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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
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 (, 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 ( applies to the data made available in this article, unless otherwise stated.
DOI(Published Version): 10.1186/s12888-019-2382-2
PubMed ID: 31829206
Appears in Collections:Journal Articles

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