Supplementary material for Machine learning analysis of wearable sensor data from mobility testing distinguishes Parkinson's disease from other forms of parkinsonism

Parkinson's Disease (PD) and other forms of parkinsonism share characteristic motor signs and symptoms, including tremor, bradykinesia, and rigidity. This overlap in the clinical presentation across the different parkinsonian disorders creates a diagnostic challenge, underscoring the need for precise and objective differentiation tools. In this study, we analyzed wearable sensor data collected during mobility testing from 260 PD participants and 18 participants with etiologically diverse forms of parkinsonism. Our findings illustrate that machine learning-based analysis of data from a single wearable sensor can effectively distinguish idiopathic PD from non-PD parkinsonism with an accuracy that closely aligns with the diagnostic precision of a movement disorder expert. Moreover, we found that enhanced diagnostic performance can be attained through severity-based partitioning of participants. Beyond its diagnostic implications, our results suggest the possibility of streamlining the testing protocol by using the Timed Up and Go test as a single high yield mobility task. Furthermore, we present a detailed analysis of several case studies of challenging scenarios commonly encountered in clinical practice, including diagnostic uncertainty at the initial visit with a movement disorder specialist, and changes in clinical diagnosis by the treating neurologist at a subsequent visit. Together, these findings demonstrate the potential of applying machine learning on sensor-based measures of mobility to distinguish between PD and other forms of parkinsonism.