Gait Kinematic Parameters in Parkinson’s Disease: A Systematic Review

Abstract

Background: Gait impairments are common and highly disabling for Parkinson's disease (PD) patients. With the development of technology-based tools, it is now possible to measure the spatiotemporal parameters of gait with a reduced margin of error, thereby enabling a more accurate characterization of impairment. Objective: To summarize and critically appraise the characteristics of technology-based gait analysis in PD and to provide mean and standard deviation values for spatiotemporal gait parameters. Methods: A systematic review was conducted using the databases CENTRAL, MEDLINE, Embase, and PEDro from their inception to September 2019 to identify all observational and experimental studies conducted in PD or atypical parkinsonism that included a technology-based gait assessment. Two reviewers independently screened citations and extracted data. Results: We included 95 studies, 82.1% (n = 78) reporting a laboratory gait assessment and 61.1% (n = 58 studies) using a wearable sensor. The most frequently reported parameters were gait velocity, stride and step length, and cadence. A statistically significant difference was found when comparing the mean values of each of these parameters in PD patients versus healthy controls. No statistically significant differences were found in the mean value of the parameters when comparing wearable versus non-wearable sensors, different types of wearable sensors, and different sensor locations. Conclusion: Our results provide useful information for performing objective technology-based gait assessment in PD, as well as mean values to better interpret the results. Further studies should explore the clinical meaningfulness of each parameter and how they behave in a free-living context and throughout disease progression.

Simple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study

“…Stride time variability is defined using the ratio of the SD of the stride times to the mean stride time within each walking trial. Longer stride time and greater stride time variability within a given walking condition (ie, single or dual task) have each been associated with aging [ 21 ], incidence of movement disorders (including PD) [ 22 ], the development of falls [ 3 ], and even the likelihood of future cognitive decline [ 23 ]. We also calculated the dual-task “costs” to stride time and stride time variability as the percent change of each metric from the single- to dual-task walking condition.…”

Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole

“…Step length and stride length (twice the step length) are two important measurements in many applications such as gait analysis and human activity recognition. As shown in [ 82 ], the increased fear of falling score was associated with increased stride length variability, and compared to heathy adults, people with PD show a reduction in their stride length [ 83 ].…”

Wearables als unterstützendes Tool für den Paradigmenwechsel in der Versorgung von Parkinson Patienten

“…Einige Gruppen verwenden an beiden Armen und Beinen Sensoren, andere nur am Handgelenk, wieder andere lediglich einen Bauchgurt oder Sensoren, die am Schuh angebracht werden [16]. [8] verglichen in einem systematischen Review Accelerometer vs. verschiedene andere Sensoren, in der Subgruppenanalyse zeigten sich keine signifikanten Unterschiede, alle Systeme konnten signifikant zwischen PmMP und gesunden Kontrollen unterscheiden, ein signifikanter Unterschied in Abhängigkeit von der Position der angebrachten Sensoren wurde auch nicht gefunden.…”

“…Neben dem FOG Questionnaire und klinischen Untersuchungen wie dem TUG-Test werden in den letzten Jahren zunehmend Studien zu Wearables durchgeführt [8]. In der sehr umfassenden Übersichtsarbeit von Silva di Lima et al werden die verschiedenen Sensoren und deren Studienlage zusammengefasst [30].…”

“…Eine Vielzahl von Wearables erfüllen diese Anforderungen und zahlreiche systematische Reviews beleuchten dies kritisch. Die zur Ganganalyse eingesetzten Algorithmen haben eine Genauigkeit, Sensitivität und Spezifität von 85-100 % [8][9][10]. Es werden verschiedene Technologien zur Beurteilung von Bradykinese [10,18,20], Tremor [19,20] und axialen Symptomen [10,20] eingesetzt.…”