Between-person heterogeneity of posttraumatic stress disorder (PTSD) is well established. Within-person analyses and the DSM-5 suggest that heterogeneity may also be evident within individuals across time as they move through social contexts and biological cycles. Modeling within-person symptom-level fluctuations may confirm such heterogeneity, elucidate mechanisms of disorder maintenance, and inform time- and person-specific interventions. The present study aimed to identify and predict discrete within-person disorder presentations, or symptom states, and explore group-level patterns of these states. Adults (N = 20, 60.0% male, M age = 38.25 years) with PTSD responded to symptom surveys four times per day for 30 days. We subjected each individual's dataset to Gaussian finite mixture modeling (GFMM) to uncover latent, within-person classes of symptom levels (i.e., states) and predicted those states with idiographic elastic net regularized regression using a set of time-based and behavioral predictors. Next, we conducted a GFMM of the within-person GFMM outputs and tested idiographic prediction models of these states. Multiple within-person states were revealed for 19 of 20 participants (Mdn = 4; 66 for the full sample). Prediction models were moderately successful, M AUC = .66 (d = 0.58), range: .50–1.00. The GFMM of the within-person model outputs revealed two states: one with above-average and one with below-average symptom levels. Prediction models were, again, moderately successful, M AUC = .66; range: .50–.89. The findings provide evidence for within-person heterogeneity of PTSD as well as between-person similarities and suggest that future work should incorporate additional contextual variables as symptom state predictors.

中文翻译：

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识别和预测患有创伤后应激障碍的成年人的创伤后应激症状状态

创伤后应激障碍 (PTSD) 的人际异质性已得到充分证实。个人内部分析和DSM-5表明，随着时间的推移，个体在社会环境和生物周期中移动时，异质性也可能很明显。对个人症状水平波动进行建模可以证实这种异质性，阐明疾病维持的机制，并为针对时间和个人的干预措施提供信息。本研究旨在识别和预测离散的人内障碍表现或症状状态，并探索这些状态的群体水平模式。成人 ( N = 20, 60.0% 男性, M年龄 = 38.25 岁）患有 PTSD，每天四次对症状调查做出回应，持续 30 天。我们将每个人的数据集置于高斯有限混合建模 (GFMM) 中以揭示潜在的、个人内部的症状水平类别（即状态），并使用一组基于时间和行为的预测因子通过具体的弹性网络正则化回归预测这些状态。接下来，我们对内部 GFMM 输出进行了 GFMM，并测试了这些状态的具体预测模型。20 名参与者中有 19 名被揭示了多种个人内部状态（Mdn = 4；完整样本为 66）。预测模型比较成功，M AUC = .66 ( d= 0.58)，范围：.50–1.00。个人内部模型输出的 GFMM 揭示了两种状态：一种症状水平高于平均水平，另一种状态低于平均水平。预测模型再次取得一定成功，M AUC = .66；范围：.50–.89。研究结果为 PTSD 的人内异质性以及人与人之间的相似性提供了证据，并建议未来的工作应纳入额外的背景变量作为症状状态预测因子。