A new approach to assess postural dynamics and its association with higher illness burden in bipolar disorder
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".