Evidence map›Paper›PMID 32667945›Full record

ArticlePloS one2020

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

Claudio Viecelli, David Graf, David Aguayo, Ernst Hafen, Rudolf M Füchslin

Open access · goldAbstract readValidation Study
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 2 countries.

Claudio ViecelliInstitute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
David GrafInstitute of Applied Mathematics and Physics, Zurich University of Applied Sciences Zurich, Winterthur, Switzerland.
David AguayoKieser Training AG, Zürich, Switzerland.
Ernst HafenInstitute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Rudolf M FüchslinInstitute of Applied Mathematics and Physics, Zurich University of Applied Sciences Zurich, Winterthur, Switzerland.
ETH Zurich · CHZHAW Zurich University of Applied Sciences · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSingle 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.

objectiveIn 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.

methodsTwenty-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).

resultsThe 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).

conclusionData 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.

Indexed as

Resistance TrainingSmartphoneAccelerometryAdultAgedFemaleHealthy VolunteersHumansMaleMiddle AgedMuscle ContractionMuscle, SkeletalReproducibility of ResultsWeight LiftingYoung Adult

Identifiers

PMID32667945
PMCPMC7363108
OpenAlexW3042319095

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.