Obstructive Sleep Apnea Clinical Trial
— CARESSOfficial title:
Evaluation of Novel Proprietary Biometric as an Aid in the Screening and Diagnosis of Sleep Breathing Disorders
| NCT number | NCT03457428 |
| Other study ID # | CARESS |
| Secondary ID | |
| Status | Terminated |
| Phase | |
| First received | |
| Last updated | |
| Start date | October 31, 2017 |
| Est. completion date | December 1, 2018 |
| Verified date | February 2019 |
| Source | Resonea, Inc |
| Contact | n/a |
| Is FDA regulated | No |
| Health authority | |
| Study type | Observational |
Earlier studies suggest that acoustical analysis of snoring sounds can identify obstructive sleep apnea (OSA). The goal of the current study is to refine and validate algorithms for OSA diagnosis.
| Status | Terminated |
| Enrollment | 83 |
| Est. completion date | December 1, 2018 |
| Est. primary completion date | December 1, 2018 |
| Accepts healthy volunteers | Accepts Healthy Volunteers |
| Gender | All |
| Age group | 18 Years and older |
| Eligibility |
Inclusion Criteria: - referred for polysomnography (PSG) because of possible obstructive sleep apnea (OSA) - owns/possesses a smartphone for use during the study. Must be: - Android models running Android OS Samsung Galaxy 5 or later - iPhone models running iOS 6 or later Exclusion Criteria: - previous PSG or home sleep testing (HST) confirmation of OSA - prior surgery for snoring or OSA - medical contraindication of HST, as applicable - congnitive impairment that might interfere with obtaining informed consent, or completing Clinical Questionnaire |
| Country | Name | City | State |
|---|---|---|---|
| United States | Peninsula Sleep Center | Burlingame | California |
| United States | Sounds Sleep Health | Kirkland | Washington |
| United States | Blue Sleep | New York | New York |
| United States | Santa Monica Clinical Trials | Santa Monica | California |
| Lead Sponsor | Collaborator |
|---|---|
| Resonea, Inc |
United States,
Abeyratne UR, de Silva S, Hukins C, Duce B. Obstructive sleep apnea screening by integrating snore feature classes. Physiol Meas. 2013 Feb;34(2):99-121. doi: 10.1088/0967-3334/34/2/99. Epub 2013 Jan 23. — View Citation
Karunajeewa AS, Abeyratne UR, Hukins C. Multi-feature snore sound analysis in obstructive sleep apnea-hypopnea syndrome. Physiol Meas. 2011 Jan;32(1):83-97. doi: 10.1088/0967-3334/32/1/006. Epub 2010 Nov 30. — View Citation
| Type | Measure | Description | Time frame | Safety issue |
|---|---|---|---|---|
| Primary | Level of agreement between Sleep Breathing Index Score obtained from analysis by the proprietary algorith, and the Apnea-Hypopnea Index (AHI) as obtained by HST, with a cut-off of 15 | Level of agreement between the Sleep Breathing Index Score obtained from analysis by the proprietary algorithm, and the Apnea-Hypopnea Index (AHI) as obtained by HST, with a cut-off of 15. | immediate |
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