A Preliminary Evaluation of Mobile Phone Apps to Curb Alcohol Consumption

Mobile apps have become increasingly prevalent in modern society, and persuasive technology has a broader market than ever. Mobile-based alcohol cessation apps can promote positive behaviour change in users and improve the overall health of our society. This research aimed to understand the various features users respond to and make design recommendations for alcohol cessation apps. This paper reports on three sources of feedback (user ratings, user reviews, MARS App Quality score) provided on 20 alcohol cessation apps in the Google Play Store. Our findings suggest that self-control type apps received much greater positive user reviews than motivational apps. In addition, this trend was not observed through numeric user ratings. We also speculate on design recommendations for apps that are meant to inhibit alcohol intake.

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How Should I Improve the UI of My App?

ACM Transactions on Software Engineering and Methodology ◽

10.1145/3447808 ◽

2021 ◽

Vol 30 (3) ◽

pp. 1-38

Author(s):

Qiuyuan Chen ◽

Chunyang Chen ◽

Safwat Hassan ◽

Zhengchang Xing ◽

Xin Xia ◽

...

Keyword(s):

Positive Impact ◽

Mobile Apps ◽

Frequent Patterns ◽

Response Mechanism ◽

Essential Factor ◽

User Reviews ◽

Information Request ◽

Comparative Review ◽

Google Play ◽

Patterns Of Interaction

UI (User Interface) is an essential factor influencing users’ perception of an app. However, it is hard for even professional designers to determine if the UI is good or not for end-users. Users’ feedback (e.g., user reviews in the Google Play) provides a way for app owners to understand how the users perceive the UI. In this article, we conduct an in-depth empirical study to analyze the UI issues of mobile apps. In particular, we analyze more than 3M UI-related reviews from 22,199 top free-to-download apps and 9,380 top non-free apps in the Google Play Store. By comparing the rating of UI-related reviews and other reviews of an app, we observe that UI-related reviews have lower ratings than other reviews. By manually analyzing a random sample of 1,447 UI-related reviews with a 95% confidence level and a 5% interval, we identify 17 UI-related issues types that belong to four categories (i.e., “Appearance,” “Interaction,” “Experience,” and “Others” ). In these issue types, we find “Generic Review” is the most occurring one. “Comparative Review” and “Advertisement” are the most negative two UI issue types. Faced with these UI issues, we explore the patterns of interaction between app owners and users. We identify eight patterns of how app owners dialogue with users about UI issues by the review-response mechanism. We find “Apology or Appreciation” and “Information Request” are the most two frequent patterns. We find updating UI timely according to feedback is essential to satisfy users. Besides, app owners could also fix UI issues without updating UI, especially for issue types belonging to “Interaction” category. Our findings show that there exists a positive impact if app owners could actively interact with users to improve UI quality and boost users’ satisfactoriness about the UIs.

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COVIDTAS COVID-19 Tracing App Scale—An Evaluation Framework

Sustainability ◽

10.3390/su13052912 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2912

Author(s):

Raghu Raman ◽

Krishnashree Achuthan ◽

Ricardo Vinuesa ◽

Prema Nedungadi

Keyword(s):

Public Health ◽

User Satisfaction ◽

Mobile Apps ◽

Evaluation Framework ◽

Contact Tracing ◽

User Reviews ◽

Average Performance ◽

Government Leadership ◽

Major Factors ◽

User Ratings

Mobile apps play an important role in COVID-19 tracing and tracking, with different countries taking different approaches. Our study focuses on 17 government owned COVID-19 Contact Tracing Apps (CTAs) and analyze them using a proposed COVIDTAS framework. User satisfaction is not directly related to the COVIDTAS score or the interaction between users and the app developers. To increase adoption of CTAs, government leadership must offer assurance to its citizens that their identify will be concealed and emphasize the benefits of CTAs as it relates to shared public health. While no country has topped the list on all three major factors (COVIDTAS Score, User Reviews, and User Ratings), the CTA from India seems to have above average performance on all three factors.

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User Perspectives of Diet-Tracking Apps: Reviews Content Analysis and Topic Modeling

Journal of Medical Internet Research ◽

10.2196/25160 ◽

2021 ◽

Vol 23 (4) ◽

pp. e25160

Author(s):

Mila Zečević ◽

Dejan Mijatović ◽

Mateja Kos Koklič ◽

Vesna Žabkar ◽

Petar Gidaković

Keyword(s):

Topic Modeling ◽

Text Processing ◽

Mobile Apps ◽

User Friendliness ◽

Self Monitoring ◽

Technical Problems ◽

User Reviews ◽

Health And Nutrition ◽

Ease Of Access ◽

Google Play

Background The availability and use of mobile apps in health and nutrition management are increasing. Ease of access and user friendliness make diet-tracking apps an important ally in their users’ efforts to lose and manage weight. To foster motivation for long-term use and to achieve goals, it is necessary to better understand users’ opinions and needs for dietary self-monitoring. Objective The aim of this study was to identify the key topics and issues that users highlight in their reviews of diet-tracking apps on Google Play Store. Identifying the topics that users frequently mention in their reviews of these apps, along with the user ratings for each of these apps, allowed us to identify areas where further improvement of the apps could facilitate app use, and support users’ weight loss and intake management efforts. Methods We collected 72,084 user reviews from Google Play Store for 15 diet-tracking apps that allow users to track and count calories. After a series of text processing operations, two text-mining techniques (topic modeling and topical n-grams) were applied to the corpus of user reviews of diet-tracking apps. Results Using the topic modeling technique, 11 separate topics were extracted from the pool of user reviews. Most of the users providing feedback were generally satisfied with the apps they use (average rating of 4.4 out of 5 for the 15 apps). Most topics referred to the positive evaluation of the apps and their functions. Negatively rated topics mostly referred to app charges and technical difficulties encountered. We identified the positive and negative topic trigrams (3-word combinations) among the most frequently mentioned topics. Usability and functionality (tracking options) of apps were rated positively on average. Negative ratings were associated with trigrams related to adding new foods, technical issues, and app charges. Conclusions Motivating users to use an app over time could help them better achieve their nutrition goals. Although user reviews generally showed positive opinions and ratings of the apps, developers should pay more attention to users’ technical problems and inform users about expected payments, along with their refund and cancellation policies, to increase user loyalty.

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User Perspectives of Diet-Tracking Apps: Reviews Content Analysis and Topic Modeling (Preprint)

10.2196/preprints.25160 ◽

2020 ◽

Author(s):

Mila Zečević ◽

Dejan Mijatović ◽

Mateja Kos Koklič ◽

Vesna Žabkar ◽

Petar Gidaković

Keyword(s):

Topic Modeling ◽

Text Processing ◽

Mobile Apps ◽

User Friendliness ◽

Self Monitoring ◽

Technical Problems ◽

User Reviews ◽

Health And Nutrition ◽

Ease Of Access ◽

Google Play

BACKGROUND The availability and use of mobile apps in health and nutrition management are increasing. Ease of access and user friendliness make diet-tracking apps an important ally in their users’ efforts to lose and manage weight. To foster motivation for long-term use and to achieve goals, it is necessary to better understand users’ opinions and needs for dietary self-monitoring. OBJECTIVE The aim of this study was to identify the key topics and issues that users highlight in their reviews of diet-tracking apps on Google Play Store. Identifying the topics that users frequently mention in their reviews of these apps, along with the user ratings for each of these apps, allowed us to identify areas where further improvement of the apps could facilitate app use, and support users’ weight loss and intake management efforts. METHODS We collected 72,084 user reviews from Google Play Store for 15 diet-tracking apps that allow users to track and count calories. After a series of text processing operations, two text-mining techniques (topic modeling and topical n-grams) were applied to the corpus of user reviews of diet-tracking apps. RESULTS Using the topic modeling technique, 11 separate topics were extracted from the pool of user reviews. Most of the users providing feedback were generally satisfied with the apps they use (average rating of 4.4 out of 5 for the 15 apps). Most topics referred to the positive evaluation of the apps and their functions. Negatively rated topics mostly referred to app charges and technical difficulties encountered. We identified the positive and negative topic trigrams (3-word combinations) among the most frequently mentioned topics. Usability and functionality (tracking options) of apps were rated positively on average. Negative ratings were associated with trigrams related to adding new foods, technical issues, and app charges. CONCLUSIONS Motivating users to use an app over time could help them better achieve their nutrition goals. Although user reviews generally showed positive opinions and ratings of the apps, developers should pay more attention to users’ technical problems and inform users about expected payments, along with their refund and cancellation policies, to increase user loyalty.

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Mobile Applications for Health Management of Older Adults: A Scoping Review (Preprint)

10.2196/preprints.22823 ◽

2020 ◽

Author(s):

Nurul Asilah Ahmad ◽

Shahrul Azman Mohd Noah ◽

Arimi Fitri Mat Ludin ◽

Suzana Shahar ◽

Noorlaili Mohd Tohit

Keyword(s):

Older Adults ◽

Goal Setting ◽

Mobile Applications ◽

Health Management ◽

Mobile Apps ◽

Personalized Feedback ◽

Self Monitoring ◽

App Store ◽

Evidenced Based ◽

Google Play

BACKGROUND Currently, the use of smartphones to deliver health-related content has experienced a rapid growth, with more than 165,000 mobile health (mHealth) applications currently available in the digital marketplace such as iOS store and Google Play. Among these, there are several mobile applications (mobile apps) that offer tools for disease prevention and management among older generations. These mobile apps could potentially promote health behaviors which will reduce or delay the onset of disease. However, no review to date that has focused on the app marketplace specific for older adults and little is known regarding its evidence-based quality towards the health of older adults. OBJECTIVE The aim of this review was to characterize and critically appraise the content and functionality of mobile apps that focuses on health management and/or healthy lifestyle among older adults. METHODS An electronic search was conducted between May 2019 to December 2019 of the official app store for two major smartphone operating systems: iPhone operating system (iTunes App Store) and Android (Google Play Store). Stores were searched separately using predetermined search terms. Two authors screened apps based on information provided in the app description. Metadata from all included apps were abstracted into a standard assessment criteria form. Evidenced based strategies and health care expert involvement of included apps was assessed. Evidenced based strategies included: self-monitoring, goal setting, physical activity support, healthy eating support, weight and/or health assessment, personalized feedback, motivational strategies, cognitive training and social support. Two authors verified the data with reference to the apps and downloaded app themselves. RESULTS A total of 16 apps met the inclusion criteria. Six out of 16 (37.5%) apps were designed exclusively for the iOS platform while ten out of 16 (62.5%) were designed for Android platform exclusively. Physical activity component was the most common feature offered in all the apps (9/16, 56.3%) and followed by cognitive training (8/16, 50.0%). Diet/nutrition (0/16, 0%) feature, however, was not offered on all reviewed mobile apps. Of reviewed apps, 56.3% (9/16) provide education, 37.5% (6/16) provide self-monitoring features, 18.8% (3/16) provide goal setting features, 18.5% (3/16) provide personalized feedback, 6.3% (1/16) provide social support and none of the reviewed apps offers heart rate monitoring and reminder features to the users. CONCLUSIONS All reviewed mobile apps for older adults in managing health did not focused on diet/nutrition component, lack of functional components and lack of health care professional involvement in their development process. There is also a need to carry out scientific testing prior to the development of the app to ensure cost effective and its health benefits to older adults. Collaborative efforts between developers, researchers, health professionals and patients are needed in developing evidence-based, high quality mobile apps in managing health prior they are made available in the app store.

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Evaluation of Persian language epilepsy-related mobile apps: A systematic review and quality assessment with a focus on content (Preprint)

10.2196/preprints.26647 ◽

2020 ◽

Author(s):

Mina Zibaei ◽

Reza Khajouei

Keyword(s):

Information Quality ◽

Mobile Apps ◽

Self Care ◽

Keywords Epilepsy ◽

Limited Health Literacy ◽

Persian Language ◽

The Mean ◽

Limited Health ◽

Google Play

BACKGROUND In Iran, around 0.05 of population suffer from epilepsy. Poorer health outcomes stem from limited health literacy. The use of mHealth, especially for educating patients in terms of self-care can be very effective. But the important thing is the content that is presented by apps, especially when unreliable or biased information can negatively affect the patient-doctor relationship, causing anxiety or stress. Also, usability of mHealth apps and their impact on behavior change are the other important issues that should be considered. OBJECTIVE The purpose of this study was to assess the quality of Persian language epilepsy-related mobile applications in terms of functionality and quality with a focus on content. METHODS The Persian equivalent of the keywords 'epilepsy' and 'seizure' were searched in the Google Play, Cafe Bazaar and IranApps app stores and the Persian language applications about epilepsy were extracted. These apps were evaluated by two trained reviewers independently using the uMARS scale and DISCERN instrument. Also apps’ prices and the number of installations were assessed. RESULTS A total of 659 applications were retrieved, 78 of which were epilepsy-related. After exclusion of non-Persian language and duplicate apps, there remained 11 relevant apps. The overall mean uMARS score was 2.8 out of 5 while six out of 11 apps (54%) scored higher than 3. The mean figures for the section-specific scores were as follows: engagement 2.2, functionality 4.0, aesthetics 3.3, and information 2.3. The overall DISCERN scores ranged from 26 to 40 out of 80, while the mean score was 34.5. The mean score of reliability was 18.5. CONCLUSIONS This study showed that the overall information quality of the epilepsy apps is poor. The most important missing characteristics of these apps include lack of functionalities for self-care, missing entry date, lack of details about additional sources and inexistence of the risks/benefits of each treatment. The findings suggest that more efforts should be made to develop evidence-based epilepsy-related apps to cover broader domains of self-care and behavioral change techniques.

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App review of anxiety and depression chatbots and their self-care features (Preprint)

10.2196/preprints.26334 ◽

2020 ◽

Author(s):

Arfan Ahmed ◽

Nashva ALi ◽

Sarah Aziz ◽

Alaa A Abd-Alrazaq ◽

Asmaa Hassan ◽

...

Keyword(s):

Mental Health ◽

Meta Analysis ◽

Self Care ◽

Anxiety And Depression ◽

Conversational Agents ◽

Eligibility Criteria ◽

On Demand ◽

User Ratings ◽

Google Play

BACKGROUND Anxiety and depression rates are at an all-time high along with other mental health disorders. Smartphone-based mental health chatbots or conversational agents can aid psychiatrists and replace some of the costly human based interaction and represent a unique opportunity to expand the availability and quality of mental health services and treatment. Regular up-to-date reviews will allow medics and individuals to recommend or use anxiety and depression related smartphone based chatbots with greater confidence. OBJECTIVE Assess the quality and characteristics of chatbots for anxiety and depression available on Android and iOS systems. METHODS A search was performed in the App Store and Google Play Store following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol to identify existing chatbots for anxiety and depression. Eligibility of the chatbots was assessed by two individuals based on predefined eligibility criteria. Meta-data of the included chatbots and their characteristics were extracted from their description and upon installation by 2 reviewers. Finally, chatbots quality information was assessed by following the mHONcode principles. RESULTS Although around 1000 anxiety and depression related chatbots exist, only a few (n=11) contained actual chatbots that could provide the user a real substitute for a human-human based interaction, even with today's Artificial Intelligence advancements, only one of these chatbots had voice as an input/output modality. Of the selected apps that contained chatbots all were clearly built with a therapeutic human substitute goal in mind. The majority had high user ratings and downloads highlighting the popularity of such chatbots and their promising future within the realm of anxiety and depression. CONCLUSIONS Anxiety and depression chatbot apps have the potential to increase the capacity of mental health self-care providing much needed assistance to professionals. In the current covid-19 pandemic, chatbots can also serve as a conversational companion with the potential of combating loneliness, especially in lockdowns where there is a lack of social interaction. Due to the ubiquitous nature of chatbots users can access them on-demand at the touch of a screen on ones’ smartphone. Self-care interventions are known to be effective and exist in various forms and some can be made available as chatbot features, such as assessment, mood tracking, medicine tracking, or simply providing conversation in times of loneliness.

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A first look at Android applications in Google Play related to COVID-19

Empirical Software Engineering ◽

10.1007/s10664-021-09943-x ◽

2021 ◽

Vol 26 (4) ◽

Cited By ~ 1

Author(s):

Jordan Samhi ◽

Kevin Allix ◽

Tegawendé F. Bissyandé ◽

Jacques Klein

Keyword(s):

Mobile Apps ◽

Contact Tracing ◽

Location Tracking ◽

Response Effort ◽

Sensitive Data ◽

Android Apps ◽

Digital World ◽

Public Health Organizations ◽

Android Applications ◽

Google Play

AbstractDue to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the COVID-19 pandemic, app developers have joined the response effort in various ways by releasing apps that target different user bases (e.g., all citizens or journalists), offer different services (e.g., location tracking or diagnostic-aid), provide generic or specialized information, etc. While many apps have raised some concerns by spreading misinformation or even malware, the literature does not yet provide a clear landscape of the different apps that were developed. In this study, we focus on the Android ecosystem and investigate Covid-related Android apps. In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a first taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing. Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the COVID-19, with dedicated apps released as early as January 2020; (2) Covid-related apps deliver digital tools to users (e.g., health diaries), serve to broadcast information to users (e.g., spread statistics), and collect data from users (e.g., for tracing); (3) Covid-related apps are less complex than standard apps; (4) they generally do not seem to leak sensitive data; (5) in the majority of cases, Covid-related apps are released by entities with past experience on the market, mostly official government entities or public health organizations.

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What do users think of the UK’s three COVID-19 contact-tracing apps? A comparative analysis

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2021-100320 ◽

2021 ◽

Vol 28 (1) ◽

pp. e100320

Author(s):

Vahid Garousi ◽

David Cutting

Keyword(s):

Mobile Apps ◽

Analysis Data ◽

End Users ◽

Contact Tracing ◽

User Reviews ◽

The Public ◽

Engineering Teams ◽

Technical Issues ◽

The Uk ◽

The Right

ObjectivesOur goal was to gain insights into the user reviews of the three COVID-19 contact-tracing mobile apps, developed for the different regions of the UK: ‘NHS COVID-19’ for England and Wales, ‘StopCOVID NI’ for Northern Ireland and ‘Protect Scotland’ for Scotland. Our two research questions are (1) what are the users’ experience and satisfaction levels with the three apps? and (2) what are the main issues (problems) that users have reported about the apps?MethodsWe assess the popularity of the apps and end users’ perceptions based on user reviews in app stores. We conduct three types of analysis (data mining, sentiment analysis and topic modelling) to derive insights from the combined set of 25 583 user reviews of the aforementioned three apps (submitted by users until the end of 2020).ResultsResults show that end users have been generally dissatisfied with the apps under study, except the Scottish app. Some of the major issues that users have reported are high battery drainage and doubts on whether apps are really working.DiscussionTowards the end of 2020, the much-awaited COVID-19 vaccines started to be available, but still, analysing the users’ feedback and technical issues of these apps, in retrospective, is valuable to learn the right lessons to be ready for similar circumstances in future.ConclusionOur results show that more work is needed by the stakeholders behind the apps (eg, apps’ software engineering teams, public-health experts and decision makers) to improve the software quality and, as a result, the public adoption of these apps. For example, they should be designed to be as simple as possible to operate (need for usability).

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Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

Journal of Intelligent Systems ◽

10.1515/jisys-2019-0197 ◽

2020 ◽

Vol 30 (1) ◽

pp. 192-208 ◽

Cited By ~ 1

Author(s):

Hamza Aldabbas ◽

Abdullah Bajahzar ◽

Meshrif Alruily ◽

Ali Adil Qureshi ◽

Rana M. Amir Latif ◽

...

Keyword(s):

Logistic Regression ◽

Language Processing ◽

Mobile Application ◽

Knowledge Engineering ◽

Machine Learning Algorithms ◽

Application Development ◽

User Reviews ◽

N Gram ◽

Logistic Regression Algorithm ◽

Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

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