Olthof Merlijn, Bunge Andrea, Maciejewski Dominique F, Hasselman Fred, Lichtwarck-Aschoff Anna
Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands.
Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands.
J Pers Oriented Res. 2024 Dec 13;10(2):89-99. doi: 10.17505/jpor.2024.27102. eCollection 2024.
Ecological momentary assessment (EMA) of affect, cognition and behavior aims to provide a 'window into a person's daily life'. But what should we look for through this window? In this paper, we compare a statistical perspective, grounded in probability theory, with a dynamic pattern perspective, grounded in complexity theory, on two common phenomena in EMA data: non-stationarity and outlying values. From a statistical perspective, these phenomena are considered nuisances that should be dealt with. From a dynamic pattern perspective, in contrast, non-stationarity may signal transitions from one dynamic pattern to another (e.g., a transition from a neutral to a persistent sad mood), whereas outlying values may signal recovery from perturbations (e.g., stressful life events). We evaluated the dynamic pattern view with a triangulation study of multiple single cases that took part in the Track your Mood EMA study, where participants reported on their emotions and daily events for 60 days. We found that non-stationarity was indeed related to a pattern transition, whereas outlying values were related to recovery after perturbations. These findings show that person-oriented EMA research would benefit from a dynamic pattern perspective that can identify highly meaningful and clinically relevant phenomena that are otherwise at risk of being missed. Complementing EMA time series with contextual information and qualitative data will be essential to genuinely understand these phenomena.
对情感、认知和行为进行生态瞬时评估（EMA）旨在提供一扇“洞察个人日常生活的窗口”。但透过这扇窗口我们应该寻找什么呢？在本文中，我们将基于概率论的统计视角与基于复杂性理论的动态模式视角，就EMA数据中的两种常见现象：非平稳性和异常值进行比较。从统计视角来看，这些现象被视为需要处理的麻烦。相比之下，从动态模式视角来看，非平稳性可能标志着从一种动态模式向另一种动态模式的转变（例如，从中性情绪转变为持续的悲伤情绪），而异常值可能标志着从干扰中恢复（例如，压力大的生活事件）。我们通过对参与“追踪你的情绪”EMA研究的多个单病例进行三角测量研究，对动态模式观点进行了评估，在该研究中，参与者报告了他们60天内的情绪和日常事件。我们发现，非平稳性确实与模式转变有关，而异常值与干扰后的恢复有关。这些发现表明，以个体为导向的EMA研究将受益于动态模式视角，该视角能够识别出那些否则可能会被遗漏的极具意义和临床相关性的现象。将EMA时间序列与背景信息和定性数据相结合对于真正理解这些现象至关重要。
