Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review

Background: The public health impact of health and well-being digital interventions is dependent upon sufficient real-world uptake and engagement. Uptake is currently largely dependent on popularity indicators (eg, ranking and user ratings on app stores), which may not correspond with effectiveness, and rapid disengagement is common. Therefore, there is an urgent need to identify factors that influence uptake and engagement with health and well-being apps to inform new approaches that promote the effective use of such tools. Objective: This review aimed to understand what is known about influences on the uptake of and engagement with health and well-being smartphone apps among adults. Methods: We conducted a systematic review of quantitative, qualitative, and mixed methods studies. Studies conducted on adults were included if they focused on health and well-being smartphone apps reporting on uptake and engagement behavior. Studies identified through a systematic search in Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (MEDLINE), EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, Scopus, Cochrane library databases, DataBase systems and Logic Programming (DBLP), and Association for Computing Machinery (ACM) Digital library were screened, with a proportion screened independently by 2 authors. Data synthesis and interpretation were undertaken using a deductive iterative process. External validity checking was undertaken by an independent researcher. A narrative synthesis of the findings was structured around the components of the capability, opportunity, motivation, behavior change model and the theoretical domains framework (TDF). Results: Of the 7640 identified studies, 41 were included in the review. Factors related to uptake (U), engagement (E), or both (B) were identified. Under capability, the main factors identified were app literacy skills (B), app awareness (U), available user guidance (B), health information (E), statistical information on progress (E), well-designed reminders (E), features to reduce cognitive load (E), and self-monitoring features (E). Availability at low cost (U), positive tone, and personalization (E) were identified as physical opportunity factors, whereas recommendations for health and well-being apps (U), embedded health professional support (E), and social networking (E) possibilities were social opportunity factors. Finally, the motivation factors included positive feedback (E), available rewards (E), goal setting (E), and the perceived utility of the app (E). Conclusions: Across a wide range of populations and behaviors, 26 factors relating to capability, opportunity, and motivation appear to influence the uptake of and engagement with health and well-being smartphone apps. Our recommendations may help app developers, health app portal developers, and policy makers in the optimization of health and well-being apps

Educational Technology Topic Guide

This guide aims to contribute to what we know about the relationship between educational technology (edtech) and educational outcomes by addressing the following overarching question: What is the evidence that the use of edtech, by teachers or students, impacts teaching and learning practices, or learning outcomes? It also offers recommendations to support advisors to strengthen the design, implementation and evaluation of programmes that use edtech. We define edtech as the use of digital or electronic technologies and materials to support teaching and learning. Recognising that technology alone does not enhance learning, evaluations must also consider how programmes are designed and implemented, how teachers are supported, how communities are developed and how outcomes are measured (see http://tel.ac.uk/about-3/, 2014). Effective edtech programmes are characterised by: a clear and specific curriculum focus the use of relevant curriculum materials a focus on teacher development and pedagogy evaluation mechanisms that go beyond outputs. These findings come from a wide range of technology use including: interactive radio instruction (IRI) classroom audio or video resources accessed via teachers’ mobile phones student tablets and eReaders computer-assisted learning (CAL) to supplement classroom teaching. However, there are also examples of large-scale investment in edtech – particularly computers for student use – that produce limited educational outcomes. We need to know more about: how to support teachers to develop appropriate, relevant practices using edtech how such practices are enacted in schools, and what factors contribute to or mitigate against successful outcomes. Recommendations: 1. Edtech programmes should focus on enabling educational change, not delivering technology. In doing so, programmes should provide adequate support for teachers and aim to capture changes in teaching practice and learning outcomes in evaluation. 2. Advisors should support proposals that further develop successful practices or that address gaps in evidence and understanding. 3. Advisors should discourage proposals that have an emphasis on technology over education, weak programmatic support or poor evaluation. 4. In design and evaluation, value-for-money metrics and cost-effectiveness analyses should be carried out

User Perceptions of Smart Home IoT Privacy

Smart home Internet of Things (IoT) devices are rapidly increasing in popularity, with more households including Internet-connected devices that continuously monitor user activities. In this study, we conduct eleven semi-structured interviews with smart home owners, investigating their reasons for purchasing IoT devices, perceptions of smart home privacy risks, and actions taken to protect their privacy from those external to the home who create, manage, track, or regulate IoT devices and/or their data. We note several recurring themes. First, users' desires for convenience and connectedness dictate their privacy-related behaviors for dealing with external entities, such as device manufacturers, Internet Service Providers, governments, and advertisers. Second, user opinions about external entities collecting smart home data depend on perceived benefit from these entities. Third, users trust IoT device manufacturers to protect their privacy but do not verify that these protections are in place. Fourth, users are unaware of privacy risks from inference algorithms operating on data from non-audio/visual devices. These findings motivate several recommendations for device designers, researchers, and industry standards to better match device privacy features to the expectations and preferences of smart home owners.Comment: 20 pages, 1 tabl

Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model

Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper's objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user pro le by following the principles of the I-Change model and maintaining the bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system's design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study.European Union's Horizon 2020 Research and Innovation Programme under Grant 68112

Culture and disaster risk management - synthesis of citizens’ reactions and opinions during 6 Citizen Summits : Romania, Malta, Italy, Germany, Portugal and the Netherlands

The analyses and results in this document are based on the data collected during six Citizen Summits held in A) Romania (Bucharest) on July 9th, 2016 B) Malta on July 16th, 2016 C) Italy (Rome) on June 17th, 2017 D) Germany (Frankfurt) on June 24th, 2017 E) Portugal (Lisbon) April 14th, 2018 F) The Netherlands (Utrecht)on May 12th, 2018. All Citizen Summits were designed as one-day events combining public information with feedback gathering through different methods of data collection, as laid out in Deliverable D5.1 (Structural design & methodology for Citizen Summits). A total of 619 citizens participated in the six events. In the morning session, the Citizen Summits started with a presentation of the CARISMAND project and its main goals and concepts. Then, several sets of questions with pre-defined answer options were posed to the audience and responses collected via an audience response system. All questions in this part of the event aimed to explore citizens’ attitudes, perceptions, and intended behaviours related to disasters and disaster risks. Between these sets of questions, additional presentations were held that informed the audience about state-of-the-art disaster preparedness and response topics (e.g., large-scale disaster scenario exercises, use of social media and mobile phone apps), as well as CARISMAND research findings. Furthermore, the last round of Citizen Summits (CS5 in Lisbon and CS6 in Utrecht) were organised and designed to additionally discuss and collect feedback on recommendations for citizens, which have all been formulated on the basis of Work Packages 2-10 results and in coordination with the Work Package 11 brief. These Toolkit recommendations will form one of the core elements of the Work Package 9 CARISMAND Toolkit. In the afternoon session of each event, small moderated group discussions (with 8-12 participants each) of approximately 2 hours’ duration were held, which aimed to gather citizens’ direct feedback on the topics presented in the morning sessions, following a detailed discussion guideline. For a detailed overview of all questions asked and topics discussed, please see Appendices A-1 to A-3. The rest of this report is structured in six main sections: After the executive summary and this introduction, the third section will present an overview of the different methods applied. The fourth section will provide a synthesis of quantitative and qualitative data collected during all Citizen Summits. The fifth section will present the evaluation of CARISMAND Toolkit recommendations for citizens, followed by a final concluding chapter.The project was co-funded by the European Commission within the Horizon2020 Programme (2014-2020).peer-reviewe

A systematic review of digital interventions for improving the diet and physical activity behaviors of adolescents

Many adolescents have poor diet and physical activity behaviors, which can lead to the development of noncommunicable diseases in later life. Digital platforms offer inexpensive means of delivering health interventions, but little is known about their effectiveness. This systematic review was conducted to synthesize evidence on the effectiveness of digital interventions to improve diet quality and increase physical activity in adolescents, to effective intervention components and to assess the cost-effectiveness of these interventions. Following a systematic search, abstracts were assessed against inclusion criteria, and data extraction and quality assessment were performed for included studies. Data were analyzed to identify key features that are associated with significant improvement in behavior. A total of 27 studies met inclusion criteria. Most (n = 15) were Web site interventions. Other delivery methods were text messages, games, multicomponent interventions, emails, and social media. Significant behavior change was often seen when interventions included education, goal setting, self-monitoring, and parental involvement. None of the publications reported cost-effectiveness. Due to heterogeneity of studies, meta-analysis was not feasible.It is possible to effect significant health behavior change in adolescents through digital interventions that incorporate education, goal setting, self-monitoring, and parental involvement. Most of the evidence relates to Web sites and further research into alternate media is needed, and longer term outcomes should be evaluated. There is a paucity of data on the cost-effectiveness of digital health interventions, and future trials should report these data