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

e-Mental Health and Cyberpsychology (930) Innovations and Technology for Physical Activity Education (540) Ecological Momentary Assessment (EMA) (239) Fitness Trackers and Smart Pedometers/Accelerometers (493) Anxiety and Stress Disorders (813)

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

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  6. Zuidersma M, Riese H, Snippe E, Booij S, Wichers M, Bos E. Single-Subject Research in Psychiatry: Facts and Fictions. Frontiers in Psychiatry 2020;11 View
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  8. Ng A, Wei B, Jain J, Ward E, Tandon S, Moskowitz J, Krogh-Jespersen S, Wakschlag L, Alshurafa N. Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation. JMIR mHealth and uHealth 2022;10(8):e33850 View
  9. Naegelin M, Weibel R, Kerr J, Schinazi V, La Marca R, von Wangenheim F, Hoelscher C, Ferrario A. An interpretable machine learning approach to multimodal stress detection in a simulated office environment. Journal of Biomedical Informatics 2023;139:104299 View
  10. Milne-Ives M, Selby E, Inkster B, Lam C, Meinert E, Narasimhan P. Artificial intelligence and machine learning in mobile apps for mental health: A scoping review. PLOS Digital Health 2022;1(8):e0000079 View