Google Play App Stores Research Articles

Analysis of Google Play Store Apps Data Using Tableau Data Visualization Application

This research aims to enhance understanding of big data management and processing. One of the challenges faced is the complexity and large volume of data, which requires effective tools and techniques for analysis and visualization. The objective of this study is to analyze Google Play Store app data based on categories and ratings, and to visualize the results using Tableau. The research method employs a quantitative approach with a framework that includes problem formulation, data collection from the Google Play Store Apps database obtained from kaggle.com, data processing, and analysis using Tableau. The results of the study indicate that the use of data visualization in the form of management graphics, such as horizontal bars and treemaps, is highly effective in identifying the comparison of the number of applications based on categories and ratings. These visualizations facilitate understanding the distribution and trends of applications on the Google Play Store. In conclusion, this research demonstrates that data visualization with Tableau can optimize big data processing and provide valuable insights into the distribution of app categories and ratings on the Google Play Store. These findings underscore the importance of using visualization tools in big data analysis to enhance understanding and improve decision-making.

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

Can the attributes of an app predict its rating?

When Google Play Store developers first publish their apps, it may seem that their success comes from the roll of a dice. Ways to improve the rating of their app may seem impossible to glean, with many relying on blind guessing to determine ways forward. In this paper, we hypothesize that the attributes of a Google Play Store apps, including maturity level, install count, and price, can estimate its rating, or stars out of five. We believed that comparing three models trained on these attributes, each with unique architectures, would lead to a higher final accuracy than one. By using Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) classifiers, we found that the attributes of an app can predict its rating, with review count, date of last update, and storage size being the most influential attributes. During modeling, we found that the RF classifier most successfully predicted the rating of an app, getting 79.3% of predictions correct. These results suggest a connection between the rating of an app and its attributes. The results from this paper can inform app developers and investors about improvement paths to increase the rating of their app, thereby increasing its success.

A content and quality analysis of free, popular mHealth apps supporting 'plant-based' diets.

There has been an increased emphasis on plant-based foods and diets. Although mobile technology has the potential to be a convenient and innovative tool to help consumers adhere to dietary guidelines, little is known about the content and quality of free, popular mobile health (mHealth) plant-based diet apps. The objective of the study was to assess the content and quality of free, popular mHealth apps supporting plant-based diets for Canadians. Free mHealth apps with high user ratings, a high number of user ratings, available on both Apple App and GooglePlay stores, and primarily marketed to help users follow plant-based diet were included. Using pre-defined search terms, Apple App and GooglePlay App stores were searched on December 22, 2020; the top 100 returns for each search term were screened for eligibility. Included apps were downloaded and assessed for quality by three dietitians/nutrition research assistants using the Mobile App Rating Scale (MARS) and the App Quality Evaluation (AQEL) scale. Of the 998 apps screened, 16 apps (mean user ratings±SEM: 4.6±0.1) met the eligibility criteria, comprising 10 recipe managers and meal planners, 2 food scanners, 2 community builders, 1 restaurant identifier, and 1 sustainability assessor. All included apps targeted the general population and focused on changing behaviors using education (15 apps), skills training (9 apps), and/or goal setting (4 apps). Although MARS (scale: 1-5) revealed overall adequate app quality scores (3.8±0.1), domain-specific assessments revealed high functionality (4.0±0.1) and aesthetic (4.0±0.2), but low credibility scores (2.4±0.1). The AQEL (scale: 0-10) revealed overall low score in support of knowledge acquisition (4.5±0.4) and adequate scores in other nutrition-focused domains (6.1-7.6). Despite a variety of free plant-based apps available with different focuses to help Canadians follow plant-based diets, our findings suggest a need for increased credibility and additional resources to complement the low support of knowledge acquisition among currently available plant-based apps. This research received no specific grant from any funding agency.

Identification and Evaluation of Mobile Applications for Self-Management of Diet and Lifestyle for Patients with Inflammatory Bowel Disease.

Mobile health applications (apps) providing diet and lifestyle self-management programs to patients with inflammatory bowel disease (IBD) are emerging. The objective of this study was to evaluate current apps available in the US and Canada based on app quality, perceived impact on diet and mental health and comprehensiveness to support self-management. The Apple iOS and Google Play app stores were searched for terms related to IBD. Apps were included if they targeted diet and lifestyle behaviours for patients living with IBD and were available to the general public. Apps were excluded if they were not specific to IBD, not available in English, did not target diet or lifestyle therapy, were not available in the US and Canada, or did not offer stand-alone self-management programs. The Mobile App Rating Scale was used to assess mobile app quality. A total of 1,512 apps were identified through the app stores. Six apps met inclusion criteria. My IBD Care: Crohn's and Colitis received the highest quality rating and LyfeMD received the highest overall app rating. Only these two apps provided behaviour tracking over time, and three (50 percent) apps provided good-quality information. While many IBD-related apps exist, few support self-management of diet and lifestyle behaviours. The My IBD Care and LyfeMD apps had the highest ratings and can be used to track lifestyle behaviours. The effectiveness of these apps to improve behaviours, and subsequently impact the disease course and quality of life, should be explored in future studies.

Market analysis and quality assessment of applications on Android-devices for patients with bronchial asthma and chronic obstructive pulmonary disease in Ukraine

Annotation. A modern and effective tool for managing patients with chronic obstructive pulmonary disease (COPD) and bronchial asthma (BA) are software applications for smartphones. In order to analyze and evaluate the quality of applications for ANDROID devices available in Ukraine, which can be used by patients with BA and COPD, relevant apps were searched in the Google Play app store. We analyzed their scope, including age restrictions, number of downloads, interface language, user rating. The quality of the apps was assessed according to the guidelines of the Health and Care Mobile App Assessment Organization (ORCHA), and the reliability checklist of an untested mobile health app. We found that Ukrainians have access to 47 mobile apps for ANDROID devices to help them adapt to living with the disease. Of these, only five applications are free and in Ukrainian, but only two are software applications in terms of quality and meet the requirements of ORCHA, so may be recommended for use in patients with AD and COPD, that is insufficient to meet the needs of Ukrainian patients.

Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM

The recent outbreaks of the COVID-19 forced people to work from home. All the educational institutes run their academic activities online. The online meeting app the “Zoom Cloud Meeting” provides the most entire supports for this purpose. For providing proper functionalities require in this situation of online supports the developers need the frequent release of new versions of the application. Which makes the chances to have lots of bugs during the release of new versions. To fix those bugs introduce developer needs users’ feedback based on the new release of the application. But most of the time the ratings and reviews are created contraposition between them because of the users’ inadvertent in giving ratings and reviews. And it has been the main problem to fix those bugs using user ratings for software developers. For this reason, we conduct this average rating calculation process based on the sentiment of user reviews to help software developers. We use BERT-based sentiment annotation to create unbiased datasets and hybridize RNN with LSTM to find calculated ratings based on the unbiased reviews dataset. Out of four models trained on four different datasets, we found promising performance in two datasets containing a necessarily large amount of unbiased reviews. The results show that the reviews have more positive sentiments than the actual ratings. Our results found an average of 3.60 stars rating, where the actual average rating found in dataset is 3.08 stars. We use reviews of more than 250 apps from the Google Play app store. The results of our can provide more promising if we can use a large dataset only containing the reviews of the Zoom Cloud Meeting app.

Journal Digest: IRT; Motivational Therapy; Mental Health Apps; and More

Journal Digest: IRT; Motivational Therapy; Mental Health Apps; and More

Accessibility and availability of smartphone apps for schizophrenia

App-based interventions have the potential to enhance access to and quality of care for patients with schizophrenia. However, less is known about the current state of schizophrenia apps in research and how those translate to publicly available apps. This study, therefore, aimed to review schizophrenia apps offered on marketplaces and research literature with a focus on accessibility and availability. A search of recent reviews, gray literature, PubMed, and Google Scholar was conducted in August 2022. A search of the U.S. Apple App Store and Google Play App Store was conducted in July 2022. All eligible studies and apps were systematically screened/reviewed. The academic research search produced 264 results; 60 eligible studies were identified. 51.7% of research apps were built on psychosis-specific platforms and 48.3% of research apps were built on non-specific platforms. 83.3% of research apps offered monitoring functionalities. Only nine apps, two designed on psychosis-specific platforms and seven on non-specific platforms were easily accessible. The search of app marketplaces uncovered 537 apps; only six eligible marketplace apps were identified. 83.3% of marketplace apps only offered psychoeducation. All marketplace apps lacked frequent updates with the average time since last update 1121 days. There are few clinically relevant apps accessible to patients on the commercial marketplaces. While research efforts are expanding, many research apps are unavailable today. Better translation of apps from research to the marketplace and a focus on sustainable interventions are important targets for the field.

Analyzing Android Taint Analysis Tools: FlowDroid, Amandroid, and DroidSafe

Numerous static taint analysis techniques have recently been proposed for identifying information flows in mobile applications. These techniques are often optimized and evaluated on a set of synthetic benchmarks, which makes the comparison results difficult to generalize. Moreover, the techniques are commonly compared under different configuration setups, rendering the comparisons inaccurate. In this paper, we provide a large, controlled, and independent comparison of the three most prominent static taint analysis tools: <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FlowDroid</small> , <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Amandroid</small> , and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DroidSafe</small> . We align the configuration setup for the tools and evaluate them on both a set of common benchmarks and on real applications from the Google Play app store. We further evaluate the effectiveness of additional reflection handling mechanism implemented by <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DroidRA</small> , applying it to each of the evaluated tools. We compare the results of our analysis to the results reported in previous studies, identify main reasons for inaccuracy in existing tools, and provide suggestions for future research.

Systematic Review of Pelvic Anatomy iOS and Android Applications for Obstetricians and Gynecologists

Resources for learning and enhancing knowledge in medicine, such as mobile applications (apps), have dramatically increased in recent years; however, there is lack of a valid source for identifying the best and most reliable mobile apps. The goal of this study was to identify the highest scoring available pelvic anatomy apps based on quality, content, and functionality for obstetrician-gynecologists using a validated tool. This was a systematic evaluation of anatomy apps in the Apple iTunes (iOS) and Google Play (Android) stores. Exclusions included apps that had nonhuman, nonanatomy content, did not include female pelvic anatomy or were deemed inappropriate for postgraduate level. The validated Mobile App Rating Scale (MARS), a mobile-health (mHealth) app scoring system was used to evaluate apps based on both objective and subjective quality. The authors' MARS scores for each variable were compiled, and the objective, subjective, and overall scores were calculated. The search criteria yielded 2,432 apps: 952 (39%) Apple iTunes store and 1,480 (60.8%) Google Play Store apps. After applying the exclusion criteria, 35 (14 iOS, 8 Android, and 13 iOS/Android) apps were included for evaluation using the MARS system. "Complete Anatomy" app, available for both iOS and Android systems, obtained the highest objective, subjective, and overall MARS score. Although there are numerous anatomy apps available, only a few meet prespecified criteria for functionality, content, quality, and are appropriate for obstetrician-gynecologists. Using a validated scoring system, we present a scored list of female pelvic anatomy apps most appropriate for obstetrician-gynecologists.

MHealth Solutions for Mental Health Screening and Diagnosis: A Review of App User Perspectives Using Sentiment and Thematic Analysis.

Mental health screening and diagnostic apps can provide an opportunity to reduce strain on mental health services, improve patient well-being, and increase access for underrepresented groups. Despite promise of their acceptability, many mental health apps on the market suffer from high dropout due to a multitude of issues. Understanding user opinions of currently available mental health apps beyond star ratings can provide knowledge which can inform the development of future mental health apps. This study aimed to conduct a review of current apps which offer screening and/or aid diagnosis of mental health conditions on the Apple app store (iOS), Google Play app store (Android), and using the m-health Index and Navigation Database (MIND). In addition, the study aimed to evaluate user experiences of the apps, identify common app features and determine which features are associated with app use discontinuation. The Apple app store, Google Play app store, and MIND were searched. User reviews and associated metadata were then extracted to perform a sentiment and thematic analysis. The final sample included 92 apps. 45.65% (n = 42) of these apps only screened for or diagnosed a single mental health condition and the most commonly assessed mental health condition was depression (38.04%, n = 35). 73.91% (n = 68) of the apps offered additional in-app features to the mental health assessment (e.g., mood tracking). The average user rating for the included apps was 3.70 (SD = 1.63) and just under two-thirds had a rating of four stars or above (65.09%, n = 442). Sentiment analysis revealed that 65.24%, n = 441 of the reviews had a positive sentiment. Ten themes were identified in the thematic analysis, with the most frequently occurring being performance (41.32%, n = 231) and functionality (39.18%, n = 219). In reviews which commented on app use discontinuation, functionality and accessibility in combination were the most frequent barriers to sustained app use (25.33%, n = 19). Despite the majority of user reviews demonstrating a positive sentiment, there are several areas of improvement to be addressed. User reviews can reveal ways to increase performance and functionality. App user reviews are a valuable resource for the development and future improvements of apps designed for mental health diagnosis and screening.

Enhancing EFL Students' Reading and Appreciation Skills with Mobile Fiction Apps

The present study proposes the integration of mobile fiction Apps (MFAs) in reading instruction to help EFL college students understand the form and content of literary works. It will give examples of fiction Apps that can be downloaded from the Google Play and iPhone App Stores, give the advantages of using MFAs, literary appreciation skills that can be developed with MFAs and instructional stages with MFAs. MFAs can be used as extension activities or as a supplement to in-class reading instruction in ESL/EFL. They are free, easy, and quick to download, update and delete, can be used anywhere, anytime and as many times as the students need. Instruction with smart mobile phones begins with downloading an MFA such as Harry Potter, Oliver Twist, Sherlock Holmes, Wuthering Heights, a novel by Agatha Christi or a collection of stories. Both simplified and original versions can be used depending on the student’s proficiency level, story/novel length and difficulty level. A story/novel video, app or e-book can be used online or offline. Some MFAs provide notes and tests. The instructor can ask pre-questions that require the students to identify the plot, setting, characters, main theme, point of view, symbolism, style, and tone of the narrative, and infer the meanings of figurative language and imagery in the story. Questions and students’ answers, summaries, comments on each other's' responses can be posted in an online discussion forum, blog or social media page. The students can discuss the elements of a literary work under the instructor's supervision. The integration of fiction Apps showed improved reading comprehension, literary appreciation and text analysis skills among participating students and increased their engagement in reading and literary analysis.

Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine

MOLA is one of the video streaming platform applications on the google play store. The mola application has been downloaded 5 million times but only has a 3.2 rating. On the Google Play Store app rating is followed by user reviews of the app. There are quite a lot of reviews that are unstructured and contain opinions from users about their satisfaction with the application so that it is often taken into consideration by potential users to choose the application used. Based on this, sentiment analysis was carried out using the Support Vector Machine algorithm to find out how the sentiments of users towards the MOLA application on the google play store were carried out. This study uses the Knowledge Discovery in Database (KDD) method. The data used is a review of the MOLA application with as many 520 data consisting of 312 positive reviews and 208 negative reviews. The best results are obtained in scenario 1 (90:10) using the RBF (Radial Basis Function) kernel which produces 92.31% accuracy, 96.3% precision, 89.66% recall, and 92.86% f1-score.&#x0D; &#x0D; Keywords: Sentiment Analysis, Support Vector Machine, MOLA

Multimedia Knowledge Translation Tools for Parents About Childhood Heart Failure: Environmental Scan.

BackgroundChildhood heart failure is a factor in many hospital admissions each year. It can impose a steep learning curve for parents who need to learn the key information to care for their child at home. In this study, we conducted an environmental scan to identify and assess web-based knowledge translation tools about childhood heart failure for parent audiences developed within North America.ObjectiveThe aim of this study is to inventory tools publicly available to parents about childhood heart failure from popular web-based venues, assess how each tool communicates health information, and explore how they were developed.MethodsOur search strategy included two commonly used multimedia-based platforms: two app stores (Google Play and Apple App Store) and one search engine (Advanced Google Search). Common search terms were used, and results were uploaded to Microsoft Excel for screening between 2 reviewers. The inclusion criteria for the tools were as follows: content focused on educating parents about their child’s heart failure, developed in the English language, and originating within Canada and the United States. A total of 2 reviewers screened the app store and internet search results for relevant tools. Each tool was assessed using the Suitability Assessment of Materials (SAM), a validated tool that objectively assesses the suitability of how health information is communicated to a particular audience. Key informants who were involved in tool development were identified and invited for a qualitative interview using a semistructured format to provide data about the development process. Key themes were identified in the semistructured interview process.ResultsFrequencies and SAM percent ratings of eligible tools were reported. No apps exist for parents relating to pediatric heart failure. Overall, 17 relevant internet tools were identified, and their suitability was assessed for the parent audience. Most tools scored well in layout and type, but they scored lower in readability and graphics. Qualitative interviews with key informants revealed three key themes: timely and introductory knowledge, credible and trustworthy knowledge, and challenges and evolution in knowledge.ConclusionsThis is the first environmental scan looking for parent tools relating to childhood heart failure in Canada and the United States. Findings from this study reveal that there are no apps on this topic and there is a small number of tools for parents on the internet (n=17). Using the SAM, no tools scored in the superior range, and further work in knowledge translation strategies needs to be done in this area to improve more effective education to parents and caregivers who have a child with heart failure. These findings will inform the development of a new resource on children’s heart failure that targets parents and caregiver audiences.

Pulse Oximeter App Privacy Policies During COVID-19: Scoping Assessment

BackgroundPulse oximeter apps became of interest to consumers during the COVID-19 pandemic, particularly when traditional over-the-counter pulse oximeter devices were in short supply. Yet, no study to date has examined or scoped the state of privacy policies and notices for the top-rated and most downloaded pulse oximeter apps during COVID-19.ObjectiveThe aim of this study was to examine, through a high-level qualitative assessment, the state and nature of privacy policies for the downloaded and top-rated pulse oximeter apps during the COVID-19 pandemic to (1) compare findings against comparable research involving other mobile health (mHealth) apps and (2) begin discussions on opportunities for future research or investigation.MethodsDuring August-October 2020, privacy policies were reviewed for pulse oximeter apps that had either at least 500 downloads (Google Play Store apps only) or a three out of five-star rating (Apple Store apps only). In addition to determining if the apps had an accessible privacy policy, other key privacy policy–related details that were extracted included, but were not limited to, app developer location (country); whether the app was free or required paid use/subscription; whether an ads disclosure was provided on the app’s site; the scope of personal data collected; proportionality, fundamental rights, and data protection and privacy issues; and privacy safeguards.ResultsSix pulse oximeter apps met the inclusion criteria and only 33% (n=2) of the six apps had an accessible privacy policy that was specific to the pulse oximeter app feature (vs the app developer’s website or at all). Variation was found in both the regulatory nature and data privacy protections offered by pulse oximeter apps, with notable privacy protection limitations and gaps, although each app provided at least some information about the scope of personal data collected upon installing the app.ConclusionsPulse oximeter app developers should invest in offering stronger privacy protections for their app users, and should provide more accessible and transparent privacy policies. This is a necessary first step to ensure that the data privacy of mHealth consumers is not exploited during public health emergency situations such as the COVID-19 pandemic, where over-the-counter personal health monitoring devices could be in short supply and patients and consumers may, as a result, turn to mHealth apps to fill such supply gaps. Future research considerations and recommendations are also suggested for mHealth technology and privacy researchers who are interested in examining privacy implications associated with the use of pulse oximeter apps during and after the COVID-19 pandemic.

Integrating User Reviews and Issue Reports of Mobile Apps for Change Requests Detection

There is abundance of mobile Apps released continuously on the App store, where developers are required to maintain these Apps to attain user satisfaction. Developers should consider all user feedback, as they are important resources for planning of next App’s release. In order to consider user feedback, many platforms host mobile Apps and allow users to submit their opinions, such as: Google Play App store and Github Open-Source Development platform. The automated consolidation of user feedback from such platforms, and transforming it into a list of change requests would result in satisfying users across different platforms, and their analysis helps developer to reduce cost of time and effort to plan for the new release of the mobile App. In this paper, a framework is proposed which integrates user feedback from different sources and analyzes them using a state-of-art user reviews analysis tool to obtain a list of change requests, such list is further examined for similarity to remove duplicates and prioritize the identified change requests. A prototype is designed to implement the proposed framework and applied to AntennaPod. Consequently, the framework experimentation results show that reviews and issue reports can be analyzed almost equally despite the difference of text’s nature.

Landscape Analysis of Oncology Mobile Health Applications.

More than 325,000 mobile health (mhealth) applications (apps) have been developed. We sought to describe the state of oncology-specific apps and to highlight areas of strength and opportunities for future development. We searched for oncology apps in the Apple iOS and Google Play app stores in January 2020. Apps were classified by English language support, date of last update, downloads, intended audience, intended purpose, and developer type. We identified 794 oncology-specific, English language applications; only 257 (32%) met basic recency standards and were considered evaluable. Of evaluable apps, almost half (47%) were found in the Medical Store Category and the majority were free (88%). The most common intended audience was health care professionals (45%), with 28% being geared toward the general public and 27% being intended for patients. The intended function was education for 36%, clinical decision support for 19.5%, and patient support for 18%. Only 23% of education apps and 40% of clinical decision support apps reported any formal app content review process. Web developers created 61.5% of apps, scientific societies created 10%, and hospitals or health care organizations created just 6%. Of 54 studies that used mobile apps in oncology identified by a recent meta-analysis, only two could be matched to commercially available apps from our study, suggesting a substantial divide between investigation and product dissemination. Relatively few oncology-related apps exist in the commercial marketplace, up-to-date apps are uncommon, and there is a notable absence of key oncology stakeholders in app development. Meaningful development opportunities exist.

Mobile App Usage Pattern Prediction Using Hierarchical Flexi-Ensemble Clustering (HFEC) for Mobile Service Rating

Nowadays, the mobile app market becomes rapidly increased in world wide. The mobile app marketers have smart enough to understand the requirements and demands of customers and perform their aspirations. They delight them. It provides growth, profitability, and creativity with lot of inventions. The main aim of this research is to analyze the customer interest and preferences of mobile service providers. This paper proposed the clustering model named as Hierarchical Flexi-Ensemble Clustering. It provides the final result with robustness and improved quality. Before clustering, the unwanted features are removed by using the Genetic Algorithm based on the Collective Materials technique. The customer preferences are analyzes with the clustering of mobile usage patterns. The analysis determined that the app usage pattern based on the most frequent word, rating category, rating character count, rating word count and content-based rating in the google play store app dataset. Finally, the results are compared with the existing methods to analyze the superior performance of proposed method. The comparison analysis is estimated based on the based on the average hit rate at different cache sizes. The work is concluded with the app pattern prediction in the form of clustering for app marketing service. From the marketing side, they can analyze the customer preferences and satisfaction.

Orthodontic apps: an assessment of quality (using the Mobile App Rating Scale (MARS)) and behaviour change techniques (BCTs)

BackgroundApps have been shown to be an effective tool in changing patients’ behaviours in orthodontics and can be used to improve their compliance with treatment. The Behaviour Change Techniques (BCTs) and quality (using MARS) within these apps have previously not been published.ObjectivesTo evaluate the quality of these apps aiming to change behaviour.To assess BCTs used in patient focused orthodontic apps.MethodsThe UK Google Play and Apple App Stores were searched to identify all orthodontic apps and 305 apps were identified. All 305 apps were assessed for the presence of BCTs using an accepted taxonomy of BCTs (Behaviour Change Wheel (BCW)), widely utilised in healthcare. Of those containing BCTs, the quality was assessed using the Mobile App Rating Scale (MARS), a validated and multi-dimensional tool which rates apps according to 19 objective criteria. Data collection was carried out by two calibrated, independent assessors and repeated after 6 weeks for 25% of the apps by both assessors.ResultsBCTs were found in 31 apps, although only 18 of them were analysed for quality and 13 apps were excluded. Six different BCTs were identified: these were most commonly ‘prompts/cues’, and ‘information about health consequences’. All apps were shown to be of moderate quality (range 3.1–3.7/5). Inter-rater and intra-rater reliability for BCT and quality assessment were excellent.ConclusionsThe current availability of orthodontic apps of sufficient quality to recommend to patients is very limited. There is therefore a need for high-quality orthodontic apps with appropriate BCTs to be created, which may be utilised to improve patients’ compliance with treatment.