Historically, the ability of coaches to prescribe training to achieve optimal athletic performance can be attributed to many years of personal experience. A more modern approach is to adopt scientific methods in the development of optimal training programmes. However, there is not much research in this area, particularly into the quantification of training programmes and their effects on physiological adaptation and subsequent performance. Several methods have been used to quantify training load, including questionnaires, diaries, physiological monitoring and direct observation. More recently, indices of training stress have been proposed, including the training impulse, which uses heart rate measurements and training load, and session rating of perceived exertion measurements, which utilizes subjective perception of effort scores and duration of exercise. Although physiological adaptations to training are well documented, their influence on performance has not been accurately quantified. To date, no single physiological marker has been identified that can measure the fitness and fatigue responses to exercise or accurately predict performance. Models attempting to quantify the relationship between training and performance have been proposed, many of which consider the athlete as a system in which the training load is the input and performance the system output. Although attractive in concept, the accuracy of these theoretical models has proven poor. A possible reason may be the absence of a measure of individuality in each athlete's response to training. Thus, in the future more attention should be directed towards measurements that reflect individual capacity to respond or adapt to exercise training rather than an absolute measure of changes in physiological variables that occur with training.
Endurance training decreases resting and submaximal heart rate, while maximum heart rate may decrease slightly or remain unchanged after training. The effect of endurance training on various indices of heart rate variability remains inconclusive. This may be due to the use of inconsistent analysis methodologies and different training programmes that make it difficult to compare the results of various studies and thus reach a consensus on the specific training effects on heart rate variability. Heart rate recovery after exercise involves a coordinated interaction of parasympathetic re-activation and sympathetic withdrawal. It has been shown that a delayed heart rate recovery is a strong predictor of mortality. Conversely, endurance-trained athletes have an accelerated heart rate recovery after exercise. Since the autonomic nervous system is interlinked with many other physiological systems, the responsiveness of the autonomic nervous system in maintaining homeostasis may provide useful information about the functional adaptations of the body. This review investigates the potential of using heart rate recovery as a measure of training-induced disturbances in autonomic control, which may provide useful information for training prescription.
Purpose:To establish the relationship between a subjective (session rating of perceived exertion [RPE]) and 2 objective (training impulse [TRIMP]) and summated-heart-rate-zone (SHRZ) methods of quantifying training load and explain characteristics of the variance not accounted for in these relationships.Methods:Thirty-three participants trained ad libitum for 2 wk, and their heart rate (HR) and RPE were recorded to calculate training load. Subjects were divided into groups based on whether the regression equations over- (OVER), under- (UNDER), or accurately predicted (ACCURATE) the relationship between objective and subjective methods.Results:A correlation of r = .76 (95% CI: .56 to .88) occurred between TRIMP and session-RPE training load. OVER spent a greater percentage of training time in zone 4 of SHRZ (ie, 80% to 90% HRmax) than UNDER (46% ± 8% vs 25% ± 10% [mean ± SD], P = .008). UNDER spent a greater percentage of training time in zone 1 of SHRZ (ie, 50% to 60% HRmax) than OVER (15% ± 8% vs 3% ± 3%, P = .005) and ACCURATE (5% ± 3%, P = .020) and more time in zone 2 of SHRZ (ie, 60% to 70%HRmax) than OVER (17% ± 6% vs 7% ± 6%, P = .039). A correlation of r = .84 (.70 to .92) occurred between SHRZ and session-RPE training load. OVER spent proportionally more time in Zone 4 than UNDER (45% ± 8% vs 25% ± 10%, P = .018). UNDER had a lower training HR than ACCURATE (132 ± 10 vs 148 ± 12 beats/min, P = .048) and spent more time in zone 1 than OVER (15% ± 8% vs 4% ± 3%, P = .013) and ACCURATE (5% ± 3%, P = .015).Conclusions:The session-RPE method provides reasonably accurate assessments of training load compared with HR-based methods, but they deviate in accuracy when proportionally more time is spent training at low or high intensity.
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