|Title:||Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach|
Furukawa, Toshi A.
|Author's alias:||熊谷, 成将|
Kessler psychological distress scale
Long sleep time
Panel vector autoregressive model
Patient health Questionnaire-9
|Publisher:||Springer Science and Business Media LLC|
|Journal title:||BMC Psychiatry|
|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.|
|Appears in Collections:||Journal Articles |
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