Baglione Anna N, Cai Lihua, Bahrini Aram, Posey Isabella, Boukhechba Mehdi, Chow Philip I
Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, United States.
Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States.
JMIR Med Inform. 2022 Jun 2;10(6):e30712. doi: 10.2196/30712.
Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success; however, the relationship between mood and engagement among patients with cancer remains poorly understood. A reason for this is the lack of a data-driven process for analyzing mood and app engagement data for patients with cancer.
This study aimed to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in patients with breast cancer.
We described the steps involved in data preprocessing, feature extraction, and data modeling and prediction. We applied this process as a case study to data collected from patients with breast cancer who engaged with a mobile mental health app intervention (IntelliCare) over 7 weeks. We compared engagement patterns over time (eg, frequency and days of use) between participants with high and low anxiety and between participants with high and low depression. We then used a linear mixed model to identify significant effects and evaluate the performance of the random forest and XGBoost classifiers in predicting weekly mood from baseline affect and engagement features.
We observed differences in engagement patterns between the participants with high and low levels of anxiety and depression. The linear mixed model results varied by the feature set; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. The accuracy of predicting depressed mood varied according to the feature set and classifier. The feature set containing survey features and overall app engagement features achieved the best performance (accuracy: 84.6%; precision: 82.5%; recall: 64.4%; F1 score: 67.8%) when used with a random forest classifier.
The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in patients with breast cancer. The ability to leverage both self-report and engagement features to analyze and predict mood during an intervention could be used to enhance decision-making for researchers and clinicians and assist in developing more personalized interventions for patients with breast cancer.
通过智能设备提供的健康干预措施越来越多地用于应对与癌症治疗相关的心理健康挑战。参与移动干预与治疗成功相关；然而，癌症患者的情绪与参与度之间的关系仍知之甚少。原因之一是缺乏用于分析癌症患者情绪和应用程序参与度数据的数据驱动流程。
本研究旨在提供一个逐步的流程，用于使用应用程序参与度指标来预测乳腺癌患者持续评估的情绪结果。
我们描述了数据预处理、特征提取以及数据建模与预测所涉及的步骤。我们将此流程作为案例研究应用于从参与为期7周的移动心理健康应用程序干预（IntelliCare）的乳腺癌患者收集的数据。我们比较了高焦虑和低焦虑参与者以及高抑郁和低抑郁参与者之间随时间的参与模式（例如，使用频率和天数）。然后，我们使用线性混合模型来确定显著影响，并评估随机森林和XGBoost分类器在根据基线情绪和参与度特征预测每周情绪方面的性能。
我们观察到高焦虑和低焦虑以及高抑郁和低抑郁参与者之间在参与模式上存在差异。线性混合模型的结果因特征集而异；这些结果显示参与度的几个特征（包括基于持续时间的指标和频率）的影响较弱。预测抑郁情绪的准确性因特征集和分类器而异。当与随机森林分类器一起使用时，包含调查特征和总体应用程序参与度特征的特征集表现最佳（准确率：84.6%；精确率：82.5%；召回率：64.4%；F1分数：67.8%）。
案例研究的结果支持了我们用于理解乳腺癌患者应用程序参与度与情绪结果之间关系的分析流程的可行性和潜力。在干预期间利用自我报告和参与度特征来分析和预测情绪的能力可用于增强研究人员和临床医生的决策制定，并有助于为乳腺癌患者开发更个性化的干预措施。
