Wearable Technology in the Detection and Evaluation of Sleep-Related Breathing Disorders
Overview
- Phase
- Not Applicable
- Intervention
- Xiaomi Mi Smart Band 8
- Conditions
- Breathing-Related Sleep Disorder
- Sponsor
- Universidade da Coruña
- Enrollment
- 300
- Locations
- 3
- Primary Endpoint
- Recording of deep sleep stage
- Status
- Completed
- Last Updated
- 4 days ago
Overview
Brief Summary
This project is an observational study that aims to evaluate the accuracy of wearable devices in detecting potential sleep-related breathing disorders (SRBD) in individuals visiting the Sleep-Related Breathing Disorders and Home Ventilation Unit. The main goal of the study is to determine if wearable devices, like sleep and activity-tracking wristbands and watches, can effectively supplement the detection of these disorders.
The study will analyze various variables related to sleep quality and quantity. Participants will be asked to wear a Xiaomi Mi Band 8 device during an overnight hospital polygraphy test, which will be conducted for one day in their usual daily environment. Additionally, at the beginning of their participation, they will need to complete a questionnaire collecting information about sociodemographic variables, daily habits, routines, and their assessment using the Epworth Sleepiness Scale.
After completing the polygraphy test and using the Xiaomi device, participants will be required to answer another questionnaire addressing aspects related to their sleep quality and habits during this period.
Detailed Description
In recent years, sleep disorders have gained importance due to their high prevalence and impact on daily life, affecting people\'s ability to perform daily tasks and reducing quality of life. These disorders include difficulties falling asleep, respiratory interruptions, and poor sleep quality, with sleep-related breathing disorders (SRBD), such as obstructive sleep apnea (OSA), being particularly significant. OSA, which involves repeated airway obstructions during sleep, is especially common in older adults, individuals with obesity, and men, but it remains frequently underdiagnosed. SRBD not only disrupts sleep but also increases the risk of chronic conditions like diabetes, hypertension, and strokes while creating an economic burden due to higher demand for medical resources. Their effects on physical and mental health lead to fatigue, reduced productivity, workplace accidents, and even disability, highlighting the need for more efficient diagnostic and management tools. While polysomnography (PSG) is the gold standard for diagnosing sleep disorders, its high cost and invasive nature limit its accessibility. Wearable devices, such as wristbands and watches, offer a more accessible and non-invasive alternative, providing real-time data on sleep, heart rate, and activity. Though promising, these devices still require further research to confirm their accuracy in detecting SRBD. This project aims to evaluate the effectiveness of wearables as complementary tools in diagnosing and managing these disorders. Specifically, it has the following specific objectives: (1) To assess the accuracy, specificity, and sensitivity of wearable devices, such as wristbands and watches, in measuring blood oxygen saturation, heart rate, and activity, compared to nocturnal polygraphy. (2) To analyze the effectiveness of these devices in identifying individuals with potential sleep-related breathing disorders (SRBD) using unsupervised learning techniques. (3) To evaluate the impact and performance of an Artificial Intelligence model for detecting and classifying potential SRBD.
Investigators
Patricia Concheiro Moscoso
Postdoctoral Research in CITIC-TALIONIS research group, Universidade da Coruña. Faculty of Health Sciences, Universidade da Coruña. PhD in Health Sciences.
Universidade da Coruña
Eligibility Criteria
Inclusion Criteria
- •Be at least 18 years of age or older.
- •Attend the Sleep Respiratory Disorders and Home Ventilation Unit for the polygraphy test.
Exclusion Criteria
- •Have significant health complications that hinder active participation in the study.
- •Present skin hypersensitivity or a known allergy to the material used in the covers or straps of the wearable devices that will be used as one of the measurement instruments in the study.
Arms & Interventions
Nocturnal polygraphy study participants
This project aims to study approximately 263 individuals from different age groups and genders who are suspected of having sleep-related breathing disorders. The participants will be those referred for a nocturnal polygraphy study at the Sleep-Related Breathing Disorders and Home Ventilation Unit. During the polygraph test, participants will also wear the Xiaomi Mi Smart Band 8 wearable device to compare its accuracy in measuring sleep parameters, oxygen saturation, and heart rate against the polygraphy results.
Intervention: Xiaomi Mi Smart Band 8
Outcomes
Primary Outcomes
Recording of deep sleep stage
Time Frame: 1 year
The Xiaomi Mi Smart Band 8 will record the duration of deep sleep, measured in minutes, to help estimate and identify potential sleep-related breathing disorders.
Recording of light sleep stage
Time Frame: 1 year
The Xiaomi Mi Smart Band 8 will record the duration of light sleep, measured in minutes, to assist in estimating and detecting potential sleep-related breathing disorders.
Recording of REM sleep stage
Time Frame: 1 year
The Xiaomi Mi Smart Band 8 will record the duration of the REM sleep stage, measured in minutes, to help estimate and detect potential sleep-related breathing disorders.
Recording of time awake after sleep onset
Time Frame: 1 year
The Xiaomi Mi Smart Band 8 will record the time spent awake after sleep onset, measured in minutes, to assist in estimating and detecting potential sleep-related breathing disorders.
Secondary Outcomes
- Tracking of step count(1 year)
- Tracking of distance(1 year)
- Tracking of physical activity duration(1 year)
- Monitoring of positional changes(1 year)
- Monitoring of body movements(1 year)
- Recording of heart rate(1 year)
- Recording of oxygen saturation(1 year)
- Sleep quality and habits measured by a sleep questionnaire(1 year)