Quantifying the Training-Intensity Distribution in Middle-Distance Runners: The Influence of Different Methods of Training-Intensity Quantification

Bellinger, Phillip; Arnold, Blayne; Minahan, Clare

doi:10.1123/ijspp.2019-0298

Cited by 26 publications

(69 citation statements)

References 29 publications

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“…Firstly, they fail to account for individual variation in the relationship among physiological variables (e.g., between heart rate and blood lactate concentration) [123]. Secondly, the method of training intensity quantification can affect the computation of the training intensity distribution [135]. Thirdly, prescribing exercise intensity based on a fixed percentage of maximal physiological anchors (e.g., VO 2max or maximal heart rate) has little merit for eliciting distinct or domain-specific homeostatic perturbations [136].…”

Section: Training Quantification Considerationsmentioning

confidence: 99%

Crossing the Golden Training Divide: The Science and Practice of Training World-Class 800- and 1500-m Runners

et al. 2021

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Despite an increasing amount of research devoted to middle-distance training (herein the 800 and 1500 m events), information regarding the training methodologies of world-class runners is limited. Therefore, the objective of this review was to integrate scientific and best practice literature and outline a novel framework for understanding the training and development of elite middle-distance performance. Herein, we describe how well-known training principles and fundamental training characteristics are applied by world-leading middle-distance coaches and athletes to meet the physiological and neuromuscular demands of 800 and 1500 m. Large diversities in physiological profiles and training emerge among middle-distance runners, justifying a categorization into types across a continuum (400–800 m types, 800 m specialists, 800–1500 m types, 1500 m specialists and 1500–5000 m types). Larger running volumes (120–170 vs. 50–120 km·week−1 during the preparation period) and higher aerobic/anaerobic training distribution (90/10 vs. 60/40% of the annual running sessions below vs. at or above anaerobic threshold) distinguish 1500- and 800-m runners. Lactate tolerance and lactate production training are regularly included interval sessions by middle-distance runners, particularly among 800-m athletes. In addition, 800-m runners perform more strength, power and plyometric training than 1500-m runners. Although the literature is biased towards men and “long-distance thinking,” this review provides a point of departure for scientists and practitioners to further explore and quantify the training and development of elite 800- and 1500-m running performance and serves as a position statement for outlining current state-of-the-art middle-distance training recommendations.

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Section: Training Quantification Considerationsmentioning

confidence: 99%

Crossing the Golden Training Divide: The Science and Practice of Training World-Class 800- and 1500-m Runners

et al. 2021

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“…The body of literature for zone 1 to zone 2 transition seems particularly confusing (Faude et al, 2009 ; Mann et al, 2013 ; Hall et al, 2016 ). In a study looking at time spent in each training zone with heart rate and speed defined by gas exchange parameters, a greater fraction of relative time was spent in zone 2 if the zone was defined by a subjects heart rate rather than running speed (Bellinger et al, 2019 ). Of importance is also the observation that both VT1 and VT2 do not correlate well with fixed percentages of either maximum heart rate, VO 2MAX or maximum aerobic power in diverse populations (Peiffer et al, 2008 ; Azevedo et al, 2011 ; Hansen et al, 2019 ; Iannetta et al, 2020 ), making training recommendations based on these pre-set metrics problematic.…”

Section: Introduction: Exercise Prescription and Intensity Distribmentioning

confidence: 99%

“…Even more desirable would be a parameter that could be derived via non-invasive, low cost and commonly available wearable devices (Düking et al, 2016 ). One of the easy accessible subjective variables is the rating of perceived exertion (RPE), which has proven as sensitive for evaluating organismic system fatigue during exercise (Eston, 2012 ), but differs regarding results of training-intensity distribution from other methods (Bellinger et al, 2019 ). In addition, various indexes of heart rate variability (HRV; providing heart rate time series by RR-intervals and refers to the potential changing patterns of cardiac beat to beat timing, which after statistical analysis may provide physiologic information on autonomic nervous system outflow on the heart; Billman, 2011 ) resulting from time- and frequency-domain analysis have been studied during dynamic exercise and have been shown to alter as work rates increase, with the greatest change occurring during lower intensities (Tulppo et al, 1996 ; Sandercock and Brodie, 2006 ; Karapetian et al, 2008 ; Michael et al, 2017 ).…”

Section: Introduction: Exercise Prescription and Intensity Distribmentioning

confidence: 99%

Fractal Correlation Properties of Heart Rate Variability: A New Biomarker for Intensity Distribution in Endurance Exercise and Training Prescription?

2020

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Exercise and training prescription in endurance-type sports has a strong theoretical background with various practical applications based on threshold concepts. Given the challenges and pitfalls of determining individual training zones on the basis of subsystem indicators (e.g., blood lactate concentration, respiratory parameters), the question arises whether there are alternatives for intensity distribution demarcation. Considering that training in a low intensity zone substantially contributes to the performance outcome of endurance athletes and exceeding intensity targets based on a misleading aerobic threshold can lead to negative performance and recovery effects, it would be desirable to find a parameter that could be derived via non-invasive, low cost and commonly available wearable devices. In this regard, analytics conducted from non-linear dynamics of heart rate variability (HRV) have been adapted to gain further insights into the complex cardiovascular regulation during endurance-type exercise. Considering the reciprocal antagonistic behavior and the interaction of the sympathetic and parasympathetic branch of the autonomic nervous system from low to high exercise intensities, it may be promising to use an approach that utilizes information about the regulation quality of the organismic system to determine training-intensity distribution. Detrended fluctuation analysis of HRV and its short-term scaling exponent alpha1 (DFA-alpha1) seems suitable for applied sport-specific settings including exercise from low to high intensities. DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance-type sports. The present perspective illustrates the potential of DFA-alpha1 for diagnostic and monitoring purposes as a "global" system parameter and proxy for organismic demands.

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“…3 Historically, most runners and coaches have used external load factors such as volume (e.g., distance or duration) and intensity (e.g., pace) to quantity running training as these methods are engrained in the running culture. 4–7 However, there may be more appropriate and effective methods to monitor training stress in runners. A better understanding of and improvements in methods to quantify training stress should help coaches optimize running performance.…”

Section: Introductionmentioning

confidence: 99%

“…26 Training-intensity distribution (i.e., time spent within specific intensity zones) is another approach used to quantify running training but input intensity metrics (e.g., HR, speed, and RPE) greatly influence the resulting training-intensity distribution in middle-distance runners. 4 Similarly, the use of biomechanically-specific external load measures could yield different week-to-week changes in training load in a homogenous group of runners who have been prescribed the same training program.…”

Section: Introductionmentioning

confidence: 99%

Comparison of different measures to monitor week-to-week changes in training load in high school runners

Ryan¹,

Napier

Greenwood³

et al. 2020

International Journal of Sports Science & Coaching

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Training load is commonly used to monitor training stress and is the product of external and internal physiological loads experienced by an athlete. With emerging wearable technology, it is possible to evolve existing external load measurement from duration or distance to runner-specific biomechanical data, which when combined with existing measures of internal load such as session rating of perceived exertion (sRPE), may improve the quantification of training stress. This study compared week-to-week changes in training duration with different training loads obtained from common and more individualized measures of external load. The training of nine male high school cross-country runners from the same team undertaking the same training program was monitored for two consecutive weeks. This two-week cycle included a “coach-prescribed” low and high training load week. Training loads were calculated with sRPE and external load measures including: duration (minutes), Step Count, “Bone Stimulus” (IMeasureU), and cumulative vertical force. Weekly distances (in miles) were also measured. Between-week percent change (%Δ) was compared among training loads and minutes using paired t-tests and Cohen’s d effect size. Different %Δ were found between sRPExMinutes (%Δ=65 ± 25%; p = 0.002, d = 1.83), sRPExStep Count (%Δ=66 ± 31%; p = 0.006, d = 2.06), sRPExForce (%Δ=66 ± 29%; p = 0.002, d = 1.91), and miles (%Δ=28 ± 13%; p = 0.019, d = 0.71) compared to minutes (%Δ=20 ± 8%). These findings highlight that only using weekly volume can greatly misrepresent changes in training stress in runners. We therefore encourage coaches and practitioners to consider training monitoring approaches beyond just weekly distance or duration. Simple measures of training load that include duration and sRPE might be sufficient.

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Quantifying the Training-Intensity Distribution in Middle-Distance Runners: The Influence of Different Methods of Training-Intensity Quantification

Cited by 26 publications

References 29 publications

Crossing the Golden Training Divide: The Science and Practice of Training World-Class 800- and 1500-m Runners

Crossing the Golden Training Divide: The Science and Practice of Training World-Class 800- and 1500-m Runners

Fractal Correlation Properties of Heart Rate Variability: A New Biomarker for Intensity Distribution in Endurance Exercise and Training Prescription?

Comparison of different measures to monitor week-to-week changes in training load in high school runners

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