German Clinical Trials Register

Acronym/abbreviation of the study

No Entry

URL of the study

https://incontalert.de/

Brief summary in lay language

Hundreds of millions of people worldwide suffer from bladder dysfunction, which involves the loss of control over the bladder. Affected patients suffer from physical and psychosocial problems that lead to significant restrictions in their everyday lives. Moreover, affected patients often cannot feel their bladder anymore. Common solutions, such as diapers or timed catheterisations, are inaccurate and burdensome. Precise and continuous bladder level monitoring, however, would give patients back control over their bladder activity. The aim of this study is to develop a software architecture for a system to continuously monitor the bladder level of patients with neurogenic bladder dysfunction and the analysis of physiological parameters using machine learning. To evaluate the results, we implement a prototype of the software architecture and conduct interviews with affected patients and physicians. From this evaluation process, we derive design principles for monitoring physiological parameters in chronic disease management.

Brief summary in scientific language

Hundreds of millions of people worldwide suffer from bladder dysfunction, which involves the loss of control over the bladder. Affected patients suffer from physical and psychosocial problems that lead to significant restrictions in their everyday lives. Moreover, affected patients often cannot feel their bladder anymore. Common solutions, such as diapers or timed catheterisations, are inaccurate and burdensome. Precise and continuous bladder level monitoring, however, would give patients back control over their bladder activity. The aim of this study is to develop a software architecture for a system to continuously monitor the bladder level of patients with neurogenic bladder dysfunction and the analysis of physiological parameters using machine learning. For this purpose, we follow a design science research approach. This approach includes problem identification, definition of design objectives, development of the design artefact, evaluation of the design artefact and communication of the results. We derive the problem identification from medical literature. We determine the design goals based on literature from the domains of business informatics and medicine. In the development phase of the design artefact, we build a holistic system for continuous bladder level monitoring. This system includes a sensor box with three different sensors (infrared, acceleration, temperature), which continuously sends data to a mobile device to display the bladder level. In addition, the patient receives a notification when the bladder is full. We illustrate the components of the system in detail in a software architecture. To evaluate the results, we implement a machine learning prototype that measures the bladder level based on infrared, acceleration and temperature data. In addition, we conduct interviews with patients affected by bladder dysfunction and with physicians who treat such patients. From this evaluation process, we derive design principles for monitoring physiological parameters in chronic disease management.