JMIR Publications

ABSTRACT

Background:

Students’ mental health crisis has been recognized before COVID-19 pandemic. Mindfulness Virtual Community (MVC), an eight-week web-based mindfulness and cognitive behavioural therapy (CBT) 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 enrols in the program is important to advise students’ accordingly.

Objective:

This study objectives were to investigate: (1) if we can predict MVC’s effectiveness using sociodemographic and self-reported features, and (2) how important was the exposure to mindfulness videos, in comparison with these features, in prediction of the intervention success.

Methods:

Machine learning models were developed to assess MVC’s effectiveness defined as success in reducing symptoms of depression, anxiety, and stress as measured using the Patient Health Questionnaire-9 (PHQ9), the Beck Anxiety Inventory (BAI), and the Perceived Stress Scale (PSS), to at least the minimal clinically important difference (MCID). A dataset representing a sample of undergraduate students (n = 209) who took the MVC intervention between Fall 2017 and Fall 2018 was used. Random forest was used to measure the features’ importance.

Results:

Gradient Boosting achieved the best performance both in terms of AUC and accuracy for predicting PHQ 9 (AUC=.85, Accuracy=.83) and PSS (AUC=1, Accuracy=1); and Random Forest had best performance for predicting BAI (AUC=.93, Accuracy=.93). The exposure to online mindfulness videos was the most important predictor for the intervention’s effectiveness for PHQ9, BAI and PSS, followed by the number of working hours per week.

Conclusions:

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