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Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the process of classifying reviews many researchers have adopted machine learning approaches. Keeping in view, the rising demand for educational applications, especially during COVID-19, this research aims to automate Android application education reviews' classification and sentiment analysis using natural language processing and machine learning techniques. A baseline corpus comprising 13,000 records has been built by collecting reviews of more than 20 educational applications. The reviews were then manually labelled with respect to sentiment and issue types mentioned in each review. User reviews are classified into eight categories and various machine learning algorithms are applied to classify users' sentiments and issues of applications. The results demonstrate that our proposed framework achieved an accuracy of 97% for sentiment identification and an accuracy of 94% in classifying the most significant issues. Moreover, the interpretability of the model is verified by using the explainable artificial intelligence technique of local interpretable model-agnostic explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
The COVID-19 pandemic has given people much free time. With this, the researchers want to encourage these people to read instead of scrolling through social media. A barrier to reading for many people is not knowing what to read and disinterest in popular books that they would find when they search online. The existing websites that encourage book reading rely on social networking for their recommendations, while the collaborative filtering algorithms applied to books do not exist in the mobile application form. Readwell is a book recommender Android app with a Point-of-Sales System created using Java, Python, and SQLite databases. The information regarding the books was web scraped from the Goodreads website. It aims to apply the more efficient collaborative filtering algorithm to an accessible mobile application that allows users to directly buy the books they are interested in, thus encouraging the reading and buying of books. The researchers created unit test cases to validate the different functionalities of the application. © 2022 IEEE.
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Mobile Financial Services (MFS) has gained significant popularity during the COVID-19 pandemic, especially among marginalized and low-income, low-literate communities around the world. Such communities have not been traditionally considered while designing MFS services via smartphone apps or USSD services in featurephones. Financial constraints limit such end-users towards basic featurephones, where recent appstore support has made it possible to deploy app-based MFS solutions beyond USSD. This new featurephone platform is a relatively underexplored area in terms of addressing design issues related to aforementioned end-users while developing MFS solutions. Our work addresses this gap by presenting qualitative findings on barriers to technology access focused on MFS solutions in marginal communities. We present a prototype non-USSD, app-based solution on an appstore-supported featurephone platform designed via a human-centered approach. This work has the potential to increase the financial inclusivity of marginalized communities in cashless MFS transactions via low-cost, appstore-enabled featurephones. © 2023 ACM.
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This article critically examines South Korea and China's COVID-19 tracking apps by bridging surveillance studies with feminist technoscience's understanding of the "politics of care". Conducting critical readings of the apps and textual analysis of discursive materials, we demonstrate how the ideological, relational, and material practices of the apps strategically deployed "care" to normalize a particular form of pandemic technogovernance in these two countries. In the ideological dimension, media and state discourse utilized a combination of vilifying and nationalist rhetoric that framed one's acquiescence to surveillance as a demonstration of national belonging. Meanwhile, the apps also performed ambivalent roles in facilitating essential care services and mobilizing self-tracking activities, which contributed to the manufacturing of pseudonormality in these societies. In the end, we argue that the Chinese and South Korean governments managed to frame their aggressive surveillance infrastructure during COVID-19 as a form of paternalistic care by finessing the blurred boundaries between care and control. [ FROM AUTHOR] Copyright of New Media & Society is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Aim Frontline health care workers (FHCWs) have endured a range of adverse mental health outcomes during the COVID-19 pandemic. Despite the widespread availability and ease-of-use of self-help mobile mental health apps, little is known about the feasibility of implementing such tools among COVID-19 FHCWs in real-world nursing settings. Methods This quality improvement project evaluated the feasibility of implementing the COVID Coach app among COVID-19 FHCWs in a skilled nursing facility. Results Participants endorsed high average ratings of the acceptability, appropriateness, feasibility, knowledge, perceived usefulness of the app. Discussion Implications for the broader dissemination of mobile self-help apps are discussed.
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Online shopping has revolutionized our daily lives in the modern era. We can purchase needed goods on mobile shopping applications (apps) anytime and anywhere without leaving home. Especially during the COVID-19 pandemic, we have become increasingly dependent on various mobile shopping activities. However, the visual design of the shopping app interface often affects the user's interactive experience and the efficiency of browsing product information. In addition, gender differences are also worth being considered in the shopping interface design process. To achieve the goal, the research conducted a user study (N=40) of a 2× 2 x 2 mixed factorial design (i.e., information layout x display mode x gender difference). Each participant performed four tasks during the experiment. The authors measured the task completion time, collected the subjective responses from the SUS and the 7-point Likert scale questionnaire, and interviewed participants. The results revealed that: (1) females perform faster in lighter mode when searching for information location, while males perform faster in darker mode. (2) The information layout affects the user's visual search performance and subjective evaluation;females prefer the list style, but men prefer the matrix style. (3) Participants (both males and females) perceived matrix style as more popular than list style in dark mode;however, the result was reversed in light mode. The findings generated from the research can serve as a good reference for the development of user experience in the user interface design of mobile shopping apps. © 2013 IEEE.
ABSTRACT
Attendance marking in a classroom is a tedious and time-consuming task. Due to a large number of students present, there is always a possibility of proxy. In recent times, the task of automatic attendance marking has been extensively addressed via the use of fingerprint-based biometric systems, radio frequency identification tags, etc. However, these RFID systems lack the factor of dependability and due to COVID-19 use of fingerprint-based systems is not advisable. Instead of using these conventional methods, this paper presents an automated contactless attendance system that employs facial recognition to record student attendance and a gesture sensor to activate the camera when needed, thereby consuming minimal power. The resultant data is subsequently stored in Google Spreadsheets, and the reports can be viewed on the webpage. Thus, this work intends to make the attendance marking process contactless, efficient and simple. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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BACKGROUND: Up to 15% of pregnant and postpartum women commonly experience undiagnosed and untreated mental health conditions, such as depression and anxiety, which may result in serious health complications. Mobile health (mHealth) apps related to mental health have been previously used for early diagnosis and intervention but not among pregnant and postpartum women. OBJECTIVE: This study aims to assess the acceptability of using mHealth to monitor and assess perinatal and postpartum depression and anxiety. METHODS: Focus group discussions with pregnant and postpartum women (n=20) and individual interviews with health care providers (n=8) were conducted to inform the acceptability of mHealth and determine its utility for assessing perinatal and postpartum mood symptoms. Participants were recruited via purposive sampling from obstetric clinics and the surrounding community. A semistructured interview guide was developed by an epidemiologist with qualitative research training in consultation with an obstetrician. The first author conducted all focus group discussions and provider interviews either in person or via Zoom (Zoom Video Communications, Inc) depending on the COVID-19 protocol that was in place during the study period. All interviews were audio recorded with consent; transcribed; and uploaded for coding to ATLAS.ti 8 (ATLAS.ti Scientific Software Development Gmb H), a qualitative data analysis and retrieval software. Data were analyzed using the deductive content analysis method using a set of a priori codes developed based on the interview guide. Methodological rigor and quality were ensured by adopting a systematic approach during the implementation, data collection, data analysis, and reporting of the data. RESULTS: Almost all women and providers had downloaded and used at least 1 health app. The respondents suggested offering short questions in layperson language that could be understood by women of all educational levels and offering no more than 2 to 3 assessments per day at preferred timings decided by the women themselves. They also suggested that the women themselves receive the alerts first, with other options being family members, spouses, or friends if the women themselves did not respond within 24 to 72 hours. Customization and snooze features were strongly endorsed by women and providers to improve acceptability and utility. Women mentioned competing demands on their time during the postpartum period, fatigue, privacy, and the security of mental health data as concerns. Health care professionals highlighted the long-term sustainability of app-based mood assessment and monitoring as an important challenge. CONCLUSIONS: The findings from this study show that mHealth would be acceptable to pregnant and postpartum women for monitoring mood symptoms. This could inform the development of clinically meaningful and inexpensive tools for facilitating the continuous monitoring of, the early diagnosis of, and an early intervention for mood disorders in this vulnerable population.
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Human mobility restriction policies have been widely used to contain the coronavirus disease-19 (COVID-19). However, a critical question is how these policies affect individuals' behavioral and psychological well-being during and after confinement periods. Here, we analyze China's five most stringent city-level lockdowns in 2021, treating them as natural experiments that allow for examining behavioral changes in millions of people through smartphone application use. We made three fundamental observations. First, the use of physical and economic activity-related apps experienced a steep decline, yet apps that provide daily necessities maintained normal usage. Second, apps that fulfilled lower-level human needs, such as working, socializing, information seeking, and entertainment, saw an immediate and substantial increase in screen time. Those that satisfied higher-level needs, such as education, only attracted delayed attention. Third, human behaviors demonstrated resilience as most routines resumed after the lockdowns were lifted. Nonetheless, long-term lifestyle changes were observed, as significant numbers of people chose to continue working and learning online, becoming "digital residents." This study also demonstrates the capability of smartphone screen time analytics in the study of human behaviors. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-023-00391-9.
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The SARS-CoV-2 pandemic is a pressing societal issue today. The German government promotes a contract tracing app named Corona-Warn-App (CWA), aiming to change citizens' health behaviors during the pandemic by raising awareness about potential infections and enable infection chain tracking. Technical implementations, citizens' perceptions, and public debates around apps differ between countries, e. g., in Germany there has been a huge discussion on potential privacy issues of the app. Thus, we analyze effects of privacy concerns regarding the CWA, perceived CWA benefits, and trust in the German healthcare system to answer why citizens use the CWA. In our initial conference publication at ICT Systems Security and Privacy Protection - 37th IFIP TC 11 International Conference, SEC 2022, we used a sample with 1752 actual users and non-users of the CWA and and support for the privacy calculus theory, i. e., individuals weigh privacy concerns and benefits in their use decision. Thus, citizens privacy perceptions about health technologies (e. g., shaped by public debates) are crucial as they can hinder adoption and negatively affect future fights against pandemics. In this special issue, we adapt our previous work by conducting a second survey 10 months after our initial study with the same pool of participants (830 participants from the first study participated in the second survey). The goal of this longitudinal study is to assess changes in the perceptions of users and non-users over time and to evaluate the influence of the significantly lower hospitalization and death rates on the use behavior which we could observe during the second survey. Our results show that the privacy calculus is relatively stable over time. The only relationship which significantly changes over time is the effect of privacy concerns on the use behavior which significantly decreases over time, i. e., privacy concerns have a lower negative effect one the CWA use indicating that it did not play such an important role in the use decision at a later point in time in the pandemic. We contribute to the literature by introducing one of the rare longitudinal analyses in the literature focusing on the privacy calculus and changes over time in the relevant constructs as well as the relationships between the calculus constructs and target variables (in our case use behavior of a contact tracing app). We can see that the explanatory power of the privacy calculus model is relatively stable over time even if strong externalities might affect individual perceptions related to the model.
ABSTRACT
Digital contact tracing has been claimed as imperative to controlling the spread of COVID19. However, the state-by-state approach in the United States led to divergences in contact tracing. This study analyzed contact-tracing apps as "boundary objects" through which each state worked toward the governance of the pandemic without having a formal consensus. Through media coverage and walkthrough analyses of three digital contacttracing apps in Alabama, California, and New York, we closely investigated both convergences and divergences of the apps. In the process, we located the implications of Google/Apple's Bluetooth-based exposure notification system for digital contact tracing within and beyond state boundaries. Our findings suggest that the development of apps shared the notion of an ideal contact-tracing method-exposure notification-while each state was also situated in their local experiences of the pandemic as reflected in distinct app features. We further discuss the implications of techno-solutionist standardization of such digital contact-tracing apps.
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This study aims at exploring the underlying determinants influencing students' continuance intention to use an e-Learning platform during the COVID-19 pandemic. Based on the technology acceptance model and expectation-confirmation model, the study investigated the role of contextual (i.e., social isolation), psychological (academic year loss and cyberchondria), and student support-related (government and institutional supports) determinants on students' continuance intention to use an e-Learning platform during the pandemic. The study collected data from 440 respondents and analyzed those with Structural Equation Modeling. The findings showed that an e-Learning continuance intention during the pandemic is affected by usefulness, ease of use, attitudes, and intention to use the e-Learning platform;while the behavioral intention is influenced by usefulness, ease of use, attitudes, contextual, psychological, and student support-related determinants;and attitudes are impacted by usefulness and ease of use. Moreover, usefulness is predicted by confirmation of expectation;e-satisfaction is forecasted by usefulness and confirmation of expectation;whereas, cyberchondria is influenced by social isolation;fear of academic year loss is influenced by cyberchondria. Finally, intention to use mediated the impact of usefulness, ease of use, attitudes, contextual, psychological, and student support-related determinants on continuance intention. The study contributes to e-Learning literature incorporating contextual, psychological, and student support-related determinants into the technology acceptance model and expectation-confirmation model, which guide policymakers to understand how all levels of students can be brought into the e-Learning platforms that eventually help to eliminate digital discrimination barrier in the academia during any emergency. The policymakers must be careful in designing eLearning platforms since students' e-learning continuance intention may vary due to unprecedented crises, such as COVID-19.
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Numerous epidemic lung diseases such as COVID-19, tuberculosis (TB), and pneumonia have spread over the world, killing millions of people. Medical specialists have experienced challenges in correctly identifying these diseases due to their subtle differences in Chest X-ray images (CXR). To assist the medical experts, this study proposed a computer-aided lung illness identification method based on the CXR images. For the first time, 17 different forms of lung disorders were considered and the study was divided into six trials with each containing two, two, three, four, fourteen, and seventeen different forms of lung disorders. The proposed framework combined robust feature extraction capabilities of a lightweight parallel convolutional neural network (CNN) with the classification abilities of the extreme learning machine algorithm named CNN-ELM. An optimistic accuracy of 90.92% and an area under the curve (AUC) of 96.93% was achieved when 17 classes were classified side by side. It also accurately identified COVID-19 and TB with 99.37% and 99.98% accuracy, respectively, in 0.996 microseconds for a single image. Additionally, the current results also demonstrated that the framework could outperform the existing state-of-the-art (SOTA) models. On top of that, a secondary conclusion drawn from this study was that the prospective framework retained its effectiveness over a range of real-world environments, including balanced-unbalanced or large-small datasets, large multiclass or simple binary class, and high- or low-resolution images. A prototype Android App was also developed to establish the potential of the framework in real-life implementation.
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While thousands of new mobile applications (i.e., apps) are being added to the major app markets daily, only a small portion of them attain their financial goals and survive in these competitive marketplaces. A key to the quick growth and success of relatively less popular apps is that they should make their way to the limited list of apps recommended to users of already popular apps;however, the focus of the current literature on consumers has created a void of design principles for app developers. In this study, employing a predictive network analytics approach combined with deep learning-based natural language processing and explainable artificial intelligence techniques, we shift the focus from consumers and propose a developer-oriented recommender model. We employ a set of app-specific and network-driven variables to present a novel approach for predicting potential recommendation relationships among apps, which enables app developers and marketers to characterize and target appropriate consumers. We validate the proposed model using a large (>23,000), longitudinal dataset of medical apps collected from the iOS App Store at two time points. From a total of 10,234 network links (rec-ommendations) formed between the two data collection points, the proposed approach was able to correctly predict 8,780 links (i.e., 85.8 %). We perform Shapley Additive exPlanation (SHAP) analysis to identify the most important determinants of link formations and provide insights for the app developers about the factors and design principles they can incorporate into their development process to maximize the chances of success for their apps.
ABSTRACT
The effects of the pandemic can translate into a variety of physical and emotional reactions that are affecting the population, particularly the elderly Panamanian population, who have not been able to overcome the mainly emerging challenges of an infectious disease with health implications. physical and has also profoundly affected their well-being and mental health. To allow the Panamanian elderly population to improve emotional self-control and mental relaxation, we propose a software architecture for the development of a recommendation system integrating: artificial intelligence (AI), internet of things (IoT) and mobile applications. This research will contribute to the elderly population in Panama having a mobile application which is beneficial as a non-pharmaceutical alternative to cope with psychological conditions caused by the Covid-19 disease. Regarding the most relevant limitations we have are the acquisition of the data set for training. As future works, we hope to have a more robust architecture to implement it in other activities related to the heath self-control of Panamanian patients. © 2022, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
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Through and after the quarantine period of the COVID-19 epidemic, Mobile Applications developed for different purposes and goals, such as contacts and patient tracing, digital services, monitoring and testing, epidemiological research, and quarantine compliance. The main aim of this study is to highlight the effect of mobile pandemic applications in Iraqi society and the trustworthiness of developers and distributors of apps. To this end, we explored differences in the attitudes of smartphone users toward pandemic apps and shared the data to conduct research. The method adopted to achieve the Survey in this study is an Email and telephone-based Survey of (318) participants adults over 18 years old in Iraq. We used a total of (315) for Statistical Analysis. This 9-item Survey examined the current use of epidemic applications, motivations for using them, trust in app distributors, data handling, willingness to share coded data with researchers using a pandemic app, attitudes toward app use among people, demographics, and personal characteristics. The results of this study showed that most participants stated they were using smartphones (307/315, 97.5), but only (77/307, 24.4) were using pandemic apps on their smartphones. Intriguingly, in this Survey, when participants asked for the preferable organizations for storing data and application division, trust in federal or state government, regional health office, public-appointed such as statutory health insurance, or government-funded organizations (research institutes) was much higher than in private organizations (private research institutions, clinics, health insurances, information technology companies). © University of Garmian. All Rights Reserved.
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Throughout the COVID-19 pandemic, a variety of digital technologies have been leveraged for public health surveillance worldwide. However, concerns remain around the rapid development and deployment of digital technologies, how these technologies have been used, and their efficacy in supporting public health goals. Following the five-stage scoping review framework, we conducted a scoping review of the peer-reviewed and grey literature to identify the types and nature of digital technologies used for surveillance during the COVID-19 pandemic and the success of these measures. We conducted a search of the peer-reviewed and grey literature published between 1 December 2019 and 31 December 2020 to provide a snapshot of questions, concerns, discussions, and findings emerging at this pivotal time. A total of 147 peer-reviewed and 79 grey literature publications reporting on digital technology use for surveillance across 90 countries and regions were retained for analysis. The most frequently used technologies included mobile phone devices and applications, location tracking technologies, drones, temperature scanning technologies, and wearable devices. The utility of digital technologies for public health surveillance was impacted by factors including uptake of digital technologies across targeted populations, technological capacity and errors, scope, validity and accuracy of data, guiding legal frameworks, and infrastructure to support technology use. Our findings raise important questions around the value of digital surveillance for public health and how to ensure successful use of technologies while mitigating potential harms not only in the context of the COVID-19 pandemic, but also during other infectious disease outbreaks, epidemics, and pandemics.
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Urinary incontinence, affecting over 300 million women worldwide, regardless of race and age, is considered one of the most important health issues in the 21st century. owing to the scale of the problem, the priority should be to provide therapy to as many patients as possible. Although effective conservative treatment measures for urinary incontinence are available, they may not cater for all individuals who seek help. Sometimes, a sense of embarrassment or a fear of stigmatization causes patients' reluctance to report urinary incontinence symptoms to their health care provider and to join therapy. That forces therapists to search for a new approach. in this field, the use of mHealth technologies seems very promising. They have become even more valuable during the CoVid-19 pandemic, when the interest in telemedicine, as a means of providing care while not being exposed to the risk of virus infection, further increased. The purpose of this work was a narrative review showing possibilities of employing conservative measures to manage stress urinary incontinence in women, with a particular emphasis on the use of mHealth technologies, as recent studies have shown that mobile applications seem to be an effective tool in terms of improving stress urinary incontinence symptoms, satisfaction, and adherence to therapy.Copyright © Wroclaw University of Health and Sport Sciences.
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Smart transformative services such as digital contact tracing apps are a means to offer transformative and economic value by selfmonitoring contacts and improving the well-being of users, while also reducing concerns when using services during pandemics such as COVID-19. In this study, we identify significant factors as well as communication and promotion strategies to encourage digital contact tracing app nonusers to use such apps in order to benefit from their value-creating potential. This research contributes to transformative service literature by identifying digital contact tracing apps as a means to regain confidence in using services during a pandemic, thereby offering transformative and economic value. By integrating two trust dimensions as meaningful mediators, this research sheds light on the conditions under which social influence and Internet privacy concerns influence nonusers' usage intention. Moreover, the results not only identify significant factors influencing intended app usage but also reveal strategies for increasing actual app usage. © 2023 C.H.BECK oHG. All rights reserved.