Reducing social diabetes distress with a conversational agent support system: a three-week technology feasibility evaluation.

Autor:	Bruijnes M; Utrecht University School of Governance, Faculty of Law, Economics, and Governance, Utrecht University, Utrecht, Netherlands.; Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Delft, Netherlands., Kesteloo M; Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Delft, Netherlands., Brinkman WP; Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Delft, Netherlands.
Jazyk:	angličtina
Zdroj:	Frontiers in digital health [Front Digit Health] 2023 Jun 13; Vol. 5, pp. 1149374. Date of Electronic Publication: 2023 Jun 13 (Print Publication: 2023).
DOI:	10.3389/fdgth.2023.1149374
Abstrakt:	Background: People with diabetes mellitus not only have to deal with physical health problems, but also with the psycho-social challenges their chronic disease brings. Currently, technological tools that support the psycho-social context of a patient have received little attention. Objective: The objective of this work is to determine the feasibility and preliminary efficacy of an automated conversational agent to deliver, to people with diabetes, personalised psycho-education on dealing with (psycho-)social distress related to their chronic illness. Methods: In a double-blinded between-subject study, 156 crowd-workers with diabetes received a social help program intervention in three sessions over three weeks. They were randomly assigned to receive support from either an interactive conversational support agent ( n = 79 ) or a self-help text from the book "Diabetes burnout" as a control condition ( n = 77 ). Participants completed the Diabetes Distress Scale (DDS) before and after the intervention, and after the intervention, the Client Satisfaction Questionnaire (CSQ-8), Feeling of Being Heard (FBH), and System Usability Scale (SUS). Results: Results indicate that people using the conversational agent have a larger reduction in diabetes distress ( M = - 0.305 , SD = 0.865 ) than the control group ( M = 0.002 , SD = 0.743 ) and this difference is statistically significant ( t ( 154 ) = 2.377 , p = 0.019 ). A hypothesised mediation effect of "attitude to the social help program" was not observed. Conclusions: An automated conversational agent can deliver personalised psycho-education on dealing with (psycho-)social distress to people with diabetes and reduce diabetes distress more than a self-help book. Ethics Study Registration and Open Science: This study has been preregistered with the Open Science Foundation (osf.io/yb6vg) and has been accepted by the Human Research Ethics Committee - Delft University of Technology under application number 1130. The data and analysis script are available: https://surfdrive.surf.nl/files/index.php/s/4xSEHCrAu0HsJ4P. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (© 2023 Bruijnes, Kesteloo and Brinkman.)
Databáze:	MEDLINE
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