App Descriptions Research Articles

Creating and Managing Transactions and Budgets: Analysis of Marketplace Descriptions and Functionality Review of Budgeting Apps

Abstract While financial practises permeate our lives, the effective management of personal finance is not trivial, as indicated in the increasing number of commercial apps aimed to support budgeting. Such apps however have been limitedly explored, despite the growing HCI interest in financial practises. To address this gap, we present the functionality review of 45 top-rated budgeting apps from Google Play and Apple Store, together with an analysis of their descriptions on marketplaces. Findings indicate the value of richer, multimodal app descriptions, support for budgeting literacy and for stronger theoretical underpinning of these apps. They also highlight main functionalities for supporting different types of transactions and accounts, for entering and managing transactions, securing data, as well as for creating and managing budgets. We conclude with five design implications to better support each of these functionalities.

A shot in the dark: the impact of online visibility on the search for an effective sleep app.

Dictated by consumer ratings and concealed algorithms, high levels of online visibility are granted to certain sleep apps on mainstream modes of app selection. Yet, it remains unclear to what extent these highly visible apps are evidence-based. The objectives of this review were to identify and describe the apps with the greatest online visibility when searching for a sleep app and to assess the claimed and actual research associated with them. A keyword search was conducted in Google Play and Google search. Titles of the most visible apps were retrieved. App descriptions were examined to identify research claims made about app effectiveness on sleep and other health-related outcomes. A follow-up search on PubMed and Google Scholar was conducted to verify claims. The keyword search identified 53 highly visible apps. Examination of app store descriptions found that no reference to research was made for the majority of apps (n = 45, 84.9%). Published research studies were available for just 3 apps, with most studies evaluating app impact on non sleep-related outcomes. There was some evidence to attesting to the effectiveness of 2 apps in improving sleep. This review demonstrates how, when carrying out a typical search for a sleep app, information about the evidence base for the majority of highly visible apps is not available. Results highlight the need for the improvement of mainstream modes of app selection in terms of better consumer-app specificity and increased transparency regarding the access to information about the evidence base for apps. Power N, Boivin DB, Perreault M. A shot in the dark: the impact of online visibility on the search for an effective sleep app. J Clin Sleep Med. 2024;20(8):1379-1386.

Assessing the Quality and Behavior Change Potential of Vaping Cessation Apps: Systematic Search and Assessment.

An increasing number of people are using vapes (e-cigarettes), and with growing evidence of associated harms, there is a need for acceptable cessation support and interventions. Smartphone apps for health and well-being have increased in popularity and use. Limited published literature assesses the potential of apps to support vaping cessation. A systematic search of vaping cessation apps currently available in Australia for iOS and Android platforms was conducted. Apps were assessed against established health app assessment tools for quality and behavior change potential. A systematic search through the Australian Apple iTunes and Google Play stores was conducted using the search terms "vape"; "vaping"; "e-cigarette"; and "cessation," "quit," or "quitting" in May 2023. Only apps that encouraged the cessation of vaping were included. App descriptions were reviewed to determine if they were relevant for inclusion in this study, and relevant apps were downloaded onto the appropriate mobile device for review. The Mobile App Rating Scale (MARS) was used to rate the quality (engagement, functionality, aesthetics, and information) of the apps using an overall score out of 5. The App Behavior Change Scale (ABACUS) was used to assess the behavior change potential of each app using a score out of 21. An initial search of the app stores yielded 220 Android apps and 124 iOS apps. Screening against the inclusion criteria left 20 iOS apps and 10 Android apps for review. Six apps were available on both operating systems, and these were downloaded, reviewed, and reported separately for each operating system. The average MARS score for all apps assessed in this review was 3.1 (SD 0.41) out of 5. The reviewed apps overall performed well for the MARS elements relating to functionality, such as ease of use and navigation, but had the lowest scores for information-related elements, such as credibility. The number of ABACUS behavior change features per app ranged from 0 to 19 out of 21, with a mean of 8.9 (SD 4.51). The apps commonly included information-related features, such as requesting baseline information. The least common behavior change features were those relating to goal-setting, such as asking about the user's willingness for behavior change and providing feedback on current actions in comparison to future goals. The identified vaping cessation apps had moderate levels of quality and some behavior change components. Future vaping cessation apps could benefit from including more features that are known to support behavior change, such as goal-setting, to improve the potential benefit of these apps to support people to stop vaping. As guidelines for vaping cessation continue to be established, future apps need to reference these in their development.

Towards System-Level Security Analysis of IoT Using Attack Graphs

Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware or communication protocols, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> , a logic programming-based framework to perform system-level security analysis for IoT systems. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> generates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In building <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> , we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> automatically translates vulnerabilities, exploits, and device dependencies to Prolog clauses and invokes MulVAL to construct attack graphs. To evaluate vulnerabilities' system-wide impact, we propose three metrics based on the attack graph, which provide guidance on hardening IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> 's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We apply <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> to 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.

STUDY COMPANION

The Study Companion Android Application is a comprehensive mobile platform designed to enhance the study experience for students and individuals preparing for academic and competitive exams. The application provides a user-friendly interface that allows users to easily download and upload notes related to their academic subjects and competitive exams. By centralizing study materials in one convenient location, the application simplifies the process of accessing and organizing study materials. The application offers various modules tailored to the specific needs of users. The Home Page module presents two options: "Academic" and "Competitive," allowing users to navigate between their academic subjects and competitive exam materials effortlessly. The My Info module provides a personalized space for users to manage their profile information and track their progress. To ensure a secure and efficient user experience, the application incorporates a Log-in and Register page module, where users can create accounts and securely access their personal information and uploaded notes. Additionally, an Admin module is connected to Firebase, enabling administrators to manage user accounts and handle backend operations effectively. The About module serves as a comprehensive resource, offering an app description and contact information for user support. Users can gain a clear understanding of the application's purpose and easily reach out for assistance or feedback. The Study Companion Android Application employs a robust system architecture, utilizing Firebase for seamless data storage, retrieval, and synchronization. The application has undergone rigorous testing, including unit testing, integration testing, and acceptance testing, to ensure functionality, performance, security, and usability. With its user-centric design, reliable architecture, and efficient functionalities, the Study Companion Android Application aims to provide a seamless and enriching study experience for users. By offering a centralized platform for downloading and uploading study materials, the application empowers users in their academic and competitive exam preparations, promoting knowledge sharing, collaboration, and effective learning.

Human-to-Computer Interactivity Features Incorporated Into Behavioral Health mHealth Apps: Systematic Search.

While there are thousands of behavioral health apps available to consumers, users often quickly discontinue their use, which limits their therapeutic value. By varying the types and number of ways that users can interact with behavioral health mobile health apps, developers may be able to support greater therapeutic engagement and increase app stickiness. The main objective of this analysis was to systematically characterize the types of user interactions that are available in behavioral health apps and then examine if greater interactivity was associated with greater user satisfaction, as measured by app metrics. Using a modified PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology, we searched several different app clearinghouse websites and identified 76 behavioral health apps that included some type of interactivity. We then filtered the results to ensure we were examining behavioral health apps and further refined our search to include apps that identified one or more of the following terms: peer or therapist forum, discussion, feedback, professional, licensed, buddy, friend, artificial intelligence, chatbot, counselor, therapist, provider, mentor, bot, coach, message, comment, chat room, community, games, care team, connect, share, and support in the app descriptions. In the final group of 34 apps, we examined the presence of 6 types of human-machine interactivities: human-to-human with peers, human-to-human with providers, human-to-artificial intelligence, human-to-algorithms, human-to-data, and novel interactive smartphone modalities. We also downloaded information on app user ratings and visibility, as well as reviewed other key app features. We found that on average, the 34 apps reviewed included 2.53 (SD 1.05; range 1-5) features of interactivity. The most common types of interactivities were human-to-data (n=34, 100%), followed by human-to-algorithm (n=15, 44.2%). The least common type of interactivity was human-artificial intelligence (n=7, 20.5%). There were no significant associations between the total number of app interactivity features and user ratings or app visibility. We found that a full range of therapeutic interactivity features were not used in behavioral health apps. Ideally, app developers would do well to include more interactivity features in behavioral health apps in order to fully use the capabilities of smartphone technologies and increase app stickiness. Theoretically, increased user engagement would occur by using multiple types of user interactivity, thereby maximizing the benefits that a person would receive when using a mobile health app.

Evaluation of asemiautomated App Store analysis for the identification of health apps for cardiac arrhythmias.

Smartphone apps are increasingly utilised by patients and physicians for medical purposes. Thus, numerous applications are provided on the App Store platforms. The aim of the study was to establish anovel, expanded approach of asemiautomated retrospective App Store analysis (SARASA) to identify and characterise health apps in the context of cardiac arrhythmias. An automated total read-out of the "Medical" category of Apple's German App Store was performed in December 2022 by analysing the developer-provided descriptions and other metadata using asemiautomated multilevel approach. Search terms were defined, based on which the textual information of the total extraction results was automatically filtered. Atotal of 435 of 31,564 apps were identified in the context of cardiac arrhythmias. Of those, 81.4% were found to deal with education, decision support, or disease management, and 26.2% (additionally) provided the opportunity to derive information on heart rhythm. The apps were intended for healthcare professionals in 55.9%, students in 17.5%, and/or patients in 15.9%. In 31.5%, the target population was not specified in the description texts. In all, 108 apps (24.8%) provided atelemedicine treatment approach; 83.7% of the description texts did not reveal any information on medical product status; 8.3% of the apps indicated that they have and 8.0% that they do not have medical product status. Through the supplemented SARASA method, health apps in the context of cardiac arrhythmias could be identified and assigned to the target categories. Clinicians and patients have awide choice of apps, although the app description texts do not provide sufficient information about the intended use and quality.

An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews.

Chatbots are an emerging technology that show potential for mental health care apps to enable effective and practical evidence-based therapies. As this technology is still relatively new, little is known about recently developed apps and their characteristics and effectiveness. In this study, we aimed to provide an overview of the commercially available popular mental health chatbots and how they are perceived by users. We conducted an exploratory observation of 10 apps that offer support and treatment for a variety of mental health concerns with a built-in chatbot feature and qualitatively analyzed 3621 consumer reviews from the Google Play Store and 2624 consumer reviews from the Apple App Store. We found that although chatbots' personalized, humanlike interactions were positively received by users, improper responses and assumptions about the personalities of users led to a loss of interest. As chatbots are always accessible and convenient, users can become overly attached to them and prefer them over interacting with friends and family. Furthermore, a chatbot may offer crisis care whenever the user needs it because of its 24/7 availability, but even recently developed chatbots lack the understanding of properly identifying a crisis. Chatbots considered in this study fostered a judgment-free environment and helped users feel more comfortable sharing sensitive information. Our findings suggest that chatbots have great potential to offer social and psychological support in situations where real-world human interaction, such as connecting to friends or family members or seeking professional support, is not preferred or possible to achieve. However, there are several restrictions and limitations that these chatbots must establish according to the level of service they offer. Too much reliance on technology can pose risks, such as isolation and insufficient assistance during times of crisis. Recommendations for customization and balanced persuasion to inform the design of effective chatbots for mental health support have been outlined based on the insights of our findings.

APIMatchmaker: Matching the Right APIs for Supporting the Development of Android Apps

Android developers are often faced with the need to learn how to use different APIs suitable for their projects. Automated API recommendation approaches have been invented to help fill this gap, and these have been demonstrated to be useful to some extent. Unfortunately, most state-of-the-art works are not proposed for Android developers, and the ones dedicated to Android app development often suffer from high redundancy and poor run-time performance, or do not target the problem of recommending API usage patterns. To address this gap we propose to the community a new tool, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIMatchmaker</i> , to recommend API usages by learning directly from similar real-world Android apps. Unlike existing recommendation approaches, which leverage a single context to find similar projects, we innovatively introduce a multi-dimensional, context-aware, collaborative filtering approach to better achieve the purpose. Specifically, in addition to code similarity, we also take app descriptions (or topics) into consideration to ensure that similar apps also provide similar functions. We evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIMatchmaker</i> on a large number of real-world Android apps and observe that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIMatchmaker</i> yields a high success rate in recommending APIs for Android apps under development, and it is also able to outperform the state-of-the-art.

BENCHMARKING DE APLICAÇÕES PARA DISPOSITIVOS MÓVEIS DIRECIONADOS À SAÚDE DA CRIANÇA

ABSTRACT Objective: to identify which of the apps available for children include information on monitoring growth and development, in a way similar to the Brazilian Children's Handbook. Method: this is an exploratory research study to survey apps designed to monitor children's growth and development. The “Benchmarking” technique was used to assist in the process. The search for apps was carried out in January 2023 in the Google Play and App Store stores. The data were tabulated in Microsoft Excel. After classifying the variables, absolute and relative frequencies were calculated. Results: a total of 624 apps were identified. Of these, 491 were found in Google Play and 133 in the App Store. After analyzing the app descriptions and excluding duplicates, 48 software options were selected for the final sample. 41% (19) of the apps are in Portuguese, 36% (17) of those selected intend to record children's development, and only 2% (1) store children's growth, development and vaccination data. Conclusion: the absence of an app similar to Children's Handbook for monitoring and recording children's health within the Unified Health System scope was evidenced.

"Money Doesn't Buy You Happiness": Negative Consequences of Using the Freemium Model for Mental Health Apps

As global rates of anxiety and depression increase, we observe millions of downloads of mobile apps addressing mental health that adopt 'freemium' charging models offering complex combinations of free and paid features. We explore potentially negative outcomes of deploying such freemium designs to vulnerable MH populations. We assess outcomes for 41 frequently downloaded mental health apps, by combining thematic analysis of 41K user reviews with audits to validate app descriptions and reviews. We propose a new analytic framework identifying three types of negative outcomes for freemium deployments: first vulnerable users currently experiencing crises may feel pressure to download unhelpful or costly apps. Second unintuitive descriptions of complex apps can lead to inappropriate treatments and expensive subscriptions. Third, limited duration offers may result in incomplete treatments or unexpected charges. We discuss ethical considerations and broader HCI consequences. We describe implications for mental health app design, including the need for greater transparency around free versus paid features, and propose new approaches to provide vulnerable users with validated information about these commonly downloaded apps.

Are Mobile Applications in Laryngology Designed for All Patients?

Mobile applications (apps) are multiplying in laryngology, with little standardization of content, functionality, or accessibility. The purpose of this study is to evaluate the quality, functionality, health literacy, readability, accessibility, and inclusivity of laryngology mobile applications. Of the 3230 apps identified from the Apple and Google Play stores, 28 patient-facing apps met inclusion criteria. Apps were evaluated using validated scales assessing quality and functionality: the Mobile App Rating Scale (MARS) and the Institute for Healthcare Informatics App Functionality Scale. The Clear Communication Index (CDC) Institute of Medicine Strategies for Creating Health Literate Mobile Applications, and Patient Education Materials Assessment Tool (PEMAT) were used to evaluate apps health literacy level. Readability was assessed using established readability formulas. Apps were evaluated for language, accessibility features, and representation of a diverse population. Twenty-six apps (92%) had adequate quality (MARS score > 3). The mean PEMAT score was 89% for actionability and 86% for understandability. On average, apps utilized 25/33 health literate strategies. Twenty-two apps (79%) did not pass the CDC index threshold of 90% for health literacy. Twenty-four app descriptions (86%) were above an 8th grade reading level. Only 4 apps (14%) showed diverse representation, 3 (11%) had non-English language functions, and 2 (7%) offered subtitles. Inter-rater reliability for MARS was adequate (CA-ICC=0.715). While most apps scored well in quality and functionality, many laryngology apps did not meet standards for health literacy. Most apps were written at a reading level above the national average, lacked accessibility features, and did not represent diverse populations. Laryngoscope, 133:1540-1549, 2023.

Why this app: Can a video‐based intervention help parents identify quality educational apps?

AbstractResearchers recommend that parents look for five benchmarks as indicators of quality educational apps (ie, scaffolding, curriculum, development team, feedback, learning theory), yet results show that parents undervalue some of these benchmarks. The current study examined if a short video‐based intervention would enhance parents' value‐judgements of apps featuring the five educational benchmarks. In original and modified app experiments (n = 100; n = 101), parents of children 4–11 years old were randomly assigned to watch either a 9‐minute video that detailed how the five benchmarks augment learning, or a 2‐minute control video. Parents evaluated 10 simulated apps containing either benchmarks or buzzwords. The original app experiment shows that a brief intervention can help parents identify quality educational apps via the benchmarks, but the modified app experiment suggests it only works if developers are using specific keywords in app descriptions. Helping parents select quality educational apps is more complicated than simply telling them what to look for. Practitioner notesWhat is already known about this topic Parents have a main role in selecting apps and deciding on how often children use them; however, they have difficulty evaluating an app's educational potential in a market mixed with high‐ and low‐quality products that lacks a standard for including educational apps on the App Store. There are five research‐based benchmarks that are indicators of quality educational apps. These include apps created by an interdisciplinary development team, having a guiding curriculum with a clear purpose, including scaffolding and feedback, and being based on a learning theory. However, parents are not valuing all of these educational benchmarks equally. Educational videos disseminated via YouTube have become an established medium to enhance people's knowledge. Mayer's cognitive theory of multimedia learning, which suggests that people learn better when there is both auditory and visual information given together, is a framework used to design educational videos. What this paper adds This study leveraged a useful, accessible medium (ie, educational YouTube videos) to make research on educational apps accessible enough such that it could influence parents' app selection. Parents of children aged 4–11 years‐old viewed either an educational intervention‐ or control‐video and assessed educational apps through measures that replicate how consumers evaluate apps on the App Store (ie, their willingness to download the app, how much they would pay, their rating, and ranking). Original and modified app experiments demonstrate that a brief, educational video designed using key features from Mayer's multimedia theory can improve parents' app selection. In the original app experiment, parents in the intervention group are valuing the guiding curriculum and development team benchmarks over others, which may be due to the structure of the intervention video (ie, worked‐examples immediately after pre‐training of benchmarks). In the modified app experiment, parents in the intervention group did not differ from the control group in their evaluations of the different benchmarks apart from rating the app with learning theory higher and ranking the guiding curriculum benchmark lower. The higher rating of learning theory may be a result of the language change in the simulated app to include more user‐friendly terms. The unexpected ranking result may be due to a limitation with the measure. Building on prior research on the educational benchmarks, these experiments show that parents are valuing them over buzzwords; however, even when shown an educational video, they are not valuing them all equally. Implications for practice and/or policy An educational video based on the five educational benchmarks could be used as a tool to easily disseminate knowledge to enhance parents' educational app selection. The experiments shed light on the complexity of knowledge dissemination to the public. While there is evidence for the use of research‐based educational videos to disseminate knowledge to parents when selecting educational apps, the App Store descriptions should include accessible language that makes the research‐based benchmarks easy to select. This study also provides suggestions for improving the development of educational videos.

Lib2Desc: automatic generation of security-centric Android app descriptions using third-party libraries

Lib2Desc: automatic generation of security-centric Android app descriptions using third-party libraries

Supporting family care: a scoping app review

BackgroundMobile applications (apps) may provide family caregivers of people with chronic diseases and conditions with access to support and good information. However, thorough understanding of how these apps meet the main needs and requirements of the users is currently lacking. The aim of this study was to review the currently available apps for family caregivers and evaluate their relevance to main domains of caregiving activities, caregivers’ personal needs, and caregivers’ groups found in previous research on family caregivers.MethodsWe conducted a scoping review on English-language and German-language apps for family caregivers on two major app stores: Google Play Store and iOS App Store. Apps were included if the main target group were family caregivers. Data were extracted from the app descriptions provided by the app producers in the app stores.ResultsThe majority of the apps was designed to assist caregivers in their caregiving activities. Apps were rarely tailored to specific groups of family caregivers such as young carers and their needs. Further, apps addressing caregivers’ personal health, financial security, and work issues were scarce. Commercial apps dominated the market, often intermediating paid services or available for users of specific hardware. Public and non-profit organizations provided best-rated and free-of-charge apps but had a very limited range of services with focus on caregivers’ health and training.ConclusionsOur results indicate that current apps for family caregivers do not distinguish specific groups of family caregivers, also they rarely address caregivers’ personal needs.

Health insurers: evolving into ecosystem-based service companies

PurposeThis study aims to investigate whether health insurers are transforming from pure payers into ecosystem-based health service companies. The authors discuss the findings’ impact on health insurers’ business model approach and their sources of competitive advantage.Design/methodology/approachThe authors conducted a multiple case study of 25 incumbent and rising health insurers from the USA, Europe and Asia-Pacific. The selection of firms was based on databases from Forbes, S&P Global Market Intelligence and Crunchbase. By performing a review of financial reports, app descriptions, corporate websites and media coverage, the authors analyzed health insurers’ digital service offerings and underlying strategic approaches for providing those services.FindingsThis study demonstrates that major health insurers are transforming into ecosystem-based health service companies. They expand the traditional insurance value chain by offering value-adding health services along the patient journey. The analysis results are summarized in a table displaying 18 digital service categories along core patient journey phases with corresponding examples of health insurers and underlying strategic approaches.Originality/valueThe role of digital services and ecosystems has been explored for many industries. This study investigates this subject area with a focus on the health insurance sector, advancing a young field of research. The analysis gives insights into the latest digital service offerings and strategic approaches of an internationally diverse set of incumbents and rising ventures.

German Mobile Apps for Patients With Psoriasis: Systematic Search and Evaluation.

BackgroundPsoriasis is a chronic inflammatory skin disease. The visibility of erythematous plaques on the skin as well as the pain and itchiness caused by the skin lesions frequently leads to psychological distress in patients. Smartphone apps are widespread and easily accessible. Earlier studies have shown that apps can effectively complement current management strategies for patients with psoriasis. However, no analysis of such apps has been published to date.ObjectiveThe aim of this study is to systematically identify and objectively assess the quality of current publicly available German apps for patients with psoriasis using the Mobile Application Rating Scale (MARS) and compile brief ready-to-use app descriptions.MethodsWe conducted a systematic search and assessment of German apps for patients with psoriasis available in the Google Play Store and Apple App Store. The identified apps were randomly assigned to 1 of 3 reviewers, who independently rated them using the German MARS (MARS-G). The MARS-G includes 15 items from 4 different sections (engagement, functionality, aesthetics, and information) to create an overall mean score for every app. Scores can range from 1 for the lowest-quality apps to 5 for the highest-quality apps. Apps were ranked according to their mean MARS-G rating, and the highest-ranked app was evaluated independently by 2 patients with psoriasis using the user version of the MARS-G (uMARS-G). Furthermore, app information, including origin, main function, and technical aspects, was compiled into a brief overview.ResultsIn total, we were able to identify 95 unique apps for psoriasis, of which 15 were available in both app stores. Of these apps, 5 were not specifically intended for patients with psoriasis, 1 was designed for clinical trials only, and 1 was no longer available at the time the evaluation process began. Consequently, the remaining 8 apps were included in the final evaluation. The mean MARS-G scores ranged from 3.51 to 4.18. The app with the highest mean MARS-G score was Psoriasis Helferin (4.18/5.00). When rated by patients, however, the app was rated lower in all subcategories, resulting in a mean uMARS-G score of 3.48. Most apps had a commercial background and a focus on symptom tracking. However, only a fraction of the apps assessed used validated instruments to measure the user’s disease activity.ConclusionsApp quality was heterogeneous, and only a minority of the identified apps were available in both app stores. When evaluated by patients, app ratings were lower than when evaluated by health care professionals. This discrepancy highlights the importance of involving patients when developing and evaluating health-related apps as the factors that make an app appealing to users may differ between these 2 groups.Trial RegistrationDeutsches Register Klinischer Studien DRKS00020963; https://tinyurl.com/ye98an5b

Evidence-Based Behavioral Strategies in Smartphone Apps for Children's Sleep: Content Analysis.

BackgroundEmpirically supported treatments for pediatric sleep problems exist, but many families turn to other sources for help with their children’s sleep, such as smartphone apps. Sleep apps are easy for families to access, but little evidence exists regarding the validity of the services and information provided in the developer descriptions of the apps.ObjectiveThe goal of this study was to examine the features and claims of developer descriptions of sleep apps for children.MethodsA search of the Apple iTunes store and Google Play was conducted using the terms “kids sleep,” “child sleep,” and “baby sleep.” Data on the type of app, price, user rating, and number of users were collected. Apps were analyzed in comparison with evidence-based behavioral strategies and were thematically coded on the basis of claims provided in developer descriptions.ResultsA total of 83 app descriptions were examined, of which only 2 (2.4%) offered sleep improvement strategies. The majority were sound and light apps (78%) and 19% were bedtime games or stories. Only 18 of 83 (21.6%) apps were identified as containing empirically supported behavioral sleep strategies. Despite this, many apps asserted claims that they will help children “fall asleep instantly,” “cry less and sleep better,” or improve child development.ConclusionsA large variety of sleep apps exist for use among children, but few include evidence-based behavioral strategies according to the developer descriptions of the apps. Addressing sleep difficulties in children is important to promote physical, cognitive, and emotional development. Collaboration between sleep researchers and technology developers may be beneficial for creating evidence-supported apps to help with children’s sleep in the future.

Growth in the development of health and fitness mobile apps amid COVID-19 pandemic.

ObjectivesTechnology in the form of mobile apps has played an essential role in facilitating, tracking, and maintaining health and fitness activities during the pandemic. When countries opted for partial or complete lockdowns to contain the spread of the coronavirus disease 2019 virus, it led to people working on their health and fitness-related activities from their homes, just as they continued working from home. This paper aims to quantify the impact of coronavirus disease 2019 on the development of health and fitness mobile apps. Specifically, we compute the effect of coronavirus disease 2019 on the growth of different sub-categories of health and fitness apps.MethodsWe scraped data about a population of 78,890 health and fitness apps from the iOS App Store. First, categories of health and fitness apps are identified using text analysis on the descriptions of apps. Second, the rise in the development of new apps is analyzed. To quantify the impact of coronavirus disease 2019 on the growth of the health and fitness apps, multiple time-series forecasting models are created for different categories of health and fitness apps.ResultsThe text analysis identified twelve different types of health and fitness apps on the app market. Our models estimated that the number of health and fitness apps on the iOS app market exceeded the expected growth by 29.9% after the pandemic. The results of all categories of health and fitness are discussed in the paper.ConclusionsOur analysis found significant growth in the development of new health and fitness apps after the pandemic outbreak. The post hoc study of the population of health and fitness apps presented the current state of this particular area of the app market. In addition, it provided potential growth areas in app markets where there are fewer apps.

Classifying Mobile Applications Using Word Embeddings

Modern application stores enable developers to classify their apps by choosing from a set of generic categories, or genres, such as health, games, and music. These categories are typically static—new categories do not necessarily emerge over time to reflect innovations in the mobile software landscape. With thousands of apps classified under each category, locating apps that match a specific consumer interest can be a challenging task. To overcome this challenge, in this article, we propose an automated approach for classifying mobile apps into more focused categories of functionally related application domains. Our aim is to enhance apps visibility and discoverability. Specifically, we employ word embeddings to generate numeric semantic representations of app descriptions. These representations are then classified to generate more cohesive categories of apps. Our empirical investigation is conducted using a dataset of 600 apps, sampled from the Education, Health&amp;Fitness, and Medical categories of the Apple App Store. The results show that our classification algorithms achieve their best performance when app descriptions are vectorized using GloVe, a count-based model of word embeddings. Our findings are further validated using a dataset of Sharing Economy apps and the results are evaluated by 12 human subjects. The results show that GloVe combined with Support Vector Machines can produce app classifications that are aligned to a large extent with human-generated classifications.