Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning-Based Exploratory Study.
Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning-Based Exploratory Study.
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使用移动和可穿戴传感器预测青少年抑郁：基于多模式机器学习的探索性研究。
DOI：
10.2196/35807
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发表时间：
2022-06-24
影响因子：
2.2
通讯作者：
Doryab, Afsaneh
中科院分区：
文献类型：
--
作者：
Mullick, Tahsin;Radovic, Ana;Shaaban, Sam;Doryab, Afsaneh
在过去几年中，青少年的抑郁水平呈上升趋势。根据2020年全国药物使用和健康调查的一项调查，410万美国青少年至少经历过一次重度抑郁发作。这一数字约占12至17岁青少年的16%。然而，只有32.3%的青少年接受了某种形式的专业或非专业治疗。使用移动的和可穿戴传感器更早地识别恶化的症状可能导致更早的干预。大多数使用基于传感器的数据预测抑郁症的研究都是针对成年人的。很少有研究预测青少年的抑郁症。我们工作的目的是研究患有抑郁症的青少年的被动感知数据，并调查两种机器学习方法预测青少年抑郁评分和抑郁水平变化的预测能力。这项工作还提供了对传感器特征的深入分析，这些特征是抑郁症状变化的关键指标，以及数据样本变化对模型准确度水平的影响。这项研究包括55名12至17岁的青少年抑郁症症状。每个参与者都通过智能手机传感器和Fitbit可穿戴设备被动监测24周。被动传感器每天收集通话、对话、位置和心率信息。经过数据预处理后，对汇总数据集中67%（37/55）的参与者进行了分析。每周患者健康问卷-参与者回答的9项调查作为基础事实。我们应用基于回归的方法来预测患者健康问卷-9抑郁评分和抑郁严重程度的变化。这些方法使用通用和个性化的建模策略进行了整合。通用策略包括Leave One Participant Out和Leave X Week Out。个性化策略模型基于累积周数和离开一周一个用户实例。训练线性和非线性机器学习算法来对数据进行建模。我们观察到，与通用方法相比，个性化方法在青少年抑郁症预测方面表现更好。最好的模型能够预测抑郁评分和抑郁水平的每周变化，均方根误差分别为2.83和3.21，遵循累积周个性化建模策略。我们的特征重要性调查表明，屏幕，呼叫和基于位置的功能的贡献影响最佳模型，并预测青少年抑郁症。这项研究提供了洞察使用被动感知数据预测青少年抑郁症的可行性。我们证明了抑郁评分和抑郁水平变化的预测能力。预测结果显示，个性化模型对青少年的预测效果优于通用方法。特征重要性提供了对抑郁症和传感器数据的更好理解。我们的研究结果可以帮助发展先进的青少年抑郁症预测。
Depression levels in adolescents have trended upward over the past several years. According to a 2020 survey by the National Survey on Drug Use and Health, 4.1 million US adolescents have experienced at least one major depressive episode. This number constitutes approximately 16% of adolescents aged 12 to 17 years. However, only 32.3% of adolescents received some form of specialized or nonspecialized treatment. Identifying worsening symptoms earlier using mobile and wearable sensors may lead to earlier intervention. Most studies on predicting depression using sensor-based data are geared toward the adult population. Very few studies look into predicting depression in adolescents. The aim of our work was to study passively sensed data from adolescents with depression and investigate the predictive capabilities of 2 machine learning approaches to predict depression scores and change in depression levels in adolescents. This work also provided an in-depth analysis of sensor features that serve as key indicators of change in depressive symptoms and the effect of variation of data samples on model accuracy levels. This study included 55 adolescents with symptoms of depression aged 12 to 17 years. Each participant was passively monitored through smartphone sensors and Fitbit wearable devices for 24 weeks. Passive sensors collected call, conversation, location, and heart rate information daily. Following data preprocessing, 67% (37/55) of the participants in the aggregated data set were analyzed. Weekly Patient Health Questionnaire-9 surveys answered by participants served as the ground truth. We applied regression-based approaches to predict the Patient Health Questionnaire-9 depression score and change in depression severity. These approaches were consolidated using universal and personalized modeling strategies. The universal strategies consisted of Leave One Participant Out and Leave Week X Out. The personalized strategy models were based on Accumulated Weeks and Leave One Week One User Instance Out. Linear and nonlinear machine learning algorithms were trained to model the data. We observed that personalized approaches performed better on adolescent depression prediction compared with universal approaches. The best models were able to predict depression score and weekly change in depression level with root mean squared errors of 2.83 and 3.21, respectively, following the Accumulated Weeks personalized modeling strategy. Our feature importance investigation showed that the contribution of screen-, call-, and location-based features influenced optimal models and were predictive of adolescent depression. This study provides insight into the feasibility of using passively sensed data for predicting adolescent depression. We demonstrated prediction capabilities in terms of depression score and change in depression level. The prediction results revealed that personalized models performed better on adolescents than universal approaches. Feature importance provided a better understanding of depression and sensor data. Our findings can help in the development of advanced adolescent depression predictions.
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