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

Wearable Devices and Sensors (857) Digital Biomarkers and Digital Phenotyping (180) mHealth for Data Collection and Research (941) mHealth for Wellness, Behavior Change and Prevention (2814) Ecological Momentary Assessment (EMA) (301)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37469, first published .
Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis

Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis

Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis

Authors of this article:

George Aalbers1, 2 Author Orcid Image ;   Andrew T Hendrickson1 Author Orcid Image ;   Mariek MP Vanden Abeele2, 3 Author Orcid Image ;   Loes Keijsers4 Author Orcid Image
Article Authors Cited by (7) Tweetations (5) Metrics

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  1. Wang X, Oussalah M, Niemilä M, Ristikari T, Virtanen P. Towards AI-governance in psychosocial care: A systematic literature review analysis. Journal of Open Innovation: Technology, Market, and Complexity 2023;9(4):100157 View
  2. Ruotsalainen P, Blobel B. Future pHealth Ecosystem-Holistic View on Privacy and Trust. Journal of Personalized Medicine 2023;13(7):1048 View
  3. Ali D, Hassan R, Othman H, Taha H, Mousavi Khaneghah A, Smaoui S. Revolutionizing detection: Smartphone-powered colorimetry for the drugs and food analysis. Microchemical Journal 2024;205:111228 View
  4. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View

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  1. Sjöberg J, Bergdahl N, Sjödén B, Nouri J. Design, Learning, and Innovation. View