Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis

Abstract

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 of 15 postural sway features derived from the chest and 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 oneminute period with a sliding window to create individualized distributions of each postural sway feature (ID method). We find traditional computation of sway features from the chest is sensitive to changes in PRMs and task differences. Distribution characteristics from the ID 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 ID method over single-observation analyses.

Assessing Free-Living Postural Sway in Persons With Multiple Sclerosis

“…This model was used to identify all remote standing bouts that were 30 seconds or longer. 30 seconds was chosen as the minimum because this is the length of the typical in-lab balance assessment [ 10 ], [ 16 ]. The first minute of the laboratory standing balance assessment was used for in-lab analysis.…”

“…The first minute of the laboratory standing balance assessment was used for in-lab analysis. Data were processed using the individualized distributions [ 16 ] approach where a 30-second window is slid 5 samples over the trial to create a distribution of each sway parameter.…”

“…The results of the strongest significant aggregation were reported. Using the results of the correlations to PRMs and previously published [ 16 ] cross-correlations between the features, we then selected a reduced feature-set for remote analysis containing features that demonstrate correlations to PRMs and that are not highly correlated to each other. When choosing between highly related features, the feature with the strongest remote PRM correlation was chosen.…”

“…However, force platforms are expensive and limit accessibility to specialized clinics or research laboratories. To address these challenges, studies have shown that postural sway can be assessed using data from just a sacral or chest accelerometer [ 10 ], [ 14 ], [ 15 ], [ 16 ]. Sensor-derived postural sway measures have been used to classify fall risk in PwMS [ 14 ], distinguish between disease states [ 10 ], [ 13 ], [ 16 ], [ 17 ], [ 18 ] and to augment current assessment techniques [ 19 ], thereby achieving similar clinical utility to the force platform.…”

“…These promising balance assessments, however, are all performed in clinical or laboratory settings, which limits their accessibility. Recent studies of chest accelerometer-based postural sway have found stronger relationships to PRMs from remotely collected measures compared to in-clinic assessments [ 16 ], [ 20 ]. These differing relationships to PRMs may be explained by differences between remote and in-clinic measures.…”