Cargando…

Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training

BACKGROUND: Single repetition, contraction-phase specific and total time-under-tension (TUT) are crucial mechano-biological descriptors associated with distinct morphological, molecular and metabolic muscular adaptations in response to exercise, rehabilitation and/or fighting sarcopenia. However, to...

Descripción completa

Detalles Bibliográficos
Autores principales: Viecelli, Claudio, Graf, David, Aguayo, David, Hafen, Ernst, Füchslin, Rudolf M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7363108/
https://www.ncbi.nlm.nih.gov/pubmed/32667945
http://dx.doi.org/10.1371/journal.pone.0235156
_version_ 1783559610528956416
author Viecelli, Claudio
Graf, David
Aguayo, David
Hafen, Ernst
Füchslin, Rudolf M.
author_facet Viecelli, Claudio
Graf, David
Aguayo, David
Hafen, Ernst
Füchslin, Rudolf M.
author_sort Viecelli, Claudio
collection PubMed
description BACKGROUND: Single repetition, contraction-phase specific and total time-under-tension (TUT) are crucial mechano-biological descriptors associated with distinct morphological, molecular and metabolic muscular adaptations in response to exercise, rehabilitation and/or fighting sarcopenia. However, to date, no simple, reliable and valid method has been developed to measure these descriptors. OBJECTIVE: In this study we aimed to test whether accelerometer data obtained from a standard smartphone placed on the weight stack can be used to extract single repetition, contraction-phase specific and total TUT. METHODS: Twenty-two participants performed two sets of ten repetitions of their 60% one repetition maximum with a self-paced velocity on nine commonly used resistance exercise machines. Two identical smartphones were attached on the resistance exercise weight stacks and recorded all user-exerted accelerations. An algorithm extracted the number of repetitions, single repetition, contraction-phase specific and total TUT. All exercises were video-recorded. The TUT determined from the algorithmically-derived mechano-biological descriptors was compared with the video recordings that served as the gold standard. The agreement between the methods was examined using Limits of Agreement (LoA). The association was calculated using the Pearson correlation coefficients and interrater reliability was determined using the intraclass correlation coefficient (ICC 2.1). RESULTS: The error rate of the algorithmic detection of single repetitions derived from two smartphones accelerometers was 0.16%. Comparing algorithmically-derived, contraction-phase specific TUT against video, showed a high degree of correlation (r>0.93) for all exercise machines. Agreement between the two methods was high on all exercise machines as follows: LoA ranged from -0.3 to 0.3 seconds for single repetition TUT (0.1% of mean TUT), from -0.6 to 0.3 seconds for concentric contraction TUT (7.1% of mean TUT), from -0.3 to 0.5 seconds for eccentric contraction TUT (4.1% of mean TUT) and from -1.9 to 1.1 seconds for total TUT (0.5% of mean TUT). Interrater reliability for single repetition, contraction-phase specific TUT was high (ICC > 0.99). CONCLUSION: Data from smartphone accelerometer derived resistance exercise can be used to validly and reliably extract crucial mechano-biological descriptors. Moreover, the presented multi-analytical algorithmic approach enables researchers and clinicians to reliably and validly report missing mechano-biological descriptors.
format Online
Article
Text
id pubmed-7363108
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-73631082020-07-27 Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training Viecelli, Claudio Graf, David Aguayo, David Hafen, Ernst Füchslin, Rudolf M. PLoS One Research Article BACKGROUND: Single repetition, contraction-phase specific and total time-under-tension (TUT) are crucial mechano-biological descriptors associated with distinct morphological, molecular and metabolic muscular adaptations in response to exercise, rehabilitation and/or fighting sarcopenia. However, to date, no simple, reliable and valid method has been developed to measure these descriptors. OBJECTIVE: In this study we aimed to test whether accelerometer data obtained from a standard smartphone placed on the weight stack can be used to extract single repetition, contraction-phase specific and total TUT. METHODS: Twenty-two participants performed two sets of ten repetitions of their 60% one repetition maximum with a self-paced velocity on nine commonly used resistance exercise machines. Two identical smartphones were attached on the resistance exercise weight stacks and recorded all user-exerted accelerations. An algorithm extracted the number of repetitions, single repetition, contraction-phase specific and total TUT. All exercises were video-recorded. The TUT determined from the algorithmically-derived mechano-biological descriptors was compared with the video recordings that served as the gold standard. The agreement between the methods was examined using Limits of Agreement (LoA). The association was calculated using the Pearson correlation coefficients and interrater reliability was determined using the intraclass correlation coefficient (ICC 2.1). RESULTS: The error rate of the algorithmic detection of single repetitions derived from two smartphones accelerometers was 0.16%. Comparing algorithmically-derived, contraction-phase specific TUT against video, showed a high degree of correlation (r>0.93) for all exercise machines. Agreement between the two methods was high on all exercise machines as follows: LoA ranged from -0.3 to 0.3 seconds for single repetition TUT (0.1% of mean TUT), from -0.6 to 0.3 seconds for concentric contraction TUT (7.1% of mean TUT), from -0.3 to 0.5 seconds for eccentric contraction TUT (4.1% of mean TUT) and from -1.9 to 1.1 seconds for total TUT (0.5% of mean TUT). Interrater reliability for single repetition, contraction-phase specific TUT was high (ICC > 0.99). CONCLUSION: Data from smartphone accelerometer derived resistance exercise can be used to validly and reliably extract crucial mechano-biological descriptors. Moreover, the presented multi-analytical algorithmic approach enables researchers and clinicians to reliably and validly report missing mechano-biological descriptors. Public Library of Science 2020-07-15 /pmc/articles/PMC7363108/ /pubmed/32667945 http://dx.doi.org/10.1371/journal.pone.0235156 Text en © 2020 Viecelli et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Viecelli, Claudio
Graf, David
Aguayo, David
Hafen, Ernst
Füchslin, Rudolf M.
Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training
title Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training
title_full Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training
title_fullStr Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training
title_full_unstemmed Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training
title_short Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training
title_sort using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7363108/
https://www.ncbi.nlm.nih.gov/pubmed/32667945
http://dx.doi.org/10.1371/journal.pone.0235156
work_keys_str_mv AT viecelliclaudio usingsmartphoneaccelerometerdatatoobtainscientificmechanicalbiologicaldescriptorsofresistanceexercisetraining
AT grafdavid usingsmartphoneaccelerometerdatatoobtainscientificmechanicalbiologicaldescriptorsofresistanceexercisetraining
AT aguayodavid usingsmartphoneaccelerometerdatatoobtainscientificmechanicalbiologicaldescriptorsofresistanceexercisetraining
AT hafenernst usingsmartphoneaccelerometerdatatoobtainscientificmechanicalbiologicaldescriptorsofresistanceexercisetraining
AT fuchslinrudolfm usingsmartphoneaccelerometerdatatoobtainscientificmechanicalbiologicaldescriptorsofresistanceexercisetraining