LAMP: a monitoring framework for mHealth application research

Abstract

The usage of mobile applications in healthcare has gained popularity in recent years. In 2018, at least, 10,000 apps related to mental health could be downloaded in the app stores. The popularity of healthcare apps, especially in the field of mental health, is based on in their simplicity in large-scale data collection scenarios used for the improvement of health-related services or research. For these apps, instruments to quantify the quality of an app and repositories for app quality ratings have emerged in recent years. What is rarely considered, however, is the degree of functional correctness of an app, which can have a serious impact on the data collection process and thus on data quality. The increasing restrictions of background services are a challenge for app developers, who need to implement recurring tasks reliably in the background, like the collection of longitudinal data based on questionnaires or sensor measurements. In this paper, we present a monitoring framework to investigate the degree of functional correctness regarding the background service implementation of apps based on notification events. With this framework, we want to enable the large-scale collection of app execution data in the wild to gain more insights into the execution of apps in different execution environments and configurations. The gained knowledge shall help to improve existing applications in the field of mental health and eventually to improve the degree of functional correctness of those apps.