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Evaluation of Digital Mobility Data to Identify Individuals at Risk of Developing Parkinson’s Disease

Study Rationale: Gait disturbances play a major role in the motor manifestation of Parkinson’s disease (PD). Traditional clinical assessments aimed at measuring motor deficits do not sufficiently identify subtle changes relating to disease progression or the earliest stages of disease. Growing evidence suggests that motor changes can be objectively detected by wearable sensors in recently diagnosed patients and even in the prodromal phase prior to diagnosis. Digital technology provides accurate, sensitive assessment of multiple, disparate mobility functions, both in controlled settings and in real-life environments. Such tools therefore have the potential for identifying early changes in mobility associated with PD.

Hypothesis: We hypothesize that digital mobility data can be used to enrich prodromal screening and to monitor progression in individuals with prodromal or early-stage PD.

Study Design: Participating subjects will complete gait assessments twice, at yearly intervals. Consenting participants will undergo a short in-clinic gait assessment while wearing sensors. During this assessment, they will also be fitted with a small wearable sensor able to continuously monitor mobility for 7 days in the home environment. Participants will be encouraged to continue their daily routine and after a week return the sensor to the site via mail.

Impact on Diagnosis/Treatment of Parkinson’s disease: Information gathered in this sub-study will help validate the use of easily deployed, self-administered digital technology for identifying individuals at risk for developing PD. The results could also reveal potentially sensitive measurements that will be suitable for use in future clinical trials.

Next Steps for Development: Motor dysfunction, a central feature of PD, is well suited for assessment via digital technology, which is capable of accurately capturing multiple mobility functions. Development of these technologies will require validation, longitudinal assessment and exploration of variance and modifiers—requirements that this sub-study will begin to address.


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