Load monitoring, strength training, and recovery in futsal: Practitioners' perspectives

This study aimed to describe the current practices in futsal regarding a variety of topics related to performance and injury risk mitigation. Thirty-seven coaches from Spain and Portugal completed a questionnaire consisting of 28 closed questions organized in four categories: a) background information; b) training load (TL) monitoring and assessment of players' physical qualities; c) strength training (ST) practices; and d) recovery (REC) methods. The results showed that coaches varied in experience (1-8 years) and age (from 20 years to >50 years). Overall, 97.3% of the participants declared monitoring TL, with rating of perceived exertion, heart rate monitors, and wearable technology being used by 86.5%, 40.5%, and 37.8%, respectively. Neuromuscular and strength testing are the most common practices to evaluate performance and fatigue during the season. ST is a significant component of futsal, being performed 3 times/week during the pre- and in-season. ST is prescribed via %1RM - XRM (59.5%), velocity-based training (21.7%), repetitions in reserve (18.9%), until failure (10.8%), and circuit training (2.7%). 'Better Monitoring', 'More Individualized', 'Better Facilities', 'More Staff', and 'More Time' were the main aspects to improve ST. Multiple post-match REC strategies are used, with durations ranging from 0-15 to 16-30 min independently of game location.

Validity of Weekly and Monthly Perceived Exertion in Runners

The development of the rating of perceived exertion (RPE) enables the easy assessment of runners' training strain as well as training load (TL). However, the long-term and retrospective validity of TL assessment using RPE scales still needs further investigation. Therefore, this study examined the validity of weekly and monthly ratings of perceived exertion (W-RPE, M-RPE) in assessing TL in runners. Healthy adult runners (n=53) rated perceived exertion using the modified category-ratio 10 (CR-10) scale for each week of a four-week period, as well as for the month, considering the four-week period. CR-10 for the week and the month were multiplied by the total training time of the week and month, respectively, to assess the W-RPE and M-RPE. Training impulse (TRIMP) was used as the criterion measure. The results indicated that the W-RPE and M-RPE may be used for monitoring TL during prolonged periods, presenting a very large correlations with the criterion measure.

"I lied a little bit." A qualitative study exploring the perspectives of elite Australian athletes on self-reported data

OBJECTIVES

Explore the perceptions and experiences of elite Australian athletes' engagement with reporting data in surveillance systems.

DESIGN

Qualitative Descriptive.

SETTING

Semi-structured interviews conducted using Zoom.

PARTICIPANTS

We recruited 13 elite Australian athletes competing at a national or international level for semi-structured interviews.

MAIN OUTCOME MEASURES

Audio recordings were transcribed using DeScript, checked for errors and imported into QSR NVIVO. Thematic analysis using QSR NVIVO was used to determine key themes from transcripts.

RESULTS

Thematic analysis uncovered four key themes: 'the paradox of reporting', 'data for data's sake', 'eyes on reporting' and 'athlete friendly reporting'.

CONCLUSION

Athletes perceived reporting as a burden and the athlete management system presented numerous technological difficulties which led to athletes to backfill data entries and compromise data accuracy. Athletes had little knowledge on how their data was used and managed and often received minimal feedback from staff accessing the data. Athletes were unaware of who has access to their data, which is of concern as sensitive information may be collected and athletes may be underage. As a result, many athletes chose to report dishonest data to avoid their performance being questioned.

Training Management of the Elite Adolescent Soccer Player throughout Maturation

Professional soccer clubs invest significantly into the development of their academy prospects with the hopes of producing elite players. Talented youngsters in elite development systems are exposed to high amounts of sports-specific practise with the aims of developing the foundational skills underpinning the capabilities needed to excel in the game. Yet large disparities in maturation status, growth-related issues, and highly-specialised sport practise predisposes these elite youth soccer players to an increased injury risk. However, practitioners may scaffold a performance monitoring and injury surveillance framework over an academy to facilitate data-informed training decisions that may not only mitigate this inherent injury risk, but also enhance athletic performance. Constant communication between members of the multi-disciplinary team enables context to build around an individual's training status and risk profile, and ensures that a progressive, varied, and bespoke training programme is provided at all stages of development to maximise athletic potential.

The Frequency and Intensity of Representative and Nonrepresentative Late Adolescent Team-Sport Athletes' Training Schedules

ABSTRACT

Scantlebury, S, Till, K, Sawczuk, T, Dalton-Barron, N, Phibbs, P, and Jones, B. The frequency and intensity of representative and nonrepresentative late adolescent team-sport athletes' training schedules. J Strength Cond Res 35(12): 3400-3406, 2021-This study aimed to identify and compare the training frequency and intensity (via session rating of perceived exertion load [sRPE load]) of representative and nonrepresentative late adolescent athletes. Thirty-six team sport athletes completed a web-based questionnaire daily over an 8-month period, reporting their training/match activities from the previous day. Athletes were categorized as representative (academy/county/international) or nonrepresentative (club/school) depending on the highest level of their sport they participated. Mean weekly frequencies and sRPE load of different training/match activities were quantified for each athlete across 5 school terms. Mann-Whitney U tests established the significance of differences and effect sizes between playing standards for mean weekly frequencies and mean sRPE load. Within-athlete weekly sRPE loads were highly variable for both playing standards; however, representative level athletes participated in significantly more activity outside of school compared with nonrepresentative athletes during November-December (effect size; 0.43-club technical training; 0.36-club matches), January-February (effect size; 0.78-club technical training; 0.75-club matches), and February-March (effect size; 0.63-club technical training; 0.44-club matches). Therefore, club and school coaches must ensure that all elements of representative athletes training schedules are coordinated and flexible to promote positive adaptions to training such as skill and physical development and prevent maladaptive responses such as overuse injury and nonfunctional overreaching. A cooperative and malleable training schedule between club/school coaches and the athlete will allow the athlete to perform on multiple fronts while also being able to meet the demands of additional stressors such as schoolwork.

Cumulative Effects of a Week's Training Loads on Shoulder Physical Qualities and Wellness in Competitive Swimmers

BACKGROUND

Competitive swimmers are exposed to high training loads, which can contribute to the development of shoulder pain. There is a lack of research investigating the interactions between the accumulation of training loads and factors associated to shoulder pain in swimmers.

PURPOSE

The primary objective was to analyze the changes in shoulder physical qualities and wellness factors over a week of training in competitive swimmers. A secondary objective was to compare the changes in these variables between different swim-training volumes performed during the week.

DESIGN

Cross-sectional.

METHODS

Thirty-one national and regional-level swimmers were included (18 females, 13 males; age= 15.5 ± 2.2 years). Active shoulder external rotation (ER) range of motion (ROM), shoulder-rotation isometric torque, and wellness factors using the Hooper questionnaire were measured twice over the week: a baseline measurement (before Monday´s training session) and a follow-up during the week. Participants were divided into a high-volume group (HVG) and low-volume group (LVG) based on the day follow-up was performed. HVL (n= 15) was tested at the end of the training week (after Saturday´s session) and LVG (n= 16) during the week (after Thursday or Friday´s session). Rating of perceived exertion (RPE) of the whole week was recorded after the follow-up session.

RESULTS

At follow-up, the LVG averaged a volume of 26.2 ± 2.2 km, whereas the HVG averaged a volume of 37.5 ± 3.7 km. LVG and HVG participants decreased active shoulder ER ROM on dominant (p= 0.002; p= 0.006) and nondominant sides (p= 0.001; p= 0.004), displayed increased muscular soreness (p= 0.001; p= 0.007) and worsened overall wellness (p< 0.001; p= 0.010). Fatigue (p= 0.008) and poor sleep quality were increased (p= 0.023) in HVG, but not in LVG. There were no changes in shoulder-rotation torque and stress in any group. Regarding between-groups differences, only weekly RPE was higher (p= 0.004) in HVG.

CONCLUSIONS

The accumulation of training loads over the week negatively affect physical and wellness factors. Greater swim-volumes were associated with an increase perception of training loads. The regular monitoring of multiple factors to assess swimmers' response to training might be necessary.

LEVEL OF EVIDENCE

3.

Training Load Monitoring in Amateur Rugby Union: A Survey of Current Practices

ABSTRACT

Griffin, A, Kenny, IC, Comyns, TM, and Lyons, M. Training load monitoring in amateur Rugby Union: A survey of current practices. J Strength Cond Res 35(6): 1568-1575, 2021-Amateur Rugby Union has an inherent risk of injury that is associated with detrimental effects on player welfare and team performance. Training load (TL) is a modifiable risk factor that, when monitored, may provide strength and conditioning (S&C) coaches with opportunities to reduce the risk of injury. The purpose of this study was to explore TL monitoring practices used by S&C coaches working with male and female amateur adult Rugby Union teams and their rationale for using such. Thirty-three (n = 31 male and n = 2 female) S&C coaches representing 62% of the total number of male clubs and 71% of female clubs, playing at the highest national amateur level, participated in the study by completing an online survey. Seventy-three percent of respondents stated that they monitor TL. The most common method of recording TL was the session rating of perceived exertion (sRPE), used in 83% of monitoring systems. Thirty-three percent of the S&C coaches surveyed use the sRPE data to calculate the acute:chronic workload ratio (ACWR) for highlighting when players are at risk of injury. This study is the first of its kind to exclusively examine the TL monitoring practices of S&C coaches working with amateur adult Rugby Union teams. Thirteen S&C coaches responded to the open-ended question and 3 higher order themes were highlighted: (a) communication with players (b) support from other staff and (c) limiting factors.

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.

25 Years of Session Rating of Perceived Exertion: Historical Perspective and Development

The session rating of perceived exertion (sRPE) method was developed 25 years ago as a modification of the Borg concept of rating of perceived exertion (RPE), designed to estimate the intensity of an entire training session. It appears to be well accepted as a marker of the internal training load. Early studies demonstrated that sRPE correlated well with objective measures of internal training load, such as the percentage of heart rate reserve and blood lactate concentration. It has been shown to be useful in a wide variety of exercise activities ranging from aerobic to resistance to games. It has also been shown to be useful in populations ranging from patients to elite athletes. The sRPE is a reasonable measure of the average RPE acquired across an exercise session. Originally designed to be acquired ∼30 minutes after a training bout to prevent the terminal elements of an exercise session from unduly influencing the rating, sRPE has been shown to be temporally robust across periods ranging from 1 minute to 14 days following an exercise session. Within the training impulse concept, sRPE, or other indices derived from sRPE, has been shown to be able to account for both positive and negative training outcomes and has contributed to our understanding of how training is periodized to optimize training outcomes and to understand maladaptations such as overtraining syndrome. The sRPE as a method of monitoring training has the advantage of extreme simplicity. While it is not ideal for the precise recording of the details of the external training load, it has large advantages relative to evaluating the internal training load.

Returning to Play after Prolonged Training Restrictions in Professional Collision Sports

The COVID-19 pandemic in 2020 has resulted in widespread training disruption in many sports. Some athletes have access to facilities and equipment, while others have limited or no access, severely limiting their training practices. A primary concern is that the maintenance of key physical qualities (e. g. strength, power, high-speed running ability, acceleration, deceleration and change of direction), game-specific contact skills (e. g. tackling) and decision-making ability, are challenged, impacting performance and injury risk on resumption of training and competition. In extended periods of reduced training, without targeted intervention, changes in body composition and function can be profound. However, there are strategies that can dramatically mitigate potential losses, including resistance training to failure with lighter loads, plyometric training, exposure to high-speed running to ensure appropriate hamstring conditioning, and nutritional intervention. Athletes may require psychological support given the challenges associated with isolation and a change in regular training routine. While training restrictions may result in a decrease in some physical and psychological qualities, athletes can return in a positive state following an enforced period of rest and recovery. On return to training, the focus should be on progression of all aspects of training, taking into account the status of individual athletes.

Achieving a desired training intensity through the prescription of external training load variables in youth sport: More pieces to the puzzle required

Identifying the external training load variables which influence subjective internal response will help reduce the mismatch between coach-intended and athlete-perceived training intensity. Therefore, this study aimed to reduce external training load measures into distinct principal components (PCs), plot internal training response (quantified via session Rating of Perceived Exertion [sRPE]) against the identified PCs and investigate how the prescription of PCs influences subjective internal training response. Twenty-nine school to international level youth athletes wore microtechnology units for field-based training sessions. SRPE was collected post-session and assigned to the microtechnology unit data for the corresponding training session. 198 rugby union, 145 field hockey and 142 soccer observations were analysed. The external training variables were reduced to two PCs for each sport cumulatively explaining 91%, 96% and 91% of sRPE variance in rugby union, field hockey and soccer, respectively. However, when internal response was plotted against the PCs, the lack of separation between low-, moderate- and high-intensity training sessions precluded further analysis as the prescription of the PCs do not appear to distinguish subjective session intensity. A coach may therefore wish to consider the multitude of physiological, psychological and environmental factors which influence sRPE alongside external training load prescription.

Absolute and Relative Load and Injury in Elite Junior Australian Football Players Over 1 Season

PURPOSE

To investigate the association between training and match loads and injury in elite junior Australian football players over 1 competitive season.

METHODS

Elite junior Australian football players (n = 290, age 17.7 [0.3] y, range 16-18 y) were recruited from the under-18 state league competition in Victoria to report load and injury information. One-week load (session rating of perceived exertion multiplied by duration) and all time-loss injuries were reported using an online sport-injury surveillance system. Absolute load measures (weekly sums) enabled the calculation of relative measures such as the acute:chronic workload ratio. Load measures were modeled against injury outcome (yes/no) using a generalized estimating equation approach, with a 1-wk lag for injury.

RESULTS

Low (<300 arbitrary units [au]) and high (>4650 au) 1-wk loads were associated with significantly higher risk of injury. Furthermore, low (<100 au) and high (>850 au) session loads were associated with a higher risk of injury. High strain values (>13,000) were associated with up to a 5-fold increase in the odds of injury. There was a relatively flat-line association between the acute:chronic workload ratio and injury.

CONCLUSIONS

This study is the first investigation of elite junior athletes demonstrating linear and nonlinear relationships between absolute and relative load measures and injury. Coaches should focus player loads on, or at least close to, the point at which injury risk starts to increase again (2214 au for 1-wk load and 458 au for session load) and use evidence-based strategies across the week and month to help reduce the risk of injury.

Are internal load measures associated with injuries in male adolescent Gaelic football players?

This study aimed to examine internal loads in male adolescent Gaelic footballers and their association with musculoskeletal injury. Written training diaries were completed by 97 male adolescent Gaelic footballers weekly and injuries, defined as any injury sustained during training or competition causing restricted performance or time lost from play, were assessed by a Certified Athletic Therapist. Daily load was determined for each player (session rating of perceived exertion by session duration) and summed to give weekly load. Univariate and multiple logistic regressions were conducted to determine the association with injury. Twenty-two injuries were recorded with match injuries significantly more common than training injuries. Periodic variations in weekly load and injuries were evident throughout the season. Univariate analysis identified weekly load (OR = 2.75; 95%CI = 1.00-7.59), monotony (OR = 4.17; 95%CI = 1.48-11.72) and absolute change in load (OR = 3.27; 95%CI = 1.15-9.32) greater than the team average were significant injury risk factors. Multiple logistic regression with 2-weekly and 3-weekly cumulative loads, absolute change, monotony, strain, ACWR and age as independent variables identified internal load measures (monotony, strain and absolute change) were associated with injury with high specificity (96.0%) but low sensitivity (25.0%). The findings highlight the need to monitor team and individual loads to avoid sudden week-to-week changes or excessive weekly loads. Open communication between players, parents, coaches and sports medicine clinicians enables effective load monitoring that can reduce injury risk and may subsequently minimise dropout, improve team success and overall sport enjoyment and promote life-long sports participation.

Development of a Human Activity Recognition System for Ballet Tasks

Background

Accurate and detailed measurement of a dancer’s training volume is a key requirement to understanding the relationship between a dancer’s pain and training volume. Currently, no system capable of quantifying a dancer’s training volume, with respect to specific movement activities, exists. The application of machine learning models to wearable sensor data for human activity recognition in sport has previously been applied to cricket, tennis and rugby. Thus, the purpose of this study was to develop a human activity recognition system using wearable sensor data to accurately identify key ballet movements (jumping and lifting the leg). Our primary objective was to determine if machine learning can accurately identify key ballet movements during dance training. The secondary objective was to determine the influence of the location and number of sensors on accuracy.

Results

Convolutional neural networks were applied to develop two models for every combination of six sensors (6, 5, 4, 3, etc.) with and without the inclusion of transition movements. At the first level of classification, including data from all sensors, without transitions, the model performed with 97.8% accuracy. The degree of accuracy reduced at the second (83.0%) and third (75.1%) levels of classification. The degree of accuracy reduced with inclusion of transitions, reduction in the number of sensors and various sensor combinations.

Conclusion

The models developed were robust enough to identify jumping and leg lifting tasks in real-world exposures in dancers. The system provides a novel method for measuring dancer training volume through quantification of specific movement tasks. Such a system can be used to further understand the relationship between dancers’ pain and training volume and for athlete monitoring systems. Further, this provides a proof of concept which can be easily translated to other lower limb dominant sporting activities

Match and Training Load Exposure and Time-Loss Incidence in Elite Rugby Union Players

Objective

To investigate the impact of match and training load on time-loss incidence in elite, professional Rugby Union players.

Materials and Methods

Eighty-nine Rugby Union players were monitored over two seasons of training and competition. Load was measured for all training sessions and matches using subjective [session ratings of perceived exertion (sRPE) load; RPE × session duration] and objective [global positioning systems (GPS); distance and high-speed running distance] methods and quantified using multiple approaches; absolute match and training load, acute:chronic workload ratio (ACWR), exponentially weighted moving average (EWMA) and cumulative 7, 14, 21, and 28 d sums. Mixed effect models were used to assess the effect of each variable on time-loss incidence.

Results

Of the 474 time-loss incidences that occurred across the two seasons, 50.0% were contact injuries (86.5% occurred during matches and 13.5% during training), 34.8% were non-contact injuries (31.5% occurred during matches and 68.5% during training) and 15.2% were cases of illness. The absolute match and training load variables provided the best explanation of the variance in time-loss incidence occurrence [sRPE load: p < 0.001, Akaike information criterion (AIC) = 2936; distance: p < 0.001, AIC = 3004; high-speed running distance: p < 0.001, AIC = 3025]. The EWMA approach (EWMA sRPE load: p < 0.001, AIC = 2980; EWMA distance: p < 0.001, AIC = 2980; EWMA high-speed running distance: p = 0.002, AIC = 2987) also explained more of the variance in time-loss incidence occurrence than the ACWR approach (ACWR sRPE load: p = 0.091, AIC = 2993; ACWR distance: p = 0.008, AIC = 2990; ACWR high-speed running distance: p = 0.153, AIC = 2994).

Conclusion

Overall, the absolute sRPE load variable best explained the variance in time-loss incidence, followed by absolute distance and absolute high-speed running distance. Whilst the model fit using the EWMA approach was not as good as the absolute load variables, it was better than when the same variables were calculated using the ACWR method. Overall, these findings suggest that the absolute match and training load variables provide the best predictors of time-loss incidence rates, with sRPE load likely to be the optimal variant of those examined here.

A Prospective Cohort Study of Load and Wellness (Sleep, Fatigue, Soreness, Stress, and Mood) in Elite Junior Australian Football Players

Purpose:To investigate associations between load (training and competition) and wellness in elite junior Australian Football players across 1 competitive season.Methods:A prospective cohort study was conducted during the 2014 playing season in 562 players from 9 teams. Players recorded their training and match intensities according to the session-rating-of-perceived-exertion (sRPE) method. Based on sRPE player loads, a number of load variables were quantified, including cumulative load and the change in load across different periods of time (including the acute-to-chronic load ratio). Wellness was quantified using a wellness index including sleep, fatigue, soreness, stress, and mood on a Likert scale from 1 to 5.Results:Players spent an average of 85 (21) min in each match and 65 (31) min per training session. Average match loads were 637 (232) arbitrary units, and average training loads were 352 (233) arbitrary units. Over the 24 wk of the 2014 season, overall wellness had a significant linear negative association with 1-wk load (B = −0.152; 95% confidence interval, −0.261 to −0.043;P = .006) and an inverseU-curve relationship with session load (B = −0.078; 95% confidence interval, 0.143 to 0.014;P = .018). Mood, stress, and soreness were all found to have associations with load.Conclusions:This study demonstrates that load (within a session and across the week) is important in managing the wellness of elite junior Australian Football players. Quantifying loads and wellness at this level will help optimize player management and has the potential to reduce the risk of adverse events such as injury.

Organized Chaos in Late Specialization Team Sports: Weekly Training Loads of Elite Adolescent Rugby Union Players

Phibbs, PJ, Jones, B, Roe, G, Read, DB, Darrall-Jones, J, Weakley, J, Rock, A, and Till, K. Organized chaos in late specialization team sports: weekly training loads of elite adolescent rugby union players. J Strength Cond Res 32(5): 1316-1323, 2018-The aim of this study was to quantify the mean weekly training load (TL) of elite adolescent rugby union players participating in multiple teams and examine the differences between playing positions. Twenty elite male adolescent rugby union players (17.4 ± 0.7 years) were recruited from a regional academy and categorized by playing position: forwards (n = 10) and backs (n = 10). Global positioning system and accelerometer microtechnology was used to quantify external TL, and session rating of perceived exertion (sRPE) was used to quantify internal TL during all sessions throughout a 10-week in-season period. A total of 97 complete observations (5 ± 3 weeks per participant) were analyzed, and differences between positions were assessed using Cohen's d effect sizes (ES) and magnitude-based inferences. Mean weekly sRPE was 1,217 ± 364 arbitrary units (AU) (between-subject coefficient of variation [CV] = 30%), with a total distance (TD) of 11,629 ± 3,445 m (CV = 30%), and PlayerLoad (PL) of 1,124 ± 330 AU (CV = 29%). Within-subject CV ranged between 5 and 78% for sRPE, 24 and 82% for TD, and 19 and 84% for PL. Mean TD (13,063 ± 3,933 vs. 10,195 ± 2,242 m) and PL (1,246 ± 345 vs. 1,002 ± 279 AU) were both likely greater for backs compared with forwards (moderate ES); however, differences in sRPE were unclear (small ES). Although mean internal TLs and volumes were low, external TLs were higher than previously reported during preseason and in-season periods in senior professional players. Additionally, the large between-subject and within-subject variation in weekly TL suggests that players participate in a chaotic training system.

Relationships Between Training Load, Sleep Duration, and Daily Well-Being and Recovery Measures in Youth Athletes

PURPOSE

To assess the relationships between training load, sleep duration, and 3 daily well-being, recovery, and fatigue measures in youth athletes.

METHODS

Fifty-two youth athletes completed 3 maximal countermovement jumps (CMJs), a daily well-being questionnaire (DWB), the perceived recovery status scale (PRS), and provided details on their previous day's training loads (training) and self-reported sleep duration (sleep) on 4 weekdays over a 7-week period. Partial correlations, linear mixed models, and magnitude-based inferences were used to assess the relationships between the predictor variables (training and sleep) and the dependent variables (CMJ, DWB, and PRS).

RESULTS

There was no relationship between CMJ and training (r = -.09; ±.06) or sleep (r = .01; ±.06). The DWB was correlated with sleep (r = .28; ±.05, small), but not training (r = -.05; ±.06). The PRS was correlated with training (r = -.23; ±.05, small), but not sleep (r = .12; ±.06). The DWB was sensitive to low sleep (d = -0.33; ±0.11) relative to moderate; PRS was sensitive to high (d = -0.36; ±0.11) and low (d = 0.29; ±0.17) training relative to moderate.

CONCLUSIONS

The PRS is a simple tool to monitor the training response, but DWB may provide a greater understanding of the athlete's overall well-being. The CMJ was not associated with the training or sleep response in this population.

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

This study assessed the influence of training load, exposure to match play and sleep duration on two daily wellbeing measures in youth athletes. Forty-eight youth athletes (age 17.3 ± 0.5 years) completed a daily wellbeing questionnaire (DWB), the Perceived Recovery Status scale (PRS), and provided details on the previous day's training loads (TL) and self-reported sleep duration (sleep) every day for 13 weeks (n = 2727). Linear mixed models assessed the effect of TL, exposure to match play and sleep on DWB and PRS. An increase in TL had a most likely small effect on muscle soreness (d = -0.43;± 0.10) and PRS (d = -0.37;± 0.09). Match play had a likely small additive effect on muscle soreness (d = -0.26;± 0.09) and PRS (d = -0.25;± 0.08). An increase in sleep had a most likely moderate effect on sleep quality (d = 0.80;± 0.14); a most likely small effect on DWB (d = 0.45;± 0.09) and fatigue (d = 0.42;± 0.11); and a likely small effect on PRS (d = 0.25;± 0.09). All other effects were trivial or did not reach the pre-determined threshold for practical significance. The influence of sleep on multiple DWB subscales and the PRS suggests that practitioners should consider the recovery of an athlete alongside the training stress imposed when considering deviations in wellbeing measures.

Elite Junior Australian Football Players Experience Significantly Different Loads Across Levels of Competition and Training Modes

Lathlean, TJH, Gastin, PB, Newstead, S, and Finch, CF. Elite junior Australian football players experience significantly different loads across levels of competition and training modes. J Strength Cond Res 32(7): 2031-2038, 2018-Well-developed physical qualities such as high jumping ability, running endurance, acceleration, and speed can help aspiring junior elite Australian football (AF) players transition to the Australian Football League competition. To do so, players need to experience sufficient load to enhance their physical resilience without increasing their risk of negative outcomes in terms of impaired wellness or injury. The aim of this study was to investigate the differences in load for different levels of competition and training modes across one competitive season. Elite junior AF players (n = 562, aged 17.7 ± 0.3, range: 16-18 years) were recruited from 9 teams across the under-18 state league competition in Victoria. All players recorded their training and match intensities according to the session rating of perceived exertion method. Training sessions were categorized according to skills, strength, conditioning, and other activities, whereas matches were identified according to level of competition. The loads in U18 state league matches (656.7 ± 210.9 au) were significantly higher (p = 0.027) than those in school matches (643.3 ± 260.9 au) and those in U18 representative matches (617.2 ± 175.4). Players, who undertook more than one match per week, experienced significantly less load in subsequent matches (p < 0.001). Furthermore, U18 state league training sessions carried the most load when compared with other training modes. This article highlights that different combinations of training and match involvement affect overall player load, which may predispose players to negative outcomes such as impaired wellness or increased injury risk.