Mobile Sensing-Based Depression Severity Assessment in Participants With Heterogeneous Mental Health Conditions

Authors

Bishal Lamichhane
Nidal Moukaddam
Ashutosh Sabharwal

Language

English

Publication Date

8-13-2024

Journal

Scientific Reports

DOI

10.1038/s41598-024-69739-z

PMID

39138328

PMCID

PMC11322485

PubMedCentral® Posted Date

8-13-2024

PubMedCentral® Full Text Version

Post-print

Abstract

Mobile sensing-based depression severity assessment could complement the subjective questionnaires-based assessment currently used in practice. However, previous studies on mobile sensing for depression severity assessment were conducted on homogeneous mental health condition participants; evaluation of possible generalization across heterogeneous groups has been limited. Similarly, previous studies have not investigated the potential of free-living audio data for depression severity assessment. Audio recordings from free-living could provide rich sociability features to characterize depressive states. We conducted a study with 11 healthy individuals, 13 individuals with major depressive disorder, and eight individuals with schizoaffective disorders. Communication logs and location data from the participants’ smartphones and continuous audio recordings of free-living from a wearable audioband were obtained over a week for each participant. The depression severity prediction model trained using communication log and location data features had a root mean squared error (rmse) of 6.80. Audio-based sociability features further reduced the rmse to 6.07 (normalized rmse of 0.22). Audio-based sociability features also improved the F1 score in the five-class depression category classification model from 0.34 to 0.46. Thus, free-living audio-based sociability features complement the commonly used mobile sensing features to improve depression severity assessment. The prediction results obtained with mobile sensing-based features are better than the rmse of 9.83 (normalized rmse of 0.36) and the F1 score of 0.25 obtained with a baseline model. Additionally, the predicted depression severity had a significant correlation with reported depression severity (correlation coefficient of 0.76, ��< 0.001). Thus, our work shows that mobile sensing could model depression severity across participants with heterogeneous mental health conditions, potentially offering a screening tool for depressive symptoms monitoring in the broader population.

Keywords

Humans, Male, Female, Adult, Middle Aged, Depressive Disorder, Major, Smartphone, Depression, Psychotic Disorders, Severity of Illness Index, Mental Health, Young Adult, Sociability, Depression severity, PHQ-9, Mobile sensing, Audio processing, Health technology, Biomedical engineering, Human behavior

Published Open-Access

yes

Recommended Citation

Bishal Lamichhane, Nidal Moukaddam, and Ashutosh Sabharwal, "Mobile Sensing-Based Depression Severity Assessment in Participants With Heterogeneous Mental Health Conditions" (2024). Faculty, Staff and Students Publications. 6054.
https://digitalcommons.library.tmc.edu/baylor_docs/6054