iOS Platform Research Articles

Mobile application "Aterostop" for a comprehensive assessment of cardiovascular risk in patients in the Russian population

The article provides an overview of an innovative software product for a comprehensive assessment of cardiovascular risk in cardiac patients in the Russian Federation. Based on the new recommendations of the Russian National Atherosclerosis Society (2020), the Aterostop application allows to assess cardiovascular risk, determine the patients achievement of target levels of low-density lipoprotein cholesterol, and also provides recommendations for correcting lipid-lowering therapy. The calculator is implemented as a web browser version, as well as a mobile application for Android and iOS platforms.

The Sustainability of Superior Performance of Platform Complementor: Evidence from the Effects of Iterative Innovation and Visibility of App in iOS Platform in China

The sustainability of superior performance is a vital goal for complementor in mobile platform ecosystem, but it is becoming increasingly difficult to realize it. The previous literature about the sustainability of superior performance is insufficient and lacks deep research the mechanisms that affect the sustainability of superior performance. Based on user-oriented perspective, this study examines the direct and interactive effects of app’s iterative innovation and visibility on the sustainability of complementor’s superior performance. Using a two-way fixed effect model and monthly panel data, with the final dataset of 4596 observations from 384 apps of top-ranking positions by revenue available from December 2018 to December 2019 on the Apple iOS App Store in China, the results show that a) app’s iterative innovation and visibility (including platform recommendations and download rankings) promote the sustainability of app’s superior performance; b) platform recommendations and iterative innovation have a synergistic effect on the sustainability of app’s superior performance; and c) the download ranking has a substitution effect on iterative innovation to promote the sustainability of app’s superior performance. The findings of this study have theoretical and practical implications for improving the sustainability of app’s superior performance in the digital age.

Comparing Smartphone Apps for Traditional Chinese Medicine and Modern Medicine in China: Systematic Search and Content Analysis.

BackgroundTraditional Chinese medicine (TCM) is an integral part of mainstream medicine in China, with theories and practices that are completely different from modern medicine. TCM should not be ignored or confused with modern medicine in the analysis of the Chinese health care system, including the analysis of mobile health (mHealth) apps. To date, differences between TCM apps and modern medicine apps have not be systematically investigated.ObjectiveThe aim of this study was to systematically compare the quality of apps for TCM and modern medicine in China.MethodsIn December 2020, we searched iOS (iTunes) and Android (Tencent, Oppo, and Huawei app stores) platforms for all mHealth apps and then categorized them as TCM or modern medicine apps if they were included in the final analysis. The included apps were downloaded on smartphones and assessed by 2 reviewers on the following 4 aspects: (1) data in the app stores, including user ratings, download counts, cost, target users, and year of last update; (2) functionality; (3) quality of the app content as determined by the Mobile App Rating Scale (MARS); and (4) analysis of the app privacy and security.ResultsIn total, 658 apps were analyzed, including 261 TCM medicine apps and 397 modern medicine apps. The average download count of modern medicine apps (approximately 5 million) was more than 10 times that of TCM apps (approximately 400,000). Regarding functionalities, 64.7% (257/397) of modern medicine apps provided telemedicine (74/261, 28.4% in TCM apps), 62.7% (249/397) provided registration (70/261, 26.8% in TCM apps), and 45.6% (181/397) provided communication (38/261, 14.6% in TCM apps). A larger proportion of TCM apps provided prescription and medication management (144/261, 55.2% in TCM apps versus 168/397, 42.3% in modern medicine apps). The majority of modern medicine apps (329/397, 82.9%) combined ≥3 functionalities compared with one-third of TCM apps (93/261, 34.6%). We then selected 81 top apps for quality and safety assessment (41 TCM apps and 40 modern medicine apps). Of these, the mean overall MARS score of TCM apps (2.7, SD 0.5) was significantly lower than modern medicine apps (3.6, SD 0.4). Almost all modern medicine apps (38/40, 95%) addressed privacy and security by providing a privacy policy and describing how to protect personal data, but less than half of the TCM apps (18/41, 44%) described this information (P<.001).ConclusionsThe different functionalities reflect the distinct innate characteristics of these two medical systems. Although great progress has been made and the Chinese mHealth market size is large, there still exist many opportunities for future development, especially for TCM.

Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation

BackgroundImmunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions.MethodsWe developed ImmunoGenius, a machine-learning-based expert system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on Bayesian theorem, the most probable diagnoses can be drawn by calculating the probabilities of the IHC results in each disease. We prepared IHC profile data of 584 antibodies in 2009 neoplasms based on the relevant textbooks. We developed the reactive native mobile application for iOS and Android platform that can provide 10 most possible differential diagnoses based on the IHC input.ResultsWe trained the software using 562 real case data, validated it with 382 case data, tested it with 164 case data and compared the precision hit rate. Precision hit rate was 78.5, 78.0 and 89.0% in training, validation and test dataset respectively. Which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases.ConclusionThe results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.

A Research on Students’ Preferences for Mobile On-Screen Keyboard

In recent years, growing technology has affected people’s communication. Not only speaking and listening but also writing has an important role in communication. Particularly, devices have changed and applications have varied thanks to spreading mobile hardware. The aim of this study is to explore the usage status and preferences of students for mobile on-screen keyboards. In this regard, 20 common keyboard programs offered for IOS and Android platforms were examined. A question pool including various items about the features of these programs was created for the survey to be used. The developed survey consists of 27 items, 26 of which are three-point Likert types and one of which is semi-structured type. During the study, 238 participants completed the survey. Frequencies and percentages were calculated for the collected data, and participants’ preferences were determined. The results conclude that student mostly has used the keyboard for the WhatsApp application. While the most preferred features are local character support, frequently used words, voice message sending, vertical or horizontal usage, key response time, typing with swipe keyboard was the least preferred one by the participants.

Prototype User Interface Mobile App E-Learning

Information technology has developed rapidly from time to time. One of the technologies commonly owned by many people today is smartphones with the Android and IOS platforms. By knowing this factor, mobile developers compete with each other to design applications with attractive user interfaces so that users are interested in using them. At this stage in mobile application development, starting from designing a user interface prototype. This stage aims to visualize user needs, improve user experience and simplify the coding process by programmers. In this study, researchers applied the prototype method. This research produces a prototype design for the e-learning application user interface which consists of a high fidelity prototype.

A Mobile App to Facilitate Socially Distanced Hospital Communication During COVID-19: Implementation Experience.

BackgroundCOVID-19 has significantly altered health care delivery, requiring clinicians and hospitals to adapt to rapidly changing hospital policies and social distancing guidelines. At our large academic medical center, clinicians reported that existing information on distribution channels, including emails and hospital intranet posts, was inadequate to keep everyone abreast with these changes. To address these challenges, we adapted a mobile app developed in-house to communicate critical changes in hospital policies and enable direct telephonic communication between clinical team members and hospitalized patients, to support social distancing guidelines and remote rounding.ObjectiveThis study aimed to describe the unique benefits and challenges of adapting an app developed in-house to facilitate communication and remote rounding during COVID-19.MethodsWe adapted moblMD, a mobile app available on the iOS and Android platforms. In conjunction with our Hospital Incident Command System, resident advisory council, and health system innovation center, we identified critical, time-sensitive policies for app usage. A shared collaborative document was used to align app-based communication with more traditional communication channels. To minimize synchronization efforts, we particularly focused on high-yield policies, and the time of last review and the corresponding reviewer were noted for each protocol. To facilitate social distancing and remote patient rounding, the app was also populated with a searchable directory of numbers to patient bedside phones and hospital locations. We monitored anonymized user activity from February 1 to July 31, 2020.ResultsOn its first release, 1104 clinicians downloaded moblMD during the observation period, of which 46% (n=508) of downloads occurred within 72 hours of initial release. COVID-19 policies in the app were reviewed most commonly during the first week (801 views). Users made sustained use of hospital phone dialing features, including weekly peaks of 2242 phone number dials, 1874 directory searches, and 277 patient room phone number searches through the last 2 weeks of the observation period. Furthermore, clinicians submitted 56 content- and phone number–related suggestions through moblMD.ConclusionsWe rapidly developed and deployed a communication-focused mobile app early during COVID-19, which has demonstrated initial and sustained value among clinicians in communicating with in-patients and each other during social distancing. Our internal innovation benefited from our team’s familiarity with institutional structures, short feedback loops, limited security and privacy implications, and a path toward sustainability provided by our innovation center. Challenges in content management were overcome through synchronization efforts and timestamping review. As COVID-19 continues to alter health care delivery, user activity metrics suggest that our solution will remain important in our efforts to continue providing safe and up-to-date clinical care.

Tablet-Based Apps for Phonics and Phonological Awareness: Protocol for Evidence-Based Appraisal of Content, Quality, and Usability.

BackgroundThe use of mobile apps to manage and promote health is becoming increasingly popular. Mobile apps are gaining popularity particularly in educational and interventional settings owing to their perceived advantages including support for and engagement of individuals with reading difficulties. In the context of COVID-19, the need for technology-based tools has increased. For practitioners and educators who wish to use apps in their practice or recommend apps to individuals with reading difficulties, it is challenging to identify high-quality apps in app stores.ObjectiveThis protocol describes a systematic search, selection, and appraisal process for tablet apps targeting phonics knowledge and phonological awareness skills. This protocol aimed to (1) provide a systematic method for identifying tablet apps targeting phonics knowledge and phonological awareness skills in the Google Play Store and Apple’s App Store and (2) describe an evidence-based approach for quality appraisal of these apps by using structured tools.MethodsThis protocol describes an evidence-based method guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to systematically search, select, and appraise apps targeting phonics knowledge and phonological awareness skills, present in Google Play and the App Store. We intend to perform a systematic and comprehensive search and a 2-step process for screening: (1) broad screening (app titles) and (2) narrow screening (marketing descriptions). Quality appraisal of the included apps will involve two structured appraisal tools: (1) the Mobile Application Rating Scale and (2) the Appraising Apps for Reading Checklist.ResultsThis method will help determine the number of apps targeting phonics knowledge and phonological awareness, present on the Android and iOS platforms. The content, quality, and usability of these apps will be determined using structured appraisal tools. We have planned to conduct searches on Google Play and the App Store in January-March 2021; broad and focused screening, from April 2021; and data extraction and quality appraisal in October 2021.ConclusionsThis protocol provides a basis for locating and evaluating apps targeting phonics knowledge and phonological awareness skills. This protocol will support practitioners, educators, and families to make informed decisions when purchasing apps for instructional use.International Registered Report Identifier (IRRID)PRR1-10.2196/23921

Designing and Developing an Agricultural Product Sales Application Catalog with a Hybrid Application Development Framework

The use of e-commerce systems continues to increase in Indonesia, but the field facts show that e-commerce in agriculture is still relatively low compared to other fields. The problems faced are limited e-commerce infrastructure, lack of the farmers' knowledge on the benefits of e-commerce, and application design, which is challenging to use. Those problems cause a lack of e-commerce use in this field. Besides, the characteristics of technology users in agriculture also have a considerable influence. In this paper, a mobile-based application is developed to facilitate farmers in promoting agricultural products sales. One of the problems faced in developing mobile applications is the diversity of mobile device platforms. A mobile application can ideally run on multiple platforms. This paper contributes to building an application by using a hybrid method for application development to be used on the Android and IOS platforms. The application built aims to display agricultural products of farmers. The filter feature also makes it easier to find agricultural products in this application. The application is built using the Flutter framework to use one source of the same code on several mobile operating systems at once. There are some slight differences between the output versions of the Android and iOS platforms. Although there are some subtle differences, the similarity rate reaches 90%.

Optimizing QR code readability for curved agro-food packages using response surface methodology to improve mobile phone-based traceability

Quick response (QR) codes are two-dimensional (2D) barcodes that are widely used in food packaging. Moreover, scanning QR codes with mobile phones has become a convenient method to ensure product traceability. However, attaching QR codes to curved agro-food products to improve traceability effects and increase customer satisfaction presents a significant challenge. This paper reports an optimization method developed for curved surfaces that uses response surface methodology (RSM) analysis. The value ranges of the following four factors affecting QR readability were selected and analyzed: reading distance, ball diameter, code size, and coded characters. A central composite inscribed experiment using four factors with five levels was designed using RSM to obtain the optimal reading parameters, and experimental equipment was designed in-house. The results indicate the primary factors that significantly affect QR code readability are reading distance, ball diameter, the interactions between reading distance and code size, and the interactions between ball diameter and code size. Optimal parameters were obtained with the established model by using apples and melons. For actual experimental testing, two solutions for apples and one solution for melon were selected to determine the requisite average values for the parameters. Tests were performed using different mobile phones, including both iOS and Android platforms. An analysis of the results reveals only slight differences between simulated readability and actual readability for different fruits and mobile phone platforms.

A Precise Tracking Algorithm Using PDR and Wi-Fi/iBeacon Corrections for Smartphones

Considering smartphone-based indoor localization and tracking, combination of Wi-Fi fingerprinting and pedestrian dead reckoning (PDR) becomes prevalent due to wide deployment of indoor Wi-Fi access points and easily access of inertial measurement units (IMUs) on smartphones. Since Wi-Fi fingerprinting depends on the similarities between online and offline received signal strengths, it suffers from the fluctuation of radio signal measurements. Meanwhile, the PDR relies on the IMUs, which undergo accumulated errors in long walking distance. In this paper, we aim to improve the indoor tracking accuracy, while filling up the gap between two popular Android and iOS platforms. In particular, we propose a hybrid tracking algorithm that integrates the PDR with Wi-Fi/iBeacon signal features. There are three key points in our paper. First, we process Wi-Fi/iBeacon signals to build conversion functions to convert signals to related proximity distances. Besides, in order to support mobile tracking, we introduce a placement methodology for iBeacon installation for a specific region of interest. Second, a mobile smartphone uses an improved PDR to localize itself, while incorporating Wi-Fi/iBeacon data for estimating a starting point and correcting its location along the walking path. Finally, we build an iOS app to implement the proposed scheme and display visual tracking results. Experiment results show that our proposed scheme is more robust and accurate compared to the conventional schemes.

Mobile Malware Classification for iOS Inspired by Phylogenetics

Cyber-attacks such as ransomware, data breaches, and phishing triggered by malware, especially for iOS (iPhone operating system) platforms, are increasing. Yet not much works on malware detection for the iOS platform have been done compared to the Android platform. Hence, this paper presents an iOS malware classification inspired by phylogenetics. It consists of mobile behaviour, exploits, and surveillance features. The new iOS classification helps to identify, detect, and predict any new malware variants. The experiment was conducted by using hybrid analysis, with twelve (12) malwares datasets from the Contagio Mobile website. As a result, twenty-nine (29) new classifications have been developed. One hundred (100) anonymous mobile applications (50 from the Apple Store and 50 from iOS Ninja) have been used for evaluation. Based on the evaluation conducted, 13% of the mobile applications matched with the developed classifications. In the future, this work can be used as guidance for other researchers with the same interest.

EVALUATION OF INFERENCE OPTIMIZED IMAGE CLASSIFICATION MODELS ON IOS PLATFORM

In this paper, we evaluate MobileNetV2 and EfficientNet network architectures performance on the iOS platform. Even though both networks are well researched, there is a certain lack of empirical data when it comes to the iOS platform. In this paper, we provide experimental data for top-1 accuracy and inference time, as well as RAM footprint collected on iPhone X and iPhone 12 Pro. The evaluation was performed using CPU, GPU, and Neural Engine (iPhone 12 only). Also, a full int8 quantization evaluation was done for MobileNetV2. Challenges around EfficientNet quantization are also described. The scoring metrics and dataset used to train models were provided as part of Mobile AI 2021 Real-Time Camera Scene Detection Challenge: https://competitions.codalab.org/compet¬iti¬o¬n¬s¬/¬2¬8¬1¬13 .

App Stores, Aftermarkets &amp;amp; Antitrust

App stores have become the subject of controversy and criticism within antitrust. For instance, app developers such as Spotify and Epic Games (creator of Fortnite) allege that Apple’s 30 percent cut of all sales in the App Store violates the antitrust laws and is indicative of monopoly power. The idea is that iPhone users are locked into Apple’s walled garden iOS platform, which frees Apple to engage in misconduct in the App Store “aftermarket” to the detriment of users and app developers. This Article challenges the recent economic and legal characterizations of app stores and the nature of the alleged harm. First, this Article builds an accessible, economic framework to illustrate how app stores do not represent the same type of aftermarkets that were condemned in the Supreme Court’s landmark Kodak case. Importantly, the differences between Kodak-like aftermarkets and app store aftermarkets raise serious questions whether the digital revival of the aftermarket doctrine conforms with the economic realities of these markets. Second, the complexity of the commercial relationships found in app stores has raised questions regarding who has standing to seek antitrust damages in this type of market setting. This Article provides an overview of the development of the current doctrine of antitrust standing—focusing on Illinois Brick and Apple v. Pepper. Further, this Article contends that Justice Kavanaugh’s opinion in Pepper, which gave iPhone users the right to sue Apple over the 30 percent commission, was right for the wrong reason. Instead, Justice Gorsuch’s dissent offers a much more economically sound approach to antitrust standing—as his “proximate cause” standard does not artificially focus on identifying the “direct purchaser,” which is unnecessarily limiting for more complex commercial relationships. As the number of antitrust claims against various app stores proliferate, the consequences of faulty characterizations of app stores will only grow.

The New Era of Teaching: Using Video Games to Teach Macroeconomics

This paper discusses how the commercial video game Civilization VI can be used as an experiential learning tool in the economics classroom. The game simulates the progress of civilizations over the past 6000 years of human history and can be used to cover principles of economics, economic history and economic growth theory. Students experience and learn the importance of natural resources, human capital, physical capital, technology, trade, government stability, property rights, geography and openness to trade to economic growth and development. The paper discusses specifically how the game relates to specific economic topics. The game is available in Mac, IOS, Windows, PS4 and most other platforms.

Methods of intellectual analysis of processes in medical information systems

Methods of data mining and intelligent analysis of processes are investigated for the develop¬ment of a mobile intelligent application “Emergency Medical Aid”, which effectively solves the problems of information support for medical purposes in a particular emergency situation for the user. With the help of Data Mining methods, a knowledge base for a personal assistant has been developed, which makes it possible to analyze indicators of a person’s condition and draw conclusions regarding the diagnosis in the field of emergency medicine. The knowledge base presented allows us to apply the inference model with the possibility of using fuzzy rules. To improve the efficiency of determining the diagnosis by the system using the Process Mining methods, models of the business process of the medical information system have been created, built on the basis of an artificially generated event log compiled with the involvement of experts in the subject areas of emergency medicine. An intelligent application on the iOS platform that plays the role of a personal assistant for decision support is presented.

Validación del uso de teléfonos inteligentes para medición de ruido ambiental urbano

El presente estudio evalúa la fiabilidad del uso de teléfonos inteligentes para monitorear el ruido urbano, como una alternativa de bajo costo a los instrumentos de monitoreo tradicionales. La metodología consistió en diseñar una herramienta de evaluación de ruido en KoBoToolbox, la cual registró las mediciones recopiladas simultáneamente con un sonómetro y dos aplicaciones para teléfonos inteligentes, Sound Meter X Standard y Sound Meter Pro, en los sistemas iOS y Android respectivamente. Las tres mediciones fueron comparadas con el método de análisis de varianza (ANOVA) de un factor. Las pruebas se realizaron en el parque El Paraíso, de la ciudad de Cuenca, basándose en la legislación ecuatoriana. Los resultados sugieren que la aplicación utilizada para la plataforma Android presenta menor grado de variación de medición con respecto a la utilizada en la plataforma iOS. Así también, muestran una relación significativa entre niveles de ruido elevados y focos de contaminación acústica por presencia de tráfico vehicular. La investigación tiene implicaciones para el uso futuro en programas de monitoreo participativo de ruido ambiente bajo un enfoque de ciencia ciudadana, complementando las normas y legislaciones vigentes.

Design, implementation, and evaluation of a game-based application for aiding chemical engineering and chemistry students to review the organic reactions

This article presents a multilingual (Portuguese, Spanish, English, and French) game-based application to the Android and the iOS platforms. The game allows undergraduate Chemical Engineering and Chemistry students to individually review organic reactions in an engaging and fun way by answering over 600 multi-choice questions. Undergraduate students tested and evaluated the game, and their opinions revealed that they liked to use the game as a complementary educational tool able to aid them in their studies. Moreover, the usability assessment of the game provided a good SUS score close to 79.

The Integration of a Big Data Framework and a Mobile Application on the iOS Platform to Support Smart Tourism

The Integration of a Big Data Framework and a Mobile Application on the iOS Platform to Support Smart Tourism

Assessing User Retention of a Mobile App: Survival Analysis.

BackgroundA mobile app generates passive data, such as GPS data traces, without any direct involvement from the user. These passive data have transformed the manner of traditional assessments that require active participation from the user. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device.ObjectiveThe primary aim of this study was to quantify user retention for the “Staff Hours” app using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as factors associated with the survival rates of user retention.MethodsWe developed an app called “Staff Hours” to automatically calculate users’ work hours through GPS data (passive data). “Staff Hours” not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in the collection of passive and active data among 342 office workers from the “Staff Hours” database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention.ResultsA total of 342 volunteers (224 men; mean age 33.8 years, SD 7.0 years) were included in this study. Passive data had higher user retention than active data (P=.011). In addition, user retention for passive data collected via Android devices was higher than that for iOS devices (P=.015). Trainee physicians had higher user retention for the collection of active data than trainees from other occupations, whereas no significant differences between these two groups were observed for the collection of passive data (P=.700).ConclusionsOur findings demonstrated that passive data collected via Android devices had the best user retention for this app that records GPS-based work hours.