The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time: A Tutorial Showcasing Potential Applications.
Arnout C SmitEvelien SchatEva CeulemansPublished in: Assessment (2022)
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.
Keyphrases
- electronic health record
- end stage renal disease
- big data
- minimally invasive
- palliative care
- healthcare
- magnetic resonance
- ejection fraction
- newly diagnosed
- chronic kidney disease
- cross sectional
- peritoneal dialysis
- sleep quality
- human health
- depressive symptoms
- physical activity
- quality improvement
- risk assessment
- climate change
- magnetic resonance imaging
- patient reported outcomes