Dataset Information

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 three diagnostic groups: individuals with schizophrenia (N = 23), individuals with depression (N = 22), and controls (N = 32), respectively, to interrogate naturalistic behavioral differences between groups. Subjects' week-long actigraphy data was processed without diagnostic labels via unsupervised machine learning clustering methods, in order to investigate the natural bounds of psychopathology. Further, actigraphic data was analyzed across time to determine timepoints influential in model outcomes.

Results

We find distinct actigraphic phenotypes, which differ between diagnostic groups, suggesting that unsupervised clustering of naturalistic data aligns with existing diagnostic constructs. Further, we found statistically significant inter-group differences, with depressed persons showing the highest behavioral variability.

Limitations

However, diagnostic group differences only consider biobehavioral trends captured by raw actigraphy information.

Conclusions

Passively-collected movement information combined with unsupervised deep learning algorithms shows promise in identifying naturalistic phenotypes in individuals with mental health disorders, specifically in discriminating between MDD and schizophrenia.

SUBMITTER: Price GD

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

REPOSITORIES: biostudies-literature

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Publications

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

Price George D GD Heinz Michael V MV Zhao Daniel D Nemesure Matthew M Ruan Franklin F Jacobson Nicholas C NC

Journal of affective disorders 20220812

<h4>Introduction</h4>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 longitudina ...[more]

PMID: 35964770

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Dataset Information

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

Introduction

Methods

Results

Limitations

Conclusions

Publications

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

Similar Datasets

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