Abstract
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.