User engagement is crucial to the long-term success of a mobile app. Several metrics, such as dwell time, have been used for measuring user engagement. However, how to effectively predict user ...engagement in the context of mobile apps is still an open research question. For example, do the mobile usage contexts (e.g., time of day) in which users access mobile apps impact their dwell time? Answers to such questions could help mobile operating system and publishers to optimize advertising and service placement. In this article, we first conduct an empirical study for assessing how user characteristics, temporal features, and the short/long-term contexts contribute to gains in predicting users’ app dwell time on the population level. The comprehensive analysis is conducted on large app usage logs collected through a mobile advertising company. The dataset covers more than 12K anonymous users and 1.3 million log events. Based on the analysis, we further investigate a novel mobile app engagement prediction problem—can we predict simultaneously what app the user will use next and how long he/she will stay on that app? We propose several strategies for this joint prediction problem and demonstrate that our model can improve the performance significantly when compared with the state-of-the-art baselines. Our work can help mobile system developers in designing a better and more engagement-aware mobile app user experience.
Software applications (apps) are now prevalent in the digital media environment. They are the site of significant sociocultural and economic transformations across many domains, from health and ...relationships to entertainment and everyday finance. As relatively closed technical systems, apps pose new methodological challenges for sociocultural digital media research. This article describes a method, grounded in a combination of science and technology studies with cultural studies, through which researchers can perform a critical analysis of a given app. The method involves establishing an app’s environment of expected use by identifying and describing its vision, operating model and modes of governance. It then deploys a walkthrough technique to systematically and forensically step through the various stages of app registration and entry, everyday use and discontinuation of use. The walkthrough method establishes a foundational corpus of data upon which can be built a more detailed analysis of an app’s intended purpose, embedded cultural meanings and implied ideal users and uses. The walkthrough also serves as a foundation for further user-centred research that can identify how users resist these arrangements and appropriate app technology for their own purposes.
The purpose of this study was to determine the meaning of application-based learning media during the pandemic and to be able to provide new innovations to the community in creating the RUBIK ...application as a learning tool, especially Indonesian language subjects. The method in this study uses a quantitative method in the form of a questionnaire conducted by researchers in order to know the respondents' assessment of the RUBIK application in learning. The results of this study indicate that the RUBIK application has meaning in learning and this application is considered by the respondents to be quite attractive, efficient and effective when applied in the learning process. The only drawback in this research is that the learning is one-way. In addition, this application also has obstacles such as the type of smartphone that does not support installing the RUBIK application. And the full memory on the respondent's cellphone hinders the learning process using this RUBIK application.
TikTok is the international twin of China’s mobile short video app, Douyin, and one of the fastest growing short video platforms in the world. Owned by Chinese tech giant, ByteDance, TikTok and ...Douyin share many similarities in terms of appearance, functionality, and platform affordances; however, they exist in radically different markets and are governed by radically different forces. Unlike other popular mobile media platforms in China and internationally, TikTok and Douyin are neither part of the big three tech giants in China nor the big five in the US. This provides an interesting case study to investigate how an emerging internet company adapts its products to better fit divergent expectations, cultures, and policy frameworks in China and abroad. Using the app walkthrough method informed by platformization of culture production theory, this study highlights the similarities and distinctions between these two platforms. We argue the co-evolution of Douyin and TikTok is a new paradigm of global platform expansion that differs from strategies of regionalization adopted by previous major social media platforms. We contribute to platformization theory by developing the concept of parallel platformization to explain ByteDance’s strategies for surviving in two opposing platform ecosystems in China and abroad.
•Mobile technology identity affects patients’ intention to adopt mHealth apps.•Self-efficacy affects patients’ intention to adopt mHealth apps.•Prior experience affects patients’ intention to adopt ...mHealth apps.•Education negatively affects patients’ intention to adopt mHealth apps.•Prior experience affects Mobile technology identity and self-efficacy.
Despite smartphone applications (apps) being key enablers of telemedicine, telehealth, and self-monitoring, adoption issues persist for mobile healthcare (mHealth) apps. This study diverged from the traditional adoption approach and drew on more innovative theories to predict the intentions of patients for adopting apps supported by clinics. More specifically, technology identity literature was explored to make this prediction and the study surveyed 292 patients who were seated in the waiting room of a local clinic. The results suggested that perceived mobile technology identity (MTI), perceived related IT experience, and perceived self-efficacy positively influences patients’ perceived intentions to adopt mHealth apps provided by clinics or hospitals. Furthermore, the results suggested that perceived related IT experience positively influences users perceived self-efficacy and perceived MTI. However, education was found to negatively influence patients’ perceived intentions to use mHealth apps. This study contributes to the growing literature on the use of these apps in trying to elevate the quality of patients’ lives. Moreover, there are implications for mHealth-app designers who are trying to make healthcare services accessible via smartphones.
•A comprehensive list of twenty process models for developing mobile apps.•Usefulness to industry of the models based on rigor/relevance framework assessment.•A synthesis of the models based on their ...Software Engineering areas and focus.
Context: An effective development model can help improve competitive advantage and shorten release cycles, which is vital in the fast paced environment of mobile app development.
Objective: The aim with this paper is to provide an extensive review of existing mobile app development models.
Method: The review is done by following a systematic literature review process. Also presented is an assessment of the usefulness and relevance to industry of the models based on a rigor and relevance framework.
Results: 20 primary studies were identified, each with distinct models. Agile methods or state-based principles are commonly adopted across the models. Relatively little effort focuses on deployment, maintenance, project evaluation activities.
Conclusion: The review reveals that the contexts in which the identified models are intended to be used vary. This benefits practitioners as they are able to select a model that suits their contexts. However, the usefulness in industry of most of the models, based on the contexts in which the models were evaluated, is questionable. There is a need for evaluating mobile app models in contexts that resemble realistic contexts. The review also calls for further research addressing special constraints of mobile apps, e.g., testing apps on multiple-platforms, user involvement in release planning and continuous deployment.
There are an increasing number of mobile apps available for adolescents with mental health problems and an increasing interest in assimilating mobile health (mHealth) into mental health services. ...Despite the growing number of apps available, the evidence base for their efficacy is unclear.
This review aimed to systematically appraise the available research evidence on the efficacy and acceptability of mobile apps for mental health in children and adolescents younger than 18 years.
The following were systematically searched for relevant publications between January 2008 and July 2016: APA PsychNet, ACM Digital Library, Cochrane Library, Community Care Inform-Children, EMBASE, Google Scholar, PubMed, Scopus, Social Policy and Practice, Web of Science, Journal of Medical Internet Research, Cyberpsychology, Behavior and Social Networking, and OpenGrey. Abstracts were included if they described mental health apps (targeting depression, bipolar disorder, anxiety disorders, self-harm, suicide prevention, conduct disorder, eating disorders and body image issues, schizophrenia, psychosis, and insomnia) for mobile devices and for use by adolescents younger than 18 years.
A total of 24 publications met the inclusion criteria. These described 15 apps, two of which were available to download. Two small randomized trials and one case study failed to demonstrate a significant effect of three apps on intended mental health outcomes. Articles that analyzed the content of six apps for children and adolescents that were available to download established that none had undergone any research evaluation. Feasibility outcomes suggest acceptability of apps was good and app usage was moderate.
Overall, there is currently insufficient research evidence to support the effectiveness of apps for children, preadolescents, and adolescents with mental health problems. Given the number and pace at which mHealth apps are being released on app stores, methodologically robust research studies evaluating their safety, efficacy, and effectiveness is promptly needed.
Discover how to build, scale, and debug native iOS and Android applications from a single codebase using the Dart programming languageKey FeaturesQuickly build and iterate on your user interface (UI) ...with hot reloadFix bugs and prevent them from reappearing using Flutter's developer tools and test suitesDiscover practical recipes for building mobile applications with FlutterBook DescriptionFlutter is an open-source mobile framework for building native mobile apps using the Dart language. This framework has consolidated the best ideas emerging from native and web development over ten years. This recipe-based guide focuses on sound app design and core principles, and shows you how to solve cross-platform mobile development issues in a practical way. Unlike other Flutter books, the Flutter cookbook features a chapter on error handling and debugging to ensure that your apps run more efficiently.You'll begin by effectively setting up Flutter in your system and customizing your development environment. Moving on, the book will explain the various tasks involved in app development such as UX/UI design, API design, and creating animations. You will then explore Dart programming for adding functionalities to your Android and iOS applications for truly native performance. Routing is an important concept for managing your application codebase, and you'll learn about the navigation and routing mechanism and material page routing for running your application. Throughout the book, you'll find recipes to help solve challenges in building a cross-platform application along with various aspects of running a single codebase on both Android and iOS mobile platforms.By the end of this Flutter book, you'll have learned everything you need to be able to write and deliver fully functional mobile apps.What you will learnBuild native iOS and Android apps from a single codebaseDiscover how to develop and think like a Dart programmerFind simple solutions to complex problems by applying the principle of composition over inheritanceCreate reusable architecture that can be applied to any type of appQuickly debug and solve problems before your users can see themEnhance the performance of your application for a truly native experience on Android and iOSEasily manage the app state with Streams and the BLoC patternUnderstand Dart programming to customize your Flutter applicationsWho This Book Is ForThis book is for beginner-to-intermediate level developers who want to become mobile developers. Basic knowledge of modern programming languages such as JavaScript, Swift, Kotlin, Java, Objective-C, or C# will help you to understand the concepts covered in the book more easily.
Table of Contents
Getting Started with Flutter: Setting up your environment and choosing an IDEDart: A programming language you probably already knowIntroduction to WidgetsMastering Layout and Taming the Widget TreeAdd Interactivity and Navigation to Your AppBasic State ManagementThe Future is Now: Introduction to Asynchronous ProgrammingData Persistence and Communicating with the InternetAdvanced State Management with StreamsFlutter Packages: Adding Maps and your own Native CodeAdding Animations to Your App;/li>Using FirebaseMachine Learning: Firebase MLKit and TensorflowLite;/li>Distributing your Mobile AppBeyond Mobile - Flutter Web and Desktop;/li>
Globally, mobile phones have achieved wide reach at an unprecedented rate, and mobile phone apps have become increasingly prevalent among users. The number of health-related apps that were published ...on the two leading platforms (iOS and Android) reached more than 100,000 in 2014. However, there is a lack of synthesized evidence regarding the effectiveness of mobile phone apps in changing people's health-related behaviors.
The aim was to examine the effectiveness of mobile phone apps in achieving health-related behavior change in a broader range of interventions and the quality of the reported studies.
We conducted a comprehensive bibliographic search of articles on health behavior change using mobile phone apps in peer-reviewed journals published between January 1, 2010 and June 1, 2015. Databases searched included Medline, PreMedline, PsycINFO, Embase, Health Technology Assessment, Education Resource Information Center (ERIC), and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Articles published in the Journal of Medical Internet Research during that same period were hand-searched on the journal's website. Behavior change mechanisms were coded and analyzed. The quality of each included study was assessed by the Cochrane Risk of Bias Assessment Tool.
A total of 23 articles met the inclusion criteria, arranged under 11 themes according to their target behaviors. All studies were conducted in high-income countries. Of these, 17 studies reported statistically significant effects in the direction of targeted behavior change; 19 studies included in this analysis had a 65% or greater retention rate in the intervention group (range 60%-100%); 6 studies reported using behavior change theories with the theory of planned behavior being the most commonly used (in 3 studies). Self-monitoring was the most common behavior change technique applied (in 12 studies). The studies suggest that some features improve the effectiveness of apps, such as less time consumption, user-friendly design, real-time feedback, individualized elements, detailed information, and health professional involvement. All studies were assessed as having some risk of bias.
Our results provide a snapshot of the current evidence of effectiveness for a range of health-related apps. Large sample, high-quality, adequately powered, randomized controlled trials are required. In light of the bias evident in the included studies, better reporting of health-related app interventions is also required. The widespread adoption of mobile phones highlights a significant opportunity to impact health behaviors globally, particularly in low- and middle-income countries.
The COVID-19 pandemic led to the implementation of worldwide restrictive measures to reduce social contact and viral spread. These measures have been reported to have a negative effect on physical ...activity (PA). Studies of PA during the pandemic have primarily used self-reported data. The single academic study that used tracked data did not report on demographics.
This study aimed to explore patterns of smartphone-tracked activity before, during, and immediately after lockdown in the United Kingdom, and examine differences by sociodemographic characteristics and prior levels of PA.
Tracked longitudinal weekly minutes of PA were captured using the BetterPoints smartphone app between January and June 2020. Data were plotted by week, demographics, and activity levels at baseline. Nonparametric tests of difference were used to assess mean and median weekly minutes of activity at significant points before and during the lockdown, and as the lockdown was eased. Changes over time by demographics (age, gender, Index of Multiple Deprivation, baseline activity levels) were examined using generalized estimating equations (GEEs).
There were 5395 users with a mean age of 41 years (SD 12) and 61% (n=3274) were female. At baseline, 26% (n=1422) of users were inactive, 23% (n=1240) were fairly active, and 51% (n=2733) were active. There was a relatively even spread across deprivation deciles (31% n=1693 in the least deprived deciles and 23% in the most n=1261). We found significant changes in PA from the week before the first case of COVID-19 was announced (baseline) to the week that social distancing restrictions were relaxed (Friedman test: χ
=2331, P<.001). By the first full week of lockdown, the median change in PA was 57 minutes less than baseline. This represents a 37% reduction in weekly minutes of PA. Overall, 63% of people decreased their level of activity between baseline and the first week of COVID-19 restrictions. Younger people showed more PA before lockdown but the least PA after lockdown. In contrast, those aged >65 years appeared to remain more active throughout and increased their activity levels as soon as lockdown was eased. Levels of PA among those classed as active at baseline showed a larger drop compared with those considered to be fairly active or inactive. Socioeconomic group and gender did not appear to be associated with changes in PA.
Our tracked PA data suggests a significant drop in PA during the United Kingdom's COVID-19 lockdown. Significant differences by age group and prior PA levels suggests that the government's response to COVID-19 needs to be sensitive to these individual differences and the government should react accordingly. Specifically, it should consider the impact on younger age groups, encourage everyone to increase their PA, and not assume that people will recover prior levels of PA on their own.
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