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Christopher P. Belcher and Cynthia Lee A. Pemberton

A training program designed to optimize athletes’ performance abilities cannot be practically planned or implemented without a valid and reliable indication of training intensity and its effect on the physiological mechanisms of the human body. The objectives of this paper are to (a) review training-intensity guidelines developed for coaches, inclusive of the associated physiologic metrics validated in a field study; (b) describe a seasonal application of the guidelines for coaches; and (c) share supporting commentary from coaches interviewed in the field study. A standardized system of training-intensity guidelines for the sports of track and field/cross country was field tested. The system was modeled after the standardized system of training-intensity guidelines used by USA Swimming. Track and field and cross country coaches were asked to comment on the perceived utility of the standardized training-intensity guidelines. Results of the field study show that coaches uniformly confirmed the utility and applicability of the training-intensity guidelines.

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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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Lotte L. Lintmeijer, A.J. “Knoek” van Soest, Freek S. Robbers, Mathijs J. Hofmijster and Peter J. Beek

consumption, such as boat velocity, stroke rate (number of cycles per minute), and heart rate, are used as indirect measures of training intensity. 7 – 10 In addition, coaches use their own subjective observations of rowers’ executed intensity to provide intensity feedback. Unfortunately, the suitability of

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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

Endurance training involves manipulation of intensity, duration, and frequency of training sessions. 1 The precise detail of this “manipulation,” however, remains an area of debate across the literature. To further guide understanding of this area, different training intensity zones have been

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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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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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Pedro Jiménez-Reyes, Fernando Pareja-Blanco, Carlos Balsalobre-Fernández, Víctor Cuadrado-Peñafiel, Manuel A. Ortega-Becerra and Juan J. González-Badillo

Purpose:

To examine the relationship between the relative load in full squats and the height achieved in jump-squat (JS) exercises and to determine the load that maximizes the power output of high-level athletes.

Method:

Fifty-one male high-level track-and-field athletes (age 25.2 ± 4.4 y, weight 77. ± 6.2 kg, height 179.9 ± 5.6 cm) who competed in sprinting and jumping events took part in the study. Full-squat 1-repetition-maximum (1-RM) and JS height (JH) with loads from 17 to 97 kg were measured in 2 sessions separated by 48 h.

Results:

Individual regression analyses showed that JH (R 2 = .992 ± .005) and the jump decrease (JD) that each load produced with respect to the unloaded countermovement jump (CMJ) (R 2 = .992 ± 0.007) are highly correlated with the full-squat %1-RM, which means that training intensities can be prescribed using JH and JD values. The authors also found that the load that maximizes JS’s power output was 0%RM (ie, unloaded CMJ).

Conclusions:

These results highlight the close relationship between JS performance and relative training intensity in terms of %1-RM. The authors also observed that the load that maximizes power output was 0%1-RM. Monitoring jump height during JS training could help coaches and athletes determine and optimize their training loads.

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Haresh T. Suppiah, Chee Yong Low and Michael Chia

Purpose:

Adolescent student-athletes face time constraints due to athletic and scholastic commitments, resulting in habitually shortened nocturnal sleep durations. However, there is a dearth of research on the effects of sleep debt on student-athlete performance. The study aimed to (i) examine the habitual sleep patterns (actigraphy) of high-level student-athletes during a week of training and academic activities, (ii) ascertain the effects of habitual sleep durations experienced by high-level student-athletes on psychomotor performance, and (iii) examine the impact of sport training intensities on the sleep patterns of high-level student-athletes that participate in low and high intensity sports.

Methods:

Sleep patterns of 29 high-level student-athletes (14.7 ± 1.3 yrs) were monitored over 7 days. A psychomotor vigilance task was administered on weekdays to ascertain the effects of habitual sleep durations.

Results:

Weekend total sleep time was longer than weekdays along with a delay in bedtime, and waketimes. Psychomotor vigilance reaction times on Monday were faster than on Thursday and Friday, with reaction times on Tuesday also faster than on Friday. False starts and lapses were greater on Friday compared with Monday.

Conclusion:

There was a negative impact of sleep debt on student-athletes’ psychomotor performance.