Vector-field statistics for the analysis of time varying clinical gait data. (January 2017)

• Record Type: Journal Article
• Authors: Donnelly, C.J.; Alexander, C.; Pataky, T.C.; Stannage, K.; Reid, S.; Robinson, M.A.
• Is Part Of: Clinical biomechanics. Volume 41(2017)
• Page Start: 87
• Page End: 91
• Publication Date: 2017-01
• Subjects: Kinematics -- Lower-limb -- Biomechanics -- Statistical parametric mapping -- SPM; Biomechanics -- Periodicals; Osteopathic medicine -- Periodicals; Biomechanics -- Periodicals; Osteopathic Medicine -- Periodicals; 612.76
• DOI: 10.1016/j.clinbiomech.2016.11.008
• Languages: English
• ISSNs: 0268-0033

Abstract

Abstract: Background: In clinical settings, the time varying analysis of gait data relies heavily on the experience of the individual(s) assessing these biological signals. Though three dimensional kinematics are recognised as time varying waveforms (1D), exploratory statistical analysis of these data are commonly carried out with multiple discrete or 0D dependent variables. In the absence of an a priori 0D hypothesis, clinicians are at risk of making type I and II errors in their analyis of time varying gait signatures in the event statistics are used in concert with prefered subjective clinical assesment methods. The aim of this communication was to determine if vector field waveform statistics were capable of providing quantitative corroboration to practically significant differences in time varying gait signatures as determined by two clinically trained gait experts. Methods: The case study was a left hemiplegic Cerebral Palsy (GMFCS I) gait patient following a botulinum toxin (BoNT-A) injection to their left gastrocnemius muscle. Findings: When comparing subjective clinical gait assessments between two testers, they were in agreement with each other for 61% of the joint degrees of freedom and phases of motion analysed. For tester 1 and tester 2, they were in agreement with the vector-field analysis for 78% and 53% of the kinematic variables analysed. When the subjective analyses of tester 1 and tester 2 were pooled together and then compared to the vector-field analysis, they were in agreement for 83% of the time varying kinematic variables analysed. Interpretation: These outcomes demonstrate that in principle, vector-field statistics corroborates with what a team of clinical gait experts would classify as practically meaningful pre- versus post time varying kinematic differences. The potential for vector-field statistics to be used as a useful clinical tool for the objective analysis of time varying clinical gait data is established. Future research is recommended to assess the usefulness of vector-field analyses during the clinical decision making process. Highlights: Vector field statistics is a useful framework for the assessment of clinical gait data. Vector field statistics can provide objective, clinically meaningful information for the assessment of clinical gait data. Vector field statistics are open-source (spm1d.org ). Vector field statistics for the analysis of clinical gait were designed to be handler centric and user friendly.