Authors:
Pedro Almir M. Oliveira
1
;
2
;
Rossana Andrade
1
and
Pedro A. Santos Neto
3
Affiliations:
1
Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Ceará, Ceará, Brazil
;
2
Federal Institute of Maranhão (IFMA), Pedreiras, Maranhão, Brazil
;
3
Laboratory of Software Optimization and Testing (LOST), Federal University of Piauí, Piauí, Brazil
Keyword(s):
Health Monitoring, Self-reported Quality of Life, Practical Report, Lessons Learned.
Abstract:
In the modern world, it is no overstatement to say that “our devices know us better than we know ourselves”. In this sense, the vast amount of data generated by wearables, mobile devices, and environmental sensors has enabled the development of increasingly personalized and intelligent services. Among them, there is a growing interest in the delivery of medical practice using mobile devices (i.e., mobile health or mHealth). mHealth makes it possible to optimize healthcare systems based on continuous and transparent health monitoring, aiming to detect the emergence of diseases. However, mHealth monitoring in the real world (i.e., uncontrolled environment or, as labeled in this paper, “in the wild”) has many challenges. Therefore, this practical report discusses ten lessons learned from the Quality of Life (QoL) monitoring of twenty-one volunteers over three months. The main objective of this QoL monitoring was to collect data capable of training Machine Learning algorithms to infer us
ers’ Quality of Life using the WHOQOL-BREF as a reference. During this period, our research team systematically recorded the problems faced and the strategies to overcome them. Such lessons can support researchers and practitioners in planning future studies to avoid or mitigate similar issues. In addition, we present strategies for dealing with each challenge using the 5W1H model.
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