Validation of the Quality of Sleep Data for Xiaomi Domestic Wristbands

Sleep Disorder Clinical Trial

Official title:

Validation of the Quality of Sleep Data for Xiaomi Domestic Wristbands

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04568408
• Other study ID #: 2020/318
• Status: Completed
• Start date: August 4, 2020
• Est. completion date: December 30, 2021

Study information

• Verified date: May 2022
• Source: Universidade da Coruña
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial Summary

Introduction: Polysomnography (PSG) is currently the accepted Gold Standard for sleep studies as it measures multiple variables that lead to a clear diagnosis of any sleep disorder. However, it has some clear drawbacks as it can only be performed by qualified technicians, has a high cost and complexity and is very invasive. In the last years, Actigraphy has been used along with PSG for sleep studies. In this study, the investigators intend to assess the capability of the new Xiaomi Mi Band 5 to be used as a sleep self-assessment tool for patients. Objective: Determine whether sleep stages recorded by the new Xiaomi Mi Band 5 can effectively replace PSG sleep stages classification in patients that undergo a sleep study. Methods and analysis: the study will be carried out with patients in a hospital from A Coruña (Galicia, Spain) that are > 18 years old. Patients who are performed a polysomnography test will be given the wearables so the investigators can record sleep stages with both techniques in order to compare both recordings afterwards. This is an observational, analytic and longitudinal study. In other words, in this study different variables from the population of interest will be observed and recorded without any direct intervention, so as to establish causality associations between these variables. It is considered as longitudinal since a six-months tracking of the variables will be performed, continually (and sometimes occasionally) recording and monitoring sleep quality (wearable wristbands). The data obtained from PSG and Xiaomi Mi Band 5 will be preprocessed and explored before extracting the features of interest for the study. Then, Paired sample T-Test will be performed to compare the means among the different variables, Bland-Altman plots will be used to assess the concordance between both techniques and, finally, Epoch by Epoch analysis will be performed to compare the classification of the sleep stages carried out by both PSG and Xiaomi.

Description:

Introduction: Sleep disorders are becoming a widespread health problem within the general population. Beyond the tiredness itself, these disorders have been found to be related to physical and mental issues like cardiovascular pathologies, obesity, depression and anxiety. This makes clear the necessity of achieving a quality rest to prevent potential chronic diseases. On this issue, Polysomnography (PSG) is currently the accepted Gold Standard for sleep studies as it measures multiple variables that lead to a clear diagnosis of any sleep disorder. However, it has some clear drawbacks as it can only be performed by qualified technicians, has a high cost and complexity and is very invasive. In the last years, several studies that focus on validating wearable devices as useful sleep trackers have emerged. In this study, the investigators intend to assess the capability of the new Xiaomi Mi Band 5 to be used as a tool for sleep self-assessment for patients. The interest in studying this smartband is due to its low price and acceptance among the population. If this device proves to be a good sleep tracker, it would allow the population to assess their own sleep and consider if they need to visit their medical practitioner, which would improve the prognosis of any underlying condition they may be developing derived from their restlessness. Objective: Determine whether sleep stages recorded by the new Xiaomi Mi Band 5 can effectively replace PSG sleep stages classification in patients that undergo a sleep study. The following features will be calculated for both devices: total sleep time, sleep efficiency, sleep latency and wake after sleep onset. Sleep stages will also be recorded. Methods and analysis: the study will be carried out with patients in a hospital from A Coruña (Galicia, Spain) that are > 18 years old. Patients who are performed a polysomnography test will be given the wearables so the investigators can record sleep stages with both techniques in order to compare both recordings afterwards. This is an observational, analytic and longitudinal study. In other words, in this study different variables from the population of interest will be observed and recorded without any direct intervention, so as to establish causality associations between these variables. It is considered as longitudinal since a six-months tracking of the variables will be performed, continually (and sometimes occasionally) recording and monitoring sleep quality (wearable wristbands). The data obtained from PSG and Xiaomi Mi Band 5 will be preprocessed and explored before extracting the features of interest for the study. Then, Paired sample T-Test will be performed to compare the means among the different variables, Bland-Altman plots will be used to assess the concordance between both techniques and, finally, Epoch by Epoch analysis will be performed to compare the classification of the sleep stages carried out by both PSG and Xiaomi.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 43
• Est. completion date: December 30, 2021
• Est. primary completion date: September 16, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Attending to the Sleep Unit to be perfomed a Polysomnography study
• Declaring a chronological age that is equal or higher than 18 years old

Exclusion Criteria:

• Having significant health complications that hinder active participation in the study
• Suffering from skin hypersensitivity or an allergic reaction due to the materials the wristbands that will be used in the study as measuring instruments are made of

Related Conditions & MeSH terms

• Sleep Disorder
• Sleep Wake Disorders

Intervention

Device:
• Xiaomi MiBand 5
Comparison of sleep-monitoring device against polysomnography (PSG)

Locations

Country	Name	City	State
Spain	Universidade da Coruña	A Coruña

Sponsors (3)

Lead Sponsor	Collaborator
Universidade da Coruña	Center on Information and Communication Technologies, IMQ San Rafael

Country where clinical trial is conducted

Spain, 

References & Publications (23)

Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986 Feb 8;1(8476):307-10. — View Citation

Bravo P, Contreras A, Perestelo-Pérez L, Pérez-Ramos J, Málaga G. [Looking for a more participative healthcare: sharing medical decision making]. Rev Peru Med Exp Salud Publica. 2013 Oct-Dec;30(4):691-7. Spanish. — View Citation

Bunce C. Correlation, agreement, and Bland-Altman analysis: statistical analysis of method comparison studies. Am J Ophthalmol. 2009 Jul;148(1):4-6. doi: 10.1016/j.ajo.2008.09.032. — View Citation

Chamorro N, Sellarés J, Millán G, Cano E, Soler N, Embid C, Montserrat JM. An integrated model involving sleep units and primary care for the diagnosis of sleep apnoea. Eur Respir J. 2013 Oct;42(4):1151-4. doi: 10.1183/09031936.00192812. — View Citation

Chen JH, Waite L, Kurina LM, Thisted RA, McClintock M, Lauderdale DS. Insomnia symptoms and actigraph-estimated sleep characteristics in a nationally representative sample of older adults. J Gerontol A Biol Sci Med Sci. 2015 Feb;70(2):185-92. doi: 10.1093/gerona/glu144. Epub 2014 Sep 8. — View Citation

Danzig R, Wang M, Shah A, Trotti LM. The wrist is not the brain: Estimation of sleep by clinical and consumer wearable actigraphy devices is impacted by multiple patient- and device-specific factors. J Sleep Res. 2020 Feb;29(1):e12926. doi: 10.1111/jsr.12926. Epub 2019 Oct 17. — View Citation

de Zambotti M, Baker FC, Willoughby AR, Godino JG, Wing D, Patrick K, Colrain IM. Measures of sleep and cardiac functioning during sleep using a multi-sensory commercially-available wristband in adolescents. Physiol Behav. 2016 May 1;158:143-9. doi: 10.1016/j.physbeh.2016.03.006. Epub 2016 Mar 9. — View Citation

de Zambotti M, Goldstone A, Claudatos S, Colrain IM, Baker FC. A validation study of Fitbit Charge 2™ compared with polysomnography in adults. Chronobiol Int. 2018 Apr;35(4):465-476. doi: 10.1080/07420528.2017.1413578. Epub 2017 Dec 13. — View Citation

Gordt K, Gerhardy T, Najafi B, Schwenk M. Effects of Wearable Sensor-Based Balance and Gait Training on Balance, Gait, and Functional Performance in Healthy and Patient Populations: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Gerontology. 2018;64(1):74-89. doi: 10.1159/000481454. Epub 2017 Nov 1. Review. — View Citation

Griessenberger H, Heib DP, Kunz AB, Hoedlmoser K, Schabus M. Assessment of a wireless headband for automatic sleep scoring. Sleep Breath. 2013 May;17(2):747-52. doi: 10.1007/s11325-012-0757-4. Epub 2012 Sep 21. — View Citation

Kahawage P, Jumabhoy R, Hamill K, de Zambotti M, Drummond SPA. Validity, potential clinical utility, and comparison of consumer and research-grade activity trackers in Insomnia Disorder I: In-lab validation against polysomnography. J Sleep Res. 2020 Feb;29(1):e12931. doi: 10.1111/jsr.12931. Epub 2019 Oct 18. — View Citation

Koffel E, McCurry SM, Smith MT, Vitiello MV. Improving pain and sleep in middle-aged and older adults: the promise of behavioral sleep interventions. Pain. 2019 Mar;160(3):529-534. doi: 10.1097/j.pain.0000000000001423. Review. — View Citation

Kubala AG, Barone Gibbs B, Buysse DJ, Patel SR, Hall MH, Kline CE. Field-based Measurement of Sleep: Agreement between Six Commercial Activity Monitors and a Validated Accelerometer. Behav Sleep Med. 2020 Sep-Oct;18(5):637-652. doi: 10.1080/15402002.2019.1651316. Epub 2019 Aug 27. — View Citation

Kurina LM, Thisted RA, Chen JH, McClintock MK, Waite LJ, Lauderdale DS. Actigraphic sleep characteristics among older Americans. Sleep Health. 2015 Dec;1(4):285-292. doi: 10.1016/j.sleh.2015.09.004. — View Citation

Lee JM, Kim Y, Welk GJ. Validity of consumer-based physical activity monitors. Med Sci Sports Exerc. 2014 Sep;46(9):1840-8. doi: 10.1249/MSS.0000000000000287. — View Citation

Lee PH, Suen LK. The convergent validity of Actiwatch 2 and ActiGraph Link accelerometers in measuring total sleeping period, wake after sleep onset, and sleep efficiency in free-living condition. Sleep Breath. 2017 Mar;21(1):209-215. doi: 10.1007/s11325-016-1406-0. Epub 2016 Sep 10. — View Citation

Merilahti J, Korhonen I. Association between Continuous Wearable Activity Monitoring and Self-Reported Functioning in Assisted Living Facility and Nursing Home Residents. J Frailty Aging. 2016;5(4):225-232. — View Citation

Peake JM, Kerr G, Sullivan JP. A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations. Front Physiol. 2018 Jun 28;9:743. doi: 10.3389/fphys.2018.00743. eCollection 2018. Review. — View Citation

Shelgikar AV, Anderson PF, Stephens MR. Sleep Tracking, Wearable Technology, and Opportunities for Research and Clinical Care. Chest. 2016 Sep;150(3):732-43. doi: 10.1016/j.chest.2016.04.016. Epub 2016 Apr 29. Review. — View Citation

Smith MT, McCrae CS, Cheung J, Martin JL, Harrod CG, Heald JL, Carden KA. Use of Actigraphy for the Evaluation of Sleep Disorders and Circadian Rhythm Sleep-Wake Disorders: An American Academy of Sleep Medicine Clinical Practice Guideline. J Clin Sleep Med. 2018 Jul 15;14(7):1231-1237. doi: 10.5664/jcsm.7230. — View Citation

Tilmanne J, Urbain J, Kothare MV, Wouwer AV, Kothare SV. Algorithms for sleep-wake identification using actigraphy: a comparative study and new results. J Sleep Res. 2009 Mar;18(1):85-98. doi: 10.1111/j.1365-2869.2008.00706.x. — View Citation

Wade AG. The societal costs of insomnia. Neuropsychiatr Dis Treat. 2010 Dec 20;7:1-18. doi: 10.2147/NDT.S15123. — View Citation

Xie J, Wen D, Liang L, Jia Y, Gao L, Lei J. Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study. JMIR Mhealth Uhealth. 2018 Apr 12;6(4):e94. doi: 10.2196/mhealth.9754. — View Citation

* Note: There are 23 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Sleep recording Reliability	Assessment of sleep measurement and sleep stages classification	6 Months