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      Improving the Well-being of Adolescents With Type 1 Diabetes During the COVID-19 Pandemic: Qualitative Study Exploring Acceptability and Clinical Usability of a Self-compassion Chatbot

      research-article
      , BA, PGDipHSci, PhD 1 , , , PhD 1 , , BSc, PGDipHSci, MSc, PhD 1 , , BA, MHealthPsych, PGDipHealthPsych 2 , , MBChB 3 , , MBChB, Dip Obs 4 , , BSc, MSc, PhD 1
      (Reviewer), (Reviewer), (Reviewer)
      JMIR Diabetes
      JMIR Publications
      self-compassion, chatbot, conversational agent, artificial intelligence, adolescence, type 1 diabetes, mental health, digital health, psychosocial interventions, COVID-19, mobile phone

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          Abstract

          Background

          Before the COVID-19 pandemic, adolescents with type 1 diabetes (T1D) had already experienced far greater rates of psychological distress than their peers. With the pandemic further challenging mental health and increasing the barriers to maintaining optimal diabetes self-management, it is vital that this population has access to remotely deliverable, evidence-based interventions to improve psychological and diabetes outcomes. Chatbots, defined as digital conversational agents, offer these unique advantages, as well as the ability to engage in empathetic and personalized conversations 24-7. Building on previous work developing a self-compassion program for adolescents with T1D, a self-compassion chatbot (COMPASS) was developed for adolescents with T1D to address these concerns. However, the acceptability and potential clinical usability of a chatbot to deliver self-compassion coping tools to adolescents with T1D remained unknown.

          Objective

          This qualitative study was designed to evaluate the acceptability and potential clinical utility of COMPASS among adolescents aged 12 to 16 years with T1D and diabetes health care professionals.

          Methods

          Potential adolescent participants were recruited from previous participant lists, and on the web and in-clinic study flyers, whereas health care professionals were recruited via clinic emails and from diabetes research special interest groups. Qualitative Zoom (Zoom Video Communications, Inc) interviews exploring views on COMPASS were conducted with 19 adolescents (in 4 focus groups) and 11 diabetes health care professionals (in 2 focus groups and 6 individual interviews) from March 2022 to April 2022. Transcripts were analyzed using directed content analysis to examine the features and content of greatest importance to both groups.

          Results

          Adolescents were broadly representative of the youth population living with T1D in Aotearoa (11/19, 58% female; 13/19, 68% Aotearoa New Zealand European; and 2/19, 11% Māori). Health care professionals represented a range of disciplines, including diabetes nurse specialists (3/11, 27%), health psychologists (3/11, 27%), dieticians (3/11, 27%), and endocrinologists (2/11, 18%). The findings offer insight into what adolescents with T1D and their health care professionals see as the shared advantages of COMPASS and desired future additions, such as personalization (mentioned by all 19 adolescents), self-management support (mentioned by 13/19, 68% of adolescents), clinical utility (mentioned by all 11 health care professionals), and breadth and flexibility of tools (mentioned by 10/11, 91% of health care professionals).

          Conclusions

          Early data suggest that COMPASS is acceptable, is relevant to common difficulties, and has clinical utility during the COVID-19 pandemic. However, shared desired features among both groups, including problem-solving and integration with diabetes technology to support self-management; creating a safe peer-to-peer sense of community; and broadening the representation of cultures, lived experience stories, and diabetes challenges, could further improve the potential of the chatbot. On the basis of these findings, COMPASS is currently being improved to be tested in a feasibility study.

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          Most cited references56

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            Qualitative research explores complex phenomena encountered by clinicians, health care providers, policy makers and consumers. Although partial checklists are available, no consolidated reporting framework exists for any type of qualitative design. To develop a checklist for explicit and comprehensive reporting of qualitative studies (in depth interviews and focus groups). We performed a comprehensive search in Cochrane and Campbell Protocols, Medline, CINAHL, systematic reviews of qualitative studies, author or reviewer guidelines of major medical journals and reference lists of relevant publications for existing checklists used to assess qualitative studies. Seventy-six items from 22 checklists were compiled into a comprehensive list. All items were grouped into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. Duplicate items and those that were ambiguous, too broadly defined and impractical to assess were removed. Items most frequently included in the checklists related to sampling method, setting for data collection, method of data collection, respondent validation of findings, method of recording data, description of the derivation of themes and inclusion of supporting quotations. We grouped all items into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. The criteria included in COREQ, a 32-item checklist, can help researchers to report important aspects of the research team, study methods, context of the study, findings, analysis and interpretations.
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                Author and article information

                Contributors
                Journal
                JMIR Diabetes
                JMIR Diabetes
                JD
                JMIR Diabetes
                JMIR Publications (Toronto, Canada )
                2371-4379
                2023
                5 May 2023
                5 May 2023
                : 8
                : e40641
                Affiliations
                [1 ] Department of Psychological Medicine Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand
                [2 ] Department of Psychology and Neuroscience, Auckland University of Technology Auckland New Zealand
                [3 ] Starship Children's Health Auckland City Hospital Auckland New Zealand
                [4 ] Liggins Institute University of Auckland Auckland New Zealand
                Author notes
                Corresponding Author: Anna Boggiss a.boggiss@ 123456auckland.ac.nz
                Author information
                https://orcid.org/0000-0002-7336-955X
                https://orcid.org/0000-0002-7691-0938
                https://orcid.org/0000-0002-4705-5362
                https://orcid.org/0000-0002-8958-350X
                https://orcid.org/0000-0002-0541-6094
                https://orcid.org/0000-0002-8995-8711
                https://orcid.org/0000-0002-4797-8351
                Article
                v8i1e40641
                10.2196/40641
                10166132
                36939680
                981845a9-ff16-43f0-8980-a8dd9137388f
                ©Anna Boggiss, Nathan Consedine, Sarah Hopkins, Connor Silvester, Craig Jefferies, Paul Hofman, Anna Serlachius. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 05.05.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Diabetes, is properly cited. The complete bibliographic information, a link to the original publication on https://diabetes.jmir.org/, as well as this copyright and license information must be included.

                History
                : 29 June 2022
                : 24 October 2022
                : 8 November 2022
                : 30 January 2023
                Categories
                Original Paper
                Original Paper

                self-compassion,chatbot,conversational agent,artificial intelligence,adolescence,type 1 diabetes,mental health,digital health,psychosocial interventions,covid-19,mobile phone

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