Digital Phenotypes of Instability and Fatigue Derived from Daily Standing Transitions in Persons with Multiple Sclerosis

Abstract

<p>Impairment in persons with multiple sclerosis (PwMS) can often be attributed to symptoms of motor instability and fatigue. Symptom monitoring and queued interventions often target these symptoms. Clinical metrics are currently limited to objective physician assessments or subjective patient reported measures. Recent research has turned to wearables for improving the objectivity and temporal resolution of assessment. Our group has previously observed wearable assessment of supervised and unsupervised standing transitions to be predictive of fall-risk in PwMS. Here we extend the application of standing transition quantification to longitudinal home monitoring of symptoms. Subjects (N=23) with varying degrees of MS impairment were recruited and monitored with accelerometry for a total of ~6 weeks each. These data were processed using a deep learning activity classifier to isolate periods of standing transition from which descriptive features were extracted for analysis. Participants completed daily and biweekly assessments describing their symptoms. From these data, Canonical Correlation Analysis was used to derive digital phenotypes of MS instability and fatigue. We find these phenotypes capable of distinguishing fallers from non-fallers, and further that they demonstrate a capacity to characterize symptoms at both daily and sub-daily resolutions. These results represent promising support for future applications of wearables, which may soon augment or replace current metrics in longitudinal monitoring of PwMS. </p>

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

“…Ryan McGinnis: modeling to enable personalized and preventative digital medicine in-the-wild The advent of conformal, skin worn sensors has enabled unobtrusive continuous monitoring of patients outside of traditional laboratory or clinical environments. These emerging sensors with advanced data analysis pipelines form a digital biomarker discovery platform [170,171]. Such platforms detect activities of daily living, quantify how patients engage in those activities, and identify potential biomarkers of symptoms or disease that can then be monitored over time to inform assessment and efficacy of interventions.…”

“…Such platforms detect activities of daily living, quantify how patients engage in those activities, and identify potential biomarkers of symptoms or disease that can then be monitored over time to inform assessment and efficacy of interventions. We have shown this platform can be used for studying biomarkers of fall risk in persons with multiple sclerosis that can be extracted from everyday walking, postural transitions, and standing [170,171]. This allows identifying several key areas of consideration for advancing remote patient monitoring, including selecting appropriate approaches for data aggregation (e.g., averages alone are probably not sufficient), considering appropriate monitoring periods (e.g., period depends on population and parameter), and the need for careful validation to ensure these technologies are fit for purpose [172].…”