Between- and within-subject variance of motor variability metrics in females performing repetitive upper-extremity precision work
Kinematic motor variability is extensively studied in occupational, clinical and sports biomechanics, but the consistency of most motor variability metrics have never been reported. In this study, fourteen subjects performed a repetitive pipetting task on three separate days. Movements of hand, arm...
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| Veröffentlicht in: | Journal of electromyography and kinesiology Jg. 25; H. 1; S. 121 - 129 |
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| Sprache: | Englisch |
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Elsevier Ltd
01.02.2015
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| ISSN: | 1050-6411, 1873-5711, 1873-5711 |
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| Abstract | Kinematic motor variability is extensively studied in occupational, clinical and sports biomechanics, but the consistency of most motor variability metrics have never been reported. In this study, fourteen subjects performed a repetitive pipetting task on three separate days. Movements of hand, arm and pipette tip were recorded in 3D and used to compute shoulder elevation, elbow flexion and shoulder-arm coordination angles, as well as pipette-tip endpoint precision. Cycle-to-cycle motor variability was quantified using linear dispersion measures of standard kinematics properties such as peak velocity, range of motion, and inter-segmental relative phase. Between- and within-subject consistencies of these variability metrics were quantified by variance components estimated using a nested random effects model. For most metrics, the variance between subjects was larger than that between days and cycles. Entering the variance components in statistical power equations showed that for most metrics, a total of 80–100 subjects will be required to detect a 20% difference between two groups with sufficient power, while this difference can typically be detected in repeated-measures (paired) designs using 25 subjects. The reported between and within-subject variance components can be used as a data base to facilitate efficient designs of future studies of kinematic motor variability. |
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| AbstractList | Kinematic motor variability is extensively studied in occupational, clinical and sports biomechanics, but the consistency of most motor variability metrics have never been reported. In this study, fourteen subjects performed a repetitive pipetting task on three separate days. Movements of hand, arm and pipette tip were recorded in 3D and used to compute shoulder elevation, elbow flexion and shoulder-arm coordination angles, as well as pipette-tip endpoint precision. Cycle-to-cycle motor variability was quantified using linear dispersion measures of standard kinematics properties such as peak velocity, range of motion, and inter-segmental relative phase. Between- and within-subject consistencies of these variability metrics were quantified by variance components estimated using a nested random effects model. For most metrics, the variance between subjects was larger than that between days and cycles. Entering the variance components in statistical power equations showed that for most metrics, a total of 80–100 subjects will be required to detect a 20% difference between two groups with sufficient power, while this difference can typically be detected in repeated-measures (paired) designs using 25 subjects. The reported between and within-subject variance components can be used as a data base to facilitate efficient designs of future studies of kinematic motor variability. Abstract Kinematic motor variability is extensively studied in occupational, clinical and sports biomechanics, but the consistency of most motor variability metrics have never been reported. In this study, fourteen subjects performed a repetitive pipetting task on three separate days. Movements of hand, arm and pipette tip were recorded in 3D and used to compute shoulder elevation, elbow flexion and shoulder-arm coordination angles, as well as pipette-tip endpoint precision. Cycle-to-cycle motor variability was quantified using linear dispersion measures of standard kinematics properties such as peak velocity, range of motion, and inter-segmental relative phase. Between- and within-subject consistencies of these variability metrics were quantified by variance components estimated using a nested random effects model. For most metrics, the variance between subjects was larger than that between days and cycles. Entering the variance components in statistical power equations showed that for most metrics, a total of 80–100 subjects will be required to detect a 20% difference between two groups with sufficient power, while this difference can typically be detected in repeated-measures (paired) designs using 25 subjects. The reported between and within-subject variance components can be used as a data base to facilitate efficient designs of future studies of kinematic motor variability. Kinematic motor variability is extensively studied in occupational, clinical and sports biomechanics, but the consistency of most motor variability metrics have never been reported. In this study, fourteen subjects performed a repetitive pipetting task on three separate days. Movements of hand, arm and pipette tip were recorded in 3D and used to compute shoulder elevation, elbow flexion and shoulder-arm coordination angles, as well as pipette-tip endpoint precision. Cycle-to-cycle motor variability was quantified using linear dispersion measures of standard kinematics properties such as peak velocity, range of motion, and inter-segmental relative phase. Between- and within-subject consistencies of these variability metrics were quantified by variance components estimated using a nested random effects model. For most metrics, the variance between subjects was larger than that between days and cycles. Entering the variance components in statistical power equations showed that for most metrics, a total of 80-100 subjects will be required to detect a 20% difference between two groups with sufficient power, while this difference can typically be detected in repeated-measures (paired) designs using 25 subjects. The reported between and within-subject variance components can be used as a data base to facilitate efficient designs of future studies of kinematic motor variability.Kinematic motor variability is extensively studied in occupational, clinical and sports biomechanics, but the consistency of most motor variability metrics have never been reported. In this study, fourteen subjects performed a repetitive pipetting task on three separate days. Movements of hand, arm and pipette tip were recorded in 3D and used to compute shoulder elevation, elbow flexion and shoulder-arm coordination angles, as well as pipette-tip endpoint precision. Cycle-to-cycle motor variability was quantified using linear dispersion measures of standard kinematics properties such as peak velocity, range of motion, and inter-segmental relative phase. Between- and within-subject consistencies of these variability metrics were quantified by variance components estimated using a nested random effects model. For most metrics, the variance between subjects was larger than that between days and cycles. Entering the variance components in statistical power equations showed that for most metrics, a total of 80-100 subjects will be required to detect a 20% difference between two groups with sufficient power, while this difference can typically be detected in repeated-measures (paired) designs using 25 subjects. The reported between and within-subject variance components can be used as a data base to facilitate efficient designs of future studies of kinematic motor variability. Kinematic motor variability is extensively studied in occupational, clinical and sports biomechanics, but the consistency of most motor variability metrics have never been reported. In this study, fourteen subjects performed a repetitive pipetting task on three separate days. Movements of hand, arm and pipette tip were recorded in 3D and used to compute shoulder elevation, elbow flexion and shoulder-arm coordination angles, as well as pipette-tip endpoint precision. Cycle-to-cycle motor variability was quantified using linear dispersion measures of standard kinematics properties such as peak velocity, range of motion, and inter-segmental relative phase. Between- and within-subject consistencies of these variability metrics were quantified by variance components estimated using a nested random effects model. For most metrics, the variance between subjects was larger than that between days and cycles. Entering the variance components in statistical power equations showed that for most metrics, a total of 80-100 subjects will be required to detect a 20% difference between two groups with sufficient power, while this difference can typically be detected in repeated-measures (paired) designs using 25 subjects. The reported between- and within-subject variance components can be used as a data base to facilitate efficient designs of future studies of kinematic motor variability. |
| Author | Srinivasan, Divya Mathiassen, Svend Erik Rudolfsson, Thomas |
| Author_xml | – sequence: 1 givenname: Divya surname: Srinivasan fullname: Srinivasan, Divya email: diasrn@hig.se organization: Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, Sweden – sequence: 2 givenname: Thomas surname: Rudolfsson fullname: Rudolfsson, Thomas organization: Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, Sweden – sequence: 3 givenname: Svend Erik surname: Mathiassen fullname: Mathiassen, Svend Erik organization: Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, Sweden |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25467549$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-16473$$DView record from Swedish Publication Index (Högskolan i Gävle) https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-95050$$DView record from Swedish Publication Index (Umeå universitet) |
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| Keywords | Kinematics reliability Precision motor control Repetitive work Movement variability Pipetting |
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| SubjectTerms | Adult Analysis of Variance Biomechanical Phenomena Female Humans Kinematics reliability Movement Movement variability Muscle Contraction Muscle, Skeletal - physiology Physical Medicine and Rehabilitation Pipetting Precision motor control Range of Motion, Articular Repetitive work Upper Extremity - physiology |
| Title | Between- and within-subject variance of motor variability metrics in females performing repetitive upper-extremity precision work |
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