This paper is in the following e-collection/theme issue:

Digital Biomarkers and Digital Phenotyping (178) Clinical Information and Decision Making (1339) Decision Support for Health Professionals (1145) Machine Learning (1400) Ecological Momentary Assessment (EMA) (297) Depression and Mood Disorders; Suicide Prevention (1290)

Published on in Vol 7, No 10 (2019): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14149, first published .
Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Authors of this article:

Heejung Kim1, 2 Author Orcid Image ;   SungHee Lee3 Author Orcid Image ;   SangEun Lee4 Author Orcid Image ;   Soyun Hong1 Author Orcid Image ;   HeeJae Kang3 Author Orcid Image ;   Namhee Kim1 Author Orcid Image
Article Authors Cited by (68) Tweetations (8) Metrics

Heejung Kim   1, 2 , GNP-BC, RN, PhD ;   SungHee Lee   3 , PhD ;   SangEun Lee   4 , PhD ;   Soyun Hong   1 , RN, MSN ;   HeeJae Kang   3 , BS ;   Namhee Kim   1 , RN, MPH

1 College of Nursing, Yonsei University, Seoul, Republic of Korea

2 Mo-Im Kim Nursing Research Institute, Yonsei University, Seoul, Republic of Korea

3 BRFrame Inc, Seoul, Republic of Korea

4 Health-IT Acceleration Platform Technology Innovation Center, College of Medicine, Yonsei University Health System, Seoul, Republic of Korea

Corresponding Author:

  • Heejung Kim, GNP-BC, RN, PhD
  • College of Nursing
  • Yonsei University
  • Room #603
  • 50-1 Yonsei-ro, Seodaemun-gu
  • Seoul, 03722
  • Republic of Korea
  • Phone: 82 2 2228 3273
  • Fax: 82 2 2227 8303
  • Email: hkim80@yuhs.ac