Designing a wearable IoT-based bladder level monitoring system for neurogenic bladder patients

ABSTRACT

Over the last years, the use of Internet of Things (IoT) systems in healthcare has increased due to technological advancements and increased availability of data. Sensor-based monitoring of physiological parameters, in particular, promises rich opportunities to promote overall health and self-management of patients suffering from chronic diseases. As such, neurogenic bladder patients lack sensation and control over their bladder while they could regain sovereignty over their bladder management through monitoring their physiological parameters. In this paper, we aim to develop a wearable IoT-based bladder level monitoring system for managing neurogenic bladder dysfunctions. We develop a set of design principles taking a stance from behaviour theory and implement the design principles in a software architecture following a design science research approach. Further, we evaluate and revise the developed artefact and implement a prototype of the software architecture. Our research contributes to IS research through prescriptive knowledge for IoT-based bladder level monitoring systems that can be transferred and generalised to similar areas of application. Further, we contribute to behaviour theory as we theorise a new type of trigger that we call a hybrid trigger.

KEYWORDS:

• Design science research
• Internet of things
• healthcare
• design principles
• neurogenic Bladder

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics vote

Ethics vote granted by the Ethics Committee of the University of Bayreuth under number: Az.O 1305/1 – GB.

Registration as clinical study

This research is enregistered in the German Register for Clinical Studies (DRKS) under the number: DRKS00026995.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. The authors are happy to provide the full dataset and source code used for prototypical implementation upon request.

Additional information

Funding

Parts of this work were supported by the publicly funded research project “inContAlert” under Grant [03EFRBY255], Address: Wittelsbacherring 10, 95444 Bayreuth, Germany.