Mood Disorder Cohort Research Consortium (MDCRC)
Initiatives
-
This study was conducted to evaluate the mood state or episode, activity, sleep, light exposure, and heart rate during a period of about 2 years by acquiring various digital log data through wearable devices and smartphone apps as well as conventional clinical assessments. We investigated a mood prediction algorithm developed with machine learning using passive data phenotypes based on circadian rhythms.
Note: All published information has been collected from the article referenced in the Marker Paper box below. Therefore, there may be variations with more advanced versions of the study.
- Start Year
- 2015
- End Year
- 2017
- Funding
- This independent research study was supported by the Korea Health 21 R&D Project funded by the Ministry of Health & Welfare, Republic of Korea (HM14C2606 and HI14C3212), and the National Research Foundation of Korea (2016M3C7A1904345 and 2017M3A9F1031220).
Design
- Study design
- Patients' cohort
Marker Paper
Cho CH, Lee T, Kim MG, In HP, Kim L, Lee HJ. Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study [published correction appears in J Med Internet Res. 2019 Oct 3;21(10):e15966]. J Med Internet Res. 2019;21(4):e11029. Published 2019 Apr 17. doi:10.2196/11029
PUBMED 30994461
Number of participants
- Number of participants
- 55
- Number of participants with biosamples
Access
Availability of data and biosamples
| Data | |
| Biosamples | |
| Other |
Timeline
patients with mood disorders
The 55 patients with mood disorders (major depressive disorder [MDD] and bipolar disorder type 1 [BD I] and 2 [BD II]) for 2 years.
Selection Criteria
- Newborns
- Twins
- Countries
-
- South Korea
- Ethnic Origin
-
- Health Status
-
- The 55 patients with mood disorders (major depressive disorder [MDD] and bipolar disorder type 1 [BD I] and 2 [BD II]) for 2 years.
Recruitment
- Sources of recruitment
-
- General population
Number of participants
- Number of participants
- 55
- Number of participants with biosamples
Data Collection Event
A smartphone app for self-recording daily mood scores and detecting light exposure (using the installed sensor) were provided. From daily worn activity trackers, digital log data of activity, sleep, and heart rate were collected. Passive digital phenotypes were processed into 130 features based on circadian rhythms, and a mood prediction algorithm was developed by random forest.
- Start Date
-
2015-03
- End Date
-
2017-12
- Data sources
-
-
Mobile data collection
- Smartphone apps
- Smartwatch and wearables
-
Mobile data collection