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

Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1515) Innovations and Technology for Physical Activity Education (742) Ecological Momentary Assessment (EMA) (311) Fitness Trackers and Smart Pedometers/Accelerometers (561) Anxiety and Stress Disorders (1053)

Published on in Vol 21, No 4 (2019): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12910, first published .
Using Machine Learning to Derive Just-In-Time and Personalized Predictors of Stress: Observational Study Bridging the Gap Between Nomothetic and Ideographic Approaches

Using Machine Learning to Derive Just-In-Time and Personalized Predictors of Stress: Observational Study Bridging the Gap Between Nomothetic and Ideographic Approaches

Using Machine Learning to Derive Just-In-Time and Personalized Predictors of Stress: Observational Study Bridging the Gap Between Nomothetic and Ideographic Approaches

Authors of this article:

Alan Rozet1 Author Orcid Image ;   Ian M Kronish1 Author Orcid Image ;   Joseph E Schwartz1 Author Orcid Image ;   Karina W Davidson2 Author Orcid Image
Article Authors Cited by (16) Tweetations (39) Metrics

Alan Rozet   1 , BA ;   Ian M Kronish   1 , MPH, MD ;   Joseph E Schwartz   1 , MS, PhD ;   Karina W Davidson   2 , MASc, PhD

1 Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, New York, NY, United States

2 Feinstein Institute for Medical Research, Northwell Health, New York, NY, United States

Corresponding Author:

  • Alan Rozet, BA
  • Center for Behavioral Cardiovascular Health
  • Columbia University Irving Medical Center
  • Presbyterian Hospital Building, 9th Floor
  • 622 W 168th Street
  • New York, NY, 10032
  • United States
  • Phone: 1 212-342-4493
  • Email: ar3793@cumc.columbia.edu