A solution for creating dynamic networks of symptoms in cognitive-behavioral functional analysis and therapy – participatory design protocol

Abstract

Purpose One of the recommendations in process-based cognitive therapies suggests that functional analysis created by the clinician should be supported by empirical data collected through daily sampling. It enables the computing of a dynamic network of psychological processes and symptoms supporting clinical decisions but also therapeutic progress monitoring. However, the experience sampling solutions available in the market do not enable the automatic creation of this kind of network; thus, the use of this approach in clinical practice is practically impossible without advanced statistical skills and significant time investment. The purpose of the present paper is to describes a protocol of a research project based on a participatory approach aiming to create a solution enabling therapists not only to set up a personalized daily sampling for their patients and collect the data but also providing a fully automated visualization of the network adapted for therapeutic purposes. Design/methodology/approach The project will require creating a platform for therapists where they can set up monitoring and receive dynamic networks visualization, creating an experience sampling application for patients connected to the platform and creating an optimal data visualization system that will enable therapists to accurately and quickly interpret the network. A series of participatory workshops, qualitative and quantitative studies are described. Findings The presented studies will enable us to evaluate the ergonomy of use of both platform and app in laboratory and ecological settings along with the evaluation of network interpretation accuracy. Originality/value To the best of the authors’ knowledge, this is the first participatory design protocol for creating a solution that might enable clinicians to use a dynamic network approach in their everyday clinical practice. The challenges and opportunities of creating this kind of mHealth solution are discussed.

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