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Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...

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Predicting Disengagement to Better Support Outcomes in a Web-Based Weight Loss Program Using Machine Learning Models:

Aida Brankovic¹, Gilly A Hendrie², Danielle L Baird²

¹The Australian e-Health Research Centre, Health & Biosecurity, Commonwealth Scientific Industrial Research Organisation, Brisbane, Australia.

Journal of Medical Internet Research

|June 26, 2023

Summary

Machine learning models can predict weight loss program disengagement by analyzing user activity. Early prediction allows for timely interventions to improve participant engagement and health outcomes.

Keywords:

artificial intelligence machine learning machine learning–driven intervention predicting engagement web-based weight loss program

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Area of Science:

Health Informatics
Machine Learning in Healthcare
Behavioral Science

Background:

Engagement is crucial for behavior change and health improvement in interventions.
Limited research exists on using machine learning (ML) to predict disengagement in commercial weight loss programs.
Predicting disengagement can help participants achieve their health goals.

Purpose of the Study:

To utilize explainable machine learning (ML) to forecast member disengagement risk weekly over a 12-week period.
To identify key predictors of disengagement in a web-based weight loss program.

Main Methods:

Developed and validated predictive models (Random Forest, Extreme Gradient Boosting, Logistic Regression) using data from 59,686 participants.
Employed 10-fold cross-validation and temporal validation on a separate cohort.
Utilized Shapley values for feature importance and prediction explanation.

Main Results:

Extreme Gradient Boosting models demonstrated the best predictive performance, with Area Under the Receiver Operating Characteristic Curve (AUC-ROC) ranging from 0.85 to 0.93.
Area Under the Precision-Recall Curve (AUC-PR) ranged from 0.57 to 0.95, showing significant improvement (20% at week 3).
Key predictors of disengagement included overall platform activity and previous weight entry frequency.

Conclusions:

Machine learning algorithms show potential for predicting and understanding participant disengagement in online weight loss programs.
These insights can inform targeted support strategies to enhance user engagement.
Improved engagement is linked to better health outcomes and greater weight loss success.