Obstructive sleep apnea (OSA) affects over 936 million adults worldwide, leading to significant morbidity and reduced quality of life. Traditional diagnostic methods such as polysomnography (PSG), although effective, rely on hospital equipment and human resources and are not widely accessible. Emerging technologies such as the jaw tracking device developed by Sunrise have appeared, using gyroscopes and accelerometers to monitor jaw movements during sleep, showing high diagnostic accuracy compared to PSG. These devices use AI algorithms, providing a practical alternative for OSA diagnosis and monitoring. However, due to the high hardware cost of Sunrise and its rental-based business model, there are logistical challenges and limited monitoring capabilities. This prospective clinical study aims to offer the same capabilities through a novel ultrasonic transmitter for tracking jaw movements during sleep, which can be directly received by using a smartphone microphone, reducing the complexity of setup operations. This study will evaluate the sensitivity of the ultrasonic jaw tracking device in detecting apnea and compare it with PSG standards, developing a deep learning AI-driven algorithm to analyze data from jaw movements, breathing sounds, and apnea-related arousal events, as a basis for continuous home monitoring and evaluation of treatment effects for OSA patients.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
100
This study investigates the effectiveness of the Ultrasonic Jaw Tracking Device, a novel non-invasive diagnostic tool designed to monitor dynamic jaw movements during sleep. The device is equipped with advanced ultrasonic sensors and proprietary algorithms to track jaw position and motion in real time. The intervention involves placing the device on the participant's mandible during polysomnography (PSG) sessions to simultaneously measure jaw movement data alongside standard PSG parameters. The primary goal is to evaluate the accuracy and efficacy of the device in detecting sleep-related conditions such as obstructive sleep apnea (OSA) compared to traditional PSG metrics.
Taipei Medical University WanFang Hospital
Taipei, Taiwan
RECRUITINGBaseline Demographic and Anthropometric Data
Collect baseline data of the subjects, including height (in meters), weight (in kilograms), age (in years), and gender; simultaneously calculate BMI (weight (kg)/height² (m²)). Data will be analyzed using descriptive statistics.
Time frame: Assessment will be conducted prior to subject enrollment (baseline).
Apnea-Hypopnea Index (AHI) measured by Polysomnography (PSG)
Record the average number of events per hour of sleep during which there is complete cessation of airflow (apnea) and partial reduction of airflow (hypopnea) using PSG. Data will be presented as the mean (events/hour) with standard deviation.
Time frame: Measured during the overnight sleep study session at baseline.
Respiratory Disturbance Index (RDI) measured by Polysomnography (PSG)
Record all respiratory disturbance events, including apnea, hypopnea, and other abnormal breathing patterns using PSG. Data will be presented as the mean (events/hour) with standard deviation.
Time frame: Measured during the overnight sleep study session at baseline.
ECG-Derived Heart Rate Variability (HRV) Parameters
ECG recordings will be performed concurrently, and heart rate variability indices will be calculated. Data will be presented as the mean with standard deviation.
Time frame: Measured during the overnight sleep study session at baseline.
Device Attachment Discomfort
Record the discomfort experienced by subjects during the attachment of the ultrasonic mandibular movement recording device.
Time frame: Assessed from the moment of device attachment until 10 minutes post-attachment.
Trial Withdrawal Rate
Calculate the proportion of subjects who withdraw from the trial from the date of enrollment until trial completion (or until withdrawal occurs, whichever comes first).
Time frame: Assessed from the date of enrollment until trial completion, over an estimated period of up to 41 weeks (i.e., from 2024/09/19 to 2025/06/30).
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