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inContAlert: Identifying Adoption Factors and Design Principles Affecting the Intention of Incontinent Patients to Adopt Mobile Health Applications

inContAlert: Identifying Adoption Factors and Design Principles Affecting the Intention of Incontinent Patients to Adopt Mobile Health Applications

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
DRKS
Registry ID
DRKS00021103
Enrollment
30
Registered
2020-05-12
Start date
2020-05-22
Completion date
Unknown
Last updated
2025-04-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Urinary incontinence as well as similar functional and sensory disorders of the urinary bladder R32 N39.3 N39.4 F98.0 N31 N32.8 G95.8

Interventions

Group 1: In this study, we aim at developing an adoption model and hence deriving design principles to support the adoption of mHealth solutions by chronic disease patients, such as urinary incontinen

Sponsors

Universität Bayreuth, Projektgruppe Wirtschaftsinformatik des Fraunhofer FIT, Kernkompetenzzentrum FIM
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: Urinary incontinence; majority age

Exclusion criteria

Exclusion criteria: No urinary incontinence; exclusive fecal incontinence; no majority age

Design outcomes

Primary

MeasureTime frame
We identified adoption factors of urinary incontinence patients regarding the adoption of mHealth solutions and derived design principles regarding the development of such mHealth. We focused on factors that foster the adoption by patients. Finally, we developed an adoption model and a catalog of specific design principles.

Secondary

MeasureTime frame
Furthermore, we developed a sensor system that noninvasively determines the filling level of the urinary bladder and displays the filling level to a digital end device.

Countries

Germany

Contacts

Public ContactMichael Burkard

Universität Bayreuth, Projektgruppe Wirtschaftsinformatik des Fraunhofer FIT, Kernkompetenzzentrum FIM

michael.burkard@uni-bayreuth.de+49 171 7022567

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026