Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number

Mood disorders are among the leading causes of disease burden worldwide. They manifest with changes in mood, sleep, and motor-activity, observable with physiological data. Despite effective treatments being available, limited specialized care availability is a major bottleneck, hindering preemptive interventions. Nearcontinuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning, could mitigate this problem, bringing mood disorders monitoring outside the doctor’s office. Previous works attempted predicting a single label, e.g. disease state or a psychometric scale total score. However, clinical practice suggests that the same label can underlie different symptom profiles, requiring personalized treatment. In this work we address this limitation by proposing a new task: inferring all items from the Hamilton Depression Rating Scale (HDRS) and the Young Mania Rating Scale (YMRS), the most-widely used standardized questionnaires for assessing depression and mania symptoms respectively, the two polarities of mood disorders. Using a naturalistic, single-center cohort of patients with a mood disorder (N=75), we develop an artificial neural network (ANN) that inputs physiological data from a wearable device and scores patients on HDRS and YMRS in moderate agreement (quadratic Cohen’s κ = 0.609) with assessments by a clinician. We also show that, when using as input physiological data recorded further away from when HDRS and YMRS were collected by the clinician, the ANN performance deteriorates, pointing to a distribution shift, likely across both psychometric scales and physiological data. This suggests the task is challenging and research into domain-adaptation should be prioritized towards real-world implementations. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was supported by a grant from Baszucki Brain Research Fund and the Instituto de Salud Carlos III (FIS PI21/00340, TIMEBASE STUDY) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was conducted in compliance with the ethical principles of medical research involving humans (WMA, Declaration of Helsinki and Hospital Clinic Ethics & Research Board (HCB/2021/104, HCB/2021/1127)) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data in de-identified form may be made available from the corresponding author upon reasonable request.