No abstract

Heart rate variability reflects fluctuations in the changes in consecutive heartbeats, providing insight into cardiac autonomic function and overall physiological state. Endurance athletes typically demonstrate better cardiac autonomic function than non-athletes, with lower resting heart rates and greater variability. The availability and use of heart rate variability metrics has increased in the broader population and may be particularly useful to endurance athletes. The purpose of this review is to characterize current practices and applications of heart rate variability analysis in endurance athletes. Important considerations for heart rate variability analysis will be discussed, including analysis techniques, monitoring tools, the importance of stationarity of data, body position, timing and duration of the recording window, average heart rate, and sex and age differences. Key factors affecting resting heart rate variability will be discussed, including exercise intensity, duration, modality, overall training load, and lifestyle factors. Training applications will be explored, including heart rate variability-guided training and the identification and monitoring of maladaptive states such as overtraining. Lastly, we will examine some alternative uses of heart rate variability, including during exercise, post-exercise, and for physiological forecasting and predicting performance.

Peak aerobic power (V Ȯ2peak) and parameters related to training are associated with long-distance running performance in master athletes. Running economy (RE) predicts performance in younger runners, but its relationship to racing ability in older athletes is unclear. Allometrically scaled RE (alloV Ȯ2; ml kg -0.66 min -1 ), energy cost (EC; kcal kg -1 km -1 ), and percent of V Ȯ2peak (%V Ȯ2peak) required in a submaximal bout represent RE more accurately than V Ȯ2 does. The VDOT score, estimating V Ȯ2peak and RE, can be used to compare races of different distances. Purpose: To determine predictors of temperature-converted VDOT in master runners training for a long-distance race (10-26.2 mi). Methods: Twenty-three master runners (age 57±9 years; eight females) performed treadmill marathon-intensity-effort (MIE) and V Ȯ2peak tests within four weeks of their goal race. The MIE occurred at 88% of predicted maximum heart rate, which corresponds to estimated marathon intensity. Participants completed online training-history surveys. Forward stepwise multiple linear regression was used to find key predictors of VDOT. The alpha level for significance was .05. Results: Converted VDOT was significantly associated with 3-year peak weekly training distance (3YP) (r = 0.454, p = .039), V Ȯ2peak (r = 0.845, p = .000), alloV Ȯ2 (r = 0.623, p = .005), and EC (r = -0.528, p = .018). The bestfitting model included V Ȯ2peak and 3YP (r = 0.898). Conclusion: Physiological and training factors are related to race performance in master runners. The best predictors of VDOT are V Ȯ2peak and 3YP. Training to enhance these variables may improve distance-running performance in masters.

The purpose of this study was to compare metabolic variables during submaximal running as predictors of marathon performance. Running economy (RE) and respiratory exchange ratio (RER) data were gathered during a 30 min incremental treadmill run completed within 2 weeks prior to running a 42.2-km marathon. Paces during the treadmill run progressed every 5 min from 75-100% of 10-km race velocity. Variables at each stage were analyzed as predictors of relative marathon performance (RMP) in competitive (COMP) and recreational (REC) runners. Twenty-nine runners were classified as COMP (n = 12; age 30 ± 8 years) or REC (n =17; age 20 ± 1 year) based on performance in shorter races. RMP was calculated as percent difference from predicted marathon finish time. Two methods of calculating RE were used: unscaled (ml . kg -1. km -1 ) and with allometric scaling of body mass (ml . kg -0.75. km -1 ). The COMP runners were significantly more economical than REC (p=0.005; p=0.015 with scaling). For the whole population, RE with and without scaling was significantly correlated with RMP. Within groups, RMP was not significantly correlated with RE unless scaling was used: COMP runners at 75% (p=0.044), 80% (p=0.040), and REC runners at 85% (p=0.038). Runners classified as COMP were more economical than REC, but RER was not different. The use of allometric scaling is important when assessing homogeneous groups. In this study, allometrically-scaled RE at 80-85% of 10-km velocity was the best predictor of RMP within groups.