Objective: To describe the design and implementation of a novel natural history study in Parkinson’s disease (PD) evaluating multi-modal technologies, including the mPower 2.0 smartphone application, MC10 wearable BioStamp nPoint® sensors, the Emerald passive in-home activity monitor, and the online Parkinson’s Analysis with Remotely-Accessed Knowledge (PARK) video analytics tool.
Background: Traditional measures of PD are insensitive, subjective, and restricted to in-clinic visits. Novel technologies can overcome these limitations and provide sensitive, objective, frequent data in real-world settings. The concurrent evaluation of multiple technologies in a single study permits cross-device comparisons, reduces travel burden on participants, and may allow for a more complete characterization of the disease.
Method: We are conducting a 2-year, prospective, observational study of up to 200 individuals with and without PD. All participants use the video analytics tool in clinic and choose one or more of the other technologies to use. Participants using at least 3 of the 4 technologies are termed “Super Users.” We plan to enroll 35 Super Users with PD and 15 age- and sex-matched Super Users without PD. In-person visits consist of standard, clinical assessments of PD alongside technology-related activities. Participants also complete remote technology-related activities. mPower participants perform quarterly 2-week bursts of tasks in the smartphone application, MC10 participants wear the biosensors for 1-week periods at home, and Emerald participants have the passive activity monitor installed in their home for 2 years.
Results: As of February 25, 2020, 55 individuals are enrolled, 32 with PD and 23 without PD. 43 participants are using the smartphone application, 33 are using the wearable biosensors, and 22 are using the in-home activity monitor. 34 participants are Super Users.
Conclusion: We have successfully initiated enrollment and data collection for this novel technology-based natural history study. This work will shed light on how Parkinson’s disease affects people in their daily lives and ideally generate novel outcome measures for clinical research and care.
To cite this abstract in AMA style:E. Waddell, S. Jensen-Roberts, T. Myers, M. Coffey, K. Lizarraga, E. Dorsey, C. Tarolli, J. Adams, R. Schneider. Deep phenotyping of Parkinson’s disease: a longitudinal study using multiple digital sensors [abstract]. Mov Disord. 2020; 35 (suppl 1). https://www.mdsabstracts.org/abstract/deep-phenotyping-of-parkinsons-disease-a-longitudinal-study-using-multiple-digital-sensors/. Accessed November 29, 2023.
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