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

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