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Assessment of Mouse Judgment Bias through an Olfactory Digging Task

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

Sonia Baee¹, Jeremy W Eberle², Anna N Baglione¹

¹Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, United States.

JMIR Mental Health

|December 20, 2024

Summary

This summary is machine-generated.

Digital mental health interventions like cognitive bias modification for interpretation (CBM-I) struggle with high dropout rates. Combining passively detected user behavior with self-reported data can predict and help prevent early attrition in these online programs.

Keywords:

CBM-I attrition prediction cognitive bias modification digital mental health intervention dropout rate personalization user engagement

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Area of Science:

Digital mental health
Computational psychiatry
Human-computer interaction

Background:

Digital mental health offers personalized, patient-driven healthcare solutions.
Cognitive bias modification for interpretation (CBM-I) is a web-based intervention targeting interpretation biases.
High attrition rates and lack of sustained engagement challenge digital mental health interventions.

Purpose of the Study:

To identify early-stage high-risk dropout participants in web-based CBM-I trials.
To determine which self-reported and passively detected features best predict dropout.

Main Methods:

Analyzed community adults with anxiety or negative future thinking across three web-based CBM-I trials (N=1277).
Created feature sets: baseline demographics, user context/reactions, clinical functioning, and passively detected website behavior.
Utilized machine learning algorithms to predict participants at high risk of not starting the second CBM-I session.

Main Results:

Extreme gradient boosting achieved high predictive performance (macro-F1 scores: .832, .770, .917).
Passively detected user behavior features significantly contributed to dropout prediction.
Combining all feature sets yielded the best overall predictive accuracy.

Conclusions:

Integrating passive behavioral indicators with self-reported data enhances early dropout prediction in CBM-I.
Findings underscore the need for personalized attrition prevention strategies in digital health.
Generalizability remains a challenge in digital health intervention studies.