Evidence map›Paper›PMID 34283863›Full record

ArticlePloS one2021

Algorithmic extraction of smartphone accelerometer-derived mechano-biological descriptors of resistance exercise is robust to changes in intensity and velocity.

Claudio Viecelli, David Aguayo, Samuel Dällenbach, David Graf, Basil Achermann, Ernst Hafen, Rudolf M Füchslin

Open access · goldAbstract read
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Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Claudio ViecelliInstitute of Molecular Systems Biology, ETH Zurich, Zurich, Zurich, Switzerland.
David AguayoKieser Training AG, Zürich, Zurich, Switzerland.
Samuel DällenbachInstitute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Zurich, Winterthur, Switzerland.
David GrafInstitute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Zurich, Winterthur, Switzerland.
Basil Achermannieffects AG, Zurich, Switzerland.
Ernst HafenInstitute of Molecular Systems Biology, ETH Zurich, Zurich, Zurich, Switzerland.
Rudolf M FüchslinInstitute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Zurich, Winterthur, Switzerland.ORCID 0000-0002-9093-4864
ETH Zurich · CHZHAW Zurich University of Applied Sciences · CHEuropean Centre for Living Technology · ITUnion Bank of Switzerland · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt 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 (slow- versus fast-movement velocity) and intensities (30% 1-RM versus 80% 1-RM) that reflect the interindividual variability during resistance exercise.

objectiveIn 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.

methodsTwenty-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.

resultsThe 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).

conclusionNeither 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.

Indexed as

SmartphoneAccelerometryAdultAgedAlgorithmsBiochemical PhenomenaExerciseExercise TherapyFemaleHumansMaleMiddle AgedMuscle, SkeletalResistance Training

Identifiers

PMID34283863
PMCPMC8291694
OpenAlexW3185831395

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.