The good, the bad, and the poorly designed: The mobile app stores are not a user-friendly experience for health and medical purposes

A shot in the dark: the impact of online visibility on the search for an effective sleep app

STUDY OBJECTIVES

Dictated by consumer ratings and concealed algorithms, high levels of online visibility are granted to certain sleep apps on mainstream modes of app selection. Yet, it remains unclear to what extent these highly visible apps are evidence-based. The objectives of this review were to identify and describe the apps with the greatest online visibility when searching for a sleep app and to assess the claimed and actual research associated with them.

METHODS

A keyword search was conducted in Google Play and Google search. Titles of the most visible apps were retrieved. App descriptions were examined to identify research claims made about app effectiveness on sleep and other health-related outcomes. A follow-up search on PubMed and Google Scholar was conducted to verify claims.

RESULTS

The keyword search identified 53 highly visible apps. Examination of app store descriptions found that no reference to research was made for the majority of apps (n = 45, 84.9%). Published research studies were available for just 3 apps, with most studies evaluating app impact on non sleep-related outcomes. There was some evidence to attesting to the effectiveness of 2 apps in improving sleep.

CONCLUSIONS

This review demonstrates how, when carrying out a typical search for a sleep app, information about the evidence base for the majority of highly visible apps is not available. Results highlight the need for the improvement of mainstream modes of app selection in terms of better consumer-app specificity and increased transparency regarding the access to information about the evidence base for apps.

CITATION

Power N, Boivin DB, Perreault M. A shot in the dark: the impact of online visibility on the search for an effective sleep app. J Clin Sleep Med. 2024;20(8):1379-1386.

Mobile Apps for Common Noncommunicable Disease Management: Systematic Search in App Stores and Evaluation Using the Mobile App Rating Scale

Background

The success of mobile apps in improving the lifestyle of patients with noncommunicable diseases through self-management interventions is contingent upon the emerging growth in this field. While users of mobile health (mHealth) apps continue to grow in number, little is known about the quality of available apps that provide self-management for common noncommunicable diseases such as diabetes, hypertension, and obesity.

Objective

We aimed to investigate the availability, characteristics, and quality of mHealth apps for common noncommunicable disease health management that included dietary aspects (based on the developer's description), as well as their features for promoting health outcomes and self-monitoring.

Methods

A systematic search of English-language apps on the Google Play Store (Google LLC) and Apple App Store (Apple Inc) was conducted between August 7, 2022, and September 13, 2022. The search terms used included weight management, obesity, diabetes, hypertension, cardiovascular diseases, stroke, and diet. The selected mHealth apps' titles and content were screened based on the description that was provided. Apps that were not designed with self-management features were excluded. We analyzed the mHealth apps by category and whether they involved health care professionals, were based on scientific testing, and had self-monitoring features. A validated and multidimensional tool, the Mobile App Rating Scale (MARS), was used to evaluate each mHealth app's quality based on a 5-point Likert scale from 1 (inadequate) to 5 (excellent).

Results

Overall, 42 apps were identified. Diabetes-specific mHealth apps accounted for 7% (n=3) of the market, hypertension apps for 12% (n=5), and general noncommunicable disease management apps for 21% (n=9). About 38% (n=16) of the apps were for managing chronic diseases, while 74% (n=31) were for weight management. Self-management features such as weight tracking, BMI calculators, diet tracking, and fluid intake tracking were seen in 86% (n=36) of the apps. Most mHealth apps (n=37, 88%) did not indicate whether there was involvement of health professionals in app development. Additionally, none of the apps reported scientific evidence demonstrating their efficacy in managing health. The overall mean MARS score was 3.2 of 5, with a range of 2.0 to 4.1. Functionality was the best-rated category (mean score 3.9, SD 0.5), followed by aesthetics (mean score 3.2, SD 0.9), information (mean score 3.1, SD 0.7), and engagement (mean score 2.9, SD 0.6).

Conclusions

The quality of mHealth apps for managing chronic diseases was heterogeneous, with roughly half of them falling short of acceptable standards for both quality and content. The majority of apps contained scant information about scientific evidence and the developer's history. To increase user confidence and accomplish desired health outcomes, mHealth apps should be optimized with the help of health care professionals. Future studies on mHealth content analysis should focus on other diseases as well.

Using data from mHealth apps to inform person-centred practice: A discussion paper

BACKGROUND

mHealth applications (apps) are tools that can enhance research by efficiently collecting and storing large amounts of data. However, data collection alone does not lead to change. Innovation and practice change occur through utilisation of evidence. The volume of data collected raises questions regarding utilisation of data by nurses and midwives, and how data from mHealth apps can be used to improve person-centred practice. There is limited empirical evidence and a lack of direction from global health authorities to guide nurses and midwives in this area.

AIM

To describe strategies for nurses and midwives that could enhance the effective use of data generated by mHealth apps to inform person-centred practice. The purpose of this paper is to stimulate reflection and generate actions for data utilisation when using mHealth apps in nursing research and practice.

METHODS

This discussion paper has been informed by current evidence, the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, and research experience as part of doctoral study.

FINDINGS

Before engaging in data collection using mHealth apps, nurses and midwives would benefit from considering the nature of the evidence collected, available technological infrastructure, and staff skill levels. When collecting data and interpreting results, use of a team approach supported by engaged leadership and external facilitation is invaluable. This provides support to operate apps, and more importantly use the data collected to inform person-centred practice.

CONCLUSIONS

This paper addresses the limited available evidence to guide nurses and midwives when using mHealth apps to collect and use data to inform practice change. It highlights the need for appropriate technology, external facilitative support, engaged leadership, and a team approach to collect meaningful evidence using mHealth apps. Clinicians, leaders, and researchers can apply the strategies provided to enhance the use of mHealth apps and ensure translation of evidence into practice.

Assessment of healthcare application quality: Development of a standardized methods for healthcare professionals

The advancement of the mobile app market is reshaping healthcare, emphasizing the imperative for quality and efficacy in health applications. This methodology has been devised to assess mobile health applications, aiming to assist healthcare professionals in selecting apps for e-healthcare consumers. Key facets of this approach are: •A stringent selection process within mobile app stores•A standardized assessment using the Mobile App Rating Scale to achieve consistent and replicable evaluations, systematically organizing app evaluations•A comprehensive framework guiding healthcare practitioners in determining which apps to integrate into clinical practice and which to exclude Central to this method is the emphasis on distinguishing apps that enhance clinical practice from those that fall short in important areas such as the effectiveness of proposed health features, data accuracy, adherence to clinical guidelines, data security, and user privacy. With heightened attention to usability and accessibility, the methodology also addresses the common risks inherent in mHealth implementation, ensuring that selected apps not only meet technical criteria but also align with the broader healthcare ecosystem's needs and challenges.