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Continuous algorithmic insights into PD motor symptoms and individual treatment response

M. Sander, FMJ. Pfister, G. Höglinger, J. Levin, A. Ceballos-Baumann, UM. Fietzek (Munich, Germany)

Meeting: 2023 International Congress

Abstract Number: 1796

Keywords: Levodopa(L-dopa), Parkinson’s, Wearing-off fluctuations

Category: Technology

Objective: To quantify the short-duration levodopa response using high resolution data derived from a wrist sensor and processed with advanced deep learning methods

Background: The fluctuating and highly heterogeneous nature of Parkinson’s disease necessitates treatment adjustments over time; however, the lack of objective, longitudinal data in Parkinson’s monitoring is a major limitation to the timely personalization of care and patient outcomes including quality of life. Currently care teams rely on brief, clinical appointments and subjective, labor-intensive tools such as rating scores or patient motor diaries to gauge treatment effects.

Method: In this investigation we deployed a novel combination of continuous monitoring and machine learning techniques to passively capture Parkinson’s disease motor fluctuations in 91 patients living with Parkinson’s in Germany. Motion data collected from a single wrist sensor provided input for a fully convolutional neural network (FCN) trained to continuously detect PD motor states and their severity. Various wrist sensor models that collect accelerometer and gyroscope data were used in the device-agnostic study design. The timing of medication intake was recorded, and longitudinal visualization and hierarchical clustering analyses applied to model output.

Results: In total 285 levodopa cycles were captured among the cohort of 91 Parkinson’s patients (59% male, mean age 68 +/- 10 years (+/- SD), disease duration 9 +/- 6 years). Hierarchical clustering classified single levodopa intakes into one of three levodopa response clusters based primarily on minimum and maximum severity of dyskinesia and bradykinesia on the 9 item scale used to label training data for the deep learning algorithm.

Conclusion: This technology-enabled approach yielded multidimensional insights into the short-duration levodopa response. Continuous data and its processing with AI methods were able to capture longitudinal motor state and treatment response information on the time frame of a single levodopa cycle at the level of an individual patient. Our results suggest that unintrusive sensors and machine learning methods in Parkinson’s disease care can provide continuous, objective, and individual-level data for monitoring treatment response and be used to infer information about treatment effect size and timing, and need for further optimization of Parkinson’s care.

To cite this abstract in AMA style:

M. Sander, FMJ. Pfister, G. Höglinger, J. Levin, A. Ceballos-Baumann, UM. Fietzek. Continuous algorithmic insights into PD motor symptoms and individual treatment response [abstract]. Mov Disord. 2023; 38 (suppl 1). https://www.mdsabstracts.org/abstract/continuous-algorithmic-insights-into-pd-motor-symptoms-and-individual-treatment-response/. Accessed May 21, 2025.
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