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

RCTs - Protocols/Proposals (eHealth) (541) Adoption and Change Management of eHealth Systems (715) mHealth for Wellness, Behavior Change and Prevention (2875) Anxiety and Stress Disorders (1055) Depression and Mood Disorders; Suicide Prevention (1364)

Published on in Vol 11, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37954, first published .
Digital Phenotyping Data to Predict Symptom Improvement and App Personalization: Protocol for a Prospective Study

Digital Phenotyping Data to Predict Symptom Improvement and App Personalization: Protocol for a Prospective Study

Digital Phenotyping Data to Predict Symptom Improvement and App Personalization: Protocol for a Prospective Study

Authors of this article:

Danielle Currey1 Author Orcid Image ;   John Torous1 Author Orcid Image
Article Authors Cited by (5) Tweetations (11) Metrics

Journals

  1. Currey D, Torous J. Digital Phenotyping Data to Predict Symptom Improvement and Mental Health App Personalization in College Students: Prospective Validation of a Predictive Model. Journal of Medical Internet Research 2023;25:e39258 View
  2. Langholm C, Byun A, Mullington J, Torous J. Monitoring sleep using smartphone data in a population of college students. npj Mental Health Research 2023;2(1) View
  3. Matthews P, Rhodes-Maquaire C. Personalisation and Recommendation for Mental Health Apps: A Scoping Review. Behaviour & Information Technology 2024:1 View
  4. 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;30(11):1108 View
  5. 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 2024:116277 View