The presentation of major depressive disorder (MDD) can vary widely due to its heterogeneity, including inter- and intraindividual symptom variability, making MDD difficult to diagnose with standard measures in clinical settings. Prior work has demonstrated that passively collected actigraphy can be used to detect MDD at a disorder level; however, given the heterogeneous nature of MDD, comprising multiple distinct symptoms, it is important to measure the degree to which various MDD symptoms may be captured by such passive data. The current study investigated whether individual depressive symptoms could be detected from passively collected actigraphy data in a (a) clinical subpopulation (i.e., moderate depressive symptoms or greater) and (b) general population. Using data from the National Health and Nutrition Examination Survey, a large nationally representative sample (N = 8,378), we employed a convolutional neural network to determine which depressive symptoms in each population could be detected by wrist-worn, minute-level actigraphy data. Findings indicated a small-moderate correspondence between the predictions and observed outcomes for mood, psychomotor, and suicide items (area under the receiver operating characteristic curve [AUCs] = 0.58-0.61); a moderate-large correspondence for anhedonia (AUC = 0.64); and a large correspondence for fatigue (AUC = 0.74) in the clinical subpopulation (n = 766); and a small-moderate correspondence for sleep, appetite, psychomotor, and suicide items (AUCs = 0.56-0.60) in the general population (n = 8,378). Thus, individual depressive symptoms can be detected in individuals who likely meet the criteria for MDD, suggesting that wrist-worn actigraphy may be suitable for passively assessing these symptoms, providing important clinical implications for the diagnosis and treatment of MDD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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使用被动收集的活动记录仪数据来检测临床亚群和一般人群中的个体抑郁症状。


由于其异质性，包括个体间和个体内症状的变异性，重度抑郁症 （MDD） 的表现可能差异很大，这使得 MDD 难以在临床环境中用标准测量来诊断。先前的工作表明，被动收集的活动记录仪可用于检测疾病水平的 MDD;然而，鉴于 MDD 的异质性，包括多种不同的症状，测量这种被动数据可能捕获各种 MDD 症状的程度是很重要的。目前的研究调查了是否可以从 （a） 临床亚群（即中度抑郁症状或更严重的）和 （b） 一般人群中被动收集的活动记录数据中检测到个体抑郁症状。使用来自全国健康和营养检查调查的数据，一个具有全国代表性的大型样本 （N = 8,378），我们采用卷积神经网络来确定每个人群中的哪些抑郁症状可以通过手腕佩戴的分钟级活动记录数据检测到。研究结果表明，情绪、精神运动和自杀项目的预测与观察到的结果之间存在小到中等的对应关系 （接受者工作特征曲线下面积 [AUC] = 0.58-0.61）;快感缺乏的中等-大对应 （AUC = 0.64）;临床亚群 （n = 766） 中疲劳 （AUC = 0.74） 的较大对应关系;在一般人群 （n = 8,378） 中，睡眠、食欲、精神运动和自杀项目 （AUCs = 0.56-0.60） 呈中小型对应关系。 因此，在可能符合 MDD 标准的个体中可以检测到个体抑郁症状，这表明腕戴式活动记录仪可能适合被动评估这些症状，为 MDD 的诊断和治疗提供重要的临床意义。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。