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Title: Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach
Authors: Kumagai, Narimasa
Tajika, Aran  kyouindb  KAKEN_id
Hasegawa, Akio
Kawanishi, Nao
Horikoshi, Masaru
Shimodera, Shinji
Kurata, Ken’ichi
Chino, Bun
Furukawa, Toshi A.
Author's alias: 熊谷, 成将
田近, 亜蘭
長谷川, 晃朗
川西, 直
堀越, 勝
下寺, 信次
倉田, 健一
茅野, 分
古川, 壽亮
Keywords: Depression
Kessler psychological distress scale
Kurashi-app
Lifelog
Long sleep time
Panel vector autoregressive model
Patient health Questionnaire-9
Issue Date: 11-Dec-2019
Publisher: Springer Science and Business Media LLC
Journal title: BMC Psychiatry
Volume: 19
Thesis number: 391
Abstract: Background: Although depression has a high rate of recurrence, no prior studies have established a method that could identify the warning signs of its recurrence. Methods: We collected digital data consisting of individual activity records such as location or mobility information (lifelog data) from 89 patients who were on maintenance therapy for depression for a year, using a smartphone application and a wearable device. We assessed depression and its recurrence using both the Kessler Psychological Distress Scale (K6) and the Patient Health Questionnaire-9. Results: A panel vector autoregressive analysis indicated that long sleep time was a important risk factor for the recurrence of depression. Long sleep predicted the recurrence of depression after 3 weeks. Conclusions: The panel vector autoregressive approach can identify the warning signs of depression recurrence; however, the convenient sampling of the present cohort may limit the scope towards drawing a generalised conclusion.
Rights: © The Author(s). 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
URI: http://hdl.handle.net/2433/254112
DOI(Published Version): 10.1186/s12888-019-2382-2
PubMed ID: 31829206
Appears in Collections:Journal Articles

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