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

Digital Biomarkers and Digital Phenotyping (75) Clinical Information and Decision Making (405) Decision Support for Health Professionals (348) Machine Learning (466) Ecological Momentary Assessment (EMA) (154) Depression and Mood Disorders; Suicide Prevention (651)

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 Kim 1, 2 Author Orcid Image ;   SungHee Lee 3 Author Orcid Image ;   SangEun Lee 4 Author Orcid Image ;   Soyun Hong 2 Author Orcid Image ;   HeeJae Kang 3 Author Orcid Image ;   Namhee Kim 2 Author Orcid Image
Article Authors Cited by (12) Tweetations (8) Metrics

Journals

  1. Kim H, Kim S, Kong S, Jeong Y, Kim H, Kim N. Possible Application of Ecological Momentary Assessment to Older Adults’ Daily Depressive Mood: Integrative Literature Review. JMIR Mental Health 2020;7(6):e13247 View
  2. Lewczuk K, Gorowska M, Li Y, Gola M. Mobile Internet Technologies, Ecological Momentary Assessment, and Intervention—Poison and Remedy for New Online Problematic Behaviors in ICD-11. Frontiers in Psychiatry 2020;11 View
  3. Niculescu I, Arora T, Iaboni A. Screening for depression in older adults with cognitive impairment in the homecare setting: a systematic review. Aging & Mental Health 2020:1 View
  4. Grossman J, Frumkin M, Rodebaugh T, Lenze E. mHealth Assessment and Intervention of Depression and Anxiety in Older Adults. Harvard Review of Psychiatry 2020;28(3):203 View
  5. Bincy K, Logaraj M, Ramraj B. Depression and its associated factors among the older adults in rural, Tamilnadu, India. Clinical Epidemiology and Global Health 2021;10:100677 View
  6. He-Yueya J, Buck B, Campbell A, Choudhury T, Kane J, Ben-Zeev D, Althoff T. Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability. npj Schizophrenia 2020;6(1) View
  7. Minaeva O, Riese H, Lamers F, Antypa N, Wichers M, Booij S. Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method. Journal of Medical Internet Research 2020;22(12):e22634 View
  8. Koo J, Son N, Yoo K. Relationship between the living-alone period and depressive symptoms among the elderly. Archives of Gerontology and Geriatrics 2021;94:104341 View
  9. Anýž J, Bakštein E, Dally A, Kolenič M, Hlinka J, Hartmanová T, Urbanová K, Correll C, Novák D, Španiel F. Validation of the Aktibipo SElf-RaTing (ASERT) questionnaire for digital self-assessment of mood and relapse-detection in bipolar disorder: Observational Study (Preprint). JMIR Mental Health 2020 View
  10. Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D. Predicting Depression from Smartphone Behavioral Markers Using Machine Learning Methods, Hyper-parameter Optimization, and Feature Importance Analysis: An Exploratory Study (Preprint). JMIR mHealth and uHealth 2020 View
  11. Massar S, Chua X, Soon C, Ng A, Ong J, Chee N, Lee T, Ghosh A, Chee M. Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data. npj Digital Medicine 2021;4(1) View