Velocity-Based Training for Monitoring Training Load and Assessing Training Effects

2021 ◽  
pp. 153-179
Author(s):  
Fernando Pareja-Blanco ◽  
Irineu Loturco
Author(s):  
Witalo K. Oliveira ◽  
Karla de Jesus ◽  
Ana D. Andrade ◽  
Fábio Y. Nakamura ◽  
Cláudio O. Assumpção ◽  
...  

2017 ◽  
Vol 181 ◽  
pp. 86-94 ◽  
Author(s):  
Léo Djaoui ◽  
Monoem Haddad ◽  
Karim Chamari ◽  
Alexandre Dellal

2017 ◽  
Vol 12 (s2) ◽  
pp. S2-2-S2-8 ◽  
Author(s):  
Carl Foster ◽  
Jose A. Rodriguez-Marroyo ◽  
Jos J. de Koning

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 heart rate (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 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 turnaround time to coaches remain to be solved.


2017 ◽  
Vol 28 ◽  
pp. e8
Author(s):  
Jason Laird ◽  
Allan Macdonald

2019 ◽  
Vol 25 (3) ◽  
pp. 226-229 ◽  
Author(s):  
Thiago Seixas Duarte ◽  
Danilo Reis Coimbra ◽  
Renato Miranda ◽  
Heglison Custódio Toledo ◽  
Francisco Zacaron Werneck ◽  
...  

ABSTRACT Introduction Monitoring training loads, along with the recovery status, is important for preventing unwanted adaptations. Knowledge of these variables over volleyball seasons is still scarce. Objective To monitor and describe the training load and recovery status of volleyball players over a competitive season. Methods The sample consisted of 14 professional volleyball players. For the entire season, the training load was monitored daily by the SPE method during the session, and the recovery status was monitored by TQR and QBE on the first and last days of training for the week. Results There was a decrease in training load between Preparatory Period I and Competitive Period I (p = 0.03), followed by an increase in Preparatory Period II (p <0.001) and a new decrease in Competitive Periods II (p = 0.01 ) and III (p = 0.003). There was a significant reduction between Pre-TQR and QBE and Post-TQR and QBE in all mesocycles. In the Pre-TQR, there was a reduction between Preparatory Period II and Competitive Period II (p = 0.006), in the Pre-QBE, there was a reduction between Preparatory Period II and Competitive Period III (p = 0.002), and in the Post-TQR, this reduction was observed between Competitive Period I and Preparatory Period II (p = 0.03). In the Post-QBE, there was an increase between Preparatory Period I and Competitive Period I (p = 0.002), followed by a decrease in Preparatory Period II (p = 0.01). Conclusion Loads varied throughout the season, along with recovery, which varied according to the loads and characteristics of each period. Level of evidence I, Therapeutic Studies – Investigating the Results of Treatment.


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