App review driven collaborative bug finding.

Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., two web browser apps) may be affected by similar bugs in their evolution process. It is therefore possible to transfer the experience of one historical app to quickly find bugs in its new counterparts. This has been referred to as collaborative bug finding in the literature. Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews. Concretely, we design the BugRMSys approach to recommend bug reports for a target app by matching historical bug reports from apps in the same category with user app reviews of the target app. We experimentally show that this approach enables us to quickly expose and report dozens of bugs for targeted apps such as Brave (web browser app). BugRMSys 's implementation relies on DistilBERT to produce natural language text embeddings. Our pipeline considers similarities between bug reports and app reviews to identify relevant bugs. We then focus on the app review as well as potential reproduction steps in the historical bug report (from a same-category app) to reproduce the bugs. Overall, after applying BugRMSys to six popular apps, we were able to identify, reproduce and report 20 new bugs: among these, 9 reports have been already triaged, 6 were confirmed, and 4 have been fixed by official development teams.

Smartphone Apps for Smoking Cessation: Systematic Framework for App Review and Analysis.

BACKGROUND: Cigarette smoking is a leading cause of preventable death, and identifying novel treatment approaches to promote smoking cessation is critical for improving public health. With the rise of digital health and mobile apps, these tools offer potential opportunities to address smoking cessation, yet the functionality of these apps and whether they offer scientifically based support for smoking cessation are unknown. OBJECTIVE: The goal of this research was to use the American Psychiatric Association app evaluation model to evaluate the top-returned apps from Android and Apple app store platforms related to smoking cessation and investigate the common app features available for end users. METHODS: We conducted a search of both Android and iOS app stores in July 2021 for apps related to the keywords "smoking," "tobacco," "smoke," and "cigarette" to evaluate apps for smoking cessation. Apps were screened for relevance, and trained raters identified and analyzed features, including accessibility (ie, cost), privacy, clinical foundation, and features of the apps, using a systematic framework of 105 objective questions from the American Psychiatric Association app evaluation model. All app rating data were deposited in mindapps, a publicly accessible database that is continuously updated every 6 months given the dynamic nature of apps available in the marketplace. We characterized apps available in July 2021 and November 2022. RESULTS: We initially identified 389 apps, excluded 161 due to irrelevance and nonfunctioning, and rated 228, including 152 available for Android platforms and 120 available for iOS platforms. Some of the top-returned apps (71/228, 31%) in 2021 were no longer functioning in 2022. Our analysis of rated apps revealed limitations in accessibility and features. While most apps (179/228, 78%) were free to download, over half had costs associated with in-app purchases or full use. Less than 65% (149/228) had a privacy policy addressing the data collected in the app. In terms of intervention features, more than 56% (128/228) of apps allowed the user to set and check in on goals, and more than 46% (106/228) of them provided psychoeducation, although few apps provided evidence-based support for smoking cessation, such as peer support or skill training, including mindfulness and deep breathing, and even fewer provided evidence-based interventions, such as acceptance and commitment therapy or cognitive behavioral therapy. Only 12 apps in 2021 and 11 in 2022 had published studies supporting the feasibility or efficacy for smoking cessation. CONCLUSIONS: Numerous smoking cessation apps were identified, but analysis revealed limitations, including high rates of irrelevant and nonfunctioning apps, high rates of turnover, and few apps providing evidence-based support for smoking cessation. Thus, it may be challenging for consumers to identify relevant, evidence-based apps to support smoking cessation in the app store, and a comprehensive evaluation system of mental health apps is critically important.

App Review Series: CTisus iPearls.

Thousands of "pearls" or facts related to radiology or imaging are accessible through a categorically organized user interface. Pearls are divided up into anatomical regions, as well as non-organ-specific categories. Information is easily accessible with an ad-free interface matched with full offline capability. User interface leaves much to be desired as it appears dated and difficult to navigate at first. Pearls are not easily searchable within the application and unexperienced users may find it difficult to quickly answer targeted questions using this application alone. This application compiles pearls of wisdom pulled from thousands of peer-reviewed publications and organizes them in an easy to use format. While this is designed with the well-informed user in mind, it can serve as an excellent resource for users of all levels of experience seeking bite-sized pieces of imaging-related information.

Patient-facing genetic and genomic mobile apps in the UK: a systematic review of content, functionality, and quality.

Close relative (consanguineous) marriage is widely practised globally, and it increases the risk of genetic disorders. Mobile apps may increase awareness and education regarding the associated risks in a sensitive, engaging, and accessible manner. This systematic review of patient-facing genetic/genomic mobile apps explores content, function, and quality. We searched the NHS Apps Library and the UK Google Play and Apple App stores for patient-facing genomic/genetic smartphone apps. Descriptive information and information on content was extracted and summarized. Readability was examined using the Flesch-Kincaid metrics. Two raters assessed each app, using the Mobile App Rating Scale (MARS) and the IMS Institute for Healthcare Informatics functionality score. A total of 754 apps were identified, of which 22 met the eligibility criteria. All apps intended to inform/educate users, while 32% analyzed genetic data, and 18% helped to diagnose genetic conditions. Most (68%) were clearly about genetics, but only 14% were affiliated with a medical/health body or charity, and only 36% had a privacy strategy. Mean reading scores were 35 (of 100), with the average reading age being equivalent to US grade 12 (UK year 13). On average, apps had 3.3 of the 11 IMS functionality criteria. The mean MARS quality score was 3.2 ± 0.7. Half met the minimum acceptability score (3 of 5). None had been formally evaluated. It was evident that there are few high-quality genomic/genetic patient-facing apps available in the UK. This demonstrates a need for an accessible, culturally sensitive, evidence-based app to improve genetic literacy within patient populations and specific communities.

Evaluation of Meditation Apps Available on Google Play and Apple Store: An App Review.

Many meditation apps have been used to improve the mental wellbeing of individuals. However, little information is available regarding the quality of the applications. This study aims to evaluate meditation apps using the Mobile Applications Rating Scale (MARS). A systematic search for meditation apps was performed on both Android Google Play and Apple iOS Store. We used two keywords to search both app stores: meditation and mindfulness. Out of 623 apps identified, 334 apps were excluded due to language, containing only reminders to meditate, or for not being accessible. A total number of 289 apps remained, of which 280 apps were excluded for being information-only focused, containing religious practices, eating habits, exercises, or for not being free. Therefore, nine apps were included in this review for evaluation. The MARS ratings used in this app review were based on scores from a prior study conducted. The mobile app Headspace had the highest average (4), which is rated as 'good' based on MARS. The remaining apps were rated as acceptable with averages that ranged from 3.2-3.7.

Data Collection Mechanisms in Health and Wellness Apps: Review and Analysis.

BACKGROUND: There has been a steady rise in the availability of health wearables and built-in smartphone sensors that can be used to collect health data reliably and conveniently from end users. Given the feature overlaps and user tendency to use several apps, these are important factors impacting user experience. However, there is limited work on analyzing the data collection aspect of mobile health (mHealth) apps. OBJECTIVE: This study aims to analyze what data mHealth apps across different categories usually collect from end users and how these data are collected. This information is important to guide the development of a common data model from current widely adopted apps. This will also inform what built-in sensors and wearables, a comprehensive mHealth platform should support. METHODS: In our empirical investigation of mHealth apps, we identified app categories listed in a curated mHealth app library, which was then used to explore the Google Play Store for health and medical apps that were then filtered using our selection criteria. We downloaded these apps from a mirror site hosting Android apps and analyzed them using a script that we developed around the popular AndroGuard tool. We analyzed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects health data. RESULTS: We retrieved 3251 apps meeting our criteria, and our analysis showed that 10.74% (349/3251) of these apps requested Bluetooth access. We found that 50.9% (259/509) of the Bluetooth service universally unique identifiers to be known in these apps, with the remainder being vendor specific. The most common health-related Bluetooth Low Energy services using known universally unique identifiers were Heart Rate, Glucose, and Body Composition. App permissions showed the most used device module or sensor to be the camera (669/3251, 20.57%), closely followed by location (598/3251, 18.39%), with the highest occurrence in the staying healthy app category. CONCLUSIONS: We found that not many health apps used built-in sensors or peripherals for collecting health data. The small number of the apps using Bluetooth, with an even smaller number of apps using standard Bluetooth Low Energy services, indicates a wider use of proprietary algorithms and custom services, which restrict the device use. The use of standard profiles could open this ecosystem further and could provide end users more options for apps. The relatively small proportion of apps using built-in sensors along with a high reliance on manual data entry suggests the need for more research into using sensors for data collection in health and fitness apps, which may be more desirable and improve end user experience.

Measuring the Quality of Clinical Skills Mobile Apps for Student Learning: Systematic Search, Analysis, and Comparison of Two Measurement Scales.

BACKGROUND: Mobile apps are widely used in health professions, which increases the need for simple methods to determine the quality of apps. In particular, teachers need the ability to curate high-quality mobile apps for student learning. OBJECTIVE: This study aims to systematically search for and evaluate the quality of clinical skills mobile apps as learning tools. The quality of apps meeting the specified criteria was evaluated using two measures-the widely used Mobile App Rating Scale (MARS), which measures general app quality, and the Mobile App Rubric for Learning (MARuL), a recently developed instrument that measures the value of apps for student learning-to assess whether MARuL is more effective than MARS in identifying high-quality apps for learning. METHODS: Two mobile app stores were systematically searched using clinical skills terms commonly found in medical education and apps meeting the criteria identified using an approach based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 9 apps were identified during the screening process. The apps were rated independently by 2 reviewers using MARS and MARuL. RESULTS: The intraclass correlation coefficients (ICCs) for the 2 raters using MARS and MARuL were the same (MARS ICC [two-way]=0.68; P<.001 and MARuL ICC [two-way]=0.68; P<.001). Of the 9 apps, Geeky Medics-OSCE revision (MARS Android=3.74; MARS iOS=3.68; MARuL Android=75; and MARuL iOS=73) and OSCE PASS: Medical Revision (MARS Android=3.79; MARS iOS=3.71; MARuL Android=69; and MARuL iOS=73) scored highly on both measures of app quality and for both Android and iOS. Both measures also showed agreement for the lowest rated app, Patient Education Institute (MARS Android=2.21; MARS iOS=2.11; MARuL Android=18; and MARuL iOS=21.5), which had the lowest scores in all categories except information (MARS) and professional (MARuL) in both operating systems. MARS and MARuL were both able to differentiate between the highest and lowest quality apps; however, MARuL was better able to differentiate apps based on teaching and learning quality. CONCLUSIONS: This systematic search and rating of clinical skills apps for learning found that the quality of apps was highly variable. However, 2 apps-Geeky Medics-OSCE revision and OSCE PASS: Medical Revision-rated highly for both versions and with both quality measures. MARS and MARuL showed similar abilities to differentiate the quality of the 9 apps. However, MARuL's incorporation of teaching and learning elements as part of a multidimensional measure of quality may make it more appropriate for use with apps focused on teaching and learning, whereas MARS's more general rating of quality may be more appropriate for health apps targeting a general health audience. Ratings of the 9 apps by both measures also highlighted the variable quality of clinical skills mobile apps for learning.

User Perceptions of Virtual Hospital Apps in China: Systematic Search.

BACKGROUND: Virtual hospital apps are mobile apps that offer functionalities of online consultation, medical guidance, health community forums, referrals, outpatient appointments or virtual hospital-to-home care services. With an increasing number of online medical and health care consulting services, virtual hospital apps have made health care more accessible and fairer for all, especially in China. However, they have occurred without control or regulation. User evaluation can provide directions to help apps optimize identification, lower risks, and guarantee service quality. OBJECTIVE: We aimed to conduct a systematic search for virtual hospital apps in China. To get a global view, virtual hospital apps were assessed and characterized by means of quantitative analysis. To get a local view, we conducted a content feedback analysis to explore user requirements, expectations, and preferences. METHODS: A search was conducted of the most popular Apple and Android app stores in China. We characterized and verified virtual hospital apps and grouped apps according to quantification analysis. We then crawled apps and paid attention to corresponding reviews to incorporate users' involvement, and then performed aspect-based content labeling and analysis using an inductive approach. RESULTS: A total of 239 apps were identified in the virtual hospital app markets in China, and 2686 informative corresponding reviews were analyzed. The evidence showed that usefulness and ease of use were vital facts for engagement. Users were likely to trust a consulting service with a high number of downloads. Furthermore, users expected frequently used apps with more optimization to improve virtual service. We characterized apps according to 4 key features: (1) app functionalities, including online doctor consultation, in-app purchases, tailored education, and community forums; (2) security and privacy, including user data management and user privacy; (3) health management, including health tracking, reminders, and notifications; and (4) technical aspects, including user interface and equipment connection. CONCLUSIONS: Virtual hospitals relying on the mobile internet are growing rapidly. A large number of virtual hospital apps are available and accessible to a growing number of people. Evidence from this systematic search can help various types of virtual hospital models enhance virtual health care experiences, go beyond offline hospitals, and continuously meet the needs of individual end users.

Mobile Apps for Blood Pressure Monitoring: Systematic Search in App Stores and Content Analysis.

BACKGROUND: Using a mobile app for self-management could make it easier for patients to get insight into their blood pressure patterns. However, little is known about the availability, quality, and features of mobile apps targeting blood pressure. OBJECTIVE: The goal of the research was to determine the availability, functionality, and quality of mobile apps that could be used for blood pressure monitoring purposes. METHODS: A systematic app search was performed based on the standards for systematic reviews. We searched the Dutch official app stores for Android and iOS platforms using predefined keywords and included all English and Dutch mobile apps targeting blood pressure. Two independent assessors determined eligibility and quality of the apps using the 5-point Mobile App Rating Scale (MARS). Quality scores of the apps with and without 17 a priori selected characteristics were compared using independent sample t tests. RESULTS: A total of 184 apps (104 Android, 80 iOS) met the inclusion criteria. The mean overall MARS score was 2.63 (95% CI 2.55-2.71) for Android and 2.64 (95% CI 2.56-2.71) for iOS. The apps Bloeddruk (4.1) and AMICOMED BP (3.6) had the highest quality scores on the Android and iOS platforms, respectively. Of the app characteristics recorded, only pricing, in-app advertisements, and local data storage were not associated with the quality scores. In only 3.8% (7/184) of the apps, involvement of medical experts in its development was mentioned, whereas none of the apps was formally evaluated with results published in a peer-reviewed journal. CONCLUSIONS: This study provides an overview of the best apps currently available in the app stores and important key features for self-management that can be used by health care providers and patients with hypertension to identify a suitable app targeting blood pressure monitoring. However, the majority of the apps targeting blood pressure monitoring were of poor quality. Therefore, it is important to involve medical experts in the developmental stage of health-related mobile apps to improve the quality of these apps.