Quantification Of Training Load Research Articles

Outil d’aide à la décision et à la prévention dans les sports collectifs. Exemple d’un centre de formation rugby

IntroductionMonitoring training load is a key factor in the optimization of the training process, whether it is to enhance performances or to reduce the risk of injury. AimsThe objective of this study was to measure the effects of using a decision-making tool based on a daily, dematerialized but centralized, quantification of training load on the occurrence of players’ injuries in a rugby-training center. Material and methodsDuring two consecutive years, information on play time and injuries was collected from respectively 39 and 35 players (U15 to U20). The first season's periodization of training was constituted of a predefined succession of cycles of development and regeneration while the second year's periodization of training was specific to each player: relative rest periods began when the real player's training load exceeded a previously individually determined threshold. ResultsBetween the first and second seasons, play time increased by 17% while the number of injuries in matches, the number of (para)medical appointments (medical imaging included) and the cost of these (para)medical acts decreased by 74%, 32% and 22% respectively (standardized data according to the number of players integrated into the training center). ConclusionThe decision-making tool, developed internally, allowing for a quick, systematic and daily quantification coupled with individualized alerts had contributed to increase play time and reduce of the number of match injuries for rugby players on their way to becoming high-level athletes.

La prévention des blessures sportives : modèles théoriques et éléments-clés d’une stratégie efficace

Research topic about sport injury prevention has importantly increased these last years. Several theoretical models have been developed with the aim of helping sport and medical staffs with preventive strategies, and many studies have demonstrated the high efficacy of preventive programs if quality conditions are met. Among these models, at least two “sports injury risk management approaches” have been recently described and their relevance needs to be confirmed by further research. In order to maximize efficacy, preventive strategies should include: pre-competition medical assessment, actions to decrease multiple risk factors at the same time, maximal compliance, training load quantification and monitoring.

Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load.

ABSTRACTPurposeThis study aimed to apply open-source analysis code to raw habitual physical activity data from wrist-worn monitors to: 1) objectively, unobtrusively, and accurately discriminate between “running” and “nonrunning” days; and 2) develop and compare simple accelerometer-derived metrics of external training load with existing self-report measures.MethodsSeven-day wrist-worn accelerometer (GENEActiv; Activinsights Ltd, Kimbolton, UK) data obtained from 35 experienced runners (age, 41.9 ± 11.4 yr; height, 1.72 ± 0.08 m; mass, 68.5 ± 9.7 kg; body mass index, 23.2 ± 2.2 kg·m−2; 19 [54%] women) every other week over 9 to 18 wk were date-matched with self-reported training log data. Receiver operating characteristic analyses were applied to accelerometer metrics (“Average Acceleration,” “Most Active-30mins,” “Mins≥400 mg”) to discriminate between “running” and “nonrunning” days and cross-validated (leave one out cross-validation). Variance explained in training log criterion metrics (miles, duration, training load) by accelerometer metrics (Mins≥400 mg, “workload (WL) 400-4000 mg”) was examined using linear regression with leave one out cross-validation.ResultsMost Active-30mins and Mins≥400 mg had >94% accuracy for correctly classifying “running” and “nonrunning” days, with validation indicating robustness. Variance explained in miles, duration, and training load by Mins≥400 mg (67%–76%) and WL400–4000 mg (55%–69%) was high, with validation indicating robustness.ConclusionsWrist-worn accelerometer metrics can be used to objectively, unobtrusively, and accurately identify running training days in runners, reducing the need for training logs or user input in future prospective research or commercial activity tracking. The high percentage of variance explained in existing self-reported measures of training load by simple, accelerometer-derived metrics of external training load supports the future use of accelerometry for prospective, preventative, and prescriptive monitoring purposes in runners.

Validity of Session Rating Perceived Exertion Method for Quantifying Internal Training Load during High-Intensity Functional Training.

The aim of this study was to validate the quantification of internal training load (session rating perceived exertion, sRPE) and the effect of recall timing of sRPE during high-intensity functional training (HIFT) sessions. Thirteen male HIFT practitioners (age 27.2 ± 33 years, height 177.1 ± 4.0 cm, body mass 81.1 ± 9.0 kg) were monitored during two common HIFT training sessions: Fight Gone Bad (FGB) and Fran. The Edwards summated heart-rate-zone method was used as a reference measure of internal training load. The session-RPE rating was obtained using the CR-10 scale modified by Foster. The training load calculated by the Edwards-TRIMP index was significantly higher (p < 0.05) during the FGB (77.7 ± 4.9) than the Fran (19.8 ± 8.4) workout. There was a strong correlation (p < 0.05) between the Edwards-TRIMP index and the training load calculated by the sRPE in all time frames (0, 10, 20, and 30 min post-exercise). The RPE and sRPE measured at 30 min post-exercise time frame was significant lower than 0, 10, and 20 min post-exercise for both workouts. The session-RPE method is an easy and valid tool to evaluate internal training load for high intensity functional training practitioners.

Quantification of Training Load in Endurance Sports: Additional Considerations

Quantification of Training Load in Endurance Sports: Additional Considerations

Quantification of Training Load in Endurance Sports: Additional Considerations: Reply

Quantification of Training Load in Endurance Sports: Additional Considerations: Reply

The Effect of Training Loads on Performance Measures and Injury Characteristics in Rugby League Players: A Systematic Review.

To conduct a systematic review into the effect of training load (TL) on both performance measures and injury characteristics in rugby league players. Based on PRISMA guidelines, a systematic search of electronic databases was performed from the earliest record to December 2016. Five electronic databases (MEDLINE, SPORTDiscus, CINAHL, Web of Science, and AusSportMed) were searched using keywords within the subcategories of population (rugby league players), intervention (training quantification), and outcomes (performance or injury). Original peer-reviewed published manuscripts were considered if they had a clear quantification of TL (eg,session rating of perceived exertion), clear measures of performance (tests of physiological parameters relative to rugby league players [eg,agility]), and/or injury characteristics (eg,injury incidence). Twelve studies met the inclusion criteria. The highest TL was observed in preseason training phases. Higher TL elicited greater improvements in maximal aerobic power, vertical jump (in cm), and agility scores (in s). Higher TL was associated with higher incidence of muscular strains and joint sprains, lower-limb injuries, and overexertion and overuse injuries. Findings suggest a dose-response relationship between TL, physical performance, and injury characteristics. However, due to study and methodological limitations, the precise nature of this relationship is, as yet, inconclusive. Therefore, the development of uniform TL definition and quantification and further research is warranted.

Answers to be Solved in Training Load Quantification

Answers to be Solved in Training Load Quantification

The Case for Adopting a Multivariate Approach to Optimize Training Load Quantification in Team Sports.

The Case for Adopting a Multivariate Approach to Optimize Training Load Quantification in Team Sports.

Validity of the Online Athlete Management System to Assess Training Load.

To validate the quantification of training load (session rating of perceived exertion [s-RPE]) in an Australian Olympic squad (women's water polo), assessed with the use of a modified RPE scale collected via a newly developed online system (athlete management system). Sixteen elite women water polo players (age = 26 [3]y, height = 1.78 [0.05]m, and body mass = 75.5 [7.1]kg) participated in the study. Thirty training sessions were monitored for a total of 303 individual sessions. Heart rate was recorded during training sessions using continuous heart-rate telemetry. Participants were asked to rate the intensity of the training sessions on the athlete management system RPE scale, using an online application within 30min of completion of the sessions. Individual relationships between s-RPE and both Banister training impulse (TRIMP) and Edwards' method were analyzed. Individual correlations with s-RPE ranged between r = .51 and .79 (Banister TRIMP) and r = .54 and .83 (Edwards' method). The percentages of moderate and large correlation were 81% and 19% between s-RPE method and Banister TRIMP, and 56% and 44% between s-RPE and Edwards' method. The online athlete management system for assessing s-RPE was shown to be a valid indicator of internal training load and can be used in elite sport.

Quantification of Training Load and Training Response for Improving Athletic Performance

ABSTRACT ONE IMPORTANT RESPONSIBILITY OF A STRENGTH AND CONDITIONING COACH IS THE DEVELOPMENT OF A TRAINING PROGRAM THAT ENHANCES PHYSIOLOGICAL QUALITIES, WITH THE GOAL OF IMPROVING ATHLETIC PERFORMANCE. TO ACHIEVE THIS GOAL, COACHES DESIGN TRAINING PROGRAMS THAT SUCCESSFULLY APPROPRIATE AND MANAGE TRAINING VOLUME AND INTENSITY DURING THE ATHLETE'S OFFSEASON, PRESEASON, AND IN-SEASON PERIODS. BY QUANTIFYING TRAINING LOADS AND RESPONSES, COACHES CAN BETTER PRESCRIBE TRAINING STIMULI WHILE ALSO MANAGING STRESS LEVELS. ATHLETE-MONITORING STRATEGIES PROVIDE A WAY FOR STRENGTH COACHES TO GATHER USEFUL INFORMATION REGARDING TRAINING DEMANDS AND ATHLETE FATIGUE.

The within-participant Correlation between s-RPE and Heart Rate in Youth Sport.

The monitoring of training load is important to ensure athletes are adapting optimally to a training stimulus. Before quantification of training load can take place, coaches must be confident that the tools available are accurate. We aimed to quantify the within-participant correlation between the session rating of perceived exertion (s-RPE) and summated heart rate zone (sHRz) methods of monitoring internal training load. Training load (s-RPE and heart rate) data were collected for rugby, soccer and field hockey field-based training sessions over a 14-week in-season period. A total of 397 sessions were monitored (rugby n=170, soccer n=114 and field hockey n=113). Within-subject correlations between s-RPE and sHRz were quantified for each sport using a general linear model. Large correlations between s-RPE and the sHRz method were found for rugby (r=0.68; 95% CI 0.59–0.75) and field hockey (r=0.60; 95% CI 0.47–0.71) with a very large correlation found for soccer (r=0.72; 95% CI 0.62–0.80). No significant differences were found between the correlations for each sport. The very large and large correlations found between s-RPE and the sHRz methods support the use of s-RPE in quantifying internal training load in youth sport.

Is Marathon Training Harder than the Ironman Training? An ECO-method Comparison.

Purpose: To compare the absolute and relative training load of the Marathon (42k) and the Ironman (IM) training in recreational trained athletes.Methods: Fifteen Marathoners and Fifteen Triathletes participated in the study. Their performance level was the same relative to the sex's absolute winner at the race. No differences were presented neither in age, nor in body weight, height, BMI, running VO2max max, or endurance training experience (p > 0.05). They all trained systematically for their respective event (IM or 42k). Daily training load was recorded in a training log, and the last 16 weeks were compared. Before this, gas exchange and lactate metabolic tests were conducted in order to set individual training zones. The Objective Load Scale (ECOs) training load quantification method was applied. Differences between IM and 42k athletes' outcomes were assessed using Student's test and significance level was set at p < 0.05.Results: As expected, Competition Time was significantly different (IM 11 h 45 min ± 1 h 54 min vs. 42k 3 h 6 min ± 28 min, p < 0.001). Similarly, Training Weekly Avg Time (IM 12.9 h ± 2.6 vs. 42k 5.2 ± 0.9), and Average Weekly ECOs (IM 834 ± 171 vs. 42k 526 ± 118) were significantly higher in IM (p < 0.001). However, the Ratio between Training Load and Training Time was superior for 42k runners when comparing ECOs (IM 65.8 ± 11.8 vs. 42k 99.3 ± 6.8) (p < 0.001). Finally, all ratios between training time or load vs. Competition Time were superior for 42k (p < 0.001) (Training Time/Race Time: IM 1.1 ± 0.3 vs. 42k 1.7 ± 0.5), (ECOs Training Load/Race Time: IM 1.2 ± 0.3 vs. 42k 2.9 ± 1.0).Conclusions: In spite of IM athletes' superior training time and total or weekly training load, when comparing the ratios between training load and training time, and training time or training load vs. competition time, the preparation of a 42k showed to be harder.

Training-Intensity Distribution in Road Cyclists: Objective Versus Subjective Measures

To evaluate training-intensity distribution using different intensity measures based on rating of perceived exertion (RPE), heart rate (HR), and power output (PO) in well-trained cyclists. Fifteen road cyclists participated in the study. Training data were collected during a 10-wk training period. Training-intensity distribution was quantified using RPE, HR, and PO categorized in a 3-zone training-intensity model. Three zones for HR and PO were based around a 1st and 2nd lactate threshold. The 3 RPE zones were defined using a 10-point scale: zone 1, RPE scores 1-4; zone 2, RPE scores 5-6; zone 3, RPE scores 7-10. Training-intensity distributions as percentages of time spent in zones 1, 2, and 3 were moderate to very largely different for RPE (44.9%, 29.9%, 25.2%) compared with HR (86.8%, 8.8%, 4.4%) and PO (79.5%, 9.0%, 11.5%). Time in zone 1 quantified using RPE was largely to very largely lower for RPE than PO (P < .001) and HR (P < .001). Time in zones 2 and 3 was moderately to very largely higher when quantified using RPE compared with intensity quantified using HR (P < .001) and PO (P < .001). Training-intensity distribution quantified using RPE demonstrates moderate to very large differences compared with intensity distributions quantified based on HR and PO. The choice of intensity measure affects intensity distribution and has implications for training-load quantification, training prescription, and the evaluation of training characteristics.

Methods of Monitoring Training Load and Their Relationships to Changes in Fitness and Performance in Competitive Road Cyclists.

To assess the dose-response relationships between different training-load methods and aerobic fitness and performance in competitive road cyclists. Training data from 15 well-trained competitive cyclists were collected during a 10-wk (December-March) preseason training period. Before and after the training period, participants underwent a laboratory incremental exercise test with gas-exchange and lactate measures and a performance assessment using an 8-min time trial (8MT). Internal training load was calculated using Banister TRIMP, Edwards TRIMP, individualized TRIMP (iTRIMP), Lucia TRIMP (luTRIMP), and session rating of perceived exertion (sRPE). External load was measured using Training Stress Score (TSS). Large to very large relationships (r = .54-.81) between training load and changes in submaximal fitness variables (power at 2 and 4 mmol/L) were observed for all training-load calculation methods. The strongest relationships with changes in aerobic fitness variables were observed for iTRIMP (r = .81 [95% CI .51-.93, r = .77 [95% CI .43-.92]) and TSS (r = .75 [95% CI .31-.93], r = .79 [95% CI .40-.94]). The strongest dose-response relationships with changes in the 8MT test were observed for iTRIMP (r = .63 [95% CI .17-.86]) and luTRIMP (r = .70 [95% CI .29-.89). Training-load quantification methods that integrate individual physiological characteristics have the strongest dose-response relationships, suggesting this to be an essential factor in the quantification of training load in cycling.

Quantification of Training Load During Return to Play After Upper- and Lower-Body Injury in Australian Rules Football.

Training volume, intensity, and distribution are important factors during periods of return to play. To quantify the effect of injury on training load (TL) before and after return to play (RTP) in professional Australian Rules football. Perceived training load (RPE-TL) for 44 players was obtained for all indoor and outdoor training sessions, while field-based training was monitored via GPS (total distance, high-speed running, mean speed). When a player sustained a competition time-loss injury, weekly TL was quantified for 3 wk before and after RTP. General linear mixed models, with inference about magnitudes standardized by between-players SDs, were used to quantify effects of lower- and upper-body injury on TL compared with the team. While total RPE-TL was similar to the team 2 wk before RTP, training distribution was different, whereby skills RPE-TL was likely and most likely lower for upper- and lower-body injury, respectively, and most likely replaced with small to very large increases in running and other conditioning load. Weekly total distance and high-speed running were most likely moderately to largely reduced for lower- and upper-body injury until after RTP, at which point total RPE-TL, training distribution, total distance, and high-speed running were similar to the team. Mean speed of field-based training was similar before and after RTP compared with the team. Despite injured athletes' obtaining comparable TLs to uninjured players, training distribution is different until after RTP, indicating the importance of monitoring all types of training that athletes complete.

Métodos de cuantificación de la carga de entrenamiento en deportes de resistencia cíclica

El presente trabajo tiene como objetivo el realizar un estado del arte con respecto a los principales sistemas y propuestas de cuantificación de cargas de entrenamiento (TL) en deportes de resistencia cíclica. En primer lugar, se expusieron los métodos propuestos para la cuantificación de la carga en función de la percepción subjetiva del esfuerzo. En segundo lugar, se presentaron los diferentes métodos encontrados en la bibliografía científica basados en variables fisiológicas. Finalmente, se presentó futuras aplicaciones prácticas sobre las posibilidades que ofrecen las nuevas tecnologías ofertadas por el mercado, en lo que a la elaboración de nuevos sistemas de cuantificación de la TL se refiere.

Individualized Internal and External Training Load Relationships in Elite Wheelchair Rugby Players

Aim: The quantification and longitudinal monitoring of athlete training load (TL) provides a scientific explanation for changes in performance and helps manage injury/illness risk. Therefore, accurate and reliable monitoring tools are essential for the optimization of athletic performance. The aim of the present study was to establish the relationship between measures of internal [heart rate (HR) and session RPE (sRPE)] and external TL specific to wheelchair rugby (WR).Methods: Fourteen international WR athletes (age = 29 ± 7 years; body mass = 58.9 ± 10.9 kg) were monitored during 18 training sessions over a 3 month period during the competitive phase of the season. Activity profiles were collected during each training session using a radio-frequency based indoor tracking system (ITS). External TL was quantified by total distance (m) covered as well as time spent and distance covered in a range of classification-specific arbitrary speed zones. Banister's TRIMP, Edwards's summated HR zone (SHRZ), and Lucia's TRIMP methods were used to quantify physiological internal TL. sRPE was calculated as the product of session duration multiplied by perceived exertion using the Borg CR10 scale. Relationships between external and internal TL were examined using correlation coefficients and the 90% confidence intervals (90% CI).Results: sRPE (r = 0.59) and all HR-based (r > 0.80) methods showed large and very large relationships with the total distance covered during training sessions, respectively. Large and very large correlations (r = 0.56 − 0.82) were also observed between all measures of internal TL and times spent and distances covered in low and moderate intensity speed zones. HR-based methods showed very large relationships with time (r = 0.71−0.75) and distance (r = 0.70−0.73) in the very high speed zone and a large relationship with the number of high intensity activities (HIA) performed (r = 0.56−0.62). Weaker relationships (r = 0.32−0.35) were observed between sRPE and all measures of high intensity activity. A large variation of individual correlation co-efficient was observed between sRPE and all external TL measures.Conclusion: The current findings suggest that sRPE and HR-based internal TL measures provide a valid tool for quantifying volume of external TL during WR training but may underestimate HIA. It is recommended that both internal and external TL measures are employed for the monitoring of overall TL during court-based training in elite WR athletes.

Quantification of training load during one-, two- and three-game week schedules in professional soccer players from the English Premier League: implications for carbohydrate periodisation

ABSTRACTMuscle glycogen is the predominant energy source for soccer match play, though its importance for soccer training (where lower loads are observed) is not well known. In an attempt to better inform carbohydrate (CHO) guidelines, we quantified training load in English Premier League soccer players (n = 12) during a one-, two- and three-game week schedule (weekly training frequency was four, four and two, respectively). In a one-game week, training load was progressively reduced (P < 0.05) in 3 days prior to match day (total distance = 5223 ± 406, 3097 ± 149 and 2912 ± 192 m for day 1, 2 and 3, respectively). Whilst daily training load and periodisation was similar in the one- and two-game weeks, total accumulative distance (inclusive of both match and training load) was higher in a two-game week (32.5 ± 4.1 km) versus one-game week (25.9 ± 2 km). In contrast, daily training total distance was lower in the three-game week (2422 ± 251 m) versus the one- and two-game weeks, though accumulative weekly distance was highest in this week (35.5 ± 2.4 km) and more time (P < 0.05) was spent in speed zones >14.4 km · h−1 (14%, 18% and 23% in the one-, two- and three-game weeks, respectively). Considering that high CHO availability improves physical match performance but high CHO availability attenuates molecular pathways regulating training adaptation (especially considering the low daily customary loads reported here, e.g., 3–5 km per day), we suggest daily CHO intake should be periodised according to weekly training and match schedules.

Quantification of Training and Competition Load Across a Season in an Elite Australian Football Club.

Load monitoring in Australian football (AF) has been widely adopted, yet team-sport periodization strategies are relatively unknown. The authors aimed to quantify training and competition load across a season in an elite AF team, using rating of perceived exertion (RPE) and GPS tracking. Weekly totals for RPE and GPS loads (including accelerometer data; PlayerLoad) were obtained for 44 players across a full season for each training modality and for competition. General linear mixed models compared mean weekly load between 3 preseason and 4 in-season blocks. Effects were assessed with inferences about magnitudes standardized with between-players SD. Total RPE load was most likely greater during preseason, where the majority of load was obtained via skills and conditioning. There was a large reduction in RPE load in the last preseason block. During in-season, half the total load came from games and the remaining half from training, predominantly skills and upper-body weights. Total distance, high-intensity running, and PlayerLoad showed large to very large reductions from preseason to in-season, whereas changes in mean speed were trivial across all blocks. All these effects were clear at the 99% level. These data provide useful information about targeted periods of loading and unloading across different stages of a season. The study also provides a framework for further investigation of training periodization in AF teams.