Sleep Apnea, Obstructive
Conditions
Keywords
Paced Breathing, OSA, sleep/wake detection
Brief summary
The purpose of this engineering trial is to develop and validate an algorithm that will deliver Paced Breathing as a ramp feature to obstructive sleep apnea (OSA) subjects using Continuous Positive Airway Pressure (CPAP) therapy. In this trial the investigators will be evaluating the algorithm's ability to correctly distinguish between sleep and wake.
Interventions
DEVICEPaced Breathing
The Paced Breathing (PB) feature(when activated) will work to relax the user and help them fall asleep by encouraging them to take deep slow breaths until they reach 10 breaths (or less) per minute. The feature will also detect when the subject has fallen asleep so the Continuous Positive Airway Pressur (CPAP) device will automatically switch from PB mode to regular CPAP mode.
Sponsors
Philips Respironics
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
21 Years to 70 Years
Healthy volunteers
No
Inclusion criteria
* Age 21-70 * Diagnosis of obstructive sleep apnea (OSA) * Current adherent CPAP user (has been using CPAP nightly for at least 2 weeks). * On CPAP pressures of 5-10cm. * Subjects wishing to complete a day or evening appointment need to have significant daytime sleepiness (Epworth Sleepiness Scale score of 8 or above) * Able and willing to provide written informed consent * English speaking
Exclusion criteria
* Participation in another interventional research study within the last 30 days * Major controlled or uncontrolled medical condition such as congestive heart failure, neuromuscular disease, renal failure etc. * Inability to tolerate nasal CPAP mask due to problems breathing solely through their nose. * Chronic respiratory failure or insufficiency with suspected or known neuromuscular disease, moderate or severe chronic obstructive pulmonary disease (COPD), or any condition with an elevation of arterial carbon dioxide levels while awake (PaCo2≥55mmHg) * Severe oxygen desaturation on the polysomnography (PSG), i.e. Sa02 \< 70% for 10% of the study. * Surgery of the upper airway, nose, sinus or middle ear within the past 90 days * Currently using supplemental oxygen * Regular use of sleeping pills or stimulants (\> 3 nights a week) * Currently working night shift or rotating day/night shift * Drowsy Driving or near miss accident in the past 6 months * Inability to tolerate or track to Paced Breathing device during initial habituation session in lab * Chronic insomnia, Restless legs syndrome, or severe periodic limb movement disorder (PLMD - PLMAI\>20/hr).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sleep/Wake Algorithm | The performance of the algorithm will be evaluated in real time while the subject is wearing the device during the sleep study, an average of 08 hours. | We tested the ability of the Sleep/Wake algorithm to identify sleep an wake periods with precision, as compared to standard polysonography (PSG) measures, which was used as the gold standard, i.e. we tested the accuracy of the algorithm. Accuracy was defined as the proportion of true results (both true positives and true negatives)in the population and it was assesed using as 2 X 2 table, i.e. accuracy = number of true positives + number of true negatives/ number of true positives + false positives + false negatives +true negatives. where True positive = the algorithm tested correctly identified sleep, False positive = the algorithm tested incorrectly identified sleep, True negative = the algorithm tested correctly rejected awake periods, and False negative = the algorithm tested incorrectly rejected awake periods. |
Countries
United States
Participant flow
Recruitment details
Participants were recruited from the general population, the sleep clinic at Sleep HealthCenters, Only subjects who had a diagnostic polysomnography and who were CPAP for at least 2 weeks, were considered for inclusion. Informed consent were be obtained by an investigator or research coordinator and was required for enrollment into the study.
Pre-assignment details
N/A All participants enrolled in the trial have completed the study
Participants by arm
| Arm | Count |
|---|---|
| Paced Breathing Sleep/Wake Detection All subjects enrolled will have OSA and will be current CPAP users. | 36 |
| Total | 36 |
Baseline characteristics
| Characteristic | Paced Breathing Sleep/Wake Detection |
|---|---|
| Age, Categorical <=18 years | 0 Participants |
| Age, Categorical >=65 years | 1 Participants |
| Age, Categorical Between 18 and 65 years | 35 Participants |
| Age Continuous | 48.1 years STANDARD_DEVIATION 11.9 |
| Region of Enrollment United States | 36 participants |
| Sex: Female, Male Female | 11 Participants |
| Sex: Female, Male Male | 25 Participants |
Adverse events
| Event type | EG000 affected / at risk |
|---|---|
| deaths Total, all-cause mortality | — / — |
| other Total, other adverse events | 10 / 36 |
| serious Total, serious adverse events | 0 / 36 |
Outcome results
Primary
Sleep/Wake Algorithm
We tested the ability of the Sleep/Wake algorithm to identify sleep an wake periods with precision, as compared to standard polysonography (PSG) measures, which was used as the gold standard, i.e. we tested the accuracy of the algorithm. Accuracy was defined as the proportion of true results (both true positives and true negatives)in the population and it was assesed using as 2 X 2 table, i.e. accuracy = number of true positives + number of true negatives/ number of true positives + false positives + false negatives +true negatives. where True positive = the algorithm tested correctly identified sleep, False positive = the algorithm tested incorrectly identified sleep, True negative = the algorithm tested correctly rejected awake periods, and False negative = the algorithm tested incorrectly rejected awake periods.
Time frame: The performance of the algorithm will be evaluated in real time while the subject is wearing the device during the sleep study, an average of 08 hours.
Population: All the participants completing the sleep study were included in the analysis
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Paced Breathing Sleep/Wake Detection | Sleep/Wake Algorithm | 62 accuracy (%) |