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

Journals

  1. Chevance G, Perski O, Hekler E. Innovative methods for observing and changing complex health behaviors: four propositions. Translational Behavioral Medicine 2021;11(2):676 View
  2. Epstein D, Tyburski M, Kowalczyk W, Burgess-Hull A, Phillips K, Curtis B, Preston K. Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data. npj Digital Medicine 2020;3(1) View
  3. Hekler E, Klasnja P, Chevance G, Golaszewski N, Lewis D, Sim I. Why we need a small data paradigm. BMC Medicine 2019;17(1) View
  4. Yan S, Hosseinmardi H, Kao H, Narayanan S, Lerman K, Ferrara E. Affect Estimation with Wearable Sensors. Journal of Healthcare Informatics Research 2020;4(3):261 View
  5. Fuller-Tyszkiewicz M, Richardson B, Little K, Teague S, Hartley-Clark L, Capic T, Khor S, Cummins R, Olsson C, Hutchinson D. Efficacy of a Smartphone App Intervention for Reducing Caregiver Stress: Randomized Controlled Trial. JMIR Mental Health 2020;7(7):e17541 View
  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