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

Abstract

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.

Keywords

• Depression
• Kessler psychological distress scale
• Kurashi-app
• Lifelog
• Long sleep time
• Panel vector autoregressive model