Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions
| dc.citation.articleNumber | 18808 | en_US |
| dc.citation.journalTitle | Scientific Reports | en_US |
| dc.citation.volumeNumber | 14 | en_US |
| dc.contributor.author | Lamichhane, Bishal | en_US |
| dc.contributor.author | Moukaddam, Nidal | en_US |
| dc.contributor.author | Sabharwal, Ashutosh | en_US |
| dc.date.accessioned | 2024-08-29T21:11:50Z | en_US |
| dc.date.available | 2024-08-29T21:11:50Z | en_US |
| dc.date.issued | 2024 | en_US |
| dc.description.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, $$p<$$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. | en_US |
| dc.identifier.digital | s41598-024-69739-z | en_US |
| dc.identifier.doi | https://doi.org/10.1038/s41598-024-69739-z | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/117747 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Springer Nature | en_US |
| dc.rights | Except where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.title | Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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