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Factors Affecting Perception of Effort (Session Rating of Perceived Exertion) During Rugby League Training
Abstract
The purpose of this study was to examine the validity of session rating of perceived exertion (sRPE) for monitoring training intensity in rugby league.
Thirty-two professional rugby league players participated in this study. Training-load (TL) data were collected during an entire season and assessed via microtechnology (heart-rate [HR] monitors, global positioning systems [GPS], and accelerometers) and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and various other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during rugby league training.
There were significant within-individual correlations between sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 62.4% of the adjusted variance in sRPE-TL could be explained by TL measures of distance, impacts, body load, and training impulse (y = 37.21 + 0.93 distance - 0.39 impacts + 0.18 body load + 0.03 training impulse). Furthermore, 35.2% of the adjusted variance in sRPE could be explained by exercise-intensity measures of percentage of peak HR (%HRpeak), impacts/min, m/min, and body load/min (y = -0.01 + 0.37%HRpeak + 0.10 impacts/min + 0.17 m/min + 0.09 body load/min).
A combination of internal and external TL factors predicts sRPE in rugby league training better than any individual measures alone. These findings provide new evidence to support the use of sRPE as a global measure of exercise intensity in rugby league training.
... This will help to optimise and personalise the training process Weaving et al., 2014). Until now, given the high frequency and variety of collisions and impacts inherent to rugby, there are several limitations that make comprehensive monitoring difficult (Lovell, Sirotic, Impellizzeri, & Coutts, 2013;Williams et al., 2017). ...
... To ensure accurate and appropriate prescribing and training monitoring, it is important that practitioners use valid methods to quantify the internal and external loads placed on players in all training modes. There are numerous measures to quantify training load -internal and external -including heart HR-based methods (Akubat et al., 2014;Weaving et al., 2014), subjective perceived exertion (RPE) (Kelly, Strudwick, Atkinson, Drust, & Gregson, 2016), global positioning systems (GPS) (Colby et al., 2014;Lovell et al., 2013;Weaving et al., 2014), and accelerometers (Gómez-Carmona, Pino-Ortega, Sánchez-Ureña, Ibáñez, & Rojas-Valverde, 2019). Methods that use HR to quantify internal load include the training impulse (TRIMP) and the individualised TRIMP (iTRIMP) (Teixeira et al., 2021a;2022a). ...
... Several studies have already related a strong correlation between HR and RPE during exercise and sports practice (Impellizzeri et al. 2004;. Some studies also contend that the individual correlation between RPE and various distance-derived measures tracked by GPS technology, in addition to the correlation between HR and RPE, establishes parameters for evaluating the relationship between the external training load and the intensity of training sessions, particularly with regard to the distances and intensities at which those distances are travelled (expressed in speed and acceleration) (Colby et al., 2014;Lovell et al., 2013;Weaving et al., 2014). The current results show that both total distance and distance travelled at high speed significantly affect RPE in Rugby training sessions (Abt & Lovell, 2009;Aughey, 2011;Delaney et al., 2018). ...
Applying appropriate training loads in accordance with the defined objectives promotes optimal physical and physiological adaptations, reduces the likelihood of illness and injury and, therefore, increases the possibility of success during Rugby. The aim of this review was to compile and systematise the information in the literature on the association between training load variables (internal and external) and performance outcomes in Rugby. As such, the main objective will be to conduct a systematic review of the published literature to identify the physical and physiological performance variables in Rugby sport to monitor the training load. Following the preferred reporting item for systematic reviews and meta-analyses (PRISMA) and PICOS approach, the search was adapted and conducted systematically only in the PubMed database, which, in itself, also restricts the search spectrum of the paper, thus conferring a limitation to the present academic work. The search was conducted in PubMed throughout the possible temporal spectrum since there is still little robustness in the literature about rugby sports performance. Articles were selected by pre-defined selection criteria, including observational, randomised clinical and clinical trial studies. After further screening, and based on the inclusion criteria of the papers, the result of the analysis of the relevance of the studies, the final set of analysis resulted in 16 articles. From the studies compiled in this review, there seems to be a strong correlation between the perceived exertion (RPE) and the prescription and definition of the training load applied in Rugby athletes. The RPE reflects the most used and analysed variable throughout all the studies. Several articles reflect a strong relationship between the training load, the inter-individual capacity of each athlete and their tolerance to the load (player load).
... The collective nature of IFTS training and use of drill based scenarios such as small sided games [9] means players are regularly prescribed the same external load. Training prescription based on external load indices (ELI) can however, result in considerable inter-individual variation in RPE [10][11][12]. This is an important consideration Data collection RPE was measured using the modified Borg CR10 scale as outlined previously [8]. ...
... RPE was subsequently categorised as low (RPE ≤ 5, n = 461), moderate (RPE 6-7, n = 710), and high (RPE ≥ 8, n = 446). These categories have been used previously in IFTS [12,23] and are associated with the three physiological exercise intensity domains [24]. ...
... In the present study, RPE was divided into three categories. This approach has been used previously when examining factors influencing RPE in IFTS [12], and with other cohorts, including endurance athletes [28]. The boundaries of these categories are reflective of the first and second ventilatory thresholds [24], and may be used to demarcate entry into the three distinct physiological exercise intensity domains [29]. ...
... 32 sRPE stands for the perception of training intensity was divided into 3 zones as low (≤4), moderate (5-6), and high intensity (≥7) in accordance with the zones, as described before. 33 The internal training load (sRPE-TL) was obtained by multiplying each athlete's sRPE value by the duration of each session in minutes (sRPE-TL = sRPE [AU] x session duration [mins], as suggested before. 31, 33 The Hooper questionnaire required athletes to rate their perceived levels of sleep quality, stress, fatigue, and delayed onset muscle soreness (DOMS) each morning, approximately 30 min before training sessions. ...
... 33 The internal training load (sRPE-TL) was obtained by multiplying each athlete's sRPE value by the duration of each session in minutes (sRPE-TL = sRPE [AU] x session duration [mins], as suggested before. 31, 33 The Hooper questionnaire required athletes to rate their perceived levels of sleep quality, stress, fatigue, and delayed onset muscle soreness (DOMS) each morning, approximately 30 min before training sessions. 34 The scale ranged from 1 (very, very low) to 7 (very, very high) for stress, fatigue, and DOMS categories. ...
Purpose
This study aimed to examine the short-term effects of SARS-CoV-2 infection and return to sport (RTS) on neuromuscular performance, body composition, and mental health in well-trained young kayakers.
Methods
17 vaccinated kayakers (8 male, 9 female) underwent body composition assessment, peak power output bench press (BP), and 40-s maximum repetition BP tests 23.9 ± 1.6 days before and 22.5 ± 1.6 days after a SARS-CoV-2 infection. A linear transducer was used to examine the BP performance. The perception of training load and mental health were quantified with Borg's CR-10 scale and the Hooper questionnaire before and after infection. The difference and relationship of variables were used Wilcoxon test, Student t-test, Pearson's, and Spearman's r correlation coefficients.
Results
There was a significant increase in body mass, fat-free mass, and skeletal muscle mass, but no significant changes in body fat, fat mass, and all BP performance after infection (p < 0.05). There was a significant reduction in training hours per week, session rating of perceived exertion (sRPE), internal training load (sRPE-TL), fatigue, muscle soreness levels, and Hooper index, but no changes in sleep quality and stress levels after infection (p < 0.05). The training and mental health during the RTS period was significantly correlated (r = −0.85 to 0.70) with physical performance after infection.
Conclusion
A SARS-CoV-2 infection did not appear to impair the upper-body neuromuscular performance and mental health of vaccinated well-trained young kayakers after a short-term RTS period. These findings can assist coaches, and medical and club staff when guiding RTS strategies after other acute infections or similar restrictions.
... As a sport, volleyball stands out for being non-invasive, which leads to it including several and fast jumps [3,4] to overcome the constraint of the net's height. To manage these demands, the integration of a training monitoring process seems to play an important role, provid-ing relevant data, which can be used to guide coaches' decision-making process and drive further coaching [5][6][7][8]. In fact, such feedback regarding the impact of the training and competition on players would help coaches to adjust the training to maintain or improve the performance and to reduce the likelihood of injuries [9][10][11]. ...
Purpose
The aim of the present study was to analyse the within-week variations according to the internal (rate of perceived exertion [RPE], and session-RPE) and external (jump height average, minimum jump, maximal jump, range of jump, number of jumps and density) intensity.
Methods
Twelve male elite/international volleyball athletes from the Portuguese 1st division (age: 21.7 ± 4.19 years of age; experience: 6.2 ± 3.8 years; body mass: 85.7 ± 8.69 kg; height: 192.4 ± 6.25 cm; body mass index: 23.1 ± 1.40 kg/m<sup>2</sup>) participated in this study. The players were monitored over 26 microcycles, 101 training sessions, and 20 matches. To assess the workload, the CR10 Borg scale and an inertial measurement unit (IMU) were used.
Results
According to the internal workload, RPE revealed significant differences between MD-4 and MD-2, MD-4 and MD1, MD-3 and MD-1, and MD-2 and MD-1 ( p < 0.05). In the same line, session RPE showed significant differences between MD-4 and MD-2, MD-4 and MD-1, MD-3 and MD-2, MD-3 and MD-1, and MD-2 and MD-1 ( p < 0.05). On the other hand, the external load demands revealed statistical differences regarding the number of jumps (MD-4 and MD-2, MD-4 and MD-1, MD-3 and MD-1, and MD-2 and MD-1) and the density of the training sessions (MD-4 and MD-1, and MD-2 and MD-1).
Conclusions
The primary findings of this study suggest that higher-intensity training sessions tend to occur during the middle of the week, with a tapering effect observed as the competition date approaches.
... According to our results, PL (derived from accelerations) is largely associated with S-RPE (Abbott et al., 2019;Alemdaroğlu, 2020;Gallo et al., 2015;Gaudino et al., 2015;Lovell et al., 2013;. As well, Gaudino et al. (2015) reported that metabolic power (derived by speed and accelerations) is more appropriated than only speed to assess performance demands, emphasizing again the role of accelerations (Gaudino et al., 2015). ...
... These results contrast the main findings of Weaving et al. (2018) who showed that all rugby union players had sRPE, TD and PL meaningfully loaded on PC1 and HSRD loaded on PC2. These differences could be attributed to the type of dataset analysed, with only skills training used in Weaving et al. (2018), which will likely yield different results due to the differences in relationships between training load variables in different modes of training (Lovell, Sirotic, Impellizzeri, & Coutts, 2013;Weaving et al., 2014). Whilst the individual variation highlights the unique training outputs between players, selecting different variables for different players would seem nonsensical and would make the evaluation of training sessions difficult for coaches. ...
The aim of this study was to explore the use of principal component analysis (PCA) in understanding multivariate relationships in soccer training load data. Training load data were collected from 20 professional male soccer players during a 28-week in-season period. Twelve training load variables (total distance, PlayerLoadTM, low-speed running distance, moderate-speed running distance, high-speed running distance, sprint distance, moderate-speed running efforts, high-speed running efforts, sprint efforts, accelerations, decelerations, and changes of direction) were collected during training sessions, with correlation analysis revealing high intercorrelation between most variables (r = 0.04-0.98). Principal component analysis was performed on datasets containing all players and on individual players. On the whole dataset, two principal components were retained explaining a total of 81% of data variance. The first component comprised variables associated with distances in speed zones and the second component changes of direction. Whilst some individual variation existed among players, distances in speed zones were loaded on the first component and inertial movement analysis variables, such as accelerations, decelerations, and changes of direction, were loaded on the second component. These findings evidence the strong relationships between several common training load variables and highlight the risk of data redundancy. By selecting variables from each component, practitioners can reduce the number of variables reported whilst retaining as much of the variation in data as possible.
This study aimed to investigate the change in adductor squeeze strength over time and to explore the relationship
between external load measured by GPS, subjective markers of recovery, training load and adductor squeeze strength.
Thirteen elite male inter-county Gaelic football players provided adductor squeeze strength scores, subjective markers
of recovery and sRPE over a 10-week period that covered pre-season (weeks 1 – 4) and in-season competition (weeks
5 – 10). GPS was used to determine total distance (TD) and high speed running (HSR). The results found a relationship
between TD and the subjective marker of muscle soreness ( r = - 0.671, p < 0.05), and stress (r = - 0.700, p < 0.01),
while HSR had a relationship with stress (r = - 0.693, p <0.01). Furthermore, a relationship was found between adductor
squeeze strength scores and the subjective marker of muscle soreness (r = - 0.610; p < 0.05), fatigue (r = - 0.645; p <
0.05) and stress (r = - 0.746; p < 0.01). A small correlation was found between Wednesday adductor squeeze strength
scores and the previous weeks training load (r = 0.195; p < 0.05). The results show that it is possible to obtain the
recovery status of athletes’ using limited resources. This may help coaches make informed decisions on player
availability and training status to help increase performance.
We sought to conduct a bibliometric analysis and review of the most cited publications relating to rugby since 2000 in order to identify topics of interest and those that warrant further investigations. Clarivate Web of Science database was used to perform a literature search using the search term “rugby.” The top 200 papers by citation count were extracted and reviewed for the inclusion criteria: all subjects were rugby players. The top 50 manuscripts were included for analysis of author, publication year, country of lead authors, institution, journal name and impact factor, topic, participant sex, and level of rugby. The total number of citations was 9,071 (average of 181.4 citations/article), with an average journal impact factor of 7.21; the top article was cited 407 times at the time of analysis. The most frequent publication was the Journal of Strength and Conditioning Research (26%), followed by the British Journal of Sports Medicine (20%) and the Journal of Sports Sciences (18%). Forty-eight (96%) of the manuscripts contained only male subjects, with 1 manuscript including females only and 1 manuscript containing mixed sexes. Thirty-three (66%) of the manuscripts focused on professional rugby players, with the next highest player group being mixed levels (10%). Twenty-eight (56%) concentrated on topics regarding strength and conditioning, 11 (22%) on injury, and 4 (8%) on physiology. Despite rugby being one of the most injurious sports and community players representing the largest component of the player pool, most of the top-cited rugby articles are cohort studies of professional male athletes focused on performance and strength and conditioning, noting the bias in research towards socially relevant topics that may not impact the majority of stakeholders and long-term health of rugby athletes. These findings highlight the need for further research among women and community athletes and on topics in injury prevention.
Background: Tracking devices, such as global (GPS) and local (LPS) positioning systems, combined with physiological measurements, have become reliable tools to characterize movement patterns, assessing the external load (EL), internal load (IL), fatigue, and performance of athletes in team sports. This scoping review aims to provide a comprehensive understanding of the applicability of tracking systems in physical performance analysis within team sports and the wellbeing of athletes based on research strategies and combined variables.
Methods: A systematic search was conducted in PubMed, Web of Knowledge, and Scopus databases according to PRISMA guidelines. The 79 studies that were reviewed met the following criteria: (1) contained relevant data regarding elite athletes′ performance; (2) athletes' EL and IL; (3) were written in the English language; (4) were related only to team sports.
Results: The findings indicate that tracking technology has been engaged in several research areas, including performance analysis, training vs. match load management, injuries, and nutrition, through characterization and correlational studies. Metrics, primarily focused on kinematic and mechanical EL aspects, have been employed in combination with IL data to analyze the performance of athletes. However, the lack of an integrative model for the analysis and integration of EL and IL metrics within each team sport suggests an interesting direction for further research.
Conclusion: There is a need for coherence between the methods and the research goals on performance analysis. The development of a framework that guides experimental studies is highly recommended, particularly on manipulating metrics analyzed between training and match sessions, injury prevention, and nutrition. This will lead to the development of the most applied sports science research to improve the preparation and decision-making of athletes based on reliable data.
Background
Tracking devices, such as global (GPS) and local (LPS) positioning systems, combined with physiological measurements, have become reliable tools to characterize movement patterns, assessing the external load (EL), internal load (IL), fatigue, and performance of athletes in team sports. This scoping review aims to provide a comprehensive understanding of the applicability of tracking systems in physical performance analysis within team sports and the wellbeing of athletes based on research strategies and combined variables.
Methods
A systematic search was conducted in PubMed, Web of Knowledge, and Scopus databases according to PRISMA guidelines. The 79 studies that were reviewed met the following criteria: (1) contained relevant data regarding elite athletes′ performance; (2) athletes' EL and IL; (3) were written in the English language; (4) were related only to team sports.
Results
The findings indicate that tracking technology has been engaged in several research areas, including performance analysis, training vs. match load management, injuries, and nutrition, through characterization and correlational studies. Metrics, primarily focused on kinematic and mechanical EL aspects, have been employed in combination with IL data to analyze the performance of athletes. However, the lack of an integrative model for the analysis and integration of EL and IL metrics within each team sport suggests an interesting direction for further research.
Conclusion
There is a need for coherence between the methods and the research goals on performance analysis. The development of a framework that guides experimental studies is highly recommended, particularly on manipulating metrics analyzed between training and match sessions, injury prevention, and nutrition. This will lead to the development of the most applied sports science research to improve the preparation and decision-making of athletes based on reliable data.
Systematic Review Registration
https://inplasy.com/?s=2022120039 , identifier 2022120039.
To assess the reliability of triaxial accelerometers as a measure of physical activity in team sports.
Eight accelerometers (MinimaxX 2.0, Catapult, Australia) were attached to a hydraulic universal testing machine (Instron 8501) and oscillated over two protocols (0.5 g and 3.0 g) to assess within- and between device reliability. A static assessment was also conducted. Secondly, 10 players were instrumented with two accelerometers during Australian football matches. The vector magnitude was calculated, expressed as Player load and assessed for reliability using typical error (TE) ± 90% confidence intervals (CI), and expressed as a coefficient of variation (CV%). The smallest worthwhile difference (SWD) in Player load was calculated to determine if the device was capable of detecting differences in physical activity.
Laboratory: Within- (Dynamic: CV 0.91 to 1.05%; Static: CV 1.01%) and between-device (Dynamic: CV 1.02 to 1.04%; Static: CV 1.10%) reliability was acceptable across each test. Field: The between-device reliability of accelerometers during Australian football matches was also acceptable (CV 1.9%). The SWD was 5.88%.
The reliability of the MinimaxX accelerometer is acceptable both within and between devices under controlled laboratory conditions, and between devices during field testing. MinimaxX accelerometers can be confidently utilized as a reliable tool to measure physical activity in team sports across multiple players and repeated bouts of activity. The noise (CV%) of Player load was lower than the signal (SWD), suggesting that accelerometers can detect changes or differences in physical activity during Australian football.
Global positioning system (GPS) technology was made possible after the invention of the atomic clock. The first suggestion that GPS could be used to assess the physical activity of humans followed some 40 y later. There was a rapid uptake of GPS technology, with the literature concentrating on validation studies and the measurement of steady-state movement. The first attempts were made to validate GPS for field sport applications in 2006. While GPS has been validated for applications for team sports, some doubts continue to exist on the appropriateness of GPS for measuring short high-velocity movements. Thus, GPS has been applied extensively in Australian football, cricket, hockey, rugby union and league, and soccer. There is extensive information on the activity profile of athletes from field sports in the literature stemming from GPS, and this includes total distance covered by players and distance in velocity bands. Global positioning systems have also been applied to detect fatigue in matches, identify periods of most intense play, different activity profiles by position, competition level, and sport. More recent research has integrated GPS data with the physical capacity or fitness test score of athletes, game-specific tasks, or tactical or strategic information. The future of GPS analysis will involve further miniaturization of devices, longer battery life, and integration of other inertial sensor data to more effectively quantify the effort of athletes.
The aims of this study were to assess (a) the validity of total body load (TBL)-obtained from the global position system (GPS) devices-to quantify soccer training load, assessing its relationship with session rating of perceived exertion (session-RPE) and (b) to analyze the differences in terms of TBL and session-RPE among defenders, midfielders, and forwards. Twenty-two professional soccer players (Spanish first division, season 2007-2008; 26.74 ± 4.2 years; height 179.74 ± 4.04 cm; weight 73.7 ± 3.35 kg) participated in the study. During 13 training sessions composed predominantly of small-sided games, TBL and RPE multiplied by the minutes of session duration were determined using GPS and the 21-point scale, respectively. In each session, data from 10 players randomly selected and classified according to player position (defenders, midfielders, and forwards) were collected. Although session-RPE was a significant predictor of TBL (β = 0.23, p < 0.05), this method only accounted for 5% of the variance in TBL. No significant differences in terms of TBL and session-RPE were found regarding player position. The results of this study suggest that TBL is not a valid measure to quantify training load because it is not strongly correlated with session-RPE. Furthermore, TBL and session-RPE in small-sided soccer games do not vary according to player positions.
The principle of training can be reduced to a simple "dose-response" relationship. The "response" in this relationship can be measured as a change in performance or the adaptation of a physiological system. The "dose" of training, or physiological stress associated with the training load, is more difficult to measure as there is no absolute "gold standard" which can be used in the field, making it difficult to validate procedures. Attempts have been made to use heart rate as a marker of intensity during training, but the theoretical attractiveness of this method is not supported by the accuracy and the practicality of using this method during training or competition. The session RPE, based on the product of training duration and perceived intensity is more practical and can be used in a variety of sports. However, the score depends on a subjective assessment, and the intersubject comparisons may be inaccurate. The demands of different sports vary and therefore the methods of assessing training need to vary accordingly. The time has come to reach consensus on assessing training accurately in different sports. There is a precedent for this consensus approach with scientists having already done so for the assessment of physical activity, and for defining injuries in rugby, football and cricket. Standardizing these methods has resulted in the quality of research in these areas increasing exponentially.
Nine games players (mean age 23.3 years, s¼2.8; height 1.73 m, s¼0.08; body mass 70.0 kg, s¼12.7) completed 14 laps of a measured circuit that incorporated intermittent running and directional changes, representative of the movements made by field hockey players during match-play. The distances and speeds recorded by a global positioning satellite (GPS) system (Spi EliteTM) were compared statistically with speed measurements made using timing gates and distances measured using a calibrated trundle wheel, to establish the criterion validity of the GPS system. A validation of the speed of movement of each participant separately was also made, using data from each timing gate, over a range of speeds. The mean distance recorded by the GPS system was 6821 m (s¼7) and the mean speed was 7.0 km h71 (s¼1.9), compared with the actual distance of 6818 m and recorded mean speed of 7.0 km h71 (s¼1.9). Pearson correlations (r) among timing gate speed and GPS speed were 0.99 (P50.001) and the mean difference and 95% limits of agreement were 0.0+0.9 km h71. These results suggest that a GPS system (Spi EliteTM) offers a valid tool for measuring speed and distance during match-play, and can quickly provide the
The two Yo-Yo intermittent recovery (IR) tests evaluate an individual’s ability to repeatedly perform intense exercise. The Yo-Yo IR level 1 (Yo-Yo IR1) test focuses on the capacity to carry out intermittent exercise leading to a maximal activation of the aerobic system, whereas Yo-Yo IR level 2 (Yo-Yo IR2) determines an individual’s ability to recover from repeated exercise with a high contribution from the anaerobic system. Evaluations of elite athletes in various sports involving intermittent exercise showed that the higher the level of competition the better an athlete performs in the Yo-Yo IR tests. Performance in the Yo- Yo IR tests for young athletes increases with rising age. The Yo-Yo IR tests have shown to be a more sensitive measure of changes in performance than maximum oxygen uptake. The Yo-Yo IR tests provide a simple and valid way to obtain important information of an individual’s capacity to perform repeated intense exercise and to examine changes in performance.
The purpose of physiological testing (J.D. MacDougall and H.A. Wenger) what do tests measure? (H.J. Green) testing strength and power (D.G. Sale) testing aerobic power (J.S. Thoden) testing anaerobic power and capacity (C. Bouchard, Albert W. Taylor, Jean-Aime Simoneau, and Serge Dulac) Kknanthropometry (WD. Ross and M.J. Marfell-Jones) testing flexibility (C.L. Hubley-Kozey) evaluating the health status of the athlete (R. Backus and D.C. Reid) modelling elite athletic performance (E.W. Banister).
Twelve elite players from an English Super League club consented to participate in the present study using portable global positioning system (GPS) devices to assess position-specific demands. Distances covered at low-intensity running, moderate-intensity running, high-intensity running, very high-intensity running, and total distance were significantly (P < 0.05) lower in forwards compared with outside backs and adjustables. Metres per minute was higher in adjustables and forwards, owing to higher values for relative distance in medium-intensity running and a rise in high-intensity running from previous absolute values. Sprint distance, sprint frequency, and peak speed were higher in outside backs than both adjustables and forwards. A moderate, significant correlation (r = 0.62, P = 0.001) was apparent between session ratings of perceived exertion and summated heart rate. Results support the requirement for position-specific conditioning and provide preliminary evidence for the use of session ratings of perceived exertion as a measure of match load.
The purpose of this study was to compare the session-RPE method for quantifying internal training load (TL) with various HR-based TL quantification methods in a variety of training modes with women soccer players.
Fifteen elite women soccer players took part in the study (age: 19.3 +/- 2.0 y and VO2max: 50.8 +/- 2.7 mL x kg(-1) x min(-1)). Session-RPE, heart rate, and duration were recorded for 735 individual training sessions and matches over a period of 16 wk. Correlation analysis was used to compare session-RPE TLs with three commonly used HR-based methods for assessing TL.
The mean correlation for session-RPE TL with Banister's TRIMP, LTzone TL and Edwards's TL were (r = 0.84, 0.83, and 0.85, all P < .01, respectively). Correlations for session-RPE TL and three HR-based methods separated by session type were all significant (all P < .05). The strongest correlations were reported for technical (r = 0.68 to 0.82), conditioning (r = 0.60 to 0.79), and speed sessions (r = 0.61 to 0.79).
The session-RPE TL showed a significant correlation with all training types common to soccer. Higher correlations were found with less intermittent, aerobic-based training sessions and suggest that HR-based TLs relate better to session-RPE TLs in less intermittent training activities. These results support previous findings showing that the session-RPE TL compares favorably with HR-based methods for quantifying internal TL in a variety of soccer training activities.
Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.