APLGMLMar 6, 2019

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

arXiv:1903.02318v14 citations
Originality Synthesis-oriented
AI Analysis

This provides an incremental solution for recreational runners to integrate lactate threshold estimation into training more easily.

The authors tackled the problem of estimating lactate threshold for recreational runners by proposing a heuristic based on 60% of 'endurance running speed reserve', which matches the accuracy of the standard protocol while improving accessibility and consistency.

In this work, a heuristic as operational tool to estimate the lactate threshold and to facilitate its integration into the training process of recreational runners is proposed. To do so, we formalize the principles for the lactate threshold estimation from empirical data and an iterative methodology that enables experience based learning. This strategy arises as a robust and adaptive approach to solve data analysis problems. We compare the results of the heuristic with the most commonly used protocol by making a first quantitative error analysis to show its reliability. Additionally, we provide a computational algorithm so that this quantitative analysis can be easily performed in other lactate threshold protocols. With this work, we have shown that a heuristic %60 of 'endurance running speed reserve', serves for the same purpose of the most commonly used protocol in recreational runners, but improving its operational limitations of accessibility and consistent use.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes