Current measures for quantifying motor symptoms in Parkinson disease (PD) and Huntington disease (HD) are subjective and categorical.  Objective, and continuous measures for symptoms such as tremor, gait disturbance, and Huntington’s chorea are needed.

To develop novel measures of quantifying the motor symptoms of Parkinson disease and Huntington disease.  

We conducted a 12 month observational study of 56 individuals: 16 with PD, 15 with HD, 5 with prodromal HD, and 20 healthy controls. Participants wore wearable sensors for an in-clinic evaluation followed by a two day at-home observation period at baseline and months 6, 9 and 12. Through the application of signal process theory, we have developed novel algorithms for quantifying parkinsonian tremor as well as Huntington’s chorea. Our tremor algorithm is capable of producing a continuous, objective, frequent score, which reflects the strength of tremor in the arms. Our chorea score quantifies the amount of non-deliberate involuntary jerky movement in the chest over a given period of time.

Based on analysis of the baseline visit, the tremor score was able to differentiate individuals with PD from controls as well as detect response to levodopa. Using the chorea algorithm, we were able to clearly distinguish individuals with HD from controls as well as individuals with prodromal HD from individuals with manifest HD.  Individuals with manifest HD were segregated based on clinician-rated Unified Huntington Disease Rating Scale (UHDRS) scores. Chorea analysis on the segregated data showed a positive correlation between the algorithmic score and the UHDRS score.

While further analysis is required to determine the reliability of these new measures, current results suggest that wearable sensors offer an effective means for quantifying motor symptoms and identifying patterns that elude current measures in PD and HD. These measures have the potential to enhance clinical trials, speed therapeutic development, and improve patient-centered care.