ACCU3RATE: A mobile health application rating scale based on user reviews

Background Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. Objective This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. Method Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Results and conclusions ACCU3RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU3RATE, matches more closely to the rating performed by experts.

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Perceptions of older men using a mobile health application to monitor lower urinary tract symptoms and tamsulosin side effects: mixed-methods study (Preprint)

10.2196/preprints.30767 ◽

2021 ◽

Author(s):

Elizabeth Y Wang ◽

Benjamin N Breyer ◽

Austin W Lee ◽

Natalie Rios ◽

Akinyemi Oni-Orisan ◽

...

Keyword(s):

Side Effects ◽

Mobile Health ◽

Mobile Application ◽

Older Men ◽

Mobile Health Application ◽

Urinary Tract Symptoms ◽

Medication Side ◽

Mobile Health Applications ◽

Health Application ◽

Health Applications

BACKGROUND Mobile health applications may provide an efficient way for patients with lower urinary tract symptoms (LUTS) to log and communicate symptoms and medication side effects with their clinicians. OBJECTIVE To explore the perceptions of older men with LUTS after using a mobile health application to track their symptoms and tamsulosin side effects. METHODS Structured phone interviews were conducted after a 2-week study piloting the daily use of a mobile application to track severity of patient-selected LUTS and tamsulosin side effects. Quantitative and qualitative data were considered. RESULTS Nineteen (100%) pilot study participants completed the post-study interviews. Most men (68%) reported that the daily questionnaires were the right length, with 32% reporting that the questionnaires were too short. Men with more severe symptoms were less likely to report changes in perception of health or changes in self-management; 47% of men reported improved awareness of symptoms and 5% of men adjusted fluid intake based on the questionnaire. All men were willing to share application data with their clinicians. Thematic analysis of qualitative data yielded 8 themes: 1) orientation (setting up app, format, symptom selection, side effect selection), 2) triggers (routine/habit, symptom timing), 3) daily questionnaire (reporting symptoms, reporting side effects, tailoring), 4) technology literacy, 5) perceptions (awareness, causation/relevance, data quality, convenience, usefulness, other apps), 6) self-management, 7) clinician engagement (communication, efficiency), and 8) improvement (reference materials, flexibility, language, management recommendations, optimize clinician engagement). CONCLUSIONS We assessed the perceptions of men using a mobile health application to monitor and improve management of LUTS and medication side effects. LUTS management may be further optimized by tailoring the mobile application experience to meet patients’ individual needs, such as tracking a greater number of symptoms and integrating the application with clinicians’ visits. Mobile health applications are likely a scalable modality to monitor symptoms and improve care of older men with LUTS. Further study is required to determine the best ways to tailor the mobile application and to communicate data to clinicians or incorporate data into the electronical medical record meaningfully.

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Pengembangan Aplikasi Mobile Health Berbasis Android untuk Monitoring dan Evaluasi Stunting

Jurnal Sehat Mandiri ◽

10.33761/jsm.v15i1.188 ◽

2020 ◽

Vol 15 (1) ◽

pp. 24-32

Author(s):

Atika Hendryani ◽

Ernia Susana

Keyword(s):

Mobile Health ◽

System Development ◽

Maternal Knowledge ◽

Ministry Of Health ◽

Mobile Health Application ◽

Mobile Health Applications ◽

The Government ◽

Two Stages ◽

Health Application ◽

Development Methods

In 2018 Indonesia still ranked fifth as the country with the highest number of stunting in the world. A better level of a mother’s knowledge can decrease about 4% to 5% in the possibility of stunting in children. Efforts to increase maternal knowledge about the importance of preventing stunting in the first thousand days of life are not only the responsibility of the government, especially the Ministry of Health. The mass media are also responsible for providing knowledge to mothers. One of the most widely used media in accessing news and information is through mobile devices such as mobile phones. From this background the problem of this research can be formulated is how to build an Android-based mobile health application for monitoring and preventing stunting. The purpose of this research is to build an Android-based mobile health for stunting monitoring and prevention. The research method is Research and Development consists of two stages, the Research Phase using qualitative methods and the Development Phase using FAST system development methods. The research was conducted at the Poltekkes of the Ministry of Health Jakarta II, Department of Electromedical Engineering from January to December 2019. The results of the study are android mobile health applications for monitoring and evaluating stunting. From the result of system testing, the mobile health application for monitoring and evaluating stunting could work well.

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Influencing Factors of Acceptance and Use Behavior of Mobile Health Application Users: Systematic Review

Healthcare ◽

10.3390/healthcare9030357 ◽

2021 ◽

Vol 9 (3) ◽

pp. 357

Author(s):

Chen Wang ◽

Huiying Qi

Keyword(s):

Systematic Review ◽

Influencing Factors ◽

Mobile Health ◽

Source Credibility ◽

Mobile Health Application ◽

Mobile Health Applications ◽

The Individual ◽

Use Behavior ◽

Health Application ◽

Health Applications

Purpose/Significance: Mobile health applications provide a convenient way for users to obtain health information and services. Studying the factors that influence users’ acceptance and use of mobile health applications (apps or Apps) will help to improve users’ actual usage behavior. Method/Process: Based on the literature review method and using the Web of Science core database as the data source, this paper summarizes the relevant research results regarding the influencing factors of the acceptance and use behavior of mobile health application users and makes a systematic review of the influencing factors from the perspectives of the individual, society, and application (app or App) design. Result/Conclusion: In terms of the individual dimension, the users’ behavior is influenced by demographic characteristics and motivations. Social attributes, source credibility, and legal issues all affect user behavior in the social dimension. In the application design dimension, functionality, perceived ease of use and usefulness, security, and cost are the main factors. At the end of the paper, suggestions are given to improve the users’ acceptability of mobile health applications and improve their use behavior.

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Domain-Adaptive Service Evaluation of Mobile Health Application

10.21203/rs.2.18220/v1 ◽

2019 ◽

Author(s):

Xiaojia Wang ◽

Kuo Du ◽

Wei Xia ◽

Shanshan Zhang ◽

Weiqun Xu ◽

...

Keyword(s):

Mobile Health ◽

Evaluation System ◽

Interaction Information ◽

Application Service ◽

Mobile Health Application ◽

Interactive Algorithm ◽

Mobile Health Applications ◽

Health Application ◽

Content Information ◽

Health Applications

Abstract Background: In the context of "Internet +" medical treatment, mobile health applications provide services for people in a new way, making it possible for people to carry out health management anytime and anywhere. According to the survey data, the most powerful consumers in the field of mobile health applications are those aged 24 to 35. Thus, it can be seen, it is particularly important to study the preferences of young people for mobile health applications.Methods: This study established a domain-adaptive mobile health application evaluation model based on users’ experience, and used an interactive algorithm combining machine learning and Delphi method to calculate the weight distribution of evaluation factors. Compared with previous studies, the establishment of evaluation index based on user experience of youth groups can more comprehensively measure users' demand for mobile health application service quality. Meanwhile, the mobile health application evaluation system established in this study adopts feedback mechanism to realize dynamic evaluation of mobile health applications.Results: The cognitive level of information (weighting 52%) was only four percentage points higher than the emotional level (weighting 48%). The importance of the four criteria is content information on cognition (weighting 31%), interaction information on emotion (weighting 29%), interaction information on cognition (weighting 21%), and content information on emotion (weighting 19%) in descending order. Among 20 sub-criteria, less disruptive (weighting 17.8%), security (weighting 10.9%), utility (weighting 9.3%), reliability (weighting 8.1%), navigational (weighting 6.7%) occupy an important position.Conclusion: We find that the weights assigned to sociability, personalization, aesthetics, and interestingness accounted for a significant proportion of the total weights assigned; however, universality and learnability were poorly weighted. These results have important reference value for the development of mobile health applications.

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Insights into mobile health application market via a content analysis of marketplace data with machine learning

PLoS ONE ◽

10.1371/journal.pone.0244302 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0244302

Author(s):

Gokhan Aydin ◽

Gokhan Silahtaroglu

Keyword(s):

Machine Learning ◽

Content Analysis ◽

Mobile Health ◽

Current Knowledge ◽

Machine Learning Algorithms ◽

Health Apps ◽

Mobile Health Application ◽

Mobile Health Applications ◽

Health Application ◽

Health Applications

Background Despite the benefits offered by an abundance of health applications promoted on app marketplaces (e.g., Google Play Store), the wide adoption of mobile health and e-health apps is yet to come. Objective This study aims to investigate the current landscape of smartphone apps that focus on improving and sustaining health and wellbeing. Understanding the categories that popular apps focus on and the relevant features provided to users, which lead to higher user scores and downloads will offer insights to enable higher adoption in the general populace. This study on 1,000 mobile health applications aims to shed light on the reasons why particular apps are liked and adopted while many are not. Methods User-generated data (i.e. review scores) and company-generated data (i.e. app descriptions) were collected from app marketplaces and manually coded and categorized by two researchers. For analysis, Artificial Neural Networks, Random Forest and Naïve Bayes Artificial Intelligence algorithms were used. Results The analysis led to features that attracted more download behavior and higher user scores. The findings suggest that apps that mention a privacy policy or provide videos in description lead to higher user scores, whereas free apps with in-app purchase possibilities, social networking and sharing features and feedback mechanisms lead to higher number of downloads. Moreover, differences in user scores and the total number of downloads are detected in distinct subcategories of mobile health apps. Conclusion This study contributes to the current knowledge of m-health application use by reviewing mobile health applications using content analysis and machine learning algorithms. The content analysis adds significant value by providing classification, keywords and factors that influence download behavior and user scores in a m-health context.

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