移动健康应用程序正在通过改善沟通，效率和服务质量来彻底改变医疗保健生态系统。在低收入国家和中等收入国家，它们还发挥了独特的作用，是有关患者和医疗保健工作者的健康成果和行为的信息来源，同时提供了一个合适的渠道来提供个性化和集体的政策干预措施。我们提出了一个框架，以研究用户与移动健康的参与度，重点关注旨在在资源贫乏环境中为其支持的医疗保健工人和数字健康应用程序。这些应用程序产生的行为日志可以转换为表征每个用户活动的每日时间序列。我们使用概率和生存分析来建立多种有意义的参与度的个性化度量，这些措施可以定制适合每个卫生工作者特定需求的内容和数字干预措施。特别注意检测流失的问题，被理解为完全脱离接触的标志。我们讨论了我们的方法应用于安全交付应用程序的印度和埃塞俄比亚用户，这是一种熟练的亲生服务员的能力建设工具。这项工作代表了对移动健康应用程序中用户参与的全面表征的重要一步，这可以显着增强卫生工作者的能力并最终挽救生命。

Maternal and child mortality is a public health problem that disproportionately affects low-and middle-income countries. Every day, 800 women and 6,700 newborns die from complications related to pregnancy or childbirth. And for every maternal death, about 20 women suffer serious birth injuries. However, nearly all of these deaths and negative health outcomes are preventable. Midwives are key to revert this situation, and thus it is essential to strengthen their capacities and the quality of their education. This is the aim of the Safe Delivery App, a digital job aid and learning tool to enhance the knowledge, confidence and skills of health practitioners. Here, we use the behavioral logs of the App to implement a recommendation system that presents each midwife with suitable contents to continue gaining expertise. We focus on predicting the click-through rate, the probability that a given user will click on a recommended content. We evaluate four deep learning models and show that all of them produce highly accurate predictions.