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

e-Mental Health and Cyberpsychology (923) Innovations and Technology for Physical Activity Education (537) Ecological Momentary Assessment (EMA) (238) Fitness Trackers and Smart Pedometers/Accelerometers (492) Anxiety and Stress Disorders (810)

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 Rozet 1 Author Orcid Image ;   Ian M Kronish 1 Author Orcid Image ;   Joseph E Schwartz 1 Author Orcid Image ;   Karina W Davidson 2 Author Orcid Image
Article Authors Cited by (10) Tweetations (40) 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 , US

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

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
  • US
  • Phone: 1 212-342-4493
  • Email: ar3793@cumc.columbia.edu