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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...

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Autores principales: Narziev, Nematjon, Goh, Hwarang, Toshnazarov, Kobiljon, Lee, Seung Ah, Chung, Kyong-Mee, Noh, Youngtae
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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).
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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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