Developing a Precision Health Approach for Obstructive Sleep Apnea: Treatment Responses Analysis and Smart Telerehabilitation Systems
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 300
- 试验地点
- 1
- 主要终点
- Apnea-Hypopnea Index
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Treatment
- 盲法
- None
入排标准
- 年龄范围
- 20 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •20 years old and above
- •Newly diagnosed with mild to severe pure obstructive sleep apnea based on polysomnography
排除标准
- •Severe allergic rhinitis
- •Sinusitis with nasal polyps
- •Body Mass Index (BMI) > 31
- •Alcohol or drug abuse within the past year
- •Pregnancy
- •Severe obstructive or restrictive pulmonary diseases
- •High-risk cardiovascular diseases during exercise (e.g., angina, myocardial infarction, heart failure, valvular heart disease)
- •History of central or peripheral neurological disorders that interfere with exercise prescription
- •Musculoskeletal or psychological disorders that interfere with exercise prescription
- •Other non-respiratory sleep disorders
- •Sleep disorders with concomitant central sleep apnea
结局指标
主要结局
Apnea-Hypopnea Index
时间窗: Baseline and 12 weeks post intervention
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.
次要结局
- Daytime Sleepiness Level(Baseline and 12 weeks post intervention)
- Sleep Quality(Baseline and 12 weeks post intervention)
- Pharyngeal Airway Volume(Baseline and 12 weeks post intervention)
- Cross Section Area on the Tip of Epiglottis(Baseline and 12 weeks post intervention)
- Anterior to Posterior Distance on the Tip of the Epiglottis(Baseline and 12 weeks post intervention)
- Lateral Distance on the Tip of Epiglottis(Baseline and 12 weeks post intervention)
- Median Usage Hours of Continuous Positive Airway Pressure(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(1-week average pre intervention, 1-week average post intervention and 1-month average during intervention for 3 months)
- Tongue Muscle Strength(Baseline and 12 weeks post intervention)
- Tongue Muscle Endurance(Baseline and 12 weeks post intervention)