The public health potential of mobile applications to increase physical activity

Background: Physical activity (PA) is an important behavioural determinant of morbidity and mortality and is a public health priority. The accessibility, convenience and wide reach of mobile applications (apps) makes these digital interventions a potential mode for delivering PA interventions at scale. At the end of 2017 there were 325,000 health apps available publicly, with “fitness” apps being the largest category of all health apps. However, most apps on the market have not been evaluated and little is known about their quality. Aim: This PhD investigated the public health potential of publicly available PA apps. Methods: The following studies were conducted: 1) a review and content analysis of the most popular PA apps on the market to assess their quality, defined as safety, likely efficacy and positive user experience; 2) a study using regression models to determine the association between popularity and quality of those apps; 3) a feasibility crossover trial assessing two apps for increasing PA; and 4) a qualitative study assessing the acceptability of the trial procedures and exploring the experiences of the two PA apps. Results: Popular apps had high usability but there were issues around their safety and likely efficacy. Popularity was not associated with likely efficacy. The feasibility trial and the qualitative study showed that such a trial would be feasible and acceptable to participants. The enablers and barriers to increasing exercise using the apps were identified. Conclusion: The discrepancy between quality and popularity represents a missed opportunity for behaviour change interventions. Hence, the public health impact of PA apps is unlikely to be achieved when market forces “prescribe” what is used by the public. The motivation to use the apps varied substantially and it is important to identify when, for whom, and in what context PA apps are most likely to facilitate behaviour change

When Private Information Settles the Bill:Money and Privacy in Google’s Market for Smartphone Applications

We shed light on a money-for-privacy trade-off in the market for smartphone applications (“apps”). Developers offer their apps at lower prices in return for greater access to personal information, and consumers choose between low prices and more privacy. We provide evidence for this pattern using data from 300,000 apps obtained from the Google Play Store (formerly Android Market) in 2012 and 2014. Our findings show that the market's supply and demand sides both consider an app's ability to collect private information, measured by the apps's use of privacy-sensitive permissions: (1) cheaper apps use more privacy-sensitive permissions; (2) given price and functionality, demand is lower for apps with sensitive permissions; and (3) the strength of this relationship depends on contextual factors, such as the targeted user group, the app's previous success, and its category. Our results are robust and consistent across several robustness checks, including the use of panel data, a difference-in-differences analysis, “twin” pairs of apps, and various measures of privacy-sensitivity and app demand

Spillover Effects and Freemium Strategy in Mobile App Market

“Freemium” whereby a basic service level is provided free of charge but consumers are charged for more advanced features has become a popular business model for firms selling digital goods. However, it is not clear whether the launch of a free version helps or hurts the sale of an existing paid version. The free version may allow consumers to sample the product before making a purchase decision and subsequently increase downloads of the paid version but it may also cannibalize downloads of the paid version. We use a comprehensive data set on game apps from Apple's App Store that tracks the launch of both the paid and the free versions of individual apps on a daily level, to identify whether a freemium strategy stimulates or hurts downloads of an existing paid version. We estimate the spillover effects between the free version and the paid version of the same app accounting for the endogeneity of the launch of the free version as well as app-level product heterogeneity. We find that the launch of a free version increases downloads of the paid version of the same app and present multiple robustness checks for these results. We then present suggestive evidence that the results are due to consumers sampling the free version and rule out alternative explanations

Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data)

Background Meeting global health challenges requires effective behaviour change interventions (BCIs). This depends on advancing the science of behaviour change which, in turn, depends on accurate intervention reporting. Current reporting often lacks detail, preventing accurate replication and implementation. Recent developments have specified intervention content into behaviour change techniques (BCTs) – the ‘active ingredients’, for example goal-setting, self-monitoring of behaviour. BCTs are ‘the smallest components compatible with retaining the postulated active ingredients, i.e. the proposed mechanisms of change. They can be used alone or in combination with other BCTs’ (Michie S, Johnston M. Theories and techniques of behaviour change: developing a cumulative science of behaviour change. Health Psychol Rev 2012;6:1–6). Domain-specific taxonomies of BCTs have been developed, for example healthy eating and physical activity, smoking cessation and alcohol consumption. We need to build on these to develop an internationally shared language for specifying and developing interventions. This technology can be used for synthesising evidence, implementing effective interventions and testing theory. It has enormous potential added value for science and global health. Objective (1) To develop a method of specifying content of BCIs in terms of component BCTs; (2) to lay a foundation for a comprehensive methodology applicable to different types of complex interventions; (3) to develop resources to support application of the taxonomy; and (4) to achieve multidisciplinary and international acceptance for future development. Design and participants Four hundred participants (systematic reviewers, researchers, practitioners, policy-makers) from 12 countries engaged in investigating, designing and/or delivering BCIs. Development of the taxonomy involved a Delphi procedure, an iterative process of revisions and consultation with 41 international experts; hierarchical structure of the list was developed using inductive ‘bottom-up’ and theory-driven ‘top-down’ open-sort procedures (n = 36); training in use of the taxonomy (1-day workshops and distance group tutorials) (n = 161) was evaluated by changes in intercoder reliability and validity (agreement with expert consensus); evaluating the taxonomy for coding interventions was assessed by reliability (intercoder; test–retest) and validity (n = 40 trained coders); and evaluating the taxonomy for writing descriptions was assessed by reliability (intercoder; test–retest) and by experimentally testing its value (n = 190). Results Ninety-three distinct, non-overlapping BCTs with clear labels and definitions formed Behaviour Change Technique Taxonomy version 1 (BCTTv1). BCTs clustered into 16 groupings using a ‘bottom-up’ open-sort procedure; there was overlap between these and groupings produced by a theory-driven, ‘top-down’ procedure. Both training methods improved validity (both p < 0.05), doubled the proportion of coders achieving competence and improved confidence in identifying BCTs in workshops (both p < 0.001) but did not improve intercoder reliability. Good intercoder reliability was observed for 80 of the 93 BCTs. Good within-coder agreement was observed after 1 month (p < 0.001). Validity was good for 14 of 15 BCTs in the descriptions. The usefulness of BCTTv1 to report descriptions of observed interventions had mixed results. Conclusions The developed taxonomy (BCTTv1) provides a methodology for identifying content of complex BCIs and a foundation for international cross-disciplinary collaboration for developing more effective interventions to improve health. Further work is needed to examine its usefulness for reporting interventions. Funding This project was funded by the Medical Research Council Ref: G0901474/1. Funding also came from the Peninsula Collaboration for Leadership in Applied Health Research and Care

Addendum to the Building America House Simulation Protocols

As Building America (BA) has grown to include a large and diverse cross-section of the home building and retrofit industries, it has become more important to develop accurate, consistent analysis techniques to measure progress towards the program's goals. The House Simulation Protocols (HSP) provides guidance to program partners and managers so that energy savings for new construction and retrofit projects can be compared alongside each other. The HSP provides the program with analysis methods that are proven to be effective and reliable in investigating the energy use of advanced energy systems and of entire houses

Measuring moral distress and moral injury:A systematic review and content analysis of existing scales

BACKGROUND: Moral distress (MD) and moral injury (MI) are related constructs describing the negative consequences of morally challenging stressors. Despite growing support for the clinical relevance of these constructs, ongoing challenges regarding measurement quality risk limiting research and clinical advances. This study summarizes the nature, quality, and utility of existing MD and MI scales, and provides recommendations for future use.METHOD: We identified psychometric studies describing the development or validation of MD or MI scales and extracted information on methodological and psychometric qualities. Content analyses identified specific outcomes measured by each scale.RESULTS: We reviewed 77 studies representing 42 unique scales. The quality of psychometric approaches varied greatly across studies, and most failed to examine convergent and divergent validity. Content analyses indicated most scales measure exposures to potential moral stressors and outcomes together, with relatively few measuring only exposures (n = 3) or outcomes (n = 7). Scales using the term MD typically assess general distress. Scales using the term MI typically assess several specific outcomes.CONCLUSIONS: Results show how the terms MD and MI are applied in research. Several scales were identified as appropriate for research and clinical use. Recommendations for the application, development, and validation of MD and MI scales are provided.</p

Measuring moral distress and moral injury:A systematic review and content analysis of existing scales

BACKGROUND: Moral distress (MD) and moral injury (MI) are related constructs describing the negative consequences of morally challenging stressors. Despite growing support for the clinical relevance of these constructs, ongoing challenges regarding measurement quality risk limiting research and clinical advances. This study summarizes the nature, quality, and utility of existing MD and MI scales, and provides recommendations for future use.METHOD: We identified psychometric studies describing the development or validation of MD or MI scales and extracted information on methodological and psychometric qualities. Content analyses identified specific outcomes measured by each scale.RESULTS: We reviewed 77 studies representing 42 unique scales. The quality of psychometric approaches varied greatly across studies, and most failed to examine convergent and divergent validity. Content analyses indicated most scales measure exposures to potential moral stressors and outcomes together, with relatively few measuring only exposures (n = 3) or outcomes (n = 7). Scales using the term MD typically assess general distress. Scales using the term MI typically assess several specific outcomes.CONCLUSIONS: Results show how the terms MD and MI are applied in research. Several scales were identified as appropriate for research and clinical use. Recommendations for the application, development, and validation of MD and MI scales are provided.</p

Supporting Evolution and Maintenance of android Apps

Mobile developers and testers face a number of emerging challenges. These include rapid platform evolution and API instability; issues in bug reporting and reproduction involving complex multitouch gestures; platform fragmentation; the impact of reviews and ratings on the success of their apps; management of crowd-sourced requirements; continuous pressure from the market for frequent releases; lack of effective and usable testing tools; and limited computational resources for handheld devices. Traditional and contemporary methods in software evolution and maintenance were not designed for these types of challenges; therefore, a set of studies and a new toolbox of techniques for mobile development are required to analyze current challenges and propose new solutions. This dissertation presents a set of empirical studies, as well as solutions for some of the key challenges when evolving and maintaining android apps. In particular, we analyzed key challenges experienced by practitioners and open issues in the mobile development community such as (i) android API instability, (ii) performance optimizations, (iii) automatic GUI testing, and (iv) energy consumption. When carrying out the studies, we relied on qualitative and quantitative analyses to understand the phenomena on a large scale by considering evidence extracted from software repositories and the opinions of open-source mobile developers. From the empirical studies, we identified that dynamic analysis is a relevant method for several evolution and maintenance tasks, in particular, because of the need of practitioners to execute/validate the apps on a diverse set of platforms (i.e., device and OS) and under pressure for continuous delivery. Therefore, we designed and implemented an extensible infrastructure that enables large-scale automatic execution of android apps to support different evolution and maintenance tasks (e.g., testing and energy optimization). In addition to the infrastructure we present a taxonomy of issues, single solutions to the issues, and guidelines to enable large execution of android apps. Finally, we devised novel approaches aimed at supporting testing and energy optimization of mobile apps (two key challenges in evolution and maintenance of android apps). First, we propose a novel hybrid approach for automatic GUI-based testing of apps that is able to generate (un)natural test sequences by mining real applications usages and learning statistical models that represent the GUI interactions. In addition, we propose a multi-objective approach for optimizing the energy consumption of GUIs in android apps that is able to generate visually appealing color compositions, while reducing the energy consumption and keeping a design concept close to the original

User Interface Matters : Analysing the Complexity of Mobile Applications from a Visual Perspective

Product-centric techniques to analyze mobile applications leverage traditional source code analysis, size, market success, complexity, and others. Many of these techniques converge in the strategy of evaluating metrics taken from the source code that delivers the functionality of the software product. However, when following the Model-View-Controller (MVC) architecture, mobile applications are typically constructed by a compound of at least two programming languages, one to deliver the functionality and the other to describe the visual aspects. The latter is commonly left out from source code analysis, even though critical parts of the application are present in the graphic User Interface (UI). In this paper, we identify an opportunity to strengthen the product-centric mobile app analysis by incorporating UI metrics. This approach aims to enhance the expressiveness of source code metrics and deliver a more comprehensive analysis of the complexity, maintainability, and effort estimation of a mobile app. To introduce the concept, we present a case study realized using a block-based programming language to create mobile apps, in which we describe and calculate functional and UI metrics, discover commonalities and differences, discuss traits, and open tracks for further research.Peer reviewe