Date: Monday, October 8, 2018
Session Title: Parkinson's Disease: Neuroimaging And Neurophysiology
Session Time: 1:15pm-2:45pm
Location: Hall 3FG
Objective: To determine whether sensor features derived from remote smartphone testing in Parkinson’s disease (PD) predicted clinical, quality of life and biomarker measures collected in a clinical trial setting.
Background: Smartphone-based assessments and sensors enable the remote monitoring of a potentially wide variety of symptoms in PD. Current research focuses on correlating sensor feature data with clinical gold standard MDS-UPDRS scores; here, a wider variety of outcomes are assessed.
Methods: PD patients (n=43; 35 men; mean age (SD)=57.5 (8.45) years) in a Phase I Multiple Ascending Dose clinical trial of PRX002/RG7935 performed daily smartphone-based assessments for 24 weeks in their homes. For “passive monitoring”, subjects carried the smartphone during their daily routine. Sensor data was recorded continuously, including movement and location data. In clinic, MDS-UPDRS, PD Questionnaire 39 (PDQ-39) and DaT-SPECT were administered among other measures. 969 features were extracted from the sensor data and submitted to a hierarchical clustering analysis (HCA) using the feature correlation matrix as the distance metric.
Results: Features clustered into 25 groups, with each cluster demonstrating high face validity, (i.e., coherent, clinically meaningful sensor features measuring similar clinical concepts). MDS-UPDRS part III scores were related to the ‘very fine motor behavior’ cluster. PDQ-39 scores were also related to the ‘very fine motor behavior’ cluster, and additionally to ‘postural tremor’ and ‘speed’ clusters. In contrast, DaT-SPECT striatal binding ratios significantly correlated with a unique group of clusters – ‘balance tremor power”, ‘balance sway, ‘gait power’. Results from additional clinical measures will be reported.
Conclusions: Remote monitoring of PD patients using a digital biomarker approach generates a large and distinctive set of feature data which cluster into clinically meaningful feature groups. Importantly, traditional clinical and biomarker measures were related to distinct sets of sensor feature clusters. This suggests that a digital biomarker approach may provide distinct and meaningful information about PD patients’ motor symptoms, quality of life, and degree of striatal dopaminergic innervation.
To cite this abstract in AMA style:F. Lipsmeier, K. Taylor, R. Postuma, D. Wolf, T. Kilchenmann, A. Scotland, J. Schjodt-Erkisen, W. Cheng, J. Siebourg-Polster, L. Jin, J. Soto, L. Verselis, F. Boess, M. Koller, M. Grundman, T. Kremer, C. Czech, C. Gossens, M. Lindemann. Remote patient monitoring with a digital biomarker approach generates clinically distinctive and meaningful sensor feature data in Parkinson’s disease: Differential relationships with MDS-UPDRS-III, PDQ-39 and DaT-SPECT [abstract]. Mov Disord. 2018; 33 (suppl 2). https://www.mdsabstracts.org/abstract/remote-patient-monitoring-with-a-digital-biomarker-approach-generates-clinically-distinctive-and-meaningful-sensor-feature-data-in-parkinsons-disease-differential-relationships-with-mds-updr/. Accessed September 28, 2023.
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MDS Abstracts - https://www.mdsabstracts.org/abstract/remote-patient-monitoring-with-a-digital-biomarker-approach-generates-clinically-distinctive-and-meaningful-sensor-feature-data-in-parkinsons-disease-differential-relationships-with-mds-updr/