Factors Affecting Perception of Effort (Session Rating of Perceived Exertion) During Rugby League Training

2013 ◽  
Vol 8 (1) ◽  
pp. 62-69 ◽  
Author(s):  
Thomas W.J. Lovell ◽  
Anita C. Sirotic ◽  
Franco M. Impellizzeri ◽  
Aaron J. Coutts
Keyword(s):  
Exercise Intensity ◽  
Perceived Exertion ◽  
Rugby League ◽  
Rating Of Perceived Exertion ◽  
Stepwise Multiple Regression ◽  
Training Intensity ◽  
Intensity Measures ◽  
Positioning Systems ◽  
Body Load ◽  
Training Impulse

Purpose:The purpose of this study was to examine the validity of session rating of perceived exertion (sRPE) for monitoring training intensity in rugby league.Methods:Thirty-two professional rugby league players participated in this study. Training-load (TL) data were collected during an entire season and assessed via microtechnology (heart-rate [HR] monitors, global positioning systems [GPS], and accelerometers) and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and various other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during rugby league training.Results:There were significant within-individual correlations between sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 62.4% of the adjusted variance in sRPE-TL could be explained by TL measures of distance, impacts, body load, and training impulse (y = 37.21 + 0.93 distance − 0.39 impacts + 0.18 body load + 0.03 training impulse). Furthermore, 35.2% of the adjusted variance in sRPE could be explained by exercise-intensity measures of percentage of peak HR (%HRpeak), impacts/min, m/min, and body load/min (y = −0.01 + 0.37%HRpeak + 0.10 impacts/min + 0.17 m/min + 0.09 body load/min).Conclusion:A combination of internal and external TL factors predicts sRPE in rugby league training better than any individual measures alone. These findings provide new evidence to support the use of sRPE as a global measure of exercise intensity in rugby league training.

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.

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.

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

2017 ◽  
Vol 12 (9) ◽  
pp. 1232-1237 ◽  
Author(s):  
Dajo Sanders ◽  
Tony Myers ◽  
Ibrahim Akubat
Keyword(s):  
Intensity Distribution ◽  
Lactate Threshold ◽  
Perceived Exertion ◽  
Rating Of Perceived Exertion ◽  
Training Data ◽  
Training Intensity ◽  
Intensity Measure ◽  
Intensity Measures ◽  
Evaluation Of Training ◽  
Road Cyclists

Purpose: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. Methods: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. Results: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). Conclusions: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.

The Influence of Exercise Intensity on the Association Between Kilojoules Spent and Various Training Loads in Professional Cycling

2019 ◽  
Vol 14 (10) ◽  
pp. 1395-1400 ◽  
Author(s):  
Teun van Erp ◽  
Marco Hoozemans ◽  
Carl Foster ◽  
Jos J. de Koning
Keyword(s):  
Exercise Intensity ◽  
Power Output ◽  
Perceived Exertion ◽  
Rating Of Perceived Exertion ◽  
Regression Coefficients ◽  
Quadratic Relation ◽  
Significant Regression ◽  
Training Stress ◽  
Training Impulse ◽  
Professional Cycling

Purpose: A valid measure for training load (TL) is an important tool for cyclists, trainers, and sport scientists involved in professional cycling. The aim of this study was to explore the influence of exercise intensity on the association between kilojoules (kJ) spent and different measures of TL to arrive at valid measures of TL. Methods: Four years of field data were collected from 21 cyclists of a professional cycling team, including 11,716 training and race sessions. kJ spent was obtained from power output measurements, and others TLs were calculated based on the session rating of perceived exertion (sRPE), heart rate (Lucia training impulse [luTRIMP]), and power output (training stress score [TSS]). Exercise intensity was expressed by the intensity factor (IF). To study the effect of exercise intensity on the association between kJ spent and various other TLs (sRPE, luTRIMP, and TSS), data from low- and high-intensity sessions were subjected to regression analyses using generalized estimating equations. Results: This study shows that the IF is significantly different for training and race sessions (0.59 [0.03] vs 0.73 [0.03]). Significant regression coefficients show that kJ spent is a good predictor of sRPE, and luTRIMP, as well as TSS. However, IF does not influence the associations between kJ spent and sRPE and luTRIMP, while the association with TSS is different when sessions are done with low or high IF. Conclusion: It seems that the TSS reacts differently to exercise intensity than sRPE and luTRIMP. A possible explanation could be the quadratic relation between IF and TSS.

Within-Subject Correlation Between Salivary IgA and Measures of Training Load in Elite Football Players

2019 ◽  
Vol 14 (6) ◽  
pp. 847-849 ◽  
Author(s):  
Pedro Figueiredo ◽  
George P. Nassis ◽  
João Brito
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Immunoglobulin A ◽  
Correlation Coefficients ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Football Players ◽  
Positioning Systems ◽  
External Training ◽  
Salivary Secretory Immunoglobulin A

Purpose: To quantify the association between salivary secretory immunoglobulin A (sIgA) and training load in elite football players. Methods: Data were obtained on 4 consecutive days during the preparation camp for the Rio 2016 Olympic Games. Saliva samples of 18 elite male football players were collected prior to breakfast. The session rating of perceived exertion (s-RPE) and external training-load metrics from global positioning systems (GPS) were recorded. Within-subject correlation coefficients between training load and sIgA concentration, and magnitude of relationships, were calculated. Results: sIgA presented moderate to large negative correlations with s-RPE (r = −.39), total distance covered (r = −.55), accelerations (r = −.52), and decelerations (r = −.48). Trivial to small associations were detected between sIgA and distance covered per minute (r = .01), high-speed distance (r = −.23), and number of sprints (r = −.18). sIgA displayed a likely moderate decrease from day 1 to day 2 (d = −0.7) but increased on day 3 (d = 0.6). The training-load variables had moderate to very large rises from day 1 to day 2 (d = 0.7 to 3.2) but lowered from day 2 to day 3 (d = −5.0 to −0.4), except for distance per minute (d = 0.8) and sprints (unclear). On day 3, all training-load variables had small to large increments compared with day 1 (d = 0.4 to 1.5), except for accelerations (d = −0.8) and decelerations (unclear). Conclusions: In elite football, sIgA might be more responsive to training volume than to intensity. External load such as GPS-derived variables presented stronger association with sIgA than with s-RPE. sIgA can be used as an additional objective tool in monitoring football players.

scholarly journals Objective and subjective measures of exercise intensity during thermo-neutral and hot yoga

2018 ◽  
Vol 43 (4) ◽  
pp. 397-402 ◽  
Author(s):  
Corinne N. Boyd ◽  
Stephanie M. Lannan ◽  
Micah N. Zuhl ◽  
Ricardo Mora-Rodriguez ◽  
Rachael K. Nelson
Keyword(s):  
Oxygen Consumption ◽  
Light Intensity ◽  
Exercise Intensity ◽  
Perceived Exertion ◽  
Maximal Oxygen Consumption ◽  
Rating Of Perceived Exertion ◽  
Moderate Intensity ◽  
Subjective Measures ◽  
Moderate Intensity Exercise ◽  
Constant Intensity

While hot yoga has gained enormous popularity in recent years, owing in part to increased environmental challenge associated with exercise in the heat, it is not clear whether hot yoga is more vigorous than thermo-neutral yoga. Therefore, the aim of this study was to determine objective and subjective measures of exercise intensity during constant intensity yoga in a hot and thermo-neutral environment. Using a randomized, crossover design, 14 participants completed 2 identical ∼20-min yoga sessions in a hot (35.3 ± 0.8 °C; humidity: 20.5% ± 1.4%) and thermo-neutral (22.1 ± 0.2 °C; humidity: 27.8% ± 1.6%) environment. Oxygen consumption and heart rate (HR) were recorded as objective measures (percentage of maximal oxygen consumption and percentage of maximal HR (%HRmax)) and rating of perceived exertion (RPE) was recorded as a subjective measure of exercise intensity. There was no difference in exercise intensity based on percentage of maximal oxygen consumption during hot versus thermo-neutral yoga (30.9% ± 2.3% vs. 30.5% ± 1.8%, p = 0.68). However, exercise intensity was significantly higher during hot versus thermo-neutral yoga based on %HRmax (67.0% ± 2.3% vs. 60.8% ± 1.9%, p = 0.01) and RPE (12 ± 1 vs. 11 ± 1, p = 0.04). According to established exercise intensities, hot yoga was classified as light-intensity exercise based on percentage of maximal oxygen consumption but moderate-intensity exercise based on %HRmax and RPE while thermo-neutral yoga was classified as light-intensity exercise based on percentage of maximal oxygen uptake, %HRmax, and RPE. Despite the added hemodynamic stress and perception that yoga is more strenuous in a hot environment, we observed similar oxygen consumption during hot versus thermo-neutral yoga, classifying both exercise modalities as light-intensity exercise.

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.

scholarly journals We know they train, but what do they do? Implications for coaches working with adolescent rugby union players

2017 ◽  
Vol 12 (2) ◽  
pp. 175-182 ◽  
Author(s):  
Padraic J Phibbs ◽  
Ben Jones ◽  
Gregory AB Roe ◽  
Dale B Read ◽  
Joshua Darrall-Jones ◽  
...  
Keyword(s):  
Heart Rate ◽  
High Speed ◽  
Injury Risk ◽  
Perceived Exertion ◽  
Rugby Union ◽  
Rating Of Perceived Exertion ◽  
Limited Information ◽  
Positioning Systems ◽  
Global Positioning ◽  
School Club

Limited information is available regarding the training loads of adolescent rugby union players. One-hundred and seventy male players (age 16.1 ± 1.0 years) were recruited from 10 teams representing two age categories (under-16 and under-18) and three playing standards (school, club and academy). Global positioning systems, accelerometers, heart rate and session-rating of perceived exertion (s-RPE) methods were used to quantify mean session training loads. Session demands differed between age categories and playing standards. Under-18 academy players were exposed to the highest session training loads in terms of s-RPE (236 ± 42 AU), total distance (4176 ± 433 m), high speed running (1270 ± 288 m) and PlayerLoad™ (424 ± 56 AU). Schools players had the lowest session training loads in both respective age categories. Training loads and intensities increased with age and playing standard. Individual monitoring of training load is key to enable coaches to maximise player development and minimise injury risk.

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