ESMvis: a tool for visualizing individual Experience Sampling Method (ESM) data.
Clinical practice
Experience sampling method
Intensive longitudinal data
Personalized feedback
Visualization
Journal
Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
ISSN: 1573-2649
Titre abrégé: Qual Life Res
Pays: Netherlands
ID NLM: 9210257
Informations de publication
Date de publication:
Nov 2021
Nov 2021
Historique:
accepted:
09
11
2020
pubmed:
23
11
2020
medline:
26
11
2021
entrez:
22
11
2020
Statut:
ppublish
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 33222049
doi: 10.1007/s11136-020-02701-4
pii: 10.1007/s11136-020-02701-4
pmc: PMC8528801
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3179-3188Subventions
Organisme : H2020 European Research Council
ID : 681466
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek
ID : 191G.037
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek
ID : 451-15-034
Informations de copyright
© 2020. The Author(s).
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