A new approach to assess postural dynamics and its association with
higher illness burden in bipolar disorder
Abstract
We recruited 53 BD participants at two Canadian academic psychiatric
hospitals (the Centre for Addiction and Mental Health, Toronto; the
Royal Ottawa Hospital, Ottawa) between April 2016 and December 2019.
Participants were provided with a BioHarness™ 3.0 wearable physiological
electronic (e-) monitoring device, which they wore continuously for 24
hours.
Posture data were recorded in units of degrees from vertical, sampled
every sec (1 Hz) with a sensitivity range of 1° to 8°, and a dynamic
range of ±180°. The sensors were configured such that a posture value of
-90° indicates a supine posture (i.e., lying face up), and a 90° posture
indicates a prone posture (i.e., lying face down). Posture was
represented as a 1 Hz-discretized single channel of angular positions of
a participant’s chest over the course of 24 hours.
We extracted a set of 9 time-domain features to characterize postural
dynamics in terms of amplitude, energy, variability, and transitions for
3 different periods: day (from 7:00 AM to 2:59 PM), evening (from 3:00
PM to 10:59 PM), and night (from 11:00 PM to 6:59 AM). To assess posture
amplitude, we computed the mean posture (angle in degrees) and its
range; to assess posture dynamics’ energy content, we computed the root
mean squared (RMS) value; to assess posture variability, we computed the
coefficient of variation (CV), interquartile range (IQR), and median
absolute deviation (MAD). Kurtosis and skewness were computed to assess
the postural statistical distribution in terms of distribution sharpness
and symmetry. Lastly, the number of postural transitions was computed
using Bayesian Online Changepoint Detection (BOCD) which identifies the
abrupt changes in sequential data generative parameters, such that each
changepoint is indicative of a postural transition (e.g., being upright
to bending over, or lying down to sitting upright).
We used the Kruskal-Wallis test to assess the level of inter-cluster
statistical significance for each posture feature, and corrected the
p-values using the Benjamini-Hochberg (BH) method. Then, in each
posture-specific cluster, we assessed the median and IQR of
cluster-specific illness burden variables. We computed pairwise Spearman
correlation coefficients to assess the strength and direction of the
association between the postural dynamics descriptors (e.g., mean, IQR)
and illness burden continuous variables (e.g., lifetime number of
depressive episodes). We used a Chi Square test to assess the
association between posture and categorical illness burden variables
(e.g., history of suicide attempts, family history of suicide). We
controlled for age, baseline functional capacity, and body mass index
(BMI) by setting them as control variables in a multiple linear
regression model. The p-values were corrected using the BH method.
To identify cluster members (i.e., participants who shared similar
postural dynamics), we performed hierarchical clustering of BD
participants using posture features as model input.