Relationship Between Various Training-Load Measures in Elite Cyclists During Training, Road Races, and Time Trials

2019 ◽  
Vol 14 (4) ◽  
pp. 493-500 ◽  
Author(s):  
Teun van Erp ◽  
Carl Foster ◽  
Jos J. de Koning
Keyword(s):  
Perceived Exertion ◽  
Mechanical Energy ◽  
Training Load ◽  
The Other ◽  
Rating Of Perceived Exertion ◽  
Heart Rate Data ◽  
Training Stress ◽  
Training Impulse ◽  
Road Races ◽  
The Relationship

Purpose: The relationship between various training-load (TL) measures in professional cycling is not well explored. This study investigated the relationship between mechanical energy spent (in kilojoules), session rating of perceived exertion (sRPE), Lucia training impulse (LuTRIMP), and training stress score (TSS) in training, races, and time trials (TT). Methods: For 4 consecutive years, field data were collected from 21 professional cyclists and categorized as being collected in training, racing, or TTs. Kilojoules (kJ) spent, sRPE, LuTRIMP, and TSS were calculated, and the correlations between the various TLs were made. Results: 11,655 sessions were collected, from which 7596 sessions had heart-rate data and 5445 sessions had an RPE score available. The r between the various TLs during training was almost perfect. The r between the various TLs during racing was almost perfect or very large. The r between the various TLs during TTs was almost perfect or very large. For all relationships between TSS and 1 of the other measurements of TL (kJ spent, sRPE, and LuTRIMP), a significant different slope was found. Conclusion: kJ spent, sRPE, LuTRIMP, and TSS all have a large or almost perfect relationship with each other during training, racing, and TTs, but during racing, both sRPE and LuTRIMP have a weaker relationship with kJ spent and TSS. Furthermore, the significant different slope of TSS vs the other measurements of TL during training and racing has the effect that TSS collected in training and road races differs by 120%, whereas the other measurements of TL (kJ spent, sRPE, and LuTRIMP) differ by only 73%, 67%, and 68%, respectively.

Technical and Tactical Training Load in Professional Volleyball Players

2019 ◽  
Vol 14 (10) ◽  
pp. 1338-1343
Author(s):  
Thiago S. Duarte ◽  
Danilo L. Alves ◽  
Danilo R. Coimbra ◽  
Bernardo Miloski ◽  
João C. Bouzas Marins ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Training Load ◽  
Technical Training ◽  
Rating Of Perceived Exertion ◽  
Different Types ◽  
Volleyball Players ◽  
Tactical Training ◽  
Training Impulse ◽  
The Relationship ◽  
And Training

Purpose: To analyze the technical and tactical training load in professional volleyball players, using subjective internal training load (session rating of perceived exertion  [SRPE]) and objective internal training load (training impulse of the heart rate [HR]) and the relationship between them. Methods: The sample was composed of 15 male professional volleyball players. They were monitored during 37 training sessions that included both technical (n = 23) and tactical (n = 14) training. Technical and training load was calculated using SRPE and training impulse of the HR. Results: Significant correlations were found between the methods in tactical (r = .616) and technical training (r = −.414). Furthermore, it was noted that technical training occurs up to 80% of HRmax (zone 3) and tactical training between 70% and 90% of HRmax (zones 3–4). Conclusions: The training impulse of the HR method has proved to be effective for training-load control during tactical training. However, it was limited compared with technical training. Thus, the use of SRPE is presented as a more reliable method in the different types of technical training in volleyball.

Training Characteristics of Male and Female Professional Road Cyclists: A 4-Year Retrospective Analysis

2020 ◽  
Vol 15 (4) ◽  
pp. 534-540 ◽  
Author(s):  
Teun van Erp ◽  
Dajo Sanders ◽  
Jos J. de Koning
Keyword(s):  
Heart Rate ◽  
Retrospective Analysis ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Training Intensity ◽  
Male And Female ◽  
Stress Score ◽  
Training Stress ◽  
Training Impulse

Purpose: To describe the training intensity and load characteristics of professional cyclists using a 4-year retrospective analysis. Particularly, this study aimed to describe the differences in training characteristics between men and women professional cyclists. Method: For 4 consecutive years, training data were collected from 20 male and 10 female professional cyclists. From those training sessions, heart rate, rating of perceived exertion, and power output (PO) were analyzed. Training intensity distribution as time spent in different heart rate and PO zones was quantified. Training load was calculated using different metrics such as Training Stress Score, training impulse, and session rating of perceived exertion. Standardized effect size is reported as Cohen’s d. Results: Small to large higher values were observed for distance, duration, kilojoules spent, and (relative) mean PO in men’s training (d = 0.44–1.98). Furthermore, men spent more time in low-intensity zones (ie, zones 1 and 2) compared with women. Trivial differences in training load (ie, Training Stress Score and training impulse) were observed between men’s and women’s training (d = 0.07–0.12). However, load values expressed per kilometer were moderately (d = 0.67–0.76) higher in women compared with men’s training. Conclusions: Substantial differences in training characteristics exist between male and female professional cyclists. Particularly, it seems that female professional cyclists compensate their lower training volume, with a higher training intensity, in comparison with male professional cyclists.

Internal Training Load Measures in Elite Adolescent Ballet Dancers

2020 ◽  
Vol 24 (4) ◽  
pp. 175-182
Author(s):  
Valeriya G. Volkova ◽  
Amanda M. Black ◽  
Sarah J. Kenny
Keyword(s):  
Heart Rate ◽  
Injury Risk ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
High Risk Population ◽  
Heart Rate Data ◽  
Ballet Dancers ◽  
Adolescent Dancers ◽  
Training Impulse

Training load has been identified as a risk factor for musculoskeletal injury in sport, but little is known about the effects of training load in dance. The purpose of this study was to describe adolescent dancers' internal training load (ITL) and compare objective and subjective measures of ITL using heart rate (HR) training impulse methods and session Rating of Perceived Exertion (sRPE), respectively. Fifteen female elite adolescent ballet dancers at a vocational dance school volunteered to participate in the study. Internal training load data using HR and sRPE were collected over 9 days of multiple technique classes at the midpoint of the dancers' training year. Heart rate data were quantified using Edwards' training impulse (ETRIMP) and Banister's training impulse (BTRIMP), and sRPE was estimated from the modified Borg 0 to 10 scale and class duration. Descriptive statistics (median [M], and interquartile range [IQR]) were determined in arbitrary units (AU), and were as follows for all classes combined: ETRIMP: M = 134 AU (IQR = 79 to 244 AU); BTRIMP: M = 67 AU (IQR = 38 to 109); sRPE: M = 407 AU (IQR = 287 to 836 AU). The association and agreement between objective and subjective ITL measures in ballet and pointe class was assessed using Spearman correlations (rs) and adjusted Bland-Altman 95% limits of agreement (LOA), respectively, with alpha set at 0.05. A significant moderate positive correlation was found between ETRIMP and BTRIMP in pointe class (rρ = 0.8000, p = 0.0031). The mean difference (LOA) between ETRIMP and BTRIMP was 121 AU (33 to 210 AU) in ballet and 43 AU (-3 to 88 AU) in pointe. It is concluded that some, but not all, measures of ITL in elite adolescent ballet dancers are comparable. Additional research is needed to examine the utilization of ITL measures for evaluating dance-related injury risk, as well as the application of ITL to inform the development of effective injury prevention strategies for this high-risk population.

Quantifying Training Load: A Comparison of Subjective and Objective Methods

2008 ◽  
Vol 3 (1) ◽  
pp. 16-30 ◽  
Author(s):  
Jill Borresen ◽  
Michael I. Lambert
Keyword(s):  
Heart Rate ◽  
High Intensity ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Regression Equations ◽  
Training Time ◽  
Ad Libitum ◽  
Training Impulse ◽  
The Relationship

Purpose:To establish the relationship between a subjective (session rating of perceived exertion [RPE]) and 2 objective (training impulse [TRIMP]) and summated-heart-rate-zone (SHRZ) methods of quantifying training load and explain characteristics of the variance not accounted for in these relationships.Methods:Thirty-three participants trained ad libitum for 2 wk, and their heart rate (HR) and RPE were recorded to calculate training load. Subjects were divided into groups based on whether the regression equations over- (OVER), under- (UNDER), or accurately predicted (ACCURATE) the relationship between objective and subjective methods.Results:A correlation of r = .76 (95% CI: .56 to .88) occurred between TRIMP and session-RPE training load. OVER spent a greater percentage of training time in zone 4 of SHRZ (ie, 80% to 90% HRmax) than UNDER (46% ± 8% vs 25% ± 10% [mean ± SD], P = .008). UNDER spent a greater percentage of training time in zone 1 of SHRZ (ie, 50% to 60% HRmax) than OVER (15% ± 8% vs 3% ± 3%, P = .005) and ACCURATE (5% ± 3%, P = .020) and more time in zone 2 of SHRZ (ie, 60% to 70%HRmax) than OVER (17% ± 6% vs 7% ± 6%, P = .039). A correlation of r = .84 (.70 to .92) occurred between SHRZ and session-RPE training load. OVER spent proportionally more time in Zone 4 than UNDER (45% ± 8% vs 25% ± 10%, P = .018). UNDER had a lower training HR than ACCURATE (132 ± 10 vs 148 ± 12 beats/min, P = .048) and spent more time in zone 1 than OVER (15% ± 8% vs 4% ± 3%, P = .013) and ACCURATE (5% ± 3%, P = .015).Conclusions:The session-RPE method provides reasonably accurate assessments of training load compared with HR-based methods, but they deviate in accuracy when proportionally more time is spent training at low or high intensity.

scholarly journals The Influence of Different Training Load Quantification Methods on the Fitness-Fatigue Model

2020 ◽  
pp. 1-9
Author(s):  
Kobe M. Vermeire ◽  
Freek Van de Casteele ◽  
Maxim Gosseries ◽  
Jan G. Bourgois ◽  
Michael Ghijs ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Time Trial ◽  
Interval Training ◽  
Cycle Ergometer ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Fatigue Model ◽  
Stress Score ◽  
Training Stress ◽  
Training Impulse

Purpose: Numerous methods exist to quantify training load (TL). However, the relationship with performance is not fully understood. Therefore the purpose of this study was to investigate the influence of the existing TL quantification methods on performance modeling and the outcome parameters of the fitness-fatigue model. Methods: During a period of 8 weeks, 9 subjects performed 3 interval training sessions per week. Performance was monitored weekly by means of a 3-km time trial on a cycle ergometer. After this training period, subjects stopped training for 3 weeks but still performed a weekly time trial. For all training sessions, Banister training impulse (TRIMP), Lucia TRIMP, Edwards TRIMP, training stress score, and session rating of perceived exertion were calculated. The fitness-fatigue model was fitted for all subjects and for all TL methods. Results: The error in relating TL to performance was similar for all methods (Banister TRIMP: 618 [422], Lucia TRIMP: 625 [436], Edwards TRIMP: 643 [465], training stress score: 639 [448], session rating of perceived exertion: 558 [395], and kilojoules: 596 [505]). However, the TL methods evolved differently over time, which was reflected in the differences between the methods in the calculation of the day before performance on which training has the biggest positive influence (range of 19.6 d). Conclusions: The authors concluded that TL methods cannot be used interchangeably because they evolve differently.

Relationship Between Pretraining Subjective Wellness Measures, Player Load, and Rating-of-Perceived-Exertion Training Load in American College Football

2018 ◽  
Vol 13 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Andrew D. Govus ◽  
Aaron Coutts ◽  
Rob Duffield ◽  
Andrew Murray ◽  
Hugh Fullagar
Keyword(s):  
Muscle Soreness ◽  
Perceived Exertion ◽  
Training Session ◽  
College Football ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Football Players ◽  
Unit Increase ◽  
Relationship Of ◽  
The Relationship

Context:The relationship between pretraining subjective wellness and external and internal training load in American college football is unclear.Purpose:To examine the relationship of pretraining subjective wellness (sleep quality, muscle soreness, energy, wellness Z score) with player load and session rating of perceived exertion (s-RPE-TL) in American college football players.Methods:Subjective wellness (measured using 5-point, Likert-scale questionnaires), external load (derived from GPS and accelerometry), and s-RPE-TL were collected during 3 typical training sessions per week for the second half of an American college football season (8 wk). The relationship of pretraining subjective wellness with player load and s-RPE training load was analyzed using linear mixed models with a random intercept for athlete and a random slope for training session. Standardized mean differences (SMDs) denote the effect magnitude.Results:A 1-unit increase in wellnessZscore and energy was associated with trivial 2.3% (90% confidence interval [CI] 0.5, 4.2; SMD 0.12) and 2.6% (90% CI 0.1, 5.2; SMD 0.13) increases in player load, respectively. A 1-unit increase in muscle soreness (players felt less sore) corresponded to a trivial 4.4% (90% CI −8.4, −0.3; SMD −0.05) decrease in s-RPE training load.Conclusion:Measuring pretraining subjective wellness may provide information about players’ capacity to perform in a training session and could be a key determinant of their response to the imposed training demands American college football. Hence, monitoring subjective wellness may aid in the individualization of training prescription in American college football players.

scholarly journals Combining Internal- and External-Training-Load Measures in Professional Rugby League

2014 ◽  
Vol 9 (6) ◽  
pp. 905-912 ◽  
Author(s):  
Dan Weaving ◽  
Phil Marshall ◽  
Keith Earle ◽  
Alan Nevill ◽  
Grant Abt
Keyword(s):  
Principal Components ◽  
High Speed ◽  
Perceived Exertion ◽  
Principal Component ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Training Dose ◽  
External Training ◽  
Training Impulse

Purpose:This study investigated the effect of training mode on the relationships between measures of training load in professional rugby league players.Methods:Five measures of training load (internal: individualized training impulse, session rating of perceived exertion; external—body load, high-speed distance, total impacts) were collected from 17 professional male rugby league players over the course of two 12-wk preseason periods. Training was categorized by mode (small-sided games, conditioning, skills, speed, strongman, and wrestle) and subsequently subjected to a principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subjected to a varimax rotation.Results:Small-sided games and conditioning extracted 1 principal component, explaining 68% and 52% of the variance, respectively. Skills, wrestle, strongman, and speed extracted 2 principal components each explaining 68%, 71%, 72%, and 67% of the variance, respectively.Conclusions:In certain training modes the inclusion of both internal and external training-load measures explained a greater proportion of the variance than any 1 individual measure. This would suggest that in training modes where 2 principal components were identified, the use of only a single internal or external training-load measure could potentially lead to an underestimation of the training dose. Consequently, a combination of internal- and external-load measures is required during certain training modes.

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

2020 ◽  
pp. 1-10 ◽  
Author(s):  
Carl Foster ◽  
Daniel Boullosa ◽  
Michael McGuigan ◽  
Andrea Fusco ◽  
Cristina Cortis ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Lactate Concentration ◽  
Training Session ◽  
Blood Lactate Concentration ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Exercise Session ◽  
Training Outcomes ◽  
External Training ◽  
Training Impulse

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.

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

2020 ◽  
Vol 15 (5) ◽  
pp. 648-653 ◽  
Author(s):  
Carolina F. Wilke ◽  
Samuel P. Wanner ◽  
Weslley H.M. Santos ◽  
Eduardo M. Penna ◽  
Guilherme P. Ramos ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Perceived Exertion ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Total Quality ◽  
Training Impulse

Purpose: To determine whether daily perceived recovery is explained from a multifactorial single-session classification of recovery (ie, faster vs slower) or other circumstantial factors (ie, previous training load, self-reported sleep, or phase of the microcycle). Methods: Nineteen elite male futsal players were initially allocated to a recovery-classification group (faster recovery, slower physiological, or slower perceptual) based on previous research using a multifactorial cluster-analysis technique. During 4 ensuing weeks of preseason, training loads were monitored via player load, training impulse, and session rating of perceived exertion. Before each day’s training, players reported their perception of recovery (Total Quality of Recovery scale [TQR]) and the number of hours and perceived quality of sleep the night prior. A hierarchical linear mixed model was used to analyze the effect of the different recovery profiles, training load, sleep, and phase of the microcycle (ie, start, middle, end) on daily TQR. Results: The recovery classification of players (P = .20), training load (training impulse, P = .32; player load, P = .23; session rating of perceived exertion, P = .46), and self-reported hours slept the night before (P = .45) did not significantly influence TQR. However, perceived sleep quality (P < .01) and phase of the microcycle (P < .01) were significantly associated with TQR (r2 = .41). Conclusions: Neither recovery classification nor prior training load influenced perceived recovery during the preseason. However, higher TQR was evident with better self-reported sleep quality, whereas lower values were associated with phases of the training week.

scholarly journals Continuous Versus Intermittent Running: Acute Performance Decrement and Training Load

2021 ◽  
pp. 1-10
Author(s):  
Antonis Kesisoglou ◽  
Andrea Nicolò ◽  
Lucinda Howland ◽  
Louis Passfield
Keyword(s):  
Perceived Exertion ◽  
Time Trial ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Performance Decrement ◽  
Running Training ◽  
Subsequent Performance ◽  
Training Stress ◽  
And Training ◽  
Opposite Response

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, ) 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, ). 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.

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