Semiautomatic Training Load Determination in Endurance Athletes

Journal article


Dausin, Christophe, Ruiz-Carmona, Sergio, De Bosscher, Ruben, Janssens, Kristel, Herbots, Lieven, Heidbuchel, Hein, Hespel, Peter, Cornelissen, Véronique, Willems, Rik, La Gerche, Andre and Claessen, Guido. (2023). Semiautomatic Training Load Determination in Endurance Athletes. Journal for the Measurement of Physical Behaviour. 6(3), pp. 193-201. https://doi.org/10.1123/jmpb.2023-0016
AuthorsDausin, Christophe, Ruiz-Carmona, Sergio, De Bosscher, Ruben, Janssens, Kristel, Herbots, Lieven, Heidbuchel, Hein, Hespel, Peter, Cornelissen, Véronique, Willems, Rik, La Gerche, Andre and Claessen, Guido
Abstract

Background: Despite endurance athletes recording their training data electronically, researchers in sports cardiology rely on questionnaires to quantify training load. This is due to the complexity of quantifying large numbers of training files. We aimed to develop a semiautomatic postprocessing tool to quantify training load in clinical studies.
Methods: Training data were collected from two prospective athlete’s heart studies (Master Athlete’s Heart study and Prospective Athlete Heart study). Using in-house developed software, maximal heart rate (MaxHR) and training load were calculated from heart rate monitored during cumulative training sessions. The MaxHR in the lab was compared with the MaxHR in the field. Lucia training impulse score, based on individually based exercise intensity zones, and Edwards training impulse, based on MaxHR in the field, were compared. A questionnaire was used to determine the number of training sessions and training hours per week.
Results: Forty-three athletes recorded their training sessions using a chest-worn heart rate monitor and were selected for this analysis. MaxHR in the lab was significantly lower compared with MaxHR in the field (183 ± 12 bpm vs. 188 ± 13 bpm, p < .01), but correlated strongly (r = .81, p < .01) with acceptable limits of agreement (±15.4 bpm). An excellent correlation was found between Lucia training impulse score and Edwards training impulse (r = .92, p < .0001). The quantified number of training sessions and training hours did not correlate with the number of training sessions (r = .20) and training hours (r = −.12) reported by questionnaires.
Conclusion: Semiautomatic measurement of training load is feasible in a wide age group. Standard exercise questionnaires are insufficiently accurate in comparison to objective training load quantification.

Keywordsexercise; automatization; cardiology
Year01 Jan 2023
JournalJournal for the Measurement of Physical Behaviour
Journal citation6 (3), pp. 193-201
PublisherHuman Kinetics Publishers
ISSN2575-6605
Digital Object Identifier (DOI)https://doi.org/10.1123/jmpb.2023-0016
Web address (URL)https://journals.humankinetics.com/view/journals/jmpb/6/3/article-p193.xml
Open accessPublished as ‘gold’ (paid) open access
Research or scholarlyResearch
Page range193-201
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online14 Oct 2023
Publication process dates
AcceptedOct 2023
Deposited19 Mar 2024
Additional information

© 2023 The Authors.

Published by Human Kinetics, Inc. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License, CC BY-NC 4.0, which permits the copy and redistribution in any medium or format, provided it is not used for commercial purposes, the original work is properly cited, the new use includes a link to the license, and any changes are indicated. See http://creativecommons.org/licenses/by-nc/4.0 . This license does not cover any third-party material that may appear with permission in the article. For commercial use, permission should be requested from Human Kinetics, Inc., through the Copyright Clearance Center (http://www.copyright.com).

Place of publicationUnited States
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