A Review of the Validity and Reliability of Accelerometer-Based Metrics From Upper Back-Mounted GNSS Player Tracking Systems for Athlete Training Load Monitoring

ABSTRACT

Dawson, L, Beato, M, Devereux, G, and McErlain-Naylor, SA. A review of the validity and reliability of accelerometer-based metrics from upper back-mounted GNSS player tracking systems for athlete training load monitoring. J Strength Cond Res 38(8): e459-e474, 2024-Athlete load monitoring using upper back-mounted global navigation satellite system (GNSS) player tracking is common within many team sports. However, accelerometer-based load monitoring may provide information that cannot be achieved with GNSS alone. This review focuses on the accelerometer-based metrics quantifying the accumulation of accelerations as an estimation of athlete training load, appraising the validity and reliability of accelerometer use in upper back-mounted GNSS player tracking systems, the accelerometer-based metrics, and their potential for application within athlete monitoring. Reliability of GNSS-housed accelerometers and accelerometer-based metrics are dependent on the equipment model, signal processing methods, and the activity being monitored. Furthermore, GNSS unit placement on the upper back may be suboptimal for accelerometer-based estimation of mechanical load. Because there are currently no feasible gold standard comparisons for field-based whole-body biomechanical load, the validity of accelerometer-based load metrics has largely been considered in relation to other measures of training load and exercise intensity. In terms of convergent validity, accelerometer-based metrics (e.g., PlayerLoad, Dynamic Stress Load, Body Load) have correlated, albeit with varying magnitudes and certainty, with measures of internal physiological load, exercise intensity, total distance, collisions and impacts, fatigue, and injury risk and incidence. Currently, comparisons of these metrics should not be made between athletes because of mass or technique differences or between manufacturers because of processing variations. Notable areas for further study include the associations between accelerometer-based metrics and other parts of biomechanical load-adaptation pathways of interest, such as internal biomechanical loads or methods of manipulating these metrics through effective training design.

Differences in Workloads of Maximal Tasks in Active-Duty Firefighters

The purpose of this study was to compare the workload of a maximal treadmill test (TREAD) and a fire suppression task (BURN) in firefighters and to examine their relationships to fitness as measured by body mass index (BMI), percent body fat (BF%), and peak aerobic capacity (VO2PEAK). The amount of time spent in the heart rate (HR) intensity ranges of 50-59% HRMAX (ZONE1), 60-69% HRMAX (ZONE2), 70-79% HRMAX (ZONE3), 80-89% HRMAX (ZONE4), and ≥90% HRMAX (ZONE5) quantified the workload as the Edward's Training Impulse for TREAD (ETRIMPTREAD) and BURN (ETRIMPBURN). The ETRIMPTREAD was significantly less than ETRIMPBURN. For TREAD, ZONE5 > ZONE2 and ZONE3. For BURN, ZONE4 > ZONE1, ZONE2, and ZONE5 > ZONE1, ZONE2, and ZONE3. A lower BF% and greater VO2PEAK were related to a greater ETRIMPTREAD and unrelated to ETRIMPBURN. For BURN only, a lower BF% and greater VO2PEAK were related to less time in ZONE5. BMI was unrelated to all workload measures. Laboratory-based maximal exercise testing does not adequately reflect the workload of simulated fire suppression and therefore may not be indicative of firefighter readiness to meet job demands. Less-fit firefighters rely on higher cardiovascular intensities to complete the same workload, and practitioners should consider this when selecting strategies to reduce job-associated cardiovascular risk.

Monitoring Within-Individual Dose-Response Relationships in Professional Soccer Players: The Importance of Fitness Level

PURPOSE

To (1) examine within-individual player dose-response associations between selected training-load measures and changes in aerobic fitness level via submaximal exercise heart rate (HRex%) and (2) measure the relationships between these dose-response associations with basal HRex% (to study the influence of fitness level on dose-response relationship).

METHODS

During an in-season phase, selected training-load measures including total minutes, total distance, mechanical work (the sum number of accelerations and decelerations > 3 m2), high metabolic load distance, and Edwards' training impulse were collected via Global Positioning System and heart-rate sensors for analyzing accumulated load. A submaximal warm-up test was used repeatedly before and after 9 phases to elicit HRex% and track fitness changes at an individual level.

RESULTS

Negative to positive extensive ranges of within-individual associations were found among players for different metrics (r = -.84 to .89). The relationship between pooled HRex% (basal fitness) and dose-response correlations showed inverse very large (r = -.71) and large (r = -.65) values for accumulated weekly minutes and distance. However, moderate values were found for all other measures (r = -.35 to -.42).

CONCLUSIONS

Individual players show extensive different ranges of dose-response associations with training measures. The dose-response association is influenced by players' fitness level, and players with lower fitness levels show stronger inverse relationships with accumulated minutes and total distance.

Intensity Zones and Intensity Thresholds Used to Quantify External Load in Competitive Basketball: A Systematic Review

BACKGROUND

Despite widespread use of intensity zones to quantify external load variables in basketball research, the consistency in identifying zones and accompanying intensity thresholds using predominant monitoring approaches in training and games remains unclear.

OBJECTIVES

The purpose of this work was to examine the external load intensity zones and thresholds adopted across basketball studies using video-based time-motion analysis (TMA), microsensors, and local positioning systems (LPS).

METHODS

PubMed, MEDLINE, and SPORTDiscus databases were searched from inception until 31 January 2023 for studies using intensity zones to quantify external load during basketball training sessions or games. Studies were excluded if they examined players participating in recreational or wheelchair basketball, were reviews or meta-analyses, or utilized monitoring approaches other than video-based TMA, microsensors, or LPS.

RESULTS

Following screening, 86 studies were included. Video-based TMA studies consistently classified jogging, running, sprinting, and jumping as intensity zones, but demonstrated considerable variation in classifying low-intensity (standing and walking) and basketball-specific activities. Microsensor studies mostly utilized a single, and rather consistent, threshold to identify only high-intensity activities (> 3.5 m·s-2 for accelerations, decelerations, and changes-in-direction or > 40 cm for jumps), not separately quantifying lower intensity zones. Similarly, LPS studies predominantly quantified only high-intensity activities in a relatively consistent manner for speed (> 18.0 m·s-1) and acceleration/deceleration zones (> 2.0 m·s-2); however, the thresholds adopted for various intensity zones differed greatly to those used in TMA and microsensor research.

CONCLUSIONS

Notable inconsistencies were mostly evident for low-intensity activities, basketball-specific activities, and between the different monitoring approaches. Accordingly, we recommend further research to inform the development of consensus guidelines outlining suitable approaches when setting external load intensity zones and accompanying thresholds in research and practice.

Methods of Monitoring Internal and External Loads and Their Relationships with Physical Qualities, Injury, or Illness in Adolescent Athletes: A Systematic Review and Best-Evidence Synthesis

BACKGROUND

With the increasing professionalisation of youth sports, training load monitoring is increasingly common in adolescent athletes. However, the research examining the relationship between training load and changes in physical qualities, injury, or illness in adolescent athletes is yet to be synthesised in a systematic review.

OBJECTIVE

The aim of this review was to systematically examine the research assessing internal and external methods of monitoring training load and physical qualities, injury, or illness in adolescent athletes.

METHODS

Systematic searches of SPORTDiscus, Web of Science, CINAHL and SCOPUS were undertaken from the earliest possible records to March 2022. Search terms included synonyms relevant to adolescents, athletes, physical qualities, injury, or illness. To be eligible for inclusion, articles were required to (1) be original research articles; (2) be published in a peer-reviewed journal; (3) include participants aged between 10 and 19 years and participating in competitive sport; (4) report a statistical relationship between a measure of internal and/or external load and physical qualities, injury or illness. Articles were screened and assessed for methodological quality. A best-evidence synthesis was conducted to identify trends in the relationships reported.

RESULTS

The electronic search yielded 4125 articles. Following screening and a review of references, 59 articles were included. The most commonly reported load monitoring tools were session ratings of perceived exertion (n = 29) and training duration (n = 22). Results of the best-evidence synthesis identified moderate evidence of positive relationships between resistance training volume load and improvement in strength, and between throw count and injury. However, evidence for other relationships between training load and change in physical qualities, injury, or illness were limited or inconsistent.

CONCLUSIONS

Practitioners should consider monitoring resistance training volume load for strength training. Additionally, where appropriate, monitoring throw counts may be useful in identifying injury risk. However, given the lack of clear relationships between singular measures of training load with physical qualities, injury, or illness, researchers should consider multivariate methods of analysing training load, as well as factors that may mediate the load-response relationship, such as maturation.

Gaussian mixture modeling of acceleration-derived signal for monitoring external physical load of tennis player

Introduction: With the widespread use of wearable sensors, various methods to evaluate external physical loads using acceleration signals measured by inertial sensors in sporting activities have been proposed. Acceleration-derived external physical loads have been evaluated as a simple indicator, such as the mean or cumulative values of the target interval. However, such a conventional simplified indicator may not adequately represent the features of the external physical load in sporting activities involving various movement intensities. Therefore, we propose a method to evaluate the external physical load of tennis player based on the histogram of acceleration-derived signal obtained from wearable inertial sensors. Methods: Twenty-eight matches of 14 male collegiate players and 55 matches of 55 male middle-aged players wore sportswear-type wearable sensors during official tennis matches. The norm of the three-dimensional acceleration signal measured using the wearable sensor was smoothed, and the rest period (less than 0.3 G of at least 5 s) was excluded. Because the histogram of the processed acceleration signal showed a bimodal distribution, for example, high- and low-intensity peaks, a Gaussian mixture model was fitted to the histogram, and the model parameters were obtained to characterize the bimodal distribution of the acceleration signal for each player. Results: Among the obtained Gaussian mixture model parameters, the linear discrimination analysis revealed that the mean and standard deviation of the high-intensity side acceleration value accurately classified collegiate and middle-aged players with 93% accuracy; however, the conventional method (only the overall mean) showed less accurate classification results (63%). Conclusion: The mean and standard deviation of the high-intensity side extracted by the Gaussian mixture modeling is found to be the effective parameter representing the external physical load of tennis players. The histogram-based feature extraction of the acceleration-derived signal that exhibit multimodal distribution may provide a novel insight into monitoring external physical load in other sporting activities.

Dose-response relationship between training load and anaerobic performance in female short-track speed skaters: A prospective cohort study

This study aimed to assess the dose-response relationship (DRR) between measures of training load (TL) and anaerobic performance in female short-track speed skaters. TL data from 22 female short-track speed skaters (mean ± SD; calendar age 20.7 ± 2.01 years, training length 4.18 ± 1.23 years, height 164.20 ± 5.53 cm, weight 52.00 ± 5.58 kg) was collected over a six week period. Participants completed two anaerobic capacity assessments (cycling parameters: total work, maximal anaerobic power [MAnP], power duration indices, maximal blood lactate, fatigue indices, and peak power output [PPO]) pre- and post-training, which included three tests (short-term, intermediate-term, and 120 s maximal anaerobic test). The mean weekly TL measures calculated were total distance, red zone (time spent > 85% of peak heart rate), Edwards training impulse (eTRIMP), Stagno training impulse (gTRIMP), Lucia training impulse, and session ratings of perceived exertion. The gTRIMP identified a curvilinear relationship and explained 79% and 63% of the variance in changes of MAnPⁱ and PPO (R² = 0.79, 95%CI = 0.64 to 1.00, turn point 621AU; R² = 0.63, 95%CI = 0.27 to 1.00, turn point 633AU, respectively). Likewise, The eTRIMP identified a curvilinear relationship and explained 62% and 54% of the variance in changes of MAnPⁱ and PPO (R² = 0.62, 95%CI = 0.11 to 1.00, turn point 485AU; R² = 0.54, 95%CI = 0.07 to 1.00, turn point 515AU, respectively). All other variables exhibited a DRR below moderate. This study investigated a TL strategy to improve the speed endurance of female short-track speed skaters and suggested the superiority of gTRIMP and eTRIMP methods of internal TL.

The dose-response relationship between training-load measures and aerobic fitness in elite academy soccer players

The aim of the current study is to examine the dose-response relationships between training load (TL) measures and the consequent changes in aerobic fitness. Data were collected over the 6-week pre-season period in elite youth soccer players. Participants completed a lactate threshold test to identify changes in treadmill speed at 2 mmol · l^-1 (S2) and 4 mmol · l^-1 (S4). Internal TL was quantified with the following training impulse (TRIMP) methods: Banister TRIMP, Edwards TRIMP, Lucia TRIMP, individual TRIMP (iTRIMP) and rate of perceived exertion was also collected. External TL measures were total distance, PlayerLoad, high speed running (14.4-19.8 km · h^-1), very high-speed running (19.8-25.2 km · h^-1) and maximal sprint distance (>25.2 km · h^-1). Individual high-speed distance was derived from each participants treadmill speed at S4. Different Bayesian regression models were run with different likelihood functions. The best-fitting models with both the lowest out-of-sample prediction error and the highest variance explained (R²) were used. iTRIMP had the strongest relationships with changes in S2 (r = 0.93, R² = 0.90) and S4 (r = 0.88, R² = 0.82). Explained variance ranged from 10%-69% and 11%-38% for all other internal TL measures and external measures, respectively. In summary, the iTRIMP method demonstrates a dose-response relationship with changes in aerobic fitness in elite youth soccer players.

Training Progression in Recreational Cyclists: No Linear Dose-Response Relationship With Training Load

ABSTRACT

Vermeire, KM, Vandewiele, G, Caen, K, Lievens, M, Bourgois, JG, and Boone, J. Training progression in recreational cyclists: no linear dose-response relationship with training load. J Strength Cond Res 35(12): 3500-3505, 2021-The purpose of the study was to assess the relationship between training load (TL) and performance improvement in a homogeneous group of recreational cyclists, training with a self-oriented training plan. Training data from 11 recreational cyclists were collected over a 12-week period. Before and after the training period, subjects underwent a laboratory incremental exercise test with blood lactate measurements to determine the power output associated with the aerobic threshold (PAT) and the anaerobic threshold (PANT), and the maximal power output (PMAX) was also determined. Mean weekly TL (calculated using the training impulse (TRIMP) of Banister, Edwards TRIMP, Lucia TRIMP and the individualized TRIMP) were correlated to the progression in fitness parameters using Pearson Correlation. Training intensity distribution (TID) was also determined (% in zone 1 as ANT). No significant correlations between mean weekly TRIMP values and the improvement on PMAX (r = -0.22 to 0.08), PANT (r = -0.56 to -0.31) and PAT (r = -0.08 to 0.41) were found. The TID was significant in a multiple regression with PANT as dependent variable (y = 0.0088 + 0.1094 × Z1 - 0.2704 × Z2 + 1.0416 × Z3; p = 0.02; R2 = 0.62). In conclusion, this study shows that the commonly used TRIMP methods to quantify TL do not show a linear dose-response relationship with performance improvement in recreational cyclists. Furthermore, the study shows that TID might be a key factor to establish a relationship with performance improvement.

The Relationship between Training Load Measures and Next-Day Well-Being in Rugby Union Players

The aim of this study is to identify the relationship between different internal and external load measures and next day subjective wellbeing. With institutional ethics approval, ten academy rugby union players (Five forwards, and five backs) with a local National League One club agreed to participate in the study (aged; 18.4 ± 1.0 years, height; 181.3 ± 5.9 cm, body mass 85.9 ± 13.0 kg, VO2max 56.2 ± 6.8 mL·kg−1·min−1). Before the 6-week in-season data collection period, participants completed an incremental treadmill test to determine lactate thresholds at 2 mmol·L−1 (LT) and 4 mmol·L−1 and the heart rate blood lactate (HR-BLa) profile for individualized training impulse (iTRIMP) calculations. Internal training load was quantified using Banister’s TRIMP, Edward’s TRIMP, Lucia’s TRIMP, individualised TRIMP and session-RPE. External training load was reported using total distance, PlayerLoadTM, high-speed distances (HSD) > 18 km∙h−1 and >15 km∙h−1, and individualized high-speed distance (iHSD) based on each player’s velocity at OBLA. On arrival and prior to all training sessions players completed a well-being questionnaire (WB). Bayesian linear mixed model analysis identified that a range of internal and external load measures explained between 30% and 37% of next-day total wellbeing and between 65% and 67% of next-day perceived stress. All other internal and external load measures demonstrated very weak to moderate relationships (R2 = 0.08 to 0.39) with all other wellbeing components. Internal sRPE, iTRIMP and bTRIMP loads alongside external HSD loads provide coaches with the most practical measures to influence players’ perceived wellbeing.

Continuous Versus Intermittent Running: Acute Performance Decrement and Training Load

PURPOSE

To examine the effect of continuous (CON) and intermittent (INT) running training sessions of different durations and intensities on subsequent performance and calculated training load (TL).

METHODS

Runners (N = 11) performed a 1500-m time trial as a baseline and after completing 4 different running training sessions. The training sessions were performed in a randomized order and were either maximal for 10 minutes (10CON and 10INT) or submaximal for 25 minutes (25CON and 25INT). An acute performance decrement (APD) was calculated as the percentage change in 1500-m time-trial speed measured after training compared with baseline. The pattern of APD response was compared with that for several TL metrics (bTRIMP, eTRIMP, iTRIMP, running training stress score, and session rating of perceived exertion) for the respective training sessions.

RESULTS

Average speed (P < .001, ηp2=.924) was different for each of the initial training sessions, which all resulted in a significant APD. This APD was similar when compared across the sessions except for a greater APD found after 10INT versus 25CON (P = .02). In contrast, most TL metrics were different and showed the opposite response to APD, being higher for CON versus INT and lower for 10- versus 25-minute sessions (P < .001, ηp2>.563).

CONCLUSION

An APD was observed consistently after running training sessions, but it was not consistent with most of the calculated TL metrics. The lack of agreement found between APD and TL suggests that current methods for quantifying TL are flawed when used to compare CON and INT running training sessions of different durations and intensities.

Variations of Trail Runner's Fitness Measures across a Season and Relationships with Workload

Trail running involves off-road running over different surfaces of positive and negative unevenness. Given these particularities and the associated physical demands, it is essential to understand this relationship and how fitness levels influence performance. This study aimed to analyze fitness level variations during different times of the season and establish a relationship between changes in fitness levels and accumulated load. Twenty-five trail running athletes (age: 36.23 ± 8.30 years) were monitored over 52 weeks. Three periods of assessment were implemented, while load between those periods was calculated. Athletes were monitored daily by global positioning systems. The collected data included distance covered, duration, and rate of perceived exertion (RPE), which were used to obtain session-RPE. Additionally, maximal aerobic speed, vertical jump, and dynamic balance were tested periodically. Moderate inverse correlations were found between assessment 1 and 2 for total sRPE and vertical jump: countermovement jump (VJ: CMJ) (r = −0.349), and Y balance test: left posterolateral (YBT: LPL) (r = −0.494). Similar correlations were found between assessment 2 and 3 for total sRPE and VJ: CMJ (r = −0.397), and vertical jump: drop jump (VJ: DJ) (r = −0.395). The results suggest that trail running coaches should monitor and assess dose–response relationships and possible anterior asymmetries of dynamic balance performance.

HIIT Models in Addition to Training Load and Heart Rate Variability Are Related With Physiological and Performance Adaptations After 10-Weeks of Training in Young Futsal Players

INTRODUCTION

The present study aimed to investigate the effects of two high-intensity interval training (HIIT) shuttle-run-based models, over 10 weeks on aerobic, anaerobic, and neuromuscular parameters, and the association of the training load and heart rate variability (HRV) with the change in the measures in young futsal players.

METHODS

Eleven young male futsal players (age: 18.5 ± 1.1 years; body mass: 70.5 ± 5.7 kg) participated in this study. This pre-post study design was performed during a typical 10 weeks training period. HIIT sessions were conducted at 86% (HIIT86; n = 6) and 100% (HIIT100; n = 5) of peak speed of the FIET. Additionally, friendly and official matches, technical-tactical and strength-power training sessions were performed. Before and after the training period, all players performed the FIET, treadmill incremental, repeated sprint ability (RSA), sprint 15-m, and vertical jump tests (CMJ and SJ), and the HRV was measured. Training load (TL) was monitored using the session rating of perceived effort. Data analysis was carried out using Bayesian inference methods.

RESULTS

The HIIT86 model showed clear improvements for the peak oxygen uptake (VO2peak), peak speed in the treadmill incremental test, first and second ventilatory thresholds, RSA best and mean times, CMJ, and SJ. The HIIT100 model presented distinct advances in VO2peak, peak speed in the treadmill incremental test, RSA mean time, and CMJ. Between HIIT models comparisons showed more favorable probabilities of improvement for HIIT86 than HIIT100 model in all parameters. TL data and HIIT models strongly explained the changes in the RSA mean and best times (R 2 = 0.71 and 0.87, respectively), as well as HRV changes, and HIIT models explained positively VO2peak changes (R 2 = 0.72). All other changes in the parameters were low to moderately explained.

CONCLUSION

The HIIT86 proved to be more effective for improving aerobic, RSA, and neuromuscular parameters than HIIT100 during a typical 10-week futsal training period. So, strength and conditioning specialists prescribing shuttle-run intermittent exercises at submaximal intensities can manage the individual acceleration load imposed on athlete increasing or decreasing either the set duration or the frequency of change of direction during HIIT programming.

The Validity and Reliability of Wearable Microtechnology for Intermittent Team Sports: A Systematic Review

BACKGROUND

Technology has long been used to track player movements in team sports, with initial tracking via manual coding of video footage. Since then, wearable microtechnology in the form of global and local positioning systems has provided a less labour-intensive way of monitoring movements. As such, there has been a proliferation in research pertaining to these devices.

OBJECTIVE

A systematic review of studies that investigate the validity and/or reliability of wearable microtechnology to quantify movement and specific actions common to intermittent team sports.

METHODS

A systematic search of CINAHL, MEDLINE, and SPORTDiscus was performed; studies included must have been (1) original research investigations; (2) full-text articles written in English; (3) published in a peer-reviewed academic journal; and (4) assessed the validity and/or reliability of wearable microtechnology to quantify movements or specific actions common to intermittent team sports.

RESULTS

A total of 384 studies were retrieved and 187 were duplicates. The titles and abstracts of 197 studies were screened and the full texts of 88 manuscripts were assessed. A total of 62 studies met the inclusion criteria. Additional 10 studies, identified via reference list assessment, were included. Therefore, a total of 72 studies were included in this review.

CONCLUSION

There are many studies investigating the validity and reliability of wearable microtechnology to track movement and detect sport-specific actions. It is evident that for the majority of metrics, validity and reliability are multi-factorial, in that it is dependent upon a wide variety of factors including wearable technology brand and model, sampling rate, type of movement performed (e.g., straight line, change of direction) and intensity of movement (e.g., walk, sprint). Practitioners should be mindful of the accuracy and repeatability of the devices they are using when making decisions on player training loads.

Performance and Training Load Profiles in Recreational Male Trail Runners: Analyzing Their Interactions during Competitions

Endurance sports like trail running constitute an extensive individual modality causing numerous physiological changes to occur in the athlete. In this sense, an adequate monitoring of training load appears to be essential to improve competition performance. The aim of this study was two-fold: (i) to analyze trail runners’ weekly load variations in the four weeks leading up to a trail running competition, and (ii) to determine the relationship between the runners’ pacing in competitions and their physical fitness and workload parameters. Twenty-five amateur male trail runners (age: 36.23 ± 8.30 years old; minimum International Trail Running Association performance index: 600) were monitored daily for the duration of a season (52 weeks). External load (distance covered, pace) and internal load (rate of perceived exertion) were measured daily. Additionally, weekly workload measures of acute:chronic workload ratio (ACWR), training monotony, and training strain were calculated. The runners were also assessed for maximal aerobic speed (MAS) every four months. No significant differences in workload measures (p > 0.05) were observed in the four weeks leading up to each short trail competition; however, leading up to the long trail, ultra-trail medium, and ultra-trail long/extra-long competitions, the differences in the runners’ workload measures were significant (p < 0.05). In the short trail, pace was found to be moderately correlated with the ACWR of total distance (r = −0.334) and with training monotony of rate of perceived exertion (RPE) (r = −0.303). In the ultra-trail, a large correlation was observed between pace and elevation accumulated (r = 0.677). We concluded that significant workload differences from one week to the next only occurred in preparation for longer-distance competitions, with sudden acute load decreases and very low ACWR values reported mainly in weeks 1 and 2 of the taper. Meaningful relationships were found between performance (pace) and MAS for longer trails and between pace and MAS for ultra-trail competitions.

Training for the HandbikeBattle: an explorative analysis of training load and handcycling physical capacity in recreationally active wheelchair users

PURPOSE

(1) to analyze training characteristics of recreationally active wheelchair users during handcycle training, and (2) to examine the associations between training load and change in physical capacity.

METHODS

Former rehabilitation patients (N = 60) with health conditions such as spinal cord injury or amputation were included. Participants trained for five months. A handcycling/arm crank graded exercise test was performed before and after the training period. Outcomes: peak power output per kg (POpeak/kg) and peak oxygen uptake per kg (VO₂peak/kg). Training load was defined as Training Impulse (TRIMP), which is rating of perceived exertion (sRPE) multiplied by duration of the session, in arbitrary units (AU). Training intensity distribution (TID) was also determined (time in zone 1, RPE ≤4; zone 2, RPE 5-6; zone 3, RPE ≥7).

RESULTS

Multilevel regression analyses showed that TRIMP_sRPE was not significantly associated with change in physical capacity. Time in zone 2 (RPE 5-6) was significantly associated with ΔVO₂peak, %ΔVO₂peak, ΔVO₂peak/kg and %ΔVO₂peak/kg.

CONCLUSION

Training at RPE 5-6 was the only determinant that was significantly associated with improvement in physical capacity. Additional controlled studies are necessary to demonstrate causality and gather more information about its usefulness, and optimal handcycle training regimes for recreationally active wheelchair users.IMPLICATIONS FOR REHABILITATIONMonitoring of handcycle training load is important to structure the training effort and intensity over time and to eventually optimize performance capacity. This is especially important for relatively untrained wheelchair users, who have a low physical capacity and a high risk of overuse injuries and shoulder pain.Training load can be easily calculated by multiplying the intensity of the training (RPE 0-10) with the duration of the training in minutes.Results on handcycle training at RPE 5-6 intensity in recreationally active wheelchair users suggests to be promising and should be further investigated with controlled studies.

Reliability and Validity of Integrated External and Internal Load Ratios as Measures of Fitness in Academy Rugby Union Players

Taylor, RJ, Sanders, D, Myers, T, and Akubat, I. Reliability and validity of integrated external and internal load ratios as measures of fitness in academy rugby union players. J Strength Cond Res 34(6): 1723-1730, 2020-This study aimed to assess the relationships between integrated ratios of external and internal load measures and aerobic fitness in 3 different training exercise protocols. Twelve academy rugby union players (aged; 17.6 ± 0.44 years, height; 179.4 ± 6.3 cm, body mass 83.3 ± 9.7 kg) performed a lactate threshold/V[Combining Dot Above]O2max test (LT/V[Combining Dot Above]O2max) followed by 3 exercise protocols; 760 m continuous shuttle running (20 m shuttle run at 9 km·h), sprint interval training (SIT: 6 × 6 seconds sprint with 54 seconds recovery), and a small-sided game (SSG: 6 vs. 6, 10 minutes on a 39 × 51 m pitch) on 2 occasions. A LT/V[Combining Dot Above]O2max test was used to determine velocity at lactate threshold (vLT) and at the onset of blood lactate accumulation (vOBLA), maximal oxygen uptake (V[Combining Dot Above]O2max) and the heart rate-blood lactate profile for the calculation of internal load (individualized training impulse or iTRIMP). The total distance (TD), PlayerLoad, metabolic power (MP), high-speed distance >15 km·h (HSD), very high-speed distance >18 km·h (VHSD) and individualized high-speed distance based on each player's vOBLA (iHSD) for the 3 exercise protocols were measured using Micro Electro Mechanical Systems/Global Positioning System technology. Bayesian analysis was used to assess the ratios validity and reliability. Ratios demonstrated large-to-very large associations with vOBLA (Rho = 0.64-0.76), vLT (Rho = 0.63-0.71). Reliability of the ratios ranged from 7.06 to 36.28% (coefficient of variation [CV]%). The results suggest that integrated load ratios from the SIT and SSG protocols provide a measure which is reliable with large-to-very large associations to submaximal aerobic fitness in rugby union.

The Training-Performance Puzzle: How Can the Past Inform Future Training Directions?

Over the past 20 years, research on the training-load-injury relationship has grown exponentially. With the benefit of more data, our understanding of the training-performance puzzle has improved. What were we thinking 20 years ago, and how has our thinking changed over time? Although early investigators attributed overuse injuries to excessive training loads, it has become clear that rapid spikes in training load, above what an athlete is accustomed, explain (at least in part) a large proportion of injuries. In this respect, it appears that overuse injuries may arise from athletes being underprepared for the load they are about to perform. However, a question of interest to both athletic trainers (ATs) and researchers is why some athletes sustain injury at low training loads, while others can tolerate much greater training loads? A higher chronic training load and well-developed aerobic fitness and lower body strength appear to moderate the training-injury relationship and provide a protective effect against spikes in load. The training-performance puzzle is complex and dynamic-at any given time, multiple inputs to injury and performance exist. The challenge facing researchers is obtaining large enough longitudinal data sets to capture the time-varying nature of physiological and musculoskeletal capacities and training-load data to adequately inform injury-prevention efforts. The training-performance puzzle can be solved, but it will take collaboration between researchers and clinicians as well as an understanding that efficacy (ie, how training load affects performance and injury in an idealized or controlled setting) does not equate to effectiveness (ie, how training load affects performance and injury in the real-world setting, where many variables cannot be controlled).

The relationship between training load and fitness indices over a pre-season in professional soccer players

BACKGROUND

An association between training load and changes in aerobic fitness has been established but the effect of training load on changes in strength/power remains controversial.

METHODS

Internal (Banister's TRIMP) and external (total distance, high-speed running and sprint distance) training load was collected from sixteen professional soccer players during and aerobic fitness and strength/power variables were measured before and after a 9-week pre-season.

RESULTS

Banister's TRIMP had a moderate correlation with changes in maximal oxygen uptake (r=0.46, 90% CI: 0.04; 0.74). Total distance had a large and a moderate correlation with changes in velocity at 2M (r=0.60, 90% CI: 0.23; 0.82) and changes in velocity at 4M (r=0.42, 90% CI: -0.01; 0.72). High-speed running had moderate correlations with changes in maximal oxygen uptake (r=0.45, 90% CI: 0.03; 0.74), velocity at 2M (r=0.45, 90% CI: 0.03; 0.74) and velocity at 4M (r=0.39, 90% CI: -0.00; 0.70). Sprint distance had a large and a moderate correlation with changes in maximal oxygen uptake (r=0.58, 90% CI: 0.20; 0.81) and velocity at 4M (r=0.46, 90% CI: 0.00; 0.74 respectively). High versus low total distance was associated with lower changes in squat jump and countermovement jump (ES=-0.90, 90% CI: -1.57; -0.24 and ES=-1.06, 90% CI: -1.89; -0.24) respectively. High versus low high-speed running was associated with higher changes in maximal oxygen uptake (ES=0.36, 90% CI: 0.02; 0.70) but lower changes in squat jump (ES=-0.58, 90% CI: -1.32; 0.15).

CONCLUSIONS

External rather internal training load had more pronounced correlations with changes in aerobic fitness. Higher compared with lower volumes of total distance and high-speed running were associated with lower gains in strength/power indices. Establishing a "dose-response" association between external/internal training load and endurance as well as strength adaptations, may maximize endurance gains with the least possible interference on strength/power gains, thus better informing soccer training practice.

The relative contribution of training intensity and duration to daily measures of training load in professional rugby league and union

This study examined the relative contribution of exercise duration and intensity to team-sport athlete's training load. Male, professional rugby league (n = 10) and union (n = 22) players were monitored over 6- and 52-week training periods, respectively. Whole-session (load) and per-minute (intensity) metrics were monitored (league: session rating of perceived exertion training load [sRPE-TL], individualised training impulse, total distance, BodyLoad™; union: sRPE-TL, total distance, high-speed running distance, PlayerLoad™). Separate principal component analyses were conducted on the load and intensity measures to consolidate raw data into principal components (PC, k = 4). The first load PC captured 70% and 74% of the total variance in the rugby league and rugby union datasets, respectively. Multiple linear regression subsequently revealed that session duration explained 73% and 57% of the variance in first load PC, respectively, while the four intensity PCs explained an additional 24% and 34%, respectively. Across two professional rugby training programmes, the majority of the variability in training load measures was explained by session duration (~60-70%), while a smaller proportion was explained by session intensity (~30%). When modelling the training load, training intensity and duration should be disaggregated to better account for their between-session variability.

Applied Sport Science for Male Age-Grade Rugby Union in England

Rugby union (RU) is a skill-collision team sport played at junior and senior levels worldwide. Within England, age-grade rugby governs the participation and talent development of youth players. The RU player development pathway has recently been questioned, regarding player performance and well-being, which sport science research can address. The purpose of this review was to summarise and critically appraise the literature in relation to the applied sport science of male age-grade RU players in England focussing upon (1) match-play characteristics, (2) training exposures, (3) physical qualities, (4) fatigue and recovery, (5) nutrition, (6) psychological challenges and development, and (7) injury. Current research evidence suggests that age, playing level and position influence the match-play characteristics of age-grade RU. Training exposures of players are described as 'organised chaos' due to the multiple environments and stakeholders involved in coordinating training schedules. Fatigue is apparent up to 72 h post match-play. Well-developed physical qualities are important for player development and injury risk reduction. The nutritional requirements are high due to the energetic costs of collisions. Concerns around the psychological characteristics have also been identified (e.g. perfectionism). Injury risk is an important consideration with prevention strategies available. This review highlights the important multi-disciplinary aspects of sport science for developing age-grade RU players for continued participation and player development. The review describes where some current practices may not be optimal, provides a framework to assist practitioners to effectively prepare age-grade players for the holistic demands of youth RU and considers areas for future research.

No Effect of Partial-Body Cryotherapy on Restoration of Countermovement Jump or Well-Being Performance in Elite Rugby Union Players During the Competitive Phase of the Season

PURPOSE

Partial body cryotherapy (PBC) has been shown to be beneficial for postexercise recovery; however, no study has demonstrated the effectiveness of PBC for recovery following elite rugby union training. Rugby union is a unique sport that involves high-velocity collisions and may induce greater performance decrements than other sports; thus, PBC could be beneficial. The application of PBC in "real world" has rarely been investigated during the competitive phase of a playing season and warranted investigation.

METHODS

In a counterbalanced sequential research design, professional rugby athletes (n = 18; age 25.4 [4.0] y; training age 7.2 [4.0] y; mass 99.8 [10.6] kg; height 188.3 [6.0] cm) were assigned to a 12-week PBC intervention, washout period (4 wk), and reassessed as their own controls. Self-reported well-being, muscle soreness, sleep quality, and countermovement jump height were assessed before and 40 hours after "real-world" training. Wilcoxon signed-rank tests and Cohen d were used for statistical analysis.

RESULTS

No differences were observed between PBC and control conditions (P > .05; d = 0.00-0.14) for well-being (-0.02% [0.08%] vs 0.01% [0.06%]), muscle soreness (-0.01% [0.11%] vs 0.01% [0.16%]), sleep quality (-0.03% [0.14%] vs 0.10% [0.29%]), or countermovement jump height (36.48-36.59 vs 38.13-37.52 cm; P = .54).

CONCLUSIONS

These results suggest PBC is ineffective for the restoration of selected performance parameters during the performance maintenance phase of the competitive season. To ascertain the appropriation of its use, future investigations should seek to assess the use of cryotherapies at various phases of the elite rugby union competitive season.

Visualizing the Complexity of the Athlete-Monitoring Cycle Through Principal-Component Analysis

PURPOSE

To discuss the use of principal-component analysis (PCA) as a dimension-reduction and visualization tool to assist in decision making and communication when analyzing complex multivariate data sets associated with the training of athletes.

CONCLUSIONS

Using PCA, it is possible to transform a data matrix into a set of orthogonal composite variables called principal components (PCs), with each PC being a linear weighted combination of the observed variables and with all PCs uncorrelated to each other. The benefit of transforming the data using PCA is that the first few PCs generally capture the majority of the information (ie, variance) contained in the observed data, with the first PC accounting for the highest amount of variance and each subsequent PC capturing less of the total information. Consequently, through PCA, it is possible to visualize complex data sets containing multiple variables on simple 2D scatterplots without any great loss of information, thereby making it much easier to convey complex information to coaches. In the future, athlete-monitoring companies should integrate PCA into their client packages to better support practitioners trying to overcome the challenges associated with multivariate data analysis and interpretation. In the interim, the authors present here an overview of PCA and associated R code to assist practitioners working in the field to integrate PCA into their athlete-monitoring process.

Associations Between Selected Training-Stress Measures and Fitness Changes in Male Soccer Players

PURPOSE

To investigate the relationship between accumulated global positioning system-accelerometer-based and heart rate-based training metrics and changes in high-intensity intermittent-running capacity during an in-season phase in professional soccer players.

METHODS

Eleven male professional players (mean [SD] age 27.2 [4.5] y) performed the 30-15 Intermittent Fitness Test (30-15IFT) before and after a 5-wk in-season training phase, and the final velocity (VIFT) was considered their high-intensity intermittent-running capacity. During all sessions, Edwards training impulse (Edwards TRIMP), Banister TRIMP, Z5 TRIMP, training duration, total distance covered, new body load (NBL), high-intensity running performance (distance covered above 14.4 km·h-1), and very-high-intensity running performance (distance covered above 19.8 km·h-1) were recorded.

RESULTS

The players' VIFT showed a most likely moderate improvement (+4.3%, 90% confidence limits 3.1-5.5%, effect size 0.70, [0.51-0.89]). Accumulated NBL, Banister TRIMP, and Edwards TRIMP showed large associations (r = .51-.54) with changes in VIFT. A very large relationship was also observed between accumulated Z5 TRIMP (r = .72) with changes in VIFT. Large to nearly perfect within-individual relationships were observed between NBL and some of the other training metrics (ie, Edwards TRIMP, Banister TRIMP, training duration, and total distance) in 10 out of 11 players.

CONCLUSIONS

Heart rate-based training metrics can be used to monitor high-intensity intermittent-running-capacity changes in professional soccer players. The dose-response relationship is also largely detected using accelerometer-based metrics (ie, NBL) to track changes in high-intensity intermittent-running capacity of professional soccer players.

Overcoming the problem of multicollinearity in sports performance data: A novel application of partial least squares correlation analysis

Objectives

Professional sporting organisations invest considerable resources collecting and analysing data in order to better understand the factors that influence performance. Recent advances in non-invasive technologies, such as global positioning systems (GPS), mean that large volumes of data are now readily available to coaches and sport scientists. However analysing such data can be challenging, particularly when sample sizes are small and data sets contain multiple highly correlated variables, as is often the case in a sporting context. Multicollinearity in particular, if not treated appropriately, can be problematic and might lead to erroneous conclusions. In this paper we present a novel ‘leave one variable out’ (LOVO) partial least squares correlation analysis (PLSCA) methodology, designed to overcome the problem of multicollinearity, and show how this can be used to identify the training load (TL) variables that influence most ‘end fitness’ in young rugby league players.

Methods

The accumulated TL of sixteen male professional youth rugby league players (17.7 ± 0.9 years) was quantified via GPS, a micro-electrical-mechanical-system (MEMS), and players’ session-rating-of-perceived-exertion (sRPE) over a 6-week pre-season training period. Immediately prior to and following this training period, participants undertook a 30–15 intermittent fitness test (30-15_IFT), which was used to determine a players ‘starting fitness’ and ‘end fitness’. In total twelve TL variables were collected, and these along with ‘starting fitness’ as a covariate were regressed against ‘end fitness’. However, considerable multicollinearity in the data (VIF >1000 for nine variables) meant that the multiple linear regression (MLR) process was unstable and so we developed a novel LOVO PLSCA adaptation to quantify the relative importance of the predictor variables and thus minimise multicollinearity issues. As such, the LOVO PLSCA was used as a tool to inform and refine the MLR process.

Results

The LOVO PLSCA identified the distance accumulated at very-high speed (>7 m·s^-1) as being the most important TL variable to influence improvement in player fitness, with this variable causing the largest decrease in singular value inertia (5.93). When included in a refined linear regression model, this variable, along with ‘starting fitness’ as a covariate, explained 73% of the variance in v30-15_IFT ‘end fitness’ (p<0.001) and eliminated completely any multicollinearity issues.

Conclusions

The LOVO PLSCA technique appears to be a useful tool for evaluating the relative importance of predictor variables in data sets that exhibit considerable multicollinearity. When used as a filtering tool, LOVO PLSCA produced a MLR model that demonstrated a significant relationship between ‘end fitness’ and the predictor variable ‘accumulated distance at very-high speed’ when ‘starting fitness’ was included as a covariate. As such, LOVO PLSCA may be a useful tool for sport scientists and coaches seeking to analyse data sets obtained using GPS and MEMS technologies.

Effects of 4 weeks of β-alanine supplementation on aerobic fitness in water polo players

The purpose of the present study was to investigate the ergogenic effects of 4 weeks of β-alanine supplementation on peak oxygen uptake (V˙O2peak) and force associated with V˙O2peak (FV˙O2peak) during a tethered swimming graded exercise test, and a three-minute all-out effort (3min_ALL-OUT) in water polo players. Twenty-two male national competitive level water polo players were randomly assigned to receive either 4 weeks of β-alanine (n = 11) or a placebo (n = 11) (i.e., 4.8 g·day^-1 for 10 days, then6.4 g·day^-1 for 18 days, resulting in 163.2 g over 28 days). The participants performed the TS_GET and 3min_ALL-OUT before and after the supplementation period. There were no significant interaction effects between-groups for any variable, however, the magnitude-based inferences analyses showed a possibly beneficial effect (74%) of β-alanine supplementation on FV˙O2peak compared to placebo treatment (Δ% [post–pre] for Placebo group = -5.2%; Δ% [post–pre] for β-alanine group = +0.7%). Only the β-alanine group presented a significant reduction in V˙O2peak expressed in absolute values (PRE = 3.3±0.6L·min^-1; POST = 3.0±0.4L·min^-1; p = .021). Similarly, only the β-alanine group presented a significant increase in critical force (PRE = 51.2±10.4N; POST = 56.5±13.1N; p = .044) and a reduction in the curvature constant parameter (W'; PRE = 2998.0±1103.7N·s; POST = 2224.6±1058.9N·s; p = .049). Thus, we can conclude that 4 weeks of β-alanine supplementation presented mixed results in water polo players, indicating that this nutritional strategy may not be effective in improving parameters of the TS_GET and 3min_{ALL-OUT .}

The Same Story or a Unique Novel? Within-Participant Principal-Component Analysis of Measures of Training Load in Professional Rugby Union Skills Training

Purpose: To identify which combination metrics of external and internal training load (TL) capture similar or unique information for individual professional players during skills training in rugby union using principal-component (PC) analysis. Methods: TL data were collected from 21 male professional rugby union players across a competitive season. This included PlayerLoad™, total distance, and individualized high-speed distance (>61% maximal velocity; all external TL) obtained from a microtechnology device (OptimEye X4; Catapult Innovations, Melbourne, Australia) that was worn by each player and the session rating of perceived exertion (RPE) (internal TL). PC analysis was conducted on each individual to extract the underlying combinations of the 4 TL measures that best describe the total information (variance) provided by the measures. TL measures with PC loadings (PCL) above 0.7 were deemed to possess well-defined relationships with the extracted PC. Results: The findings show that from the 4 TL measures, the majority of an individual’s TL information (first PC: 55–70%) during skills training can be explained by session RPE (PCL: 0.72–0.95), total distance (PCL: 0.86–0.98), or PlayerLoad (PCL: 0.71–0.98). High-speed distance was the only variable to relate to the second PC (PCL: 0.72–1.00), which captured additional TL information (+19–28%). Conclusions: Findings suggest that practitioners could quantify the TL of rugby union skills training with one of PlayerLoad, total distance, or session RPE plus high-speed distance while limiting omitted information of the TL imposed during professional rugby union skills training.

Methods of Monitoring Training Load and Their Association With Changes Across Fitness Measures in Hurling Players

Malone, S, Hughes, B, Collins, K, and Akubat, I. Methods of monitoring training load and their association with changes across fitness measures in hurling players. J Strength Cond Res 34(1): 225-234, 2020-The aim of the current investigation was to assess the dose-response relationship for various methods of monitoring training load (TL) and changes in aerobic and anaerobic fitness in hurling players. Training and match load measures were collected from 30 hurling players (speed at different blood lactate [vLT, vOBLA], maximal oxygen uptake [VO2max], speed at VO2max [vVO2max], peak treadmill velocity [PTV] running economy [RE] Yo-Yo intermittent recovery test (Yo-YoIR1, Yo-YoIR2), speed at 5, 10 , 20 m, and repeated sprint ability [6 × 35 m]) before and after during a 12-week in-season period. Mean weekly training and match loads as determined by s-RPE, bTRIMP, luTRIMP, eTRIMP, iTRIMP, and gTRIMP were correlated with each other, percentage change in VO2max, vVO2max, RE, PTV, the speed at blood lactate concentrations of 2 mmol·L (vLT) and 4 mmol·L (vOBLA), and YoYoIR1, Yo-YoIR2 performance. iTRIMP showed a trivial association with s-RPE and a small to moderate association with other heart rate-based methods. Small to large changes (effect size: 0.38-1.12) were observed in aerobic and anaerobic fitness measures were observed across the 12-week period. Trivial to very large associations (r = 0.11-0.78) were observed between TL variables and changes in fitness depending on the variable analyzed. iTRIMP was shown to have large to very large associations with changes in vOBLA {r = 0.78 (95% confidence interval [CI]: 0.71-0.88)}, VO2max (r = 0.77 [95% CI: 0.68-0.82]), RE (r = 0.77 [95% CI: 0.67-0.82]), Yo-YoIR1 (r = 0.69 [95% CI: 0.51-0.72]), and RSAb (r = 0.66 [95% CI: 0.60-0.72]) with moderate associations reported between iTRIMP and other variables. All other measures of TL showed trivial to moderate associations (r = 0.11-0.66). The current investigation shows that fully individualized training load (iTRIMP) measures may potentially offer a better understanding of dose-response relationships when compared with subjective and nonindividualized measures of TL to changes in aerobic and anaerobic fitness in team sports athletes; as such, it may be suggested that practitioners should use individualized measures of TL assessment within their day-to-day practices.

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.