Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting

Katja Oberhofer; Raphael Erni; Mark Sayers; Dominik Huber; Fabian Luthy; Silvio Lorenzetti

doi:10.3390/sports9090118

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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

Journal article

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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

Katja Oberhofer, Raphael Erni, Mark Sayers, Dominik Huber, Fabian Luthy and Silvio Lorenzetti

Sports, Vol.9(9), pp.1-11

2021

DOI: https://doi.org/10.3390/sports9090118

PMCID: PMC8471343

PMID: 34564323

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Abstract

Biomechanics

resistance training

muscle strength

physical conditioning

smartphone

wearable electronic devices

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 Zurich
Raphael Erni (Author) - Swiss Federal Institute of Sport Magglingen SFISM
Mark Sayers (Author) - University of the Sunshine Coast, Queensland, School of Health and Behavioural Sciences - Legacy
Dominik Huber (Author) - Institute for Biomechanics - ETH Zürich
Fabian Luthy (Author) - Swiss Federal Institute of Sport Magglingen SFISM
Silvio 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
PMID: 34564323; PMC8471343
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