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

Journal: JMIR mental health
PMID: 39705068

Abstract

BACKGROUND: Digital mental health is a promising paradigm for individualized, patient-driven health care. For example, cognitive bias modification programs that target interpretation biases (cognitive bias modification for interpretation [CBM-I]) can provide practice thinking about ambiguous situations in less threatening ways on the web without requiring a therapist. However, digital mental health interventions, including CBM-I, are often plagued with lack of sustained engagement and high attrition rates. New attrition detection and mitigation strategies are needed to improve these interventions.

Authors

  • Sonia Baee
    Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, United States.
  • Jeremy W Eberle
    Department of Psychology, University of Virginia, Charlottesville, VA, United States.
  • Anna N Baglione
    Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, United States.
  • Tyler Spears
    Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States.
  • Elijah Lewis
    Department of Computer Science, University of Virginia, Charlottesville, VA, United States.
  • Hongning Wang
    Department of Computer Science and Technology, Tsinghua University, Beijing, China.
  • Daniel H Funk
    Sartography, Staunton, VA, United States.
  • Bethany Teachman
    Department of Psychology, University of Virginia, Charlottesville, VA, United States.
  • Laura E Barnes
    Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, United States.

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