The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of ecological momentary assessment (EMA) and mobile crowdsensing (MCS) in the healthcare domain. This combination not only allows researchers to collect ecologically valid data, but also to use smartphones as well as external sensors to capture the context in which these data are collected. By correlating subjective EMA data collected through self-reports with objective MCS sensor measurements in mobile applications, new valuable insights about participants and patients can be gained. However, the operationalization of EMA and MCS is not clearly defined. In addition, engineering of software systems that enable both EMA and MCS is a challenging endeavor that is accompanied by a multitude of conceptual, legal, architectural, and technical challenges.
This thesis identifies these challenges based on a literature review and the insights gained in several large-scale, long-term eHealth & mHealth projects. Based on the state of the art, the conceptual and operational foundations of combining EMA and MCS in the healthcare domain are introduced.
In particular, characteristic requirements for an integrated EMA & MCS platform are elaborated in a structured manner and implemented in a conceptual framework. The framework fosters the operationalization of EMA & MCS endeavors in eHealth & mHealth by leveraging the synergies between the two concepts and providing a holistic view on the overall topic.
Major aspects of the conceptual framework include the design of a reference software architecture, the management of heterogeneous sensor measurements in MCS, and addressing the specifics of data analysis in the context of EMA & MCS.
Moreover, it is shown how the concepts and artifacts proposed in the thesis were iteratively validated and their practical feasibility was evaluated.
Altogether, the contributions presented by this thesis intend to establish a profound and holistic understanding of EMA, MCS, and their combined use in eHealth and mHealth. The proposed conceptual framework and each of its components contribute to operationalizing EMA & MCS endeavors in generic healthcare scenarios and provide technical support for the entire workflow of realizing EMA & MCS software solutions, from their development to the analysis of EMA & MCS healthcare data.
Finally, the knowledge gained and the artifacts created shall serve as a foundation for new EMA & MCS endeavors and eHealth research in general, for example, in the context of personalized treatments.