Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data.
Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data.
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DOI：
10.1038/s41398-023-02669-y
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发表时间：
2023-12-09
影响因子：
6.8
通讯作者：
Jacobson, Nicholas C.
中科院分区：
文献类型：
--
作者：
Price, George D.;Heinz, Michael V.;Song, Seo Ho;Nemesure, Matthew D.;Jacobson, Nicholas C.
重度抑郁障碍（MDD）由于症状的变异性，对诊断和管理提出了相当大的挑战。只有最近的工作强调了询问抑郁症状变异性的临床意义。因此，本研究调查了社会人口学、合并症、运动和睡眠数据与长期抑郁症状变异性的关系。参与者信息包括（N = 939）基线社会人口统计和合并症数据，纵向被动收集的可穿戴数据，以及12个月内收集的患者健康问卷-9 （PHQ-9）得分。使用集成机器学习方法通过：(i)领域驱动的特征选择方法和（ii）详尽的特征包含方法来检测长期抑郁症状的可变性。SHapley加性解释（SHAP）用于询问变量的重要性和方向性。复合领域驱动模型和穷极包含模型都能够适度地检测长期抑郁症状变异性（r = 0.33和r = 0.39）。我们的研究结果表明，社会人口学、合并症和被动收集的可穿戴运动和睡眠数据在检测长期抑郁症状变异性方面具有递增的预测有效性。
Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting long-term depression symptom variability.
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