The influence of training load, exposure to match play and sleep duration on daily wellbeing measures in youth athletes

Relationship Between Training Load, Neuromuscular Fatigue, and Daily Well-Being in Elite Young Wrestlers

Purpose: This study investigated acute workload (wAW), chronic workload (wCW), acute: chronic workload ratio (wACWR), training monotony (wTM), perceived load training strain indicators (wTS), and countermove- ment jump (CMJ) as indicators of wellness in one season and defined weekly variations. In addition, we analyzed the relationships between training load measurements and weekly reports. Methods: 16 elite young wrestlers were monitored daily with individual observations for 46 consecutive weeks throughout the season. Training load was obtained using the session rating of perceived effort. wSleep, wStress, wFatigue & wMuscle Soreness well-being were monitored daily using the Hooper index. Results: As a result of the analysis, it was found that there is a moderate relationship (r = 0.51, p = .003) between ACWR and w mean load (A.U.) and a high relationship (r = 0.81, p < .001) between monotony and strain. Conclusion: All variables other than ACWR, w mean load, strain, and monotony presented small and statistically insignificant relationships. These results provide coaches and practitioners with new insights into perceived loads and health changes during a season at the elite youth level.

Evaluation of Athlete Monitoring Tools across 10 Weeks of Elite Youth Basketball Training: An Explorative Study

The growth of sport science technology is enabling more sporting teams to implement athlete monitoring practices related to performance testing and load monitoring. Despite the increased emphasis on youth athlete development, the lack of longitudinal athlete monitoring literature in youth athletes is concerning, especially for indoor sports such as basketball. The aim of this study was to evaluate the effectiveness of six different athlete monitoring methods over 10 weeks of youth basketball training. Fourteen state-level youth basketball players (5 males and 9 females; 15.1 ± 1.0 years) completed this study during their pre-competition phase prior to their national basketball tournament. Daily wellness and activity surveys were completed using the OwnUrGoal mobile application, along with heart rate (HR) and inertial measurement unit (IMU) recordings at each state training session, and weekly performance testing (3x countermovement jumps [CMJs], and 3x isometric mid-thigh pulls [IMTPs]). All of the athlete monitoring methods demonstrated the coaching staff's training intent to maintain performance and avoid spikes in workload. Monitoring IMU data combined with PlayerLoad™ data analysis demonstrated more effectiveness for monitoring accumulated load (AL) compared to HR analysis. All six methods of athlete monitoring detected similar trends for all sessions despite small-trivial correlations between each method (Pearson's correlation: -0.24 < r < 0.28). The use of subjective monitoring questionnaire applications, such as OwnUrGoal, is recommended for youth sporting clubs, given its practicability and low-cost. Regular athlete education from coaches and support staff regarding the use of these questionnaires is required to gain the best data.

Are Measurement Instruments Responsive to Assess Acute Responses to Load in High-Level Youth Soccer Players?

Purpose

The aim of this study was to assess the short-term responsiveness of measurement instruments aiming at quantifying the acute psycho-physiological response to load in high-level adolescent soccer players.

Methods

Data were collected from 16 high-level male youth soccer players from the Under 15 age group. Players were assessed on two occasions during the week: after 2 days of load accumulation (“high load”) and after at least 48 h of rest. Measurements consisted of the Short Recovery and Stress Scale (SRSS), a countermovement jump (CMJ) and a sub-maximal run to assess exercise heart-rate (HRex) and heart-rate recovery (HRR60s). Training load was quantified using total distance and high-speed running distance to express external and sRPE training load to express internal load. It was expected that good instruments can distinguish reliably between high load and rest.

Results

Odd ratios (0.74–1.73) of rating one unit higher or lower were very low for athlete-reported ratings of stress and recovery of the SRSS. Standardized mean high load vs. rest differences for CMJ parameters were trivial to small (−0.31 to 0.34). The degree of evidence against the null hypothesis that changes are interchangeable ranged from p = 0.04 to p = 0.83. Moderate changes were observed for HRex (−0.62; 90% Cl −0.78 to −0.47; p = 3.24 × 10−9), while small changes were evident for HRR60s (0.45; 90% Cl 0.08–0.80; p = 0.04). Only small to moderate repeated-measures correlations were found between the accumulation of load and acute responses across all measurement instruments. The strongest relationships were observed between HRex and total distance (rm-r = −0.48; 90% Cl −0.76 to −0.25).

Conclusion

Results suggest that most of the investigated measurement instruments to assess acute psycho-physiological responses in adolescent soccer players have limited short-term responsiveness. This questions their potential usefulness to detect meaningful changes and manage subsequent training load and program adequate recovery.

Off-training physical activity and training responses as determinants of sleep quality in young soccer players

This study aimed to quantify and assess the relationship of young soccer players' off-training physical activity (PA) and training responses on sleep quality. Eleven adolescent soccer players (13 ± 0.5 years old) were monitored during weekdays for four consecutive weeks, throughout soccer practice days. Off-training PA and sleep quality were assessed using 100 Hz tri-axial accelerometers and training responses analyzed using 20 Hz global positioning measurement units. A cluster analysis classified all cases into three different dimensions, (1) off-training PA, (2) training responses and (3) sleep quality. For each dimension, the most important variables for classifying the cases into clusters were sedentary PA and moderate-to-vigorous PA; total distance covered and impacts; and sleep onset latency and sleep fragmentation index, respectively. Afterwards, a correspondence analysis was used to identify whether off-training PA and training responses affected sleep quality. Results exposed that high to medium off-training PA combined with medium to high training responses may have decreased sleep quality. Conversely, no correspondence was observed between off-training PA and training responses, with higher sleep quality. This study emphasizes the importance of sports organizations adopting a holistic approach to youth soccer players' development, that appropriately considers the inter-relationship between lifestyle, performance and health-related information.

Influence of Perceptions of Sleep on Well-Being in Youth Athletes

ABSTRACT

Sawczuk, T, Jones, B, Scantlebury, S, and Till, K. Influence of perceptions of sleep on well-being in youth athletes. J Strength Cond Res 35(4): 1066-1073, 2021-To date, most research considering well-being questionnaires has only considered the training stress imposed on the athlete, without evaluating the questionnaire's relationship with a measure of recovery (e.g., sleep). This study aimed to assess the influence of sleep duration (Sduration), sleep quality (Squality), and sleep index (Sindex; Sduration × Squality) on well-being in youth athletes, while accounting for the known training stressors of training load and exposure to match play. Forty-eight youth athletes (age 17.3 ± 0.5 years) completed a daily questionnaire including well-being (DWBno-sleep; fatigue, muscle soreness, stress, and mood) measures, Perceived Recovery Status Scale (PRS), the previous day's training loads, Sduration, and Squality every day for 13 weeks. Linear mixed models assessed the impact of Sduration, Squality, and Sindex on DWBno-sleep, its individual subscales, and PRS. Sduration had a small effect on DWBno-sleep (d = 0.31; ±0.09), fatigue (d = 0.42; ±0.11), and PRS (d = 0.25; ±0.09). Squality had a small effect on DWBno-sleep (d = 0.47; ±0.08), fatigue (d = 0.53; ±0.11), stress (d = 0.35; ±0.07), mood (d = 0.41; ±0.09), and PRS (d = 0.37; ±0.08). Sindex had a small effect on DWBno-sleep (d = 0.44; ±0.08), fatigue (d = 0.55; ±0.11), stress (d = 0.29; ±0.07), mood (d = 0.37; ±0.09), and PRS (d = 0.36; ±0.09). The results indicate that an athlete's perceptions of sleep are associated with deviations in well-being measures and should be used as an input to the monitoring process rather than as part of the outcome well-being score. The sleep index is suggested as a potential input because it provides information on both the duration and quality of the sleep experienced.

Athlete-Reported Outcome Measures for Monitoring Training Responses: A Systematic Review of Risk of Bias and Measurement Property Quality According to the COSMIN Guidelines

BACKGROUND

Athlete-reported outcome measures (AROMs) are frequently used in research and practice but no studies have examined their psychometric properties.

OBJECTIVES

Part 1-identify the most commonly used AROMs in sport for monitoring training responses; part 2-assess risk of bias, measurement properties, and level of evidence, based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines.

STUDY APPRAISAL AND SYNTHESIS METHODS

Methodological quality of the studies, quality of measurement properties, and level of evidence were determined using the COSMIN checklist and criteria.

RESULTS

Part 1-from 9446 articles screened for title and abstract, 310 out of 334 full texts were included; 53.9% of the AROMs contained multiple items, while 46.1% contained single items. Part 2-from 1895 articles screened for title and abstract, 71 were selected. Most measurement properties of multiple-item AROMs were adequate, but content validity and measurement error were inadequate. With the exclusion of 2 studies examining reliability and responsiveness, no validity studies were found for single items.

CONCLUSIONS

The measurement properties of multiple-item AROMs derived from psychometrics were acceptable (with the exclusion of content validity and measurement error). The single-item AROMs most frequently used in sport science have not been validated. Additionally, nonvalidated modified versions of the originally nonvalidated items are common. Until proper validation studies are completed, all conclusions based on these AROMs are questionable. Established reference methods, such as those of clinimetrics, should be used to develop and assess the validity of AROMs.

Managing Load to Optimize Well-Being and Recovery During Short-Term Match Congestion in Elite Basketball

In elite basketball, players are exposed to intensified competition periods when participating in both national and international competitions. How coaches manage training between matches and in reference to match scheduling for a full season is not yet known.

PURPOSE

First, to compare load during short-term match congestion (ie, ≥2-match weeks) with regular competition (ie, 1-match weeks) in elite male professional basketball players. Second, to determine changes in well-being, recovery, neuromuscular performance, and injuries and illnesses between short-term match congestion and regular competition.

METHODS

Sixteen basketball players (age 24.8 [2.0] y, height 195.8 [7.5] cm, weight 94.8 [14.0] kg, body fat 11.9% [5.0%], VO2max 51.9 [5.3] mL·kg-1·min-1) were monitored during a full season. Session rating of perceived exertion (s-RPE) was obtained, and load was calculated (s-RPE × duration) for each training session or match. Perceived well-being (fatigue, sleep quality, general muscle soreness, stress levels, and mood) and total quality of recovery were assessed each training day. Countermovement jump height was measured, and a list of injuries and illnesses was collected weekly using the adapted Oslo Sports Trauma Research Center Questionnaire on Health Problems.

RESULTS

Total load (training sessions and matches; P < .001) and training load (P < .001) were significantly lower for ≥2-match weeks. Significantly higher well-being (P = .01) and less fatigue (P = .001) were found during ≥2-match weeks compared with 1-match weeks.

CONCLUSION

Total load and training load were lower during short-term match congestion compared with regular competition. Furthermore, better well-being and less fatigue were demonstrated within short-term match congestion. This might indicate that coaches tend to overcompensate training load in intensified competition.

Discrepancies between self-reported current and ideal sleep behaviors of adolescent athletes

It is well documented that many adolescents are not meeting current sleep duration recommendations, with a growing body of literature suggesting adolescent athletes also fall into this category. What is less known is the relationship between current and ideal sleep behaviors. We sought to quantify sleep behaviors in a group of athletes and to understand how their current behaviors compare to their self-reported ideal behaviors. One hundred ninety six competitive, male and female athletes (15.7 ± 1.3 y) completed the Pittsburgh Sleep Quality Index (PSQI) and a questionnaire that captured usual sleep habits. The PSQI was analyzed for habitual bedtime, wake time, sleep duration, and sleep quality. The usual sleep habits questionnaire was analyzed for ideal bedtime, wake time, and calculated sleep duration. Reported mean sleep duration was 7:45 ± 1:06 h:min. Actual bedtime was later (+0:44 ± 0:05 h:min, p<0.001) than ideal bedtime, actual wake time was earlier (-0:50 ± 0:08 h:min, p<0.001) than ideal wake time, and actual sleep duration was less (-2:11 ± 1:27 h:min, p<0.001) than ideal sleep duration. Adolescent athletes are not meeting current sleep duration recommendations and there are significant discrepancies between self-reported current and ideal sleep behaviors in this group.

Influence of Faster and Slower Recovery-Profile Classifications, Self-Reported Sleep, Acute Training Load, and Phase of the Microcycle on Perceived Recovery in Futsal Players

PURPOSE

To determine whether daily perceived recovery is explained from a multifactorial single-session classification of recovery (ie, faster vs slower) or other circumstantial factors (ie, previous training load, self-reported sleep, or phase of the microcycle).

METHODS

Nineteen elite male futsal players were initially allocated to a recovery-classification group (faster recovery, slower physiological, or slower perceptual) based on previous research using a multifactorial cluster-analysis technique. During 4 ensuing weeks of preseason, training loads were monitored via player load, training impulse, and session rating of perceived exertion. Before each day's training, players reported their perception of recovery (Total Quality of Recovery scale [TQR]) and the number of hours and perceived quality of sleep the night prior. A hierarchical linear mixed model was used to analyze the effect of the different recovery profiles, training load, sleep, and phase of the microcycle (ie, start, middle, end) on daily TQR.

RESULTS

The recovery classification of players (P = .20), training load (training impulse, P = .32; player load, P = .23; session rating of perceived exertion, P = .46), and self-reported hours slept the night before (P = .45) did not significantly influence TQR. However, perceived sleep quality (P < .01) and phase of the microcycle (P < .01) were significantly associated with TQR (r2 = .41).

CONCLUSIONS

Neither recovery classification nor prior training load influenced perceived recovery during the preseason. However, higher TQR was evident with better self-reported sleep quality, whereas lower values were associated with phases of the training week.

High Match Load’s Relation to Decreased Well-Being During an Elite Women’s Rugby Sevens Tournament

During rugby sevens tournaments, it is crucial to balance match load and recovery to strive for optimal performance. Purpose: To determine changes in well-being, recovery, and neuromuscular performance during and after an elite women’s rugby sevens tournament and assess the influence of match-load indicators. Methods: Twelve elite women rugby sevens players (age = 25.3 [4.1]y, height = 169.0 [4.0] cm, weight = 63.9 [4.9] kg, and body fat = 18.6% [2.7%]) performed 5 matches during a 2-d tournament of the Women’s Rugby Sevens World Series. Perceived well-being (fatigue, sleep quality, general muscle soreness, stress levels, and mood), total quality of recovery, and countermovement-jump flight time were measured on match days 1 and 2, 1 d posttournament, and 2 d posttournament. Total distance; low-, moderate-, and high-intensity running; and physical contacts during matches were derived from global positioning system–based time–motion analysis and video-based notational analysis, respectively. Internal match load was calculated by session rating of perceived exertion and playing time (rating of perceived exertion × duration). Results: Well-being (P < .001), fatigue (P < .001), general muscle soreness (P < .001), stress levels (P < .001), mood (P = .005), and total quality of recovery (P < .001) were significantly impaired after match day 1 and did not return to baseline values until 2 d posttournament. More high-intensity running was related to more fatigue (r = −.60, P = .049) and a larger number of physical contacts with more general muscle soreness (r = −.69, P = .013). Conclusion: Perceived well-being and total quality of recovery were already impaired after match day 1, although performance was maintained. High-intensity running and physical contacts were predominantly related to fatigue and general muscle soreness, respectively.