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      To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App

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          Abstract

          Background

          Mobile health (mHealth) apps provide an opportunity for easy, just-in-time access to health promotion and self-management support. However, poor user engagement with these apps remains a significant unresolved challenge.

          Objective

          This study aimed to assess the effect of sending versus not sending a push notification containing a contextually tailored health message on proximal engagement, measured here as self-monitoring via the app. Secondary aims were to examine whether this effect varies by the number of weeks enrolled in the program or by weekday versus weekend. An exploratory aim was to describe how the effect on proximal engagement differs between weekday versus weekend by the time of day.

          Methods

          The study analyzes the causal effects of push notifications on proximal engagement in 1255 users of a commercial workplace well-being intervention app over 89 days. The app employs a microrandomized trial (MRT) design to send push notifications. At 1 of 6 times per day (8:30 am, 12:30 pm, 5:30 pm, 6:30 pm, 7:30 pm, and 8:30 pm; selected randomly), available users were randomized with equal probability to be sent or not sent a push notification containing a tailored health message. The primary outcome of interest was whether the user self-monitored behaviors and feelings at some time during the next 24 hours via the app. A generalization of log-linear regression analysis, adapted for use with data arising from an MRT, was used to examine the effect of sending a push notification versus not sending a push notification on the probability of engagement over the next 24 hours.

          Results

          Users were estimated to be 3.9% more likely to engage with the app in the next 24 hours when a tailored health message was sent versus when it was not sent (risk ratio 1.039; 95% CI 1.01 to 1.08; P<.05). The effect of sending the message attenuated over the course of the study, but this effect was not statistically significant ( P=.84). The effect of sending the message was greater on weekends than on weekdays, but the difference between these effects was not statistically significant ( P=.18). When sent a tailored health message on weekends, the users were 8.7% more likely to engage with the app (95% CI 1.01 to 1.17), whereas on weekdays, the users were 2.5% more likely to engage with the app (95% CI 0.98 to 1.07). The effect of sending a tailored health message was greatest at 12:30 pm on weekends, when the users were 11.8% more likely to engage (90% CI 1.02 to 1.13).

          Conclusions

          Sending a push notification containing a tailored health message was associated with greater engagement in an mHealth app. Results suggested that users are more likely to engage with the app within 24 hours when push notifications are sent at mid-day on weekends.

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          Most cited references27

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          Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions

          Background Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence. Objective This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention. Methods We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence. Results We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p = .004), setup (p < .001), updates (p < .001), frequency of interaction with a counselor (p < .001), the system (p = .003) and peers (p = .017), duration (F = 6.068, p = .004), adherence (F = 4.833, p = .010) and the number of primary task support elements (F = 5.631, p = .005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence. Conclusions Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere.
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            Understanding tailoring in communicating about health.

            'Tailoring' refers to any of a number of methods for creating communications individualized for their receivers, with the expectation that this individualization will lead to larger intended effects of these communications. Results so far have been generally positive but not consistently so, and this paper seeks to explicate tailoring to help focus future research. Tailoring involves either or both of two classes of goals (enhancing cognitive preconditions for message processing and enhancing message impact through modifying behavioral determinants of goal outcomes) and employs strategies of personalization, feedback and content matching. These goals and strategies intersect in a 2 x 3 matrix in which some strategies and their component tactics match better to some goals than to others. The paper illustrates how this framework can be systematically applied in generating research questions and identifying appropriate study designs for tailoring research.
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              What is user engagement? A conceptual framework for defining user engagement with technology

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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
                November 2018
                29 November 2018
                : 6
                : 11
                : e10123
                Affiliations
                [1 ] Personal Health Informatics, College of Medicine & Public Health Adelaide Australia
                [2 ] Insitute for Social Research Michigan University Ann Arbor, MI United States
                [3 ] Department of Statistics Harvard University Boston, MA United States
                [4 ] Jool Health Ann Arbor, MI United States
                [5 ] School of Public Health University of Michigan Ann Arbor, MI United States
                Author notes
                Corresponding Author: Niranjan Bidargaddi niranjan.bidargaddi@ 123456flinders.edu.au
                Author information
                http://orcid.org/0000-0003-2868-9260
                http://orcid.org/0000-0002-9697-6600
                http://orcid.org/0000-0002-2032-4286
                http://orcid.org/0000-0001-6138-9089
                http://orcid.org/0000-0003-4232-2473
                http://orcid.org/0000-0003-4468-2720
                http://orcid.org/0000-0001-8227-2402
                http://orcid.org/0000-0002-0587-2305
                Article
                v6i11e10123
                10.2196/10123
                6293241
                30497999
                828eabbd-878b-48ec-a224-c8caac31641c
                ©Niranjan Bidargaddi, Daniel Almirall, Susan Murphy, Inbal Nahum-Shani, Michael Kovalcik, Timothy Pituch, Haitham Maaieh, Victor Strecher. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 29.11.2018.

                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
                : 26 February 2018
                : 12 April 2018
                : 20 June 2018
                : 10 July 2018
                Categories
                Original Paper
                Original Paper

                mobile applications,smartphone,self report,health promotion,lifestyle,ubiquitous computing,push notification

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