Review Of Apps Research Articles

A73 CANADIAN INFLAMMATORY BOWEL DISEASE MOBILE APPS: CURRENT LANDSCAPE AND NEEDS

BackgroundEvidence-based digital health applications (apps) offering comprehensive lifestyle therapies for inflammatory bowel disease (IBD) patients are limited in Canada.AimsThe aims of this study were to explore the Canadian IBD digital app landscape and review preliminary data from a recently launched digital app for IBD, LyfeMD. (www.lyfemd.ca).Methods“IBD”, “Inflammatory bowel disease”, “UC”, “Ulcerative colitis”, “Crohns” and “Crohn’s disease (CD)” were searched by one team member (BC) on the App Store. Apps were included if they offered any type of lifestyle therapy, including education. The mobile application rating system (MARS) was used to evaluate each app and is a validated tool used to assess the quality of mobile health apps. For the LyfeMD app, 35 IBD users completed a baseline assessment survey to identify: 1) physical activity, sitting, and screen time, and; 2) stress, sleep, depression and anxiety. Eleven participants completed in-depth user experience evaluations after 4 weeks. Survey scores were calculated using published scoring protocols and descriptive data were prepared.ResultsThe LyfeMD and My IBD Care app scored highest on the MARS with a total score of 4.8/5. Of the other eight apps identified, scores ranged from 2.4 to 4.6 (overall mean=4.0). LyfeMD differentiated itself from other apps by providing lifestyle programs to improve nutrition, physical activity and mental health. Of the LyfeMD users, 74% had CD (median Harvey Bradshaw index=3.1, IQR=1.1–4.8) and 26% had ulcerative colitis (median partial mayo score=1.0, 0.5–6.0), 60% had a BMI ≥25 kg/m2, 57% were meeting 150 minute/week activity guidelines, 49% had high sitting time, 100% had high screen time, 69% had a moderate to high level of stress, 100% experienced sleep problems, 69% reported depression, and 49% reported anxiety. Eleven people completed the detailed user experience evaluations. They reported the app helped them identify behaviour changes to improve overall wellness; most often what they eat (64%), overall well-being (64%) and physical activity (46%).ConclusionsTwo IBD apps available in Canada had a high MARS rating, however only the LyfeMD app offered comprehensive lifestyle therapies. The growing literature supports benefit for lifestyle therapies in IBD, and the LyfeMD app may be effective to identify areas amenable to lifestyle modification.Table 1: MARS ScoresIBD HealthlineMy IBD Care: Crohn’s and ColitisGali HealthMyGutmyIBDLyfeMDPhyla HealthMy Crohn’s and Colitis SupportAGA IBD ManagerCrohn’s Disease by AZoMedicalEngagement3.004.754.003.802.004.803.603.603.002.40Functionality5.004.754.005.003.004.755.003.753.753.75Aesthetics4.335.005.005.001.675.004.333.334.334.67Information Quality3.864.713.294.432.864.713.863.714.294.00Mean MARS4.054.804.074.562.384.824.203.603.843.71Funding AgenciesAscend, Alberta Innovates

A Review of Smartphone Apps Used for Toric Intraocular Lens Calculation and Alignment.

Smartphone apps are becoming increasingly popular in ophthalmology, one specific area of their application being toric intraocular lens (IOL) surgery for astigmatism correction. Our objective was to identify, review and objectively score smartphone apps applicable to toric IOL calculation and/or axis alignment. This review was divided into three phases. A review was conducted on four major app databases (phase I): National Health Service (NHS) Apps Library, Google Play Store, Apple App Store and Amazon Appstore. A systematic literature review (phase II) was conducted to identify studies for included apps in phase I of our study. Keywords used in both searches included: “toric lens”, “toric IOL”, “refraction”, “astigmatism”, “ophthalmology”, “eye calculator”, “ophthalmology calculator” and “refractive calculator”. Included apps were objectively scored (phase III) by three independent reviewers using the mobile app rating scale (MARS), a validated tool that ranks the quality of mobile health apps using a calculated mean app quality (MAQ) score. Phase I of our study screened 2428 smartphone apps, of which six apps for toric IOL calculation and four apps for axis marking were eligible and were selected for quantitative analysis. Phase II of our study screened 477 studies from PubMed, Medline and Google Scholar. Three studies validating two apps (toriCAM, iToric Patwardhan) in a clinical setting as adjunct tools for preoperative axis marking were identified. Phase III ranked Toric Calculator for iPhone (Apple iOS, MAQ 4.13; average MAQ 3.34 ± 0.54) as the highest-scoring toric IOL calculator, and iToric Patwardhan (Android OS, MAQ 4.13; average MAQ 3.41 ± 0.44) was the highest-scoring axis marker in our study. Our review identified and objectively scored ten smartphone apps available for toric IOL surgery adjuncts. Toric Calculator for iPhone and iToric Patwardhan were the highest-scoring toric IOL calculator and axis marker, respectively. Current literature, though limited, suggests that axis marking smartphone apps can achieve similar levels of misalignment reduction when compared to digital systems.

The Content, Quality, and Behavior Change Techniques in Nutrition-Themed Mobile Apps for Children in Canada: App Review and Evaluation Study.

BackgroundChildren increasingly use mobile apps. Strategies to increase child engagement with apps include the use of gamification and images that incite fun and interaction, such as food. However, the foods and beverages that children are exposed to while using apps are unknown and may vary by app type.ObjectiveThe aim of this study is to identify the app content (ie, types of foods and beverages) included in nutrition-themed apps intended for children, to assess the use of game-like features, and to examine app characteristics such as overall quality and behavior change techniques (BCTs).MethodsThis analysis used a cross-sectional database of nutrition-themed apps intended for children (≤12 years), collected between May 2018 and June 2019 from the Apple App Store and Google Play Store (n=259). Apps were classified into four types: food games or nongames that included didactic nutrition guides, habit trackers, and other. Food and beverages were identified in apps and classified into 16 food categories, as recommended (8/16, 50%) and as not recommended (8/16, 50%) by dietary guidelines, and quantified by app type. Binomial logistic regression assessed whether game apps were associated with foods and beverages not recommended by guidelines. App quality, overall and by subscales, was determined using the Mobile App Rating Scale. The BCT Taxonomy was used to classify the different behavioral techniques that were identified in a subsample of apps (124/259, 47.9%).ResultsA total of 259 apps displayed a median of 6 (IQR 3) foods and beverages. Moreover, 62.5% (162/259) of apps were classified as food games, 27.4% (71/259) as didactic nutrition guides, 6.6% (17/259) as habit trackers, and 3.5% (9/259) as other. Most apps (198/259, 76.4%) displayed at least one food or beverage that was not recommended by the dietary guidelines. Food game apps were almost 3 times more likely to display food and beverages not recommended by the guidelines compared with nongame apps (β=2.8; P<.001). The overall app quality was moderate, with a median Mobile App Rating Scale score of 3.6 (IQR 0.7). Functionality was the subscale with the highest score (median 4, IQR 0.3). Nutrition guides were more likely to be educational and contain informative content on healthy eating (score 3.7), compared with the other app types, although they also scored significantly lower in engagement (score 2.3). Most apps (105/124, 84.7%) displayed at least one BCT, with the most common BCT being information about health consequences.ConclusionsFindings suggest nutrition-themed apps intended for children displayed food and beverage content not recommended by dietary guidelines, with gaming apps more likely to display not recommended foods than their nongame counterparts. Many apps have a moderate app quality, and the use of consequences (instead of rewards) was the most common BCT.

MHealth Apps for Self-Management of Cardiovascular Diseases: A Scoping Review.

The use of mHealth apps for the self-management of cardiovascular diseases (CVDs) is an increasing trend in patient-centered care. In this research, we conduct a scoping review of mHealth apps for CVD self-management within the period 2014 to 2021. Our review revolves around six main aspects of the current status of mHealth apps for CVD self-management: main CVDs managed, main app functionalities, disease stages managed, common approaches used for data extraction, analysis, management, common wearables used for CVD detection, monitoring and/or identification, and major challenges to overcome and future work remarks. Our review is based on Arksey and O’Malley’s methodological framework for conducting studies. Similarly, we adopted the PRISMA model for reporting systematic reviews and meta-analyses. Of the 442 works initially retrieved, the review comprised 38 primary studies. According to our results, the most common CVDs include arrhythmia (34%), heart failure (32%), and coronary heart disease (18%). Additionally, we found that the majority mHealth apps for CVD self-management can provide medical recommendations, medical appointments, reminders, and notifications for CVD monitoring. Main challenges in the use of mHealth apps for CVD self-management include overcoming patient reluctance to use the technology and achieving the interoperability of mHealth applications with other systems.

Persuasive Design Solutions for a Sustainable Workforce: Review of Persuasive Apps for Real-Time Capability Support for Rural Health Care Professionals.

BackgroundThere is a need to further investigate how persuasive design principles can change rural health professionals’ behaviors to look after their own health workforce capability. Several theories are used when developing apps to persuade people to change behavior, including the Persuasive System Design Model, consisting of primary task, dialogue, system credibility, and social support categories, and Cialdini’s principles of persuasion. These have not been analyzed yet in the field of health workforce capability.ObjectiveThis study aims to determine the persuasive design techniques used in capability building–related apps and to provide recommendations for designing a health workforce app to increase their persuasiveness.MethodsA Python script was used to extract a total of 3060 apps from Google Play. Keywords centered around health workforce capability elements. App inclusion criteria were as follows: been updated since 2019, rated by users on average 4 and above, and more than 100,000 downloads. Next, 2 experts reviewed whether 32 persuasive strategies were used in the selected apps, and these were further analyzed by capability categories: competencies and skills, health and personal qualities, values and attitudes, and work organization.ResultsIn all, 53 mobile apps were systematically reviewed to identify the persuasive design techniques. The most common were surface credibility (n=48, 90.6%) and liking (n=48), followed by trustworthiness (n=43, 81.1%), reminders (n=38, 71.7%), and suggestion (n=30, 56.6%). The techniques in the social support domain were the least used across the different apps analyzed for health workforce capability, whereas those in the primary task support domain were used most frequently. The recommendations reflect learnings from our analysis. These findings provided insight into mobile app design principles relevant to apps used in improving health workforce capability.ConclusionsOur review showed that there are many persuasive design techniques that can assist in building health workforce capability. Additionally, several apps are available in the market that can assist in improving health workforce capability. There is, however, a specific lack of digital, real-time support to improve health workforce capability. Social support strategies through using social support persuasive design techniques will need to be integrated more prominently into a health workforce capability app. An app to measure and monitor health workforce capability scores can be used in conjunction with direct real-world person and real-time support to discuss and identify solutions to improve health workforce capability for rural and remote health professionals who are at high risk of burnout or leaving the rural health workforce.

A Comprehensive Review of Progressive Web Apps: Bridging the Gap Between Web and Native Experiences

A Comprehensive Review of Progressive Web Apps: Bridging the Gap Between Web and Native Experiences

What Improves Customer Satisfaction in Mobile Banking Apps? An Application of Text Mining Analysis

Consumer-generated reviews reflect consumers’ experiences and perceptions toward a product or service. In this context, we propose a text mining approach to identify factors that improve customer satisfaction in the mobile banking app service. To do so, we collect 96,140 mobile app reviews for four U.S. banks: Bank of America, Capital One, Chase, and Wells Fargo. Using the Latent Dirichlet Allocation (LDA) topic model, we first derive the critical quality dimensions such as ease of use, convenience, security, and customer support. Analysis of weekly panel data shows that positive responses to the security and convenience of mobile banking apps improve app ratings. However, increased comments about insecurity, negative customer support experiences, discomfort, and complexity lower user ratings. Overall, the empirical results support that security is the most influential factor in customer satisfaction with mobile financial services.

Understanding in-app advertising issues based on large scale app review analysis

In-app advertising closely relates to app revenue. Reckless ad integration could adversely impact app quality and user experience, leading to loss of income. It is very challenging to balance the ad revenue and user experience for app developers.

User Experience, Engagement, and Popularity in Mental Health Apps: Secondary Analysis of App Analytics and Expert App Reviews.

BackgroundUser experience and engagement are critical elements of mental health apps’ abilities to support users. However, work examining the relationships among user experience, engagement, and popularity has been limited. Understanding how user experience relates to engagement with and popularity of mental health apps can demonstrate the relationship between subjective and objective measures of app use. In turn, this may inform efforts to develop more effective and appealing mental health apps and ensure that they reach wide audiences.ObjectiveWe aimed to examine the relationship among subjective measures of user experience, objective measures of popularity, and engagement in mental health apps.MethodsWe conducted a preregistered secondary data analysis in a sample of 56 mental health apps. To measure user experience, we used expert ratings on the Mobile App Rating Scale (MARS) and consumer ratings from the Apple App Store and Google Play. To measure engagement, we acquired estimates of monthly active users (MAU) and user retention. To measure app popularity, we used download count, total app revenue, and MAU again.ResultsMARS total score was moderately positively correlated with app-level revenue (Kendall rank [T]=0.30, P=.002), MAU (T=0.39, P<.001), and downloads (T=0.41, P<.001). However, the MARS total score and each of its subscales (Engagement, Functionality, Aesthetics, and Information) showed extremely small correlations with user retention 1, 7, and 30 days after downloading. Furthermore, the total MARS score only correlated with app store rating at T=0.12, which, at P=.20, did not meet our threshold for significance.ConclusionsMore popular mental health apps receive better ratings of user experience than less popular ones. However, user experience does not predict sustained engagement with mental health apps. Thus, mental health app developers and evaluators need to better understand user experience and engagement, as well as to define sustained engagement, what leads to it, and how to create products that achieve it. This understanding might be supported by better collaboration between industry and academic teams to advance a science of engagement.

Listening to Stakeholders Involved in Speech-Language Therapy for Children With Communication Disorders: Content Analysis of Apple App Store Reviews.

BackgroundWith the plethora of mobile apps available on the Apple App Store, more speech-language pathologists (SLPs) have adopted apps for speech-language therapy services, especially for pediatric clients. App Store reviews are publicly available data sources that can not only create avenues for communication between technology developers and consumers but also enable stakeholders such as parents and clinicians to share their opinions and view opinions about the app content and quality based on user experiences.ObjectiveThis study examines the Apple App Store reviews from multiple key stakeholders (eg, parents, educators, and SLPs) to identify and understand user needs and challenges of using speech-language therapy apps (including augmentative and alternative communication [AAC] apps) for pediatric clients who receive speech-language therapy services.MethodsWe selected 16 apps from a prior interview study with SLPs that covered multiple American Speech-Language-Hearing Association Big Nine competencies, including articulation, receptive and expressive language, fluency, voice, social communication, and communication modalities. Using an automatic Python (Python Software Foundation) crawler developed by our research team and a Really Simple Syndication feed generator provided by Apple, we extracted a total of 721 app reviews from 2009 to 2020. Using qualitative coding to identify emerging themes, we conducted a content analysis of 57.9% (418/721) reviews and synthesized user feedback related to app features and content, usability issues, recommendations for improvement, and multiple influential factors related to app design and use.ResultsOur analyses revealed that key stakeholders such as family members, educators, and individuals with communication disorders have used App Store reviews as a platform to share their experiences with AAC and speech-language apps. User reviews for AAC apps were primarily written by parents who indicated that AAC apps consistently exhibited more usability issues owing to violations of design guidelines in areas of aesthetics, user errors, controls, and customization. Reviews for speech-language apps were primarily written by SLPs and educators who requested and recommended specific app features (eg, customization of visuals, recorded feedback within the app, and culturally diverse character roles) based on their experiences working with a diverse group of pediatric clients with a variety of communication disorders.ConclusionsTo our knowledge, this is the first study to compile and analyze publicly available App Store reviews to identify areas for improvement within mobile apps for pediatric speech-language therapy apps from children with communication disorders and different stakeholders (eg, clinicians, parents, and educators). The findings contribute to the understanding of apps for children with communication disorders regarding content and features, app usability and accessibility issues, and influential factors that impact both AAC apps and speech-language apps for children with communication disorders who need speech therapy.

Analysing app reviews for software engineering: a systematic literature review

App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

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.

Digitized thought records: a practitioner-focused review of cognitive restructuring apps

Abstract Mental health (MH) apps can be used as adjunctive tools in traditional face-to-face therapy to help implement components of evidence-based treatments. However, practitioners interested in using MH apps face a variety of challenges, including knowing which apps would be appropriate to use. Although some resources are available to help practitioners identify apps, granular analyses of how faithfully specific clinical skills are represented in apps are lacking. This study aimed to conduct a review and analysis of MH apps containing a core component of cognitive behaviour therapy (CBT) – cognitive restructuring (CR). A keyword search for apps providing CR functionality on the Apple App and Android Google Play stores yielded 246 apps after removal of duplicates, which was further reduced to 15 apps following verification of a CR component and application of other inclusionary/exclusionary criteria. Apps were coded based on their inclusion of core elements of CR, and general app features including app content, interoperability/data sharing, professional involvement, ethics, and data safeguards. They were also rated on user experience as assessed by the Mobile App Rating Scale (MARS). Whereas a majority of the CR apps include most core CR elements, they vary considerably with respect to more granular sub-elements of CR (e.g. rating the intensity of emotions), other general app features, and user experience (average MARS = 3.53, range from 2.30 to 4.58). Specific apps that fared best with respect to CR fidelity and user experience dimensions are highlighted, and implications of findings for clinicians, researchers and app developers are discussed. Key learning aims (1) To identify no-cost mobile health apps that practitioners can adopt to facilitate cognitive restructuring. (2) To review how well the core elements of cognitive restructuring are represented in these apps. (3) To characterize these apps with respect to their user experience and additional features. (4) To provide examples of high-quality apps that represent cognitive restructuring with fidelity and facilitate its clinical implementation.

Paving the Way for Digital Transformation: Investigate Customer Experiences of Using Mobile Apps

Background: Mobile apps have become a critical channel for retailers to interact with their customers and digitalize shopping behaviors in the customer journeys. In the transition of digital transformation, convenience stores have launched mobile apps to interact with and particularly collecting data from their customers. It is critical to investigate customer experiences in using mobile apps because it paves the way for successful digital transformation. This paper aims to investigate the customer experiences of convenience stores’ mobile apps from the online reviews. Method: This study proposed a mobile apps quality model as the theoretical framework to empirically test the online reviews of mobile apps. Large volumes of online reviews generated by customers provide important strategic values for business and service design for mobile apps. This paper collected 40,521 online reviews of two leading convenience stores in Taiwan and analyzed with text analysis and qualitative analysis. Results: Applying text analysis and qualitative analysis, this paper identified the critical quality attributes of mobile apps in the convenience stores that need to be improved. In addition, software quality is the major concern that 7-11 Open Point needs to improve, followed by service quality and information quality respectively. Software quality is also the major concern that FamilyMart app needs to improve the customer experiences, followed by service quality and information quality. Moreover, customer dissatisfaction primarily resulted from problems in software quality and information quality. Instead, convenience, ease of use, and practicability of mobile apps lead to customer satisfaction. Conclusion: The results demonstrate that software quality, information quality, and service quality are critical dimensions in affecting customer experiences in using mobile apps. Although different mobile apps may have different priorities of quality attributes that are needed to be improved, these improvements of mobile apps help to enhance customer experiences and accelerate digital transformation of the convenience stores.

MeTag App and MeTag Analyze

Our review of the MeTag app and the MeTagAnalyze web app is based on an initial test project involving undergraduate students, and, later, its adoption as part of a mixed-methods longitudinal research project on the datafication of childhood and family life. We contextualise the value of media diaries as a research tool in phenomenological and user-centred research traditions, namely the Domestication of Technology approach and the constructivist mediatization research tradition. Based on our preliminary findings and these theoretical frameworks, we highlight the main methodological advantages of the MeTag app for researchers who aim for a nuanced understanding of media practices and communication repertoires within different contexts and across various timeframes.

A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

Arabic language incurs from the shortage of accessible huge datasets for Sentiment Analysis (SA), Machine Learning (ML), and Deep Learning (DL) applications. In this paper, we present MASR, a simple Mobile Applications Arabic Slang Reviews dataset for SA, ML, and DL applications which comprises of 2469 Egyptian Mobile Apps reviews, and help app developers meet user requirements evolution. Our methodology consists of six phases. We collect mobile apps reviews dataset, then apply preprocessing steps, in addition perform SA tasks. To evaluate MASR datasets, first we apply ML classification techniques: K-Nearest Neighbors (K-NN), Support vector machine (SVM), Logistic Regression (LR), and Random Forest (RF), and DL classification technique: Multi-layer Perceptron Neural Network (MLP-NN). From the examination for pervious classification techniques, we adopted a hybrid classification approach combined from the top two ML classifier accuracy results (LR, RF), and DL classifier (MLP-NN). The findings prove the adequacy of a hybrid supervised classification approach for MASR datasets.

Semi-Supervised Learning Applied to Mobile App Reviews

Semi-Supervised Learning Applied to Mobile App Reviews

Perceiving university students' opinions from Google app reviews

AbstractGoogle app market captures the school of thought of users from every corner of the globe via ratings and text reviews, in a multilinguistic arena. The critique's viewpoint regarding an app is proportional to their satisfaction level. The potential information from the reviews cannot be extracted manually, due to its exponential growth. So, sentiment analysis, by machine learning and deep learning algorithms employing NLP, explicitly uncovers and interprets the emotions. This study performs the sentiment classification of the app reviews and identifies the university students' behavior toward the app market via exploratory analysis. We applied machine learning algorithms using the TP, TF, and TF‐IDF text representation scheme and evaluated its performance on Bagging, an ensemble learning method. We used word embedding, GloVe, on the deep learning paradigms. Our model was trained on Google app reviews and tested on students' app reviews (SAR). The various combinations of these algorithms were compared among each other using F‐score and accuracy and inferences were highlighted graphically. SVM, among other classifiers, gave fruitful accuracy (93.41%), F‐score (0.89) on bi‐gram + TF‐IDF scheme. Bagging enhanced the performance of LR and NB with accuracy 87.88% and 86.69% and F‐score 0.86 and 0.78 respectively. Overall, LSTM on Glove embedding recorded the highest accuracy (95.2%) and F‐score (0.88).

Emergency Preparedness and Disaster Response: There's an App for That 2.0.

In disaster response, smartphone applications (or apps) are being used by the layperson, emergency first responders, and health care providers to aid in everything from incident reporting to clinical decision making. However, quality apps are often diluted by the overwhelming number of apps that exist for both the lay public and first responders in the Apple iTunes (Apple Inc.; Cupertino, California USA) and Google Play (Google LLC; Mountain View, California USA) stores. A systematic review of disaster response apps was originally completed in 2015; a follow-up review was completed here to evaluate trends and explore novel apps. A search of the Apple iTunes and Google Play stores was performed using the following terms obtained from PubMed (National Center for Biotechnology Information; Bethesda, Maryland USA) Medical Subject Headings Database: Emergency Preparedness; Emergency Responders; Disaster; Disaster Planning; Disaster Medicine; Bioterrorism; Chemical Terrorism; Hazardous Materials; and the Federal Emergency Management Agency (FEMA [Washington, DC USA]). After excluding any unrelated apps, a working list of apps was formed and categorized based on topics. Apps were categorized by intended user (first responders or the public) and sub-categorized by topic for discussion. Sub-categories included News/Information, Reference/Education, Weather/Natural Disasters, Travel/Navigation, and Communication/Reunification. A search of the Apple iTunes store revealed 394 unique apps and was narrowed to 342 based on relevance to the field and availability on the iPhone. A search of the Google Play store yielded 645 unique applications and was narrowed to 634 based on relevance. Of note, 49 apps appeared in both app stores using the search terms. An aggregate 927 apps from the Apple iTunes and Google Play stores were then critically reviewed by the authors. Apps were sub-categorized based on intended audience, layperson or first responder, and sorted into one of five disaster response categories. Two apps were chosen for discussion from each of the five sub-categories. The highest quality apps were determined from each group based on relevance to emergency preparedness and disaster response, rating, and number of reviews. After comparisons with the 2015 article, many new apps have been developed and previously described apps have been updated, highlighting that this is a constantly changing field deserving of continued analysis and research.

User Reviews of Depression App Features: Sentiment Analysis

BackgroundMental health in general, and depression in particular, remain undertreated conditions. Mobile health (mHealth) apps offer tremendous potential to overcome the barriers to accessing mental health care and millions of depression apps have been installed and used. However, little is known about the effect of these apps on a potentially vulnerable user population and the emotional reactions that they generate, even though emotions are a key component of mental health. App reviews, spontaneously posted by the users on app stores, offer up-to-date insights into the experiences and emotions of this population and are increasingly decisive in influencing mHealth app adoption.ObjectiveThis study aims to investigate the emotional reactions of depression app users to different app features by systematically analyzing the sentiments expressed in app reviews.MethodsWe extracted 3261 user reviews of depression apps. The 61 corresponding apps were categorized by the features they offered (psychoeducation, medical assessment, therapeutic treatment, supportive resources, and entertainment). We then produced word clouds by features and analyzed the reviews using the Linguistic Inquiry Word Count 2015 (Pennebaker Conglomerates, Inc), a lexicon-based natural language analytical tool that analyzes the lexicons used and the valence of a text in 4 dimensions (authenticity, clout, analytic, and tone). We compared the language patterns associated with the different features of the underlying apps.ResultsThe analysis highlighted significant differences in the sentiments expressed for the different features offered. Psychoeducation apps exhibited more clout but less authenticity (ie, personal disclosure). Medical assessment apps stood out for the strong negative emotions and the relatively negative ratings that they generated. Therapeutic treatment app features generated more positive emotions, even though user feedback tended to be less authentic but more analytical (ie, more factual). Supportive resources (connecting users to physical services and people) and entertainment apps also generated fewer negative emotions and less anxiety.ConclusionsDevelopers should be careful in selecting the features they offer in their depression apps. Medical assessment features may be riskier as users receive potentially disturbing feedback on their condition and may react with strong negative emotions. In contrast, offering information, contacts, or even games may be safer starting points to engage people with depression at a distance. We highlight the necessity to differentiate how mHealth apps are assessed and vetted based on the features they offer. Methodologically, this study points to novel ways to investigate the impact of mHealth apps and app features on people with mental health issues. mHealth apps exist in a rapidly changing ecosystem that is driven by user satisfaction and adoption decisions. As such, user perceptions are essential and must be monitored to ensure adoption and avoid harm to a fragile population that may not benefit from traditional health care resources.