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

IF 1.9 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
Christo El Morr, Farideh Tavangar, Farah Ahmad, Paul Ritvo
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引用次数: 0

Abstract

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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

预测大学生正念虚拟社区干预措施的效果:机器学习模型
背景:在 COVID-19 大流行之前,人们就已经意识到学生的心理健康危机。正念虚拟社区(MVC)是一个为期八周的网络正念和认知行为疗法(CBT)项目,已被证明是一个有效的网络项目,可减轻抑郁、焦虑和压力症状。在学生报名参加该项目之前预测 MVC 的成功率对于向学生提供相应建议至关重要:本研究的目的是调查:(1)我们是否能利用社会人口学和自我报告特征预测 MVC 的有效性;(2)接触正念视频是否能高度预测干预的成功:我们开发了机器学习模型来预测 MVC 的有效性,MVC 的定义是成功减少抑郁、焦虑和压力症状,这些症状是使用患者健康问卷-9(PHQ9)、贝克焦虑量表(BAI)和感知压力量表(PSS)测量的,至少达到最小临床重要差异(MCID)。本次二次分析使用的数据集代表了在 2017 年秋季至 2018 年秋季期间参加 MVC 干预的本科生样本(n = 209)。随机森林用于衡量特征的重要性:梯度提升法在预测PHQ 9(AUC=.85,准确率=.83)和PSS(AUC=1,准确率=1)方面的AUC和准确率都达到了最佳性能;而随机森林在预测BAI方面的性能最佳(AUC=.93,准确率=.93)。在PHQ9、BAI和PSS方面,接触在线正念视频是干预效果的最重要预测因素,其次是每周工作小时数:预测 MVC 对抑郁、焦虑和压力的干预效果的模型具有很高的性能。这些模型可能有助于专业人士及早建议学生接受干预或选择其他方法。学生接触在线正念视频是预测MVC干预效果的最重要因素:ISRCTN Registry ISRCTN12249616; http://www.isrctn.com/ISRCTN12249616.
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来源期刊
Interactive Journal of Medical Research
Interactive Journal of Medical Research MEDICINE, RESEARCH & EXPERIMENTAL-
自引率
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发文量
45
审稿时长
12 weeks
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