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STDD: Short-Term Depression Detection with Passive Sensing
It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depres...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085564/ https://www.ncbi.nlm.nih.gov/pubmed/32143358 http://dx.doi.org/10.3390/s20051396 |
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author | Narziev, Nematjon Goh, Hwarang Toshnazarov, Kobiljon Lee, Seung Ah Chung, Kyong-Mee Noh, Youngtae |
author_facet | Narziev, Nematjon Goh, Hwarang Toshnazarov, Kobiljon Lee, Seung Ah Chung, Kyong-Mee Noh, Youngtae |
author_sort | Narziev, Nematjon |
collection | PubMed |
description | It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and food intake and extracted features related to each symptom cluster from mobile devices’ sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are normal, mildly depressed, moderately depressed, and severely depressed and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone and a wearable device that constantly reported the metrics (sensor data and self-reports) to perform depression group classification. The result of this pilot study revealed high correlations between participants` Ecological Momentary Assessment (EMA) self-reports and passive sensing (sensor data) in physical activity, mood, and sleep levels; STDD demonstrated the feasibility of group classification with an accuracy of 96.00% (standard deviation (SD) = 2.76). |
format | Online Article Text |
id | pubmed-7085564 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70855642020-03-23 STDD: Short-Term Depression Detection with Passive Sensing Narziev, Nematjon Goh, Hwarang Toshnazarov, Kobiljon Lee, Seung Ah Chung, Kyong-Mee Noh, Youngtae Sensors (Basel) Article It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and food intake and extracted features related to each symptom cluster from mobile devices’ sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are normal, mildly depressed, moderately depressed, and severely depressed and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone and a wearable device that constantly reported the metrics (sensor data and self-reports) to perform depression group classification. The result of this pilot study revealed high correlations between participants` Ecological Momentary Assessment (EMA) self-reports and passive sensing (sensor data) in physical activity, mood, and sleep levels; STDD demonstrated the feasibility of group classification with an accuracy of 96.00% (standard deviation (SD) = 2.76). MDPI 2020-03-04 /pmc/articles/PMC7085564/ /pubmed/32143358 http://dx.doi.org/10.3390/s20051396 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Narziev, Nematjon Goh, Hwarang Toshnazarov, Kobiljon Lee, Seung Ah Chung, Kyong-Mee Noh, Youngtae STDD: Short-Term Depression Detection with Passive Sensing |
title | STDD: Short-Term Depression Detection with Passive Sensing |
title_full | STDD: Short-Term Depression Detection with Passive Sensing |
title_fullStr | STDD: Short-Term Depression Detection with Passive Sensing |
title_full_unstemmed | STDD: Short-Term Depression Detection with Passive Sensing |
title_short | STDD: Short-Term Depression Detection with Passive Sensing |
title_sort | stdd: short-term depression detection with passive sensing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085564/ https://www.ncbi.nlm.nih.gov/pubmed/32143358 http://dx.doi.org/10.3390/s20051396 |
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