Feedback based on experience sampling data: Examples of current approaches and considerations for future research

Sara Laureen Bartels*, Catherine van Zelst, Bernardo Melo Moura, Naomi E.M. Daniëls, Claudia J.P. Simons, Machteld Marcelis, Fionneke M. Bos, Michelle N. Servaas

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    6 Citations (Scopus)
    78 Downloads (Pure)

    Abstract

    Methodologies such as the Experience Sampling Method (ESM) or Ecological Momentary Assessment allow the gathering of fine-graded, dynamic, personal data within a patient's daily life. Currently, it is studied whether feedback based on experience sampling data (ESM-based feedback) can be used as a clinical tool to inform shared decision-making in clinical practice. Although the potential of feedback is recognized, little is known on how to generate, use, and implement it. This article (i) presents n = 15 ongoing ESM projects within the Belgian-Dutch network for ESM research wherein ESM-based feedback is provided to various patient populations, and (ii) summarizes qualitative data on experiences with ESM-based feedback of researchers (n = 8) with extensive expertise with ESM (average of 10 years) involved in these ongoing studies. The following aspects appear to be of relevance when providing ESM-based feedback: training for healthcare professionals and researchers, the use of online interfaces and graphical visualizations to present data, and interacting with patients in a face-to-face setting when discussing the contextual relevance and potential implications. Prospectively, research may build on these aspects and create coherent consensus-based guidelines for the use of ESM-based feedback.

    Original languageEnglish
    Article numbere20084
    Number of pages14
    JournalHeliyon
    Volume9
    Issue number9
    DOIs
    Publication statusPublished - Sept-2023

    Keywords

    • Ecological momentary assessment
    • ESM-based feedback
    • Experience sampling method
    • Guidelines
    • Momentary data

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