App Behavior Research Articles

The Use of Smartphone Fitness Applications: The Role of Self-Efficacy and Self-Regulation.

With the popularity of the health and wellness trend in recent years, smartphone fitness applications have become more and more popular. Thus, this study explored factors affecting the behavioral intention to use and the actual usage behavior of smartphone fitness apps from technical, health, and social perspectives by integrating the Social Cognitive Theory (SCT) and Unified Theory of Acceptance and Use of Technology (UTAUT). We examined whether perceived usefulness, perceived ease-of-use, social influence, self-efficacy, goal-setting, and self-monitoring predict usage behavior. Based on the survey responses of 1066 smartphone fitness apps users, we revealed that all of the variables, except for self-monitoring, significantly influence usage behavior, while behavioral intention acts as a total mediator between perceived usefulness, perceived ease-of-use and usage behavior. Drawing on the research findings, we suggest that influencing behavioral intention to use a fitness app can be an effective method to increase its adoption. Therefore, app developers need to pay attention to interventions that seek to enhance the usefulness of the app, provide professional counseling, as well as an opportunity for effortless goal setting features.

The Influencing Factors of Note-Taking APP Users Accept Behavioral—Empirical Analysis Based on UTAUT Model

With the continuous popularization of smart phones, mobile applications have also grown rapidly. Note-taking APP as a means of information recording has also emerged. This study attempts to explore the user's acceptance behavior of note-taking APP. Based on the development of user acceptance theory and understanding, UTAUT, the integrated model of technology adoption and utilization, was selected as the research model, and related assumptions were put forward. By means of publishing questionnaire and using SPSS and AMOS software for data analysis and testing. The following conclusions were obtained from the study: 1. The factors affecting the user's use of note-taking APP are ranked according to the size of the influence: willingness to act BI (0.745)> performance expectation PE (0.613)> facilitator FC (0.551)> social influence SI (0.206)> Efforts to expect EE (0.064). 2. Performance expectations and social influences have a significant positive impact on behavioral intentions, and the behavioral intention of note-taking APP positively affects usage behaviors. 3. Efforts to expect no significant statistical significance in the effects of the willingness to use note-taking APP. 4. The acceptance factor in note-taking APP has a significant positive effect on the promotion factor and on the use behavior. 5. There are significant differences in performance expectations and effort expectations among users of different professional types in the acceptance of note-taking APP, and there are significant differences in the expectations of users in different grades.

MashReDroid: enabling end-user creation of Android mashups based on record and replay

To allow end users to combine different apps for accomplishing various goals, it is desired that they can create mashups of mobile apps in an on-demand fashion. The end user creation of mobile mashups, however, is complicated by the fact that many apps do not expose interfaces for mashup and the lack of user friendly interfaces for end user programming. In this paper, we propose MashReDroid, an end user programming approach for the creation of Android mashups that incorporates the behaviors of backend apps into the execution of a host app. MashReDroid automatically transforms Android apps into mashup enabled apps. It then allows end users to create mashups by recording the interactions between host apps and backend apps and run mashups by replaying the interactions. Our evaluation shows that MashReDroid supports a variety of real scenarios and users can easily create and use mashups with a very low overhead.

AppAngio: Revealing Contextual Information of Android App Behaviors by API-Level Audit Logs

Android users are now suffering severe threats from unwanted behaviors of various apps. The analysis of apps' audit logs is one of the essential methods for the security analysts of various companies to unveil the underlying maliciousness within apps. We propose and implement AppAngio, a novel system that reveals contextual information in Android app behaviors by API-level audit logs. Our goal is to help security analysts understand how the target apps worked and facilitate the identification of the maliciousness within apps. The key module of AppAngio is identifying the path matched with the logs on the app's control-flow graphs (CFGs). The challenge, however, is that the limited-quantity logs may incur high computational complexity in the log matching, where there are a large number of candidates caused by the coupling relation of successive logs. To address the challenge, we propose a divide and conquer strategy that precisely positions the nodes matched with log records on the corresponding CFGs and connects the nodes with as few backtracks as possible. Our experiments show that AppAngio reveals contextual information of behaviors in real-world apps. Moreover, the revealed results assist the analysts in identifying the maliciousness of app behaviors and complement existing analysis schemes. Meanwhile, AppAngio incurs negligible performance overhead on the real device in the experiments.

Millennials’ Adoption of Radio Station Apps: The Roles of Functionality, Technology, Media, and Brand Factors

To adapt to Millennials’ mobile-centric lifestyle, the U.S. radio broadcasters are integrating mobile apps into their services. Through a national survey, this study explores Millennial radio users’ functionality preferences regarding station-specific apps from radio stations, and factors affecting their adoption intentions and actual adoption behaviors of such apps. The potential contribution of these apps to brand loyalty is also examined. Three types of factors are investigated: technology, brand, and media factors. The results suggest that all of them affect adoption and loyalty building. As such, this study contributes to the literature of technology acceptance, mobile marketing, and media management.

How bright and dark personality traits predict dating app behavior

Facilitating looking for a new partner or a hook-up, dating apps are highly popular. Explaining their continuous use and the reflection of personality on behavior we investigated the question whether classically bright or rather dark personality traits influence their use, combining behavioral and questionnaire data. In the analysis the Big Five and the Dark Triad were included and compared; additionally, the motives love and sex and the current relationship status were examined. Predictions were established on whether dating apps were used at all, and their average daily using time; behavioral data allowed distinguishing between the dependent variables. Results (N = 555) suggest that the Dark Triad is a better predictor compared to the Big Five in the context of dating apps for any usage as well as usage time. Narcissism, and the motives love and sex were the most relevant predictors for any usage, and machiavellianism was the only relevant predictor for usage time. Previous findings on the Big Five and psychopathy could not be replicated. Passive selection of narcissists and tactical aspects of machiavellianism regarding usage are discussed. The outcomes support and extend theory on personality-based behavior.

A Personalized Physical Activity Coaching App for Breast Cancer Survivors: Design Process and Early Prototype Testing.

BackgroundExisting evidence supports the many benefits of physical activity (PA) in breast cancer survival. However, few breast cancer survivors adhere to the recommended levels of activity. A PA coaching app that provides personalized feedback, guidance, and motivation to the user might have the potential to engage these individuals in a more active lifestyle, in line with the general recommendations. To develop a successful tool, it is important to involve the end users in the design process and to make theoretically grounded design decisions.ObjectiveThis study aimed to execute the design process and early prototype evaluation of a personalized PA coaching app for posttreatment breast cancer survivors. In particular, the study explored a design combining behavioral theory and tailored coaching strategies.MethodsThe design process was led by a multidisciplinary team, including technical and health professionals, and involved input from a total of 22 survivors. The process comprised 3 stages. In stage 1, the literature was reviewed and 14 patients were interviewed to understand the needs and considerations of the target population toward PA apps. In stage 2, the global use case for the tool was defined, the features were ideated and refined based on theory, and a digital interactive prototype was created. In stage 3, the prototype went through usability testing with 8 patients and was subjected to quality and behavior change potential evaluations by 2 human-computer interaction experts.ResultsThe design process has led to the conceptualization of a personalized coaching app for walking activities that addresses the needs of breast cancer survivors. The main features of the tool include a training plan and schedule, adaptive goal setting, real-time feedback and motivation during walking sessions, activity status through the day, activity history, weekly summary reports, and activity challenges. The system was designed to measure users’ cadence during walking, use this measure to infer their training zone, and provide real-time coaching to control the intensity of the walking sessions. The outcomes from user testing and expert evaluation of the digital prototype were very positive, with scores from the system usability scale, mobile app rating scale, and app behavior change scale of 95 out of 100, 4.6 out of 5, and 15 out of 21, respectively.ConclusionsImplementing a user-centered design approach for the development and early evaluation of an app brings essential considerations to tailor the solution to the user’s needs and context. In addition, informing the design on behavioral and tailored coaching theories supports the conceptualization of the PA coaching system. This is critical for optimizing the usability, acceptability, and long-term effectiveness of the tool. After successful early in-laboratory testing, the app will be developed and evaluated in a pilot study in a real-world setting.

VIDE - Vault App Identification and Extraction System for iOS Devices

Content hiding (or vault) apps are a class of applications that allow users to hide photos, videos, documents and other content securely. A subclass of these applications called decoy apps further supports secret hiding by having a mode which mimics standard apps such as calculators but can turn into a vault app through entering a specific input. In this work we focus on iOS devices and first describe how to identify content hiding applications from the App Store. We consider not only the US Store but also give results for App Stores in Russia, India and China. We show an effective and very fast identification of content hiding apps through a two-phase process: initial categorization using keywords followed by more precise binary classification. We next turn to understanding the behavior and features of these vault apps and how to extract the hidden information from artifacts of the app's stored data. Based on this work, we have designed and built a fully automated vault app identification and extraction system that first identifies and then extracts the hidden data from the apps on an iOS smartphone. Using our vault identification and data extraction system (VIDE), law enforcement investigators can more easily identify and extract data from such apps as needed. Although vault apps are removed regularly from the App Store, VIDE can still identify removed apps as our system continues to maintain information on such apps in our vault database.

Analysis Of Physical Activity-Related Smartphone Apps For Behavior Change Potential

The majority of Americans do not meet physical activity (PA) guidelines. Adoption and adherence may be improved with personalized intervention. Smartphone app use has increased exponentially in the past decade, extending to many areas of life and crossing socioeconomic status, ethnicity, and gender. Free smartphone apps that address habitual PA may be a useful tool, but their potential to support PA behavior change is not well understood. PURPOSE: To evaluate top-ranked free health and fitness apps for PA behavior change potential and to determine the relationship between this evaluation and consumer perceptions of quality, such as app store ranking and consumer ratings. METHODS: The top 100 "free" health and fitness apps in the US iOS app store were screened for rinclusion. Apps were excluded if the descriptions did not address any aspects of PA or fitness; required an external device, purchase/subscription after a trial period, or a health club membership or specific health insurance. Apps were also excluded if their descriptions was not written in English. Two investigators downloaded and evaluated the remaining apps that met the inclusion criteria using the App Behavior Change Scale (ABACUS). Apps were scored on 21 strategies within 4 categories (knowledge/information, goals/planning, feedback/monitoring, and actions), and prevalence in the sample was calculated. Pearson correlations were estimated for the relationship between ABACUS total scores and relative app ranking, as well as consumer rating. Significance was set at p < 0.05. RESULTS: 23 apps met the inclusion criteria. 100% offered some form of personalization; 96% incorporated self-monitoring and/or rehearsal; and 70% included options for goal-setting. Only 13% recommended restructuring of the environment, and 4% provided advice on distraction or avoidance. There were no significant correlations between ABACUS score and store ranking (r2= -0.15, p = 0.49) or consumer rating (r2= -0.26, p = 0.91) . CONCLUSIONS: Free apps may have the potential to support PA behavior change, especially in the areas of planning and self-monitoring. There is no correlation between the total number of evidence-based behavior change strategies and app popularity, however, so it may be beneficial for wellness professionals to counsel patients and clients on app choice.

Automated Spatiotemporal Classification Based on Smartphone App Logs

In this paper, a framework for user app behavior analysis using an automated supervised learning method in smartphone environments is proposed. This framework exploits the collective location data of users and their smartphone app logs. Based on these two datasets, the framework determines the apps with a high probability of usage in a geographic area. The framework extracts the app-usage behavior data of a mobile user from an Android phone and transmits them to a server. The server learns the representative trajectory patterns of the user by combining the collected app usage patterns and trajectory data. The proposed method performs supervised learning with automated labeled trajectory data using the user app data. Furthermore, it uses the behavioral characteristics data of users linked to the app usage data by area without a labeling cost.

Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach

Due to the low adoption rate of mHealth apps, the apps designers need to understand the factors behind adoption. But understanding the determinants of mHealth apps adoption remains unclear. Comparatively less attention has been given to the factors affecting the adoption of mHealth apps among the young generation. This study aims to examine the factors influencing behavioral intention and actual usage behavior of mHealth apps among technology prone young generation. The research model has extracted variables from the widely accepted Unified Theory of Acceptance and Use of Technology (UTAUT2) alongside privacy, lifestyles, self-efficacy and trust. Required data were collected from mHealth apps users in Bangladesh. Firstly, this study confirmed that performance expectancy, social influence, hedonic motivation and privacy exerted a positive influence on behavioral intention whereas facilitating conditions, self-efficacy, trust and lifestyle had an influence on both behavioral intention and actual usage behavior. Secondly, the Neural Network Model was employed to rank relatively significant predictors obtained from structural equation modeling (SEM). This study contributes to the growing literature on the use of mHealth apps in trying to elevate the quality of patients' lives. The new methodology and findings from this study will significantly contribute to the extant literature of technology adoption and mHealth apps adoption intention especially. Therefore, for practitioners concerned with fostering mHealth apps adoption, the findings stress the importance of adopting an integrated approach centered on key findings of this study.

Notfall-Apps: vergeudete Zeit oder sinnvolles Instrument in der präklinischen Versorgung von Traumapatienten?

HintergrundUm in Notfallsituationen eine effektive Versorgung des Patienten zu gewährleisten, sind Informationen über Vorerkrankungen und bisherige Medikamenteneinnahme essenziell. Daher haben Smartphonehersteller entsprechende Anwendungssoftware (App) entwickelt, auf die im Notfall zugegriffen werden kann, um jene Informationen zu erhalten. Ziel der Studie war es herauszufinden, ob Notfall-Apps von Smartphoneinhabern aktiv genutzt werden und ob diese in Notfallsituationen von den behandelnden Notärzten eingesehen werden.MethodeZur Datenerhebung wurde eine anonymisierte Umfrage über das Nutzungsverhalten der Notfall-Apps bei verunfallten Patienten der unfallchirurgischen Ambulanz eines universitären Maximalversorgers über einen Zeitraum von 3 Monaten durchgeführt. Parallel fand eine Befragung von Notärzten verschiedener Standorte zu ihren beruflichen Erfahrungen mit den Apps statt.ErgebnisseInsgesamt wurden 192 Patienten und 103 Notärzte befragt. Die Notfall-Apps waren 45 % (n = 79) der Befragten nicht bekannt; nur bei 10 % (n = 19) der Befragten war die App mit Daten hinterlegt. Weiterhin zeigte sich, dass insgesamt 21 % (n = 41) der Personen einen Notizzettel mit Vorerkrankungen und Medikamenten bei sich trugen. Von den Befragten Ärzten gaben 42 % (n = 44) an, schon einmal von der App gehört zu haben; nur 6 % (n = 5) durchsuchten jedoch routinemäßig bei nichtansprechbaren Patienten das Smartphone. Erfolgreich genutzt wurde die App bisher nur von 14 % der Ärzte (n = 14).SchlussfolgerungAufgrund der geringen Bekanntheit erscheint es in zeitkritischen Situationen nicht empfehlenswert, das Smartphone der Patienten nach Notfall-Apps zu durchsuchen. Bei Patienten über 55 ist es zurzeit erfolgsversprechender, die Brieftasche nach Informationen zu Vorerkrankungen zu kontrollieren.

Mapping online App hate: Determinants and consequences

According to Lee’s emergent brand avoidance theoretical framework and self-congruity theory, this research conceptualizes and examines the application (App) hate framework. Data are collected from Chinese smartphone users who provided the possible reasons for hating a specific App. This study first validates Lee’s brand avoidance framework in relation to App hate. The empirical investigation certifies that four key determinants, namely, symbolic, relationship quality, moral, and deficit-value avoidance, are responsible for App hate among online users. Moreover, findings show that App hate is positively and significantly related to negative word of mouth and switching App behavior. This study also discusses the theoretical and managerial implications of Apps.

A study of run-time behavioral evolution of benign versus malicious apps in android

Abstract Context The constant evolution of the Android platform and its applications have imposed significant challenges both to understanding and securing the Android ecosystem. Yet, despite the growing body of relevant research, it remains unclear how Android apps evolve in terms of their run-time behaviors in ways that impede our gaining consistent empirical knowledge about the workings of the ecosystem and developing effective technical solutions to defending it against security threats. Intuitively, an essential step towards addressing these challenges is to first understand the evolution itself. Among others, one avenue to examining a program’s run-time behavior is to dissect the program’s execution in terms of its syntactic and semantic structure. Objective In this paper, we study how benign Android apps execute differently from malware over time, in terms of their execution structures measured by the distribution and interaction among functionality scopes, app components, and callbacks. In doing so, we attempt to reveal how relevant app execution structure is to app security orientation (i.e., benign or malicious). Method By tracing the method calls and inter-component communications (ICCs) of 15,451 benign apps and 15,183 malware developed during eight years (2010–2017), we systematically characterized the execution structure of malware versus benign apps and revealed similarities and disparities between them that are not previously known. Results Our results show, among other findings, that (1) despite their similarity in execution distribution over functionality scopes, malware accessed framework functionalities mainly through third-party libraries, while benign apps were dominated by calls within the framework; (2) use of Activity component had been rising in malware while benign apps saw continuous drop in such uses; (3) malware invoked significantly more Services but less Content Providers than benign apps during the evolution of both groups; (4) malware carried ICC data significantly less often via standard data fields than benign apps, albeit both groups did not carry any data in most ICCs; and (5) newer malware tended to have more even distribution of callbacks among event-handler categories, while the distribution remained constant in benign apps over time. Conclusion We discussed how these findings inform understanding app behaviors, optimizing static and dynamic code analysis of Android apps, and developing sustainable app security defense solutions.

DINA: Detecting Hidden Android Inter-App Communication in Dynamic Loaded Code

Android inter-app communication (IAC) allows apps to request functionalities from other apps, which has been extensively used to provide a better user experience. However, IAC has also become an enticing target by attackers to launch malicious activities. Dynamic class loading (DCL) and reflection are effective features to enhance the functionality of the apps. In this paper, we expose a new attack that leverages these features in conjunction with inter-app communication to conceal malicious attacks with the ability to bypass existing security mechanisms. To counteract such attack, we present DINA, a novel hybrid analysis approach for identifying malicious IAC behaviors concealed within dynamically loaded code through reflective/DCL calls. DINA appends reflection and DCL invocations to control-flow graphs and continuously performs incremental dynamic analysis to detect the misuse of reflection and DCL that obfuscates malicious Intent communications. DINA utilizes string analysis and inter-procedural analysis to resolve hidden IAC and achieves superior detection performance. Our extensive evaluation on 49,000 real-world apps corroborates the prevalent usage of reflection and DCL, and reveals previously unknown and potentially harmful, hidden IAC behaviors in real-world apps.

Privacy issues of android application permissions: A literature review

AbstractAndroid is an application platform for mobile devices. It comprises of the operating system, software framework, and core programs. This platform uses permissions to hide precious information about the user from untrusted apps. However, to install an application, device feature uses permissions that are granted by the user. User has the ability to analyze permissions and abort the setup if the permissions are unfriendly or unrestrained. Android permission analysis schemes show a significant role to fight against these undesirable behaviors of untrusted android apps in the aspect of security and privacy. This survey attempts to deal with the android application permissions that are related security and privacy challenges. It includes various research articles published in computers and security, digital investigation, decision support systems, systems and software security, and information forensics journals in the last 10 years. The survey is based on the following considerations: research issues motivated by the scheme, the methodology used, ability of result analysis conducted, and android features considered for performance evaluation.

Research on Patient Acceptance Behavior of Online Medical APP Based on UTAUT Improved Model

In recent years, with the continuous development of mobile communication technology and the Internet, Chinese mobile medical industry has entered an explosive development stage. The gap between medical supply and demand has brought a bright future to the mobile medical industry. From the perspective of "patient" We use UTAUT to improve the model and study the behavior of patients' mobile medical APP acceptance. Through the combination of literature research and questionnaire survey, collect relevant data and conduct empirical tests. Finally, by analyzing the main factors affecting the behavior of users using mobile medical APP and the impact, it puts forward effective suggestions and Countermeasures for the development of mobile medical APP.

Are free Android app security analysis tools effective in detecting known vulnerabilities?

Increasing interest in securing the Android ecosystem has spawned numerous efforts to assist app developers in building secure apps. These efforts have resulted in tools and techniques capable of detecting vulnerabilities (and malicious behaviors) in apps. However, there has been no evaluation of the effectiveness of these tools and techniques in detecting known vulnerabilities. The absence of such evaluations puts app developers at a disadvantage when choosing security analysis tools to secure their apps. In this regard, we evaluated the effectiveness of vulnerability detection tools for Android apps. We reviewed 64 tools and empirically evaluated 14 vulnerability detection tools (incidentally along with five malicious behavior detection tools) against 42 known unique vulnerabilities captured by Ghera benchmarks, which are composed of both vulnerable and secure apps. Of the 24 observations from the evaluation, the main observation is existing vulnerability detection tools for Android apps are very limited in their ability to detect known vulnerabilities -- all of the evaluated tools together could only detect 30 of the 42 known unique vulnerabilities. More effort is required if security analysis tools are to help developers build secure apps. We hope the observations from this evaluation will help app developers choose appropriate security analysis tools and persuade tool developers and researchers to identify and address limitations in their tools and techniques. We also hope this evaluation will catalyze or spark a conversation in the software engineering and security communities to require a more rigorous and explicit evaluation of security analysis tools and techniques.

Using Health and Well-Being Apps for Behavior Change: A Systematic Search and Rating of Apps.

BackgroundSmartphones have allowed for the development and use of apps. There is now a proliferation of mobile health interventions for physical activity, healthy eating, smoking and alcohol cessation or reduction, and improved mental well-being. However, the strength or potential of these apps to lead to behavior change remains uncertain.ObjectiveThe aim of this study was to review a large sample of healthy lifestyle apps at a single point in time (June to July 2018) to determine their potential for promoting health-related behavior change with a view to sharing this information with the public. In addition, the study sought to test a wide range of apps using a new scale, the App Behavior Change Scale (ABACUS).MethodsApps focusing on 5 major modifiable lifestyle behaviors were identified using a priori key search terms across the Australian Apple iTunes and Google Play stores. Lifestyle behavior categories were selected for their impact on health and included smoking, alcohol use, physical activity, nutrition, and mental well-being. Apps were included if they had an average user rating between 3 and 5, if they were updated in the last 18 months, if the description of the app included 2 of 4 behavior change features, and if they were in English. The selected behavior change apps were rated in 2 ways using previously developed rating scales: the Mobile App Rating Scale (MARS) for functionality and the ABACUS for potential to encourage behavior change.ResultsThe initial search identified 212,352 apps. After applying the filtering criteria, 5018 apps remained. Of these, 344 were classified as behavior change apps and were reviewed and rated. Apps were given an average MARS score of 2.93 out of 5 (SD 0.58, range 1.42-4.16), indicating low-to-moderate functionality. Scores for the ABACUS ranged from 1 to 17, out of 21, with an average score of 7.8 (SD 2.8), indicating a low-to-moderate number of behavior change techniques included in apps. The ability of an app to encourage practice or rehearsal, in addition to daily activities, was the most commonly identified feature across all apps (310/344, 90.1%), whereas the second most common feature was the ability of the user to easily self-monitor behavior (289/344, 84.0%).ConclusionsThe wide variety of apps included in this 2018 study and the limited number of behavior change techniques found in many apps suggest an opportunity for improvement in app design that will promote sustained and significant lifestyle behavior change and, therefore, better health. The use of the 2 scales for the review and rating of the apps was successful and provided a method that could be replicated and tested in other behavior change areas.

Controlling Interactions with Libraries in Android Apps Through Runtime Enforcement

Android applications are executed on smartphones equipped with a variety of resources that must be properly accessed and controlled, otherwise the correctness of the executions and the stability of the entire environment might be negatively affected. For example, apps must properly acquire, use, and release microphones, cameras, and other multimedia devices, otherwise the behavior of the apps that use the same resources might be compromised. Unfortunately, several apps do not use resources correctly, for instance, due to faults and inaccurate design decisions. By interacting with these apps, users may experience unexpected behaviors, which in turn may cause instability and sporadic failures, especially when resources are accessed. In this article, we present an approach that lets users protect their environment from the apps that use resources improperly by enforcing the correct usage protocol. This is achieved by using software enforcers that can observe executions and change them when necessary. For instance, enforcers can detect that a resource has been acquired but not released and automatically perform the release operation, thus giving the possibility to use that same resource to the other apps. The main idea is that software libraries, in particular, the ones controlling access to resources, can be augmented with enforcers that can be activated and deactivated on demand by users to protect their environment from unwanted app behaviors. We call the software libraries augmented with one or more enforcers proactive libraries , because the activation of the enforcer decorates the library with proactive behaviors that can guarantee the correctness of the execution despite the invocation of the operations implemented by the library. For example, enforcers can detect that a resource has not been released on time and proactively release it. Our experimental results with 27 possible misuses of resources in real Android apps reveal that proactive libraries are able to effectively correct library misuses with negligible runtime overheads.