Association Between Wearable Sensor Data and Clinical Scores in Individuals with Early-stage Multiple System Atrophy
Leah Mann1, Jose Casado2, Ram Kinker Mishra3, Adonay Nunes3, Paula Trujillo Diaz1, Jessie Iregui1, Amy Wynn1, Cynthia Wong4, David Stamler4, Ashkan Vaziri5, Daniel Claassen1
1Vanderbilt University Medical Center, 2BioSensics LLC, 3Biosensics, 4Alterity Therapeutics, 5Biosensics LLC
Objective:
We sought to determine the clinical meaningfulness of actigraphy metrics as they relate to typical motor symptoms of multiple system atrophy (MSA).
Background:
The symptomology of MSA, including parkinsonism, autonomic dysfunction, cerebellar symptoms, and pyramidal signs, reflects the disorder’s heterogeneity and challenges associated with diagnosis and disease monitoring. While traditional assessments are limited by a single clinic visit, digital at-home monitoring instruments overcome this disadvantage by allowing for continuous data collection. However, the value of wearable sensors in MSA has not been well defined.
Design/Methods:
We recruited 18 patients with clinically probable MSA and continuously monitored their physical activity using PAMSysTM sensors (BioSensics LLC, Newton, MA, USA) during a 12-month, 5-visit study. We applied Spearman’s rank correlations to determine associations between baseline clinical measures and sensor-derived parameters of posture, locomotion, and postural transitions over a one-week period. Additionally, we developed machine learning models to investigate the ability of sensor-derived measures to predict clinical scores.
Results:
Total walking time was negatively correlated with tandem walk (rho=-0.705) and Timed Up and Go (TUG) (rho=-0.811), while sedentary time was positively correlated with tandem walk (rho=0.626) and TUG (rho=0.597). We found a negative association between daily step count and walking episodes with tandem walk and TUG. Additionally, we identified positive relationships between average sit-to-stand and stand-to-sit durations with UMSARS-II (rho=0.722, 0.628), the motor section of NNIPPS-PPS (rho=0.690, 0.689), and TUG (rho=0.644, 0.596). Finally, regression models established successful prediction of clinical scores, with TUG demonstrating the highest explained variance.
Conclusions:
These correlations suggest that sensor-derived metrics, specifically those measuring walking and postural transitions, may increase our understanding of impairments associated with MSA. Our results contribute meaningfulness to digital outcomes in MSA, underlining potential benefits sensors could hold for these patients. Additional longitudinal results will elucidate the value of activity parameters throughout disease course and will be shown at presentation.
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