Assessments of postural sway are associated with disease status and fall risk in Persons with Multiple Sclerosis (PwMS). However, these assessments, which leverage force platforms or wearable accelerometers, are most often conducted in laboratory environments and are thus not broadly accessible. Remote measures of postural sway captured during daily life may provide a more accessible alterative, but their ability to capture disease status and fall risk has not yet been established. We explore the utility of remote measures of postural sway in a sample of 33 PwMS. Remote measures of sway differed significantly from lab-based measures, but still demonstrated moderately strong associations with patient reported measures of balance and mobility impairment. Machine learning models for predicting fall risk trained on lab data provided an AUC of 0.79, while remote data only achieved an AUC of 0.51. Remote model performance improved to an AUC of 0.74 after a new, subject-specific k-means clustering approach was applied for identifying the remote data most appropriate for modelling. This cluster-based approach for analysing remote data also strengthened associations with patient-reported measures, increasing their strength above those observed in the lab. This work introduces a new framework for analysing data from remote patient monitoring technologies and demonstrates the promise of remote postural sway for assessing fall risk and characterizing balance impairment in PwMS.

Typical assessments of balance impairment are subjective or require data from cumbersome and expensive force platforms. Researchers have utilized lower back (sacrum) accelerometers to enable more accessible, objective measurement of postural sway for use in balance assessment. However, new sensor patches are broadly being deployed on the chest for cardiac monitoring, opening a need to determine if measurements from these devices can similarly inform balance assessment. Our aim in this work is to validate postural sway measurements from a chest accelerometer. To establish concurrent validity, we considered data from 16 persons with multiple sclerosis (PwMS) asked to stand on a force platform while also wearing sensor patches on the sacrum and chest. We found five (five) of 15 postural sway features derived from chest (sacrum) were significantly correlated with force platform-derived features, which is in line with prior sacrum-derived findings. Clinical significance was established using a sample of 39 PwMS who performed eyes-open, eyes-closed, and tandem standing tasks. This cohort was stratified by fall status and completed several patient-reported measures (PRM) of balance and mobility impairment. We also compared sway features derived from a single 30-second period to those derived from a one-minute period with a sliding window to create subject-specific distributions of each postural sway feature (SSD method). We find traditional computation of sway features from the chest is sensitive to changes in PRMs and task differences. Distribution characteristics from the SSD method establish additional relationships with PRMs, detect differences in more tasks, and distinguish between fall status groups. Overall, the chest was found to be a valid location to monitor postural sway and we recommend utilizing the SSD method over single-observation analyses.

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.