Evaluation of Home-based Sensor System to Detect Health Decompensation in Elderly Patients With History of CHF

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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05865197

Enrollment

Registered

2023-05-18

Start date

2022-11-28

Completion date

2024-09-01

Last updated

2024-01-16

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

Conditions

CHF, Congestive Heart Failure

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.

Interventions

OTHERData collection

Data collection of clinically relevant signals using home-based sensor system

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

55 Years to No maximum

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
Recall of AI in passive sensor system	6 months	Evaluation of AI ability to prospectively detect hospital utilization event
Precision of AI in passive sensor system	6 months	Evaluation of AI ability to precisely predict hospital utilization event

Secondary

Measure	Time frame	Description
Recall of sensor data review by trained nurses	6 months	Evaluation of nurse ability to prospectively detect hospital utilization event
Precision of sensor data review by trained nurses	6 months	Evaluation of nurse ability to precisely predict hospital utilization event

Countries

United States

Contacts

Primary ContactChristine Fernandez

christine@sensorum.ai(973) 786-3573

Backup ContactAndrew Hotchkiss

andrew@sensorum.ai(973) 946-8382

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026