Affect Variability and Predictability: Using Recurrence Quantification Analysis to Better Understand How the Dynamics of Affect Relate to Health
Affect Variability and Predictability: Using Recurrence Quantification Analysis to Better Understand How the Dynamics of Affect Relate to Health
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DOI：
10.1037/emo0000556
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发表时间：
2020-04-01
期刊：
影响因子：
4.2
通讯作者：
Pressman, Sarah D.
中科院分区：
文献类型：
--
作者：
Jenkins, Brooke N.;Hunter, John F.;Pressman, Sarah D.
随着时间的推移，情感的变化与健康结果有关。然而，先前利用的测量方法集中于影响的可变性（例如，标准差、均方根连续差），并忽略随时间影响的更复杂的时间模式。这些模式可能是理解情感动态如何与健康相关的重要特征。递归量化分析（RQA）可能有助于缓解这个问题，通过评估过去的方法未评估的时间特性。RQA指标，如确定性和重复性，可以提供一个衡量影响随时间变化的可预测性的指标，索引情感体验中模式重复的频率。在研究1中，我们首先将RQA指标与常用的可变性指标进行了对比，以证明RQA可以进一步区分影响模式。在研究2中，我们分析了这些新指标与健康之间的关联，即抑郁和躯体症状。我们发现，RQA指标预测的健康水平高于平均水平，并且随着时间的推移，影响会发生变化。最理想的健康结果是在那些具有高平均积极影响，低平均消极影响，低影响可变性和高影响可预测性的人中观察到的。这些研究首次证明了RQA在确定情感体验的时间模式对健康结果的重要性方面的实用性。
Changes in affect over time have been associated with health outcomes. However, previously utilized measurement methods focus on variability of affect (e.g., standard deviation, root mean squared successive difference) and ignore the more complex temporal patterns of affect over time. These patterns may be an important feature in understanding how the dynamics of affect relate to health. Recurrence quantification analysis (RQA) may help alleviate this problem by assessing temporal characteristics unassessed by past methods. RQA metrics, such as determinism and recurrence, can provide a measure of the predictability of affect over time, indexing how often patterns within affective experiences repeat. In Study 1, we first contrasted RQA metrics with commonly used measures of variability to demonstrate that RQA can further differentiate among patterns of affect. In Study 2, we analyzed the associations between these new metrics and health, namely, depressive and somatic symptoms. We found that RQA metrics predicted health above and beyond mean levels and variability of affect over time. The most desirable health outcomes were observed in people who had high mean positive affect, low mean negative affect, low affect variability, and high affect predictability. These studies are the first to demonstrate the utility of RQA for determining how temporal patterns in affective experiences are important for health outcomes.