Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach
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 dat...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-12-01
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| Series: | BMC Psychiatry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12888-019-2382-2 |
| _version_ | 1818392275441418240 |
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| author | Narimasa Kumagai Aran Tajika Akio Hasegawa Nao Kawanishi Masaru Horikoshi Shinji Shimodera Ken’ichi Kurata Bun Chino Toshi A. Furukawa |
| author_facet | Narimasa Kumagai Aran Tajika Akio Hasegawa Nao Kawanishi Masaru Horikoshi Shinji Shimodera Ken’ichi Kurata Bun Chino Toshi A. Furukawa |
| author_sort | Narimasa Kumagai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T05:26:50Z |
| format | Article |
| id | doaj.art-a0451fecdd71419d83ddb0d818a6f6f1 |
| institution | Directory Open Access Journal |
| issn | 1471-244X |
| language | English |
| last_indexed | 2024-12-14T05:26:50Z |
| publishDate | 2019-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj.art-a0451fecdd71419d83ddb0d818a6f6f12022-12-21T23:15:28ZengBMCBMC Psychiatry1471-244X2019-12-0119111210.1186/s12888-019-2382-2Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approachNarimasa Kumagai0Aran Tajika1Akio Hasegawa2Nao Kawanishi3Masaru Horikoshi4Shinji Shimodera5Ken’ichi Kurata6Bun Chino7Toshi A. Furukawa8Department of Economics, Seinan Gakuin UniversityDepartment of Psychiatry, Kyoto University HospitalAdvanced Telecommunications Research Institute InternationalSonas Inc.National Center for Cognitive Behavior Therapy and Research, National Center of Neurology and PsychiatryGinza Shimodera ClinicKabe Mental Health ClinicGinza Taimei ClinicDepartment of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine / School of Public HealthAbstract 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.https://doi.org/10.1186/s12888-019-2382-2DepressionKessler psychological distress scaleKurashi-appLifelogLong sleep timePanel vector autoregressive model |
| spellingShingle | Narimasa Kumagai Aran Tajika Akio Hasegawa Nao Kawanishi Masaru Horikoshi Shinji Shimodera Ken’ichi Kurata Bun Chino Toshi A. Furukawa Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach BMC Psychiatry Depression Kessler psychological distress scale Kurashi-app Lifelog Long sleep time Panel vector autoregressive model |
| title | Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach |
| title_full | Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach |
| title_fullStr | Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach |
| title_full_unstemmed | Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach |
| title_short | Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach |
| title_sort | predicting recurrence of depression using lifelog data an explanatory feasibility study with a panel var approach |
| topic | Depression Kessler psychological distress scale Kurashi-app Lifelog Long sleep time Panel vector autoregressive model |
| url | https://doi.org/10.1186/s12888-019-2382-2 |
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