Kim Jinhyuk, Marcusson-Clavertz David, Togo Fumiharu, Park Hyuntae
Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA.
Department of Psychology, Lund University, Lund, Sweden.
Comput Math Methods Med. 2018 Jul 9;2018:8652034. doi: 10.1155/2018/8652034. eCollection 2018.
There is growing interest in within-person associations of objectively measured physical and physiological variables with psychological states in daily life. Here we provide a practical guide with SAS code of multilevel modeling for analyzing physical activity data obtained by accelerometer and self-report data from intensive and repeated measures using ecological momentary assessments (EMA). We review previous applications of EMA in research and clinical settings and the analytical tools that are useful for EMA research. We exemplify the analyses of EMA data with cases on physical activity data and affect and discuss the future challenges in the field.
对于日常生活中客观测量的身体和生理变量与心理状态之间的个体内关联，人们的兴趣与日俱增。在此，我们提供一份实用指南，并附上SAS代码，用于多级建模，以分析通过加速度计获得的身体活动数据，以及来自使用生态瞬时评估（EMA）的密集和重复测量的自我报告数据。我们回顾了EMA在研究和临床环境中的先前应用，以及对EMA研究有用的分析工具。我们以身体活动数据和情感方面的案例为例，对EMA数据进行分析，并讨论该领域未来面临的挑战。
