Validation of Sleep Healthcare System

Study Overview
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Brief Summary
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Sleep quality affect working and learning performance; poor quality of sleep is one of the common problems of modern people. Traditionally, polysomnography is a recognized standard for sleep quality assessment. Subjects are put adhesive electrodes, chest and abdomen band, oximetery, and oronasal cannula and stay in certified sleep laboratory for monitoring. These sensors setup are cumbersome and be likely to induce discomfort. An alternative to assess the quality of sleep is actigraphy, which allows users to wear for more than two weeks. In recent years, many of the smart watches, which often measure wrist photoplethysmography (PPG) signal and body movement, are prevailing to make long-term sleep monitoring feasible, but its accuracy and effectiveness still need to be verified. Obstructive sleep apnea (OSA) is a common disorder characterized by intermittent hypoxia and sleep fragmentation. OSA is associated with cardiovascular morbidity and mortality, metabolic dysregulation, and neurocognitive dysfunction, which results in the negative impact on prognosis. PSG is the gold standard for OSA diagnosis which is expensive and less accessible. Therefore, modality other than PSG is necessary to speed up diagnosis and treatment. Center of Sleep Disorder in National Taiwan University Hospital has been operated since June 2006. Up to Dec.2015, totally 8,819 patients have been referred for sleep studies (NTUH cohort) where 1,435 patients are under long-term CPAP and 396 patients are under MAD. Using data from 4,618 patients in NTUH cohort, we have already established an OSA prediction mode (apnea-hypopnea index, AHI≥5/hr) with accuracy 82.37% (sensitivity 87.03%, positive predictive value 91%). Regarding the molecular mechanism, our previous study showed that by plasma metabolomics profiling, we could identify candidate metabolites associated with OSA severity. The 11 candidate metabolites were identified by comparing profiling in 100 patients with AHI <15/hr and with AHI>=15/hr, respectively. Six identified metabolites were selected to establish an AHI prediction model which gave sensitivity 66%, specificity 72%, and AUROC 0.736. Furthermore, 15 plasma metabolites associated with excessive daytime sleepiness (EDS) or polysomnographic parameters were identified. Among those metabolites, L-Kynurenine and g-Glutamylleucine were metabolites associated with EDS which generated the AUROC to EDS prediction as 63% in study group and 76.7% in validation group. The online system (Good Sleep) for diagnosis of sleep disorder has been set up under the collaboration between, NTU, NTUH, and MediaTek. It aims on population with low probability of sleep disorder which compliments the NTUH cohort, high probability of sleep disorder. The online system provides the diagnosis and solution of sleep disorder, sleep tracking, and education via both website and App. The system is almost set and needs the input from general population to validate the accuracy. The sleep healthcare system, which includes questionnaires, smart watches,24-hr BP and LARGANECG Holter for long-term home sleep monitoring, is proposed to allow users to detect potential subjects who have sleep disorders by filling out the questionnaire. The aims of the present project include: (1) All 300 voluntary.Stage1, Recruit 140 voluntary participants from MediaTek to validate agreement of sleep efficiency via online system, actigraph devices, smart watches and daily blood pressure for one week.Stage2, Recruit 160 voluntary participants from patients with moderate-severe OSA (AHI≥15/hr) to validate agreement of sleep efficiency via online system, actigraph devices, smart watches, ECG Holter and 24 hour blood pressure for one day. (2) All participants will take an overnight PSG test, blood sampling, basal metabolism measurement, ECG Holter, body composition and E-Prime at the sleep center to validate the performance of online system on diagnosis of OSA in low risk population. (3) Analyze the of PSG parameters in both low and high risk population (to build up the out of center devices for OSA home testing). (4) Integrate the clinical parameters and plasma metabolic profile, before and after treatment, to identify factors associated with OSA related sequels and long-term prognosis.

Official Title
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Validation of Sleep Healthcare System

Conditions
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Validation of Sleep Healthcare System

Intervention / Treatment
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* Device: CPAP


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Stage1:>=20 year-old Stage2:>=20 year-old 2-1patients with moderate-severe OSA (AHI≥15/hr) Exclusion Criteria: Stage1:Have been diagnosed with obstructive sleep apnea Stage2:Have been diagnosed with obstructive sleep apnea 2-1BMI≧40 kg/m2 2-2Acute disease 2-3Chronic disease 2-3Systemic inflammatory state 2-4Use anti-inflammatory drugs 2-5shift worker

Ages Eligible for Study
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Minimum Age: 20 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: cpap<br>usage cpap 3month | Device: CPAP<br>* CPAP useg 3month<br>|
| No Intervention: Usual care<br>Usual care 3month | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Validation of sleep healthcare system | This study aims to validate effectiveness of the online questionnaires and commercial wearable devices for sleep quality assessment. The validation results can enable future research on sleep healthcare. | 24month | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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sleep healthcare, actigraph devices, smart watches, questionnaires