An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia.

ABSTRACT:

Introduction

Schizophrenia and Major Depressive Disorder (MDD) are highly burdensome mental disorders, with significant cost to both individuals and society. Despite these disorders representing distinct clinical categories, they are each heterogenous in their symptom profiles, with considerable transdiagnostic features. Although movement and sleep abnormalities exist in both disorders, little is known of the precise nature of these changes longitudinally. Passively-collected longitudinal data from wearable sensors is well suited to characterize naturalistic features which may cross traditional diagnostic categories (e.g., highlighting behavioral markers not captured by self-report information).

Methods

The present analyses utilized raw minute-level actigraphy data from thre

SUBMITTER: Price GD 

PROVIDER: S-EPMC10064481 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature