Abstract & Details
Description
Award ID: 2126867
The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve care for the 27 million American women with involuntary urine leakage. Such leakage is highly prevalent, affecting one in three adult women and worsening with age. Most women avoid medical treatment, either because they accept their leakage as natural or hold an unfavorable view of treatments like surgery and pelvic floor physical therapy. This research will explore a scalable, high-tech approach to at-home treatments recommended by clinicians. Patients will interact virtually with health coaches and routinely receive personalized, semi-automated help and encouragement. Health coaches will be able to monitor the simultaneous progress of many patients remotely. Benefits include (1) helping more women at lower cost versus current practice and (2) increasing clinician productivity. It could save individual women up to $1,000 annually and eliminate tens of thousands of unnecessary surgeries. This Small Business Innovation Research (SBIR) Phase I project will develop a healthcare system workflow technology enabled by AI algorithms for tailoring an incontinence home health intervention to a wide variety of patient needs, preferences, and idiosyncratic behaviors. Study use cases will include medical device usage training and exercise regimen adherence. To address these scenarios, researchers will create a clinically-valid, user-specific, dynamic incontinence treatment solution comprising a mobile app for symptomatic women and a health coaching platform that integrates user data and generates tailored interactions. Solution elements will be built to (1) collect and manage high quantities of rich user data, (2) develop and train real-time inference algorithms, and (3) incorporate machine learning approaches to learn from user responses to system outputs (feedback, recommendations, encouragement, etc.). Known principles of health behavior change will be incorporated in adapting interactions for users. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Alastair Monk
The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve care for the 27 million American women with involuntary urine leakage. Such leakage is highly prevalent, affecting one in three adult women and worsening with age. Most women avoid medical treatment, either because they accept their leakage as natural or hold an unfavorable view of treatments like surgery and pelvic floor physical therapy. This research will explore a scalable, high-tech approach to at-home treatments recommended by clinicians. Patients will interact virtually with health coaches and routinely receive personalized, semi-automated help and encouragement. Health coaches will be able to monitor the simultaneous progress of many patients remotely. Benefits include (1) helping more women at lower cost versus current practice and (2) increasing clinician productivity. It could save individual women up to $1,000 annually and eliminate tens of thousands of unnecessary surgeries. This Small Business Innovation Research (SBIR) Phase I project will develop a healthcare system workflow technology enabled by AI algorithms for tailoring an incontinence home health intervention to a wide variety of patient needs, preferences, and idiosyncratic behaviors. Study use cases will include medical device usage training and exercise regimen adherence. To address these scenarios, researchers will create a clinically-valid, user-specific, dynamic incontinence treatment solution comprising a mobile app for symptomatic women and a health coaching platform that integrates user data and generates tailored interactions. Solution elements will be built to (1) collect and manage high quantities of rich user data, (2) develop and train real-time inference algorithms, and (3) incorporate machine learning approaches to learn from user responses to system outputs (feedback, recommendations, encouragement, etc.). Known principles of health behavior change will be incorporated in adapting interactions for users. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Alastair Monk
| Status | Closed |
|---|---|
| Effective start/end date | 01/15/22 → 07/31/23 |
Funding
- SBIR Phase I: $255,078.00
Active Fiscal Year
- FY2023
- FY2022
Start Fiscal Year
- FY2022
TIP Programs
- SBIR Phase I
Small Business
- Yes
Key Technology Areas
- Artificial Intelligence
- (confidence score: 100%)
Technology Foci
- Artificial Intelligence (Broad)
- (confidence score: 100%)
Congressional District at Award
- District n. 05 of Georgia
Current Congressional District
- District n. 05 of Georgia
United States
- Georgia
Core Based Statistical Area (CBSA)
- Atlanta-Sandy Springs-Roswell, GA
County
- County: DeKalb, GA
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