NCT07254026

Precision Health and Smart Telerehabilitation in OSA

Developing a Precision Health Approach for Obstructive Sleep Apnea: Treatment Responses Analysis and Smart Telerehabilitation Systems

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 300 (estimated)
• Sponsor: National Cheng-Kung University Hospital · Academic / Other
• Sex: All
• Age: 20 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

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.

Conditions

• Obstructive Sleep Apnea

Interventions

Type	Name	Description
PROCEDURE	Surgery	Including septomeatoplasty, uvulopalatopharyngoplasty (UPPP), or/and tongue base reduction surgery.
DEVICE	Continuous positive airway pressure	Continuous positive airway pressure, 1-5 days a week for three months.
OTHER	Oropharyngeal Exercise with diary	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.
OTHER	Oropharyngeal Exercise with smartphone application	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.

Timeline

• Start date: 2025-10-15
• Primary completion: 2029-12-31
• Completion: 2029-12-31
• First posted: 2025-11-28
• Last updated: 2025-11-28

Locations

1 site across 1 country: Taiwan