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

Wearable Devices and Sensors (903) Digital Biomarkers and Digital Phenotyping (198) mHealth for Data Collection and Research (981) mHealth for Wellness, Behavior Change and Prevention (2922) Ecological Momentary Assessment (EMA) (321)

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 (13) Tweetations (5) Metrics

Journals

  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
  5. Levinson C, Cusack C, Hunt R, Fitterman-Harris H, Ralph-Nearman C, Hooper S. The future of the eating disorder field: Inclusive, aware of systems, and personalized. Behaviour Research and Therapy 2024;183:104648 View
  6. Patel J, Hung C, Katapally T. Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review. Psychiatry Research 2025;343:116277 View
  7. Monarca I, Cibrian F, Hurtado I, Tentori M. Smartphone Haptics Can Uncover Differences in Touch Interactions Between ASD and Neurotypicals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(4):1 View
  8. Takeshige M, Oka T, Ohwan M, Hirai K. Exploring the Utility of a Machine Learning Approach with Mobile‐Based Cognitive Function Tasks for Detecting Depression. Japanese Psychological Research 2024 View
  9. Schaab B, Calvetti P, Hoffmann S, Diaz G, Rech M, Cazella S, Stein A, Barros H, Silva P, Reppold C. How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review. Cadernos de Saúde Pública 2024;40(11) View

Books/Policy Documents

  1. Sjöberg J, Bergdahl N, Sjödén B, Nouri J. Design, Learning, and Innovation. View
  2. Owotoki W, Enseroth A, Mbugua R, Owotoki P. Digital Technologies for Learning and Psychological Interventions. View