Privacy-preserving Metrics for an mHealth App in the Context of Neuropsychological Studies

Alexander Gabel, Funda Ertas, Michael Pleger, Ina Schiering, Sandra Müller

Abstract

The potential of smart devices as smartphones, smart watches and wearables in healthcare and rehabilitation, so-called mHealth applications, is considerable. It is especially interesting, that these devices accompany patients during their normal life. Hence they are able to track activities and support users in activities of daily life. But beside the benefits for patients, mHealth applications also constitute a considerable privacy and security risk. The central question investigated here is how data about the usage of mobile applications in empirical studies with mHealth technologies can be collected in a privacy-friendly way based on the ideas of Privacy by Design. The context for the proposed approach are neuropsychological studies where a mobile application for Goal Management Training, a therapy for executive dysfunctions, is investigated. There a privacy-friendly concept for collecting data about the usage of the app based on metrics which are derived from research questions is proposed. The main ideas underlying the proposed concept are a decentralized architecture, where only aggregated data is gathered for the study, and a consequent data minimization approach.

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Paper Citation


in Harvard Style

Gabel A., Ertas F., Pleger M., Schiering I. and Müller S. (2020). Privacy-preserving Metrics for an mHealth App in the Context of Neuropsychological Studies.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-398-8, pages 166-177. DOI: 10.5220/0008982801660177


in Bibtex Style

@conference{healthinf20,
author={Alexander Gabel and Funda Ertas and Michael Pleger and Ina Schiering and Sandra Müller},
title={Privacy-preserving Metrics for an mHealth App in the Context of Neuropsychological Studies},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2020},
pages={166-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008982801660177},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Privacy-preserving Metrics for an mHealth App in the Context of Neuropsychological Studies
SN - 978-989-758-398-8
AU - Gabel A.
AU - Ertas F.
AU - Pleger M.
AU - Schiering I.
AU - Müller S.
PY - 2020
SP - 166
EP - 177
DO - 10.5220/0008982801660177