ESMvis: a tool for visualizing individual Experience Sampling Method (ESM) data
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SPECIAL SECTION: FEEDBACK TOOLS
ESMvis: a tool for visualizing individual Experience Sampling Method (ESM) data Laura F. Bringmann1 · Date C. van der Veen2 · Marieke Wichers3 · Harriëtte Riese3 · Gert Stulp4 Accepted: 9 November 2020 © The Author(s) 2020
Abstract Purpose The experience sampling method (ESM) is used for intensive longitudinal time-series data collection during normal daily life. ESM data give information on momentary affect, activities and (social) context of, for example, patients suffering from mental disorders, and allows for person-specific feedback reports. However, current personalized feedback reports only display a selection of measured variables, and typically involve only summary statistics, thus not reflecting the dynamic fluctuations in affect and its influencing factors. To address this shortcoming, we developed a tool for dynamically visualizing ESM data. Methods We introduce a new framework, ESMvis, for giving descriptive feedback, focusing on direct visualization of the dynamic nature of raw data. In this ESM feedback approach, raw ESM data are visualized using R software. We applied ESMvis to data collected for over 52 weeks on a patient diagnosed with an obsessive–compulsive disorder with comorbid depression. Results We provided personalized feedback, in which both the overall trajectory and specific time moments were captured in a movie format. Two relapses during the study period could be visually determined, and subsequently confirmed by the therapist. The therapist and patient evaluated ESMvis as an insightful add-on tool to care-as-usual. Conclusion ESMvis is a showcase on providing personalized feedback by dynamic visualization of ESM time-series data. Our tool is freely available and adjustable, making it widely applicable. In addition to potential applications in clinical practice, ESMvis can work as an exploratory tool that can lead to new hypotheses and inform more complex statistical techniques. Keywords Visualization · Experience sampling method · Clinical practice · Intensive longitudinal data · Personalized feedback
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11136-020-02701-4) contains supplementary material, which is available to authorized users. * Laura F. Bringmann [email protected] 1
Department of Psychometrics and Statistics, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands
2
Department of Psychiatry, University Centre Psychiatry, UMCG, Groningen, The Netherlands
3
Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Department of Psychiatry, University Medical Center Groningen (UMCG), University of Groningen, Groningen, The Netherlands
4
Department of Sociology, University of Groningen/Inter-University Center for Social Science Theory and Methodology (ICS), Groningen, The Netherlands
Introduction … sometimes all that is required is a useful visualization … Spiegelhalter, p. 15 [1] Increasingly intensive longitudinal data
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