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      Methods for Evaluating the Content, Usability, and Efficacy of Commercial Mobile Health Apps

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          Abstract

          Commercial mobile apps for health behavior change are flourishing in the marketplace, but little evidence exists to support their use. This paper summarizes methods for evaluating the content, usability, and efficacy of commercially available health apps. Content analyses can be used to compare app features with clinical guidelines, evidence-based protocols, and behavior change techniques. Usability testing can establish how well an app functions and serves its intended purpose for a target population. Observational studies can explore the association between use and clinical and behavioral outcomes. Finally, efficacy testing can establish whether a commercial app impacts an outcome of interest via a variety of study designs, including randomized trials, multiphase optimization studies, and N-of-1 studies. Evidence in all these forms would increase adoption of commercial apps in clinical practice, inform the development of the next generation of apps, and ultimately increase the impact of commercial apps.

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          Most cited references77

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          Three approaches to qualitative content analysis.

          Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.
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            The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions.

            CONSORT guidelines call for precise reporting of behavior change interventions: we need rigorous methods of characterizing active content of interventions with precision and specificity. The objective of this study is to develop an extensive, consensually agreed hierarchically structured taxonomy of techniques [behavior change techniques (BCTs)] used in behavior change interventions. In a Delphi-type exercise, 14 experts rated labels and definitions of 124 BCTs from six published classification systems. Another 18 experts grouped BCTs according to similarity of active ingredients in an open-sort task. Inter-rater agreement amongst six researchers coding 85 intervention descriptions by BCTs was assessed. This resulted in 93 BCTs clustered into 16 groups. Of the 26 BCTs occurring at least five times, 23 had adjusted kappas of 0.60 or above. "BCT taxonomy v1," an extensive taxonomy of 93 consensually agreed, distinct BCTs, offers a step change as a method for specifying interventions, but we anticipate further development and evaluation based on international, interdisciplinary consensus.
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              Evidence based medicine: what it is and what it isn't

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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                December 2017
                18 December 2017
                : 5
                : 12
                : e190
                Affiliations
                [01] 1 Division of Preventive and Behavioral Medicine University of Massachusetts Medical School Worcester, MA United States
                [02] 2 Department of Allied Health Sciences University of Connecticut Storrs, CT United States
                [03] 3 Department of Quantitative Health Sciences University of Massachusetts Medical School Worcester, MA United States
                [04] 4 Department of Obstetrics and Gynecology University of Massachusetts Medical School Worcester, MA United States
                [05] 5 Department of Emergency Medicine University of Massachusetts Medical School Worcester, MA United States
                [06] 6 Division of Health Informatics and Implementation Science Department of Quantitative Health Sciences University of Massachusetts Medical School Worcester, MA United States
                [07] 7 Department of Kinesiology & Community Health University of Illinois at Urbana-Champaign Urbana, IL United States
                [08] 8 Division of Consultation/Liaison Psychiatry and Psychology Department of Psychiatry Virginia Commonwealth University Richmond, VA United States
                [09] 9 Department of Psychology & Neuroscience Duke University Durham, NC United States
                [10] 10 Duke Digital Health Science Center Duke University Durham, NC United States
                Author notes
                Corresponding Author: Danielle E Jake-Schoffman danielle.jakeschoffman@ 123456umassmed.edu
                Author information
                http://orcid.org/0000-0001-6381-7323
                http://orcid.org/0000-0001-8505-4726
                http://orcid.org/0000-0002-9884-9824
                http://orcid.org/0000-0002-3223-6371
                http://orcid.org/0000-0001-8406-6207
                http://orcid.org/0000-0002-8200-4026
                http://orcid.org/0000-0002-0758-565X
                http://orcid.org/0000-0002-2590-3404
                http://orcid.org/0000-0002-6904-8534
                http://orcid.org/0000-0002-4740-9175
                http://orcid.org/0000-0002-2462-8797
                Article
                v5i12e190
                10.2196/mhealth.8758
                5748471
                29254914
                957ad367-8686-4e66-bb18-c10c6e4fbcc8
                ©Danielle E Jake-Schoffman, Valerie J Silfee, Molly E Waring, Edwin D Boudreaux, Rajani S Sadasivam, Sean P Mullen, Jennifer L Carey, Rashelle B Hayes, Eric Y Ding, Gary G Bennett, Sherry L Pagoto. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 18.12.2017.

                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 JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 16 August 2017
                : 27 September 2017
                : 11 October 2017
                : 29 October 2017
                Categories
                Viewpoint
                Viewpoint

                mhealth,mobile health,mobile applications,telemedicine/methods ,treatment efficacy,behavioral medicine,chronic disease

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