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.

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 centres. 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. synchronisation, different sampling frequencies) but to date this has not been systematically examined. The aims of this study were to: (i) quantify a comprehensive range of gait characteristics measured using a single tri-axial accelerometer-based monitor, (ii) examine outcomes and monitor performance in measuring gait in older adults and those with Parkinson's disease (PD) and (iii) 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 4 (ICCs 0.913 - 0.983); moderate for 4 (ICCs 0.508 - 0.766); and poor for 6 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.