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      The Effect of Timing and Frequency of Push Notifications on Usage of a Smartphone-Based Stress Management Intervention: An Exploratory Trial

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          Abstract

          Push notifications offer a promising strategy for enhancing engagement with smartphone-based health interventions. Intelligent sensor-driven machine learning models may improve the timeliness of notifications by adapting delivery to a user’s current context (e.g. location). This exploratory mixed-methods study examined the potential impact of timing and frequency on notification response and usage of Healthy Mind, a smartphone-based stress management intervention. 77 participants were randomised to use one of three versions of Healthy Mind that provided: intelligent notifications; daily notifications within pre-defined time frames; or occasional notifications within pre-defined time frames. Notification response and Healthy Mind usage were automatically recorded. Telephone interviews explored participants’ experiences of using Healthy Mind. Participants in the intelligent and daily conditions viewed (d = .47, .44 respectively) and actioned (d = .50, .43 respectively) more notifications compared to the occasional group. Notification group had no meaningful effects on percentage of notifications viewed or usage of Healthy Mind. No meaningful differences were indicated between the intelligent and non-intelligent groups. Our findings suggest that frequent notifications may encourage greater exposure to intervention content without deterring engagement, but adaptive tailoring of notification timing does not always enhance their use. Hypotheses generated from this study require testing in future work.

          Trial registration number: ISRCTN67177737

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          Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide.

          Without a complete published description of interventions, clinicians and patients cannot reliably implement interventions that are shown to be useful, and other researchers cannot replicate or build on research findings. The quality of description of interventions in publications, however, is remarkably poor. To improve the completeness of reporting, and ultimately the replicability, of interventions, an international group of experts and stakeholders developed the Template for Intervention Description and Replication (TIDieR) checklist and guide. The process involved a literature review for relevant checklists and research, a Delphi survey of an international panel of experts to guide item selection, and a face to face panel meeting. The resultant 12 item TIDieR checklist (brief name, why, what (materials), what (procedure), who provided, how, where, when and how much, tailoring, modifications, how well (planned), how well (actual)) is an extension of the CONSORT 2010 statement (item 5) and the SPIRIT 2013 statement (item 11). While the emphasis of the checklist is on trials, the guidance is intended to apply across all evaluative study designs. This paper presents the TIDieR checklist and guide, with an explanation and elaboration for each item, and examples of good reporting. The TIDieR checklist and guide should improve the reporting of interventions and make it easier for authors to structure accounts of their interventions, reviewers and editors to assess the descriptions, and readers to use the information.
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            Opportunities and Challenges for Smartphone Applications in Supporting Health Behavior Change: Qualitative Study

            Background There is increasing interest from academics and clinicians in harnessing smartphone applications (apps) as a means of delivering behavioral interventions for health. Despite the growing availability of a range of health-related apps on the market, academic research on the development and evaluation of such apps is in the relatively early stages. A few existing studies have explored the views of various populations on using mobile phones for health-related issues and some studies are beginning to report user feedback on specific apps. However, there remains little in depth research on users’ (and potential users’) experiences and views on a wide range of features and technologies that apps are, or will soon be, capable of. In particular, research on young adults is lacking, which is an unfortunate omission considering that this group comprises of a good number of mobile technology adoptors. Objective The current study sought to explore young adults’ perspectives on apps related to health behavior change. It sought their experiences and views of features that might support health behavior change and issues that contribute to interest in and willingness to use such apps. Methods Four focus groups were conducted with 19 students and staff at a University in the United Kingdom. Participants included 13 females and 6 males with a mean age of 23.79 (SD 7.89). The focus group discussions centred on participants’ experiences of using smartphone apps to support a healthy lifestyle, and their interest in and feelings about features and capabilities of such apps. The focus groups were recorded, transcribed, and analyzed using inductive thematic analysis. Results Study findings suggested that young, currently healthy adults have some interest in apps that attempt to support health-related behavior change. Accuracy and legitimacy, security, effort required, and immediate effects on mood emerged as important influences on app usage. The ability to record and track behavior and goals and the ability to acquire advice and information “on the go” were valued. Context-sensing capabilities and social media features tended to be considered unnecessary and off-putting. Conclusions This study provided insight into the opportunities and challenges involved in delivering health-related behavioral interventions through smartphone apps. The findings suggested a number of valued features and characteristics that app developers may wish to consider when creating health behavior apps. Findings also highlighted several major challenges that appeared to need further consideration and research to ensure the development of effective and well-accepted behavior change apps.
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              Confidence intervals rather than P values: estimation rather than hypothesis testing.

              Overemphasis on hypothesis testing--and the use of P values to dichotomise significant or non-significant results--has detracted from more useful approaches to interpreting study results, such as estimation and confidence intervals. In medical studies investigators are usually interested in determining the size of difference of a measured outcome between groups, rather than a simple indication of whether or not it is statistically significant. Confidence intervals present a range of values, on the basis of the sample data, in which the population value for such a difference may lie. Some methods of calculating confidence intervals for means and differences between means are given, with similar information for proportions. The paper also gives suggestions for graphical display. Confidence intervals, if appropriate to the type of study, should be used for major findings in both the main text of a paper and its abstract.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                3 January 2017
                2017
                : 12
                : 1
                : e0169162
                Affiliations
                [1 ]Psychology, Faculty of Social, Human, and Mathematical Sciences, University of Southampton, Southampton, Hampshire, United Kingdom
                [2 ]Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, Hampshire, United Kingdom
                [3 ]Department of Creative Technology, Faculty of Science and Technology, Bournemouth University, Poole, Dorset, United Kingdom
                [4 ]Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
                [5 ]Redcar & Cleveland Borough Council, Redcar, Yorkshire, United Kingdom
                [6 ]Health and Social Care Institute, School of Health and Social Care, Teesside University, Middlesbrough, Tees Valley, United Kingdom
                [7 ]Fuse, Centre for Translational Research in Public Health, Newcastle University, Newcastle upon Tyne, Tyne and Wear, United Kingdom
                [8 ]Centre for Public Policy and Health, School of Medicine, Pharmacy and Health, Durham University, Stockton on Tees, United Kingdom
                [9 ]Gateshead Council, Gateshead, Tyne and Wear, United Kingdom
                [10 ]Electronics and Computer Science, University of Southampton, Southampton, Hampshire, United Kingdom
                [11 ]Department of Geography, University College London, London, United Kingdom
                University of Rochester, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: LGM CH VP DTM MM MJW LY.

                • Data curation: LGM CH VP DTM.

                • Formal analysis: LGM AW.

                • Funding acquisition: MM MJW LY.

                • Investigation: LGM CH VP DTM.

                • Methodology: LGM CH VP AWAG DTM MM MJW LY.

                • Project administration: LGM CH VP.

                • Resources: LGM CH VP AWAG SL NG DTM.

                • Software: LGM CH VP DTM.

                • Supervision: MM MJW LY.

                • Validation: LGM CH VP AW.

                • Visualization: LGM.

                • Writing – original draft: LGM LY.

                • Writing – review & editing: LGM CH VP AWAG SL NG DTM AW MM MJW LY.

                Author information
                http://orcid.org/0000-0002-9961-551X
                Article
                PONE-D-16-32583
                10.1371/journal.pone.0169162
                5207732
                28046034
                09aa40ae-92ab-4a45-b915-28400ff7b3d5
                © 2017 Morrison et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 August 2016
                : 13 December 2016
                Page count
                Figures: 1, Tables: 4, Pages: 15
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/I032673/1
                Award Recipient :
                The study was funded by the UK Engineering and Physical Sciences Research Council (EP/I032673/1: UBhave: Ubiquitous and social computing for positive behaviour change led by Professor Lucy Yardley); https://www.epsrc.ac.uk/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Custom metadata
                Participants in our study provided consent to share anonymised data with the academic community for the purposes of research. Therefore, our ethical approval dictates that we must include the restriction that the data is available on request to bona fide researchers. Access to request the data will be available through the following DOI ( http://dx.doi.org/10.5258/SOTON/381004) and through the University of Southampton repository via http://library.soton.ac.uk/datarequest.

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