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.

Aim A robust and user-friendly software tool was developed for the prediction of dopamine D2 receptor occupancy (RO) in patients with schizophrenia treated with either olanzapine or risperidone. This tool can facilitate clinician exploration of the impact of treatment strategies on RO using sparse plasma concentration measurements. Methods Previously developed population pharmacokinetic (PPK) models for olanzapine and risperidone were combined with a PD model for D2 receptor occupancy (RO) and implemented in the R programming language. MAP Bayesian estimation was used to provide predictions of plasma concentration and receptor occupancy and based on sparse PK measurements. Results The average (standard deviation) response times of the tools were 2.8 (3.1) and 5.3 (4.3) seconds for olanzapine and risperidone, respectively. The mean error (95% confidence interval) and root mean squared error (RMSE, 95% CI) of predicted versus observed concentrations were 3.73 ng/mL (-2.42 – 9.87) and 10.816 (6.71 – 14.93) for olanzapine, and 0.46 ng/mL (-4.56 – 5.47) and 6.68 (3.57 – 9.78) for risperidone and its active metabolite (9-OH risperidone). Mean error and RMSE of RO were -1.47% (-4.65 – 1.69) and 5.80 (3.89 – 7.72) for olanzapine and -0.91% (-7.68 – 5.85) and 8.87 (4.56 – 13.17) for risperidone. Conclusion Treatment of schizophrenia with antipsychotics offers unique challenges and requires careful monitoring to establish the optimal dosing regimen. Our monitoring software predicts RO in a reliable and accurate form.