Reliability of a commercially available and algorithm-based kinetic analysis software compared to manual-based software
(Zuverlässigkeit einer kommerziell verfügbaren und algorithmikbasierten kinetischen Analysesoftware im Vergleich zu manuell-basierter Software)
There is a need for reliable analysis techniques for kinetic data for coaches and sport scientists who employ athlete monitoring practices. The purpose of the study was: (1) to determine intra- and inter-rater reliability within a manual-based kinetic analysis program; and (2) to determine test-retest reliability of an algorithm-based kinetic analysis program. Five independent raters used a manual analysis program to analyse 100 isometric mid-thigh pull (IMTP) trials obtained from previously collected data. Each trial was analysed three times. The same IMTP trials were analysed using an algorithm-based analysis software. Variables measured were peak force, rate of force development from 0 to 50 ms (RFD50) and RFD from 0 to 200 ms (RFD200). Intraclass correlation coefficients (ICC) and coefficient of variation (CV) were used to assess intra- and inter-rater reliability. Nearly perfect reliability was observed for the manual-based (ICC > 0.92). However, poor intra- and inter-rater CV was observed for RFD (CV > 16.25% and CV > 32.27%, respectively). The algorithm-based method resulted in perfect reliability in all measurements (ICC = 1.0, CV = 0%). While manual methods of kinetic analysis may provide sufficient reliability, the perfect reliability observed within the algorithm-based method in the current study suggest it is a superior method for use in athlete monitoring programs.
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| Schlagworte: | |
|---|---|
| Notationen: | Trainingswissenschaft Naturwissenschaften und Technik Biowissenschaften und Sportmedizin |
| Tagging: | Vergleich |
| Veröffentlicht in: | Sports Biomechanics |
| Sprache: | Englisch |
| Veröffentlicht: |
2019
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| Online-Zugang: | https://doi.org/10.1080/14763141.2017.1372514 |
| Jahrgang: | 18 |
| Heft: | 1 |
| Seiten: | 1-9 |
| Dokumentenarten: | Artikel |
| Level: | hoch |