Abstract
The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain.
Highlights
For many use cases in the healthcare domain, e.g., in the assessment of chronic diseases and disorders, there is a need for the collection of large, qualitative, longitudinal, and ecologically valid data sets
There are numerous challenges when implementing a software solution in order to provide the desired functionality, to cope with technical aspects, as well as to comply with high standards and regulations in the healthcare domain. We discuss these challenges, derive several recommendations and propose a reference architecture for a respective software platform. These insights were mainly gained through several studies that combined mobile crowdsensing (MCS) and ecological momentary assessments (EMA) based on mHealth apps that we have developed in the last years
We discussed the combination of mobile crowdsensing (MCS) and ecological momentary assessment (EMA) in the healthcare domain
Summary
For many use cases in the healthcare domain, e.g., in the assessment of chronic diseases and disorders, there is a need for the collection of large, qualitative, longitudinal, and ecologically valid data sets. We discuss these challenges, derive several recommendations and propose a reference architecture for a respective software platform. These insights were mainly gained through several studies that combined MCS and EMA based on mHealth apps that we have developed in the last years. The mentioned studies, in turn, address different healthcare questions and are mostly running for many years This provides us with a proper basis for the proposed reference architecture as well as the introduced set of recommendations.
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