App Store Research Articles

Attributes, Quality, and Downloads of Dementia-Related Mobile Apps for Patients With Dementia and Their Caregivers: App Review and Evaluation Study.

The adoption of mobile health (mHealth) apps among older adults (>65 years) is rapidly increasing. However, use of such apps has not been fully effective in supporting people with dementia and their caregivers in their daily lives. This is mainly attributed to the heterogeneous quality of mHealth apps, highlighting the need for improved app quality in the development of dementia-related mHealth apps. The aims of this study were (1) to assess the quality and content of mobile apps for dementia management and (2) to investigate the relationship between app quality and download numbers. We reviewed dementia-related mHealth apps available in the Google Play Store and Apple App Store in Taiwan. The identified mobile apps were stratified according to a random sampling approach and evaluated by five independent reviewers with sufficient training and proficiency in the field of mHealth and the related health care sector. App quality was scored according to the user version of the Mobile Application Rating Scale. A correlation analysis was then performed between the app quality score and number of app downloads. Among the 17 apps that were evaluated, only one was specifically designed to provide dementia-related education. The mean score for the overall app quality was 3.35 (SD 0.56), with the engagement (mean 3.04, SD 0.82) and information (mean 3.14, SD 0.88) sections of the scale receiving the lowest ratings. Our analyses showed clear differences between the top three- and bottom three-rated apps, particularly in the entertainment and interest subsections of the engagement category where the ratings ranged from 1.4 to 5. The top three apps had a common feature in their interface, which included memory, attention, focus, calculation, and speed-training games, whereas the apps that received lower ratings were found to be deficient in providing adequate information. Although there was a correlation between the number of downloads (5000 or more) and app quality (t15=4.087, P<.001), this may not be a significant determinant of the app's perceived impact. The quality of dementia-related mHealth apps is highly variable. In particular, our results show that the top three quality apps performed well in terms of engagement and information, and they all received more than 5000 downloads. The findings of this study are limited due to the small sample size and possibility of disregarding exceptional occurrences. Publicly available expert ratings of mobile apps could help people with dementia and their caregivers choose a quality mHealth app.

Sentiment analysis on google play store app users’ reviews based on deep learning approach

Sentiment analysis on google play store app users’ reviews based on deep learning approach

Prioritizing user requirements for digital products using explainable artificial intelligence: A data-driven analysis on video conferencing apps

The advent of Industry 5.0 has brought a wealth of digital information to mobile app stores. With the help of emerging technologies such as machine learning and explainable artificial intelligence (XAI), these large amounts of user-generated data can be efficiently captured and analyzed. In this study, we propose an app store analysis framework and demonstrate the utility of the framework by mining and prioritizing user requirements in three popular video conferencing apps. We used the Sentistrength sentiment analysis tool, structural topic modeling, the Gephi web analysis tool, machine learning, and XAI techniques to conduct an in-depth analysis of user requirements in Microsoft Teams, ZOOM Cloud Meetings, and Google Meet. The findings indicated that Steal data, Audio and video quality, Customer service, Hacker issues, Meeting and account passwords, Mute and unmute, Features, and Office platform were the web conferencing system's key areas for improvement. The study demonstrated the usability of app store analysis frameworks and the great potential of XAI to provide insights about requirements prioritization by interpreting machine learning models. Additionally, it offered valuable suggestions for app developers on using the massive data in app stores to improve their apps.

Research on Apple's Marketing Strategy

With the rapid development of science and technology, electronics continue to emerge, and the market competition is increasingly fierce. Apple, with successful marketing strategies, and strong technical support, emerged among many electronics products, becoming the industry giant. Since Apple launched the App Store, the online application market has been developing rapidly, which not only promotes mobile Internet. The development of the network has changed the revenue structure of mobile value-added services and promoted the re-integration and improvement of the mobile network industry chain. In this stage, how to enter and seize the market better and faster has become the primary problem of Apple. This paper discusses the current market situation of Apple, analyzes the target market selection, and focuses on the main competitive advantages of Apple. Then, by introducing a marketing information management system, a series of marketing channels are organized for Apple and marketing countermeasures. The management system enables to effective implementation of the marketing and promotion plan, as well as evaluation, monitoring, and update of the implementation results in a reasonable and timely manner, laying a good foundation for successful marketing. The work provided valuable advice. Through an in-depth analysis of Apple, people can deeply understand its core competitiveness and enterprise development of the layout of ideas. The paper also provides examples and references for enterprises to optimize their business models.

Regulatory Standards and Guidance for the Use of Health Apps for Self-Management in Sub-Saharan Africa: Scoping Review.

Health apps are increasingly recognized as crucial tools for enhancing health care delivery. Many countries, particularly those in sub-Saharan Africa, can substantially benefit from using health apps to support self-management and thus help to achieve universal health coverage and the third sustainable development goal. However, most health apps published in app stores are of unknown or poor quality, which poses a risk to patient safety. Regulatory standards and guidance can help address this risk and promote patient safety. This review aims to assess the regulatory standards and guidance for health apps supporting evidence-based best practices in sub-Saharan Africa with a focus on self-management. A methodological framework for scoping reviews was applied. A search strategy was built and applied across the following databases, gray literature sources, and institutional websites: PubMed, Scopus, World Health Organization (WHO) African Index Medicus, OpenGrey, WHO Regional Office for Africa Library, ICTworks, WHO Directory of eHealth policies, HIS Strengthening Resource Center, International Telecommunication Union, Ministry of Health websites, and Google. The search covered the period between January 2005 and January 2024. The findings were analyzed using a deductive descriptive content analysis. The policy analysis framework was adapted and used to organize the findings. The Reporting Items for Stakeholder Analysis tool guided the identification and mapping of key stakeholders based on their roles in regulating health apps for self-management. The study included 49 documents from 31 sub-Saharan African countries. While all the documents were relevant for stakeholder identification and mapping, only 3 regulatory standards and guidance contained relevant information on regulation of health apps. These standards and guidance primarily aimed to build mutual trust; promote integration, inclusion, and equitable access to services; and address implementation issues and poor coordination. They provided guidance on systems quality, software acquisition and maintenance, security measures, data exchange, interoperability and integration, involvement of relevant stakeholders, and equitable access to services. To enhance implementation, the standards highlight that legal authority, coordination of activities, building capacity, and monitoring and evaluation are required. A number of stakeholders, including governments, regulatory bodies, funders, intergovernmental and nongovernmental organizations, academia, and the health care community, were identified to play key roles in regulating health apps. Health apps have huge potential to support self-management in sub-Saharan Africa, but the lack of regulatory standards and guidance constitutes a major barrier. Hence, for these apps to be safely and effectively integrated into health care, more attention should be given to regulation. Learning from countries with effective regulations can help sub-Saharan Africa build a more robust and responsive regulatory system, ensuring the safe and beneficial use of health apps across the region. RR2-10.1136/bmjopen-2018-025714.

Balancing security and contestability in the DMA: the case of app stores

ABSTRACT The EU’s Digital Markets Act will require certain large tech firms’ ecosystems to become more open. The Act contains few exceptions – but law-makers did include special protections for undertakings providing operating systems, like Apple and Google, to protect security. This paper explores how the Commission should assess these measures. It argues that security is not a “trump card” to undermine the Act’s objectives – instead, Apple and Google must balance security benefits of their measures against any limitations on contestability. Furthermore, the Act should continue to allow both firms to differentiate their products’ approach to security. However, Apple’s (and to a lesser extent Google’s) security measures are likely to be heavily disputed. The Act does not give the Commission effective powers to efficiently resolve such disputes nor to force gatekeepers to change their approaches to security.

Detection System Based on Text Adversarial and Multi-Information Fusion for Inappropriate Comments in Mobile Application Reviews

With the rapid development of mobile application technology, the content and forms of comments disseminated on the internet are becoming increasingly complex. Various comments serve as users’ firsthand reference materials for understanding the application. However, some comments contain a significant amount of inappropriate content unrelated to the app itself, such as gambling, loans, pornography, and game account recharging, seriously impacting the user experience. Therefore, this article aims to assist users in filtering out irrelevant and inappropriate messages, enabling them to quickly obtain useful and relevant information. This study focuses on analyzing actual comments on various Chinese apps on the Apple App Store. However, these irrelevant comments exhibit a certain degree of concealment, sparsity, and complexity, which increases the difficulty of detection. Additionally, due to language differences, the existing English research methods exhibit relatively poor adaptability to Chinese textual data. To overcome these challenges, this paper proposes a research method named “blend net”, which combines text adversarial and multi-information fusion detection to enhance the overall performance of the system. The experimental results demonstrate that the method proposed in this paper achieves precision and recall rates both exceeding 98%, representing an improvement of at least 2% compared to existing methods.

Immediate Effects of Instrument-Assisted Soft-Tissue Mobilization Versus Dry Needling on Trigger Point Pain and Tightness of Calf Muscles in Long-Distance Runners.

Introduction Muscle tightness is frequently identified as a potential precursor to muscle injuries. Reclaiming flexibility and enhancing range of motion (ROM) is crucial for preventing injuries and achieving improvements in performance.The present study examines the immediate effects of instrument-assisted soft-tissue mobilization (IASTM) and dry needling (DN) in reducing trigger point pain and calf tightness in long-distance runners. Methodology A total of 40 long-distance runners were recruited in the study (30 males and 10 females). The procedure was performed under the author's surveillance at the sports complex. These recruited players were placed into two groups: the IASTM (n=20) and the DN (n=20) group.The outcome measures used were the pressure algometer for assessing pain pressure threshold and the lunge test. An iPhone Measure app (Measure app, Apple App Store 2023) is used to assess ankle dorsiflexion ROM. The evaluation took place both prior to and immediately following the interventionand 48 hours after the intervention. Result The analysis within each group revealed a significant alteration in pain pressure threshold for both the IASTM and DN groups (p≤0.05), along with a relative enhancement in ankle dorsiflexion ROMobserved in the IASTM group (p≤0.05). Between-group analysis revealed a notable difference with an effect size difference of Cohen's d=1.06 (large difference) in pain pressure threshold, d=0.21 (small difference) in lunge test, and d=0.57 (medium difference) in ankle dorsiflexion ROM. Conclusion The present study concludes that both groups, IASTM and DN, showed significant effects in improving pain pressure threshold in long-distance runners. However, DN showed better results. IASTM showed significant results in enhancing the ankle dorsiflexion ROM immediately. This implies thatit can be used in conjunction with stretching to decrease pain and enhance flexibility, hence improving performance and preventing injuries.

Validated mobile applications in otolaryngology head and neck surgery for patient and physicians: A systematic literature review

ImportanceMobile apps in the field of ORL-HNS, are widely used by patients and physicians, but neither necessarily developed in collaboration with healthcare professionals nor subjected to regulations by the United States Food and Drug Administration guidelines, with a resultant potential of risk for its users. ObjectiveTo provide the ORL-HNS physician with an updated list of scientific peer review literature- validated mobile apps for safe use for both the clinician and the patients, for screening, diagnosis, therapy and follow up for various ORL-HNS pathologies. Evidence reviewA comprehensive systematic review of the scientific literature was conducted in “PubMed,” “EMBASE,” and “Web of Science” without limitation of publication date up to January 1st, 2023. The included papers validated mobile apps in the ORL-HNS discipline. Each study was evaluated using the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) tool. FindingsFrom the thousands of unregulated ORL-HNS mobile apps available for download and use in the various app stores, only 17 apps were validated for safe use by the clinician and/or patient. Their information is listed. Conclusions and relevanceThe limited number of validated mobile apps highlights the importance to use validated apps in clinical practice, to improve evidence-based medicine and patient safety. Physician are encouraged to use and recommend their patients to use validated mobile apps only, like any other tool in clinical practice in the evidence-based era.

AI and agents

AbstractEarlier this year, OpenAI released their GPTs framework, allowing users to set up Large Language Model (LLM)‐based personas, orchestrate them into a workflow and even offering their AI apps within an app store. This is the latest, and maybe the easiest to set up, in a string of agent‐based LLM orchestration platforms in the past year, harkening a new age of agent‐based engineering. But, like most breakthroughs, this one is also rooted in many years of research, and the reason the world is paying attention to it now is that, thanks to Generative AI and Large Language Models, we finally have artificial agents that are useful enough to scale to more serious problems.

Fashion Finder : Ai-Powered Image Analysis and Online Shopping Integration App

The proposed mobile application aims to revolutionize the fashion industry by providing users with a seamless way to discover and purchase clothing items through image recognition technology. The app allows users to either capture images of clothing items through their device's camera or upload images from their stored files. Leveraging machine learning and computer vision algorithms, the app identifies the corresponding clothing items within the images. Upon identification, the app interfaces with e-commerce platforms such as Amazon and Flipkart to search for similar items available for purchase. The development process involves extensive research, design, and implementation of features including image recognition, camera/file upload functionality, API integration with e-commerce platforms, user authentication, and additional user-friendly features. Through rigorous testing, deployment on major app stores, and continuous maintenance, the app aims to offer a seamless and engaging experience for fashion enthusiasts while adhering to user privacy and data security standards.

The death of privacy policies: How app stores shape GDPR compliance of apps

The death of privacy policies: How app stores shape GDPR compliance of apps

Why data about people are so hard to govern

AbstractHow data on individuals are gathered, analyzed, and stored remains largely ungoverned at both domestic and global levels. We address the unique governance problem posed by digital data to provide a framework for understanding why data governance remains elusive. Data are easily transferable and replicable, making them a useful tool. But this characteristic creates massive governance problems for all of us who want to have some agency and choice over how (or if) our data are collected and used. Moreover, data are co‐created: individuals are the object from which data are culled by an interested party. Yet, any data point has a marginal value of close to zero and thus individuals have little bargaining power when it comes to negotiating with data collectors. Relatedly, data follow the rule of winner take all—the parties that have the most can leverage that data for greater accuracy and utility, leading to natural oligopolies. Finally, data's value lies in combination with proprietary algorithms that analyze and predict the patterns. Given these characteristics, private governance solutions are ineffective. Public solutions will also likely be insufficient. The imbalance in market power between platforms that collect data and individuals will be reproduced in the political sphere. We conclude that some form of collective data governance is required. We examine the challenges to the data governance by looking a public effort, the EU's General Data Protection Regulation, a private effort, Apple's “privacy nutrition labels” in their App Store, and a collective effort, the First Nations Information Governance Centre in Canada.

Data Safety vs. App Privacy: Comparing the Usability of Android and iOS Privacy Labels

Privacy labels---standardized, compact representations of data collection and data use practices---are often presented as a solution to the shortcomings of privacy policies. Apple introduced mandatory privacy labels for apps in its App Store in December 2020; Google introduced mandatory labels for Android apps in July 2022. iOS app privacy labels have been evaluated and critiqued in prior work. In this work, we evaluated Android Data Safety Labels and explored how differences between the two label designs impact user comprehension and label utility. We conducted a between-subjects, semi-structured interview study with 12 Android users and 12 iOS users. While some users found Android Data Safety Labels informative and helpful, other users found them too vague. Compared to iOS App Privacy Labels, Android users found the distinction between data collection groups more intuitive and found explicit inclusion of omitted data collection groups more salient. However, some users expressed skepticism regarding elided information about collected data type categories. Most users missed critical information due to not expanding the accordion interface, and they were surprised by collection practices excluded from Android's definitions. Our findings also revealed that Android users generally appreciated information about security practices included in the labels, and iOS users wanted that information added.

Analisis Faktor-Faktor yang Berpengaruh terhadap Customer Satisfaction pada Aplikasi Mobile Health

The COVID-19 pandemic has brought positive changes to the digital health industry in Indonesia. Kimia Farma Mobile is a mobile health application that offers comprehensive features for its users. The difference in ratings on Google Play Store and Apple App Store can influence trust (brand trust) and customer satisfaction with this application. The quality of electronic services, such as application interface, delivery quality, and transaction security, is increasingly crucial in influencing customer evaluations and ratings. Therefore, this research aims to analyze the factors influencing customer satisfaction with this application. The research was conducted quantitatively online through Google Form. The sampling method used convenience sampling and was analyzed using Structural Equation Modeling-Analysis Moment of Structural (SEM-AMOS). The research findings indicate that e-service quality significantly affects brand trust and customer satisfaction. This suggests that to improve brand trust and customer satisfaction, the mobile health application should be user-friendly and secure. Furthermore, brand trust significantly influences customer satisfaction. This implies that to enhance customer satisfaction, the mobile health application should provide educational and accurate information and offer customer guarantees.

Pendampingan Penggunaan Aplikasi Budgeting Pada PT Jaya Selalu Juwana Jawa Tengah Guna Meningkatkan Pendapatan Usaha

The emerging issue in the latest technological advancements has become the most powerful mechanism for advancing living standards and environmental sustainability in the era of the 4.0 revolution, leading to the Society 5.0 era. The combination of technology, which also focuses on humans, will be balanced to advance economic growth. PT Jaya Selalu Juwana, located in Central Java, is a cold storage company engaged in the export of fresh products. This dedication to society is expected to increase knowledge and utilize technological advancements packaged through an application called "budgeting" that can be used to increase the revenue of PT Jaya Selalu Juwana, Central Java. In the future, PT Jaya Selalu Juwana, Central Java, can also analyze business activities ranging from income planning to projecting profit/loss through budgeting software applications that can be downloaded through the web or the Play Store and App Store.

Exploring Sentiment Trends: Deep Learning Analysis of Social Media Reviews on Google Play Store by Netizens

This study explores sentiment analysis of Instagram app reviews using Long Short-Term Memory (LSTM) algorithms. The rise of app stores has transformed digital interactions, particularly for social media apps. Leveraging LSTM, we aim to understand user sentiments expressed in Instagram application reviews, offering insights to enhance user experience and address concerns. The methodology involves data crawling, preprocessing, LSTM model training, and evaluation metrics. Our findings reveal promising results in accurately identifying user sentiments, with an accuracy of 77.77%, precision of 0.45, recall of 0.089, and F1-score of 0.15. This study underscores the importance of sentiment analysis in understanding user feedback and its implications for app development and user engagement.

Understanding smartphone users’ app usage with restricted permissions

PurposeSmartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal information. This may impact the effectiveness of in-app advertising. However, research has not yet demonstrated what factors impact app users' decisions to use apps with restricted permissions. This study is aimed to bridge this gap.Design/methodology/approachUsing a quantitative research method, the authors collected the data from 384 app users via a structured questionnaire. The data were analysed using AMOS and fuzzy-set qualitative comparative analysis (fsQCA).FindingsThe findings suggest privacy concerns and risks have a significant positive effect on app usage with restricted permissions, whilst reputation, trust and perceived benefits have significant negative impact on it. Some app-related factors, such as the number of apps installed and type of apps, also impact app usage with restricted permissions.Practical implicationsBased on the findings, the authors provided several implications for app stores, app developers and app marketers.Originality/valueThis study examines the factors that influence smartphone users' decisions to use apps with restricted permission requests. By doing this, the authors' study contributes to the consumer behaviour literature in the context of smartphone app usage. Also, by explaining the underlying mechanisms through which the principles of communication privacy management theory operate in smartphone app context, the authors' research contributes to the communication privacy management theory.

The growing partisan politicization of non-political online spaces: A mixed-method analysis of news app reviews on Google Play between 2009 and 2022

Drawing on theories of identity politics and partisan polarization, we explored the politicization of Google Play’s news app reviews—an explicitly non-political domain. Using a mixed-methods approach, Analysis of Topic Model Networks (ANTMNs), combining topic modeling, network analysis, community detection, and theory-driven qualitative reading, we analyzed 759,143 reviews from 2009 to 2022 across 46 news apps. Three themes emerged: Technical, Content Quality, and Political. The political discourse in reviews has intensified over the years, with notable spikes around election periods. Accusations of bias were found to correlate most strongly with lower app ratings. The findings provide alarming empirical evidence for the politicization of non-political spaces, such as the app reviews section on app stores. With identity politics on the rise, this study sheds light on the importance of considering non-political online spaces for the study of political discourse.

Acceptance Analysis of Electronic Application Ready for Marriage and Pregnancy (Elsimil) Based on the Technology Acceptance Model (Tam) Approach

Purpose: The Elsimil application is an intervention effort to prevent stunting by conducting health screening which is followed up with assistance for marriage and pregnancy readiness to prospective brides. The Elsimil application review rating on the Play Store / App Store on February 01, 2023, which is 3.2, is still low and there are many user complaints related to difficulties in registering, operating the application and frequent system problems. The purpose of this study was to analyze the acceptance of the Elsimil application using the Technology Acceptance Model approach. Metod: This study uses analytical quantitative research with a cross sectional approach. Primary data obtained from a closed questionnaire, which explores the perceived ease, perceived usefulness, user attitude, and interest in using the Elsimil application. The number of samples was 33 Catin, then the data obtained was analyzed using the Partial Least Square (PLS) method using smartPLS software. Results and Discussion: Based on the results of the analysis, it shows that there is a significant effect of perceived convenience on perceived usefulness (p- value 0.045), effect of perceived convenience on user attitudes (p-value 0.000), effect of perceived usefulness on user attitudes (p-value 0.029), influence between user attitudes on interest (p-value 0.000). and influence between perceived usefulness on interest in using (p-value 0.132) in using the Elsimil application. In conclusion, of the five hypotheses proposed, four of them have a significant effect and one has no significant effect on the use of the Elsimil application.