Prediction of performance by heart rate-derived parameters in recreational runners

Abstract

We investigated whether heart rate (HR)-derived parameters are accurate performance predictors in endurance recreational runners. One hundred thirty recreational athletes completed an incremental running test (4´running + 1´rest). After each stage, we recorded HR, % of maximum HR (%HRmax), and blood lactate. We also assessed HR after each recovery period, and calculated lactate and HR recovery thresholds and HR deflection point. We tested these parameters for associations with running performance, as measured by peak treadmill speed (PTS) and personal best International Association of Athletics Federations (IAAF) score. The %HRmax at 14.5 km·h correlated with PTS (r = -0.92), and IAAF score (rho = -0.80). The magnitudes of the correlations of lactate-related parameters with PTS (|r| = 0.84 to 0.86) or IAAF score (|rho| = 0.70 to 0.77) in absolute values were slightly lower. The correlations detected between other HR-derived parameters and running performance were weaker (|r or rho| = 0.24 to 0.70). Regression models identified %HRmax at 14.5 km·h as the strongest predictor of both PTS (β = -0.72) and IAAF score (β = -0.72). Consequently, tests based on %HRmax may provide a non-invasive and inexpensive alternate method for predicting the performance of these athletes.

Factors Affecting Training and Physical Performance in Recreational Endurance Runners

“…Other field-testing evaluations include the calculation of critical speed (CS; i.e., the highest sustainable running speed that can be maintained without a continual rise in VO 2 ) from a number of maximum time trials [56], the identification of HR-running velocity associations at submaximal intensities [57], and AT determination with the changes produced in blood lactate concentration with intensity [58,59]. However, CS ideally requires maximal testing on different days, therefore it does not represent an appealing option for some recreational runners, while the blood lactate measures required for AT testing may also represent a barrier in some cases.…”

Use of fitness trackers in a blended learning model to personalize fitness running lessons

“…an oxygen cost to generate a given running speed) (Joyner & Coyle, 2008). In recreational runners the performance can alternatively be evaluated and predicted by a non-invasive and easy method of measures based on the percentage of maximal heart rate (Etxegarai et al, 2018).…”

A heuristic approach for lactate threshold estimation for training decision-making: An accessible and easy to use solution for recreational runners

“…System accuracy = 87.1% From the application perspective, we have shown that a simple heuristic (60% of endurance speed reserve) is capable of providing a estimation as good as the commonly used Dmax LT protocol for the recreational runners population. Unlike the Dmax LT protocol, our heuristic is an accessible solution that facilitates its consistent use as it relies on PTS: an easily measurable, noninvasive and robust feature that is well established for performance evaluation [17,8,14]. Its calculation is detailed below: We can further compare the usefulness of this heuristic with the Dmax protocol according to the three most common use cases:…”

“…In this work, a feature transformation is used to represent the LT in a way that it is decoupled from the PTS, so that it can be represented in relative terms whit respect to the individual endurance running speed reserve (5) As we highlighted in [8,14], there is a consistent association between the PTS and the LT. As shown in figure 6, once we dis-attach the LT from the effect of the PTS, the variability of the LT is highly reduced and the problem is simplified. Then, using the % of endurance running speed reserve corresponding to the LT as estimand, we propose to use the mean as an extremely parsimonious estimator.…”

“…• Prediction of performance: The LT here presented is derived from the PTS and therefore has a perfect positive correlation (R=1) with it. Hence, they both share the prediction power of PTS [17,8,14] and can be interchangeably used for a qualitative prediction of performance.…”