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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 Setting1.03 MB
Published VersionCC BY V4.0, Open Access
Journal article
Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting
Sports, Vol.9(9), pp.1-11
2021
Abstract
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 RepetitionMaximum (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.
Details
- Title
- Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting
- Authors
- Katja Oberhofer (Author) - Swiss Federal Institute of Technology in ZurichRaphael Erni (Author) - Swiss Federal Institute of Sport Magglingen SFISMMark Sayers (Author) - University of the Sunshine Coast, Queensland, School of Health and Behavioural Sciences - LegacyDominik Huber (Author) - Institute for Biomechanics - ETH ZürichFabian Luthy (Author) - Swiss Federal Institute of Sport Magglingen SFISMSilvio Lorenzetti (Author) - Institute for Biomechanics - ETH Zürich
- Publication details
- Sports, Vol.9(9), pp.1-11
- Publisher
- MDPI AG
- Date published
- 2021
- DOI
- 10.3390/sports9090118
- ISSN
- 2075-4663
- Copyright note
- Copyright: © 2021 by the authors. 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/)
- Organisation Unit
- School of Health - High Performance Sport; University of the Sunshine Coast, Queensland; School of Health - Sports & Exercise Science; School of Health and Behavioural Sciences - Legacy
- Language
- English
- Record Identifier
- 99565508902621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Sport Sciences