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 Na\"ive 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.

翻译：背景 尽管应用市场（如Google Play商店）推广的大量健康应用带来了诸多益处，但移动健康和电子健康应用的广泛采用尚未实现。目的 本研究旨在调查当前专注于改善和维持健康福祉的智能手机应用格局。理解热门应用关注的类别及其为用户提供的相关功能（这些功能带来了更高的用户评分和下载量），将为推动大众更广泛采用提供启示。这项对1000个移动健康应用的研究旨在揭示某些应用被喜爱和采用而许多其他应用却未获青睐的原因。方法 我们从应用市场收集了用户生成数据（即评分）和公司生成数据（即应用描述），并由两位研究人员进行手动编码和分类。分析采用了人工神经网络、随机森林和朴素贝叶斯人工智能算法。结果 分析得出了能吸引更多下载行为和更高用户评分的特征。研究结果表明，提及隐私政策或在描述中提供视频的应用可获得更高的用户评分，而提供应用内购买选项、社交网络与分享功能以及反馈机制的免费应用则能获得更高的下载量。此外，移动健康应用的不同子类别在用户评分和总下载量方面存在差异。结论 本研究通过结合文本分析和机器学习算法对移动健康应用进行审查，为当前关于移动健康应用使用的知识体系做出了贡献。文本分析通过提供分类、关键词以及影响移动健康情境下下载行为和用户评分的因素，增添了重要价值。