Using Digital Phenotyping for Depression Screening in Community-Dwelling Older Adults: Bayesian Multilevel Hurdle Model Machine Learning Approach
Moo-Kwon Chung, Hyo-Sang Lim, Sang Yup Lee, Hyo Seok Baek, Jinhee Lee, Kyoung Joung Lee, Taeksoo Shin, Min-Hyuk Kim, Sangwon Hwang, Erdenebayar Urtnasan, Ji Young Park, Dan Hee Kwon, Jin-kyung Lee, Andrew Coristine · 2026
Read Using Digital Phenotyping for Depression Screening in Community-Dwelling Older Adults: Bayesian Multilevel Hurdle Model Machine Learning Approach by Moo-Kwon Chung, Hyo-Sang Lim, Sang Yup Lee, Hyo Seok Baek, Jinhee Lee, Kyoung Joung Lee, Taeksoo Shin, Min-Hyuk Kim, Sangwon Hwang, Erdenebayar Urtnasan, Ji Young Park, Dan Hee Kwon, Jin-kyung Lee, Andrew Coristine (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13179049.txt.
Description
Moo-Kwon Chung and co-authors present a 2026 study of phone and watch data as a depression screen for older adults. Older people face high rates of low mood but often shun clinics due to stigma. Daily data from wearables, a method called digital phenotyping, could spot slumps early. The team built a learning model that respects both gaps between people and swings within one person.
Data came from 147 older Korean adults tracked for two years, with over a thousand mood reports. Each report used a standard nine-item mood scale. The model sorts first whether symptoms are present, then how deep they run. That two-step form suits mood data, where many reports sit at zero. Screens of this kind could reach elders who never book a clinic visit.
Catalog record
| CREATOR | Moo-Kwon Chung, Hyo-Sang Lim, Sang Yup Lee, Hyo Seok Baek, Jinhee Lee, Kyoung Joung Lee, Taeksoo Shin, Min-Hyuk Kim, Sangwon Hwang, Erdenebayar Urtnasan, Ji Young Park, Dan Hee Kwon, Jin-kyung Lee, Andrew Coristine |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 61 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |