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Guellich Arne, Seiler Stephen, and Emrich Eike

Purpose:

To describe the distribution of exercise types and rowing intensity in successful junior rowers and its relation to later senior success.

Methods:

36 young German male rowers (31 international, 5 national junior finalists; 19.2 ± 1.4 y; 10.9 ± 1.6 training sessions per week) reported the volumes of defined exercise and intensity categories in a diary over 37 wk. Training categories were analyzed as aggregates over the whole season and also broken down into defined training periods. Training organization was compared between juniors who attained national and international senior success 3 y later.

Results:

Total training time consisted of 52% rowing, 23% resistance exercise, 17% alternative training, and 8% warm-up programs. Based on heart rate control, 95% of total rowing was performed at intensities corresponding to <2 mmol·L-1, 2% at 2 to 4 mmol·L-1, and 3% at >4 mmol·L-1 blood lactate. Low-intensity work remained widely unchanged at ~95% throughout the season. In the competition period, the athletes exhibited a shift within <2 mmol exercise toward lower intensity and within the remaining ~5% of total rowing toward more training near maximal oxygen consumption (VO2max) intensity. Retrospectively, among subjects going on to international success 3 y later had their training differed significantly from their peers only in slightly higher volumes at both margins of the intensity scope.

Conclusion:

The young world-class rowers monitored here exhibit a constant emphasis on low-intensity steady-state rowing exercise, and a progressive polarization in the competition period. Possible mechanisms underlying a potential association between intensity polarization and later success require further investigation.

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Jan G. Bourgois, Gil Bourgois, and Jan Boone

time, that is, training intensity distribution (TID), 7 has been considered as a key issue within the design of the training program to optimize performance for endurance sports. A conceptual 3-zone intensity distribution model 8 , 9 based on physiological (heart rate, gas exchange, and blood lactate

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Miguel Ángel Galán-Rioja, José María Gonzalez-Ravé, Fernando González-Mohíno, and Stephen Seiler

threshold, first and second ventilatory thresholds), as well as other determinants and results of study. Figure 1 —Flowchart of the search strategy. Figure 2 —Training intensity distribution for the block periodization model. HIT indicates high-intensity training; HS, heavy strength training; LIT, low

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José María González-Ravé, Francisco Hermosilla, Fernando González-Mohíno, Arturo Casado, and David B. Pyne

. The retrospective study of Hellard et al 19 initially stated 5 zones, but only reported 3 zones of training intensity distribution (TID) for the swimmers analyzed. The proportion of training in each zone differs for every endurance sport, depending on the duration of the event and, in consequence

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Mark Kenneally, Arturo Casado, and Jordan Santos-Concejero

]). 2 Three training intensity zones of endurance athletes are most commonly used in the literature 1 , 3 and are considered similar regardless of the method used to determine them. However, up to 7 intensity zones can be also used to describe the training intensity distribution (TID). 4 Both TID and

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Dajo Sanders, Tony Myers, and Ibrahim Akubat

Purpose:

To evaluate training-intensity distribution using different intensity measures based on rating of perceived exertion (RPE), heart rate (HR), and power output (PO) in well-trained cyclists.

Methods:

Fifteen road cyclists participated in the study. Training data were collected during a 10-wk training period. Training-intensity distribution was quantified using RPE, HR, and PO categorized in a 3-zone training-intensity model. Three zones for HR and PO were based around a 1st and 2nd lactate threshold. The 3 RPE zones were defined using a 10-point scale: zone 1, RPE scores 1–4; zone 2, RPE scores 5–6; zone 3, RPE scores 7–10.

Results:

Training-intensity distributions as percentages of time spent in zones 1, 2, and 3 were moderate to very largely different for RPE (44.9%, 29.9%, 25.2%) compared with HR (86.8%, 8.8%, 4.4%) and PO (79.5%, 9.0%, 11.5%). Time in zone 1 quantified using RPE was largely to very largely lower for RPE than PO (P < .001) and HR (P < .001). Time in zones 2 and 3 was moderately to very largely higher when quantified using RPE compared with intensity quantified using HR (P < .001) and PO (P < .001).

Conclusions:

Training-intensity distribution quantified using RPE demonstrates moderate to very large differences compared with intensity distributions quantified based on HR and PO. The choice of intensity measure affects intensity distribution and has implications for training-load quantification, training prescription, and the evaluation of training characteristics.

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Phillip Bellinger, Blayne Arnold, and Clare Minahan

across the training-intensity spectrum (ie, training-intensity distribution [TID]) is considered a key determinant of training and performance adaptations. 1 – 5 Training intensity can be measured via external work rate (running speed or power output), 6 , 7 an internal physiological response (ie

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Arturo Casado, Fernando González-Mohíno, José María González-Ravé, and Carl Foster

performance. 6 Differences in adaptive responses to training between untrained and trained runners are well-documented. 7 , 8 For example, VO 2 max, VO 2 kinetics, and time to exhaustion at vVO 2 max (Tlim) are responsive to the volume/intensity/and training intensity distribution (TID). 8 Shaw et al 9

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Farhan Juhari, Dean Ritchie, Fergus O’Connor, Nathan Pitchford, Matthew Weston, Heidi R. Thornton, and Jonathan D. Bartlett

, the aim of this study was to quantify the session intensity, duration, and intensity distribution of Australian Rules football across various stages of a season using the s-RPE method. Methods Subjects A total of 45 professional male AF players (mean [SD]: age, 24.7 [4.3] y; height, 187.2 [7.5] cm

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Daniel J. Plews, Paul B. Laursen, Andrew E. Kilding, and Martin Buchheit

Purpose:

Elite endurance athletes may train in a polarized fashion, such that their training-intensity distribution preserves autonomic balance. However, field data supporting this are limited.

Methods:

The authors examined the relationship between heart-rate variability and training-intensity distribution in 9 elite rowers during the 26-wk build-up to the 2012 Olympic Games (2 won gold and 2 won bronze medals). Weekly averaged log-transformed square root of the mean sum of the squared differences between R-R intervals (Ln rMSSD) was examined, with respect to changes in total training time (TTT) and training time below the first lactate threshold (>LT1), above the second lactate threshold (LT2), and between LT1 and LT2 (LT1–LT2).

Results:

After substantial increases in training time in a particular training zone or load, standardized changes in Ln rMSSD were +0.13 (unclear) for TTT, +0.20 (51% chance increase) for time >LT1, –0.02 (trivial) for time LT1–LT2, and –0.20 (53% chance decrease) for time >LT2. Correlations (±90% confidence limits) for Ln rMSSD were small vs TTT (r = .37 ± .80), moderate vs time >LT1 (r = .43 ± .10), unclear vs LT1–LT2 (r = .01 ± .17), and small vs >LT2 (r = –.22 ± .50).

Conclusion:

These data provide supportive rationale for the polarized model of training, showing that training phases with increased time spent at high intensity suppress parasympathetic activity, while low-intensity training preserves and increases it. As such, periodized low-intensity training may be beneficial for optimal training programming.