Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, United States; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, 03755, United States.
Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, United States; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, 03755, United States.
Behav Res Ther. 2023 Sep;168:104382. doi: 10.1016/j.brat.2023.104382. Epub 2023 Aug 2.
Wearable technology enables unobtrusive collection of longitudinally dense data, allowing for continuous monitoring of physiology and behavior. These digital phenotypes, or device-based indicators, are frequently leveraged to study depression. However, they are usually considered alongside questionnaire sum-scores which collapse the symptomatic gamut into a general representation of severity. To explore the contributions of passive sensing streams more precisely, associations of nine passive sensing-derived features with self-report responses to Center for Epidemiologic Studies Depression (CES-D) items were modeled. Using data from the NetHealth study on N=469 college students, this work generated mixed ordinal logistic regression models to summarize contributions of pulse, movement, and sleep data to depression symptom detection. Emphasizing the importance of the college context, wearable features displayed unique and complementary properties in their heterogeneously significant associations with CES-D items. This work provides conceptual and exploratory blueprints for a reductionist approach to modeling depression within passive sensing research.
可穿戴技术能够实现对纵向密集数据的非侵入式采集，从而实现对生理和行为的持续监测。这些数字表型或基于设备的指标经常被用于研究抑郁症。然而，它们通常与问卷总分一起使用，将症状谱简化为严重程度的一般表示。为了更精确地探索被动感知流的贡献，本研究使用混合有序逻辑回归模型对 9 种被动感知衍生特征与对中心流行病学研究抑郁量表（CES-D）项目的自我报告反应之间的关联进行建模。本研究使用了来自 NetHealth 研究的 469 名大学生的数据，生成了混合有序逻辑回归模型，以总结脉搏、运动和睡眠数据对抑郁症状检测的贡献。本研究强调了大学背景的重要性，可穿戴特征在与 CES-D 项目的异质显著关联中显示出独特且互补的特性。这项工作为被动感知研究中抑郁的简化建模方法提供了概念和探索性蓝图。
