noncontact injuries, both external load (ie, global positioning system) 15 and internal load (ie, session rating of perceived exertion [sRPE]) 13 variables have been used. However, according to the UEFA Elite Club Injury Study, 16 internal load markers have greater relevance as a risk factor than the
Javier Raya-González, Fabio Yuzo Nakamura, Daniel Castillo, Javier Yanci and Maurizio Fanchini
Shaun J. McLaren, Michael Graham, Iain R. Spears and Matthew Weston
To investigate the sensitivity of differential ratings of perceived exertion (dRPE) as measures of internal load.
Twenty-two male university soccer players performed 2 maximal incremental-exercise protocols (cycle, treadmill) on separate days. Maximal oxygen uptake (V̇O2max), maximal heart rate (HRmax), peak blood lactate concentration (B[La]peak), and the preprotocol-to-postprotocol change in countermovement-jump height (ΔCMJH) were measured for each protocol. Players provided dRPE (CR100) for breathlessness (RPE-B) and leg-muscle exertion (RPE-L) immediately on exercise termination (RPE-B0, RPE-L0) and 30 min postexercise (RPE-B30, RPE-L30). Data were analyzed using magnitude-based inferences.
There were clear between-protocols differences for V̇O2max (cycle 46.5 ± 6.3 vs treadmill 51.0 ± 5.1 mL · kg−1 · min−1, mean difference –9.2%; ±90% confidence limits 3.7%), HRmax (184.7 ± 12.7 vs 196.7 ± 7.8 beats/min, –6.0%; ±1.7%), B[La]peak (9.7 ± 2.1 vs 8.5 ± 2.0 mmol/L, 15%; ±10%), and ΔCMJH (–7.1 ± 4.2 vs 0.6 ± 3.6 cm, –23.2%; ±5.4%). Clear between-protocols differences were recorded for RPE-B0 (78.0 ± 11.7 vs 94.7 ± 9.5 AU, –18.1%; ±4.5%), RPE-L0 (92.6 ± 9.7 vs 81.3 ± 14.1 AU, 15.3%; ±7.6%), RPE-B30 (70 ± 11 vs 82 ± 13 AU, –13.8%; ±7.3%), and RPE-L30 (86 ± 12 vs 65 ± 19 AU, 37%; ±17%). A substantial timing effect was observed for dRPE, with moderate to large reductions in all scores 30 min postexercise compared with scores collected on exercise termination.
dRPE enhance the precision of internal-load measurement and therefore represent a worthwhile addition to training-load-monitoring procedures.
Renato Barroso, Diego F. Salgueiro, Everton C. do Carmo and Fábio Y. Nakamura
To assess swimmers’ session rating of perceived exertion (sRPE) after standardized sets of interval swimming training performed at the same relative intensity but with different total volume and repetition distance.
Thirteen moderately trained swimmers (21.1 ± 1.1 y, 178 ± 6 cm, 74.1 ± 8.3 kg, 100-m freestyle 60.2 ± 2.9 s) performed 4 standardized sets (10 × 100-m, 20 × 100-m, 10 × 200-m, and 5 × 400-m) at the same relative intensity (ie, critical speed), and 1 coach (age 31 y, 7 y coaching experience) rated their efforts. Swimmers’ sRPE was assessed 30 min after the training session. Coach sRPE was collected before each training session. Internal load was calculated by multiplying sRPE by session duration.
When bouts with the same repetition distance and different volumes (10 × 100-m vs 20 × 100-m) are compared, sRPE and internal load are higher in 20 × 100-m bouts. When maintaining constant volume, sRPE and internal load (20 × 100-m, 10 × 200-m, and 5 × 400-m) are higher only in 5 × 400-m bouts. The coach’s and swimmers’ sRPE differed in 10 × 200-m and 5 × 400-m.
These results indicate that sRPE in swimming is affected not only by intensity but also by volume and repetition distance. In addition, swimmers’ and the coach’s sRPE were different when longer repetition distances were used during training sessions. Therefore, care should be taken when prescribing swimming sessions with longer volume and/or longer repetition distances.
Nacho Torreño, Diego Munguía-Izquierdo, Aaron Coutts, Eduardo Sáez de Villarreal, Jose Asian-Clemente and Luis Suarez-Arrones
To analyze the match running profile, distance traveled over successive 15 min of match play, heart rates (HRs), and index of performance efficiency (effindex) of professional soccer players with a global positioning system (GPS) and HR in official competition.
Twenty-six professional players were investigated during full matches in competitive club-level matches (N = 223). Time–motion data and HR were collected using GPS and HR technology.
The relative total distance was 113 ± 11 m/min, with substantial differences between halves. For all playing positions, a substantial decrease in total distance and distance covered at >13.0 km/h was observed in the second half in comparison with the first. The decrease during the second half in distance covered at >13.0 km/h was substantially higher than in total distance. The average HR recorded was 86.0% maximal HR, and the relationship between external and internal load (effindex) was 1.3, with substantial differences between halves in all playing positions, except strikers for effindex. Wide midfielders reflected substantially the lowest mean HR and highest effindex, whereas center backs showed substantially the lowest effindex of all playing positions.
The current study confirmed the decrement in a player’s performance toward the end of a match in all playing positions. Wide midfielders displayed the highest and fittest levels of physical and physiological demands, respectively, whereas center backs had the lowest and least-fit levels of physical and physiological demands, respectively. The position-specific relationship between external and internal load confirms that players with more overall running performance during the full match were the best in effindex.
Marco Cardinale and Matthew C. Varley
The need to quantify aspects of training to improve training prescription has been the holy grail of sport scientists and coaches for many years. Recently, there has been an increase in scientific interest, possibly due to technological advancements and better equipment to quantify training activities. Over the last few years there has been an increase in the number of studies assessing training load in various athletic cohorts with a bias toward subjective reports and/or quantifications of external load. There is an evident lack of extensive longitudinal studies employing objective internal-load measurements, possibly due to the cost-effectiveness and invasiveness of measures necessary to quantify objective internal loads. Advances in technology might help in developing better wearable tools able to ease the difficulties and costs associated with conducting longitudinal observational studies in athletic cohorts and possibly provide better information on the biological implications of specific external-load patterns. Considering the recent technological developments for monitoring training load and the extensive use of various tools for research and applied work, the aim of this work was to review applications, challenges, and opportunities of various wearable technologies.
Training quantification is basic to evaluate an endurance athlete’s responses to training loads, ensure adequate stress/recovery balance, and determine the relationship between training and performance. Quantifying both external and internal workload is important, because external workload does not measure the biological stress imposed by the exercise sessions. Generally used quantification methods include retrospective questionnaires, diaries, direct observation, and physiological monitoring, often based on the measurement of oxygen uptake, heart rate, and blood lactate concentration. Other methods in use in endurance sports include speed measurement and the measurement of power output, made possible by recent technological advances such as power meters in cycling and triathlon. Among subjective methods of quantification, rating of perceived exertion stands out because of its wide use. Concurrent assessments of the various quantification methods allow researchers and practitioners to evaluate stress/recovery balance, adjust individual training programs, and determine the relationships between external load, internal load, and athletes’ performance. This brief review summarizes the most relevant external- and internal-workload-quantification methods in endurance sports and provides practical examples of their implementation to adjust the training programs of elite athletes in accordance with their individualized stress/recovery balance.
Gerald K. Cole, Benno M. Nigg, Gordon H. Fick and Michael M. Morlock
A 3-D model was used in this study to determine the influence of midsole hardness, as well as the influence of running in shoes in comparison to barefoot, on the contact forces in the joints of the foot and ankle during running. The results showed that there were no statistical differences in the magnitude and rate of joint loading for changing midsole hardness, nor were there any general trends observed in the measured variables. However, both the magnitude and rate of loading in the subtalar and ankle joints during the impact phase were found to be greater in the barefoot condition than the shod condition. The results suggest that if running injuries are assumed to be related to the impact of heel-strike, running in shoes may aid in preventing injuries, whereas it is still questionable whether changes in the midsole hardness have a general influence on the incidence of impact-related injuries.
Jamie Highton, Thomas Mullen, Jonathan Norris, Chelsea Oxendale and Craig Twist
This aim of this study was to examine the validity of energy expenditure derived from microtechnology when measured during a repeated-effort rugby protocol. Sixteen male rugby players completed a repeated-effort protocol comprising 3 sets of 6 collisions during which movement activity and energy expenditure (EEGPS) were measured using microtechnology. In addition, energy expenditure was estimated from open-circuit spirometry (EEVO2). While related (r = .63, 90%CI .08–.89), there was a systematic underestimation of energy expenditure during the protocol (–5.94 ± 0.67 kcal/min) for EEGPS (7.2 ± 1.0 kcal/min) compared with EEVO2 (13.2 ± 2.3 kcal/min). High-speed-running distance (r = .50, 95%CI –.66 to .84) was related to EEVO2, while PlayerLoad was not (r = .37, 95%CI –.81 to .68). While metabolic power might provide a different measure of external load than other typically used microtechnology metrics (eg, high-speed running, PlayerLoad), it underestimates energy expenditure during intermittent team sports that involve collisions.
Caoimhe Tiernan, Mark Lyons, Tom Comyns, Alan M. Nevill and Giles Warrington
exertion [sRPE] × session duration). Training load has been found to be a more valid and reliable measure of training response than training volume as it takes into account the players’ internal load. 3 Cunniffe et al 8 conducted an 11-month longitudinal saliva study with rugby union players; however
Daniele Conte, Nicholas Kolb, Aaron T. Scanlan and Fabrizio Santolamazza
013e3181d7552a 10.1519/JSC.0b013e3181d7552a 20386474 10. Moreira A , McGuigan MR , Arruda AF , Freitas CG , Aoki MS . Monitoring internal load parameters during simulated and official basketball matches . J Strength Cond Res . 2012 ; 26 ( 3 ): 861 – 866 . PubMed ID: 22289698 doi:10