Abstract
Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson's (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III). Seventy-five patients suffering from Parkinson's disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient's home. Convergence between the algorithm and the scale was evaluated by using the Spearman's correlation coefficient. Correlation with the UPDRS-III was moderate (rho -0.56; p < 0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho -0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: "axial function, balance, and gait." The correlation between the algorithm outputs and this factor of the UPDRS-III was -0.67 (p < 0.01). The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson's disease and motor fluctuations.
Highlights
No assessment methods can substitute the clinical judgment, subjective and objective measures in PD complement each other, each method having strengths and weaknesses [1]
The factorial analysis of the UPDRS-III had previously shown that the following items are clustered in one factor: speech, facial expression, arising from a chair, gait, postural stability, posture, and body bradykinesia [Factor 1: “axial function, balance, and gait” [15]]
According to the most widely used interpretation of the correlation coefficient, the algorithm outputs are moderately correlated with the UPDRS-III [16]
Summary
No assessment methods can substitute the clinical judgment, subjective and objective measures in PD complement each other, each method having strengths and weaknesses [1]. Accelerometers can detect different motor symptoms and fluctuations in patients with Parkinson’s disease [6,7,8,9]. Our group earlier developed an algorithm capable of detecting the motor state in patients with motor fluctuations (On and Off) based on accelerometry data from a single inertial sensor located on the patient’s waist. The algorithm first detects gait, identifies strides and extracts a frequency characteristic of them, which has been shown to be related to the motor state [10] This frequency characteristic consists in the power spectra between 0 and 10 Hz. Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson’s (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson’s Disease Rating Scale part-III (UPDRS-III)
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