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01.12.2019 | Research article | Ausgabe 1/2019 Open Access

BMC Psychiatry 1/2019

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

Zeitschrift:
BMC Psychiatry > Ausgabe 1/2019
Autoren:
Narimasa Kumagai, Aran Tajika, Akio Hasegawa, Nao Kawanishi, Masaru Horikoshi, Shinji Shimodera, Ken’ichi Kurata, Bun Chino, Toshi A. Furukawa
Wichtige Hinweise

Supplementary information

Supplementary information accompanies this paper at https://​doi.​org/​10.​1186/​s12888-019-2382-2.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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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