# Predicting the Effectiveness of a Mindfulness Virtual Community Intervention for University Students: Machine Learning Model

## TL;DR

This study uses machine learning to predict which university students will benefit most from an 8-week mindfulness program to reduce depression, anxiety, and stress.

## Contribution

The study introduces a machine learning model that predicts the effectiveness of a mindfulness intervention using student data.

## Key findings

- Gradient boosting and random forest models accurately predicted the effectiveness of the mindfulness program for depression, anxiety, and stress.
- Exposure to online mindfulness videos was the strongest predictor of program success.
- The models achieved high accuracy and AUC scores, especially for predicting stress and depression outcomes.

## Abstract

Students’ mental health crisis was recognized before the COVID-19 pandemic. Mindfulness virtual community (MVC), an 8-week web-based mindfulness and cognitive behavioral therapy program, has proven to be an effective web-based program to reduce symptoms of depression, anxiety, and stress. Predicting the success of MVC before a student enrolls in the program is essential to advise students accordingly.

The objectives of this study were to investigate (1) whether we can predict MVC’s effectiveness using sociodemographic and self-reported features and (2) whether exposure to mindfulness videos is highly predictive of the intervention’s success.

Machine learning models were developed to predict MVC’s effectiveness, defined as success in reducing symptoms of depression, anxiety, and stress as measured using the Patient Health Questionnaire-9 (PHQ-9), the Beck Anxiety Inventory (BAI), and the Perceived Stress Scale (PSS), to at least the minimal clinically important difference. A data set representing a sample of undergraduate students (N=209) who took the MVC intervention between fall 2017 and fall 2018 was used for this secondary analysis. Random forest was used to measure the features’ importance.

Gradient boosting achieved the best performance both in terms of area under the curve (AUC) and accuracy for predicting PHQ-9 (AUC=0.85 and accuracy=0.83) and PSS (AUC=1 and accuracy=1), and random forest had the best performance for predicting BAI (AUC=0.93 and accuracy=0.93). Exposure to online mindfulness videos was the most important predictor for the intervention’s effectiveness for PHQ-9, BAI, and PSS, followed by the number of working hours per week.

The performance of the models to predict MVC intervention effectiveness for depression, anxiety, and stress is high. These models might be helpful for professionals to advise students early enough on taking the intervention or choosing other alternatives. The students’ exposure to online mindfulness videos is the most important predictor for the effectiveness of the MVC intervention.

ISRCTN Registry ISRCTN12249616; https://www.isrctn.com/ISRCTN12249616