Algorithmic extraction of smartphone accelerometer-derived mechano-biological descriptors of resistance exercise is robust to changes in intensity and velocity
dc.contributor.author
Viecelli, Claudio
dc.contributor.author
Aguayo, David
dc.contributor.author
Dällenbach, Samuel
dc.contributor.author
Graf, David
dc.contributor.author
Achermann, Basil
dc.contributor.author
Hafen, Ernst
dc.contributor.author
Füchslin, Rudolf M.
dc.date.accessioned
2021-08-04T05:58:07Z
dc.date.available
2021-08-04T00:16:21Z
dc.date.available
2021-08-04T05:58:07Z
dc.date.issued
2021-07-20
dc.identifier.issn
1932-6203
dc.identifier.other
10.1371/journal.pone.0254164
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/499246
dc.identifier.doi
10.3929/ethz-b-000499246
dc.description.abstract
Background It was shown that single repetition, contraction-phase specific and total time-under-tension (TUT) can be extracted reliably and validly from smartphone accelerometer-derived data of resistance exercise machines using user-determined resistance exercise velocities at 60% one repetition maximum (1-RM). However, it remained unclear how robust the extraction of these mechano-biological descriptors is over a wide range of movement velocities (slowversus fast-movement velocity) and intensities (30% 1-RM versus 80% 1-RM) that reflect the interindividual variability during resistance exercise. Objective In this work, we examined whether the manipulation of velocity or intensity would disrupt an algorithmic extraction of single repetitions, contraction-phase specific and total TUT. Methods Twenty-seven participants performed four sets of three repetitions of their 30% and 80% 1- RM with velocities of 1 s, 2 s, 6 s and 8 s per repetition, respectively. An algorithm extracted the number of repetitions, single repetition, contraction-phase specific and total TUT. All exercises were video-recorded. The video recordings served as the gold standard to which algorithmically-derived TUT was compared. The agreement between the methods was examined using Limits of Agreement (LoA). The Pearson correlation coefficients were used to calculate the association, and the intraclass correlation coefficient (ICC 2.1) examined the interrater reliability. Results The calculated error rate for the algorithmic detection of the number of single repetitions derived from two smartphones accelerometers was 1.9%. The comparison between algorithmically- derived, contraction-phase specific TUT against video, revealed a high degree of correlation (r > 0.94) for both exercise machines. The agreement between the two methods was high on both exercise machines, intensities and velocities and was as follows: LoA ranged from -0.21 to 0.22 seconds for single repetition TUT (2.57% of mean TUT), from -0.24 to 0.22 seconds for concentric contraction TUT (6.25% of mean TUT), from -0.22 to 0.24 seconds for eccentric contraction TUT (5.52% of mean TUT) and from -1.97 to 1.00 seconds for total TUT (5.13% of mean TUT). Interrater reliability for single repetition, contraction- phase specific TUT was high (ICC > 0.99). Conclusion Neither intensity nor velocity disrupts the proposed algorithmic data extraction approach. Therefore, smartphone accelerometers can be used to extract scientific mechano-biological descriptors of dynamic resistance exercise with intensities ranging from 30% to 80% of the 1-RM with velocities ranging from 1 s to 8 s per repetition, respectively, thus making this simple method a reliable tool for resistance exercise mechano-biological descriptors extraction.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
PLOS
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Algorithmic extraction of smartphone accelerometer-derived mechano-biological descriptors of resistance exercise is robust to changes in intensity and velocity
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
PLoS ONE
ethz.journal.volume
16
en_US
ethz.journal.issue
7
en_US
ethz.journal.abbreviated
PLoS ONE
ethz.pages.start
e0254164
en_US
ethz.size
22 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
San Francisco, CA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02538 - Institut für Molekulare Systembiologie / Institute for Molecular Systems Biology::03710 - Hafen, Ernst (emeritus) / Hafen, Ernst (emeritus)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02538 - Institut für Molekulare Systembiologie / Institute for Molecular Systems Biology::03710 - Hafen, Ernst (emeritus) / Hafen, Ernst (emeritus)
ethz.date.deposited
2021-08-04T00:16:34Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-08-04T05:58:13Z
ethz.rosetta.lastUpdated
2022-03-29T10:54:24Z
ethz.rosetta.versionExported
true
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