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

Use and User Demographics of mHealth (346) Evaluation and Research Methodology for mHealth (211) mHealth for Wellness, Behavior Change and Prevention (2958) Depression and Mood Disorders; Suicide Prevention (1415)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41815, first published .
Trajectories of Symptoms in Digital Interventions for Depression and Anxiety Using Routine Outcome Monitoring Data: Secondary Analysis Study

Trajectories of Symptoms in Digital Interventions for Depression and Anxiety Using Routine Outcome Monitoring Data: Secondary Analysis Study

Trajectories of Symptoms in Digital Interventions for Depression and Anxiety Using Routine Outcome Monitoring Data: Secondary Analysis Study

Authors of this article:

Diana Catalina Cumpanasoiu1 Author Orcid Image ;   Angel Enrique1, 2 Author Orcid Image ;   Jorge E Palacios1, 2 Author Orcid Image ;   Daniel Duffy1, 2 Author Orcid Image ;   Scott McNamara1 Author Orcid Image ;   Derek Richards1, 2 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Hisler G, Young K, Cumpanasoiu D, Palacios J, Duffy D, Enrique A, Keegan D, Richards D. Incorporating a deep‐learning client outcome prediction tool as feedback in supported internet‐delivered cognitive behavioural therapy for depression and anxiety: A randomised controlled trial within routine clinical practice. Counselling and Psychotherapy Research 2025;25(1) View
  2. McBain R, Schuler M, Rukundo T, Wanyenze R, Wagner G. Trajectories of perinatal depression among women living with HIV in Uganda. Journal of Global Health 2024;14 View
  3. Park J, Shin Y, Jung D, Hur J, Pack S, Lee H, Lee H, Cho C. Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions. Frontiers in Psychiatry 2025;15 View