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      Clinical Targets and Attitudes Toward Implementing Digital Health Tools for Remote Measurement in Treatment for Depression: Focus Groups With Patients and Clinicians

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          Abstract

          Background

          Remote measurement technologies, such as smartphones and wearable devices, can improve treatment outcomes for depression through enhanced illness characterization and monitoring. However, little is known about digital outcomes that are clinically meaningful to patients and clinicians. Moreover, if these technologies are to be successfully implemented within treatment, stakeholders’ views on the barriers to and facilitators of their implementation in treatment must be considered.

          Objective

          This study aims to identify clinically meaningful targets for digital health research in depression and explore attitudes toward their implementation in psychological services.

          Methods

          A grounded theory approach was used on qualitative data from 3 focus groups of patients with a current diagnosis of depression and clinicians with >6 months of experience with delivering psychotherapy (N=22).

          Results

          Emerging themes on clinical targets fell into the following two main categories: promoters and markers of change. The former are behaviors that participants engage in to promote mental health, and the latter signal a change in mood. These themes were further subdivided into external changes (changes in behavior) or internal changes (changes in thoughts or feelings) and mapped with potential digital sensors. The following six implementation acceptability themes emerged: technology-related factors, information and data management, emotional support, cognitive support, increased self-awareness, and clinical utility.

          Conclusions

          The promoters versus markers of change differentiation have implications for a causal model of digital phenotyping in depression, which this paper presents. Internal versus external subdivisions are helpful in determining which factors are more susceptible to being measured by using active versus passive methods. The implications for implementation within psychotherapy are discussed with regard to treatment effectiveness, service provision, and patient and clinician experience.

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          Most cited references38

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          Using thematic analysis in psychology

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            Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology

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              Realizing the Mass Public Benefit of Evidence-Based Psychological Therapies: The IAPT Program

              Empirically supported psychological therapies have been developed for many mental health conditions. However, in most countries only a small proportion of the public benefit from these advances. The English Improving Access to Psychological Therapies (IAPT) program aims to bridge the gap between research and practice by training over 10,500 new psychological therapists in empirically supported treatments and deploying them in new services for the treatment of depression and anxiety disorders. Currently IAPT treats over 560,000 patients per year, obtains clinical outcome data on 98.5% of these individuals, and places this information in the public domain. Around 50% of patients treated in IAPT services recover, and two-thirds show worthwhile benefits. The clinical and economic arguments on which IAPT is based are presented, along with details of the service model, how the program was implemented, and recent findings about service organization. Limitations and future directions are outlined.
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                Author and article information

                Contributors
                Journal
                JMIR Ment Health
                JMIR Ment Health
                JMH
                JMIR Mental Health
                JMIR Publications (Toronto, Canada )
                2368-7959
                August 2022
                15 August 2022
                : 9
                : 8
                : e38934
                Affiliations
                [1 ] Department of Psychological Medicine Institute of Psychiatry, Psychology and Neuroscience King's College London London United Kingdom
                [2 ] NIHR Maudsley Biomedical Research Centre  South London and Maudsley NHS Foundation Trust London United Kingdom
                [3 ] Department of Psychology University of Bath Bath United Kingdom
                [4 ] School of Psychology, University of Sussex, Falmer East Sussex United Kingdom
                Author notes
                Corresponding Author: Valeria de Angel valeria.de_angel@ 123456kcl.ac.uk
                Author information
                https://orcid.org/0000-0002-5109-3636
                https://orcid.org/0000-0002-4962-9673
                https://orcid.org/0000-0002-0925-0175
                https://orcid.org/0000-0002-4055-904X
                https://orcid.org/0000-0002-3980-4466
                Article
                v9i8e38934
                10.2196/38934
                9425163
                35969448
                1dfc70c8-2377-4edb-baa1-7fc2528c182d
                ©Valeria de Angel, Serena Lewis, Katie M White, Faith Matcham, Matthew Hotopf. Originally published in JMIR Mental Health (https://mental.jmir.org), 15.08.2022.

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

                History
                : 22 April 2022
                : 2 June 2022
                : 13 June 2022
                : 13 June 2022
                Categories
                Original Paper
                Original Paper

                depression,digital health tools,implementation,qualitative,wearable devices,smartphone,passive sensing,sensor data,mobile health,mhealth,mood disorders,digital phenotyping,mobile phone

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