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A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity
The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is expe...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920816/ https://www.ncbi.nlm.nih.gov/pubmed/36772625 http://dx.doi.org/10.3390/s23031585 |
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author | Ross, Mindy K. Tulabandhula, Theja Bennett, Casey C. Baek, EuGene Kim, Dohyeon Hussain, Faraz Demos, Alexander P. Ning, Emma Langenecker, Scott A. Ajilore, Olusola Leow, Alex D. |
author_facet | Ross, Mindy K. Tulabandhula, Theja Bennett, Casey C. Baek, EuGene Kim, Dohyeon Hussain, Faraz Demos, Alexander P. Ning, Emma Langenecker, Scott A. Ajilore, Olusola Leow, Alex D. |
author_sort | Ross, Mindy K. |
collection | PubMed |
description | The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input. |
format | Online Article Text |
id | pubmed-9920816 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99208162023-02-12 A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity Ross, Mindy K. Tulabandhula, Theja Bennett, Casey C. Baek, EuGene Kim, Dohyeon Hussain, Faraz Demos, Alexander P. Ning, Emma Langenecker, Scott A. Ajilore, Olusola Leow, Alex D. Sensors (Basel) Article The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input. MDPI 2023-02-01 /pmc/articles/PMC9920816/ /pubmed/36772625 http://dx.doi.org/10.3390/s23031585 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ross, Mindy K. Tulabandhula, Theja Bennett, Casey C. Baek, EuGene Kim, Dohyeon Hussain, Faraz Demos, Alexander P. Ning, Emma Langenecker, Scott A. Ajilore, Olusola Leow, Alex D. A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity |
title | A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity |
title_full | A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity |
title_fullStr | A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity |
title_full_unstemmed | A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity |
title_short | A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity |
title_sort | novel approach to clustering accelerometer data for application in passive predictions of changes in depression severity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920816/ https://www.ncbi.nlm.nih.gov/pubmed/36772625 http://dx.doi.org/10.3390/s23031585 |
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