Effectiveness of the AI-Supporter in Reducing Urinary Tract Infections
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-supporter.
- Who it may be relevant to
- Registry conditions: Incontinence, Urinary Tract Infection, Incontinence-associated Dermatitis, Cost-effectiveness. Basic parameters: from 20 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Taiwan
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Testing the Effectiveness of the AI-Supporter in Reducing Urinary Tract Infections, Incontinence-associated Dermatitis and Caregiving Costs for Incontinence Patients
Overview
The "AI Supporter," an intelligent excretion management robot, leverages artificial intelligence-based vision recognition to autonomously detect and cleanse affected areas, followed by drying and changing the diaper, thereby reducing caregiver strain and enhancing care quality. This study aims to assess the efficacy of the "AI Supporter" in decreasing the incidence of urinary tract infections and incontinence-associated dermatitis among incontinent patients, in addition to exploring its cost-effectiveness. Adopting an experimental (two groups) and longitudinal design, this research utilizes both convenience and random sampling strategies. The study anticipates recruiting 60 female subjects who have been confined to bed for more than three months with urinary and/or fecal incontinence. Participants will intermittently use the AI Supporter over a 14-day period. Measurement tools include routine urine analysis.
Detailed description
Background: As Taiwan progresses medically, the aging demographic has become a significant challenge, leading to an escalation in the disabled population. The lack of caregiving manpower represents a critical bottleneck in the provision of long-term care. Diaper changing, a daily and labor-intensive task for caregivers, involves bending motions that pose a risk of musculoskeletal injuries. Consequently, the imperative development of automated caregiving technologies has emerged. The "AI Supporter," an intelligent excretion management robot, leverages artificial intelligence-based vision recognition to autonomously detect and cleanse affected areas, followed by drying and changing the diaper, thereby reducing caregiver strain and enhancing care quality.
Objective: This study aims to assess the efficacy of the "AI Supporter" in decreasing the incidence of urinary tract infections and incontinence-associated dermatitis among incontinent patients, in addition to exploring its cost-effectiveness.
Methods: Adopting an experimental (two groups) and longitudinal design, this research utilizes both convenience and random sampling strategies. Scheduled from November 2024 to October 2025 at a residential long-term care facility in Central Taiwan, the study anticipates recruiting 60 female subjects who have been confined to bed for more than three months with urinary and/or fecal incontinence. Participants will intermittently use the AI Supporter over a 14-day period. Measurement tools include routine urine analysis, incontinence-associated dermatitis rating scales, pressure sore assessments, skin pH measurements, caregiver hours, and cost analyses pertaining to diapers and the AI Supporter. The principal analytical method employed will be Generalized Estimating Equations (GEE), with statistical significance defined at p \< 0.05.
Expected Outcomes: The AI Supporter is expected to significantly reduce the occurrence of urinary tract infections and incontinance-associated dermatitis in patients, concurrently alleviating caregiver workload and diminishing associated costs.
Interventions
- Device AI-supporter
rticipants in the experimental group will use the AI-supporter, an intelligent excretion management robot. This device utilizes AI-driven visual recognition technology to automatically detect urine and feces, followed by a cleaning and drying process. When the AI-supporter detects excretion, it activates an automated sequence that washes, dries, and sanitizes the perineal area without requiring the caregiver to remove the diaper. The AI-supporter also records relevant data, such as the time, fre
Primary outcome measures
- white blood cells [Time frame: 14 days after intervention]
- Bacterial count [Time frame: 14 days after intervention]
Eligibility criteria
Inclusion criteria
- Participants must have been bedridden for at least 3 months and have urinary and/or fecal incontinence.
- Female participants aged over 20 years old.
- Participants must be capable of wearing the AI-supporter device during the study period.
Exclusion criteria
- Participants with severe skin conditions unrelated to incontinence.
- Participants with current urinary tract infections or incontinence-associated dermatitis at the time of enrollment.
- Participants who are unable to provide informed consent or have a legal representative to do so.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Health services research
Study locations
Taiwan · 1 center
- Rom A Master List, Extracted From This Organization'S Records. — Taichung
Publications
- Borchert K, Bliss DZ, Savik K, Radosevich DM. The incontinence-associated dermatitis and its severity instrument: development and validation. J Wound Ostomy Continence Nurs. 2010 Sep-Oct;37(5):527-35. doi: 10.1097/WON.0b013e3181edac3e. PMID 20736860
- Buckingham KW, Berg RW. Etiologic factors in diaper dermatitis: the role of feces. Pediatr Dermatol. 1986 Feb;3(2):107-12. doi: 10.1111/j.1525-1470.1986.tb00499.x. PMID 3513143
- Fader M, Clarke-O'Neill S, Cook D, Dean G, Brooks R, Cottenden A, Malone-Lee J. Management of night-time urinary incontinence in residential settings for older people: an investigation into the effects of different pad changing regimes on skin health. J Clin Nurs. 2003 May;12(3):374-86. doi: 10.1046/j.1365-2702.2003.00731.x. PMID 12709112
- Ferreira M, Abbade L, Bocchi SCM, Miot HA, Boas PV, Guimaraes HQCP. Incontinence-associated dermatitis in elderly patients: prevalence and risk factors. Rev Bras Enferm. 2020;73 Suppl 3:e20180475. doi: 10.1590/0034-7167-2018-0475. Epub 2020 Jul 13. English, Portuguese. PMID 32696899
- Francis K, Pang SM, Cohen B, Salter H, Homel P. Disposable Versus Reusable Absorbent Underpads for Prevention of Hospital-Acquired Incontinence-Associated Dermatitis and Pressure Injuries. J Wound Ostomy Continence Nurs. 2017 Jul/Aug;44(4):374-379. doi: 10.1097/WON.0000000000000337. PMID 28549048
- Gray M. Optimal management of incontinence-associated dermatitis in the elderly. Am J Clin Dermatol. 2010;11(3):201-10. doi: 10.2165/11311010-000000000-00000. PMID 20131923
- Hachem JP, Crumrine D, Fluhr J, Brown BE, Feingold KR, Elias PM. pH directly regulates epidermal permeability barrier homeostasis, and stratum corneum integrity/cohesion. J Invest Dermatol. 2003 Aug;121(2):345-53. doi: 10.1046/j.1523-1747.2003.12365.x. PMID 12880427
- Hahnel E, Blume-Peytavi U, Trojahn C, Kottner J. Associations between skin barrier characteristics, skin conditions and health of aged nursing home residents: a multi-center prevalence and correlational study. BMC Geriatr. 2017 Nov 13;17(1):263. doi: 10.1186/s12877-017-0655-5. PMID 29132305
Identifiers
NCT: NCT06613503 · CMUH113-REC3-109