Factors influencing the download of mobile health apps: Content review-led regression analysis

Attitudes Toward Cisgender Women's Participation in Sex Work: Opportunity for Agency or Harmful Exchange?

Despite the stigmatization of sex work in society, little empirical research has examined attitudes toward sex work, especially its modern incarnations (e.g., sugar relationships, webcamming). Here, a sample of 298 US residents (Mage = 40.06 years; 59.1% male, 40.9% female) was recruited through Amazon Mechanical Turk. Various theoretical predictors (e.g., right-wing authoritarianism [RWA]), sociosexuality) were set to predict the degree to which four sex work domains (prostitution, pornography, sugar relationships, webcamming) provide cisgender women agency (beneficial) or harm them (detrimental). We found that the domains of sex work were organized hierarchically, as theorized by the so-called "whorearchy," whereby the more "unfavorable" domains (e.g., prostitution) fall at the bottom, and the more "favorable" ones (e.g., webcamming) sit at the top. Additionally, multiple regression analyses revealed that RWA (negatively) and sociosexuality (positively) were the strongest predictors of sex work agency across various domains. In predicting harm, RWA, feminism, religiosity, and age were unique positive predictors, whereas sociosexuality and male (vs. female) self-identified sex were unique negative predictors, across the four domains of sex work. Moreover, individual differences (e.g., RWA) were often significantly stronger predictors of agency or harm among female than male participants. The results suggest that although sex work domains vary in agency and harm ratings, individual differences (most notably, RWA and sociosexuality) are important predictors across domains, especially for cisgender women. Given the growing prevalence of such online forms of sex work, along with growing evidence of sugar relationships, it will become increasingly important to track reactions as these forms of sex work evolve.

mHealth Apps Use and Their Associations With Healthcare Decision-Making and Health Communication Among Informal Caregivers: Evidence From the National Cancer Institute's Health Information National Trends Survey

PURPOSE

The current study investigates associations between mHealth apps and healthcare decision-making and health communication among informal caregivers in the US.

DESIGN

Cross-sectional study employing secondary data.

SETTING

The Health Information National Trends Survey (HINTS5, Cycles 2 through 4, 2018 - 2020).

SAMPLE

Self-identified informal caregivers (n = 1386; had mHealth apps = 61.3%, female = 63.2%, some college or more in education = 80.3%) who reported owning at least a smartphone or a tablet computer (i.e., ownership of a "smart device").

MEASURES

Sociodemographic characteristics, reports of having mHealth apps, smart device utilization in healthcare decision-making and health communication.

ANALYSIS

Accounting for the complex design features of the HINTS data, we constructed multiple hierarchical logistic regressions to compute adjusted odds ratios (aOR) and their 95% confidence intervals (CI).

RESULTS

Compared to caregivers without mHealth apps, those with the apps had higher odds of utilizing their smart devices to make a health-related decision, such as how to treat a disease or a medical condition (aOR = 1.65; 95% CI: 1.13-2.39, P < .01), or engage in health-related discussions with a healthcare provider (aOR = 2.36; 95% CI: 1.54-3.61, P < .001).

CONCLUSION

Having mHealth apps was associated with a higher likelihood of using smart devices in healthcare decision-making and health communication by informal caregivers. Empowering caregivers to make informed health-related decisions and communicate effectively with healthcare providers are both crucial to health promotion and well-being. Future studies should investigate facilitators as well as barriers to using mHealth apps and smart devices in health-promoting strategies involving informal caregivers.

Systematic Evaluation of the Behavior Change Techniques and Quality of Commercially Available Cancer Self-Management Apps

PURPOSE

Apps have the potential to aid in cancer self-management, but there is limited guidance available for selecting among currently available options. The purpose of this study is to evaluate the behavior change techniques (BCTs) and quality of publicly available cancer self-management apps.

METHODS

Cancer self-management apps were identified from the Apple and Google Play stores in April 2022. Trained study team members coded the BCTs included in each app and rated its quality using the Mobile App Rating Scale (MARS). BCTs supported by previous literature were coded as cancer management BCTs.

RESULTS

The 39 apps meeting inclusion criteria included an average of 5.85 BCTs (standard deviation [SD], 3.49; range, 0-15) and 3.54 cancer management BCTs (SD, 1.90; range, 0-8). The most commonly included BCTs were educational or informational strategies: provide information about behavior-health link, provide instruction, and provide information on consequences. The overall app quality ranged from 1.69 to 4.20 (M, 3.29; SD, 0.67).

CONCLUSION

No cancer self-management apps were of excellent quality, and less than half included multiple cancer management BCTs beyond education. Clinical implications are discussed, and opportunities to improve the content and quality of apps to address the critical self-management needs of patients diagnosed with cancer are highlighted.

Diabetes mellitus and inequalities in the equipment and use of information technologies as a socioeconomic determinant of health in Spain

Inequalities in the equipment and use of information and communications technology (ICT) in Spanish households can lead to users being unable to access certain information or to carry out certain procedures. Accessibility to ICT is considered a social determinant of health (SDOH) because it can generate inequalities in access to information and in managing access to health services. In the face of a chronic illness such as diabetes mellitus (DM)-for which a comprehensive approach is complex and its complications have a direct impact on current healthcare systems-all the resources that patients may have are welcome. We aimed to analyze hospitalizations and amputations as direct consequences of DM among the autonomous communities of Spain (ACS) in 2019, along with socioeconomic factors related to health, including inequalities in access to ICT between territories, as well as citizens' interest in online information searches about DM. We used different databases such as that of the Ministerio de Sanidad (Spain's health ministry), Ministerio de Asuntos Económicos y transformación (Ministry of Economic Affairs and Digital Transformation), Google Trends (GT), and the Instituto Nacional de Estadística (Spain's national institute of statistics). We examined the data with R software. We employed a geolocation approach and performed multivariate analysis (specifically factor analysis of mixed data [FAMD]) to evaluate the aggregate interest in health information related to DM in different regions of Spain grounded in online search behavior. The use of FAMD allowed us to adjust the techniques of principal component analysis (PCA) and multiple correspondence analysis (MCA) to detect differences between the direct consequences of DM, citizen's interest in this non-communicable disease, and socioeconomic factors and inequalities in access to ICT in aggregate form between the country's different ACS. The results show how SDOH, such as poverty and education level, are related to the ACS with the highest number of homes that cite the cost of connection or equipment as the reason for not having ICT at home. These regions also have a greater number of hospitalizations due to DM. Given that in Spain, there are certain differences in accessibility in terms of the cost to households, in the case of DM, we take this issue into account from the standpoint of an integral approach by health policies.

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.

Developing a Fuzzy TOPSIS Model Combining MACBETH and Fuzzy Shannon Entropy to Select a Gamification App

Due to the important advantages it offers, gamification is one of the fastest-growing industries in the world, and interest from the market and from users continues to grow. This has led to the development of more and more applications aimed at different fields, and in particular the education sector. Choosing the most suitable application is increasingly difficult, and so to solve this problem, our study designed a model which is an innovative combination of fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) and Shannon entropy theory, to choose the most suitable gamification application for the Industrial Manufacturing and Organisation Systems course in the degree programmes for Electrical Engineering and Industrial and Automatic Electronics at the Higher Technical School of Industrial Engineering of Ciudad Real, part of the University of Castilla-La Mancha. There is no precedent in the literature that combines MACBETH and fuzzy Shannon entropy to simultaneously consider the subjective and objective weights of criteria to achieve a more accurate model. The objective weights computed from fuzzy Shannon entropy were compared with those calculated from De Luca and Termini entropy and exponential entropy. The validity of the proposed method is tested through the Preference Ranking Organisation METHod for Enrichment of Evaluations (PROMETHEE) II, ELimination and Choice Expressing REality (ELECTRE) III, and fuzzy VIKOR method (VIsekriterijumska optimizacija i KOmpromisno Resenje). The results show that Quizizz is the best option for this course, and it was used in two academic years. There are no precedents in the literature using fuzzy multicriteria decision analysis techniques to select the most suitable gamification application for a degree-level university course.

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

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.

Mobile Health Apps on COVID-19 Launched in the Early Days of the Pandemic: Content Analysis and Review

Background

Mobile health (mHealth) app use is a major concern because of the possible dissemination of misinformation that could harm the users. Particularly, it can be difficult for health care professionals to recommend a suitable app for coronavirus disease (COVID-19) education and self-monitoring purposes.

Objective

This study aims to analyze and evaluate the contents as well as features of COVID-19 mobile apps. The findings are instrumental in helping health care professionals to identify suitable mobile apps for COVID-19 self-monitoring and education. The results of the mobile apps’ assessment could potentially help mobile app developers improve or modify their existing mobile app designs to achieve optimal outcomes.

Methods

The search for the mHealth apps available in the android-based Play Store and the iOS-based App Store was conducted between April 18 and May 5, 2020. The region of the App Store where we performed the search was the United States, and a virtual private network app was used to locate and access COVID-19 mobile apps from all countries on the Google Play Store. The inclusion criteria were apps that are related to COVID-19 with no restriction in language type. The basic features assessment criteria used for comparison were the requirement for free subscription, internet connection, education or advisory content, size of the app, ability to export data, and automated data entry. The functionality of the apps was assessed according to knowledge (information on COVID-19), tracing or mapping of COVID-19 cases, home monitoring surveillance, online consultation with a health authority, and official apps run by health authorities.

Results

Of the 223 COVID-19–related mobile apps, only 30 (19.9%) found in the App Store and 28 (44.4%) in the Play Store matched the inclusion criteria. In the basic features assessment, most App Store (10/30, 33.3%) and Play Store (10/28, 35.7%) apps scored 4 out of 7 points. Meanwhile, the outcome of the functionality assessment for most App Store apps (13/30, 43.3%) was a score of 3 compared to android-based apps (10/28, 35.7%), which scored 2 (out of the maximum 5 points). Evaluation of the basic functions showed that 75.0% (n=36) of the 48 included mobile apps do not require a subscription, 56.3% (n=27) provide symptom advice, and 41.7% (n=20) have educational content. In terms of the specific functions, more than half of the included mobile apps are official mobile apps maintained by a health authority for COVID-19 information provision. Around 37.5% (n=18) and 31.3% (n=15) of the mobile apps have tracing or mapping and home monitoring surveillance functions, respectively, with only 17% (n=8) of the mobile apps equipped with an online consultation function.

Conclusions

Most iOS-based apps incorporate infographic mapping of COVID-19 cases, while most android-based apps incorporate home monitoring surveillance features instead of providing focused educational content on COVID-19. It is important to evaluate the contents and features of COVID-19 mobile apps to guide users in choosing a suitable mobile app based on their requirements.