Google Play App Research Articles

GUing: A Mobile GUI Search Engine using a Vision-Language Model

Graphical User Interfaces (GUIs) are central to app development projects. App developers may use the GUIs of other apps as a means of requirements refinement and rapid prototyping or as a source of inspiration for designing and improving their own apps. Recent research has thus suggested retrieving relevant GUI designs that match a certain text query from screenshot datasets acquired through crowdsourced or automated exploration of GUIs. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements, neglecting visual information such as icons or background images. In addition, retrieved screenshots are not steered by app developers and lack app features that require particular input data. To overcome these limitations, this paper proposes GUing, a GUI search engine based on a vision-language model called GUIClip, which we trained specifically for the problem of designing app GUIs. For this, we first collected from Google Play app introduction images which display the most representative screenshots and are often captioned (i.e. labelled) by app vendors. Then, we developed an automated pipeline to classify, crop, and extract the captions from these images. This resulted in a large dataset which we share with this paper: including 303k app screenshots, out of which 135k have captions. We used this dataset to train a novel vision-language model, which is, to the best of our knowledge, the first of its kind for GUI retrieval. We evaluated our approach on various datasets from related work and in a manual experiment. The results demonstrate that our model outperforms previous approaches in text-to-GUI retrieval achieving a Recall@10 of up to 0.69 and a HIT@10 of 0.91. We also explored the performance of GUIClip for other GUI tasks including GUI classification and sketch-to-GUI retrieval with encouraging results.

Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

Fraud and Malware Detection of Apps in Google Play

Abstract: Fraudulent Behaviors in Google Play, the Most Popular Android App Market, Fuel Search Rank Abuse and Malware Proliferation. To Identify Malware, Previous Work Has Focused on App Executable and Permission Analysis. In This Paper, We Introduce FairPlay, a Novel System That Discovers and Leverages Traces Left Behind by Fraudsters, to Detect Both Malware and Apps Subjected to Search Rank Fraud. FairPlay Correlates Review Activities and Uniquely Combines Detected Review Relations with Linguistic and Behavioral Signals Gleaned from Google Play App Data (87K Apps, 2.9M Reviews, and 2.4M Reviewers, Collected Over Half a Year), in Order to Identify Suspicious Apps. FairPlay Achieves Over 95% Accuracy in Classifying Gold Standard Datasets of Malware, Fraudulent and Legitimate Apps. We Show That 75% of the Identified Malware Apps Engage in Search Rank Fraud. FairPlay Discovers Hundreds of Fraudulent Apps That Currently Evade Google Bouncer’s Detection Technology. FairPlay Also Helped the Discovery of More Than 1,000 Reviews, Reported for 193 Apps, That Reveal a New Type of “Coercive” Review Campaign: Users Are Harassed into Writing Positive Reviews, and Install and Review Other Apps

DARK DESIGN PATTERNS AND GAMIFICATION AS THE HEART OF DATING APPLICATIONS’ BUSINESS MODELS

Dating applications represent a paradox: on the one hand, they provide a monetized platform for people to form relationships, yet on the other hand, the more people who find partners, the less revenue goes to the company. With this paradox as a backdrop, this extended abstract examines the business model of dating applications by address the following research question: What characterizes the business models of dating applications? To provide a nuanced picture of this, we conducted a 2022-study combining data about dating apps and app users: (A) 22 in-depth interviews with current and previous users (aged 24–49 years) of Norwegian dating applications users; (B) a systematic analysis of 30 serious dating apps in Google Play and Apple’s App Store; and (C) a diary study of four of the 30 dating applications. The findings reveals that dating apps follows a business model that creates revenue through a mix of an in-app purchase-model and subscription fees. The apps typically market themselves as free, yet they use several dark design and gamification features to create and spark emotional curiosity and engagement that in turn brings in revenue to dating app companies. Several of the users stated that they have experienced the apps as manipulative, emotionally exhausting and that they created the same kind of “addiction” or “cravings” they experienced in social media. Clearly, design is not only power, design gives the dating app companies power where designing for emotional engagement is a key value-creating element in their business models.

UiAnalyzer: Evaluating whether the UI of apps is at the risk of violating the design conventions in terms of function layout

There are many unofficial but widely accepted design conventions on the function layout of the user interface (UI) of apps, which include the position of functions and position relationship between functions. If an app fails to follow these design conventions, users may feel uncomfortable when using the app and this can negatively affect its market value. Unfortunately, no research is available to help app developers analyze whether the UI of their products is at the risk of violating the design conventions in terms of the function layout. To fill this gap, we propose a novel function layout analysis method called UiAnalyzer. This method compares the function layout of a UI with that of similar UIs to evaluate the acceptability of its function layout. Specifically, UiAnalyzer first generates semantic wireframes for the analyzed UI and similar UIs according to their function layout. Then, it extracts visual features from the semantic wireframes using a convolutional neural network and applies an anomaly detection algorithm to process the obtained visual features to determine whether the visual feature corresponding to the analyzed UI is anomalous. The experiment based on Google Play apps shows that UiAnalyzer is capable of evaluating the function layout of UIs of apps.

Identification and Evaluation of Mobile Applications for Self-Management of Diet and Lifestyle for Patients with Inflammatory Bowel Disease.

Mobile health applications (apps) providing diet and lifestyle self-management programs to patients with inflammatory bowel disease (IBD) are emerging. The objective of this study was to evaluate current apps available in the US and Canada based on app quality, perceived impact on diet and mental health and comprehensiveness to support self-management. The Apple iOS and Google Play app stores were searched for terms related to IBD. Apps were included if they targeted diet and lifestyle behaviours for patients living with IBD and were available to the general public. Apps were excluded if they were not specific to IBD, not available in English, did not target diet or lifestyle therapy, were not available in the US and Canada, or did not offer stand-alone self-management programs. The Mobile App Rating Scale was used to assess mobile app quality. A total of 1,512 apps were identified through the app stores. Six apps met inclusion criteria. My IBD Care: Crohn's and Colitis received the highest quality rating and LyfeMD received the highest overall app rating. Only these two apps provided behaviour tracking over time, and three (50 percent) apps provided good-quality information. While many IBD-related apps exist, few support self-management of diet and lifestyle behaviours. The My IBD Care and LyfeMD apps had the highest ratings and can be used to track lifestyle behaviours. The effectiveness of these apps to improve behaviours, and subsequently impact the disease course and quality of life, should be explored in future studies.

Market analysis and quality assessment of applications on Android-devices for patients with bronchial asthma and chronic obstructive pulmonary disease in Ukraine

Annotation. A modern and effective tool for managing patients with chronic obstructive pulmonary disease (COPD) and bronchial asthma (BA) are software applications for smartphones. In order to analyze and evaluate the quality of applications for ANDROID devices available in Ukraine, which can be used by patients with BA and COPD, relevant apps were searched in the Google Play app store. We analyzed their scope, including age restrictions, number of downloads, interface language, user rating. The quality of the apps was assessed according to the guidelines of the Health and Care Mobile App Assessment Organization (ORCHA), and the reliability checklist of an untested mobile health app. We found that Ukrainians have access to 47 mobile apps for ANDROID devices to help them adapt to living with the disease. Of these, only five applications are free and in Ukrainian, but only two are software applications in terms of quality and meet the requirements of ORCHA, so may be recommended for use in patients with AD and COPD, that is insufficient to meet the needs of Ukrainian patients.

Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM

The recent outbreaks of the COVID-19 forced people to work from home. All the educational institutes run their academic activities online. The online meeting app the “Zoom Cloud Meeting” provides the most entire supports for this purpose. For providing proper functionalities require in this situation of online supports the developers need the frequent release of new versions of the application. Which makes the chances to have lots of bugs during the release of new versions. To fix those bugs introduce developer needs users’ feedback based on the new release of the application. But most of the time the ratings and reviews are created contraposition between them because of the users’ inadvertent in giving ratings and reviews. And it has been the main problem to fix those bugs using user ratings for software developers. For this reason, we conduct this average rating calculation process based on the sentiment of user reviews to help software developers. We use BERT-based sentiment annotation to create unbiased datasets and hybridize RNN with LSTM to find calculated ratings based on the unbiased reviews dataset. Out of four models trained on four different datasets, we found promising performance in two datasets containing a necessarily large amount of unbiased reviews. The results show that the reviews have more positive sentiments than the actual ratings. Our results found an average of 3.60 stars rating, where the actual average rating found in dataset is 3.08 stars. We use reviews of more than 250 apps from the Google Play app store. The results of our can provide more promising if we can use a large dataset only containing the reviews of the Zoom Cloud Meeting app.

Journal Digest: IRT; Motivational Therapy; Mental Health Apps; and More

Journal Digest: IRT; Motivational Therapy; Mental Health Apps; and More

Execution Recording and Reconstruction for Detecting Information Flows in Android Apps

Security researchers utilize taint analyses to uncover suspicious behaviors in Android apps. Current static taint analyzers cannot handle ICC, reflection, and lifecycles dependably, increasing the result verification cost. On the other hand, current dynamic taint trackers accurately detect execution paths. However, they depend on specific Android versions and modified devices, reducing their usability and applicability. In addition, they require app exercise every time running the taint analysis. This paper presents a new dynamic taint tracker called T-Recs, tracking information flows by recording and reconstructing the app execution. First, before the taint analysis, the app’s runtime data are obtained by instrumenting logging code into the app’s bytecode and running the app to be independent of specific Android versions and devices. Then, T-Recs performs the taint analysis accurately with the logged data and separately from the app exercise. This paper is an extended version of our work published. Previously, T-Recs’ accuracy was mainly evaluated in privacy leak detection. The results show that T-Recs outperforms compared analyzers, which are FlowDroid (w/ and w/o IC3), Amandroid, DroidSafe, and TaintDroid (w/ and w/o IntelliDroid). This paper also involves DroidRA and IccTA. This paper shows that T-Recs detects ICC- and reflection-related leaks missed by FlowDroid in popular Google Play apps. The other static analyzers fail to analyze most of the apps. These experiments also indicate an advantage of T-Recs: its users can re-execute T-Recs’ taint analysis without re-exercising the app. T-Recs’ app-runtime overhead and parallel execution performance were also evaluated, and the results are acceptable.

Country- and app-level factors affecting the adoption and evaluation of COVID-19 mobile apps

BackgroundCountries across the globe have released many COVID-19 mobile apps. However, there is a lack of systematic empirical investigation into the factors affecting the adoption and evaluation of COVID-related apps. This study explores what factors at the country level and the app levels would influence the adoption and evaluation of COVID-19 apps.MethodsWe collected data on 267 COVID-19 apps in App Store and Google Play. The number of installs, ratings, reviews and rating scores were used as indicators of adoption and evaluation. Country-level predictors include the number of infected cases and the political system (i.e., democratic vs. non-democratic). App-level predictors include developer (i.e., government vs. non-government) and functions. Four app functions were coded for analysis: providing health information, contact tracing, home monitoring, and consultation. Negative binomial regression and OLS (Ordinary Least Square) regression were used to analyze the data.ResultsOur analyses show that apps developed by countries with more infected cases (B = 0.079, CI (Confidence Interval) = 0.000, 0.158; P = .049) and by non-governmental institutions (B=-0.369, CI=-0.653, -0.083; P = .01) received more positive rating scores. Apps with home monitoring function received lower rating scores (B=-0.550, CI=-0.971, -0.129; P = .01). Regarding adoption, apps developed by governments were more likely to be installed (IRR (Incident Rate Ratio) = 8.156, CI = 3.389, 19.626; P < .001), to be rated (IRR = 26.036, CI = 7.331, 92.468; P < .001), and to receive user comments (IRR = 12.080, CI = 3.954, 37.568; p < .001). Apps with functions of contact tracing or consultation were more likely to be installed (IRR = 4.533, CI = 2.072, 9.918; p < .001; IRR = 4.885, CI = 1.970, 12.111; p < .001), to be rated (IRR = 11.634, CI = 3.486, 38.827; p < .001; IRR = 17.194, CI = 5.309, 55.680; p < .001), and to receive user comments (IRR = 5.688, CI = 2.052, 5.770; p < .001; IRR = 16.718, CI = 5.363, 52.113; p < .001). Apps with home monitoring functions were less likely to be rated (IRR = 0.206, CI = 0.047, 0.896; P = .04) but more likely to receive user comments (IRR = 3.874, CI = 1.044, 14.349; P = .04). Further analysis shows that apps developed in democratic countries (OR (Odd Ratio) = 3.650, CI = 1.238, 10.758; P = .02) or by governments (OR = 7.987, CI = 4.106, 15.534, P < .001) were more likely to include the function of contact tracing.ConclusionThis study systematically investigates factors affecting the adoption and evaluation of COVID-19 apps. Evidence shows that government-developed apps and the inclusion of contact tracing and consultation app functions strongly predict app adoption.

Breast cancer-related apps in Google Play and App store: evaluate their functionality and quality.

This study aimed to evaluate the functionality and quality of breast cancer apps that can be accessed from Turkey. In this study, a systematic search strategy was used to identify free mobile apps on breast cancer available in the App and Google Play stores. According to the study's PRISMA flowchart, 707 apps met the inclusion criteria. The Mobile App Rating Scale (MARS) assessed the quality of the apps. Mobile apps focused on the categories of communication, education, and treatment order/plan. The mean MARS quality score for all apps was 3.4, and according to the mean score of the subscales, they were categorized as functionality, aesthetics, information quality, and participation. In addition, the mean scores of the subscales were listed on MARS as functionality, aesthetics, knowledge quality, and participation. Nowadays, "e-health" apps are attracting attention in the treatment of health problems. While the apps in download stores appear to provide basic information about breast cancer, they offer limited and personalized problem management related to prevention, early detection, and screening. Support from healthcare professionals and further efforts can be made to develop innovative technologies and apps that include evidence-based informational content about breast cancer. In the process of breast cancer information, survivors can be encouraged to manage this process using e-health services.

Analyzing Android Taint Analysis Tools: FlowDroid, Amandroid, and DroidSafe

Numerous static taint analysis techniques have recently been proposed for identifying information flows in mobile applications. These techniques are often optimized and evaluated on a set of synthetic benchmarks, which makes the comparison results difficult to generalize. Moreover, the techniques are commonly compared under different configuration setups, rendering the comparisons inaccurate. In this paper, we provide a large, controlled, and independent comparison of the three most prominent static taint analysis tools: <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FlowDroid</small> , <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Amandroid</small> , and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DroidSafe</small> . We align the configuration setup for the tools and evaluate them on both a set of common benchmarks and on real applications from the Google Play app store. We further evaluate the effectiveness of additional reflection handling mechanism implemented by <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DroidRA</small> , applying it to each of the evaluated tools. We compare the results of our analysis to the results reported in previous studies, identify main reasons for inaccuracy in existing tools, and provide suggestions for future research.

Search Rank Fraud and Malware Detection in Mobile Apps

Abstract:The financial success of Android software stores like Google Play, as well as the incentive scheme they provide for popular apps, make them attractive targets for fraud and malicious behavior. Some fraudulent developers deceptively boost the search rank and popularity of their apps while malicious developers use app markets as a launch pad for their malware. In this project, introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. FairPlay correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from Google Play app data in order to identify suspicious apps. Adversaries can have chances to launch attacks by gathering victim’s information continuously. This project shows that an adversary can successfully infer a victim’s vertex identity and community identity by the knowledge of degrees within a time period. The project also includes a new supervised clustering algorithm to find groups of data (coarse and finer cluster). It directly incorporates the information of sample categories into the fraud clustering process. Keywords: -Android market, search rank fraud, malware detection.

MHealth Solutions for Mental Health Screening and Diagnosis: A Review of App User Perspectives Using Sentiment and Thematic Analysis.

Mental health screening and diagnostic apps can provide an opportunity to reduce strain on mental health services, improve patient well-being, and increase access for underrepresented groups. Despite promise of their acceptability, many mental health apps on the market suffer from high dropout due to a multitude of issues. Understanding user opinions of currently available mental health apps beyond star ratings can provide knowledge which can inform the development of future mental health apps. This study aimed to conduct a review of current apps which offer screening and/or aid diagnosis of mental health conditions on the Apple app store (iOS), Google Play app store (Android), and using the m-health Index and Navigation Database (MIND). In addition, the study aimed to evaluate user experiences of the apps, identify common app features and determine which features are associated with app use discontinuation. The Apple app store, Google Play app store, and MIND were searched. User reviews and associated metadata were then extracted to perform a sentiment and thematic analysis. The final sample included 92 apps. 45.65% (n = 42) of these apps only screened for or diagnosed a single mental health condition and the most commonly assessed mental health condition was depression (38.04%, n = 35). 73.91% (n = 68) of the apps offered additional in-app features to the mental health assessment (e.g., mood tracking). The average user rating for the included apps was 3.70 (SD = 1.63) and just under two-thirds had a rating of four stars or above (65.09%, n = 442). Sentiment analysis revealed that 65.24%, n = 441 of the reviews had a positive sentiment. Ten themes were identified in the thematic analysis, with the most frequently occurring being performance (41.32%, n = 231) and functionality (39.18%, n = 219). In reviews which commented on app use discontinuation, functionality and accessibility in combination were the most frequent barriers to sustained app use (25.33%, n = 19). Despite the majority of user reviews demonstrating a positive sentiment, there are several areas of improvement to be addressed. User reviews can reveal ways to increase performance and functionality. App user reviews are a valuable resource for the development and future improvements of apps designed for mental health diagnosis and screening.

Enhancing EFL Students' Reading and Appreciation Skills with Mobile Fiction Apps

The present study proposes the integration of mobile fiction Apps (MFAs) in reading instruction to help EFL college students understand the form and content of literary works. It will give examples of fiction Apps that can be downloaded from the Google Play and iPhone App Stores, give the advantages of using MFAs, literary appreciation skills that can be developed with MFAs and instructional stages with MFAs. MFAs can be used as extension activities or as a supplement to in-class reading instruction in ESL/EFL. They are free, easy, and quick to download, update and delete, can be used anywhere, anytime and as many times as the students need. Instruction with smart mobile phones begins with downloading an MFA such as Harry Potter, Oliver Twist, Sherlock Holmes, Wuthering Heights, a novel by Agatha Christi or a collection of stories. Both simplified and original versions can be used depending on the student’s proficiency level, story/novel length and difficulty level. A story/novel video, app or e-book can be used online or offline. Some MFAs provide notes and tests. The instructor can ask pre-questions that require the students to identify the plot, setting, characters, main theme, point of view, symbolism, style, and tone of the narrative, and infer the meanings of figurative language and imagery in the story. Questions and students’ answers, summaries, comments on each other's' responses can be posted in an online discussion forum, blog or social media page. The students can discuss the elements of a literary work under the instructor's supervision. The integration of fiction Apps showed improved reading comprehension, literary appreciation and text analysis skills among participating students and increased their engagement in reading and literary analysis.

Crowdsourcing Platform for Website and Application Testing

Crowdourcing has gained or achieved a great deal or path of interest in the operation or usage or exercise during the last years of the professional generation. Many teams have done a lot of procedures and tasks in this regard to appear to be a special obligation and income-generating games. However, regardless of its reputation and its use in this generation of security technologies and the best consumer interface and value, there may be well-informed information compared to crowdsourcing, especially with reference to crowdsourcing consultants or activities or courses. Crowdsourcing coordinators or course studies play an important role in multi-person projects or processes or activities as they ensure the relationships or cooperation established between crowdsourcing groups and the gang. However, the current problem or situation of crowdsourcing mediators or courses that misleads crowdsourcing projects and their functionality and the corresponding contexts they need are no longer addressed in the course of studies. We deal with or address those issues in a way that addresses a well-known case with an average person called TestCloud which has helped us to get to the top of the platform providing software experiments or software engineering. the stadium or donations of teams that progress slowly or completely without their efforts or sending sports and programs to a good crowd. Criticism shows that the testing of the said service Cloud meets the 3 most required conditions if you want to improve or enhance its use and, these are: process management, team management and collaboration with technology. In every size, we explain or highlight many of the methods or processes that this app uses to get through the difficult situations or problems related to the collection activities that need to be done properly at the same time. The term 'crowdsourcing' was changed to be added or introduced in 2006 to describe a problem-solving model distributed by remote internet users. Since then it has been extensively studied and practiced to assist software program engineering and objective software applications. In this course we provide a comprehensive or specific survey of the use and importance of crowdsourcing on the software engineering program and software program you are trying, we aim to cover all the books and topics that may be relevant to this topic. We first assess and attempt to find definitions and application functions, crowdsourcing performance and to define and define our Crowdsourcing Software Engineering ideas together in terms of their value or application. Then we summarize or integrate the many required features within the commercial fitness regions in software system engineering and related case research. Similarly we explore software programming engineering or experimenting with domains, obligations and programs, functions, crowdsourced operations and systems and stakeholders involved in knowing Crowdsourced Software Engineering solutions. We are concluding or would like to really focus on the process of developing styles, open-ended problems and opportunities for conclusion lessons in Crowdsourced Software Engineering or Software Testing. We may also want to show or demonstrate that statistics released from the crowd are primarily based entirely on experiments that can adhere to multiple regions or activities associated with an automated web site or experimental cell. We would really like to integrate the creation of POLARIS, which produces repetitive views of texts from the crowd — primarily based entirely on experiments, rendering the events 'motif' of a different application: a series of high-level automated easy-to-use integrated low-stage action actions. Our strong focus is on the mobilization staff deployed from Mechanical Turk to fulfill 1,350 test commitments in nine popular Google Play apps, each with at least 1 million customer subscriptions.

Multimedia Knowledge Translation Tools for Parents About Childhood Heart Failure: Environmental Scan.

BackgroundChildhood heart failure is a factor in many hospital admissions each year. It can impose a steep learning curve for parents who need to learn the key information to care for their child at home. In this study, we conducted an environmental scan to identify and assess web-based knowledge translation tools about childhood heart failure for parent audiences developed within North America.ObjectiveThe aim of this study is to inventory tools publicly available to parents about childhood heart failure from popular web-based venues, assess how each tool communicates health information, and explore how they were developed.MethodsOur search strategy included two commonly used multimedia-based platforms: two app stores (Google Play and Apple App Store) and one search engine (Advanced Google Search). Common search terms were used, and results were uploaded to Microsoft Excel for screening between 2 reviewers. The inclusion criteria for the tools were as follows: content focused on educating parents about their child’s heart failure, developed in the English language, and originating within Canada and the United States. A total of 2 reviewers screened the app store and internet search results for relevant tools. Each tool was assessed using the Suitability Assessment of Materials (SAM), a validated tool that objectively assesses the suitability of how health information is communicated to a particular audience. Key informants who were involved in tool development were identified and invited for a qualitative interview using a semistructured format to provide data about the development process. Key themes were identified in the semistructured interview process.ResultsFrequencies and SAM percent ratings of eligible tools were reported. No apps exist for parents relating to pediatric heart failure. Overall, 17 relevant internet tools were identified, and their suitability was assessed for the parent audience. Most tools scored well in layout and type, but they scored lower in readability and graphics. Qualitative interviews with key informants revealed three key themes: timely and introductory knowledge, credible and trustworthy knowledge, and challenges and evolution in knowledge.ConclusionsThis is the first environmental scan looking for parent tools relating to childhood heart failure in Canada and the United States. Findings from this study reveal that there are no apps on this topic and there is a small number of tools for parents on the internet (n=17). Using the SAM, no tools scored in the superior range, and further work in knowledge translation strategies needs to be done in this area to improve more effective education to parents and caregivers who have a child with heart failure. These findings will inform the development of a new resource on children’s heart failure that targets parents and caregiver audiences.

Are iPhones Really Better for Privacy? A Comparative Study of iOS and Android Apps

Abstract While many studies have looked at privacy properties of the Android and Google Play app ecosystem, comparatively much less is known about iOS and the Apple App Store, the most widely used ecosystem in the US. At the same time, there is increasing competition around privacy between these smartphone operating system providers. In this paper, we present a study of 24k Android and iOS apps from 2020 along several dimensions relating to user privacy. We find that third-party tracking and the sharing of unique user identifiers was widespread in apps from both ecosystems, even in apps aimed at children. In the children’s category, iOS apps tended to use fewer advertising-related tracking than their Android counterparts, but could more often access children’s location. Across all studied apps, our study highlights widespread potential violations of US, EU and UK privacy law, including 1) the use of third-party tracking without user consent, 2) the lack of parental consent before sharing personally identifiable information (PII) with third-parties in children’s apps, 3) the non-data-minimising configuration of tracking libraries, 4) the sending of personal data to countries without an adequate level of data protection, and 5) the continued absence of transparency around tracking, partly due to design decisions by Apple and Google. Overall, we find that neither platform is clearly better than the other for privacy across the dimensions we studied.

An analysis of a third-party food delivery app during the COVID-19 pandemic

PurposeThe study aims to analyze guest comments on the Uber Eats food delivery app (FDA) in the USA during the April–June 2020 COVID-19 pandemic lockdown period. Three aspects influencing customer satisfaction with the FDA were evaluated in this study: (1) performance on the app, (2) product quality and (3) service quality.Design/methodology/approachOne thousand customer comments posted on the Uber Eats Google Play app from April 1 to June 30, 2020 were analyzed in this study. The text mining technique was applied to discover the hidden, but meaningful patterns from the unstructured text. Content analysis was applied to systematically analyze the text into organized categories and themes.FindingsAmong the three dimensions evaluated in this study, the most important dimension regarding customers' perceptions toward the FDA was the service quality dimension (40.02%), followed by the FDA's performance dimension (39.43%) and the product quality dimension (20.54%) was least important. Additionally, customers' perceptions towards the three dimensions were all unfavorable and there were more negative comments than the positive comments: FDAs (P/N = 0.728), product quality (P/N = 0.60) and service quality (P/N = 0.865).Originality/valuePrevious studies investigating FDAs assessed solely the performance of the app. However, customers' experience of a food delivery service is comprised of multiple components including the app, the restaurant and the delivery driver. To fill the void, this study evaluated a third-party app performance, product quality and service quality to capture the totality of customers' food delivery service experience.