Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39258, first published .
Digital Phenotyping Data to Predict Symptom Improvement and Mental Health App Personalization in College Students: Prospective Validation of a Predictive Model

Digital Phenotyping Data to Predict Symptom Improvement and Mental Health App Personalization in College Students: Prospective Validation of a Predictive Model

Digital Phenotyping Data to Predict Symptom Improvement and Mental Health App Personalization in College Students: Prospective Validation of a Predictive Model

Authors of this article:

Danielle Currey1, 2 Author Orcid Image ;   John Torous1 Author Orcid Image

Journals

  1. Currey D, Torous J. Increasing the value of digital phenotyping through reducing missingness: a retrospective review and analysis of prior studies. BMJ Mental Health 2023;26(1):e300718 View
  2. Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. Journal of Medical Internet Research 2023;25:e44502 View
  3. Cohen A, Naslund J, Lane E, Bhan A, Rozatkar A, Mehta U, Vaidyam A, Byun A, Barnett I, Torous J. Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample. Acta Psychiatrica Scandinavica 2024 View
  4. Matthews P, Rhodes-Maquaire C. Personalisation and Recommendation for Mental Health Apps: A Scoping Review. Behaviour & Information Technology 2024:1 View
  5. Gray L, Marcynikola N, Barnett I, Torous J. The Potential for Digital Phenotyping in Understanding Mindfulness App Engagement Patterns: A Pilot Study. Journal of Integrative and Complementary Medicine 2024 View