Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study.
Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study.
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DOI：
10.2196/30712
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发表时间：
2022-06-02
影响因子：
3.2
通讯作者：
中科院分区：
文献类型：
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通过智能设备提供的健康干预越来越多地被用于应对与癌症治疗相关的心理健康挑战。移动干预的参与与治疗的成功相关；然而，癌症患者的情绪和参与之间的关系仍然知之甚少。其中一个原因是缺乏一个数据驱动的过程来分析癌症患者的情绪和应用程序参与度数据。这项研究旨在提供一个循序渐进的过程，使用APP敬业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、数据建模和预测所涉及的步骤。我们将这一过程作为案例研究，应用于从接受移动精神健康应用干预(IntelliCare)超过7周的乳腺癌患者收集的数据。我们比较了焦虑程度高和焦虑程度低的参与者以及抑郁程度高和程度低的参与者随着时间的推移(例如，使用频率和使用天数)的投入模式。然后，我们使用线性混合模型来识别显著影响，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。我们观察到焦虑和抑郁水平较高和较低的参与者在参与模式上的差异。线性混合模型的结果因特征集的不同而不同；这些结果揭示了参与的几个特征的弱影响，包括基于持续时间的指标和频率。预测抑郁情绪的准确率因特征集和分类器的不同而不同。当使用随机森林分类器时，包含调查功能和整体应用程序参与度功能的功能集获得了最佳性能(准确率：84.6%；准确率：82.5%；召回率：64.4%；F1得分：67.8%)。案例研究的结果支持了我们的分析过程的可行性和潜力，以了解乳腺癌患者APP参与度和情绪结果之间的关系。利用自我报告和参与度功能来分析和预测干预期间的情绪的能力可以用来增强研究人员和临床医生的决策，并帮助为乳腺癌患者开发更个性化的干预措施。
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.
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DOI：
10.1016/0749-5978(91)90020-t
发表时间：
1991-12-01
影响因子：
4.6
作者：
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