Digital Endpoint Resource Guide: Parkinson's Disease

The development of drugs for movement disorders has been a challenging endeavor. Despite extensive research and billions of dollars invested, no disease-modifying treatments have been approved for Parkinson's disease (PD). A key obstacle in drug development is the absence of reliable clinical endpoints to measure disease progression and assess drug efficacy. Currently, the motor and non-motor symptoms of PD are typically evaluated through self-reported patient diaries or the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) questionnaire. However, these subjective assessments are susceptible to recall bias and variability between different raters, and they only provide episodic snapshots that fail to fully capture disease fluctuations. As a result, these evaluations can be incomplete and unreliable.

To address these shortcomings, continuous, objective data from wearable digital health technologies (DHTs) with minimal burden on the patient can fill gaps in the current "gold standard" measures. These technologies offer critical insights that can enhance study design and improve the accuracy and specificity of efficacy assessments.

Motor symptoms like bradykinesia, dyskinesia, dystonia, and gait freezing, along with non-motor symptoms such as sleep disturbances, severely impact the daily lives of individuals with PD. Regulatory bodies and payers are increasingly requiring patient-centered outcomes that demonstrate meaningful clinical benefits in terms of how patients feel and function. With the rise of sensor-based DHTs, there is now an unprecedented opportunity to directly and objectively measure the daily functioning of individuals with neurological movement disorders, including Parkinson’s disease, Huntington’s disease, and Essential Tremor.