were no statistically significant differences in average weekly training load (in iTrimp per week), training time (in hours per week), or intensity distribution between the groups during the final 4 weeks of the competitive period. During the 3-week transition period, both groups were instructed to
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The Inclusion of Sprints in Low-Intensity Sessions During the Transition Period of Elite Cyclists Improves Endurance Performance 6 Weeks Into the Subsequent Preparatory Period
Madison Taylor, Nicki Almquist, Bent Rønnestad, Arnt Erik Tjønna, Morten Kristoffersen, Matt Spencer, Øyvind Sandbakk, and Knut Skovereng
The Effects of an Intensive 2-wk Resistance Training Period on Strength Performance and Nocturnal Heart Rate Variability
Piia Kaikkonen, Esa Hynynen, Arto Hautala, and Juha P. Ahtiainen
-body resistance training is also known to affect cardiovascular function, for example, by decreasing resting blood pressure. 5 The relationship between training load (TL) and cardiac autonomic modulation has been investigated during both endurance and resistance training periods of different lengths. Typically
Examining Perceptions of Teammates’ Burnout and Training Hours in Athlete Burnout
Ralph Appleby, Paul Davis, Louise Davis, and Henrik Gustafsson
is proposed as a key contributor to the development of burnout ( Gould & Dieffenbach, 2002 ; Kenttä, Hassmén, & Raglin, 2001 ; Raglin & Wilson, 2000 ), with qualitative research outlining the link between high training load and the development of burnout ( Cresswell & Eklund, 2006a ; 2007a
Player Session Rating of Perceived Exertion: A More Valid Tool Than Coaches’ Ratings to Monitor Internal Training Load in Elite Youth Female Basketball
Corrado Lupo, Alexandru Nicolae Ungureanu, Riccardo Frati, Matteo Panichi, Simone Grillo, and Paolo Riccardo Brustio
Although internal training load (ITL) has been successfully monitored in several sport conditions, 1 – 3 the complexity of team sports can make this procedure more difficult, especially because of the presence of different goals and workouts, even within a single training session. 4 For instance
Predicting Soccer Players’ Fitness Status Through a Machine-Learning Approach
Mauro Mandorino, Jo Clubb, and Mathieu Lacome
the subjects who had poor training continuity due to injuries or absence. 17 External training load was collected using the WIMU Pro system (RealTrack Systems) whose validity and reliability have been previously tested. 18 – 20 Twelve different parameters were considered (Table 1 ). Table 1
Monitoring the Heart Rate Variability Responses to Training Loads in Competitive Swimmers Using a Smartphone Application and the Banister Impulse-Response Model
Eva Piatrikova, Nicholas J. Willsmer, Marco Altini, Mladen Jovanović, Lachlan J.G. Mitchell, Javier T. Gonzalez, Ana C. Sousa, and Sean Williams
monitoring of the cardiac autonomic nervous system, specifically its parasympathetic arm via the measurement of resting heart rate variability (HRV) and its day-to-day variation. 3 Indeed, HRV has been shown to be related to training load, 4 – 7 performance, 5 , 6 health, 8 and psychological status of
Monitoring Training Load and Well-Being During the In-Season Phase in National Collegiate Athletic Association Division I Men’s Basketball
Daniele Conte, Nicholas Kolb, Aaron T. Scanlan, and Fabrizio Santolamazza
potential stressors faced during the season, it is especially important to monitor player training load (TL) and well-being status. In fact, the use of these monitoring tools has been considered useful to maximize physical performance in players while preventing overtraining symptoms. 2 Monitoring
The Same Story or a Unique Novel? Within-Participant Principal-Component Analysis of Measures of Training Load in Professional Rugby Union Skills Training
Dan Weaving, Nicholas E. Dalton, Christopher Black, Joshua Darrall-Jones, Padraic J. Phibbs, Michael Gray, Ben Jones, and Gregory A.B. Roe
regularly prescribed by practitioners within a holistic training program. 1 , 2 However, due to their differing characteristics (eg, duration, intensity, energy system stimulus), balancing the training load (TL) imposed across these modes is challenging, yet important to manage negative training outcomes
Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures
Sean Williams, Grant Trewartha, Matthew J. Cross, Simon P.T. Kemp, and Keith A. Stokes
Purpose:
Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner’s perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures.
Methods:
Training loads were collected from 173 professional rugby union players during the 2013–14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models.
Results:
Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component.
Conclusions:
The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of “load” in the data.
Relationships Between External- and Internal-Workload Variables in an Elite Female Netball Team and Between Playing Positions
Marni J. Simpson, David G. Jenkins, Aaron T. Scanlan, and Vincent G. Kelly
training sessions included general netball skills, position-specific skills, conditioning drills, and practice matches. External and internal training load data were collected during all court-based training sessions, and single competitive matches played weekly (Monday to Sunday) during the competition
