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      Developing and Evaluating Digital Interventions to Promote Behavior Change in Health and Health Care: Recommendations Resulting From an International Workshop

      research-article
        , MA, MPhil, DPhil 1 , , , BSc, MSc, PhD, CPsychol 2 , 3 , , BSc, PhD 4 , , MD, MS 5 , 6 , , BMBCh, PhD 7 , 8
      (Reviewer), (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      health behavior, psychological theory, mobile applications, behavioral medicine, mHealth, eHealth

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          Abstract

          Devices and programs using digital technology to foster or support behavior change (digital interventions) are increasingly ubiquitous, being adopted for use in patient diagnosis and treatment, self-management of chronic diseases, and in primary prevention. They have been heralded as potentially revolutionizing the ways in which individuals can monitor and improve their health behaviors and health care by improving outcomes, reducing costs, and improving the patient experience. However, we are still mainly in the age of promise rather than delivery. Developing and evaluating these digital interventions presents new challenges and new versions of old challenges that require use of improved and perhaps entirely new methods for research and evaluation. This article discusses these challenges and provides recommendations aimed at accelerating the rate of progress in digital behavior intervention research and practice. Areas addressed include intervention development in a rapidly changing technological landscape, promoting user engagement, advancing the underpinning science and theory, evaluating effectiveness and cost-effectiveness, and addressing issues of regulatory, ethical, and information governance. This article is the result of a two-day international workshop on how to create, evaluate, and implement effective digital interventions in relation to health behaviors. It was held in London in September 2015 and was supported by the United Kingdom’s Medical Research Council (MRC), the National Institute for Health Research (NIHR), the Methodology Research Programme (PI Susan Michie), and the Robert Wood Johnson Foundation of the United States (PI Kevin Patrick). Important recommendations to manage the rapid pace of change include considering using emerging techniques from data science, machine learning, and Bayesian approaches and learning from other disciplines including computer science and engineering. With regard to assessing and promoting engagement, a key conclusion was that sustained engagement is not always required and that for each intervention it is useful to establish what constitutes “effective engagement,” that is, sufficient engagement to achieve the intended outcomes. The potential of digital interventions for testing and advancing theories of behavior change by generating ecologically valid, real-time objective data was recognized. Evaluations should include all phases of the development cycle, designed for generalizability, and consider new experimental designs to make the best use of rich data streams. Future health economics analyses need to recognize and model the complex and potentially far-reaching costs and benefits of digital interventions. In terms of governance, developers of digital behavior interventions should comply with existing regulatory frameworks, but with consideration for emerging standards around information governance, ethics, and interoperability.

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          Most cited references39

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          The impact of guidance on Internet-based mental health interventions — A systematic review

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            Cost-effectiveness analysis alongside clinical trials II-An ISPOR Good Research Practices Task Force report.

            Clinical trials evaluating medicines, medical devices, and procedures now commonly assess the economic value of these interventions. The growing number of prospective clinical/economic trials reflects both widespread interest in economic information for new technologies and the regulatory and reimbursement requirements of many countries that now consider evidence of economic value along with clinical efficacy. As decision makers increasingly demand evidence of economic value for health care interventions, conducting high-quality economic analyses alongside clinical studies is desirable because they broaden the scope of information available on a particular intervention, and can efficiently provide timely information with high internal and, when designed and analyzed properly, reasonable external validity. In 2005, ISPOR published the Good Research Practices for Cost-Effectiveness Analysis Alongside Clinical Trials: The ISPOR RCT-CEA Task Force report. ISPOR initiated an update of the report in 2014 to include the methodological developments over the last 9 years. This report provides updated recommendations reflecting advances in several areas related to trial design, selecting data elements, database design and management, analysis, and reporting of results. Task force members note that trials should be designed to evaluate effectiveness (rather than efficacy) when possible, should include clinical outcome measures, and should obtain health resource use and health state utilities directly from study subjects. Collection of economic data should be fully integrated into the study. An incremental analysis should be conducted with an intention-to-treat approach, complemented by relevant subgroup analyses. Uncertainty should be characterized. Articles should adhere to established standards for reporting results of cost-effectiveness analyses. Economic studies alongside trials are complementary to other evaluations (e.g., modeling studies) as information for decision makers who consider evidence of economic value along with clinical efficacy when making resource allocation decisions.
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              Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist

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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
                June 2017
                29 June 2017
                : 19
                : 6
                : e232
                Affiliations
                [1] 1 Centre for Behaviour Change Department of Clinical, Educational and Health Psychology University College London London United Kingdom
                [2] 2 Department of Psychology University of Southampton Southampton United Kingdom
                [3] 3 Nuffield Department of Primary Care Health Sciences University of Oxford Oxford United Kingdom
                [4] 4 Health Behaviour Research Centre University College London United Kingdom
                [5] 5 Department of Family Medicine and Public Health The Qualcomm Institute - Calit2 University of California San Diego, CA United States
                [6] 6 The Center for Wireless and Population Health Systems The Qualcomm Institute - Calit2 University of California San Diego, CA United States
                [7] 7 Public Health England Skipton House London United Kingdom
                [8] 8 Department of Primary Care and Public Health Imperial College London London United Kingdom
                Author notes
                Corresponding Author: Susan Michie s.michie@ 123456ucl.ac.uk
                Author information
                http://orcid.org/0000-0003-0063-6378
                http://orcid.org/0000-0002-3853-883X
                http://orcid.org/0000-0002-0291-5760
                http://orcid.org/0000-0002-7334-3042
                http://orcid.org/0000-0001-9393-3122
                Article
                v19i6e232
                10.2196/jmir.7126
                5509948
                28663162
                a97051df-f87e-435d-bb72-358e7ba229c2
                ©Susan Michie, Lucy Yardley, Robert West, Kevin Patrick, Felix Greaves. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.06.2017.

                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 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
                : 14 December 2016
                : 18 February 2017
                : 28 February 2017
                : 22 April 2017
                Categories
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                Medicine
                health behavior,psychological theory,mobile applications,behavioral medicine,mhealth,ehealth
                Medicine
                health behavior, psychological theory, mobile applications, behavioral medicine, mhealth, ehealth

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