Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Viecelli C

PROVIDER: S-EPMC7363108 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Viecelli Claudio C Graf David D Aguayo David D Hafen Ernst E Füchslin Rudolf M RM

PloS one 20200715 7

<h4>Background</h4>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.<h4>Objective</h4>In this study we aimed to test whether accelerometer data obtained from a standard ...[more]

PMID: 32667945

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Project description:Although core stability (CS) training is largely used to enhance motor performance and prevent musculoskeletal injuries, the lack of methods to quantify CS training intensity hinders the design of CS programs and the comparison and generalization of their effects. The aim of this study was to analyze the reliability of accelerometers integrated into smartphones to quantify the intensity of several CS isometric exercises. Additionally, this study analyzed to what extent the pelvic acceleration data represent the local stability of the core structures or the whole-body postural control. Twenty-three male and female physically-active individuals performed two testing-sessions spaced one week apart, each consisting of two 6-second trials of five variations of frontal bridge, back bridge, lateral bridge and bird-dog exercises. In order to assess load intensity based on the postural control challenge of CS exercises, a smartphone accelerometer and two force platforms were used to measure the mean pelvic linear acceleration and the mean velocity of the centre of pressure displacement, respectively. Reliability was assessed through the intra-class correlation coefficient (ICC3,1) and the standard error of measurement (SEM). In addition, Pearson coefficient was used to analyze the correlation between parameters. The reliability analysis showed that most CS exercise variations obtained moderate-to-high reliability scores for pelvic acceleration (0.71<ICC<0.88; 13.23%?SEM?22.99%) and low-to-moderate reliability scores for centre of pressure displacement (0.24<ICC<0.89; 9.88%?SEM?35.90%). Regarding the correlation analysis, correlations between pelvic acceleration and centre of pressure displacement were moderate-to-high (0.52?r?0.81). Based on these results, smartphone accelerometers seem reliable devices to quantify isometric CS exercise intensity, which is useful to identify the individuals' CS status and to improve the dose-response characterization of CS programs.

Project description:BackgroundPoor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.ResultsThe models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/-bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.ConclusionThe logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.

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

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

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