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      Accelerating Digital Mental Health Research From Early Design and Creation to Successful Implementation and Sustainment

      research-article
      , PhD 1 , , , PhD 2 , , PhD 1 , , PhD 3 , , PhD 1
      (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      eHealth, mHealth, methodology

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          Abstract

          Mental health problems are common and pose a tremendous societal burden in terms of cost, morbidity, quality of life, and mortality. The great majority of people experience barriers that prevent access to treatment, aggravated by a lack of mental health specialists. Digital mental health is potentially useful in meeting the treatment needs of large numbers of people. A growing number of efficacy trials have shown strong outcomes for digital mental health treatments. Yet despite their positive findings, there are very few examples of successful implementations and many failures. Although the research-to-practice gap is not unique to digital mental health, the inclusion of technology poses unique challenges. We outline some of the reasons for this gap and propose a collection of methods that can result in sustainable digital mental health interventions. These methods draw from human-computer interaction and implementation science and are integrated into an Accelerated Creation-to-Sustainment (ACTS) model. The ACTS model uses an iterative process that includes 2 basic functions (design and evaluate) across 3 general phases (Create, Trial, and Sustain). The ultimate goal in using the ACTS model is to produce a functioning technology-enabled service (TES) that is sustainable in a real-world treatment setting. We emphasize the importance of the service component because evidence from both research and practice has suggested that human touch is a critical ingredient in the most efficacious and used digital mental health treatments. The Create phase results in at least a minimally viable TES and an implementation blueprint. The Trial phase requires evaluation of both effectiveness and implementation while allowing optimization and continuous quality improvement of the TES and implementation plan. Finally, the Sustainment phase involves the withdrawal of research or donor support, while leaving a functioning, continuously improving TES in place. The ACTS model is a step toward bringing implementation and sustainment into the design and evaluation of TESs, public health into clinical research, research into clinics, and treatment into the lives of our patients.

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          Most cited references57

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          Reenvisioning Clinical Science: Unifying the Discipline to Improve the Public Health.

          We present a vision of clinical science, based on a conceptual framework of intervention development endorsed by the Delaware Project. This framework is grounded in an updated stage model that incorporates basic science questions of mechanisms into every stage of clinical science research. The vision presented is intended to unify various aspects of clinical science toward the common goal of developing maximally potent and implementable interventions, while unveiling new avenues of science in which basic and applied goals are of equally high importance. Training in this integrated, translational model may help students learn how to conduct research in every domain of clinical science and at each stage of intervention development. This vision aims to propel the field to fulfill the public health goal of producing implementable and effective treatment and prevention interventions.
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            Core Implementation Components

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              Computer-aided psychotherapy for anxiety disorders: a meta-analytic review.

              Computer-aided psychotherapy (CP) is said to (1) be as effective as face-to-face psychotherapy, while requiring less therapist time, for anxiety disorder sufferers, (2) speed access to care, and (3) save traveling time. CP may be delivered on stand-alone or Internet-linked computers, palmtop computers, phone-interactive voice response, DVDs, and cell phones. The authors performed a meta-analysis of 23 randomised controlled studies (RCTs) that compared CP with non-CP in anxiety disorders: phobias, n = 10; panic disorder/agoraphobia, n = 9; PTSD, n = 3; obsessive-compulsive disorder, n = 1. Overall mean effect size of CP compared with non-CP was 1.08 (95% confidence interval: 0.84-1.32). CP and face-to-face psychotherapy did not differ significantly from each other (13 comparisons, d = -0.06). Much caution is needed when interpreting the findings indicating that outcome was unrelated to type of disorder, type of comparison group, mode of CP delivery (Internet, stand-alone PC, palmtop), and recency of the CP system and that effect size decreased when more therapist time was replaced by the computer. Because CP as a whole was as effective as face-to-face psychotherapy, certain forms of CP deserve to be integrated into routine practice.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                May 2017
                10 May 2017
                : 19
                : 5
                : e153
                Affiliations
                [1] 1Center for Behavioral Intervention Technologies Department of Preventive Medicine Northwestern University Chicago, ILUnited States
                [2] 2Department of Psychiatry and Behavioral Sciences University of Washington Seattle, WAUnited States
                [3] 3Department of Communication Studies Northwestern University Evanston, ILUnited States
                Author notes
                Corresponding Author: David C. Mohr d-mohr@ 123456northwestern.edu
                Author information
                http://orcid.org/0000-0002-5443-7596
                http://orcid.org/0000-0003-3657-5060
                http://orcid.org/0000-0002-3069-5996
                http://orcid.org/0000-0002-6330-3637
                http://orcid.org/0000-0002-1003-0399
                Article
                v19i5e153
                10.2196/jmir.7725
                5443926
                28490417
                770aa7e2-d2d2-4a11-b0b9-fe9216835c75
                ©David C. Mohr, Aaron R Lyon, Emily G Lattie, Madhu Reddy, Stephen M Schueller. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 10.05.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 20 March 2017
                : 5 April 2017
                : 15 April 2017
                : 16 April 2017
                Categories
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                Medicine
                ehealth,mhealth,methodology
                Medicine
                ehealth, mhealth, methodology

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