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Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage
Franziska Wenger1, Caroline Allenhof2, Simon Schreynemackers3
1Clinic for Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University Frankfurt, Frankfurt am Main, Germany.
JMIR Formative Research
|November 15, 2024
Summary
Early user engagement in online depression tools is key to adherence. Analyzing first-week usage behavior predicts adherence better than demographics, enabling tailored interventions for better outcomes.
Area of Science:
- Digital mental health
- Computational psychiatry
- Machine learning in healthcare
Background:
- Online interventions offer accessible alternatives for depression treatment.
- Low adherence rates can limit the effectiveness of digital mental health tools.
- Predicting adherence is crucial for optimizing user engagement and therapeutic outcomes.
Purpose of the Study:
- To develop and evaluate a random forest model for predicting adherence to the iFightDepression (iFD) tool.
- Identify users at risk of noncompletion for early intervention.
- Enhance the effectiveness of online depression interventions.
Main Methods:
- Utilized log data from 4187 adult patients using the iFD tool.
- Trained a random forest model using baseline and first-week usage data.
- Evaluated model performance using accuracy, F1-score, and AUC, analyzing variable importance.
Main Results:
- A model incorporating first-week usage behavior significantly predicted adherence (P<.001).
- Achieved 0.82 accuracy and 0.83 AUC in predicting adherence.
- Key predictors included early usage patterns like logs, word count, and time spent on the tool.
Conclusions:
- Early engagement and first-week usage behavior are stronger predictors of adherence than sociodemographic or clinical factors.
- Analyzing early usage patterns can identify at-risk users for tailored interventions.
- Proactive interventions based on predicted adherence can improve user engagement and optimize online mental health support.
Keywords:
adherencedepressiondigital interventionsiFDiFightDepressionmachine learningonline interventionrandom forestMore Related Videos
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