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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22039
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dc.contributor.authorViecelli, Claudio-
dc.contributor.authorGraf, David-
dc.contributor.authorAguayo, David-
dc.contributor.authorHafen, Ernst-
dc.contributor.authorFüchslin, Rudolf M.-
dc.date.accessioned2021-03-15T09:49:43Z-
dc.date.available2021-03-15T09:49:43Z-
dc.date.issued2020-
dc.identifier.issn1932-6203de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22039-
dc.description.abstractBackground 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.de_CH
dc.language.isoende_CH
dc.publisherPublic Library of Science de_CH
dc.relation.ispartofPLOS ONEde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectAccelerometryde_CH
dc.subjectAdultde_CH
dc.subjectAgedde_CH
dc.subjectFemalede_CH
dc.subjectHealthy volunteerde_CH
dc.subjectHumande_CH
dc.subjectMalede_CH
dc.subjectMiddle agedde_CH
dc.subjectMuscle contractionde_CH
dc.subjectMuscle, skeletalde_CH
dc.subjectReproducibility of resultsde_CH
dc.subjectWeight liftingde_CH
dc.subjectYoung adultde_CH
dc.subjectResistance trainingde_CH
dc.subjectSmartphonede_CH
dc.subject.ddc613: Persönliche Gesundheitde_CH
dc.titleUsing smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise trainingde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
dc.identifier.doi10.1371/journal.pone.0235156de_CH
dc.identifier.doi10.21256/zhaw-22039-
dc.identifier.pmid32667945de_CH
zhaw.funding.euNode_CH
zhaw.issue7de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.starte0235156de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume15de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedAngewandte Gerontologiede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Viecelli, C., Graf, D., Aguayo, D., Hafen, E., & Füchslin, R. M. (2020). Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training. Plos One, 15(7), e0235156. https://doi.org/10.1371/journal.pone.0235156
Viecelli, C. et al. (2020) ‘Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training’, PLOS ONE, 15(7), p. e0235156. Available at: https://doi.org/10.1371/journal.pone.0235156.
C. Viecelli, D. Graf, D. Aguayo, E. Hafen, and R. M. Füchslin, “Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training,” PLOS ONE, vol. 15, no. 7, p. e0235156, 2020, doi: 10.1371/journal.pone.0235156.
VIECELLI, Claudio, David GRAF, David AGUAYO, Ernst HAFEN und Rudolf M. FÜCHSLIN, 2020. Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training. PLOS ONE. 2020. Bd. 15, Nr. 7, S. e0235156. DOI 10.1371/journal.pone.0235156
Viecelli, Claudio, David Graf, David Aguayo, Ernst Hafen, and Rudolf M. Füchslin. 2020. “Using Smartphone Accelerometer Data to Obtain Scientific Mechanical-Biological Descriptors of Resistance Exercise Training.” Plos One 15 (7): e0235156. https://doi.org/10.1371/journal.pone.0235156.
Viecelli, Claudio, et al. “Using Smartphone Accelerometer Data to Obtain Scientific Mechanical-Biological Descriptors of Resistance Exercise Training.” Plos One, vol. 15, no. 7, 2020, p. e0235156, https://doi.org/10.1371/journal.pone.0235156.


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