To assess the validity of methods for quantifying training load, fitness and fatigue in endurance athletes using a mathematical model. Seven trained runners (
 $$\dot{V}$$
 O2max: 51.7 ± 4.5 mL kg−1 min−1, age: 38.6 ± 9.4 years, mean ± SD) completed 15 weeks of endurance running training. Training sessions were assessed using a heart rate (HR), running pace and rating of perceived exertion (RPE). Training dose was calculated using the session-RPE method, Banisters TRIMP and the running training stress score (rTSS). Weekly running performance (1,500-m time trial), fitness (submaximal HR, resting HR) and fatigue [profile of mood states, heart rate variability (HRV)] were measured. A mathematical model was applied to the training data from each runner to provide individual estimates of performance, fitness and fatigue. Correlations assessed the relationships between the modelled and actual weekly performance, fitness and fatigue measures within each runner. Training resulted in 5.4 ± 2.6 % improvement in 1,500-m performance. Modelled performance was correlated with actual performance in each subject, with relationships being r = 0.70 ± 0.11, 0.60 ± 0.10 and 0.65 ± 0.13 for the rTSS, session-RPE and TRIMP input methods, respectively. There were moderate correlations between modelled and actual fitness (submaximal HR) for the session-RPE (−0.43 ± 0.37) and TRIMP (−0.48 ± 0.39) methods and moderate-to-large correlations between modelled and actual fatigue measured through HRV indices for both session-RPE (−0.48 ± 0.39) and TRIMP (−0.59 ± 0.31) methods. These findings showed that each of the training load methods investigated are appropriate for quantifying endurance training dose and that submaximal HR and HRV may be useful for monitoring fitness and fatigue, respectively.

A comparison of methods for quantifying training load: relationships between modelled and actual training responses.

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. ...

Heart-rate variability and training-intensity distribution in elite rowers.

We sought to determine whether improved cycling performance following ‘Live High-Train Low’ (LHTL) occurs if increases in haemoglobin mass (Hbmass) are prevented via periodic phlebotomy during hypoxic exposure. Eleven, highly trained, female cyclists completed 26 nights of simulated LHTL (16 h day−1, 3000 m). Hbmass was determined in quadruplicate before LHTL and in duplicate weekly thereafter. After 14 nights, cyclists were pair-matched, based on their Hbmass response (ΔHbmass) from baseline, to form a response group (Response, n = 5) in which Hbmass was free to adapt, and a Clamp group (Clamp, n = 6) in which ΔHbmass was negated via weekly phlebotomy. All cyclists were blinded to the blood volume removed. Cycling performance was assessed in duplicate before and after LHTL using a maximal 4-min effort (MMP4min) followed by a ride time to exhaustion test at peak power output (T
 lim). VO2peak was established during the MMP4min. Following LHTL, Hbmass increased in Response (mean ± SD, 5.5 ± 2.9%). Due to repeated phlebotomy, there was no ΔHbmass in Clamp (−0.4 ± 0.6%). VO2peak increased in Response (3.5 ± 2.3%) but not in Clamp (0.3 ± 2.6%). MMP4min improved in both the groups (Response 4.5 ± 1.1%, Clamp 3.6 ± 1.4%) and was not different between groups (p = 0.58). T
 lim increased only in Response, with Clamp substantially worse than Response (−37.6%; 90% CL −58.9 to −5.0, p = 0.07). Our novel findings, showing an ~4% increase in MMP4min despite blocking an ~5% increase in Hbmass, suggest that accelerated erythropoiesis is not the sole mechanism by which LHTL improves performance. However, increases in Hbmass appear to influence the aerobic contribution to high-intensity exercise which may be important for subsequent high-intensity efforts.

The contribution of haemoglobin mass to increases in cycling performance induced by simulated LHTL.

A number of professions rely on exercise prescription to improve health or athletic performance, including coaching, fitness/personal training, rehabilitation, and exercise physiology. It is theref...

Rationale and Resources for Teaching the Mathematical Modeling of Athletic Training and Performance.

In order to quantify the seasonal variability of haemoglobin mass (Hbmass) in cyclists during a competitive season, and investigate whether variability is associated with changes in training load or performance, Hbmass was measured in 10 internationally competitive female road cyclists approximately once per month for 2–10 months via CO-rebreathing. Power meters were used to quantify daily load (Training Stress Scores) during training and racing, from which cumulative training load units for 7, 14, 28 and 42 day were calculated. Maximal mean power (MMP) for 1, 4, 10 and 25 min, performed during training or racing was used as a surrogate for performance. The relationship between changes in training load (%ΔTraining) and changes in Hbmass (%ΔHbmass), and between %ΔHbmass and changes in MMP (%ΔMMP) was established via regression analysis. Individual coefficients of variation (CV) in Hbmass ranged from 2.0 to 4.4%. The weighted mean CV in Hbmass was 3.3% (90% Confidence Limits: 2.9–3.8%) or 23 g over the average 6.6 ± 2.3 month monitoring period. The effect of %ΔTraining on %ΔHbmass was small for 7 and 14 day (r = 0.22 and 0.29), moderate for 42 day (r = 0.35) and large for 28 day (r = 0.56). The regression slope was greatest for 42 day, with a 10% change in training associated with a ~1% change in Hbmass. The relationship between %ΔHbmass and %ΔMMP was ~0.5:1 for MMP1min,10min and 25min and ~1:1 for MMP4min, respectively. Hbmass varies by ~3% in female cyclists during a competitive season. Some of the variation may be related to oscillations in chronic training load.

Seasonal variation of haemoglobin mass in internationally competitive female road cyclists

Training with power means that you are utilising the latest tool in the cycling industry today in order to maximize your athletic potential.With every crank of the pedals, power meters can bring about improved performance for both cyclists and triathletes. But understanding how to use the technology is key. Here's the first book to fully explore the benefits and methodology of power-based training. Step-by-step instructions explain how to incorporate power into regular training, identify strengths and weaknesses, track progress throughout the season, and peak for the most important races."Training & Racing with a Power Meter" is for all power meter users or those thinking about taking the plunge. It can help the advanced users better understand their current power data and delve deeper into the numbers, while new users will find out the steps to take in order to better use their power meter. And if you don't own a power meter yet, let "Training & Racing with a Power Meter" be your guide to the products available with recommendations, installation notes and clear directions on how to interpret the data they provide.Authors Allen and Coggan explode the myth that power meter training is too complicated for the average cyclist. This title includes a thorough guide to help cyclists find the right power meter to suit their goals. Comprehensive illustrations explain key concepts.

Training and Racing with a Power Meter

Chapter 1. Evaluating Cycling Science Chapter 3. Tracking Effort and Performance Chapter 5. Preventing Overtraining and Enhancing Recovery Chapter 6. Finding Your Perfect Pace Chapter 7. Optimizing Bike Fit Chapter 8. Pedaling for Peak Efficiency Chapter 9. Understanding Hydration and Supplements.

Hunter Allen

Papers

Training and Racing with a Power Meter

Cutting-Edge Cycling