Validation of an Accelerometer to Quantify a Comprehensive Battery of Gait Characteristics in Healthy Older Adults and Parkinson's Disease: Toward Clinical and at Home Use

Abstract

Measurement of gait is becoming important as a tool to identify disease and disease progression, yet to date its application is limited largely to specialist centers. Wearable devices enables gait to be measured in naturalistic environments, however questions remain regarding validity. Previous research suggests that when compared with a laboratory reference, measurement accuracy is acceptable for mean but not variability or asymmetry gait characteristics. Some fundamental reasons for this have been presented, (e.g., synchronization, different sampling frequencies) but to date this has not been systematically examined. The aims of this study were to: 1) quantify a comprehensive range of gait characteristics measured using a single triaxial accelerometer-based monitor; 2) examine outcomes and monitor performance in measuring gait in older adults and those with Parkinson's disease (PD); and 3) carry out a detailed comparison with those derived from an instrumented walkway to account for any discrepancies. Fourteen gait characteristics were quantified in 30 people with incident PD and 30 healthy age-matched controls. Of the 14 gait characteristics compared, agreement between instruments was excellent for four (ICCs 0.913-0.983); moderate for four (ICCs 0.508-0.766); and poor for six characteristics (ICCs 0.637-0.370). Further analysis revealed that differences reflect an increased sensitivity of accelerometry to detect motion, rather than measurement error. This is most likely because accelerometry measures gait as a continuous activity rather than discrete footfall events, per instrumented tools. The increased sensitivity shown for these characteristics will be of particular interest to researchers keen to interpret "real-world" gait data. In conclusion, use of a body-worn monitor is recommended for the measurement of gait but is likely to yield more sensitive data for asymmetry and variability features.

A model of free-living gait: A factor analysis in Parkinson’s disease

“…Interestingly, step length asymmetry loaded onto the asymmetry domain in free-living for both groups. BWM are more sensitive at detecting characteristics of asymmetry [10] but in addition, perhaps due to environment complexity, asymmetry increased in free-living [13] thereby emphasising it.…”

“…Reasons may be twofold: similarly to asymmetry, BWM analysis appears to be more sensitive to variability characteristics compared to GaitRite [10] and; measures of variability become more accurate with increased step count [16].…”

“…Raw acceleration data for controlled and free-living assessments were analysed using a bespoke MATLAB ® (Version 2015a) program, see [10] and [13] for further details of controlled and free-living data processing respectively. 14 previously validated spatiotemporal gait characteristics [10] were quantified (Figure 1).…”

“…As a result, BWM demonstrate increased sensitivity to asymmetry and variability characteristics [10]. In addition, BWM derive 14 of 16 characteristics due to limitations measuring step width and step width variability with single tri-axial accelerometers [10]. Thus, we hypothesise that free-living characteristics will load differently onto a conceptual gait model.…”

“…Differences occur in gait metrics when comparing GaitRite™ with BWM as the latter measures continuous motion and the former discrete events (separate foot-falls). As a result, BWM demonstrate increased sensitivity to asymmetry and variability characteristics [10]. In addition, BWM derive 14 of 16 characteristics due to limitations measuring step width and step width variability with single tri-axial accelerometers [10].…”