Article

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

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Abstract

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.

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... There are only a few reports of potential links between activity, conditioning, or training practices and risk of injury in agility dogs (3,13). In contrast, there is an abundance of information on this topic related to human athletes in a wide variety of sports (14)(15)(16)(17) and load management has emerged as an important factor in injury risk (14). Objective exercise data are also used to study factors predisposing racing horses to injury (18,19). ...
... The rating of perceived exertion (RPE) as reported by the athlete after each training session was first described by Borg (21). This simple, subjective measure has been modified in numerous ways to fit athletes in multiple sports (15)(16)(17)(22)(23)(24). Despite its simplicity, the RPE and its modifications have often been more valuable in monitoring training load than objective parameters such as training days, training volume, or repetitions of individual training events. ...
... In the ensuing years, this scale has been adapted in a variety of ways for specific sports and user groups. For this canine RPE logging tool, a 10-point scale was used, similar to visual analog scales which are widely used for assessment of pain, and similar to modified RPE scales used for assessment of exercise intensity or training load in people and horses (15,19). One previous description of a perceived exertion scale used for dogs on a treadmill used a 5-point scale ranging from 0, no effort noted, to 4, significant effort. ...
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Objective To develop a web-based tool for daily use by agility handlers to log rating of perceived exertion (RPE) for dogs as an aid in quantifying daily exercise and training load and to improve training and conditioning strategies. Procedures Focus group meetings with small groups of handlers were conducted via internet—based video conferencing using a semi-structured interview format. Meeting notes were coded for reflexive thematic analysis. The RPE logging tool was revised based on handler feedback. Each handler was asked to log their dog’s daily RPE data for 1 week. Data were analyzed to assess compliance and timeliness of entries. Participants completed a post-logging questionnaire to provide feedback about their experiences. Results Eighteen agility dog handlers participated in all phases of the project. Handler and dog demographics were similar to previously reported demographics of agility participants in the United States. Reflexive thematic analysis of their comments related to the initial draft RPE logging tool yielded 3 initial themes, all of which supported a fourth and major theme: the need for specific and detailed training resources before agility handlers utilized the RPE tool. Of 18 handlers, 14 (78%) submitted logging records for the full week. Median time for data entry was 87 s (IQR = 56–117 s), and 92% of logging records were entered within 1 day of the events which were being recorded. In the follow-up questionnaire the handlers did not identify any major concerns. Based on all available data from the handlers, a final version of the RPE logging tool was produced. Conclusion and clinical relevance Agility dog handlers are very interested in developing and validating tools to quantify training load for their dogs. The final RPE logging tool was quick and easy to use. Further validation of this logging tool is required with a larger number of handlers and comparison to physiologic data from exercising dogs.
... Furthermore, very large correlations (r = 0.96-0.97) have been reported across multiple measures of internal and external training load in cyclists during racing and training [15], suggesting other metrics such as session rating of perceived exertion (sRPE) [16], Lucia training impulse (LuTRIMP) [17], and training stress score (TSS) [18] can also provide relevant information to athletes and coaches. However, there is no gold standard measure of training load [13], and measures of external and internal load are not always consistent. ...
... Presumably, greater priorday sRPE-TL values imply lower levels of starting muscle glycogen, which is known to shift substrate use away from carbohydrate and towards fat oxidation [34]. Practical application of the carbohydrate models may be challenged by people who do not use sRPE-TL; however this is among the easiest measures for someone to record, with the caveat that a validated scale is used [16]. A less optimal, yet potentially viable option for athletes who only use TSS would be to convert prior-day TSS values into estimated sRPE-TL using the regression equation − 65.4 + (TSS × 5.18). ...
... Measures of training load and their calculationsFTP functional threshold power, HR heart rate, IF intensity factor, LuTRIMP Lucia training impulse, NP normalized power, TSS training stress score, VT 1 , first entilator threshold, VT 2 second entilator threshold Total work done A measure of total mechanical energy spent (kJ), collected from the ergometer (cycling and kayaking) or Stryd power meter (running) Session rating of perceived exertiontraining load Session rating of perceived exertion × duration (minutes)[16]. This value was divided by 10 to account for the 100point scale, allowing easier comparisons to other research using the 10-point scale NP × IF)/(FTP × 3600)] × 100[18]. ...
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Background Sports nutrition guidelines recommend carbohydrate (CHO) intake be individualized to the athlete and modulated according to changes in training load. However, there are limited methods to assess CHO utilization during training sessions. Objectives We aimed to (1) quantify bivariate relationships between both CHO and overall energy expenditure (EE) during exercise and commonly used, non-invasive measures of training load across sessions of varying duration and intensity and (2) build and evaluate prediction models to estimate CHO utilization and EE with the same training load measures and easily quantified individual factors. Methods This study was undertaken in two parts: a primary study, where participants performed four different laboratory-based cycle training sessions, and a validation study where different participants performed a single laboratory-based training session using one of three exercise modalities (cycling, running, or kayaking). The primary study included 15 cyclists (five female; maximal oxygen consumption [V˙V˙\dot{V}O2max], 51.9 ± 7.2 mL/kg/min), the validation study included 21 cyclists (seven female; V˙V˙\dot{V}O2max 53.5 ± 11.0 mL/kg/min), 20 runners (six female; V˙V˙\dot{V}O2max 57.5 ± 7.2 mL/kg/min), and 18 kayakers (five female; V˙V˙\dot{V}O2max 45.6 ± 4.8 mL/kg/min). Training sessions were quantified using six training load metrics: two using heart rate, three using power, and one using perceived exertion. Carbohydrate use and EE were determined separately for aerobic (gas exchange) and anaerobic (net lactate accumulation, body mass, and O2 lactate equivalent method) energy systems and summed. Repeated-measures correlations were used to examine relationships between training load and both CHO utilization and EE. General estimating equations were used to model CHO utilization and EE, using training load alongside measures of fitness and sex. Models were built in the primary study and tested in the validation study. Model performance is reported as the coefficient of determination (R²) and mean absolute error, with measures of calibration used for model evaluation and for sport-specific model re-calibration. Results Very-large to near-perfect within-subject correlations (r = 0.76–0.96) were evident between all training load metrics and both CHO utilization and EE. In the primary study, all models explained a large amount of variance (R² = 0.77–0.96) and displayed good accuracy (mean absolute error; CHO = 16–21 g [10–14%], EE = 53–82 kcal [7–11%]). In the validation study, the mean absolute error ranged from 16–50 g [15–45%] for CHO models to 53–182 kcal [9–31%] for EE models. The calibrated mean absolute error ranged from 9–20 g [8–18%] for CHO models to 36–72 kcal [6–12%] for EE models. Conclusions At the individual level, there are strong linear relationships between all measures of training load and both CHO utilization and EE during cycling. When combined with other measures of fitness, EE and CHO utilization during cycling can be estimated accurately. These models can be applied in running and kayaking when used with a calibration adjustment.
... Furthermore, very large correlations (r = 0.96-0.97) have been reported across multiple measures of internal and external training load in cyclists during racing and training [15], suggesting other metrics such as session rating of perceived exertion (sRPE) [16], Lucia training impulse (LuTRIMP) [17], and training stress score (TSS) [18] can also provide relevant information to athletes and coaches. ...
... Session RPE * Duration (min) [16]. This value was divided by 10 to account for the 100-pt scale, allowing easier comparisons to other research using the 10-pt scale. ...
... Presumably, greater prior-day sRPE-TL values imply lower levels of starting muscle glycogen, which is known to shift substrate use away from carbohydrate and towards fat oxidation [34]. Practical application of the carbohydrate models may be challenged by people who do not use sRPE-TL, however this is among the easiest measures for someone to record, with the caveat that a validated scale is used [16]. The use of squared training load terms are included in half of the models, allowing non-linear relationships to be modelled. ...
... Measures of sRPE have been shown to be a valid and reliable assessment of training load, and has been used previously in soccer (Impellizzeri et al., 2004). Another benefit of using sRPE to monitor training load is its affordability in comparison to other methods (Foster et al., 2021). ...
... Whilst sRPE, and sRPE-TL, have been proposed as suitable methods for measuring training load it has been suggested that, due to it being a global measure of load, it may lack the sensitivity to appreciate changes in load profiles. However, sRPE may be an attractive option to practitioners who do not have the financial budget to invest in technologies which allow the assessment of objective measures of load (Foster et al., 2021). Therefore, the broad aim of the subsequent experimental portion of this thesis is to better understand the relationships between objective and subjective measures of load in professional youth soccer players within the specific context of a professional soccer club. ...
... Despite this, little quantitative research has been carried out comparing youth and senior load profiles. Whilst sRPE-TL has been shown to be a valid, reliable, and costeffective measure of monitoring training load (Foster et al., 2021, Impellizzeri et al., 2004, its ability to detect changes in load across transitions is unclear. Considering this, the aim of this thesis is firstly to quantify and describe the relationships between subjective and objective training load measures to assess their suitability for monitoring load. ...
... Considering the VO 2 Max plateau may not be achieved by all participants, we used the 75%VO 2 Max predicted Oduyomi and Fasanmade Bulletin of Faculty of Physical Therapy (2025) 30:2 by age (220-age) throughout the study [26]. The participant's condition was continuously monitored during the submaximal-graded exercise test by perceived exertion (modified Borg scale, RPE 10-scale) [27] oxygen saturation, and cuff sphygmomanometer. Participants were given 2-h period of rest in between the two graded exercises. ...
... VO 2 was predicted using predictive equations by Jackson et al. (1990), Heil et al. (1995), Matthews et al. (1999), and de Souza et al. (2018)[20][21][22][23][24][25][26][27][30][31][32][33][34][35][36]. ...
Research
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Background Maximum oxygen consumption is a measure of an individual’s cardiorespiratory fitness which is a singular predictor of an array of diseases. Several exercise and non-exercise assessments are frequently compared to know which method(s) provide the most accurate estimation of aerobic capacity due to difficulties in using the direct method. There is a need to know if an estimation method is the right fit for a population without huge overestimation or underestimation due to ethnical variation. This study was undertaken to assess the estimations of the cardiorespiratory fitness of healthy African males by the submaximal exercise-based and the non-exercise-based equations in undergraduate students of the University of Ibadan, Nigeria. Results VO2 was significantly higher in the undergraduate students of the University of Ibadan in all the equations used (44.38 ± 39.07, 62.46 ± 27.61, 44.38 ± 39.07, 62.46 ± 27.61, 46.37 ± 3.31, 46.16 ± 3.64, 47.08 ± 3.19). The two submaximal exercises compared using the Bland–Altman Plot showed a high degree of agreement, further linear regression performed showed no proportional bias on the distribution of data around the mean difference line (p > 0.05). Cross-validation using the Bland–Altman plot and linear regression for the five non–exercise predicted equations with the YMCA submaximal exercise test and Bruce submaximal exercise test all showed a significance difference of (P < 0.05) showed that data from all the methods provided proportional bias on the distribution of data around the mean difference line. Conclusion This study asserts that caution should be taken when using a non-exercise equation to predict VO2 in the African population. It is therefore recommended to carry out various assessment methods of VO2 estimation in a wider population using various protocols, and also develop a predictive equation for VO2 specific to the African population.
... Complementary to EL, internal load (IL) monitoring describes the psychophysiological response to the imposed external load demands [8]. While there are several ways to assess IL (e.g., heart rate zones, heart rate variability, biomarkers, subjective ratings of soreness and fatigue), session rating of perceived exertion (sRPE) is the most widely utilized measure in basketball athletes [14][15][16][17]. sRPE (sRPE = RPE × session duration) is an easy-to-implement, cost-effective, time-efficient, and valid measure of IL [14,15]. ...
... While there are several ways to assess IL (e.g., heart rate zones, heart rate variability, biomarkers, subjective ratings of soreness and fatigue), session rating of perceived exertion (sRPE) is the most widely utilized measure in basketball athletes [14][15][16][17]. sRPE (sRPE = RPE × session duration) is an easy-to-implement, cost-effective, time-efficient, and valid measure of IL [14,15]. A dose-response relationship exists between EL and IL, but IL stress is highly individual and appears to be based on a variety of factors such as fitness level, playing status, and sport [18]. ...
Article
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External (EL) and internal (IL) load are commonly used methods used to quantify training load in team sports. Playing time and playing position may influence the training loads for specific athletes throughout a season. The purpose of the current study was to evaluate the effect of athlete playing status and individual in-season practices on EL and IL across a collegiate women’s basketball season. Female basketball athletes were classified as high-minute (HMA; ≥15 min/game) or low-minute (LMA; <15 min/game) and wore microsensors during 53 practices for a total of 583 data points. EL was obtained via an inertial measurement unit (IMU) device that contained a triaxial accelerometer to obtain three-dimensional positioning data. IL and strength training (ST) load were determined via session rating of perceived exertion (sRPE) to create a daily summated value. Descriptive statistics indicate that athletes experienced individual differences in EL, ST, and IL throughout the season. A growth model showed that HMAs experienced higher EL than LMAs at the start of the season for practices (90.21 AU). Across all athletes, IL increased across the season (40.11 AU) and for each 1 unit of change in EL, IL increased by 1.04 AU. Repeated measures correlations identified a large relationship between IL and EL (r = 0.51, p < 0.001). A location-scale model indicated that the within-person variability of IL across all athletes was 3.29 AU but was not due to athlete playing status. It is recommended to base in-season training on individual loads and game demands to promote athlete readiness and improved sport performance.
... This way, external factors, such as peer pressure and replicating other scores that could influence the RPE answer were minimized. 20 The ITL value was obtained by multiplying the session duration, in minutes, by each s-RPE score. 20 The weekly ITL resulted from the sum of the past 7 ITL values. ...
... 20 The ITL value was obtained by multiplying the session duration, in minutes, by each s-RPE score. 20 The weekly ITL resulted from the sum of the past 7 ITL values. Monotony (calculated as daily load divided by the daily load SD) and strain (calculated as the product of monotony and load) were also obtained. ...
Article
Purpose: This cohort study aimed to investigate the relationship between subjective (wellness and internal training load [ITL]) and objective (neuromuscular fatigue) monitoring markers and performance aspects (reception quality [RQ] and attack efficiency [AE]) in professional female volleyball players. Methods: The study was conducted over an 8-week period during the final mesocycle of the competitive phase. A total of 24 training sessions and 10 matches were included in the analysis. Subjective measures of wellness and ITL were assessed, and neuromuscular fatigue was evaluated using countermovement-jump (CMJ) height. RQ and AE were determined based on game statistics. Results: The study found a positive relationship between wellness and RQ, particularly affecting outside hitters and liberos. ITL showed a positive association with AE, primarily impacting outside hitters, opposite hitters, and middle blockers. Additionally, ITL demonstrated a negative correlation with RQ, mainly affecting outside hitters and liberos. CMJ performance was associated with AE, where a decrease in CMJ height was linked to reduced AE. Conclusions: The findings highlight the importance of considering players' wellness scores in training and match strategies for different positions. Careful management of training loads, considering both physical and technical demands, is crucial for optimizing performance outcomes. Monitoring neuromuscular fatigue, as indicated by CMJ performance, is particularly relevant for outside hitters, opposite hitters, and middle blockers involved in attack actions. Coaches, trainers, and sports practitioners can use these insights to develop position-specific training protocols and implement effective strategies for maintaining or improving performance metrics under various stressors.
... O treinador pode mensurar a intensidade com a escala de percepção subjetiva do esforço (PSE) (Foster et al., 2021). Foster et al. (1995) aplicaram os resultados da escala de PSE nos cálculos matemáticos para determinar a carga interna, a monotonia das cargas (verifica se a organização da carga vem sendo adequada) e o estresse das cargas (é o índice de fadiga da carga). ...
... A ANOVA two way detectou diferença estatística (p≤0,05) e o post hoc Tamhane identificou a média da carga de treino interna de cada tipo de sessão é importante para o treinador saber distribuir o volume e a intensidade conforme a etapa da periodização.Os resultados informaram que a carga de treino interna do voleibol costuma ser mais elevada no período preparatório do que o período competitivo(Aoki et al., 2017; Debien et al., 2018; Seixas et al., 2019;Horta et al., 2019). O motivo desse ocorrido não foi encontrado nas referências sobre esse tema(Foster et al., 2021;Martins et al., 2018). Entretanto, isso pode estar relacionado com o menor preparo físico do voleibolista e/ou com a aplicação de maiores cargas de treino no período preparatório visando que o atleta atinja adequada forma esportiva no período competitivo(Matveev, 1997). ...
Article
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The trainer can measure intensity with the rating of perceived exertion (RPE) scale. Through of the RPE scale, it is possible to make mathematical calculations to determine the internal load (IC), the monotony of the loads, and the strain of the loads. The results of the RPE scale applied in mathematical calculations are accurate to monitor IC, several volleyball coaches and physical trainers use this content during the season. The objective of the review was to present some volleyball studies on IC using the RPE scale. The studies in this review were collected from Google Scholar and Research Gate during 2019 to 2023. The results on IC were presented in four different parts for better understanding of the reader. The main results of the review article were the following: the preparatory period has a higher IC than the competitive period, the mesocycle of the preparatory period has a higher IC than the competitive period, the increase in the number of sets played causes greater intensity in the match, and the volleyball game has a higher IC than other types of training. In conclusion, volleyball IC is important content to monitor the training.
... The studies research protocol duration varies between 6 weeks and 2 seasons. IL was assessed with RPE proposed by Foster (Foster, 1998;Foster et al., 2021), S-RPE (derived multiplying RPE x duration) (Foster et al., 2017(Foster et al., , 2021, and/or HR, while EL using GPS (Rossi et al., 2018) and/or accelerometer indicators. Twenty-one studies (84%) used standard statistical analysis (Alemdaroğlu, 2020;Derbidge et al., 2020;Enes et al., 2021;Gaudino et al., 2015;Gomez-Piriz et al., 2011;Grünbichler et al., 2020;Lacome et al., 2018;Lu et al., 2017;Marynowicz et al., 2020;Maughan et al., 2020Maughan et al., , 2021Montini & Rocchi, 2020;Rago et al., 2019;Silva et al., 2018;Suarez-Arrones et al., 2015;Torreño et al., 2016;Wiig et al., 2020) and only four (16%) (Geurkink et al., 2019;Alessio;Rossi et al., 2019;Vallance et al., 2020) used ML approaches to analyse data. ...
... The studies research protocol duration varies between 6 weeks and 2 seasons. IL was assessed with RPE proposed by Foster (Foster, 1998;Foster et al., 2021), S-RPE (derived multiplying RPE x duration) (Foster et al., 2017(Foster et al., , 2021, and/or HR, while EL using GPS (Rossi et al., 2018) and/or accelerometer indicators. Twenty-one studies (84%) used standard statistical analysis (Alemdaroğlu, 2020;Derbidge et al., 2020;Enes et al., 2021;Gaudino et al., 2015;Gomez-Piriz et al., 2011;Grünbichler et al., 2020;Lacome et al., 2018;Lu et al., 2017;Marynowicz et al., 2020;Maughan et al., 2020Maughan et al., , 2021Montini & Rocchi, 2020;Rago et al., 2019;Silva et al., 2018;Suarez-Arrones et al., 2015;Torreño et al., 2016;Wiig et al., 2020) and only four (16%) (Geurkink et al., 2019;Alessio;Rossi et al., 2019;Vallance et al., 2020) used ML approaches to analyse data. ...
... All exercise was recorded in Training Peaks software (Train-ingPeaks, Louisville, CO, USA). Each session was noted for modality (e.g., bike, run, swim), duration, and session rating of perceived exertion (sRPE [14]) using the Borg CR100 ® scale, which offers additional precision compared with the CR10 scale [10]. Participants were instructed to rate their perceived effort for the whole training session within 1-h of exercise, although sRPE scores are temporally robust from minutes to days following a bout of exercise [14]. ...
... Each session was noted for modality (e.g., bike, run, swim), duration, and session rating of perceived exertion (sRPE [14]) using the Borg CR100 ® scale, which offers additional precision compared with the CR10 scale [10]. Participants were instructed to rate their perceived effort for the whole training session within 1-h of exercise, although sRPE scores are temporally robust from minutes to days following a bout of exercise [14]. ...
Article
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Purpose Research findings are typically reported at the group level but applied to individuals. However, an emerging issue in sports science concerns nonergodicity—whereby group-level data cannot be generalized to individuals. The purpose of this study was to determine if the relationship between daily carbohydrate intake and perceived recovery status displays nonergodicity. Methods Fifty-five endurance athletes recorded daily measures of self-selected dietary intake, training, sleep, and subjective wellbeing for 12 weeks. We constructed linear models to measure the influence of daily carbohydrate intake on perceived recovery status while accounting for training load, sleep duration, sleep quality, and muscle soreness. Using linear model coefficients for carbohydrate intake we tested whether the distributions (mean and SD) differed at the group and individual levels (indicating nonergodicity). Additionally, a decision tree was created to explore factors that could provide an indication of an individual athlete’s relationship between carbohydrate intake and perceived recovery status. Results Mean values were not different between group- and individual-level analyses, but SDs at the individual level were ~2.4 times larger than at the group level, indicating nonergodicity. Model coefficients for carbohydrate intake were negative for three participants, positive for four participants, and non-significant for 37 participants. The κ value measuring accuracy of the decision tree was 0.52, indicating moderate prediction accuracy. Conclusion For most individuals, carbohydrate intake did not influence recovery status. However, the influence of dietary carbohydrate intake on daily recovery differs at the group and individual level. Therefore, practical recommendations should be based on individual-level analysis.
... Training impulse (TRIMP) and rating of perceived exertion (RPE) [12] are currently the most common methods to evaluate internal loads. TRIMP was first produced by Banister et al. [13] in 1975 and the Banister's TRIMP [13] was calculated by training time and training intensity based on heart rate reserve. ...
... The MINORS is suitable for single-arm tests and is used to access the methodological quality of criterion-related validity studies. The total score for each study was used to rank the risk of bias as low (13)(14)(15)(16), moderate (9)(10)(11)(12), or high (0-8). ...
Article
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Background The objective of this study was to establish the criterion-related validity of the session-rating of perceived exertion (s-RPE) method in adolescent athletes. Methods According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, a meta-analysis (PROSPERO ID: CRD42022373126) was performed using Stata 15.1 software. Eight data�bases using the following terms: (‘s-RPE’ OR ‘Rating Perceived Exertion session’ OR ‘RPE session’ OR ‘RPE’ OR ‘Rate of Perceived Exertion’ OR ‘Rated of Perceived Exertion’) AND (‘Adolescen*’ OR ‘Youth*’ OR ‘Teen*’) AND (‘validity’ OR ‘cor�relation’ OR ‘concurrent validity’) were searched up to 2022. Articles meeting the inclusion criteria were screened and adopted the “Methodological Index for Non-Randomized Studies (MINORS)” to evaluate the risk of bias. Results An initial 1798 studies using the s-RPE method were identifed and fnally, a total of 16 studies were included for further analysis. The relationship between assessment instruments CR-10 or CR-100 modifed methods of s-RPE and the heart rate measures of these selected studies were calculated using correlation coefcient (r values) and Fish�er’s z-score. A strong to very strong correlation between s-RPE and HR was observed (overall: r=0.74; CR-10: r=0.69; CR-100: r=0.80). CR-100 scale (Fisher’s z=1.09) was shown to have a higher criterion validity than that of the CR-10 scale (Fisher’s z=0.85). Conclusion Preliminary fndings showed that s-RPE using either CR-10 or CR-100 scales can be used "stand�alone" for monitoring internal training load for children and adolescent athletes. Future studies should focus on whether CR-100 could better perform than CR-10 for junior and children athletes in diferent age groups and sports as well as the causes leading to potential scoring biases.
... 3 Sport science encompasses a diverse range of disciplines, ranging from exercise physiology and medicine to sport biomechanics and psychology. In this context, practitioners rely on both objective (eg, Global navigation satellite system) 4,5 and subjective (eg, rate of perceived exertion) [6][7][8] data to be interpreted to enhance athletic performance. However, confirmation bias introduces a subtle yet powerful distortion, which is difficult to control and quantify. ...
Article
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Purpose: Confirmation bias, a cognitive phenomenon deeply ingrained in human thinking, shapes our perception of reality. It leads individuals to favor information that aligns with their preexisting beliefs while dismissing or undervaluing the contrary evidence. In sport science, where precision and evidence-based practices are critical aspects to optimize performance, confirmation bias can decisively impact decision-making processes. In this context, practitioners rely on both objective and subjective data to be interpreted to enhance athletic performance. However, confirmation bias introduces a subtle yet powerful distortion, which is difficult to control and quantify. We usually believe that our observations can justify expectations or predictions about future observations, as well as broader generalizations beyond what we have directly observed-inductive inferences. Researchers and practitioners may therefore unknowingly seek out evidence that confirms their beliefs, inadvertently overlooking alternative viewpoints and interpretations. Conclusions: This paper explores the concept of confirmation bias in sport science, providing practical examples in the contexts of testing, training, and monitoring. By understanding how confirmation bias operates (ie, heuristic of decision making), we may develop strategies to mitigate its effects and promote more accurate and effective decision making in sport science (eg, performance).
... This essay is a collection of thoughts that come from 50 years of personal experiences relevant to exercise science and human endurance performance. These experiences range from participation as an athlete to research to advising coaches, athletes, and sports policy-makers [1,2]. I started training for sport seriously in the 1970s. ...
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This essay summarizes and integrates my experiences and observations—starting in the middle 1970s—as an athlete, scientist interested in human performance, biomedical researcher, and “expert,” who sometimes advises athletes, coaches, and sports policy‐makers. In this context, my focus has been primarily on endurance sports and five concepts underpin what I have learned over the last 50 years. (1) The “competitive significance principle” whereby athletes, coaches, and policy‐makers are frequently interested in performance improvements of 1% or less. This is especially true at the elite level. (2) Science frequently explains what coaches and athletes have already discovered in the field. (3) Many science‐based performance improvements have reduced the energy cost of moving a given speed. (4) Varied and eclectic training programs have been used by elite athletes with similar performance outcomes. (5) A few things that are actionable matter. Coaches, athletes, and administrators should focus their efforts on what is actionable.
... The internal training load (ITL) was calculated immediately after each session based on the players' perceived exertion (RPE), measured using the ten-level Borg scale (Borg, 1982), and training time: ITL = RPE x time (a.u.), where the RPE was the perceived exertion (a.u.) and time was the training time (Foster et al., 2021). All participants also completed a short version of the Physical Activity EnjoymentScale (PACES) (Raedeke, 2007). ...
Article
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The purpose of this study was to compare the effects of on-court tennis training (OTT) combined with high-intensity interval training (HIIT) or repeated sprint training (RST) on the physiological, kinematic, kinetic, and perceptual responses of young tennis players. Twenty-four male tennis players (age 13.6 ± 0.3 years) were randomly assigned to either the OTT + HIIT group (n = 12) or the OTT + RST group (n = 12) three times per week for six weeks. Both groups trained for the same total training time with passive rest in each session. A number of physiological, performance and perceptual responses were measured before and after the 6-week training intervention. All variables showed a significant improvement over time, with maximal oxygen uptake showing the greatest improvement (p < 0.001, η² = 0.97). The 5-m sprint (p = 0.044, η² = 0.17), repeated sprint ability (p = 0.021, η² = 0.22), and T-drill agility (p = 0.048, η² = 0.17) showed a significant group effect. The OTT + RST group had a lower internal training load (better scores), a lower rate of perceived exertion (better scores), and higher scores in the Physical Activity Enjoyment Scale (PACES) at both times compared to the OTT + HITT group. These results demonstrate that OTT + RST appears to be a more effective training approach to improve speed and agility-based performance responses with more enjoyment in young tennis players.
... This specialized equipment is able to measure the pulse from the wrist in addition to recording the pulse. The perceived exertion of the players was evaluated using a validated 20-point Borg scale after each session of the SSGs in order to measure the internal training intensity (Foster et al., 2021). The validity and reliability of this scale in evaluating the amount of effort have been demonstrated in prior study (Impellizzeri et al., 2004). ...
... 33,65 As reported by Coyne et al., 33 inconsistent findings examining the relationship between TL and injury and performance 66 may be related to TL (computed as duration x intensity) that is defined as "a relatively simplistic and somewhat limited tool for accurately modeling training responses in elite athletes". 35,67,68 According to Renfree et al., 67 TL (given by the product of exercise intensity and duration) may be limited as does not account for nonlinearities in the biological response to stress. In agreement with the previous authors, the TL may not be fully satisfactory even if it remains the only tool available to monitor soccer training. ...
Article
Data-driven training prescription based on previous training or match data is thought to be associated with better training outcome, compared to prescription without considering any monitoring data. Understanding the complex relationship between training load, physical performance, fitness status, fatigue and injury risk represents a challenge for health and performance practitioners and researchers. Although studies have revealed a positive correlation between training load and injury risk, this cause-effect relation cannot be determined given the multifactorial nature of injuries. Additionally, conflicting findings have been published explaining the relationship between training load and injuries, underlining the importance of training load management, prescription, and communication within the multidisciplinary team to improve physical performance and reduce injury risk. In this sense, practitioners may benefit from practical examples based on training load data to make informed decisions for prescribing training. This narrative review provides real-world examples of training decisions based on training load data in soccer, including training prescription, drill design and multidisciplinary team communication. Finally, a framework was provided to make informed training prescription from a physiological standpoint and elucidate the relationship between training load and injury risk.
... As an example, a young player playing half a game may perceive the load on them very differently than a player playing a full game with multiple years of consistent training. Further, a tactically proficient Table 7 Conditioning format types based on HIIT recommendations compared with the ball-in-play demands of women's rugby sevens measures of training load, such as ratings of perceived exertion (RPE) and training duration to capture the internal load (session RPE) (42) and subjective measures that include subscales to monitor nontraining stress, fatigue, physical recovery, health, and well-being as the athletes' response to training (84). Monitoring external load is desirable; however, monitoring external load accurately may be challenging depending on the university's budget and staffing, limiting access to technologies like GNSS units, which are ideal for quantifying external load during training and competition (8,29,30,32). ...
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With increasing investments and resources, rugby sevens is growing internationally and domestically in many countries. Within Canada, women's rugby sevens is a popular sport at the regional and national levels in university settings and centralized training programs. Given the importance of strength and conditioning for success in sevens, the purpose of this article is to highlight some of the frameworks used to develop sevens athletes from the university to the international level within the Canadian context. As such, the match demands and physical characteristics relevant to rugby sevens are discussed based on a comprehensive needs analysis. This is further contextualized by a training philosophy and framework tailored for university-aged female athletes, which play a key role in the Canadian talent development pathway.
... RPE, VAS and enjoyment were collected between bouts and after in all games, and MRF-3 was measured before and after the games by researchers in five minutes. Players' perceived exertion was assessed using a validated 20-point Borg scale following each session of the SSGs to determine internal training intensity (Foster et al., 2021). A previous study demonstrated the validity and reliability of this scale to estimate the intensity of effort (Impellizzeri et al., 2004). ...
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This study explored responses to 2v2 and 4v4 soccer small-sided games (SSGs) in possession, mini-goal (MG), and goalkeeper (GK) formats among 24 young players. The rating of perceived exertion (RPE) was used to calculate the perceived exertion, enjoyment scale (PACES) measures the enjoyment of exercise, visual analogue scale (VAS) that assesses mental fatigue during exercise sessions and psychological preparedness for physical activity was assessed using the Mental Readiness Form-3 during exercise sessions. In the 2v2 games, ratings of perceived exertion (RPE) and visual analogue scale (VAS) scores differed significantly between possession, MG, and GK games (P
... One of the most practical and commonly used methods to estimate internal training load is session rating of perceived exertion (sRPE). This simple subjective evaluation correlates strongly with objective indicators of training load, such as heart rate reserve percentage and blood lactate concentration [4]. Additional variables derived from sRPE, such as acute: chronic workload ratio (ACWR), training monotony, and training strain, can provide additional insight into both positive and negative training outcomes [5]. ...
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Background The long-term monitoring of internal and external training load is crucial for the training effectiveness of athletes. This study aims to quantify the internal and external training loads of collegiate male volleyball players during the competitive season. The internal and external training load variables were analyzed across mesocycles and playing positions. Methods Fourteen participants with age of 20.2 ± 1.3 years, height of 1.81 ± 0.05 m, and body weight of 70.8 ± 5.9 kg were recruited. The data were collected over a 29-week period that was divided into four mesocycles: preparation 1 (P1, weeks 1–7), competition 1 (C1, weeks 8–14, including a 5-day tournament in week 14), preparation 2 (P2, weeks 15–23), and competition 2 (C2, weeks 24–29, including a 6-day tournament in week 29). Each participant wore an inertial measurement unit and reported the rating of perceived exertion in each training session. The internal training load variables included weekly session rating of perceived exertion, acute: chronic workload ratio, and training monotony and strain. The external training load variables included jump count and height and the percentage of jumps exceeding 80% of maximal height. Results C2 had the highest average weekly internal training load (3022 ± 849 AU), whereas P2 had the highest average weekly acute: chronic workload ratio (1.46 ± 0.13 AU). The number of weekly jumps in C1 (466.0 ± 176.8) was significantly higher than in other mesocycles. Weekly jump height was significantly higher in C1, P2, and C2. Internal training load was positively correlated with jump count (ρ = 0.477, p < 0.001). Jump count was negatively correlated with jump height (ρ = −0.089, p = 0.006) and the percentage of jumps exceeding 80% of maximal height (ρ = −0.388, p < 0.001). The internal and external training load variables were similar among different playing positions. Conclusion The participants exhibited significantly higher internal training load in C2 and higher jump height after P1. A high jump count was associated with higher internal training load and lower jump height. Excessive jumps may result in fatigue and reduce height.
... Простая и доступная для повсеместного использования шкала Борга становится общепринятым методом переносимости нагрузки, хорошо коррелирующим с частотой сердечных сокращений и концентрацией лактата в капиллярной крови [17][18][19][20][21]. Первый вариант шкалы Борга (шкала Борга 6-20) субъективно оценивал уровень нагрузки с помощью числового диапазона от 6 до 20. ...
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ABSTRACT Objective: evaluation of the feasibility of using the Borg CR10 perceived exertion scale in the training process of young football players and its role in injury prevention. Material and methods: 48 athletes aged 15–17 were examined by scale of perceived exertion during the 7 month every day within 20 minutes after the training. The coaches also assessed the degree of perceived exertion. A comparative retrospective analysis of injuries was done for the periods of the mycrocycle of the youth football league of the seasons 2020–2021 and 2021–2022 based on muscle injuries, ligament and joint capsular injuries. Results: Athletes’ scores on the Borg CR10 scale in the preparation, competition and transitional stages of the competitive macrocycle are 5.08 ± 1.28, 5.00 ± 1.16 and 4.67 ± 1.38, respectively. The coaches’ scores for the aforementioned periods are 4.78 ± 0.89, 5.09 ± 1.05 and 4.82 ± 0.99. The total scores of players and coaches is 5.20 ± 1.85 and 4.96 ± 1 respectively. Moreover, there is no statistically significant difference between the rate of perceived exertion between players and coaches for the whole season (p = 0.288). There is a decreased injury rate in young professional athletes group when the Borg CR10 scale of perceived exertion is measured in a season with the use of this scale compared to a season where the Borg CR 10 scale was not used (p = 0,028). Conclusion: the Borg СR10 scale is a useful methodology for assessing the level of perceived exertion of a young athlete and can be used for early training corrections in order to reduce injuries. Keywords: soccer, load tolerance, young athletes, sports injuries
... Простая и доступная для повсеместного использования шкала Борга становится общепринятым методом переносимости нагрузки, хорошо коррелирующим с частотой сердечных сокращений и концентрацией лактата в капиллярной крови [17][18][19][20][21]. Первый вариант шкалы Борга (шкала Борга 6-20) субъективно оценивал уровень нагрузки с помощью числового диапазона от 6 до 20. ...
Article
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Objective: evaluation of the feasibility of using the Borg CR10 perceived exertion scale in the training process of young football players and its role in injury prevention. Material and methods: 48 athletes aged 15–17 were examined by scale of perceived exertion during the 7 month every day within 20 minutes after the training. The coaches also assessed the degree of perceived exertion. A comparative retrospective analysis of injuries was done for the periods of the mycrocycle of the youth football league of the seasons 2020–2021 and 2021–2022 based on muscle injuries, ligament and joint capsular injuries. Results: Athletes’ scores on the Borg CR10 scale in the preparation, competition and transitional stages of the competitive macrocycle are 5.08 ± 1.28, 5.00 ± 1.16 and 4.67 ± 1.38, respectively. The coaches’ scores for the aforementioned periods are 4.78 ± 0.89, 5.09 ± 1.05 and 4.82 ± 0.99. The total scores of players and coaches is 5.20 ± 1.85 and 4.96 ± 1 respectively. Moreover, there is no statistically significant difference between the rate of perceived exertion between players and coaches for the whole season (p = 0.288). There is a decreased injury rate in young professional athletes group when the Borg CR10 scale of perceived exertion is measured in a season with the use of this scale compared to a season where the Borg CR 10 scale was not used (p = 0,028). Conclusion: the Borg СR10 scale is a useful methodology for assessing the level of perceived exertion of a young athlete and can be used for early training corrections in order to reduce injuries. Keywords: soccer, load tolerance, young athletes, sports injuries
... The players' subjective perception of their effort, known as the Rating of Perceived effort (RPE), was evaluated after each session of the Small-Sided Games (SSGs) using a reliable 20-point scale. This assessment was conducted to establish the internal training intensity, as described by Foster et al. (2021). Impellizzeri et al. (2004) conducted a study that established the validity and reliability of this scale in order to measure the level of effort that was being exerted. ...
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This study was conducted to investigate the responses of 4 x 6 minute (min) matches to 6 x 4 min small sided games (SSG) on heart rate (HR), perceived exertion rates (RPE), enjoyment, total distance covered and technical abilities in young soccer players. The SSG took place separately with a 4v4 format on a 25 × 32-sized field, 4 × 6 min bouts, and 6 × 4 min bouts. Twenty-four amateur soccer players (age = 16.83 ± 0.381 years; height= 175.83 ± 6.75; weight: 66.21 ± 7.19; body mass index; 21.36 ± 1.36) participated in the study voluntarily. Based on the study results, 6 × 4 min SSG was higher than 4 × 6 min SSG of the RPE response and successfully passed the technical ability results (p0.05). The findings of this study emphasize that a 6 × 4 min SSG is more effective than a 4 × 6 min SSG in RPE and successfully passes the technical abilities of soccer players. Therefore, coaches may prefer short bout matches in SSGs to significantly increase the training load and successfully pass technical performance.
... Foster et al. [56] have also indicated that session RPE is mode dependent, but more studies are warranted to verify this feature. ...
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Background Our scientific understanding of the mechanistic and practical connections between training session prescriptions, their execution by athletes, and adaptations over time in elite endurance sports remains limited. These connections are fundamental to the art and science of coaching. Objective By using successful Norwegian endurance coaches as key informants, the aim of this study is to describe and compare best practice session models across different exercise intensities in Olympic endurance sports. Methods Data collection was based on a four-step pragmatic qualitative study design, involving questionnaires, training logs from successful athletes, and in-depth and semi-structured interviews, followed by negotiation among researchers and coaches to assure our interpretations. Twelve successful and experienced male Norwegian coaches from biathlon, cross-country skiing, long-distance running, road cycling, rowing, speed skating, swimming, and triathlon were chosen as key informants. They had been responsible for the training of world-class endurance athletes who altogether have won > 370 medals in international championships. Results The duration of low-intensity training (LIT) sessions ranges from 30 min to 7 h across sports, mainly due to modality-specific constraints and load tolerance considerations. Cross-training accounts for a considerable part of LIT sessions in several sports. Moderate (MIT)- and high-intensity training (HIT) sessions are mainly conducted as intervals in specific modalities, but competitions also account for a large proportion of annual HIT in most sports. Interval sessions are characterized by a high accumulated volume, a progressive increase in intensity throughout the session, and a controlled, rather than exhaustive, execution approach. A clear trend towards shorter intervals and lower work: rest ratio with increasing intensity was observed. Overall, the analyzed sports implement considerably more MIT than HIT sessions across the annual cycle. Conclusions This study provides novel insights on quantitative and qualitative aspects of training session models across intensities employed by successful athletes in Olympic endurance sports. The interval training sessions revealed in this study are generally more voluminous, more controlled, and less exhaustive than most previous recommendations outlined in research literature.
... In another study, a maximal fitness test was not conducted to obtain HR max before an amputee football match to analyze the match related individual HR responses in an amputee football match (Simim et al., 2018). Furthermore, [BLa-], one of the internal load measures to quantify a physiological response (Foster et al., 2021;Mara et al., 2016), was not collected to determine the internal load in amputee football matches (Maehana et al., 2018). Including [BLa-] collection as an internal load parameters may assist in understanding some of the aspects that HR and RPE do not clarify (Halson, 2014). ...
Article
Literature is scarce related to the evaluation of internal and external load demands in simulated amputee football. This study aimed to quantify internal and external load demands on players during amputee football matches (n=22). Heart rate (HR), blood lactate ([BLa-]), and ratings of perceived exertion (RPE) were analyzed as internal load. Total distance covered (TD), TD at five speed categories (SC), percentages of TD at five SC, time spent in four HR zones and peak speed were considered external load. The results revealed that mean RPE and [BLa-] were 10.59±2.93 and 5.36±2.93 mmol.L-1 , respectively, and were similar between the first and the second half (p>0.05). Mean HR was 145.5±12.5 bpm and mean HR was higher in the first half compared to the second half (p=0.049). Amputee football players covered 2445.40±416.36 m in matches and the mean TD in the second half was lower than the first half (p=0.000). TD at very low speed category corresponded to ~73 % efforts. Low, moderate, high, and very high efforts corresponded to ~27 % of the TD. Amputee football players spent ~46 min at moderate and high intensity HR zones while these corresponded only about ~4 min at very low and low intensity HR zones during amputee football matches. This study concludes that match demands are high during amputee football matches. Internal and external load parameters have a great importance in the design of amputee football specific training protocols to enhance physical capacity, minimize injury risk and improve well-being.
... On the other hand, internal load is defined as the athlete's physiological responses to the imposed external load during training or competition [11]. Measuring internal load is more challenging because it involves the evaluation of both psychological (e.g., rating of perceived exertion [RPE]) and physiological aspects (e.g., heart rate [HR], lactate concentration in blood or oxygen consumption) [12]. ...
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Objective This study aims to comprehend the impact of handball practice on sub-elite athletes by investigating transcriptomic changes that occur during a match. The primary focus encompasses a dual objective: firstly, to identify and characterize these transcriptomic alterations, and secondly, to establish correlations between internal factors (gene expression), and external loads measured through Electronic Performance and Tracking Systems (EPTS variables). Ultimately, this comprehensive analysis seeks to evaluate both acute and chronic responses to exercise within the context of handball training. Methods The study included sixteen elite male athletes from the FC Barcelona handball second team. Blood samples were extracted at three different time points: before the match at baseline levels (T1), immediately upon completion (T2), and 24 hours after completion (T3). Differential gene expression, Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted in two comparisons: Comparison 1 (T1 vs T2) and Comparison 2 (T1 vs T3). Further, the correlation between gene expression levels and training variables (external load) was conducted. Results In T1 vs T2, 3717 of the 14632 genes detected were differentially expressed (adjusted p-value < 0.05), and enrichment of terms related to the immune system, mitochondria, and metabolic processes was found. Further, significant linear correlations were obtained between High-Speed running (HSR) and high-intensity variables such as acceleration ACC and deceleration DEC values with amino acids, and inflammatory and oxidative environment-related pathways, both in chronic and acute response. Conclusions This research highlights the effects of external workload on elite athletes during a handball match and throughout the season. The study identifies deregulation in the immune system, mitochondrial functions, and various metabolic pathways during the match. Additionally, it establishes correlations between the external load and pathways associated with amino acids, inflammation, oxidative environment, and regulation. These findings offer insights into the immediate and chronic responses of athletes to physical effort.
... Therefore, in this research, load monitoring variables were applied through the method of subjective load monitoring, which represents a simple, free, and effective method of load monitoring. The previous sentence can be confirmed by its high correlation with heart rate, blood parameters, and the level of lactate concentration in the blood (21). In addition, the correlation and connection with external load parameters show that subjective load assessment can be a very good tool for assessing the total training load in football players of younger age categories (22). ...
Article
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Background. One of the most effective methods for progression in fast and explosive movements which are important in football player's performance is plyometric training which includes activities and movements whose performance depends on the stretch and shortening cycle. Objectives. The problem of this research is related to the monitoring of the subjective feeling of load and the feeling of physiological and psychological stress under the influence of plyometric training, and whether the said low-volume training has an impact on the internal experiences of the load of young football players. Methods. The sample of respondents consisted of 33 junior football players who played club football at the age of 17.3±0.9. The experimental training program lasted six weeks and subjective parameters for training load were monitored daily through RPE and wellness questionnaires. Results. The results of this study showed that additional plyometric training does not significantly affect the subjective feeling of load during six weeks in the participants. Significant differences between the experimental and control groups of participants were found in wellness in the first and second weeks of the experimental procedure (<0.005 and 0.017), while no significant differences were found in wellness from the third to the sixth week of the experimental procedure. Conclusion. Daily monitoring of the subjective feeling of load is a good tool for controlling athletes. This especially applies to younger athletes because it is an extremely effective tool that is available to everyone and does not require financial expenses.
... The RPE rating was multiplied by the session minutes to determine the sRPE. 22 Well-Being Questionnaire ...
Article
Purpose: The monitoring of training loads and quantification of physical performance are common practices in youth soccer academies to support coaches in prescribing and programming training for individuals. The interaction between training load and physical performance is unknown during a preseason period in youth soccer players. The current study assessed changes in training load and physical assessments across a 4-week preseason period. The relationship between physical performance and match playing time in youth male soccer players was also investigated. Methods: The training loads of 25 professional youth academy male soccer players were monitored throughout a 4-week preseason period. Assessments of power, agility, speed, and aerobic capacity were undertaken in the first training session. Session ratings of perceived exertion (sRPE) and well-being questionnaires were collected during all training sessions and preseason matches. Playing time during subsequent competitive matches was recorded. Results: T test and 30-m-sprint assessments, conducted on the first day of preseason, were predictors of sRPE throughout preseason (t test χ 2 /df = 2.895, poor adjustment; 30-m sprint χ 2 /df = 1.608, good adjustment). YoYo Test performance was related with changes in perceived fatigue (χ 2 /df = 0.534, very good adjustment). Faster players reported higher values of sRPE, and players with higher aerobic capacity reported higher levels of fatigue across preseason. Well-being, perceived fatigue and soreness, and sRPE decreased across preseason. Greater match durations were related to higher levels of fatigue during preseason (P < .05). Conclusion: The current study highlights the relationship between training load, physical assessments, and playing time. Coaches and practitioners can use physical test data at the start of preseason as an indication of players that report higher sRPE, perceived fatigue, and reduced well-being across preseason, supporting decisions around individualized training prescriptions. Keywords: athlete monitoring, load management, physical assessment, recovery In recent years, there has been an increase in the profile of youth soccer. 1,2 According to recent data derived from 27 European countries, more than half a million under-14-year-old participants compete in soccer. 3 However, given the rapid annual changes in growth and maturation, 4 injury propensity and overreaching in adolescent athletes are higher versus both adults and younger athletes. 5,6 Therefore, an appropriate balance between training, competition, and recovery is required to minimize injury risk and overreaching in youth soccer. 2 Injury susceptibility and overreaching in youth soccer players are also likely attributed to seasonal variations in load, with peaks in injury observed following periods of inactivity or during rapid spikes in training load, 7 such as during a soccer preseason. 7 Accordingly, there is growing concern relating to heightened injury and overreaching due to high training loads across certain periods within a season in youth soccer. 8 Attempts to quantify the accumulated weekly in-season training load under-taken by young soccer players have been made. 9 A separate investigation has also assessed the in-season changes in physical qualities of elite youth soccer players according to maturity status. 10 However, there is a lack of research quantifying training and match loads across a preseason period in youth soccer players. Training loads can be measured through external or internal load, depending on whether measurements are external or internal to the athlete. 11 External loads relate to the objective measurements of physical work (eg, distances, speeds, and number of movements), 12 whereas internal load refers to the stress imposed on the athlete. 11 For example, the quantification of internal training load is commonly assessed among youth soccer players using session ratings of perceived exertion (sRPE), with well-being questionnaires used to assess the response to training loads. 13,14 Significant correlations between physical performance (ie, sprint, total distance , maximum speed, and average speed) and RPE in training sessions have been found in adolescent soccer players examined during 6 weeks of preseason. 15 However, how these relationships change when match play is considered and how external load is linked with internal load and changing perceptions of well-being
... Thus, subjective measures of athlete well-being, such as rate of perceived exertion (RPE), have been proposed as an alternative assessment modality capable of detecting changes in internal loads that athletes are exposed to (Saw et al., 2016). The Borg CR-10 scale method of measuring RPE has been shown to possess strong relationships with objective measures of internal load (e.g., heart rate and blood lactate; Foster et al., 2001;Foster et al., 2021;Hadad et al., 2017). This lends credence to RPE as being a time-efficient testing modality that has been widely used for the assessment of internal load in basketball players (Lupo et al., 2017;Montgomery et al., 2010;Moreira et al., 2012;Paulauskas et al., 2019;Sansone et al., 2019;Svilar et al., 2018a;Svilar et al., 2018b). ...
... Perceived fatigue was assessed using a visual analog scale ranging from 0 (not fatigued at all) to 10 (maximal fatigue and exhaustion; Micklewright et al., 2017). Perceived exertion was assessed with the Borg scale, which ranges from 6 (no exertion) to 20 (maximal exertion; Borg, 1982;Foster et al., 2021). For both visual analogue scales, rating scales were shown to the participants, and they verbally provided their answers. ...
Article
Perceptual cognitive skills in real game settings, under conditions of fatigue, such as the ability to gather relevant visual information, are key factors in achieving motor goals in sports. The objectives were to evaluate the effects of acute physical fatigue on gaze behavior during a badminton game (Study 1) and in an unfavorable force ratio situation (Study 2). Six international-level badminton players played two sets and unfavorable force ratio situations while wearing eye-tracking glasses before and after a fatiguing task. During the set, fatiguing physical exercise led to fewer fixations per exchange and more fixations on one area of interest. During unfavorable force ratio situations, fatiguing physical exercise led to shorter fixation durations per exchange, shorter fixation durations on two areas of interest, and longer fixation durations on one area of interest. The results showed that gaze behaviors were adapted in acute physical fatigue conditions to maintain performance.
... The participants were allowed to change and maintain their pace during each training session without any guidance on their activity intensity. The rating of perceived exertion (RPE) was determined using the category level (CR-20) Borg scale immediately after each session [35]. The Brunel Mood Scale (BRUMS) was used to determine mood profiles [36,37] within 10 min before and after the training sessions. ...
Article
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This study aims to investigate the effects of self-paced high-intensity interval training (Sp-HIIT) vs. self-paced moderate-intensity continuous training (Sp-MICT) on aerobic fitness levels, psychophysiological responses, and antioxidant status to assess the relationship between aerobic fitness levels and antioxidant markers. Physically active young adults were randomised into Sp-HIIT and Sp-MICT groups. The intervention consisted of three weekly sessions during an eight-week period. Sp-HIIT consisted of performing two sets of 12-24 × 30 s high-intensity runs ≥ 85% HR max followed by 30 s rest periods, while Sp-MICT consisted of performing 24-48 min of continuous running at 60-75% HR max. Pre-and post-intervention testing included a maximal oxygen uptake (VO 2max) assessment during a 30-15 intermittent fitness test (30-15 IFT), as well as resting blood samples, which were analysed for oxidative stress markers (malondialdehyde (MDA)) and activity of intracellular antioxidant enzymes (catalase (CAT), superoxide dismutase (SOD) and reduced (GSH) and oxidized glutathione (GSSG)). The Sp-HIIT group showed a greater improvement in velocity of 30-15 IFT, VO 2max , and MDA responses. Furthermore, the Sp-HIIT group demonstrated higher psychophysiological responses than the Sp-MICT group, except for anger responses. In conclusion, these results suggest that Sp-HIIT has a higher level of beneficial exercise-induced effects in physiological responses with greater perceived exertion in physically active young adults.
... Our participants were allowed to change and maintain their pace during each training session without receiving any guidance on the intensity of their activity. The rating of perceived exertion (RPE) was determined using the category level (CR-20) Borg scale immediately after each session [31]. Brunel Mood Scale (BRUMS) was used to determine their mood profile [32,33] before and after the training sessions. ...
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This study aims to investigate the effects of self-paced high-intensity interval training (Sp-HIIT) vs self-paced moderate-intensity continuous training (Sp-MICT) on the aerobic fitness level, psy-chophysiological responses and antioxidant status, to assess the relationship between aerobic fit-ness level and antioxidant markers. Well-trained males were randomised into Sp-HIIT and Sp-MICT. The intervention consisted of 3 weekly sessions during an 8-wk period. Sp-HIIT per-formed two sets of 12–24 x 30-s high-intensity runs ≥85 %HRmax followed by 30-s rest periods, while Sp-MICT performed 24-48 min of continuous running at 60-75 %HRmax. Pre and post-intervention testing included maximal oxygen uptake (VO2max) assessment during a 30-15 intermittent fitness test (30-15 IFT), as well as resting blood samples analysed for oxidative stress markers (Malondialdehyde (MDA)) and intracellular antioxidant enzymes activity (Catalase (CAT), Superoxide Dismutase (SOD) and Reduced (GSH) and Oxidized Glutathione (GSSG)). The Sp-HIIT showed meaningfully greater improvement in velocity of 30-15 IFT, VO2max and MDA responses. Furthermore, the Sp-HIIT demonstrated higher psychophysiological responses than the Sp-MICT, except for anger responses. In conclusion, these results suggest that Sp-HIIT has higher level of exercise-induced beneficial effects in physiological responses with greater perceived exer-tion in young well-trained adults.
... Owing to the differences in the effective activity time in the match between positions, this suggests that individualized training loads must be used when managing volume and intensity of subsequent sessions to prevent injuries and optimize performance. In addition, a more accurate indicator of training or match intensity may be obtained when multiplying internal load data (e.g., rating of perceived exertion, RPE on a 1-10 scale) by the effective activity time instead of using traditional formulas (i.e., RPE 3 session duration in minutes) (9). In summary, this study helps sports performance practitioners better understand effective activity time in both training and match contexts. ...
Article
Oliva-Lozano, JM, Chmura, P, Granero-Gil, P, and Muyor, JM. Using microtechnology and the Fourier Transform for the analysis of effective activity time in professional soccer. J Strength Cond Res XX(X): 000–000, 2023—This study aimed to investigate the use of the fast Fourier transform (FFT) for the analysis of effective activity time in professional soccer by (a) exploring the relationship between this variable and standard external load parameters and (b) analyzing the effective activity time during official professional soccer matches. Twenty-six male players participated in the study. Each player was categorized as midfielder, central defender, full-back, wide-midfielder, or forward. Tracking systems based on inertial sensors (4 3D accelerometers, 3 3D gyroscopes, and 1 magnetometer), and global positioning systems technology were used to collect external load measures for 35 matches. Each match was analyzed considering 15-minute periods to explore the evolution of effective activity time during the matches. The extra time from each match was also included. Fast Fourier transform duration may be a representative variable of effective activity time, given the strong positive correlation with the external load variables ( p < 0.001). The linear regression analysis showed that the variables that significantly contributed to the model ( R ² = 0.97) were the total of steps and the distance covered. The mean effective activity time in soccer match play was ∼48.69 minutes. This time significantly changed depending on factors such as the period of the match ( F = 239.05; p < 0.001; ηp ² = 0.60) or playing position ( F = 16.99; p < 0.001; ηp ² = 0.06). The greatest effective activity time was observed for all playing positions in the 0′–15′ period. However, the 60′–75′ period showed the lowest effective activity times compared with the rest of the 15-minute periods for all positions except for forwards (75′–90′). From a practical standpoint, sports performance practitioners may consider these results to improve the individualization of training and match demands. Also, a more accurate indicator of exercise intensity may be obtained (e.g., multiplying the rating of perceived exertion by the effective activity time).
... The session rating of perceived exertion (sRPE) is a scale used to control the perceived training load (Freitas et al., 2014;Hernández-Cruz et al., 2017). This scale has been suggested as a non-expensive and effective tool for controlling training load in an easier and less invasive way, being widely used to assess changes in team and individual sports, both in training and in competition (Foster et al., 2021;Halson, 2014). ...
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Objectives. – The aim of the study was to analyze the behavior of the modified total quality recovery (TQR) scale during four microcycles of accumulation in volleyball players and its relationship with physiological markers as heart rate variability (HRV) and training impulse (TRIMP), as well as load perception by the session rating of perceived exertion (s-RPE), and perceived recovery-stress state using the RESTQ-Sport questionnaire. Equipment and methods. – Seven female players of a national volleyball team (age: 24.26 ± 3.37 years; weight: 71.63 ± 6.84 kg; height: 176.97 ± 8.04 cm; % fat: 32.64 ± 1.57%) were evaluated during a pre-competitive camp. HRV, TRIMP, TQR scale, and the s-RPE were assessed daily. While the RESTQ-Sport was applied at the beginning and at the end of the concentration camp. The Spearman correlation for all variables, as well as Friedman Test for HRV, TRIMP, TQR and S-RPE and Wilcoxon test for RESTQ-Sport were performed. Furthermore, the smallest worthwhile change (SWC) was applied to identify the magnitudes of change in the intra-subject data. Results. – The TQR scale showed significant correlations (p < .05) with HRV parameters (LnrMSSD and Stress Score), S-RPE and RESTQ-Sport dimensions. Conclusion. – The TQR scale shows to be sensitivity to microcycles of accumulation in female volleyball players. In addition, the study proves the TQR concurrent validity by correlating with different physiological and psychological parameters for monitoring load and recovery. Keywords: assessment; recovery; sports performance; volleyball; internal training load
... Thus, an equilibrium between simplicity and complexity should be warranted to allow the correct flux of information for appropriate diagnosis and decision making. For instance, while the session RPE (sRPE) is a valid and simple monitoring tool including both the volume and intensity in a single metric, 18 this simplicity would not allow the differentiation of what physiological system is more taxed (eg, cardiometabolic vs neuromuscular). Thus, depending on specific requirements of each sport, additional measures are needed for fine-tuning the athlete's training adaptations. ...
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... Athlete A3 reported RPE consistently lower than their peers; athlete A2 mainly rated sessions as moderate to hard; finally, athlete A1 reported RPE across the whole scale of values available. These individual tendencies have been observed in previous athlete cohorts as well (34), and could be explained by environmental factors, accumulated fatigue that propagates from day to day or even athletes' interpretation of the scale (35). In contrast, IMU playerload showed no significant differences across participants (p = 0.81). ...
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Identifying indicators of non-functional overreaching during periods of increased training volume and/or intensity is particularly relevant for understanding the detrimental impacts incurred, as well as how these factors contribute to heightened injury risks among exposed athletes. This study aimed to compare the effects of a congested training period versus a standard training period on the strength levels and landing forces of female young aerobic gymnastics athletes. A prospective cohort study design was implemented, spanning four weeks. Fifty athletes (aged 16.2 ± 1.1 years old) at a trained/developmental level, competing at the regional level, were observed throughout the study. During two of these weeks (specifically weeks 2 and 3), half of the group was subjected to a congested training period consisting of six sessions per week (HTF), while the other half continued with their regular four sessions per week (STF). During each week of observation, participants underwent evaluation using the countermovement jump test (CMJ), squat jump test (SJ), and the leg land and hold test (LHT), with measurements taken on a force platform. The main outcomes repeatedly observed over the four weeks were CMJ peak landing force, CMJ peak power, SJ peak power, SJ maximum negative displacement, LHT time to stabilization, and LHT peak drop landing force. Significant interactions (time*group) were observed in CMJ peak power (p < 0.001), CMJ peak landing force (p < 0.001), SJ peak power (p < 0.001), SJ maximum negative displacement (p < 0.001), LHT time to stabilization (p < 0.001), and LHT peak drop landing force (p < 0.001). Furthermore, the results of the final assessment revealed significantly lower CMJ peak power (p = 0.008) and SJ peak power (p = 0.002) in the HTF group compared to the STF group. Additionally, significantly higher values of CMJ peak landing force (p = 0.041), SJ maximum negative displacement (p = 0.015), and LHT peak drop landing force (p = 0.047) were observed in the HTF group compared to the STF group. In conclusion, the increase in training frequency over two weeks significantly contributed to declines in neuromuscular power performance and peak landing forces. This indicates that intensified training periods may acutely expose athletes not only to performance drops but also to an increased risk of injury due to reduced capacity to absorb landing forces.
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Purpose The aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, sleep, HRV, and subjective well-being measures. Methods Self-selected nutrition intake, exercise training, sleep habits, HRV, and subjective well-being of 43 endurance athletes ranging from professional to recreationally trained were monitored daily for 12 weeks (3572 days of tracking). Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model. The model performance was compared with a baseline intercept-only model. Results Prediction error (root mean square error [RMSE]) was lower than baseline for the group models (11.8 vs. 14.1 and 0.22 vs. 0.29 for AM PRS and HRV change, respectively). At the individual level, prediction accuracy outperformed the baseline model but varied greatly across participants (RMSE range 5.5–23.6 and 0.05–0.44 for AM PRS and HRV change, respectively). Conclusion At the group level, daily recovery measures can be predicted based on commonly measured variables, with a small subset of variables providing most of the predictive power. However, at the individual level, the key variables may vary, and additional data may be needed to improve the prediction accuracy.
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There are various categorization models of high-intensity interval training (HIIT) in the literature that need to be more consistent in definition, terminology, and concept completeness. In this review, we present a training goal-oriented categorization model of HIIT, aiming to find the best possible consensus among the various defined types of HIIT. This categorization concludes with six different types of HIIT derived from the literature, based on the interaction of interval duration, interval intensity and interval:recovery ratio. We discuss the science behind the defined types of HIIT and shed light on the possible effects of the various types of HIIT on aerobic, anaerobic, and neuromuscular systems and possible transfer effects into competition performance. We highlight various research gaps, discrepancies in findings and not yet proved know-how based on a lack of randomized controlled training studies, especially in well-trained to elite athlete cohorts. Our HIIT “toolbox” approach is designed to guide goal-oriented training. It is intended to lay the groundwork for future systematic reviews and serves as foundation for meta-analyses.
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The vigilant monitoring of training load contributes to evidence-based decision-making aimed at reducing injury risks and enhancing player performance. In the realm of soccer, particularly at the highest levels, the employment of sport scientists in this capacity has become commonplace, with professionals fulfilling this role on a daily basis. Technological advancements have significantly expanded the arsenal of tools available to sport scientists for monitoring training load. This chapter explores various methods for monitoring training load, highlighting the integration of electronic performance and tracking systems like GPS, coupled with internal load measurement tools such as the rating of perceived exertion, wellness assessments, sleep monitoring tools, and more.
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This study aimed to verify the validity of session-RPE method to monitor the internal training load (ITL) in beach volleyball players by considering sessions related to different genders, competition levels (elite or amateur), and types of session (conditioning, technical, or tactical/game). Session-RPE and Edwards’ methods were applied to quantify the ITLs of 12 elite (18 players; 197 individual sessions) and 12 amateur (18 players; 189 individual sessions) training sessions. Very large relationships between the two methods emerged for both competition level (Elite: r=.77; Amateur: r=.75) and gender (male: r=.76; female: r=.75) subgroups, and conditioning sessions (r=.75). Large relationships emerged for technical (r=.61) sessions, whereas tactical/game sessions resulted only in moderate relationships (r=.36). Beach volleyball coaches could adequately use session-RPE method to monitor training for players of different genders, competition levels, and types of session, although tactical/game sessions should be considered with some caution.
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Rating of perceived exertion (RPE) and session RPE (sRPE) are reliable tools for predicting exercise intensity and are alternatives to more technological and physiological measurements, such as blood lactate (HLa) concentration, oxygen consumption and heart rate (HR). As sRPE may also convey some insights into accumulated fatigue, the purpose of this study was to examine the effects of progressive fatigue in response to heavier-than-normal training on sRPE, with absolute training intensity held constant, and determine its validity as marker of fatigue. Twelve young adults performed eight interval workouts over a two-week period. The percentage of maximal HR (%HRmax), HLa, RPE and sRPE were measured for each session. The HLa/RPE ratio was calculated as an index of fatigue. Multilevel regression analysis showed significant differences for %HRmax (p = 0.004), HLa concentration (p = 0.0001), RPE (p < 0.0001), HLa/RPE ratio (p = 0.0002) and sRPE (p < 0.0001) across sessions. Non-linear regression analysis revealed a very large negative relationship between HLa/RPE ratio and sRPE (r = −0.70, p < 0.0001). These results support the hypothesis that sRPE is a sensitive tool that provides information on accumulated fatigue, in addition to training intensity. Exercise scientists without access to HLa measurements may now be able to gain insights into accumulated fatigue during periods of increased training by using sRPE.
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The Rating of Perceived Exertion (RPE) is an important measure of exercise intensity, which is useful both as a primary and adjunctive method of exercise prescription. However, there are multiple variants of the Borg RPE scale, primarily the Borg 6-20 RPE scale (BORG-RPE) and the Borg Category-Ratio-10 scale (BORG-CR10). There are inadequate data available to address the comparability and interchangeability of these two widely used scales. Well-trained non-athletes performed two increment cycle tests, with each scale used in a random sequence. Subjects also performed interval sessions at three intensities (50, 75 and 85% of peak power output) with each scale used in a random sequence. There were very large correlations during the incremental exercise between the conventional physiological measures (% heart rate reserve – r=0.89 & r=.87); and %VO2reserve (r=.88 & r=.90) and RPE measured by either the BORG-RPE or the BORGCR10, respectively. This pattern was also evident during the interval exercise (% heart rate reserve – r=.85 & r=.84; and blood lactate concentration – r=.74 & r=.78) and RPE measured by either the BORG-RPE or the BORG-CR10, respectively. The relationship between RPE measured by the BORG-RPE and the BORGCR10 was large and best described by a non-linear relationship for both the incremental (R2=.89) and the interval (R2=.89) exercise. The incremental and interval curves were virtually overlapping. We concluded that the two most popular versions of the RPE scale, BORG-RPE and BORG-CR10, were both highly related to the conventional physiological measures and very strongly related to each other, with an easily described conversion.
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Purpose:: The relationship between various training load (TL) measures in professional cycling is not well explored. This study investigates the relationship between mechanical energy spent (in kJ), sRPE, LuTRIMP and TSS in training, races and time trials (TT). Methods:: From 4 consecutive years field data was collected from 21 professional cyclists and categorized as being collected in training, racing or TT's. kJ spent, sRPE, LuTRIMP and TSS were calculated and the correlations between the various TL's were made. Results:: 11,655 sessions were collected from which 7,596 sessions had heart rate (HR) data and 5,445 sessions had an RPE-score available. The r between the various TL's during training was almost perfect. The r between the various TL's during racing was almost perfect or very large. The r between the various TL's during TT's was almost perfect or very large. For all relationships between TSS and one of the other measurements of TL (kJ spent, sRPE and LuTRIMP) a significant different slope was found. Conclusions:: kJ spent, sRPE, LuTRIMP and TSS have all a large or almost perfect relationship with each other during training, racing and TT's but during racing both sRPE and LuTRIMP have a weaker relationship with kJ spent and TSS. Further, the significant different slope of TSS versus the other measurements of TL during training and racing has the effect that TSS collected in training and road-races differ by 120% while the other measurements of TL (kJ spent, sRPE and LuTRIMP) differ by only 73%, 67%, and 68% respectively).
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An athlete’s pacing strategy is widely recognised as an essential determinant for performance during individual events. Previous research focussed on the importance of internal bodily state feedback, revealed optimal pacing strategies in time-trial exercise, and explored concepts such as teleoanticipation and template formation. Recently, human–environment interactions have additionally been emphasized as a crucial determinant for pacing, yet how they affect pacing is not well understood. Therefore, this literature review focussed on exploring one of the most important human–environment interactions in sport competitions: the interaction among competitors. The existing literature regarding the regulation of exercise intensity and the effect of competition on pacing and performance is critically reviewed in this paper. The PubMed, CINAHL and Web of Science electronic databases were searched for studies about pacing in sports and (interpersonal) competition between January 2000 to October 2017, using the following combination of terms: (1) Sports AND (2) Pacing, resulting in 75 included papers. The behaviour of opponents was shown to be an essential determinant in the regulation of exercise intensity, based on both observational (N = 59) and experimental (N = 16) studies. However, adjustment in the pacing response related to other competitors appears to depend on the competitive situation and the current internal state of the athlete. The findings of this review emphasize the importance of what is happening around the athlete for the outcome of the decision-making process involved in pacing, and highlight the necessity to incorporate human–environment interactions into models that attempt to explain the regulation of exercise intensity in sports and exercise.
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Purpose: The aim of this review is to (1) retrieve all data validating the Session-rating of perceived exertion (RPE)-method using various criteria, (2) highlight the rationale of this method and its ecological usefulness, and (3) describe factors that can alter RPE and users of this method should take into consideration. Method: Search engines such as SPORTDiscus, PubMed, and Google Scholar databases in the English language between 2001 and 2016 were consulted for the validity and usefulness of the session-RPE method. Studies were considered for further analysis when they used the session-RPE method proposed by Foster et al. in 2001. Participants were athletes of any gender, age, or level of competition. Studies using languages other than English were excluded in the analysis of the validity and reliability of the session-RPE method. Other studies were examined to explain the rationale of the session-RPE method and the origin of RPE. Results: A total of 950 studies cited the Foster et al. study that proposed the session RPE-method. 36 studies have examined the validity and reliability of this proposed method using the modified CR-10. Conclusion: These studies confirmed the validity and good reliability and internal consistency of session-RPE method in several sports and physical activities with men and women of different age categories (children, adolescents, and adults) among various expertise levels. This method could be used as “standing alone” method for training load (TL) monitoring purposes though some recommend to combine it with other physiological parameters as heart rate.
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To alleviate issues arising from the over/under prescription of training load, coaches must ensure that desired athlete responses to training are being achieved. The present study aimed to assess the level of agreement between the coach intended (pre-session) and observed (post-session) rating of perceived exertion (RPE), with athlete RPE during different training intensities (easy, moderate, hard). Coach intended RPE was taken prior to all field based training sessions over an 8 week in-season period. Following training, all coaches and athletes, whom were participants in hockey, netball, rugby and soccer were asked to provide an RPE measure for the completed session. Sessions were then classified based on the coaches intended RPE, with a total of 28, 125 and 66 easy, moderate and hard training sessions collected respectively. A univariate analysis of variance was used to calculate within-participant correlations between coach intended/observed RPE and athlete RPE. Moderate correlations were found between coach intended and athlete RPE for sessions intended to be moderate and hard whilst a small correlation was found for sessions intended to be easy. The level of agreement between coach and athlete RPE improved following training with coaches altering their RPE to align with those of the athlete. Despite this, moderate and small differences between coach observed and athlete RPE persisted for sessions intended to be easy and moderate respectively. Coaches should therefore incorporate strategies to monitor training load to increase the accuracy of training periodisation and reduce potential over/under prescription of training.
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Purpose: The aim of this study was to determine the reliability and validity of several submaximal variables that can be easily obtained by monitoring cyclists' performance. Methods: Eighteen professional cyclists participated in this study. In a first part (n=15) the test-retest reliability of HR and RPE during a progressive maximal test was measured. Derived submaximal variables based on HR, RPE and power output (PO) responses were analyzed. In a second part (n=7) the pattern of the submaximal variables according to cyclists' training status was analyzed. Cyclists were assessed 3 times during the season: at the beginning of the season, before the Vuelta a España and the day after this Grand Tour. Results: Part 1: no significant differences in maximal and submaximal variables between test-retest were found. Excellent ICCs (0.81-0.98) were obtained in all variables. Part 2: the HR and RPE showed a rightward shift from early to peak season. In addition, RPE showed a left shift after the Vuelta a España. Submaximal variables based on RPE had the best relationship with both performance and changes in performance. Conclusion: The present study showed the reliability of different maximal and submaximal variables used to assess cyclists' performance. Submaximal variables based on RPE seem to be the best to monitor changes in training status over a season.
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Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of HR evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds, and the possibility of trackside measurement of HR, lactate, VO2 and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise, but often require laboratory testing for calibration, and tend to produce too much information, in too slow of a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training LOAD. Although the original TRIMP concept was mathematically complex, the development of the Session RPE and similar low tech methods has demonstrated a way to evaluate training LOAD, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high resolution data of the external training load. These methods are promising, but problems relative to information overload and turn-around time to coaches remain to be solved.
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Athlete–environment interactions are crucial factors in understanding the regulation of exercise intensity in head-to-head competitions. Previously, we have proposed a framework based on the interdependence of perception and action, which allows us to explore athletic behavior in the more complex pacing situations occurring when athletes need to respond to actions of their opponents. In the present perspective we will further explore whether opponents, crucial external factors in competitive sports, could indeed be perceived as social invitations for action. Decisions regarding how to expend energy over the race are based on internal factors such as the physiological/biomechanical capacity of the athlete in relation to external factors such as those presented by opponents. For example: Is the athlete able to overtake competitors, or not? We present several experimental studies that demonstrate that athletes regulate their exercise intensity differently in head-to-head competition compared to time-trial exercises: Relational athlete-environment aspects seem to outweigh benefits of the individual optimal energy distribution. Also, the behavior of the opponents has been shown to influence pacing strategies of competing athletes, again demonstrating the importance of relational athlete–environment aspects in addition to strictly internal factors. An ecological perspective is presented in which opponents are proposed to present social affordances, and decision-making is conceptualized as a resultant of affordance-competition. This approach will provide novel insights in tactical decision-making and pacing behavior in head-to-head competitions. Future research should not only focus on the athlete's internal state, but also try to understand opponents in the context of the social affordances they provide.
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Session rating of perceived exertion (sRPE) is known to significantly relate to heart rate (HR) based methods of quantifying internal training load (TL) in a variety of sports. However, to date this has not been investigated in fencing and was therefore the aim of this study. TL was calculated by multiplying the session rating of perceived exertion (sRPE) with exercise duration, and through Heart rate (HR) -based methods calculated using Banister's and Edward's TRIMP. Seven male elite foil fencers (Mean ± SD: Age = 22.3 ± 1.6 years, height = 181.3 ± 6.5 cm, body mass = 77.7 ± 7.6 kg) were monitored over the period of one competitive season. The sRPE and HR of 67 training sessions and three competitions (87 poule bouts and 12 knockout rounds) were recorded and analysed. Correlation analysis was used to determine any relationships between sRPE and HR-based methods, accounting for individual variation, mode of training (footwork drills vs. sparring sessions) and stage of competition (poules vs. knockouts). Across two footwork sessions, sRPE, Banister's and Edward's TRIMP were found to be reliable, with coefficient of variation values of 6.0, 5.2 and 4.5% respectively. Significant correlations with sRPE for individual fencers (r = 0.84 - 0.98) and across mode of exercise (r = 0.73 - 0.85) and competition stages (r = 0.82 - 0.92) were found with HR-based measures. sRPE is a simple and valuable tool coaches can use to quantify TL in fencing.
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Purpose: The session RPE (sRPE) has gained popularity as a "user friendly" method for evaluating internal training load. sRPE has historically been obtained 30-min following exercise. This study evaluated the effect of post-exercise measurement time on sRPE following steady-state and interval cycle exercise. Methods: Well-trained subjects (N=15) (VO2max=51+4 & 36+4 ml.kg-1 (cycle ergometer) for men & women, respectively) completed counterbalanced 30-min steady-state and interval training bouts. The steady-state ride was at 90% of ventilatory threshold (VT). The work-to-rest ratio of the interval rides was 1:1 and the interval segment durations were 1-, 2- & 3-min. The high-intensity component of each interval bout was 75% peak power output (PPO), which was accepted as a surrogate of the respiratory compensation threshold, critical power or maximal lactate steady state. Heart rate (HR), blood lactate [BLa], and Rating of Perceived Exertion (RPE) were measured. The sRPE (Category Ratio Scale) was measured at 5-, 10-, 15-, 20-, 2-, 30-, 60-min and 24-hr following each ride, using a Visual Analog Scale (VAS) to prevent bias associated with specific RPE verbal anchors. Results: sRPE, at 30-min post exercise, followed a similar trend: steady state=3.7, 1-min=3.9, 2-min=4.7, 3-min=6.2. No significant differences (p > 0.05) in sRPE were found based on post-exercise sampling times, from 5-min to 24-hr post-exercise. Conclusion: Post-exercise time does not appear to have a significant effect on sRPE after either steady-state or interval exercise. Thus, sRPE appears to be temporally robust and is not necessarily limited to the 30-min post exercise window historically used with this technique, although the presence/absence of a cool-down period after the exercise bout may be of importance.
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Background: The session rating of perceived exertion (session-RPE) proved to be a valuable method to quantify the internal training load (ITL) in taekwondo. However, no study validated this method in youth taekwondo athletes performing different training sessions. Thus this study aimed at evaluating the reliability of the session-RPE to monitor the ITL of prepubescent taekwondo athletes during pre-competitive (PC) and competitive (C) training sessions. Methods: Five female (age: 12.0 ± 0.7 y; height: 1.54 ± 0.08 m; body mass: 48.8 ± 7.3 kg) and four male (age: 12.0 ± 0.8 yrs; height: 1.55 ± 0.07 m; body mass: 47.3 ± 5.3 kg) taekwondo athletes were monitored during 100 individual sessions (PC: n = 33; C: n = 67). The Edwards' HR method was used as reference measure of ITL; the CR-10 RPE scale was administered at 1- and 30-minutes from the end of each session. Results: No difference for gender emerged. The ITLs of C (Edwards: 228 ± 40 arbitrary units, AU) resulted higher than that of PC (192 ± 26 AU; P = .04). Although all training typologies and data collections achieved significant correlations between Edwards' and session-RPE methods, a large relationship (r = .71, P < .001) emerged only for PC sessions evaluated at 30 minutes of the recovery phases. Conclusion: Findings support coaches of prepubescent taekwondo athletes to successfully use session-RPE to monitor the ITL of different training typologies. However, PC training evaluated at 30 minutes of the recovery phase represents the best condition for a highly reliable ITL perception.
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Objective. The Session Rating of Perceived Extertion (RPE) is a method of measuring exercise intensity that may be useful for the quantitative assessment of exercise training programmes. However, there are inadequate data regarding the validity and reliability of the Session RPE method. This study was designed to evaluate both the validity and reliability of the Session RPE method in comparison to objective measures (%HRpeak, %HRreserve and %VO2peak) of exercise intensity. Methods. Fourteen healthy volunteers (7 male, 7 female) performed 6 randomly ordered 30-minute constant-load exercise bouts at 3 different intensities, with each intensity being repeated. Oxygen consumption (VO2) and heart rate (HR) were measured throughout each exercise bout and normalised to maximal values obtained during a preliminary maximal exercise test. Thirty minutes following the conclusion of each exercise bout, the subject rated the global intensity of the bout using a modification of the Category Ratio (CR) (0 - 10) RPE scale. This rating was compared to the mean value of objectively measured exercise intensity across the duration of the bout. Results. There were significant non-linear relationships between Session RPE and %VO2peak (R2 = 0.76), %HRpeak (R2 = 0.74) and %HRreserve (R2 = 0.71). There were no significant differences between test and retest values of %VO2peak, %HRpeak, %HRreserve and Session RPE during the easy (47 v. 47%, 65 v. 66%, 47 v. 48% and 2.0 v. 1.9), moderate (69 v. 70%, 83 v. 84%, 74 v. 75%, and 4.2 v. 4.3) and hard (81 v. 81%, 94 v. 94%, 91 v. 91% and 7.3 v. 7.4) exercise bouts. Correlations between repeated bouts for %VO2peak (r = 0.98), %HRpeak (r = 0.98), %HRreserve (r = 0.98) and Session RPE (r = 0.88) were significant and strong. Conclusions. The results support the validity and reliability of the Session RPE method of monitoring exercise intensity, although as might be predicted for a subjective method the Session RPE was less precise than the objective measures of exercise training intensity. South African Journal of Sports Medicine Vol. 18 (1) 2006: pp. 14-17
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Describe the internal training load (ITL) of common training sessions performed during a typical week and to determine the relationships between different indicators of ITL commonly employed in professional football. Session-RPE TL (sRPE-TL) and HR-derived measurements of ITL as Edward's-TL and Stagno training impulses (TRIMPMOD) were used in nine players during three periods of the season. The relationships between them were analyzed in different training sessions during a typical week: Skill Drills/Circuit Training+Small-Sided Games (SCT+SSGs), Ball-Possession Games+Technical-Tactical Exercises (BPG+TTE), Tactical-Training (TT) and Pre-Match activation (PMa). HR values obtained during SCT+SSGs and BPG+TTE were substantially greater than the other two sessions, all the ITL markers and session duration were substantially greater in SCT+SSGs than in any other session, and all ITL measures in BPG+TTE were substantially greater than in TT and PMa sessions. Large relationships were found between HR>80% HRmax- and HR>90% HRmax - sRPR-TL during BPG+TTE and TT sessions (r = 0.61 to 0.68). Very large relationships were founded between Edward's TL - sRPE-TL and between TRIMPMOD - sRPE-TL in sessions with BPG+TTE and TT (r = 0.73 to 0.87). Correlations between the different HR-based methods were always extremely large (r = 0.92 to 0.98), and unclear correlations were observed for other relationships between variables. Session-RPE provided variable magnitude within-individual correlations with HR-derived measures of training intensity and load during different types of training sessions typically performed during a week in professional soccer. Caution should be applied when using RPE- or HR-derived measures of exercise intensity/load in soccer training interchangeably.
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Background: Exercise prescription in cardiac patients is based on heart rate (HR) response to exercise. How to prescribe long-term exercise training outside medically-supervised settings also considering changes in individual physical capacity over time is unknown. In this study we hypothesized that in patients with chronic heart failure (CHF) the session-rate of perceived exertion (RPE), a subjective-based training methodology, provides autonomic and functional capacity changes superimposable to those observed with HR-based Training Impulses (TRIMPi) method. Methods: Twenty patients with stable CHF were randomized to either aerobic continuous training (ACT) or aerobic interval training (AIT) for 12 weeks. For each TRIMPi-guided exercise session, the session-RPE was recorded. By this method, internal training load (TL) is quantified by multiplying the RPE of the whole training session, using the Borg CR10-scale, by its duration. Heart rate variability (HRV), and baroreflex sensitivity (BRS) were assessed at baseline and at 3 weeks intervals. Results: Significant correlations were found between TRIMPi and individual session-RPE, for both ACT and AIT (r=0.63 to 0.81), (P<0.05). The same occurred when ACT and AIT groups were pooled together (r=0.72; P<0.01). R-R interval, HRV and BRS were significantly and very highly correlated with weekly RPE-session (r(2) ranged from 0.77 to 0.97; P<0.001). A significant relationship between session-RPE and performance at the 6MWT was also found. Conclusions: Session-RPE is an easy-to-use, inexpensive and valid method for exercise prescription and health maintenance, consistent with objective physiological indices of training, that could be used for long-term physical activity in patients with CHF.
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This study aimed to verify the ecological validity of the session rating of perceived exertion (session RPE) method for quantifying internal training load (ITL) in competitive tennis players. Twelve professional tennis players (18.5 ± 0.4 years; 178.0 ± 4.0 cm; 72.4 ± 6.0 kg) participated in this study. In total, 384 on-court tennis training sessions, 23 simulated matches and 13 official matches were monitored. ITL was then calculated for each session using session RPE and HR-based methods. All individual correlations between the session RPE method and the HR-based method were significant (r = 0.58 – 0.89: p < 0.01). The correlation coefficients between methods during on court tennis training sessions, simulated and official matches were r = 0.74, r = 0.57 and r = 0.99, respectively. The results support the validity of session RPE as a practical method for quantifying ITL in professional tennis players.
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The aim of this study was to compare the perception concerning of the training load intensity of Judo coaches and athletes. The sample consisted of 4 coaches and 40 athletes of the Brazilian National Judo Team. The comparison between the intensity planned by the coach and the intensity experienced by the athletes was determined by the Session RPE method during a "Training camp". In order to assess lactate responses to training, blood samples were collected pre- and post training session. The intensity experienced by athletes was higher than the intensity planned by coaches in all training sessions. Regarding lactate concentration, it was observed an increase at post-training as compared to pre-training in all sessions, with no differences between sessions. The results of this study demonstrate that although the training session has been developed by experienced coaches, significant differences were detected between the intensity of external training load planned by the coach and the intensity of the internal training load experienced by the athletes. These data reinforce the relevance of training monitoring in order to maximize performance of elite athletes.
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The purpose of this study was to compare the influence of measuring the overall session rating of perceived exertion (session-RPE) at 10 vs. 30 minutes following exercise. Eight boxers completed three different standardized training sessions of different intensities (easy, moderate and hard) in a matchedpairs, randomized research design. Exercise intensity was assessed during each bout by measuring heart rate, blood lactate concentration and session-RPE. To assess the effect of measurement timing on session-RPE, RPE data were collected either 10 or 30 minutes post-exercise. There was no significant effect of measurement time on session-RPE values following easy (10 minutes: session-RPE = 1.3 ± 1.0 Arbitrary Unit (AU), %Heart Rate Reserve (HRR) = 49.5 ± 11.1, and ΔBlood lactate = -2.3 ± 16.3%; 30 minutes: session-RPE = 1.7 ± 1.0 AU, %HRR = 51.3 ± 10.8, and ΔBlood lactate = 0.7 ± 25.2%), moderate (10 minutes: session-RPE = 2.7 ± 1.6 AU, %HRR = 67.2 ± 10.8, and ΔBlood lactate = 2.2 ± 19%; 30 minutes: session-RPE = 2.5 ± 0.9 AU, %HRR = 67.2 ± 5.9, and ΔBlood lactate = 24.5 ± 17.1%) and hard (10 minutes: session-RPE = 5.7 ± 1.0 AU, %HRR = 88.1 ± 6.3, and ΔBlood lactate = 146.3 ± 87.9%; 30 minutes: session-RPE = 5.8 ± 1.9 AU, %HRR = 83.3 ± 8.0, and ΔBlood lactate = 91.6 ± 39%) sessions. In conclusion, our findings suggest that session-RPE can be used in boxing training routines across a range of intensities and accurate measurements can be determined as early as 10 minutes after exercise.