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BMC Psychiatry
Published in: BMC Psychiatry 1/2019

Open Access 01-12-2019 | Affective Disorder | Research article

Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach

Authors: Narimasa Kumagai, Aran Tajika, Akio Hasegawa, Nao Kawanishi, Masaru Horikoshi, Shinji Shimodera, Ken’ichi Kurata, Bun Chino, Toshi A. Furukawa

Published in: BMC Psychiatry | Issue 1/2019

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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.
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Metadata
Title
Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach
Authors
Narimasa Kumagai
Aran Tajika
Akio Hasegawa
Nao Kawanishi
Masaru Horikoshi
Shinji Shimodera
Ken’ichi Kurata
Bun Chino
Toshi A. Furukawa
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Psychiatry / Issue 1/2019
Electronic ISSN: 1471-244X
DOI
https://doi.org/10.1186/s12888-019-2382-2

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