Department of Health Management, Istanbul Medipol University, Beykoz, Istanbul, Turkey.
Department of Management Information Systems, Istanbul Medipol University, Beykoz, Istanbul, Turkey.
PLoS One. 2021 Jan 6;16(1):e0244302. doi: 10.1371/journal.pone.0244302. eCollection 2021.
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.
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.
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.
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.
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.
尽管应用市场（例如 Google Play 商店）上提供了大量健康应用带来的好处，但移动健康和电子健康应用的广泛采用尚未实现。
本研究旨在调查专注于改善和维持健康的智能手机应用程序的现状。了解热门应用程序关注的类别以及为用户提供的相关功能，这些功能可以提高用户评分和下载量，从而为提高普通大众的采用率提供深入了解。本研究对 1000 个移动健康应用程序进行了分析，旨在阐明为什么某些应用程序受到欢迎和采用，而许多应用程序则没有。
从应用市场收集用户生成的数据（即评论评分）和公司生成的数据（即应用描述），并由两名研究人员手动进行编码和分类。为了进行分析，使用了人工神经网络、随机森林和朴素贝叶斯人工智能算法。
分析导致了吸引更多下载行为和更高用户评分的功能。研究结果表明，在描述中提到隐私政策或提供视频的应用程序会获得更高的用户评分，而具有应用内购买可能性、社交网络和共享功能以及反馈机制的免费应用程序会获得更高的下载量。此外，在不同的移动健康应用程序子类别中检测到用户评分和总下载量的差异。
本研究通过使用内容分析和机器学习算法来审查移动健康应用程序，为移动健康应用程序的使用提供了当前知识。内容分析通过提供分类、关键词和影响移动健康背景下下载行为和用户评分的因素，增加了重要价值。
