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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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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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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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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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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.

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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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Erling A. Algrøy, Ken J. Hetlelid, Stephen Seiler and Jørg I. Stray Pedersen

Purpose:

This study was designed to quantify the daily distribution of training intensity in a group of professional soccer players in Norway based on three different methods of training intensity quantification.

Methods:

Fifteen male athletes (age, 24 ± 5 y) performed treadmill test to exhaustion to determine heart rate and VO2 corresponding to ventilatory thresholds (VT1, VT2), maximal oxygen consumption (VO2max) and maximal heart rate. VT1 and VT2 were used to delineate three intensity zones based on heart rate. During a 4 wk period in the preseason (N = 15), and two separate weeks late in the season (N = 11), all endurance and on-ball training sessions (preseason: N = 378, season: N= 78) were quantified using continuous heart rate registration and session rating of perceived exertion (sRPE). Three different methods were used to quantify the intensity distribution: time in zone, session goal and sRPE.

Results:

Intensity distributions across all sessions were similar when based on session goal or by sRPE. However, intensity distribution based on heart rate cut-offs from standardized testing was significantly different (time in zone).

Conclusions:

Our findings suggest that quantifying training intensity by using heart rate based total time in zone is not valid for describing the effective training intensity in soccer. The results also suggest that the daily training intensity distribution in this representative group of high level Norwegian soccer players is organized after a pattern where about the same numbers of training sessions are performed in low lactate, lactate threshold, and high intensity training zones.

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Maurizio Fanchini, Roberto Ghielmetti, Aaron J. Coutts, Federico Schena and Franco M. Impellizzeri

Purpose:

To examine the effect of different exercise-intensity distributions within a training session on the session rating of perceived exertion (RPE) and to examine the timing of measure on the rating.

Methods:

Nineteen junior players (age 16 ± 1 y, height 173 ± 5 cm, body mass 64 ± 6 kg) from a Swiss soccer team were involved in the study. Percentage of heart rate maximum (%HR) and RPE (Borg CR100®) were collected in 4 standardized training sessions (conditions). The Total Quality of Recovery scale (TQR) and a visual analogue scale (VAS) for pain of the lower limbs were used to control for the effect of pretraining fatigue. Every session consisted of three 20-min blocks of different intensities (ie, low-moderate-high) performed in a random order. RPE was collected after every block (RPE5), immediately after the session (RPE-end), and 30 min after the session (RPE30).

Results:

RPE5s of each block were different depending on the distribution sequence (P < .0001). RPE-end, TQR, and VAS values were not different between conditions (P = .57, P = .55, and P = .96, respectively). The %HR was significantly different between conditions (P = .008), with condition 3 higher than condition 2 (74.1 vs 70.2%, P = .02). Edwards training loads were not significantly different between conditions (P = .09). RPE30 was not different from RPE-end (P > .05).

Conclusions:

The current results show that coaches can design training sessions without concern about the influence of the within-session distribution of exercise intensity on session-RPE and that RPE can be collected at the end of the session or 30 min later.

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Hongjun Yu, Xiaoping Chen, Weimo Zhu and Chunmei Cao

Purpose:

To examine the effectiveness of threshold and polarized models in the training organization of Chinese top-level sprint speed skaters using a 2-y quasi-experimental design.

Methods:

Two years (2004–05 and 2005–06 seasons) of the Chinese national speed-skating team’s daily training load (N = 9; 5 men, 23.6 ± 1.7 y, weight 76.6 ± 4.1 kg, competitive experience 5.0 ± 0.8 y, 500-m time 35.45 ± 0.72 s, 1000-m time 71.18 ± 2.28 s; 4 women, 25.3 ± 6.8 y, 73.0 ± 8.5 kg, 6.3 ± 3.5 y, 37.81 ± 0.46 s, 75.70 ± 0.81 s) were collected and analyzed. Each season’s training load included overall duration (calculated in min and km), frequency (calculated by overall sessions), and training intensity (measured by ear blood lactate or estimated by heart rate), Their performances at national, World Cup, and Olympic competitions during the 2 seasons (2004–06), as well as lactate data measured 15 and 30 min after these competitions, were also collected and analyzed. Based on the lactate data (<2, 2–4, >4 mmol/L), training zones were classified as low, moderate, and high intensity.

Results:

The total durations and frequencies of the training load were similar across the seasons, but a threshold-training model distribution was used in 2004–05, and a polarized-training-load organization in 2005–06. Under the polarized-training model, or load organization, all speed skaters’ performance improved and their lactate after competition decreased considerably.

Conclusion:

Training-intensity distribution based on a polarized-training model led to the success in top Chinese sprint speed skaters in the 2005–06 season.