Google Play Research Articles

Assessment of online pharmacy applications in India by employing the mobile application rating scale

IntroductionOver the past few years, the technology powering mobile devices such as smartphones has made significant progress. Furthermore, the healthcare industry is always progressing and actively embracing the latest technological advancements to achieve the highest level of efficiency. With the rising prevalence of smartphones and internet connection, customers are benefiting from reduced prices, convenient home delivery, and effortless accessibility through online pharmacies. Internet-based pharmacies facilitate the internet-based transaction of health-related products, such as drugs, dietary supplements, and various other wellbeing products.Objective of studyThe study assessed digital pharmacy applications in India using the Mobile App Rating Scale (MARS) on Android and iOS devices, aiming to evaluate their quality.MethodsAn investigation examined the digital pharmacy applications in India that were accessible via the Android Market and App Store. The applications were assessed by two researchers using the MARS questionnaire, a tool that evaluates 23 variables categorized into five domains: Engagement, Functionality, Aesthetics, Information, and Subjective Quality. The grading system spanned from one to five for every category.ResultsA Google Play Store and App Store investigation revealed 40 online pharmacy apps in India, with 13 rejected. Seven were non-English language-related apps and seven were not downloaded. Thirteen were chosen and evaluated using the MARS Scale. The MARS demonstrated significant positive associations across its components, namely Engagement, Functionality, Aesthetics, and Information. Specifically, greater levels of user functionality were shown to be indicative of superior app aesthetics and engagement. The mean rating of the 13 apps fell between the range of 3.11 to 4.32 on a 5-point scale.ConclusionThis is the first study to utilize the MARS scale to assess the efficacy of online pharmacy applications in India. This research enhanced the functionality and quality of various online pharmacy applications utilized in India.

GUing: A Mobile GUI Search Engine using a Vision-Language Model

Graphical User Interfaces (GUIs) are central to app development projects. App developers may use the GUIs of other apps as a means of requirements refinement and rapid prototyping or as a source of inspiration for designing and improving their own apps. Recent research has thus suggested retrieving relevant GUI designs that match a certain text query from screenshot datasets acquired through crowdsourced or automated exploration of GUIs. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements, neglecting visual information such as icons or background images. In addition, retrieved screenshots are not steered by app developers and lack app features that require particular input data. To overcome these limitations, this paper proposes GUing, a GUI search engine based on a vision-language model called GUIClip, which we trained specifically for the problem of designing app GUIs. For this, we first collected from Google Play app introduction images which display the most representative screenshots and are often captioned (i.e. labelled) by app vendors. Then, we developed an automated pipeline to classify, crop, and extract the captions from these images. This resulted in a large dataset which we share with this paper: including 303k app screenshots, out of which 135k have captions. We used this dataset to train a novel vision-language model, which is, to the best of our knowledge, the first of its kind for GUI retrieval. We evaluated our approach on various datasets from related work and in a manual experiment. The results demonstrate that our model outperforms previous approaches in text-to-GUI retrieval achieving a Recall@10 of up to 0.69 and a HIT@10 of 0.91. We also explored the performance of GUIClip for other GUI tasks including GUI classification and sketch-to-GUI retrieval with encouraging results.

A Narrative Review of the Digital Equity Gap of Apps for Cigarette Smoking Cessation for Persons Living in the Hispanosphere

Abstract Purpose of Review The Hispanosphere is a vast region of the world that has received little attention in the digital health literature. No study to date has examined the availability and quality of publicly available mobile applications (apps) for cigarette smoking cessation in this region. Three coders utilized the American Psychiatry Association (APA)’s Brief App Evaluation Model Screener (Brief-AEM Screener) to evaluate the quality of the label and public-facing screens of smoking cessation apps in Spanish. Availability of apps in the Hispanosphere was compared to availability of apps in the Anglosphere. Recent Findings We identified and reviewed 19 apps in Spanish in Google Play. The median score using the Brief-AEM Screener was 63 out of 100 suggesting generally acceptable app quality and features according to the quality standards for digital health tools proposed by the APA. However, we found (1) notable inaccurate and misleading labelling claims, (2) poor grammar or incomplete translations, and (3) a lack of cultural and linguistic adaptation to countries in the Hispanosphere. Our comparison of smoking cessation apps between the Hispanosphere and the Anglosphere suggested that there is a large digital equity gap between these two regions, with a four to sevenfold gap in app availability. Summary There is a relative shortage of quality and quantity of digital health apps for smoking cessation in the Hispanosphere. To ensure the cultural appropriateness of those digital interventions, it is essential that developers of digital health tools establish community partners in the region prior to developing apps for smoking cessation.

Analyzing User Reviews on Digital Detox Apps: A Text Mining and Sentiment Analysis Approach

ABSTRACTDue to the growing concerns around problematic smartphone use and its negative impact, there is a rising interest in digital detox. While many digital detox apps have been developed in recent years, there is still limited understanding of the long‐term effectiveness of digital detox applications and the attitude of people towards these apps. This study fills this gap by identifying the topics that people post in their reviews on the Google Play Store about digital detox apps and the emotion‐based sentiment of those reviews. A total of 3500 reviews of 25 digital detox apps were collected from the Google Play Store using a scraping tool called “Parsehub.” Data was analyzed using R studio. Sentiment analysis results suggest that positive sentiments dominated the data frame. “Trust” and “anticipation” were the two most expressed emotions in the reviews. Regression analysis confirmed that sentiment scores could explain the ratings of the apps. Through LDA topic modeling four major topics of the reviews were identified and are discussed in detail in the later section of the research paper. The findings of this study may help app developers and marketers improve digital detox apps so that people can learn and practice mindful smartphone use with the help of these apps. This study fills a gap in digital detox research by adopting a new methodological approach and procedure since it combines text mining, sentiment analysis (NRC Lexicon using Syuzhet package), regression analysis, and LDA topic modeling. To the best of our knowledge, this is the first study which uses this research approach in the context of digital detox apps.

Smartphone Apps for Pulmonary Hypertension: Systematic Search and Content Evaluation.

Pulmonary hypertension (PH) is a chronic and complex condition, requiring consistent management and education. The widespread use of smartphones has opened possibilities for mobile health apps to support both patients and health care professionals in monitoring and managing PH more effectively. This study aimed to identify and assess the quality of free smartphone apps for PH targeted at either patients or health care professionals. A systematic search was conducted on freely available apps for patients with PH and health care professionals, accessed from a Spanish IP address, on Android (Google Play) and iOS (App Store) platforms. Searches were performed in October 2022 and 2023. Apps were independently analyzed by two reviewers, focusing on general characteristics. Quality assessment was based on the Mobile Application Rating Scale (MARS) framework, and Mann-Whitney U tests compared mean MARS scores against specific variables. In the overall study, 21 apps were identified. In the 2022 search, 19 apps were listed (9 iOS, 7 Android, 3 available on both platforms). In the subsequent 2023 search, 16 apps were identified (6 Android, 7 iOS, 3 available on both platforms). Of those identified in 2022, 14 remained available in 2023, with only 7 updated since 2022. In addition, 12 apps targeted patients or the general population, while 9 targeted health care professionals; none involved patients in the development or design. Conversely, 13 apps involving health care professionals were identified. There were 10 apps that received pharmaceutical industry funding. The primary goal for 81% (17/21) of the apps was to disseminate general information about PH. The overall mean MARS quality was acceptable in 2022 and 2023, with mean ratings of 3.1 (SD 0.6) and 3.3 (SD 0.5), respectively. The functionality category achieved the highest scores in both years, indicating ease of use and intuitive navigation. In contrast, the subjective quality domain consistently received the lowest ratings in the MARS assessment across both years. None of the apps underwent clinical testing themselves; however, 2 incorporated tools or algorithms derived from trials. The overall quality of iOS apps statistically outperformed that of Android apps in both years (P<.05). Furthermore, the involvement of health care professionals in app development was associated with enhanced quality, a trend observed in both years (P=.003 for both years). This review of mobile health apps for PH reveals their emergent development stage, with generally acceptable quality but lacking refinement. It highlights the critical role of health care professionals in app development, as they contribute significantly to quality and reliability. Despite this, a notable stagnation in app quality and functionality improvement over 2 years points to a need for continuous innovation and clinical validation for effective clinical integration. This research advocates for future app developers to actively engage with health care professionals, integrate patient insights, and mandate rigorous clinical validation for PH management.

Review of Alleged Abuse of Google Play Billing's Dominant Position in Indonesia: A Study of South Korea's Anti-Google Law

The author examines how the Anti-Google Law is implemented in South Korea and whether the law can be a solution for Indonesia in preventing giant digital platforms from abusing their dominant position. To review the alleged abuse of the dominant position of Google Pay Billing in Indonesia and conduct a study of the implementation of the Anti-Google Law in South Korea. This study uses doctrinal legal research methods through document and literature studies. The research results: For now, there have been several cases related to digital platforms that have entered ICC, such as GoTo in 2022, Google, and Shopee, which are about to enter the trial. It can be seen that there has been an increase in incoming cases related to digital platforms in ICC, therefore ICC must issue rules both in the form of laws, or technical regulations in the form of guidelines to regulate digital platforms, so that in the future.

Method Digital Learning for Increase Ability Early Childhood Literacy

One of the learning media is a digital fairy tale application available with pictures​ for download on the Google Play Store. The objective of this study is to learn in a way comprehensive how using digital learning media increases the ability of literacy children age early. Research quantitative This uses articles and journals accredited nationally as data sources and references. Research This in a way special see how digital learning media can help children Study literacy. Knowledge of letters, knowledge of sound letters, consciousness of phonemics, and comprehension of the concept of writing are indicators important to skills literacy. Digital applications that can be accessible to everyone, including teachers, parents, and students, used in the study This For looks for solutions for problem literacy. Study This is classified as in type study qualitative and using approach studies case. Analysis results study show that using learning media digital can also increase the skills and literacy child age early in PAUD institutions with give teachers the tools they need need for use it. Research This shows that tool digital learning can be used to increase ability literacy child age early PAUD institutions.

Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

User-centric AI: evaluating the usability of generative AI applications through user reviews on app stores

This article presents a usability evaluation and comparison of generative AI applications through the analysis of user reviews from popular digital marketplaces, specifically Apple’s App Store and Google Play. The study aims to bridge the research gap in real-world usability assessments of generative AI tools. A total of 11,549 reviews were extracted and analyzed from January to March 2024 for five generative AI apps: ChatGPT, Bing AI, Microsoft Copilot, Gemini AI, and Da Vinci AI. The dataset has been made publicly available, allowing for further analysis by other researchers. The evaluation follows ISO 9241 usability standards, focusing on effectiveness, efficiency, and user satisfaction. This study is believed to be the first usability evaluation for generative AI applications using user reviews across digital marketplaces. The results show that ChatGPT achieved the highest compound usability scores among Android and iOS users, with scores of 0.504 and 0.462, respectively. Conversely, Gemini AI scored the lowest among Android apps at 0.016, and Da Vinci AI had the lowest among iOS apps at 0.275. Satisfaction scores were critical in usability assessments, with ChatGPT obtaining the highest rates of 0.590 for Android and 0.565 for iOS, while Gemini AI had the lowest satisfaction rate at −0.138 for Android users. The findings revealed usability issues related to ease of use, functionality, and reliability in generative AI tools, providing valuable insights from user opinions and feedback. Based on the analysis, actionable recommendations were proposed to enhance the usability of generative AI tools, aiming to address identified usability issues and improve the overall user experience. This study contributes to a deeper understanding of user experiences and offers valuable guidance for enhancing the usability of generative AI applications.

Digital Storytelling for People With Cognitive Impairment Using Available Mobile Apps: Systematic Search in App Stores and Content Analysis.

Growing evidence suggests cognitive and social health benefits can be derived from digital storytelling for older adults with cognitive impairment. Digital storytelling apps offer the potential to serve as an on-demand, easy-to-access platform for enhancing cognitive abilities and promoting social well-being. Yet, despite the increasing quantity of such apps being available on the market, there is a gap in research investigating their quality. This app review aims to assess the digital storytelling apps available in the Chinese market and evaluate them in accordance with the Mobile Application Rating Scale (MARS). The goal was to identify key features and evaluate the overall quality in the context of cognitively impaired users. A systematic search was conducted in both the Google Play store (Google LLC) and iTunes store (Apple Inc), using English and Chinese keywords. Apps were chosen according to specific criteria that included features, including (but not limited to) memory capture, story saving, cue-based reminiscing, and the ability to share stories or memories with others. The MARS was used by 3 individual researchers to independently assess app quality across several domains, such as engagement, functionality, aesthetics, and information quality, for both Android and iOS apps. From an initial screening of 297 apps, only 9 (3%) met the criteria for detailed evaluation using MARS. The reviewed apps featured capture memory, save, reminisce, and share functions, which are critical in supporting cognitive functions and enhancing user engagement. The analysis revealed patterns in platform diversity and geographical distribution of developers, with apps available on both iOS and Android. Memoirs of Life and Memorize: Diaries, Memories, Notes, Ideas, Timelines, Categories (Fair Apps Mobile) had the highest mean MARS scores of 3.35, indicating strong engagement, functionality, and information quality, while the lowest score was 2.33. The overall mean score across all apps was only 3.03 (SD 0.60), highlighting significant variation, particularly in information quality. User feedback also showed considerable variability, ranging from 0 comments for apps such as Grand Storyteller (VarIT Inc) and PWI Storyteller (Project World Impact, LLC) to as many as 5361 comments for FamilySearch, which received extensive positive reviews. This wide range of user feedback underscores the importance of continuous improvement and user-centered design, particularly in enhancing information quality and content accuracy. The systematic search and evaluation highlight the diverse capabilities yet variable quality of digital storytelling apps available within the Chinese market, reflecting user experiences, satisfaction levels, and efficacy in supporting cognitively impaired users. While some apps excel in engagement and functionality, others need significant improvements in information quality and user interface design to better serve those with cognitive impairments. Future research is recommended to investigate regional limitations and features that would result in more inclusive and effective digital storytelling apps.

Structure and functions of talking book mobile applications: a comparative study of Google Play Books, Audible, LibriVox, Storytel and Loyal Books

PurposeTalking book mobile applications have introduced the concept of reading through listening. Though conventional reading methods are preferable, talking book mobile applications have the advantage that they treat every user equally. The purpose of this paper is to compare the five popular talking book mobile applications namely Google Play Books, Audible, LibriVox, Storytel and Loyal Books.Design/methodology/approachThis research compares and evaluates features and functionalities of five different talking book mobile applications using a checklist method. A structured checklist is prepared to compare the five popular talking book mobile applications comprising 261 dichotomous questions divided into 13 categories.FindingsAfter evaluating the features, an attempt was made to rate the selected applications. According to the evaluation, Audible application performed better than others. This study about the features and functions of talking book mobile applications considered a total number of 261 features from the selected five mobile applications. Audible is ranked first with 182 (69.7%) features and is rated “Above average”; Storytel (68.2%) is second with 178 features, also rated “Above average”; Google Play Books ranked third with 161 (61.7%) features, rated “Average”; LibriVox ranked fourth with 151 (57.9%) features and is rated “Below average”. Fifth is Loyal Books with 130 (49.8%) features, rated “Below average”.Research limitations/implicationsThe study covers only five talking book mobile applications. The checklist used in the study is generic covering limited features. Further, the perceptions of users were also not included while comparing the talking book mobile applications. The study does not comprehend whether the talking book mobile applications fulfil the information needs of the users.Practical implicationsUsers can use the findings of the study in choosing the suitable talking book mobile application suitable to their requirements. Instructors of information literacy programmes can use the checklist to train the users in using talking book mobile applications that can accelerate the usage of these mobile applications.Originality/valueThe study identifies the features that should be available in a model talking book mobile application. Features of the talking book mobile applications were evaluated after preparing the detailed checklist. Features were divided into 13 different categories. The checklist used in the study is original and the first of its kind. The findings of the study can be used by developers of talking book mobile applications to improve their functionalities.

IMPROVING WATER QUALITY MONITORING AND PARTICIPATORY MONITORING OF THE AQUA PROJECT APPLICATION

Introduction Water quality monitoring plays a key role in promoting environmental sustainability. Water is a vital resource for life and its availability and quality are intrinsically linked to human well-being and health. Through constant monitoring, it is possible to identify and mitigate pollutants, the Aqua project proposes to present a solution for evaluating and monitoring water quality, using a cell phone application and a very low-cost kit, integrating everything into an online platform. line with the aim of establishing a participatory monitoring network for rivers and water bodies. Results and Discussion The initial version of the Aqua1,2 project application, which is capable of determining: nitrite, organic matter, ortho-phosphate, ammonia and iron, all contaminants of water bodies3, stored the information only on the cell phone itself, and the georeferencing was done in a manual, where the operator himself had to manually record where the sample was taken. Furthermore, it was not available on Google Play, which made it difficult for less skilled people to install it. The application was rewritten, and is no longer local, running exclusively on the cell phone and was migrated to the cloud, now the data is also stored in a public database, where other researchers, or interested entities can have access, the account is no longer be local and is now stored on the internet, georeferencing is also now more accurate, as the application reads the location through the phone's own GPS system, and installation can be done via Google Play, removing one of the difficulties for use of the application. Conclusions With these modifications, the objective of participatory monitoring has now become easier to achieve, as the data of all users can be accessed via the internet, they have become more secure as they are no longer on the device, and the location of the sample location has become much easier. more accurate, in addition to allowing data integration.

Sentiment Analysis of User Reviews on BSI Mobile and Action Mobile Applications on the Google Play Store Using Multinomial Naive Bayes Algorithm

Mobile banking services are designed to facilitate customer transactions. Bank Syariah Indonesia (BSI) and Bank Aceh also provide these online services through their respective applications, BSI Mobile and Action Mobile. The mobile banking apps aim to simplify customer transactions, which can be conducted remotely via several features, from transfers, payments, and purchases to zakat payments, by simply connecting to the internet. Therefore, this research aims to classify the sentiment of user reviews for BSI Mobile and Action Mobile applications on Google Play Store to understand the users' experiences. The Multinomial Naïve Bayes algorithm is used in this study, where the algorithm analyzes and classifies the user reviews into positive and negative sentiment categories. The study involves several stages, such as text preprocessing, sentiment visualization, splitting the data into an 80:20 ratio for training and testing datasets, and training the model using the Multinomial Naïve Bayes algorithm. The results of this study show that the Multinomial Naïve Bayes algorithm performs well in analyzing user sentiment for BSI Mobile and Action Mobile, achieving an accuracy of 78.7%, precision of 76.5%, recall of 86.2%, and an F1-score of 80.6% for BSI Mobile, and an accuracy of 85.6%, precision of 75%, recall of 75%, and an F1-score of 75% for Action Mobile. Additionally, the sentiment classification results reveal that 52.8% of BSI Mobile user reviews are positive and 47.2% are negative, while for Action Mobile, 35.1% are positive and 64.9% are negative. For BSI Mobile, 21,497 reviews express a positive sentiment with dominant keywords such as "updated," "good," "balance," "transaction," and "thank." Meanwhile, for Action Mobile, 274 reviews express a negative sentiment with dominant keywords such as "transaction," "application," "network," "register," "please," and "update."

Sentiment Analysis of Google Maps User Reviews on the Play Store Using Support Vector Machine and Latent Dirichlet Allocation Topic Modeling

These days, traveling is made easier by utilizing easily accessible online directions such as Google Maps. Google Maps provides real-time routes by displaying and presenting the closest routes that users can take. However, lately, the routes provided by Google Maps services often get users lost by presenting routes such as forests, narrow roads, and even dead ends. Therefore, this study aims to determine the level of user satisfaction and sentiment into two categories, namely positive and negative, based on reviews on the Google Play Store platform using the Support Vector Machine (SVM) algorithm and topic modeling using Latent Dirichlet Allocation (LDA) to find out the collection of topics that are the main topics of conversation by users regarding Google Maps services. The results of this study show that the SVM algorithm is feasible to use in sentiment analysis classification with an accuracy value of 86%, precision of 93%, recall of 53%, and f1-score of 52%. In addition, topic modeling is applied to generate coherence values for each topic, which shows that the higher the coherence value, the more specific the topic is. The highest coherence value generated in this study was two topic models with a coherence value of 35.15%, but this study took five with a coherence value of 33.39%. The five topic models to be applied in this study are selected because they have a good enough coherence value to identify the main topics and hidden topics in Google Maps user reviews with the Latent Dirichlet Allocation model. The topic model shows five aspects users often discuss: Google Maps route accuracy, system and service errors, navigation application directions, lost time history, and convoluted route provision.

A Data-Driven Approach for Game Evaluation Using Latent Dirichlet Allocation Method Based on Players’ Reviews

Garena is a global game developer and publisher. Garena provides users with access to popular and engaging online games for mobile and PC, developed, curated, and localized for each market. The Battle Royale genre is relatively new, and this research will evaluate Free Fire, one of the games in this genre made by Garena. Analyzing end-user reviews is considered important for evaluating software quality. Researchers need to understand which aspects need to be evaluated based on player reviews on Google Play and how the model's performance is generated using feedback from players who have played Free Fire. In this study, researchers use the Latent Dirichlet Allocation (LDA) method to model topics and generate clusters in discussions for each topic. LDA is a generative probabilistic model of a corpus. This research on topic modeling using Google Play reviews and LDA has identified the topics users are most concerned with. The research shows three main aspects: bugs, graphics and performance, and game rules/punishment policy, as aspects that need to be evaluated based on player reviews on Google Play.

ANALISIS SENTIMEN APLIKASI MICROSOFT TEAMS BERDASARKAN ULASAN GOOGLE PLAY STORE MENGGUNAKAN MODEL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENT ESTIMATION (ADAM)

The Covid-19 virus pandemic with its rapid spread has made the government enact several policies that can reduce community activities that have the potential to cause crowds including social distancing, work from home (WFH) and online and hybrid learning. One of the platforms that was widely used during the WFH and online learning period was Microsoft Teams. Microsoft Teams is an app that lets people and organizations collaborate and interact online. Microsoft Teams can be downloaded through the Google Play Store and users can leave reviews. Sentiment analysis is used to classify user sentiment towards Microsoft Teams into positive and negative sentiments. Review data obtained from January to November 2022. The classification is carried out using the Neural Network which is a computational system inspired by biological neural networks. Weight optimization is done using Adaptive Moment Estimation which is popular in the field of deep learning because it achieves good results quickly. The classification model in this paper was built with a training test ratio of 80:20. The best hyperparameter combination is node hidden layer 100, learning rate 0.0001, and batch size 32. The accuracy of the ANN classification model was obtained by 88.16%.Keywords: Microsoft Teams, Google Play Store, Sentiment Analysis, Neural Network, Adaptive Moment Estimation

Nutrition-Related Mobile Apps in the Spanish App Stores: Quality and Content Analysis.

Mobile apps represent accessible and cost-effective tools to improve nutrition and prevent chronic diseases. However, most of these apps have been characterized as having limited functionality, raising concerns about their effectiveness, acceptability, and efficacy. The aims of the study were to assess the quality of popular nutrition-related app platforms in Spain and to describe their characteristics and functionalities. We screened apps providing information on dietary advice, food advice, and nutritional content in the Apple App Store and Google Play Store in Spain from March 2 to March 16, 2024. Apps with a star rating of ≥4 (of 5 stars), those available in Spanish, those that were free of charge, those last updated after January 2022, those with >500 reviews, and those with >500,000 downloads were included. The quality of apps was assessed using the user version of the Mobile App Rating Scale (uMARS). General characteristics and nutritional, health, and market-related functionalities of the nutrition-related apps were described. Correlations among total and uMARS sections, star ratings, and number of reviews and downloads were evaluated. Among the 1460 apps identified in the search, 42 apps met the criteria. The majority of these (n=20, 48%) aimed at recording and analyzing food intake, followed by those providing nutritional plans or diets (n=9, 21%), advising on healthy habits (n=7, 17%), and offering recipes (n=6, 14%). The most prevalent nutritional functionalities offered were recording and monitoring body measurements (n=30, 71%), food tracking (n=26, 62%), and dietary analysis (n=25, 60%), whereas nutrition education was less common (n=16, 38%). Among market-related functionalities, advertisements were the most common among the study apps (n=30, 71%), followed by the option of sharing on social media (n=29, 69%) and customizable reminders (n=26, 62%). Sharing the recorded information in the app with health professionals was infrequent (n=1, 2%). The mean (SD) total uMARS score (maximum 5 points) was 3.78 (0.35), while the mean (SD) uMARS scores for functionality, aesthetics, engagement, and information were 4.21 (0.38), 3.94 (0.54), 3.51 (0.46), and 3.48 (0.44), respectively. Lower mean scores were observed for the subjective quality (mean 2.65, SD 0.56) and perceived impact (mean 3.06, SD 0.67). Moderate to strong positive significant correlations were mostly observed between total uMARS and section-specific uMARS scores, while the correlations between the uMARS section scores were mostly moderate positive. Total uMARS scores were very weakly correlated with user rating, number of reviews, and number of downloads. The quality of popular nutrition-related app platforms in Spain was acceptable, with observed remarkable differences between sections. The majority of the apps were appealing due to their user-friendly interfaces. Only a few apps, however, provided dietary structure analysis or nutritional education. Further research is needed to assess the long-term impact of these apps on users.

A Systematic Review of Cervical Cancer Mobile Applications and a Future Directions for Developers.

The objective of this study is to evaluate the quality of mobile health (mHealth) applications that promote cervical cancer awareness and provide screening assistance, with an emphasis on apps available on the Google Play Store and iOS. From December 2023 to February 2024, we assessed mobile applications focused on cervical cancer screening that are available on Google Play and Apple iTunes. The "Cervical Cancer," "Mobile Application," "Pap Test," "Cervical Cancer Guide," "Human Papillomavirus," plus "Cervical Screening are the keywords used to search the applications." Data collection includes features such as application name, pricing, download metrics, invention date, last update, affiliation, online access, login requirements, and notification functionality, which were gathered in Excel. Interrater reliability based on four reviewers' independent judgments, varied from 0.75 to 0.83. In our research, we found 25 apps (16 on the Google Play Store and 9 on iOS). After a thorough review, only 14 relevant apps were included. According to the MARS rating, Rise Against Cancer received the highest score (3.9), followed by FightHPV and Cervical Cancer Forum (3.8). Rise Against Cancer (29), HPV Vaccine (28), and CDC STI Tx Guidelines (28) scored highest in the APPLICATIONS rating system. Hope 4 All and OCI Cervibreast closely matched the statements, meeting seven of the thirteen requirements each. Future app developers should produce user-friendly, often updated mHealth applications that include high-quality cervical cancer awareness and screening content. These apps should provide validated information and pleasant graphic effects.

MHealth Apps in the Digital Marketplace for Pediatric Patients With Cancer: Systematic Search and Analysis.

The substantial increase in smartphone ownership has led to a rise in mobile health (mHealth) app use. Developing tailored features through mHealth apps creates a pathway to address the health care needs of pediatric patients with cancer and their families who have complex care needs. However, few apps are designed specifically to integrate with pediatric cancer care. This study reports a systematic search and analysis of mHealth apps available on the Apple App (iOS) and Google Play (Android) stores designed for pediatric cancer through a list of features that serve (1) patients, (2) caregivers, or (3) both audiences. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we reviewed apps for pediatric patients with cancer and caregivers available as of January 30, 2024. We searched the Apple App and Google Play stores with a list of keyword combinations focusing on pediatric cancer care. The inclusion criteria were (1) specifically apps targeted toward pediatric patients with cancer, their families, or both; (2) available in either app store; and (3) available in English. Apps were assessed using the Mobile Application Rating Scale (MARS). The MARS is a quality assessment for mHealth apps, including components of engagement, functionality, aesthetics, and informational quality (5-point Likert scale items-1: low and 5: high quality). In total, 22 apps were identified and 17 of those apps were available on both platforms. The most popular features (n=12) were resource sharing, symptom tracking, reminders, care team connections, journaling, community support, medication tracking, data visualizations, and appointment tracking. Features and interfaces were designed for caregivers (n=9) more frequently than the patients (n=7) while a subset of apps created options for both users (n=6). A total of 16 apps received positive reviews (mean 4.4, SD 0.59; Min=3.1, Max=5.0). A small subset (n=3) achieved over 5000 downloads; however, the majority (n=15) had fewer than 500. More than half (n=12) of the apps were not available in English. Apps requested access to a range of device functionalities to operate (mean 2.72, SD 3.13; Min=0, Max=10). Out of 22, a total of 17 apps were publicly accessible. The mean MARS scores for the apps ranged from 1.71 (SD 0.75) to 4.33 (SD 0.82). Overall, apps scored high on functionality (mean 3.72, SD 0.54) but low on engagement (mean 3.02, SD 0.93). Our review highlights the promising yet underdeveloped potential of mHealth apps in pediatric oncology care, underscoring the need for more inclusive, comprehensive, and integrative digital health solutions. Future developments should actively involve key stakeholders from the pediatric oncology community, including patients, families, and health care professionals, to ensure the apps meet specific needs while addressing linguistic and cultural barriers.

Guessing Number: A Game-Based Mobile Application for Children Learning Numbers

The brains of young kids grow in spurts known as crucial periods. The first happens at the age of two, and the second throughout puberty. Mathematics is one of the most important subjects to study and practice in daily life. Nonetheless, it must be admitted that learning numbers for young kids is difficult, particularly for today’s generation. Since it is more interesting and effective than traditional learning, game-based mobile learning applications have recently been used as a learning tool for young kids. Therefore, this paper proposes a learning application for young kids to empower them in learning numbers named “Guessing Number”. The objective of this project is to build an application that will help young kids to learn numbers and play games at the same time to stimulate their brains to remember numbers faster. The features focused on this application is to help them recognize numbers, remember numbers by playing games, and stimulate their brain to think fast. This mobile based application is developed using MIT App Inventor along with Canva to design the interface. The proposed educational application has been successfully built, tested, and uploaded into Google Play Store.