and student-athletes. Analysis of sleep patterns showed only possibly to likely small differences in sleep schedule, sleep onset latency, and subjective sleep quality between groups. IIV analysis showed likely moderate to possibly small differences between groups, suggesting more variable sleep patterns among student-athletes. This study highlights that sleep issues are prevalent within the university student population and that student-athletes may be at greater risk due to more variable sleep patterns. University coaches should consider these results to optimize sleep habits of their student-athletes.

Previous studies have shown that variation in PlayerLoad (PL) could be used to detect fatigue in soccer players. Machine learning techniques (ML) were used to develop a new locomotor efficiency index (LEI) based on the prediction of PL. Sixty-four elite soccer players were monitored during an entire season. GPS systems were employed to collect external load data, which in turn were used to predict PL during training/matches. Random Forest Regression (RF) produced the best performance (mean absolute percentage error = 0.10 ± 0.01) and was included in further analyses. The difference between the PL value predicted by the ML model and the real one was calculated, individualized for each player using a z-score transformation (LEI), and interpreted as a sign of fatigue (negative LEI) or neuromuscular readiness (positive LEI). A linear mixed model was used to analyze how LEI changed according to the period of the season, day of the week, and weekly load. Regarding seasonal variation, the lowest and highest LEI values were recorded at the beginning of the season and in the middle of the season, respectively. On a weekly basis, our results showed lower values on match day − 2, while high weekly training loads were associated with a reduction in LEI.

Purpose: To investigate the convergent validity, reliability, and sensitivity over a week of training of a standardized running test to measure neuromuscular fatigue. Methods: Twenty male rugby union players were recruited for the study, which took place during preseason. The standardized running test consisted of four 60-m runs paced at ~5 m·s⁻¹ with 33 seconds of recovery between trials. Data from micromechanical electrical systems were used to calculate a running-load index (RLI), which was a ratio between the mechanical load and the speed performed during runs. RLI was calculated by using either the entire duration of the run or a constant-velocity period. For each type of calculation, either an individual directional or the sum of the 3 components of the accelerometer was used. A measure of leg stiffness was used to assess the convergent validity of the RLI. Results: Unclear to large relationships between leg stiffness and RLI were found (r ranged from −.20 to .62). Regarding reliability, small to moderate (.47–.86) standardized typical errors were found. The sensitivity analysis showed that the leg stiffness presented a very likely trivial change over the course of 1 week of training, whereas RLI showed very likely small to a most likely large change. Conclusions: This study showed that RLI is a practical method to measure neuromuscular fatigue. In addition, such a methodology aligns with the constraint of elite team-sport setup due to its ease of implementation in practice.

Background: Practices to routinely monitor athletes are rapidly changing. With the concurrent exponential rise in wearable technologies and advanced data analysis, tracking training exposures and responses is widespread and more frequent in the athlete–coach decision-making process. Within this scenario, the concept of invisible monitoring emerged, which was initially vaguely defined as testing athletes without testing them. Despite sound practical applications and benefits (eg, reduced burden on player staff and more frequent measurement), a clear lack of constitutive definition has led to multiple cleavages in both research and practice, including ethical concerns. Purpose: The purpose of this study is to (1) extend the current conceptualization of invisible monitoring by considering subdimensions of the concept and (2) its data-related and ethical challenges and (3) provide practical considerations to implement invisible monitoring. Monitoring burden (degree of obtrusion and frequency of measurement) and the number of constructs a single measurement tool can assess have been proposed as subdimensions of the concept of invisible monitoring. Challenges include the governance and analysis of data required to make estimates, validity and reliability of an invisible monitoring measure, and communication to athletes. Conclusions: This commentary presents a first attempt to conceptualize invisible monitoring in the context of elite sport and provide subdimensions of the concept that can be used to classify choices of measurement tools. A consensus is required from both researchers and practitioners regarding its definition and operationalization to optimize current monitoring services to elite athletes.

Leduc, C, Jones, B, Robineau, J, Piscione, J, and Lacome, M. Sleep quality and quantity of international rugby sevens players during pre-season. J Strength Cond Res 33(7): 1878–1886, 2019—The aim of this study was to investigate the influence of training load on objective and subjective sleep measures among elite rugby sevens players during pre-season. Nine international male rugby sevens players participated in this study. Actigraphic and subjective sleep assessment were performed on a daily basis to measure sleep parameters. Training load was measured during the entire pre-season period, and sleep data from the highest and lowest training load week were used in the analysis through magnitude-based inferences. During the highest training load, likely to possibly small, moderate decreases in time in bed (effect sizes; ±90% confidence limits: −0.42; ±0.44 for session rating of perceived exertion [sRPE], −0.69; ±0.71 for total distance covered [TDC]) and total sleep time (−0.20; ±0.37 for sRPE, −0.23; ±0.35 for TDC) were found. Possibly small (−0.21; ±0.35 for high-speed distance, −0.52; ±0.73 for acceleration/deceleration [A/D]) and likely moderate (−074; ±0.67 for TDC) decreases were observed in subjective sleep quality. Possibly small to very likely moderate changes in sleep schedule were observed. Sleep quantity and subjective quality seem to be deteriorated during higher loads of training. This study highlights the necessity to monitor and improve sleep among elite rugby sevens players, especially for the intense period of training.

Background: Elite rugby players experience poor sleep quality and quantity. This lack of sleep could compromise post-exercise recovery. Therefore, it appears central to encourage sleep in order to improve recovery kinetics. However, the effectiveness of an acute ergogenic strategy such as sleep extension on recovery has yet to be investigated among athletes. Aim: To compare the effects of a single night of sleep extension to an active recovery session (CON) on post-exercise recovery kinetics. Methods: In a randomised cross-over design, 10 male rugby union players participated in two evening training sessions (19:30) involving collision activity, 7-days apart. After each session, participants either extended their sleep to 10 hours or attended an early morning recovery session (07:30). Prior to (PRE), immediately after (POST 0 hour [h]), 14h (POST 14) and 36h (POST 36) post training, neuromuscular, perceptual and cognitive measures of fatigue were assessed. Objective sleep parameters were monitored two days before the training session and over the two-day recovery period. Results: The training session induced substantial decreases in countermovement jump mean power and wellness across all time points, while heart rate recovery decreased at POST 0 in both conditions. Sleep extension resulted in greater total sleep time (effect size [90% confidence interval]: 5.35 [4.56 to 6.14]) but greater sleep fragmentation than CON (2.85 [2.00 to 3.70]). Between group differences highlight a faster recovery of cognitive performance following sleep extension (-1.53 [-2.33 to -0.74]) at POST 14, while autonomic function (-1.00 [-1.85 to -0.16]) and upper-body neuromuscular function (-0.78 [-1.65 to 0.08]) were better in CON. However, no difference in recovery status between groups was observed at POST 36. Conclusion: The main finding of this study suggests that sleep extension could affect cognitive function positively but did not improve neuromuscular function the day after a late exercise bout.