Exploring Dynamics in Mood Regulation-Mixture Latent Markov Modeling of Ambulatory Assessment Data
Exploring Dynamics in Mood Regulation-Mixture Latent Markov Modeling of Ambulatory Assessment Data
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DOI：
10.1097/psy.0b013e31825474cb
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发表时间：
2012-05-01
影响因子：
3.3
通讯作者：
Vermunt, Jeroen K.
中科院分区：
文献类型：
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作者：
Crayen, Claudia;Eid, Michael;Vermunt, Jeroen K.
目的：阐明混合隐马尔可夫模型如何充分分析动态评估数据中类别项少、测量误差和变化模式中的异质性等特征的波动模式。波动模式的识别对关注功能障碍行为或认知的心身研究具有重要价值，例如成瘾行为或不顺从行为。在我们的应用中，识别出在情绪调节过程方面不同的未观察到的个体亚群，例如情绪维持和情绪修复。方法：在一项动态评估研究中，收集了164名学生一周内56次的情绪评分。愉快-不愉快情绪维度通过不舒服-好和坏-好两个分类项目进行评定。对不同状态数、不同类别、不同不变性程度的混合隐马尔可夫模型进行了检验，并根据信息准则对最优模型进行了解释。结果：识别出两个在白天情绪调节模式不同的潜在类别。该模型的平均分类概率很高(>；0.88)。大班学生倾向于保持并回到适度愉快的情绪状态，而小班学生更有可能进入一种非常愉快的情绪状态，并以更高的概率停留在那里。结论：混合隐马尔可夫模型适合于获取动态评估数据中个体间稳定性和变化差异的信息。已识别的情绪调节模式可作为健康年轻人典型情绪波动的参考。
Objective: To illustrate how fluctuation patterns in ambulatory assessment data with features such as few categorical items, measurement error, and heterogeneity in the change pattern can adequately be analyzed with mixture latent Markov models. The identification of fluctuation patterns can be of great value to psychosomatic research concerned with dysfunctional behavior or cognitions, such as addictive behavior or noncompliance. In our application, unobserved subgroups of individuals who differ with regard to their mood regulation processes, such as mood maintenance and mood repair, are identified. Methods: In an ambulatory assessment study, mood ratings were collected 56 times during 1 week from 164 students. The pleasant-unpleasant mood dimension was assessed by the two ordered categorical items unwell-well and bad-good. Mixture latent Markov models with different number of states, classes, and degrees of invariance were tested, and the best model according to information criteria was interpreted. Results: Two latent classes that differed in their mood regulation pattern during the day were identified. Mean classification probabilities were high (>0.88) for this model. The larger class showed a tendency to stay in and return to a moderately pleasant mood state, whereas the smaller class was more likely to move to a very pleasant mood state and to stay there with a higher probability. Conclusions: Mixture latent Markov models are suitable to obtain information about interindividual differences in stability and change in ambulatory assessment data. Identified mood regulation patterns can serve as reference for typical mood fluctuation in healthy young adults.