This study aims to improve treatment strategies for Obstructive Sleep Apnea (OSA), a disorder characterized by recurrent upper airway collapse during sleep, resulting in reduced oxygenation, sleep fragmentation, and excessive daytime sleepiness. The objectives are twofold: to evaluate whether an artificial intelligence (AI)-based model can accurately predict the most effective treatment for individual patients, and to assess whether a mobile health application can enhance adherence to oropharyngeal rehabilitation (OPR) and improve therapeutic outcomes. The study will be conducted in two phases. In Phase I, a retrospective analysis will be performed using a large dataset of polysomnography (PSG) records obtained from the Sleep Center at National Cheng Kung University Hospital. Machine learning algorithms will be applied to identify predictive features that differentiate responders from non-responders across Continuous Positive Airway Pressure (CPAP), surgical, and OPR interventions. These findings will inform the development of a predictive treatment recommendation model. In Phase II, a prospective clinical trial will validate the predictive accuracy and clinical utility of the model. Patients newly diagnosed with OSA will be assigned to CPAP, surgery, or OPR interventions according to the model's recommendations, in combination with physician judgment and patient preference. Each intervention will last 12 weeks, followed by repeat PSG and clinical assessments. Within the OPR arm, participants will be further randomized to monitor adherence via an exercise diary or a smartphone application equipped with a pressure sensor and facial motion recognition technology, enabling real-time feedback and remote monitoring. This trial is expected to determine whether AI can provide clinically reliable treatment recommendations and whether digital telerehabilitation can improve adherence and outcomes, thereby advancing precision medicine in OSA management.
This study is designed to improve treatment strategies for Obstructive Sleep Apnea (OSA), a disorder characterized by reduced oxygenation and recurrent sleep disturbances. The research has two primary objectives: first, to evaluate whether an artificial intelligence (AI)-based model can accurately predict the most effective treatment for individual patients; and second, to assess whether a mobile health application can facilitate oropharyngeal exercise training, thereby enhancing adherence and therapeutic outcomes. The study will be conducted in two phases. In Phase I, researchers will analyze a large dataset of polysomnography (PSG) records obtained from the Sleep Center at National Cheng Kung University Hospital. Machine learning methods will be applied to identify predictive patterns that distinguish responders from non-responders across treatments such as Continuous Positive Airway Pressure (CPAP), surgical intervention, and oropharyngeal rehabilitation (OPR). In Phase II, a prospective clinical trial will be implemented. Patients newly diagnosed with OSA will be allocated to CPAP, surgical, or OPR interventions (with either an exercise diary or a smartphone application) according to the AI-generated treatment recommendations, supplemented by physician judgment and patient preference. Each intervention will last 12 weeks, after which repeat PSG and clinical evaluations will be conducted to assess treatment efficacy. Participants in the CPAP arm will undergo 12 weeks of nightly CPAP use. Participants in the surgical group will receive operative treatment for OSA. Those assigned to the OPR arm will complete 12 weeks of telerehabilitation training focused on oropharyngeal exercises. Within the OPR group, participants will be further divided into two subgroups: one will record adherence using an exercise diary, while the other will train with a smartphone application integrated with a pressure sensor and facial motion recognition technology. This system will provide real-time feedback, record adherence, and transmit performance data to a secure cloud platform, enabling remote monitoring and clinician-guided adjustments. The study aims to determine whether AI can deliver clinically reliable, personalized treatment recommendations and whether app-based telerehabilitation can improve adherence and treatment outcomes. The anticipated results are expected to advance precision medicine approaches in OSA management and enhance both patient care and healthcare efficiency.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Enrollment
300
Including septomeatoplasty, uvulopalatopharyngoplasty (UPPP), or/and tongue base reduction surgery.
Continuous positive airway pressure, 1-5 days a week for three months.
Oropharyngeal exercise conducted via the telerehabilitation method. Participants are required to attend online supervised sessions of exercises 1-5 days a week for three months. Participants are required to fill out the exercise diary upon completion of the training each day.
Oropharyngeal exercise conducted via the telerehabilitation method. Participants are required to attend online supervised sessions of exercises 1-5 days a week for three months. In addition to attending the weekly supervised telerehabilitation sessions online, these participants will independently perform the exercises using the smartphone application incorporated with ASMT one to three times per week, with each session lasting approximately 45-60 minutes.
National Cheng Kung University Hospital
Tainan, Taiwan
RECRUITINGApnea-Hypopnea Index
The apnea-hypopnea index will be obtained from the overnight Polysomnography (PSG) study. PSG will be performed in the sleep center of National Cheng Kung University Hospital. Less than 5 events/hour indicates normal; AHI between 5-14 events/hour indicates mild Obstructive Sleep Apnea(OSA); AHI between 15-30 events/hour indicates moderate OSA; and AHI more than 30 events/hour indicates severe OSA.
Time frame: Baseline and 12 weeks post intervention
Daytime Sleepiness Level
Epworth Sleepiness Score(ESS) will be used to measure the daytime sleepiness of OSA patients. The total score of ESS range from 0-24. A score greater than 10 indicates greater daytime sleepiness.
Time frame: Baseline and 12 weeks post intervention
Sleep Quality
Sleep quality will be measured using Pittsburgh Sleep Quality Index (PSQI).The total score ranges from 0 to 21 with a higher total score equal to or more than 5 indicating worse sleep quality.
Time frame: Baseline and 12 weeks post intervention
Pharyngeal Airway Volume
Computer Tomography (CT) will be performed. The pharyngeal airway volume will be calculated from the hard palate to the epiglottis and the data will be presented in cm\^3. The minimum score is 0 and a higher score indicates greater in pharyngeal airway volume.
Time frame: Baseline and 12 weeks post intervention
Cross Section Area on the Tip of Epiglottis
Computer Tomography (CT) will be performed. Cross section area on the tip of the epiglottis was measured and the data will be presented in cm\^2. The minimum score is 0 and a higher score indicates greater in the cross-sectional area of the region.
Time frame: Baseline and 12 weeks post intervention
Anterior to Posterior Distance on the Tip of the Epiglottis
The distance between the anterior and posterior pharyngeal wall on the tip of the epiglottis will be measured and presented in cm. The minimal value will be 0 and the greater value indicates a greater distance between the anterior to posterior in this area.
Time frame: Baseline and 12 weeks post intervention
Lateral Distance on the Tip of Epiglottis
The distance between the lateral distance on the tip of the epiglottis will be measured and presented in cm. The minimal value will be 0 and the greater value indicates a greater distance between the lateral wall.
Time frame: Baseline and 12 weeks post intervention
Median Usage Hours of Continuous Positive Airway Pressure
The average number of hours per night that participants use CPAP will be recorded from CPAP usage reports. Greater nightly usage is considered indicative of better compliance and treatment effectiveness.
Time frame: 1-week average pre intervention, 1-week average post intervention and 1-month average during intervention for 3 months
Median Pressure in Continuous Positive Airway Pressure
The change in therapeutic CPAP pressure required to maintain airway patency will be assessed using CPAP usage reports. A reduction in required pressure is expected to indicate improved upper airway muscle tone and stability.
Time frame: 1-week average pre intervention, 1-week average post intervention and 1-month average during intervention for 3 months
Tongue Muscle Strength
The maximal muscle strength of genioglossus muscles using The Iowa Oral Performance Instrument (IOPI) system, model 2.2 (Northwest, Co., LLC, Carnation, WA, USA) (kPa)
Time frame: Baseline and 12 weeks post intervention
Tongue Muscle Endurance
The endurance of the genioglossus muscles using The Iowa Oral Performance Instrument (IOPI) system, model 2.2 (Northwest, Co., LLC, Carnation, WA, USA) (in seconds).
Time frame: Baseline and 12 weeks post intervention
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