, 20 and cross-country skiing, 21 this new training prescription strategy has not been used in road cycling yet. Therefore, the purpose of this study was to determine the effect of an HRV-guided and a traditionally periodized training program on road cycling performance. Methods Subjects Seventeen
Alejandro Javaloyes, Jose Manuel Sarabia, Robert Patrick Lamberts, and Manuel Moya-Ramon
Pedro L. Valenzuela, Guillermo Sánchez-Martínez, Elaia Torrontegi, Javier Vázquez-Carrión, Zigor Montalvo, and G. Gregory Haff
abovementioned studies support the assessment of the F–v profile with the Samozino method and particularly the development of a F–v IMB score that can be used to guide training prescription, there is still debate on the optimal method for its determination. Whereas some authors 9 – 13 have conducted the
Jonathan D. Bartlett, Fergus O’Connor, Nathan Pitchford, Lorena Torres-Ronda, and Samuel J. Robertson
The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches.
TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39–89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis.
Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players.
This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.
Benoit Capostagno, Michael I. Lambert, and Robert P. Lamberts
Finding the optimal balance between high training loads and recovery is a constant challenge for cyclists and their coaches. Monitoring improvements in performance and levels of fatigue is recommended to correctly adjust training to ensure optimal adaptation. However, many performance tests require a maximal or exhaustive effort, which reduces their real-world application. The purpose of this review was to investigate the development and use of submaximal cycling tests that can be used to predict and monitor cycling performance and training status. Twelve studies met the inclusion criteria, and 3 separate submaximal cycling tests were identified from within those 12. Submaximal variables including gross mechanical efficiency, oxygen uptake (VO2), heart rate, lactate, predicted time to exhaustion (pTE), rating of perceived exertion (RPE), power output, and heart-rate recovery (HRR) were the components of the 3 tests. pTE, submaximal power output, RPE, and HRR appear to have the most value for monitoring improvements in performance and indicate a state of fatigue. This literature review shows that several submaximal cycle tests have been developed over the last decade with the aim to predict, monitor, and optimize cycling performance. To be able to conduct a submaximal test on a regular basis, the test needs to be short in duration and as noninvasive as possible. In addition, a test should capture multiple variables and use multivariate analyses to interpret the submaximal outcomes correctly and alter training prescription if needed.
Tahleya Eggers, Rebecca Cross, Dean Norris, Lachlan Wilmot, and Ric Lovell
practitioners within collision-based sports have recognized the physical cost of previous match and athlete recovery status as the most influential factors of subsequent training prescription, 10 deliberate changes in prescription by coaches/support staff may have contributed to the variation in T skills
Jace A. Delaney, Heidi R. Thornton, John F. Pryor, Andrew M. Stewart, Ben J. Dascombe, and Grant M. Duthie
To quantify the duration and position-specific peak running intensities of international rugby union for the prescription and monitoring of specific training methodologies.
Global positioning systems (GPS) were used to assess the activity profile of 67 elite-level rugby union players from 2 nations across 33 international matches. A moving-average approach was used to identify the peak relative distance (m/min), average acceleration/deceleration (AveAcc; m/s2), and average metabolic power (Pmet) for a range of durations (1–10 min). Differences between positions and durations were described using a magnitude-based network.
Peak running intensity increased as the length of the moving average decreased. There were likely small to moderate increases in relative distance and AveAcc for outside backs, halfbacks, and loose forwards compared with the tight 5 group across all moving-average durations (effect size [ES] = 0.27–1.00). Pmet demands were at least likely greater for outside backs and halfbacks than for the tight 5 (ES = 0.86–0.99). Halfbacks demonstrated the greatest relative distance and Pmet outputs but were similar to outside backs and loose forwards in AveAcc demands.
The current study has presented a framework to describe the peak running intensities achieved during international rugby competition by position, which are considerably higher than previously reported whole-period averages. These data provide further knowledge of the peak activity profiles of international rugby competition, and this information can be used to assist coaches and practitioners in adequately preparing athletes for the most demanding periods of play.
Context: Combat sports are composed of high-intensity actions (eg, attacks, defensive actions, and counterattacks in both grappling and striking situations depending on the specific sport) interspersed with low-intensity actions (eg, displacement without contact, stepping) or pauses (eg, referee stoppages), characterizing an intermittent activity. Therefore, high-intensity interval training (HIIT) is at the essence of combat-sport-specific training and is used as complementary training, as well. HIIT prescription can be improved by using intensity parameters derived from combat-sport-specific tests. Specifically, the assessment of physiological indexes (intensity associated with the maximal blood lactate steady state, maximal oxygen consumption, and maximal sprint) or of time–motion variables (high-intensity actions, low-intensity actions, and effort:pause ratio) is a key element for a better HIIT prescription because these parameters provide an individualization of the training loads imposed on these athletes. Purpose: To present a proposal for HIIT prescription for combat-sport athletes, exemplifying with different HIIT protocols (HIIT short intervals, HIIT long intervals, repeated-sprint training, and sprint interval training) using combat-sport-specific actions and the parameters for the individualization of these protocols. Conclusions: The use of combat-sport-specific tests is likely to improve HIIT prescription, allowing coaches and strength and conditioning professionals to elaborate HIIT short intervals, HIIT long intervals, repeated-sprint training, and sprint interval training protocols using combat-sport actions, providing more specificity and individualization for the training sessions.
Stephen Seiler and Øystein Sylta
The purpose of this study was to compare physiological responses and perceived exertion among well-trained cyclists (n = 63) performing 3 different high-intensity interval-training (HIIT) prescriptions differing in work-bout duration and accumulated duration but all prescribed with maximal session effort. Subjects (male, mean ± SD 38 ± 8 y, VO2peak 62 ± 6 mL · kg–1 · min–1) completed up to 24 HIIT sessions over 12 wk as part of a training-intervention study. Sessions were prescribed as 4 × 16, 4 × 8, or 4 × 4 min with 2-min recovery periods (8 sessions of each prescription, balanced over time). Power output, HR, and RPE were collected during and after each work bout. Session RPE was reported after each session. Blood lactate samples were collected throughout the 12 wk. Physiological and perceptual responses during >1400 training sessions were analyzed. HIIT sessions were performed at 95% ± 5%, 106% ± 5%, and 117% ± 6% of 40-min time-trial power during 4 × 16-, 4 × 8-, and 4 × 4-min sessions, respectively, with peak HR in each work bout averaging 89% ± 2%, 91% ± 2%, and 94% ± 2% HRpeak. Blood lactate concentrations were 4.7 ± 1.6, 9.2 ± 2.4, and 12.7 ± 2.7 mmol/L. Despite the common prescription of maximal session effort, RPE and sRPE increased with decreasing accumulated work duration (AWD), tracking relative HR. Only 8% of 4 × 16-min sessions reached RPE 19–20, vs 61% of 4 × 4-min sessions. The authors conclude that within the HIIT duration range, performing at “maximal session effort” over a reduced AWD is associated with higher perceived exertion both acutely and postexercise. This may have important implications for HIIT prescription choices.
Annemiek J. Roete, Marije T. Elferink-Gemser, Ruby T.A. Otter, Inge K. Stoter, and Robert P. Lamberts
overreaching and how much consensus there is about these markers. This information is highly valuable for coaches and trainers when monitoring and fine-tuning the training prescription of their endurance athletes. A state of functional overreaching can not only be reflected by a maximal performance test or a
Robert W. Pettitt
The use of personal records (PRs) for running different distances may be used to derive critical speed (CS) and the finite capacity for running speeds exceeding CS (D′). Using CS and D′, individualized speed-time and distance-time relationships can be modeled (ie, time limits associated with running at a given speed or a given distance can be derived via linear regression with a high degree of accuracy). The running 3-min all-out exercise test (3 MT) has emerged as a method for estimating CS and D′ on a large group of athletes in a single visit. Such a procedure is useful when PRs are not readily available (eg, team-sport athletes). This article reviews how to administer and interpret the running 3 MT, how CS and D′ can inform racing strategy, and how CS and D′ can be used to prescribe and evaluate high-intensity interval training (HIIT). Directions for deriving HIIT bouts using either fixed distances or fixed speeds are provided along with CS dose-responses to short-term HIIT programs.