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      Dropout From an Internet-Delivered Cognitive Behavioral Therapy Intervention for Adults With Depression and Anxiety: Qualitative Study

      research-article
      , BA, MSc 1 , 2 , , , MSc 1 , 2 , , PhD 1 , , PhD 1 , 2 , , PhD 1 , 2
      (Reviewer), (Reviewer), (Reviewer)
      JMIR Formative Research
      JMIR Publications
      depression, anxiety, iCBT, dropout, internet interventions

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          Abstract

          Background

          Treatment dropout continues to be reported from internet-delivered cognitive behavioral therapy (iCBT) interventions, and lower completion rates are generally associated with lower treatment effect sizes. However, evidence is emerging to suggest that completion of a predefined number of modules is not always necessary for clinical benefit or consideration of the needs of each individual patient.

          Objective

          The aim of this study is to perform a qualitative analysis of patients’ experiences with an iCBT intervention in a routine care setting to achieve a deeper insight into the phenomenon of dropout.

          Methods

          A total of 15 purposively sampled participants (female: 8/15, 53%) from a larger parent randomized controlled trial were interviewed via telephone using a semistructured interview schedule that was developed based on the existing literature and research on dropout in iCBT. Data were analyzed using a descriptive-interpretive approach.

          Results

          The experience of treatment leading to dropout can be understood in terms of 10 domains: relationship to technology, motivation to start, background knowledge and attitudes toward iCBT, perceived change in motivation, usage of the program, changes due to the intervention, engagement with content, experience interacting with the supporter, experience of web-based communication, and termination of the supported period.

          Conclusions

          Patients who drop out of treatment can be distinguished in terms of their change in motivation: those who felt ready to leave treatment early and those who had negative reasons for dropping out. These 2 groups of participants have different treatment experiences, revealing the potential attributes and nonattributes of dropout. The reported between-group differences should be examined further to consider those attributes that are strongly descriptive of the experience and regarded less important than those that have become loosely affiliated.

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          Most cited references49

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          Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups.

          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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            The Law of Attrition

            In an ongoing effort of this Journal to develop and further the theories, models, and best practices around eHealth research, this paper argues for the need for a “science of attrition”, that is, a need to develop models for discontinuation of eHealth applications and the related phenomenon of participants dropping out of eHealth trials. What I call “law of attrition” here is the observation that in any eHealth trial a substantial proportion of users drop out before completion or stop using the appplication. This feature of eHealth trials is a distinct characteristic compared to, for example, drug trials. The traditional clinical trial and evidence-based medicine paradigm stipulates that high dropout rates make trials less believable. Consequently eHealth researchers tend to gloss over high dropout rates, or not to publish their study results at all, as they see their studies as failures. However, for many eHealth trials, in particular those conducted on the Internet and in particular with self-help applications, high dropout rates may be a natural and typical feature. Usage metrics and determinants of attrition should be highlighted, measured, analyzed, and discussed. This also includes analyzing and reporting the characteristics of the subpopulation for which the application eventually “works”, ie, those who stay in the trial and use it. For the question of what works and what does not, such attrition measures are as important to report as pure efficacy measures from intention-to-treat (ITT) analyses. In cases of high dropout rates efficacy measures underestimate the impact of an application on a population which continues to use it. Methods of analyzing attrition curves can be drawn from survival analysis methods, eg, the Kaplan-Meier analysis and proportional hazards regression analysis (Cox model). Measures to be reported include the relative risk of dropping out or of stopping the use of an application, as well as a “usage half-life”, and models reporting demographic and other factors predicting usage discontinuation in a population. Differential dropout or usage rates between two interventions could be a standard metric for the “usability efficacy” of a system. A “run-in and withdrawal” trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users.
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              The generalizability of the psychoanalytic concept of the working alliance.

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                Author and article information

                Contributors
                Journal
                JMIR Form Res
                JMIR Form Res
                JFR
                JMIR Formative Research
                JMIR Publications (Toronto, Canada )
                2561-326X
                November 2021
                12 November 2021
                : 5
                : 11
                : e26221
                Affiliations
                [1 ] E-Mental Health Research Group School of Psychology Trinity College Dublin Dublin Ireland
                [2 ] Clinical Research and Innovation SilverCloud Health Dublin Ireland
                Author notes
                Corresponding Author: Kate Lawler lawlerka@ 123456tcd.ie
                Author information
                https://orcid.org/0000-0001-6459-9831
                https://orcid.org/0000-0001-5303-0893
                https://orcid.org/0000-0003-2785-0753
                https://orcid.org/0000-0003-0585-4008
                https://orcid.org/0000-0003-0871-4078
                Article
                v5i11e26221
                10.2196/26221
                8663602
                34766909
                d89b6a4d-841d-4ddf-b03e-a5e84bc3af80
                ©Kate Lawler, Caroline Earley, Ladislav Timulak, Angel Enrique, Derek Richards. Originally published in JMIR Formative Research (https://formative.jmir.org), 12.11.2021.

                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 Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

                History
                : 3 December 2020
                : 25 January 2021
                : 16 March 2021
                : 1 August 2021
                Categories
                Original Paper
                Original Paper

                depression,anxiety,icbt,dropout,internet interventions
                depression, anxiety, icbt, dropout, internet interventions

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