Quantification of discrete behavioral components of the MDS-UPDRS. (March 2019)

• Record Type: Journal Article
• Authors: Brooks, Chris; Eden, Gabrielle; Chang, Andrew; Demanuele, Charmaine; Kelley Erb, Michael; Shaafi Kabiri, Nina; Moss, Mark; Bhangu, Jaspreet; Thomas, Kevin
• Is Part Of: Journal of clinical neuroscience. Volume 61(2019)
• Page Start: 174
• Page End: 179
• Publication Date: 2019-03
• Subjects: Parkinson's disease -- Video coding -- MDS-UPDRS -- Wearable sensors; Brain -- Surgery -- Periodicals; Neurosciences -- Periodicals; Nervous system -- Surgery -- Periodicals; Brain -- surgery -- Periodicals; Neurosurgical Procedures -- Periodicals; Neurosciences -- Periodicals; Electronic journals; 616.8
• DOI: 10.1016/j.jocn.2018.10.043
• Languages: English
• ISSNs: 0967-5868

Abstract

Highlights: Durations of gold standard clinical motor tasks were quantified with video coding. Tasks were defined using anatomically-based criteria derived from their instructions. Non-clinician coders achieved high agreement with each other and clinician raters. Timestamps of task performance can be aligned to objective data from wearable sensors. Abstract: Introduction: The Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the current gold standard means of assessing disease state in Parkinson's disease (PD). Objective measures in the form of wearable sensors have the potential to improve our ability to monitor symptomology in PD, but numerous methodological challenges remain, including integration into the MDS-UPDRS. We applied a structured video coding scheme to temporally quantify clinical, scripted, motor tasks in the MDS-UPDRS for the alignment and integration of objective measures collected in parallel. Methods: 25 PD subjects completed two video-recorded MDS-UPDRS administrations. Visual cues of task performance reliably identifiable in video recordings were used to construct a structured video coding scheme. Postural transitions were also defined and coded. Videos were independently coded by two trained non-expert coders and a third expert coder to derive indices of inter-rater agreement. Results: 50 videos of MDS-UPDRS performance were fully coded. Non-expert coders achieved a high level of agreement (Cohen's κ > 0.8) on all postural transitions and scripted motor tasks except for Postural Stability (κ = 0.617); this level of agreement was largely maintained even when more stringent thresholds for agreement were applied. Durations coded by non-expert coders and expert coders were significantly different (p < 0.05) for only Postural Stability and Rigidity, Left Upper Limb. Conclusions: Non-expert coders consistently and accurately quantified discrete behavioral components of the MDS-UPDRS using a structured video coding scheme; this represents a novel, promising approach for integrating objective and clinical measures into unified, longitudinal datasets.