Feasibility of Home-based, Ambient Passive Sensor Technology to Provide Early Warning of Health Decompensation by Detecting Deviations in Activities of Daily Living (ADLs) of Elderly Subjects With Diagnosed Chronic Heart Failure
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Sensorum Health Inc.
- Enrollment
- 20
- Locations
- 1
- Primary Endpoint
- Recall of AI in passive sensor system
Study Overview
Brief Summary
Sensorum Health (Sensorum) is conducting a pilot study to determine if Sensorum's proprietary passive sensor network can be used to identify signals of early health decompensation in subjects prior to a hospitalization for chronic disease exacerbation or other ambulatory care sensitive conditions. Successful early detection would provide a window of opportunity to intervene outside of the acute setting in future interventional studies.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 55 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Current Patient at Weill Cornell Medicine
- •Aged 55 years or older
- •Able to consent
- •Documented diagnosis of congestive heart failure (CHF)
- •At least 1 of the following prior hospital utilization events in the past 12 months
- •Inpatient admission for any reason
- •Facility observation stay for any reason
- •Emergency Department visit for any reason
Exclusion Criteria
- •Significant cardiac valvular disease
- •End-Stage Renal Disease (ESRD)
- •End-Stage CHF
- •End-Stage COPD
Outcomes
Primary Outcomes
Recall of AI in passive sensor system
Time Frame: 6 months
Evaluation of AI ability to prospectively detect hospital utilization event
Precision of AI in passive sensor system
Time Frame: 6 months
Evaluation of AI ability to precisely predict hospital utilization event
Secondary Outcomes
- Recall of sensor data review by trained nurses(6 months)
- Precision of sensor data review by trained nurses(6 months)
