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Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting
The goal of this study was to assess the validity, reliability and accuracy of a smartwatch-based workout analysis application in exercise recognition, repetition count and One Repetition Maximum (1RM) prediction in the strength training-specific setting. Thirty recreationally trained athletes perfo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471343/ https://www.ncbi.nlm.nih.gov/pubmed/34564323 http://dx.doi.org/10.3390/sports9090118 |
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author | Oberhofer, Katja Erni, Raphael Sayers, Mark Huber, Dominik Lüthy, Fabian Lorenzetti, Silvio |
author_facet | Oberhofer, Katja Erni, Raphael Sayers, Mark Huber, Dominik Lüthy, Fabian Lorenzetti, Silvio |
author_sort | Oberhofer, Katja |
collection | PubMed |
description | The goal of this study was to assess the validity, reliability and accuracy of a smartwatch-based workout analysis application in exercise recognition, repetition count and One Repetition Maximum (1RM) prediction in the strength training-specific setting. Thirty recreationally trained athletes performed four consecutive sets of barbell deadlift, barbell bench press and barbell back squat exercises with increasing loads from 60% to 80% of their estimated 1RM with maximum lift velocity. Data was measured using an Apple Watch Sport and instantaneously analyzed using an iOS workout analysis application called StrengthControl. The accuracies in exercise recognition and repetition count, as well as the reliability in predicting 1RM, were statistically analyzed and compared. The correct strength exercise was recognised in 88.4% of all the performed sets (N = 363) with accurate repetition count for the barbell back squat (p = 0.68) and the barbell deadlift (p = 0.09); however, repetition count for the barbell bench press was poor (p = 0.01). Only 8.9% of attempts to predict 1RM using the StrengthControl app were successful, with failed attempts being due to technical difficulties and time lag in data transfer. Using data from a linear position transducer instead, significantly different 1RM estimates were obtained when analysing repetition to failure versus load-velocity relationships. The present results provide new perspectives on the applicability of smartwatch-based strength training monitoring to improve athlete performance. |
format | Online Article Text |
id | pubmed-8471343 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84713432021-09-27 Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting Oberhofer, Katja Erni, Raphael Sayers, Mark Huber, Dominik Lüthy, Fabian Lorenzetti, Silvio Sports (Basel) Article The goal of this study was to assess the validity, reliability and accuracy of a smartwatch-based workout analysis application in exercise recognition, repetition count and One Repetition Maximum (1RM) prediction in the strength training-specific setting. Thirty recreationally trained athletes performed four consecutive sets of barbell deadlift, barbell bench press and barbell back squat exercises with increasing loads from 60% to 80% of their estimated 1RM with maximum lift velocity. Data was measured using an Apple Watch Sport and instantaneously analyzed using an iOS workout analysis application called StrengthControl. The accuracies in exercise recognition and repetition count, as well as the reliability in predicting 1RM, were statistically analyzed and compared. The correct strength exercise was recognised in 88.4% of all the performed sets (N = 363) with accurate repetition count for the barbell back squat (p = 0.68) and the barbell deadlift (p = 0.09); however, repetition count for the barbell bench press was poor (p = 0.01). Only 8.9% of attempts to predict 1RM using the StrengthControl app were successful, with failed attempts being due to technical difficulties and time lag in data transfer. Using data from a linear position transducer instead, significantly different 1RM estimates were obtained when analysing repetition to failure versus load-velocity relationships. The present results provide new perspectives on the applicability of smartwatch-based strength training monitoring to improve athlete performance. MDPI 2021-08-27 /pmc/articles/PMC8471343/ /pubmed/34564323 http://dx.doi.org/10.3390/sports9090118 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Oberhofer, Katja Erni, Raphael Sayers, Mark Huber, Dominik Lüthy, Fabian Lorenzetti, Silvio Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting |
title | Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting |
title_full | Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting |
title_fullStr | Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting |
title_full_unstemmed | Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting |
title_short | Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting |
title_sort | validation of a smartwatch-based workout analysis application in exercise recognition, repetition count and prediction of 1rm in the strength training-specific setting |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471343/ https://www.ncbi.nlm.nih.gov/pubmed/34564323 http://dx.doi.org/10.3390/sports9090118 |
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