Search Results (84)

Mobile apps are essential for communication, transactions and leisure and frequently rely on access to personal data. This study examines Google Play’s Data Safety section and declared permissions five years after the GDPR came into force, focusing on how developers disclose data collection, sharing, security practices and deletion controls. We use metadata from 49,578 Android apps and analyze self-reported disclosures in relation to permission categories, app categories, installs and user ratings. The results show that free apps request broader permission access than paid ones and that declared permission use has gradually increased over time. In addition, 25.44% of the sampled apps had not completed any part of the Data Safety section and non-completion was associated with app age, installation band and pricing model. Among apps with completed relevant Data Safety section disclosures, 11% of developers explicitly declared that data are not encrypted in transit and 34% explicitly declared that no user-initiated data deletion mechanism is available. Category-level differences in declared data collection and sharing were modest, while the relationship between permission breadth and user ratings was small. Overall, the findings indicate that structured disclosure mechanisms can improve visibility of privacy-related information, but do not necessarily ensure its completeness or consistency. Full article

The rapid digitalization of healthcare has led to the widespread availability of mobile health (mHealth) applications, including those aimed at mental health and well-being. The present study followed the PRISMA guidelines and systematically reviewed English and/or German mental health apps available in the Google Play Store to evaluate their functional quality and behavior-change potential. It utilized the Mobile App Rating Scale (MARS) to assess app quality, including engagement, functionality, esthetics, and information quality, and the App Behavior Change Scale (ABACUS) to evaluate the potential for behavior change by inclusion of behavior change techniques (BCTs). A total of 77 apps were reviewed, with findings indicating an average functional quality and moderate behavior-change potential, as the reviewed apps only utilized a limited amount of BCTs. Notably, only a small fraction of apps had been evaluated in randomized controlled trials (RCTs). Further analysis showed that MARS and ABACUS scores had limited predictive power regarding app popularity as measured by stars awarded by users and number of user ratings in the Google Play Store. The study highlights the need for more rigorous testing of mHealth apps and suggests that factors beyond those measured by MARS and ABACUS may influence app popularity. In addition to the scientific value, this review provides insights for both users interested in mental health support via apps and developers aiming to enhance the quality and impact of mental health applications. Full article

The rapid development of Green Information Technology has contributed to reducing the environmental footprint and supporting more efficient resource management. Within this context, food waste reduction is a key priority of the European Green Deal and the Farm to Fork Strategy. This study records and analyzes 25 food waste-related smart applications identified during the first quarter of 2025 in Google Play and the Apple Store based on defined selection criteria. Their basic characteristics were analyzed and compared. Results indicate that redistribution applications dominate, whereas prevention-oriented applications remain limited in number. Although most of the applications enjoy positive ratings, their real effectiveness depends on user adoption and behavioral change, making prevention-oriented applications essential for achieving the EU 2030 target of reducing food waste by 50%. Full article

The global increase in dementia cases, which is predicted to exceed 152 million by 2050, poses substantial challenges to healthcare systems and caregiving structures. Concurrently, the expansion of mobile health (mHealth) technologies offers scalable, cost-effective opportunities for dementia care. This study systematically reviews 100 publicly available dementia-related mobile applications on the Apple App Store (iOS) and the Google Play Store (Android), categorised using the Mobile App Rating Scale (MARS), as well as the targeted end-users, Internet of Things (IoT) integration, data protection, and cost burden. Applications were evaluated for their utility in cognitive training, memory support, carer education, clinical decision-making, and emotional well-being. Findings indicate a predominance of carer resources and support tools, while clinically integrated platforms, cognitive assessments, and adaptive memory aids remain underrepresented. Most apps lack empirical validation, inclusive design, and integration with electronic health records, raising ethical concerns around data privacy, transparency, and informed consent. In parallel, the study identifies promising pathways for energy-optimised IoT systems, Artificial Intelligence (AI), and Ambient Assisted Living (AAL) technologies in fostering dementia-friendly, sustainable environments. Key gaps include limited use of low-power wearables, energy-efficient sensors, and smart infrastructure tailored to therapeutic needs. Application domains such as cognitive training (19 apps) and carer resources (28 apps) show early potential, while emerging innovations in neuroadaptive architecture and emotional computing remain underexplored. The findings emphasize the need for co-designed, evidence-based digital solutions that align with the evolving needs of people with dementia, carers, and clinicians. Future innovations must integrate sustainability principles, promote interoperability, and support global aging populations through ecologically responsible, person-centred dementia care ecosystems. Full article

Introduction: The digital era has provided the development of innovative health devices that enable the precise characterization of health and disease, facilitating diagnoses and interventions. This study aimed to systematically review and verify the quality of mobile applications (apps) available for the monitoring and assessment of breast cancer-related lymphedema (BCRL). Methods: A systematic search was conducted in the Apple App Store and Google Play Store for apps related to BCRL monitoring and assessment. Two independent reviewers extracted descriptive data and evaluated app quality using the validated User Mobile App Rating Scale (uMARS). Results: Out of 630 apps screened, four met the inclusion criteria and were analyzed. Two Korean apps targeted patients, providing educational content, self-assessment tools, and bilingual interfaces. Two British apps, LymVol and LymphaTech Lite, focused on volumetric measurement and clinical use, although LymVol lacked compatibility with recent Android versions. Quality assessment using the uMARS indicated that the included applications performed consistently across the evaluated domains, despite low download numbers and the absence of user ratings. Conclusions: Although mobile apps have the potential to enhance lymphedema monitoring and assessment, more accessible and scientifically validated tools are needed to ensure effective use by healthcare professionals and patients. Developers are encouraged to create accessible, linguistically inclusive smartphone apps that incorporate standardized assessment protocols and regular updates to ensure usability and accuracy. Rigorous validation studies covering reproducibility, diagnostic accuracy, and real-world clinical outcomes should be conducted by researchers to guarantee safety and reliability. Full article

Mobile banking applications play a crucial role in providing users with access to financial services, and the quality of user experience is a key factor for their sustainability. This study investigates the predictability of application quality signals derived from user ratings of five leading mobile banking apps in Türkiye. The main problem addressed is understanding how these user-driven quality indicators evolve over time and identifying effective methods for forecasting them. This research problem is critical for understanding how banks can monitor customer satisfaction and reputational risk in real time, as fluctuations in app ratings directly affect user trust and engagement. For this purpose, daily average rating series collected from the Google Play Store were analyzed using LSTM-based time series models, and the results were benchmarked against the seasonal naïve (SNaive) method. The findings show that LSTM consistently achieved lower error rates across all banks, with particularly reliable forecasts for YapıKredi and Akbank, where MAPE values ranged between 16% and 28%. However, low R² values for some banks suggest limitations in long-term forecasting. The contribution of this study lies in demonstrating that user experience signals in mobile banking can be systematically monitored from a time series perspective, and that LSTM-based approaches provide a more effective method for capturing these quality dynamics. Full article

Market-Driven Requirements Engineering (MDRE) integrates traditional Requirements Engineering (RE) practices, such as Requirements Elicitation and requirements prioritization, with market analysis. Offering software products to an open market has become a trend, yet it has many challenges. In MDRE, there are diverse sources for requirements including support teams, subcontractors, sales, and marketing teams. So, the MDRE process must provide ongoing requirements gathering techniques to ensure no crucial requirements are overlooked. It is generally possible for users to search and download software applications through app stores (such as the Google Play Store and Apple App Store) for various purposes. Users are allowed to express their opinions about the software applications by writing text messages which are widely known as “app reviews”. Utilizing these app reviews as a source of requirements while planning to develop a similar software application may have promising results. Therefore, the concept of “App Reviews Utilization” has emerged and can be applied for various purposes. This research utilizes app reviews in Requirements Elicitation while developing a software product in the market-driven development context. Furthermore, these reviews may be noisy and informally expressed. This paper proposes a framework, Automatic Requirements Elicitation from App Reviews (AREAR), that integrates Natural Language Processing (NLP) techniques with pre-trained Language Models to automatically elicit requirements from available app reviews while developing a market-driven software product. AREAR employed the Bidirectional Encoder Representation from the Transformers (BERT) Language Model. The proposed framework achieved an improved Accuracy and ${\mathrm{F}}_1$ score as compared to previous research. Full article

The proper use of Android app permissions is crucial to the success and security of these apps. Users must agree to permission requests when installing or running their apps. Despite official Android platform documentation on proper permission usage, there are still many cases of permission abuse. This study provides a comprehensive analysis of the Android permission landscape, highlighting trends and patterns in permission requests across various applications from the Google Play Store. By distinguishing between benign and malicious applications, we uncover developers’ evolving strategies, with malicious apps increasingly requesting fewer permissions to evade detection, while benign apps request more to enhance functionality. In addition to examining permission trends across years and app features such as advertisements, in-app purchases, content ratings, and app sizes, we leverage association rule mining using the FP-Growth algorithm. This allows us to uncover frequent permission combinations across the entire dataset, specific years, and 16 app genres. The analysis reveals significant differences in permission usage patterns, providing a deeper understanding of co-occurring permissions and their implications for user privacy and app functionality. By categorizing permissions into high-level semantic groups and examining their application across distinct app categories, this study offers a structured approach to analyzing the dynamics within the Android ecosystem. The findings emphasize the importance of continuous monitoring, user education, and regulatory oversight to address permission misuse effectively. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

Global clothing production and waste continues to increase while garment utilisation is decreasing. Many consumers wear only a fraction of the clothing they own, often forgetting or overlooking items already in their wardrobes. This disconnection from existing garments encourages unnecessary purchases and contributes to the environmental and psychological burden of fashion overconsumption. The purpose of this study was to explore the role of wardrobe management apps (WMAs) and their potential for reducing fashion overconsumption. This paper has two core aims: first, to assess how WMAs contribute to sustainability through organisation, extended use, and behavioural change, and second, to examine their emotional and practical benefits for users. A content analysis of 27 WMAs was conducted alongside a thematic analysis of all 5953 user reviews from Apple and Google Play stores. Our analysis showed that 470 users reported behavioural shifts related to reduced overconsumption and increased garment utilisation, while a further 210 users described psychological benefits such as improved mental clarity and reduced anxiety. These results demonstrate the potential of WMAs to support more sustainable fashion habits and enhanced consumer well-being, offering a digital means to promote responsible consumption and align with the UN Sustainable Development Goals, especially SDG 12. Full article

This paper discusses three algorithmic strategies tailored for distinct applications, each aiming to tackle specific operational challenges. The first application unveils an innovative SMS messaging system that substitutes manual typing with voice interaction. The key algorithm facilitates real-time conversion from speech to text for message creation and from text to speech for message playback, thus turning SMS communication into an audio-focused exchange while preserving conventional messaging standards. The second application suggests a secure file management system for Android, utilizing encryption and access control algorithms to safeguard user privacy. Its mathematical framework centers on cryptographic methods for file security and authentication processes to prevent unauthorized access. The third application redefines flashlight functionality using an optimized touch interface algorithm. By employing a screen-wide double-tap gesture recognition system, this approach removes the reliance on a physical button, depending instead on advanced event detection and hardware control logic to activate the device’s flash. All applications are fundamentally based on mathematical modeling and algorithmic effectiveness, emphasizing computational approaches over implementation specifics. Full article

Background: Mobile gaming applications are increasingly marketed as cognitive training tools for older adults, yet their scientific validity and accessibility remain uncertain. This scoping review evaluates their effectiveness and inclusivity. Methods: A systematic search of the Apple App Store and Google Play Store identified 227 applications, with 14 meeting inclusion criteria. Apps were assessed for scientific validity, theoretical foundation, accessibility, cognitive targeting, user engagement, and monetization models. Results: While all 14 apps claimed cognitive benefits, only one cited empirical research. None included baseline cognitive assessments or progress tracking. Accessibility was limited—eight apps had visual accommodations, but none provided auditory support. Six apps were English-only, restricting linguistic inclusivity. Monetization varied, with eight requiring in-app purchases or subscriptions, posing financial barriers. Conclusions: This review highlights critical gaps in the current cognitive gaming application market for older adults. Despite their popularity, cognitive training apps for older adults lack scientific validation and accessibility, limiting their effectiveness as cognitive interventions. Developers should integrate evidence-based training, adaptive assessments, and inclusive accessibility features such as voice guidance and multilingual support. Future research should prioritize longitudinal studies to assess real-world efficacy, refine interventions targeting memory, executive function, and processing speed, and enhance inclusive design for diverse aging populations. Full article

The increasing frequency and severity of disasters in urban areas demand sustainable, smart disaster management strategies to leverage technological advancements. This study provides a comprehensive review of mobile apps for disaster awareness available in the Google Play Store, with a particular emphasis on addressing flood disaster readiness and response. Mobile apps have become indispensable tools for disseminating immediate notifications, facilitating emergency communication, and coordinating response activities. A total of 77 mobile apps in the Google Play Store were identified and evaluated using a systematic search. The evaluation criteria included user ratings, download counts, and key crisis management functionalities such as real-time alerts, emergency contact directories, preparedness checklists, and user reporting capabilities. The findings emphasised the following: (a) the importance of integrating cutting-edge technologies, i.e., AI and IoT, to enhance functionality, accuracy, and capacity in mobile applications; (b) the use of crowdsourcing as a valuable mechanism for enriching inclusive and responsible data; (c) enabling timely updates and fostering community engagement; and (d) establishing agency engagements, gamified elements, and real-time reciprocal communication tools, i.e., push-to-talk features to ensure the long-term sustainability of mobile apps. By incorporating these insights, disaster management apps can significantly enhance community resilience and improve the effectiveness of responding to natural disasters in this digital age. Full article

Amidst the rapid proliferation of mHealth applications, questions persist regarding their efficacy, usability, and integration into oral health practice. This review aims to inform practitioners and stakeholders in the field of oral health about the potential of digital technologies to transform healthcare delivery and promote healthy behaviors. Key terms included “dental”, “dentistry”, “oral health”, “dental treatment”, and “tooth care”. The Google Play Store search identified 130 applications, out of which 18 met our study objectives. Most apps (n = 13) focused on providing dental appointments, oral health education, and promotion. The Mobile Application Rating Scale (MARS) quality rating revealed that only 50% of these apps were of high quality. Engagement and information were the lowest scored subscales. This review highlights the many benefits that these digital tools provide, including online appointments, teleconsultations, and access to oral health educational resources. Nevertheless, despite their potential, the current state of dental health applications left substantial room for development, especially in the areas of user involvement and information quality. The lack of reliable and accurate information in many apps may be harmful to users’ health. To fully realize the potential of these digital technologies and to enhance oral health outcomes on a larger scale, coordinated actions involving stakeholders from the technology and dentistry sectors are imperative. Full article

In the current digital era, the Google Play Store and the App Store are major platforms for the distribution of mobile applications and games. Billions of users regularly download mobile games and provide reviews, which serve as a valuable resource for game vendors and developers, offering insights into bug reports, feature suggestions, and documentation of existing functionalities. This study showcases an innovative application of fine-tuned RoBERTa for detecting bugs in mobile phone games, highlighting advanced classification capabilities. This approach will increase player satisfaction, lead to higher ratings, and improve brand reputation for game developers, while also reducing development costs and saving time in creating high-quality games. To achieve this goal, a new bug detection dataset was created. Initially, data were sourced from four top-rated mobile games from multiple domains on the Google Play Store and the App Store, focusing on bugs, using the Google Play API and App Store API. Subsequently, the data were categorized into two classes: binary and multi-class. The Logistic Regression, Convolutional Neural Network (CNN), and pre-trained Robustly Optimized BERT Approach (RoBERTa) algorithms were used to compare the results. We explored the strength of pre-trained RoBERTa, which demonstrated its ability to capture both semantic nuances and contextual information within textual content. The results showed that pre-trained RoBERTa significantly outperformed the baseline models (Logistic Regression), achieving superior performance with a 5.49% improvement in binary classification and an 8.24% improvement in multi-class classification, resulting in cross-validation scores of 96% and 92%, respectively. Full article