Automated cardiac arrest detection using a smartwatch: A simulation study to DETECT sudden falls

Automated cardiac arrest detection using a smartwatch: A simulation study to DETECT sudden falls - DETECT-2

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON56025

Enrollment

Registered

2023-03-30

Start date

2023-08-18

Completion date

Unknown

Last updated

2025-09-01

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

Conditions

Detectie van een val fall detection

Interventions

None listed

Eligibility

Age

18 Years to 64 Years

Inclusion criteria

Inclusion criteria: - Aged between 18 and 40 - Fitting the wristband

Exclusion criteria

Exclusion criteria: - Unwilling or unable to provide informed consent - Medical issues that interfere with wearing of the wristband (e.g., skin  disorders) - (Physically) unable or unwilling to perform the (fall-)motions - Relevant health issues (e.g. osteoporosis)

Design outcomes

Primary

Measure	Time frame
To construct an algorithm for detection of cardiac arrest related falls using wrist-derived accelerometer signals from simulated sudden falls and non-fall movements and study the sensitivity and specificity of the developed algorithm.	—

Secondary

Measure	Time frame
1. To study wrist-derived accelerometry signal characteristics in relation to sudden falls, soft falls and non-fall-motions in healthy subjects who simulate falls. 2. To study positive and negative predictive value of the algorithm for sudden falls. 3. To study sensitivity and false positives of an algorithm to detect soft falls. 4. To identify sources of noise interfering with correct accelerometry-based measurement of movements. 5. To study false positive rates of the recently developed first PPG-based cardiac arrest detection algorithm (DETECT-1) in this controlled study setting 6. To validate the steps per minute recorded by the CardioWatch by the steps per minute recorded by the CE/FDA certified Actigraph. 7. To validate the active calories per minute recorded by the CardioWatch by the active calories per minute recorded by the CE/FDA certified Actigraph.	—

Measure

Time frame

1. To study wrist-derived accelerometry signal characteristics in relation to sudden falls, soft falls and non-fall-motions in healthy subjects who simulate falls. 2. To study positive and negative predictive value of the algorithm for sudden falls. 3. To study sensitivity and false positives of an algorithm to detect soft falls. 4. To identify sources of noise interfering with correct accelerometry-based measurement of movements. 5. To study false positive rates of the recently developed first PPG-based cardiac arrest detection algorithm (DETECT-1) in this controlled study setting 6. To validate the steps per minute recorded by the CardioWatch by the steps per minute recorded by the CE/FDA certified Actigraph. 7. To validate the active calories per minute recorded by the CardioWatch by the active calories per minute recorded by the CE/FDA certified Actigraph.

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Countries

Netherlands

Outcome results