Monitoring of the Bladder Filling
Conditions
Keywords
bladder monitoring, digital Health, bladder filling level, incontinence, neurourology, neurogenic bladder, spinal cord injury
Brief summary
The aim of this study is to evaluate the bladder filling level of the study participants using the inContAlert sensor. The generated data will be used for the evaluation and optimization of the machine learning algorithms to be able to make precise predictions about the individual bladder fill level. In particular, the hypothesis that the bladder filling level can be estimated by the algorithm will be tested. When testing the hypothesis, it should be determined which deviation (measured by the mean absolute percentage error) of the estimation/prediction differs from the actual value (obtained by measuring the urine output using a measuring cup in combination with kitchen scales).
Interventions
DEVICEinContAlert
InContAlert is a non-invasive sensor technology to measure the bladder filling level for incontinence patients. The device is fixed about 2cm above the pubic bone using a patch or strap and does not require surgery. The data collected from the patient is analyzed using deep learning algorithms. The bladder filling level determined in this way is then displayed on an app.
Sponsors
University of Bayreuth
inContAlert GmbH
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* informed consent
Exclusion criteria
* Missing informed consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Difference between the predicted bladder filling level and the actual value | December 2023 | Difference (measured as mean absolute error in percent) of the predicted bladder filling level (measured in ml) and the actual value (determined by measuring the volume of urine in ml with a measuring cup in combination with a kitchen scale). |
Countries
Germany
Outcome results
None listed