Nathan E. Townsend

Ventilatory acclimatisation is beneficial for high-intensity exercise at altitude in elite cyclists

Abstract Aim: The aim of this study was to examine the relationship between ventilatory adaptation and performance during altitude training at 2700 m. Methods: Seven elite cyclists (age: 21.2 ± 1.1 yr, body mass: 69.9 ± 5.6 kg, height 176.3 ± 4.9 cm) participated in this study. A hypoxic ventilatory response (HVR) test and a submaximal exercise test were performed at sea level prior to the training camp and again after 15 d at altitude (ALT15). Ventilation (VE), end-tidal carbon-dioxide partial pressure (PETCO2) and oxyhaemoglobin saturation via pulse oximetry (SpO2) were measured at rest and during submaximal cycling at 250 W. A hill climb (HC) performance test was conducted at sea level and after 14 d at altitude (ALT14) using a road of similar length (5.5–6 km) and gradient (4.8–5.3%). Power output was measured using SRM cranks. Average HC power at ALT14 was normalised to sea level power (HC%). Multiple regression was used to identify significant predictors of performance at altitude. Results: At ALT15, there was a significant increase in resting VE (10.3 ± 1.9 vs. 12.2 ± 2.4 L·min−1) and HVR (0.34 ± 0.24 vs. 0.71 ± 0.49 L·min−1·%−1), while PETCO2 (38.4 ± 2.3 vs. 32.1 ± 3.3 mmHg) and SpO2 (97.9 ± 0.7 vs. 94.0 ± 1.7%) were reduced (P < .05). Multiple regression revealed ΔHVR and exercise VE at altitude as significant predictors of HC% (adjusted r2 = 0.913; P = 0.003). Conclusions: Ventilatory acclimatisation occurred during a 2 wk altitude training camp in elite cyclists and a higher HVR was associated with better performance at altitude, relative to sea level. These results suggest that ventilatory acclimatisation is beneficial for cycling performance at altitude.

Prediction of Critical Power and W′ in Hypoxia: Application to Work-Balance Modelling

Purpose: Develop a prediction equation for critical power (CP) and work above CP (W′) in hypoxia for use in the work-balance (WBAL′) model. Methods: Nine trained male cyclists completed cycling time trials (TT; 12, 7, and 3 min) to determine CP and W′ at five altitudes (250, 1,250, 2,250, 3,250, and 4,250 m). Least squares regression was used to predict CP and W′ at altitude. A high-intensity intermittent test (HIIT) was performed at 250 and 2,250 m. Actual and predicted CP and W′ were used to compute W′ during HIIT using differential (WBALdiff′) and integral (WBALint′) forms of the WBAL′ model. Results: CP decreased at altitude (P < 0.001) as described by 3rd order polynomial function (R2 = 0.99). W′ decreased at 4,250 m only (P < 0.001). A double-linear function characterized the effect of altitude on W′ (R2 = 0.99). There was no significant effect of parameter input (actual vs. predicted CP and W′) on modelled WBAL′ at 2,250 m (P = 0.24). WBALdiff′ returned higher values than WBALint′ throughout HIIT (P < 0.001). During HIIT, WBALdiff′ was not different to 0 kJ at completion, at 250 m (0.7 ± 2.0 kJ; P = 0.33) and 2,250 m (−1.3 ± 3.5 kJ; P = 0.30). However, WBALint′ was lower than 0 kJ at 250 m (−0.9 ± 1.3 kJ; P = 0.058) and 2,250 m (−2.8 ± 2.8 kJ; P = 0.02). Conclusion: The altitude prediction equations for CP and W′ developed in this study are suitable for use with the WBAL′ model in acute hypoxia. This enables the application of WBAL′ modelling to training prescription and competition analysis at altitude.

Effect of Intermittent Hypobaric Hypoxia on Erythropoiesis

We examined the effect of 3 hours of intermittent hypobaric hypoxia on erythropoiesis. METHODS: 23 trained athletes were randomly assigned to either hypobaric hypoxia (HYPO; simulated altitude of 4000-5500m) or normoxia (NORM; 0-500m) in a double-blind, placebo controlled design. Both groups rested in a hypobaric chamber for 3 h/day, 5 day/wk, for 4 wks. Total Hb mass (CO rebreathing) was measured twice before and twice after treatment. Blood was drawn 8 times during the 10-wk study, (twice before, once per week during and twice after) and analyzed for [Hb], reticulocyte Hb, soluble transferin receptor (sTfr) and erythropoietin (EPO). Blood was also drawn twice (Wk 2, Wk 4) within 3hrs of chamber exposure and assayed for EPO.

Hemodynamic Responses to Intermittent Hypoxia Exposure in Trained Athletes

1UNTHSC-Texas College of Osteopathic Medicine, Fort Worth, TX. 2Australian Institute of Sport, Canberra, Australia. 3Institute for Exercise and Environmental Medicine, Presbyterian Hospital/UT Southwestern Medical Center, Dallas, TX, Dallas, TX. 4Universtitat de Barcelona, Barcelona, Spain. 5Vrije Universteit Amsterdam, Amsterdam, Netherlands. 6Institute for Exercise and Environmental Medicine, Presebyterian Hospital/UT Southwestern Medical Center, Dallas, TX, Dallas, TX.

The Critical Power Model as a Potential Tool for Anti-doping

Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W′. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation.