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      A Framework for Analyzing and Measuring Usage and Engagement Data (AMUsED) in Digital Interventions: Viewpoint

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          Abstract

          Trials of digital interventions can yield extensive, in-depth usage data, yet usage analyses tend to focus on broad descriptive summaries of how an intervention has been used by the whole sample. This paper proposes a novel framework to guide systematic, fine-grained usage analyses that better enables understanding of how an intervention works, when, and for whom. The framework comprises three stages to assist in the following: (1) familiarization with the intervention and its relationship to the captured data, (2) identification of meaningful measures of usage and specifying research questions to guide systematic analyses of usage data, and (3) preparation of datasheets and consideration of available analytical methods with which to examine the data. The framework can be applied to inform data capture during the development of a digital intervention and/or in the analysis of data after the completion of an evaluation trial. We will demonstrate how the framework shaped preparation and aided efficient data capture for a digital intervention to lower transmission of cold and flu viruses in the home, as well as how it informed a systematic, in-depth analysis of usage data collected from a separate digital intervention designed to promote self-management of colds and flu. The Analyzing and Measuring Usage and Engagement Data (AMUsED) framework guides systematic and efficient in-depth usage analyses that will support standardized reporting with transparent and replicable findings. These detailed findings may also enable examination of what constitutes effective engagement with particular interventions.

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          Effectiveness and Cost-Effectiveness of eHealth Interventions in Somatic Diseases: A Systematic Review of Systematic Reviews and Meta-Analyses

          Background eHealth potentially enhances quality of care and may reduce health care costs. However, a review of systematic reviews published in 2010 concluded that high-quality evidence on the benefits of eHealth interventions was still lacking. Objective We conducted a systematic review of systematic reviews and meta-analyses on the effectiveness/cost-effectiveness of eHealth interventions in patients with somatic diseases to analyze whether, and to what possible extent, the outcome of recent research supports or differs from previous conclusions. Methods Literature searches were performed in PubMed, EMBASE, The Cochrane Library, and Scopus for systematic reviews and meta-analyses on eHealth interventions published between August 2009 and December 2012. Articles were screened for relevance based on preset inclusion and exclusion criteria. Citations of residual articles were screened for additional literature. Included papers were critically appraised using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement before data were extracted. Based on conclusions drawn by the authors of the included articles, reviews and meta-analyses were divided into 1 of 3 groups: suitable, promising, or limited evidence on effectiveness/cost-effectiveness. Cases of uncertainty were resolved by consensus discussion. Effect sizes were extracted from papers that included a meta-analysis. To compare our results with previous findings, a trend analysis was performed. Results Our literature searches yielded 31 eligible reviews, of which 20 (65%) reported on costs. Seven papers (23%) concluded that eHealth is effective/cost-effective, 13 (42%) underlined that evidence is promising, and others found limited or inconsistent proof. Methodological quality of the included reviews and meta-analyses was generally considered high. Trend analysis showed a considerable accumulation of literature on eHealth. However, a similar percentage of papers concluded that eHealth is effective/cost-effective or evidence is at least promising (65% vs 62%). Reviews focusing primarily on children or family caregivers still remained scarce. Although a pooled (subgroup) analysis of aggregate data from randomized studies was performed in a higher percentage of more recently published reviews (45% vs 27%), data on economic outcome measures were less frequently reported (65% vs 85%). Conclusions The number of reviews and meta-analyses on eHealth interventions in patients with somatic diseases has increased considerably in recent years. Most articles show eHealth is effective/cost-effective or at least suggest evidence is promising, which is consistent with previous findings. Although many researchers advocate larger, well-designed, controlled studies, we believe attention should be given to the development and evaluation of strategies to implement effective/cost-effective eHealth initiatives in daily practice, rather than to further strengthen current evidence.
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            The common-sense model of self-regulation of health and illness

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              An internet-delivered handwashing intervention to modify influenza-like illness and respiratory infection transmission (PRIMIT): a primary care randomised trial.

              Handwashing to prevent transmission of respiratory tract infections (RTIs) has been widely advocated, especially during the H1N1 pandemic. However, the role of handwashing is debated, and no good randomised evidence exists among adults in non-deprived settings. We aimed to assess whether an internet-delivered intervention to modify handwashing would reduce the number of RTIs among adults and their household members.
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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
                February 2019
                15 February 2019
                : 21
                : 2
                : e10966
                Affiliations
                [1 ] Center for Clinical and Community Applications of Health Psychology Department of Psychology University of Southampton Southampton United Kingdom
                [2 ] Department of Psychology University of Bath Bath United Kingdom
                [3 ] School of Psychological Science University of Bristol Bristol United Kingdom
                [4 ] Web and Internet Science Group School of Electronics and Computer Science University of Southampton Southampton United Kingdom
                [5 ] Department of Social Statistics and Demography School of Economic, Social and Political Sciences University of Southampton Southampton United Kingdom
                [6 ] Primary Care and Population Sciences School of Medicine University of Southampton Southampton United Kingdom
                Author notes
                Corresponding Author: Sascha Miller S.J.Miller@ 123456soton.ac.uk
                Author information
                http://orcid.org/0000-0002-1949-5774
                http://orcid.org/0000-0002-5098-1092
                http://orcid.org/0000-0002-3853-883X
                http://orcid.org/0000-0002-2207-2537
                http://orcid.org/0000-0001-6251-8786
                http://orcid.org/0000-0003-4423-5410
                http://orcid.org/0000-0002-9961-551X
                Article
                v21i2e10966
                10.2196/10966
                6396072
                30767905
                222bedf7-4e81-48e8-af54-c2afb6103611
                ©Sascha Miller, Ben Ainsworth, Lucy Yardley, Alex Milton, Mark Weal, Peter Smith, Leanne Morrison. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.02.2019.

                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
                : 4 May 2018
                : 7 August 2018
                : 17 October 2018
                : 30 October 2018
                Categories
                Viewpoint
                Viewpoint

                Medicine
                behavioral research,internet,health,patient engagement,data analysis
                Medicine
                behavioral research, internet, health, patient engagement, data analysis

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