Wearable Technology in the Detection and Evaluation of Sleep-Related Breathing Disorders

Last updated: June 30, 2025
Sponsor: Universidade da Coruña
Overall Status: Active - Recruiting

Phase

N/A

Condition

Sleep Disorders

Treatment

Xiaomi Mi Smart Band 8

Clinical Study ID

NCT06606691
2024/260
  • Ages > 18
  • All Genders
  • Accepts Healthy Volunteers

Study 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.

Eligibility Criteria

Inclusion

Inclusion Criteria:

  • Be at least 18 years of age or older.

  • Attend the Sleep Respiratory Disorders and Home Ventilation Unit for the polygraphytest.

Exclusion

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 coversor straps of the wearable devices that will be used as one of the measurementinstruments in the study.

Study Design

Total Participants: 263
Treatment Group(s): 1
Primary Treatment: Xiaomi Mi Smart Band 8
Phase:
Study Start date:
February 18, 2025
Estimated Completion Date:
December 31, 2025

Study 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.

Connect with a study center

  • Hospital Álvaro Cunqueiro

    Vigo, Pontevedra 36312
    Spain

    Site Not Available

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