Identifying the presence and timing of discrete mood states prior to therapy
Identifying the presence and timing of discrete mood states prior to therapy
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DOI：
10.1016/j.brat.2020.103596
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发表时间：
2020-05-01
影响因子：
4.1
通讯作者：
Bosley, Hannah G.
中科院分区：
文献类型：
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作者：
Fisher, Aaron J.;Bosley, Hannah G.
本研究测试了一种新的，个人特定的方法来识别离散的情绪曲线的时间序列数据，并检查这些配置文件可以预测滞后的情绪和焦虑变量和基于时间的变量，包括趋势（线性，二次，立方），周期（12小时，24小时，7天），一周中的一天，一天中的时间。我们分析了45名治疗前患有情绪和焦虑障碍的患者的动态数据。每天收集四次数据，持续至少30天。潜在的个人档案分析，离散化每个人的连续多变量时间序列的沉思，担心，恐惧，愤怒，易怒，快感缺乏，绝望，抑郁情绪，回避。也就是说，每个时间点根据其独特的情绪状态混合进行分类，并为每个参与者确定代表离散情绪特征的潜在类别。我们发现，每个人的潜在类的模态数为3（平均值= 3.04，中位数= 3），范围为2至4类。在将每个人的时间序列随机分成两半进行训练和测试后，我们使用弹性网络正则化来识别训练集中每个情绪曲线存在或不存在的时间和滞后预测因子。在测试集中评估预测准确度。在127个模型中，平均曲线下面积为0.77，灵敏度为0.81，特异性为0.75。Brier评分表明平均预测准确率为83%。
The present study tested a novel, person-specific method for identifying discrete mood profiles from time-series data, and examined the degree to which these profiles could be predicted by lagged mood and anxiety variables and time-based variables, including trends (linear, quadratic, cubic), cycles (12-hr, 24-hr, and 7-day), day of the week, and time of day. We analyzed ambulatory data from 45 individuals with mood and anxiety disorders prior to therapy. Data were collected four-times-daily for at least 30 days. Latent profile analysis was applied person-by-person to discretize each individual's continuous multivariate time series of rumination, worry, fear, anger, irritability, anhedonia, hopelessness, depressed mood, and avoidance. That is, each time point was classified according to its unique blend of emotional states, and latent classes representing discrete mood profiles were identified for each participant. We found that the modal number of latent classes per person was three (mean = 3.04, median = 3), with a range of two to four classes. After splitting each individual's time series into random halves for training and testing, we used elastic net regularization to identify the temporal and lagged predictors of each mood profile's presence or absence in the training set. Prediction accuracy was evaluated in the testing set. Across 127 models, the average area under the curve was 0.77, with sensitivity of 0.81 and specificity of 0.75. Brier scores indicated an average prediction accuracy of 83%.