AUTHOR=Rodríguez-Molinero Alejandro , Samà Albert , Pérez-López Carlos , Rodríguez-Martín Daniel , Alcaine Sheila , Mestre Berta , Quispe Paola , Giuliani Benedetta , Vainstein Gabriel , Browne Patrick , Sweeney Dean , Quinlan Leo R. , Moreno Arostegui J. Manuel , Bayes Àngels , Lewy Hadas , Costa Alberto , Annicchiarico Roberta , Counihan Timothy , Laighin Gearòid Ò. , Cabestany Joan TITLE=Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales JOURNAL=Frontiers in Neurology VOLUME=8 YEAR=2017 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2017.00431 DOI=10.3389/fneur.2017.00431 ISSN=1664-2295 ABSTRACT=Background

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).

Method

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.

Results

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).

Conclusion

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.