Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach
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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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- Authors
- Kumagai, N., Tajika, A., Hasegawa, A., Kawanishi, N., Horikoshi, M., Shimodera, S., Kurata, K., Chino, B., & Furukawa, T. A.
- Affiliations
- Seinan Gakuin University (JP)
- Kyoto University Hospital (JP)
- Advanced Telecommunications Research Institute International (JP)
- National Center Hospital of Neurology and Psychiatry (JP)
- Kyoto University (JP)
- Corresponding author
- Tajika, Aran
- ORCID
- Venue
- BMC Psychiatry
- Published
- 2019-12
- Volume
- 19
- Issue
- 1
- Pages
- 391-391
- ISSN
- 1471-244X (ISSN-L)
- PMC
- PMC6907185
- OpenAlex
- W2996333298
- Cited by
- 20
- References
- 33
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 7
- FWCI
- 2.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Psychiatry (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Kyoto University Research Information Repository (Kyoto University) (submittedVersion, cc-by)
- Funders
- National Institute of Information and Communications Technology (178A05)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- depression recurrence, long sleep, warning signs, wearable devices, panel vector autoregression, digital phenotyping
- MeSH terms
- Patient Health Questionnaire, Wearable Electronic Devices, Adult, Depression, Feasibility Studies, Female, Humans, Male, Middle Aged, Recurrence, Regression Analysis, Risk Factors, Software, Early Diagnosis
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