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| Name | Class |
|---|---|
| Center on Information and Communication Technologies | OTHER |
| NeumoVigo I+i research group | UNKNOWN |
| Hospital Álvaro Cunqueiro | OTHER |
| TALIONIS research group |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Xiaomi Mi Smart Band 8 | Device | The wearable device, Xiaomi Mi Smart Band 8, will be used solely for observational purposes to assess its accuracy in measuring sleep parameters, oxygen saturation, and heart rate in comparison to nocturnal polygraphy. Participants are receiving routine care as prescribed by their clinicians, and the wearable device is not part of their medical treatment but is being observed alongside standard polygraphy tests. |
| Measure | Description | Time Frame |
|---|---|---|
| Recording of deep sleep stage | 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. | 1 year |
| Recording of light sleep stage | 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. | 1 year |
| Recording of REM sleep stage | 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. | 1 year |
| Recording of time awake after sleep onset | 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. | 1 year |
| Measure | Description | Time Frame |
|---|---|---|
| Tracking of step count | The Xiaomi Mi Smart Band 8 device will track the total number of steps taken by the participant throughout a 24-hour period. The data will be used to assess physical activity levels and will be recorded as the total step count during the monitoring period. | 1 year |
| Tracking of distance |
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Inclusion Criteria:
Exclusion Criteria:
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Adult population with a potential sleep-related breathing disorder.
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| Name | Affiliation | Role |
|---|---|---|
| Patricia Concheiro-Moscoso, PhD | CITIC-TALIONIS research group, Universidade da Coruña. Faculty of Health Sciences, Universidade da Coruña. | Principal Investigator |
| Mar Mosteiro-Añon, Physician | Hospital Álvaro Cunqueiro | Principal Investigator |
| José Alberto Fernández-Villar, PhD, Physician | NeumoVigo I+i. Hospital Álvaro Cunqueiro. | Study Chair |
| Javier Pereira, PhD | CITIC-TALIONIS research group, Universidade da Coruña. Faculty of Health Sciences, Universidade da Coruña. | Study Chair |
| María Luisa Torres-Durán, PhD, Physician | NeumoVigo I+i. Hospital Álvaro Cunqueiro. | Study Chair |
| Betania Groba, PhD | CITIC-TALIONIS research group, Universidade da Coruña. Faculty of Health Sciences, Universidade da Coruña. | Study Chair |
| Manuel Casal-Guisande, PhD | NeumoVigo I+i. Hospital Álvaro Cunqueiro. | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital Álvaro Cunqueiro | Vigo | Pontevedra | 36312 | Spain | ||
| Hospital Álvaro Cunqueiro |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34713186 | Background | 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. | |
| 30789439 | Background | 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. |
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| ID | Term |
|---|---|
| D012893 | Sleep Wake Disorders |
| ID | Term |
|---|---|
| D009422 | Nervous System Diseases |
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| UNKNOWN |
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|
The Xiaomi Mi Smart Band 8 device will measure the total distance covered by the participant, recorded in meters. |
| 1 year |
| Tracking of physical activity duration | The Xiaomi Mi Smart Band 8 device will track the total duration of physical activity, recorded in minutes. | 1 year |
| Monitoring of positional changes | The nocturnal polygraph will track changes in body position of participants. This data will help assess physical activity, rest patterns, and potential sleep-related issues. The changes in position will be recorded without a specific unit of measurement. | 1 year |
| Monitoring of body movements | The nocturnal polygraph will monitor and record body movements of the participants. These movements will be used to assess physical activity and rest patterns. The data will be recorded without a specific unit of measurement, focusing on the detection of movement occurrence and intensity. | 1 year |
| Recording of heart rate | Heart rate monitoring using the Xiaomi Mi Smart Band 8 device and nocturnal polygraph, to compare data from both devices. Both devices record the average heart rate, as well as the maximum and minimum (in BPM). | 1 year |
| Recording of oxygen saturation | Oxygen saturation using the Xiaomi Mi Smart Band 8 device and nocturnal polygraph, to compare data from both devices. Both devices record the average oxygen saturation, as well as the maximum and minimum (in percentage). | 1 year |
| Sleep quality and habits measured by a sleep questionnaire | This custom-designed questionnaire includes questions related to bedtime and wake-up time, time taken to fall asleep, number of nighttime awakenings, reasons for waking up, perceived sleep quality, pre-sleep activities, and perceived stress levels during the day. It also includes items assessing the quality and quantity of sleep using a 6-point Likert scale, ranging from 0 (strongly disagree) to 5 (strongly agree). The total score ranges from 0 to 45, with higher scores indicating a greater perception of sleep quality and quantity. | 1 year |
| Vigo |
| Pontevedra |
| Spain |
| 30625225 | Background | 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. |
| 32219183 | Background | 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. |
| 38067885 | Background | 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. |
| 37296808 | Background | 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. |
| 37204853 | Background | 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. |
| 32605640 | Background | 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. |
| 32436658 | Background | 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. |
| 27815846 | Background | 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. |
| 35322331 | Background | 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. |
| 31085749 | Background | 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. |
| 35594257 | Background | 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. |
| 28709510 | Background | 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. |
| 30292731 | Background | 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. |
| 29788034 | Background | 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. |
| 29280728 | Background | 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. |
| 40812810 | Derived | Concheiro-Moscoso P, Pereira J, Mosteiro-Anon M, Torres-Duran M, Casal-Guisande M, Groba B. ReSTech project on Xiaomi wearable devices for monitoring and detecting obstructive sleep apnoea: observational study protocol. BMJ Open. 2025 Aug 13;15(8):e101824. doi: 10.1136/bmjopen-2025-101824. |
| D001523 | Mental Disorders |