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

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 Disease

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05865184
Enrollment
100
Registered
2023-05-18
Start date
2022-09-28
Completion date
2024-06-06
Last updated
2023-05-18

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

Conditions

CHF, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, COPD, COPD Exacerbation

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

Sponsors

Sensorum Health Inc.
Lead SponsorINDUSTRY

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Aged 65 years or older * Able to consent * Documented history of diagnosis of chronic obstructive pulmonary disease (COPD) and/or congestive heart failure (CHF) * Currently admitted to JSUMC for observation or as an inpatient with any diagnosis ≥1 prior hospital utilization events with any diagnosis (inpatient admissions, facility observation stays, or ED visits) in the past 12 months

Exclusion criteria

* Significant cardiac valvular disease * End-Stage Renal Disease (ESRD) * End-Stage CHF * End-Stage COPD

Design outcomes

Primary

MeasureTime frameDescription
Recall of AI in passive sensor system90 daysEvaluation of AI ability to prospectively detect hospital utilization event
Precision of AI in passive sensor system90 daysEvaluation of AI ability to precisely predict hospital utilization event

Secondary

MeasureTime frameDescription
Recall of sensor data review by trained nurses90 daysEvaluation of nurse ability to prospectively detect hospital utilization event
Precision of sensor data review by trained nurses90 daysEvaluation 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