Affect, behavior, and severity of psychopathological symptoms do not remain static throughout the life of an individual, but rather they change over time. Since the rise of the smartphone, longitudinal data can be obtained at higher frequencies than ever before, providing new opportunities for investigating these person-specific changes in real-time. Since 2019, researchers have started using the exponentially weighted moving average (EWMA) procedure, as a statistically sound method to reach this goal. Real-time, person-specific change detection could allow (a) researchers to adapt assessment intensity and strategy when a change occurs to obtain the most useful data at the most useful time and (b) clinicians to provide care to patients during periods in which this is most needed. The current paper provides a tutorial on how to use the EWMA procedure in psychology, as well as demonstrates its added value in a range of potential applications.

精神病理症状的影响、行为和严重程度在个体的一生中不会保持不变，而是随着时间的推移而变化。自智能手机兴起以来，纵向数据的获取频率比以往任何时候都高，为实时调查这些特定于个人的变化提供了新的机会。自 2019 年以来，研究人员开始使用指数加权移动平均 (EWMA) 程序作为实现这一目标的统计上合理的方法。实时、针对个人的变化检测可以允许（a）研究人员在变化发生时调整评估强度和策略，以便在最有用的时间获得最有用的数据；（b）临床医生在变化期间为患者提供护理。这是最需要的。本文提供了有关如何在心理学中使用 EWMA 程序的教程，并展示了其在一系列潜在应用中的附加价值。