Sense4Safety fall-prevention program for older adults with mild cognitive impairment
Official title: Sense4Safety Intervention
Run by University of Pennsylvania · NCT07220668 · Data updated 2026-11-12
This project will test whether a home sensor system with machine-learning alerts can detect rising fall risk and help prevent falls in adults 65 and older with mild cognitive impairment.
Quick facts
| Phase | Phase1; Phase2 |
|---|---|
| Study type | Interventional |
| Ages | 65 Years and up |
| Sex | All |
| Enrollment | 200 (estimated) |
| Sponsor | University of Pennsylvania Academic / other |
| Locations | 1 site (Philadelphia, Pennsylvania) |
| Status | Recruiting |
| Started | February 19, 2026 |
| Main results expected | February 28, 2030 |
| Study ends | March 2030 |
| Registry record updated | March 19, 2026 |
| Trial ID | NCT07220668 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are community-dwelling adults aged 65 or older who live in senior living communities, can walk household distances, and meet criteria for mild cognitive impairment (MoCA ≥17) with minimal physical disability (SPPB ≥1).
✕Probably not a fit
People with dementia, major neurologic conditions (such as stroke or Parkinson's disease), terminal illness, severe vision or hearing loss, or who rely on a wheelchair or scooter inside their living quarters are unlikely to benefit or be eligible.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
Sense4Safety combines passive in-home sensors with machine-learning algorithms to identify escalating fall risk in real time. The system is designed to generate individualized alerts when movement patterns or behaviors suggest increased risk. The intervention is being piloted in community-dwelling older adults with mild cognitive impairment who live in senior living communities. Participants who can walk household distances will be monitored over time to see whether alerts correspond to falls or near-misses and can guide preventive actions.
Why it matters
Potential benefit: If successful, Sense4Safety could lower the number of falls and related injuries by detecting risk earlier and prompting timely prevention actions.
How similar studies have gone: Pilot studies of home sensors and machine-learning for fall detection and prediction have shown promising but limited results, so applying this approach specifically for preventive individualized alerts in older adults with MCI is relatively novel and early-stage.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * aged ≥ 65 years * reside in senior living communities and plan to be in the area for more than 6 months * be able to walk household distances * score of at least 1 of 12 on the Short Physical Performance Battery (SPPB) * meet MCI criteria (MCI is defined as a score ≥17 Montreal Cognitive Assessment) Exclusion Criteria: Exclusion criteria include terminal illness, central nervous system condition such as stroke or Parkinson's disease, diagnosis of dementia, severe vision or hearing deficits, and reliant on wheelchair or scooter within living quarters.
Where it's running
-
University of Pennsylvania
Philadelphia, Pennsylvania, United States Recruiting
Study contacts
- Study coordinator: Sean Harrison, MPH
- Email: seanha@nursing.upenn.edu
- Phone: 215-898-0757
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.