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

Innovations in Mental Health Systems (484) Web-based and Mobile Health Interventions (4018) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2111) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (638) Behavior Change (863) Anxiety and Stress Disorders (1330) Machine Learning (2076)

Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51567, first published .
Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study

Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study

Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study

Authors of this article:

Sonia Baee1 Author Orcid Image ;   Jeremy W Eberle2 Author Orcid Image ;   Anna N Baglione1 Author Orcid Image ;   Tyler Spears3 Author Orcid Image ;   Elijah Lewis4 Author Orcid Image ;   Hongning Wang5 Author Orcid Image ;   Daniel H Funk6 Author Orcid Image ;   Bethany Teachman2 Author Orcid Image ;   Laura E Barnes1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Eberle J, Baee S, Wolfe E, Boukhechba M, Funk D, Teachman B, Barnes L, Ayatollahi H. Behavioral engagement patterns and psychosocial outcomes in web-based interpretation bias training for anxiety. PLOS Digital Health 2025;4(7):e0000945 View
  2. Zahed K, Hallak Y, Azad K, Haidar J, Ghomrawi H. Utility of wearable technology in predicting panic attacks: A scoping review. DIGITAL HEALTH 2025;11 View