Teachers College, Columbia University, New York, New York, USA.
The Pennsylvania State University, State College, Pennsylvania, USA.
Br J Clin Psychol. 2022 Jan;61 Suppl 1(Suppl 1):31-50. doi: 10.1111/bjc.12295. Epub 2021 May 7.
Using two intensive longitudinal data sets with different timescales (90 minutes, daily), we examined emotion network density, a metric of emotional inflexibility, as a predictor of clinical-level anxiety and depression.
Mobile-based intensive longitudinal assessments.
119 participants (61 anxious and depressed, 58 healthy controls) completed ecological momentary assessment (EMA) to rate a variety of negative (NE) and positive emotions (PE) 9 times per day for 8 days using a mobile phone application. 169 participants (97 anxious and depressed and 72 healthy controls) completed an online daily diary on their NE and PE for 50 days. Multilevel vector autoregressive models were run to compute NE and PE network densities in each data set.
In the EMA data set, both NE and PE network densities significantly predicted participants' diagnostic status above and beyond demographics and the mean and standard deviation of NE and PE. Greater NE network density and lower PE network density were associated with anxiety and depression diagnoses. In the daily diary data set, NE and PE network densities did not significantly predict the diagnostic status.
Greater inflexibility of NE and lower inflexibility of PE, indexed by emotion network density, are potential clinical markers of anxiety and depressive disorders when assessed at intra-daily levels as opposed to daily levels. Considering emotion network density, as well as the mean level and variability of emotions in daily life, may contribute to diagnostic prediction of anxiety and depressive disorders.
Emotion network density, or the degree to which prior emotions predict and influence current emotions, indicates an inflexible or change-resistant emotion system. Emotional inflexibility or change resistance over a few hours, but not daily, may characterize anxiety and depressive disorders. Inflexible negative emotion systems are associated with anxiety and depressive disorders, whereas inflexible positive emotion systems may indicate psychological health. Considering emotional inflexibility within days may provide additional information beyond demographics and mean level and variability of emotions in daily life for detecting anxiety and depressive disorders.
利用两个具有不同时间尺度(90 分钟,每日)的密集纵向数据,我们检验了情绪网络密度,一种衡量情绪灵活性的指标,作为预测临床水平焦虑和抑郁的指标。
基于移动的密集纵向评估。
119 名参与者(61 名焦虑和抑郁,58 名健康对照)使用移动电话应用程序,每天 9 次,每次评估 9 次,评估各种负性(NE)和正性情绪(PE)。169 名参与者(97 名焦虑和抑郁,72 名健康对照)完成了 50 天的在线日常日记,记录他们的 NE 和 PE。使用多层向量自回归模型计算每个数据集的 NE 和 PE 网络密度。
在 EMA 数据集中,NE 和 PE 网络密度均显著预测了参与者的诊断状态,超过了人口统计学以及 NE 和 PE 的均值和标准差。较高的 NE 网络密度和较低的 PE 网络密度与焦虑和抑郁诊断相关。在日常日记数据集中,NE 和 PE 网络密度并未显著预测诊断状态。
当以日内水平而非每日水平评估时,NE 网络密度和 PE 网络密度较大,表明 NE 灵活性较低,PE 灵活性较高,这是焦虑和抑郁障碍的潜在临床标志物。考虑情绪网络密度,以及日常生活中的情绪均值和变异性,可能有助于焦虑和抑郁障碍的诊断预测。
情绪网络密度,或先前情绪预测和影响当前情绪的程度,表明情绪系统不灵活或难以改变。数小时内而不是每天的情绪不灵活性或难以改变可能是焦虑和抑郁障碍的特征。不灵活的负性情绪系统与焦虑和抑郁障碍相关,而不灵活的正性情绪系统可能表明心理健康。考虑数日内的情绪不灵活性可能会提供比人口统计学和日常生活中的情绪均值和变异性更多的信息,以检测焦虑和抑郁障碍。