Clinical trial NCT06606691: Wearable Technology in the Detection and Evaluation of Sleep-Related Breathing Disorders
Official title: Wearable Technology in the Detection and Evaluation of Sleep-Related Breathing Disorders
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Breathing-Related Sleep Disorder
Sleep Disorder (Disorder)
Keywords: Participatory Health
Internet of Things
Sleep
Polygraphy
Wearable devices
Occupational Performance
Lead sponsor: Universidade da Coruña
Sponsor class: OTHER
Collaborators: name: Center on Information and Communication Technologies
name: NeumoVigo I+i research group
name: Hospital Álvaro Cunqueiro
name: TALIONIS research group
First posted: 2024-09-23
Enrollment: 263
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult population with a potential sleep-related breathing disorder.

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\&#39;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.

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.

Primary outcomes:
measure: Recording of deep sleep stage
timeFrame: 1 year
measure: Recording of light sleep stage
timeFrame: 1 year
measure: Recording of REM sleep stage
timeFrame: 1 year
measure: Recording of time awake after sleep onset
timeFrame: 1 year
Secondary outcomes:
measure: Tracking of step count
timeFrame: 1 year
measure: Tracking of distance
timeFrame: 1 year
measure: Tracking of physical activity duration
timeFrame: 1 year
measure: Monitoring of positional changes
timeFrame: 1 year
measure: Monitoring of body movements
timeFrame: 1 year
measure: Recording of heart rate
timeFrame: 1 year
measure: Recording of oxygen saturation
timeFrame: 1 year
measure: Sleep quality and habits measured by a sleep questionnaire
timeFrame: 1 year
Other outcomes:
Locations:
city: Vigo
country: Spain
facility: Hospital Álvaro Cunqueiro
state: Pontevedra
Study officials:
affiliation: CITIC-TALIONIS research group, Universidade da Coruña. Faculty of Health Sciences, Universidade da Coruña.
name: Patricia Concheiro-Moscoso, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Hospital Álvaro Cunqueiro
name: Mar Mosteiro-Añon, Physician
role: PRINCIPAL_INVESTIGATOR
affiliation: NeumoVigo I+i. Hospital Álvaro Cunqueiro.
name: José Alberto Fernández-Villar, PhD, Physician
role: STUDY_CHAIR
affiliation: CITIC-TALIONIS research group, Universidade da Coruña. Faculty of Health Sciences, Universidade da Coruña.
name: Javier Pereira, PhD
role: STUDY_CHAIR
affiliation: NeumoVigo I+i. Hospital Álvaro Cunqueiro.
name: María Luisa Torres-Durán, PhD, Physician
role: STUDY_CHAIR
affiliation: CITIC-TALIONIS research group, Universidade da Coruña. Faculty of Health Sciences, Universidade da Coruña.
name: Betania Groba, PhD
role: STUDY_CHAIR
affiliation: NeumoVigo I+i. Hospital Álvaro Cunqueiro.
name: Manuel Casal-Guisande, PhD
role: STUDY_CHAIR
References:
citation: Lujan MR, Perez-Pozuelo I, Grandner MA. Past, Present, and Future of Multisensory Wearable Technology to Monitor Sleep and Circadian Rhythms. Front Digit Health. 2021 Aug 16;3:721919. doi: 10.3389/fdgth.2021.721919. eCollection 2021.
citation: de Zambotti M, Cellini N, Goldstone A, Colrain IM, Baker FC. Wearable Sleep Technology in Clinical and Research Settings. Med Sci Sports Exerc. 2019 Jul;51(7):1538-1557. doi: 10.1249/MSS.0000000000001947.
citation: Gruwez A, Bruyneel AV, Bruyneel M. The validity of two commercially-available sleep trackers and actigraphy for assessment of sleep parameters in obstructive sleep apnea patients. PLoS One. 2019 Jan 9;14(1):e0210569. doi: 10.1371/journal.pone.0210569. eCollection 2019.
citation: Perez-Pozuelo I, Zhai B, Palotti J, Mall R, Aupetit M, Garcia-Gomez JM, Taheri S, Guan Y, Fernandez-Luque L. The future of sleep health: a data-driven revolution in sleep science and medicine. NPJ Digit Med. 2020 Mar 23;3:42. doi: 10.1038/s41746-020-0244-4. eCollection 2020.
citation: Espinosa MA, Ponce P, Molina A, Borja V, Torres MG, Rojas M. Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study. Sensors (Basel). 2023 Nov 30;23(23):9512. doi: 10.3390/s23239512.
citation: Teplitzky TB, Zauher AJ, Isaiah A. Alternatives to Polysomnography for the Diagnosis of Pediatric Obstructive Sleep Apnea. Diagnostics (Basel). 2023 Jun 3;13(11):1956. doi: 10.3390/diagnostics13111956.
citation: Concheiro-Moscoso P, Groba B, Alvarez-Estevez D, Miranda-Duro MDC, Pousada T, Nieto-Riveiro L, Mejuto-Muino FJ, Pereira J. Quality of Sleep Data Validation From the Xiaomi Mi Band 5 Against Polysomnography: Comparison Study. J Med Internet Res. 2023 May 19;25:e42073. doi: 10.2196/42073.
citation: Hashimoto Y, Sakai R, Ikeda K, Fukui M. Association between sleep disorder and quality of life in patients with type 2 diabetes: a cross-sectional study. BMC Endocr Disord. 2020 Jun 30;20(1):98. doi: 10.1186/s12902-020-00579-4.
citation: Lyons MM, Bhatt NY, Pack AI, Magalang UJ. Global burden of sleep-disordered breathing and its implications. Respirology. 2020 Jul;25(7):690-702. doi: 10.1111/resp.13838. Epub 2020 May 21.
citation: Kang JM, Kang SG, Cho SJ, Lee YJ, Lee HJ, Kim JE, Shin SH, Park KH, Kim ST. The quality of life of suspected obstructive sleep apnea patients is related to their subjective sleep quality rather than the apnea-hypopnea index. Sleep Breath. 2017 May;21(2):369-375. doi: 10.1007/s11325-016-1427-8. Epub 2016 Nov 4.
citation: Chen L, Bai C, Zheng Y, Wei L, Han C, Yuan N, Ji D. The association between sleep architecture, quality of life, and hypertension in patients with obstructive sleep apnea. Sleep Breath. 2023 Mar;27(1):191-203. doi: 10.1007/s11325-022-02589-z. Epub 2022 Mar 23.
citation: Morsy NE, Farrag NS, Zaki NFW, Badawy AY, Abdelhafez SA, El-Gilany AH, El Shafey MM, Pandi-Perumal SR, Spence DW, BaHammam AS. Obstructive sleep apnea: personal, societal, public health, and legal implications. Rev Environ Health. 2019 Jun 26;34(2):153-169. doi: 10.1515/reveh-2018-0068.
citation: Borsoi L, Armeni P, Donin G, Costa F, Ferini-Strambi L. The invisible costs of obstructive sleep apnea (OSA): Systematic review and cost-of-illness analysis. PLoS One. 2022 May 20;17(5):e0268677. doi: 10.1371/journal.pone.0268677. eCollection 2022.
citation: Kaufmann CN, Susukida R, Depp CA. Sleep apnea, psychopathology, and mental health care. Sleep Health. 2017 Aug;3(4):244-249. doi: 10.1016/j.sleh.2017.04.003. Epub 2017 May 26.
citation: K Pavlova M, Latreille V. Sleep Disorders. Am J Med. 2019 Mar;132(3):292-299. doi: 10.1016/j.amjmed.2018.09.021. Epub 2018 Oct 4.
citation: Van Ryswyk E, Mukherjee S, Chai-Coetzer CL, Vakulin A, McEvoy RD. Sleep Disorders, Including Sleep Apnea and Hypertension. Am J Hypertens. 2018 Jul 16;31(8):857-864. doi: 10.1093/ajh/hpy082.
citation: Tester NJ, Foss JJ. Sleep as an Occupational Need. Am J Occup Ther. 2018 Jan/Feb;72(1):7201347010p1-7201347010p4. doi: 10.5014/ajot.2018.020651.