Quality of Sleep Data Validation From the Xiaomi Mi Band 5 Against Polysomnography: Comparison Study

Patricia Concheiro-Moscoso, Betania Groba, Diego Alvarez-Estevez, María Del Carmen Miranda-Duro, Thais Pousada, Laura Nieto-Riveiro, Francisco Javier Mejuto-Muiño, Javier Pereira, Patricia Concheiro-Moscoso, Betania Groba, Diego Alvarez-Estevez, María Del Carmen Miranda-Duro, Thais Pousada, Laura Nieto-Riveiro, Francisco Javier Mejuto-Muiño, Javier Pereira

Abstract

Background: Polysomnography is the gold standard for measuring and detecting sleep patterns. In recent years, activity wristbands have become popular because they record continuous data in real time. Hence, comprehensive validation studies are needed to analyze the performance and reliability of these devices in the recording of sleep parameters.

Objective: This study compared the performance of one of the best-selling activity wristbands, the Xiaomi Mi Band 5, against polysomnography in measuring sleep stages.

Methods: This study was carried out at a hospital in A Coruña, Spain. People who were participating in a polysomnography study at a sleep unit were recruited to wear a Xiaomi Mi Band 5 simultaneously for 1 night. The total sample consisted of 45 adults, 25 (56%) with sleep disorders (SDis) and 20 (44%) without SDis.

Results: Overall, the Xiaomi Mi Band 5 displayed 78% accuracy, 89% sensitivity, 35% specificity, and a Cohen κ value of 0.22. It significantly overestimated polysomnography total sleep time (P=.09), light sleep (N1+N2 stages of non-rapid eye movement [REM] sleep; P=.005), and deep sleep (N3 stage of non-REM sleep; P=.01). In addition, it underestimated polysomnography wake after sleep onset and REM sleep. Moreover, the Xiaomi Mi Band 5 performed better in people without sleep problems than in those with sleep problems, specifically in detecting total sleep time and deep sleep.

Conclusions: The Xiaomi Mi Band 5 can be potentially used to monitor sleep and to detect changes in sleep patterns, especially for people without sleep problems. However, additional studies are necessary with this activity wristband in people with different types of SDis.

Trial registration: ClinicalTrials.gov NCT04568408; https://ichgcp.net/clinical-trials-registry/NCT04568408.

International registered report identifier (irrid): RR2-10.3390/ijerph18031106.

Keywords: Internet of Things; Xiaomi Mi Band 5; health promotion; occupation; polysomnography; sleep.

Conflict of interest statement

Conflicts of Interest: None declared.

©Patricia Concheiro-Moscoso, Betania Groba, Diego Alvarez-Estevez, María del Carmen Miranda-Duro, Thais Pousada, Laura Nieto-Riveiro, Francisco Javier Mejuto-Muiño, Javier Pereira. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.05.2023.

Figures

Figure 1
Figure 1
Bland-Altman plots for initial sleep onset, total sleep time (TST), total sleep period duration (TSPD), wake after sleep onset (WASO), awakenings, sleep onset latency (SOL), sleep efficiency (SE), light sleep, deep sleep, rapid eye movement (REM) sleep, and awake time. The PSG-Xiaomi Mi Band 5 differences for sleep parameters (y-axis) are plotted as a function of the PSG-Xiaomi Mi Band 5 means (x-axis) for sleep parameters. Circles represent participants without sleep disorders (No SDis group; n=20), and triangles represent participants with sleep disorders (SDis group; n=25). Zero lines are marked and represent perfect agreement. The dotted lines represent the biases and Bland-Altman 95% limits of agreement (mean observed difference ± 1.96 × SD of observed differences). PSG: polysomnography.

References

    1. Boop C, Cahill SM, Davis C, Dorsey J, Gibbs V, Herr B. Occupational therapy practice framework: domain and process fourth edition. Am J Occup Ther. 2020 Aug 15;74(Supplement_2):7412410010p1–87. doi: 10.5014/ajot.2020.74S2001.8382
    1. Selvaraj S, Sundaravaradhan S. Challenges and opportunities in IoT healthcare systems: a systematic review. SN Appl Sci. 2020 Jan 30;2(1):139. doi: 10.1007/s42452-019-1925-y.
    1. Safdar Z, Farid S, Qadir M, Asghar K, Iqbal J, Hamdani FK. A novel architecture for internet of things based e-Health systems. J Med Imaging Health Inform. 2020 Oct 01;10(10):2378–88. doi: 10.1166/jmihi.2020.3184.
    1. Bent B, Goldstein BA, Kibbe WA, Dunn JP. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit Med. 2020 Feb 10;3:18. doi: 10.1038/s41746-020-0226-6. doi: 10.1038/s41746-020-0226-6.226
    1. Worldwide quarterly wearable device tracker. International Data Corporation. 2021. [2022-07-04]. .
    1. Nieto-Riveiro L, Groba B, Miranda MC, Concheiro P, Pazos A, Pousada T, Pereira J. Technologies for participatory medicine and health promotion in the elderly population. Medicine (Baltimore) 2018 May;97(20):e10791. doi: 10.1097/MD.0000000000010791. 00005792-201805180-00052
    1. Sadek I, Demarasse A, Mokhtari M. Internet of things for sleep tracking: wearables vs. nonwearables. Health Technol. 2020;10(1):333–40. doi: 10.1007/s12553-019-00318-3.
    1. Chong KP, Guo JZ, Deng X, Woo BK. Consumer perceptions of wearable technology devices: retrospective review and analysis. JMIR Mhealth Uhealth. 2020 Apr 20;8(4):e17544. doi: 10.2196/17544. v8i4e17544
    1. Topalidis P, Florea C, Eigl ES, Kurapov A, Leon CA, Schabus M. Evaluation of a low-cost commercial actigraph and its potential use in detecting cultural variations in physical activity and sleep. Sensors (Basel) 2021 May 29;21(11):3774. doi: 10.3390/s21113774. s21113774
    1. 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;18(5):637–52. doi: 10.1080/15402002.2019.1651316.
    1. 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):1538–57. doi: 10.1249/MSS.0000000000001947.
    1. Borger JN, Huber R, Ghosh A. Capturing sleep-wake cycles by using day-to-day smartphone touchscreen interactions. NPJ Digit Med. 2019 Jul 29;2:73. doi: 10.1038/s41746-019-0147-4. doi: 10.1038/s41746-019-0147-4.147
    1. Tester NJ, Foss JJ. Sleep as an occupational need. Am J Occup Ther. 2018 Jan;72(1):7201347010p1–4. doi: 10.5014/ajot.2018.020651.
    1. Hale L, Troxel W, Buysse DJ. Sleep health: an opportunity for public health to address health equity. Annu Rev Public Health. 2020 Apr 02;41:81–99. doi: 10.1146/annurev-publhealth-040119-094412.
    1. 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. doi: 10.1038/s41746-020-0244-4.244
    1. Jafari B, Mohsenin V. Polysomnography. Clin Chest Med. 2010 Jun;31(2):287–97. doi: 10.1016/j.ccm.2010.02.005.S0272-5231(10)00028-6
    1. Zhang X, Kou W, Chang EI, Gao H, Fan Y, Xu Y. Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device. Comput Biol Med. 2018 Dec 01;103:71–81. doi: 10.1016/j.compbiomed.2018.10.010.S0010-4825(18)30303-2
    1. Massar SA, Chua XY, Soon CS, Ng AS, Ong JL, Chee NI, Lee TS, Ghosh A, Chee MW. Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data. NPJ Digit Med. 2021 Jun 02;4(1):90. doi: 10.1038/s41746-021-00466-9. doi: 10.1038/s41746-021-00466-9.10.1038/s41746-021-00466-9
    1. Mahadevan N, Christakis Y, Di J, Bruno J, Zhang Y, Dorsey ER, Pigeon WR, Beck LA, Thomas K, Liu Y, Wicker M, Brooks C, Kabiri NS, Bhangu J, Northcott C, Patel S. Development of digital measures for nighttime scratch and sleep using wrist-worn wearable devices. NPJ Digit Med. 2021 Mar 03;4(1):42. doi: 10.1038/s41746-021-00402-x. doi: 10.1038/s41746-021-00402-x.10.1038/s41746-021-00402-x
    1. Li X, Zhao H. Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms. PLoS Genet. 2020 Oct 19;16(10):e1009089. doi: 10.1371/journal.pgen.1009089. PGENETICS-D-20-00503
    1. Bélanger MÈ, Bernier A, Paquet J, Simard V, Carrier J. Validating actigraphy as a measure of sleep for preschool children. J Clin Sleep Med. 2013 Jul 15;9(7):701–6. doi: 10.5664/jcsm.2844.
    1. Girschik J, Fritschi L, Heyworth J, Waters F. Validation of self-reported sleep against actigraphy. J Epidemiol. 2012;22(5):462–8. doi: 10.2188/jea.je20120012. DN/JST.JSTAGE/jea/JE20120012
    1. Svetnik V, Wang TC, Ceesay P, Snyder E, Ceren O, Bliwise D, Budd K, Hutzelmann J, Stevens J, Lines C, Michelson D, Herring WJ. Pilot evaluation of a consumer wearable device to assess sleep in a clinical polysomnography trial of suvorexant for treating insomnia in patients with Alzheimer's disease. J Sleep Res. 2021 Dec;30(6):e13328. doi: 10.1111/jsr.13328.
    1. Roberts DM, Schade MM, Mathew GM, Gartenberg D, Buxton OM. Detecting sleep using heart rate and motion data from multisensor consumer-grade wearables, relative to wrist actigraphy and polysomnography. Sleep. 2020 Jul 13;43(7):zsaa045. doi: 10.1093/sleep/zsaa045. 5811697
    1. Crowley O, Pugliese L, Kachnowski S. The impact of wearable device enabled health initiative on physical activity and sleep. Cureus. 2016 Oct 11;8(10):e825. doi: 10.7759/cureus.825.
    1. Wan J, Gu X, Chen L, Wang J. Internet of things for ambient assisted living: challenges and future opportunities. Proceedings of the 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery; CyberC '17; October 12-14, 2017; Nanjing, China. 2017. pp. 354–7.
    1. Van de Water AT, Holmes A, Hurley DA. Objective measurements of sleep for non-laboratory settings as alternatives to polysomnography--a systematic review. J Sleep Res. 2011 Mar;20(1 Pt 2):183–200. doi: 10.1111/j.1365-2869.2009.00814.x. JSR814
    1. Baron KG, Abbott S, Jao N, Manalo N, Mullen R. Orthosomnia: are some patients taking the quantified self too far? J Clin Sleep Med. 2017 Feb 15;13(2):351–4. doi: 10.5664/jcsm.6472. jc-00177-16
    1. Singh J, Keer N. Overview of telemedicine and sleep disorders. Sleep Med Clin. 2020 Sep;15(3):341–6. doi: 10.1016/j.jsmc.2020.05.005. S1556-407X(20)30045-X
    1. Ameen MS, Cheung LM, Hauser T, Hahn MA, Schabus M. About the accuracy and problems of consumer devices in the assessment of sleep. Sensors (Basel) 2019 Sep 25;19(19):4160. doi: 10.3390/s19194160. s19194160
    1. 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.S0012-3692(16)48652-6
    1. Khosla S, Deak MC, Gault D, Goldstein CA, Hwang D, Kwon Y, O'Hearn D, Schutte-Rodin S, Yurcheshen M, Rosen IM, Kirsch DB, Chervin RD, Carden KA, Ramar K, Aurora RN, Kristo DA, Malhotra RK, Martin JL, Olson EJ, Rosen CL, Rowley JA, American Academy of Sleep Medicine Board of Directors Consumer sleep technology: an American academy of sleep medicine position statement. J Clin Sleep Med. 2018 May 15;14(5):877–80. doi: 10.5664/jcsm.7128. jc-18-00212
    1. Consumer Technology Association. Approved American National Standards. National Sleep Foundation . Performance Criteria and Testing Protocols for Features in Sleep Tracking Consumer Technology Devices and Applications (ANSI/CTA/NSF-2052.3) Arlington, VA, USA: Consumer Technology Association; 2019. Apr,
    1. Kahawage P, Jumabhoy R, Hamill K, de Zambotti M, Drummond SP. 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.
    1. 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 01;158:143–9. doi: 10.1016/j.physbeh.2016.03.006. S0031-9384(16)30093-2
    1. Moreno-Pino F, Porras-Segovia A, López-Esteban P, Artés A, Baca-García E. Validation of Fitbit charge 2 and Fitbit Alta HR against polysomnography for assessing sleep in adults with obstructive sleep apnea. J Clin Sleep Med. 2019 Nov 15;15(11):1645–53. doi: 10.5664/jcsm.8032.
    1. 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):465–76. doi: 10.1080/07420528.2017.1413578.
    1. 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.
    1. de Zambotti M, Rosas L, Colrain IM, Baker FC. The sleep of the ring: comparison of the ŌURA sleep tracker against polysomnography. Behav Sleep Med. 2019 Mar;17(2):124–36. doi: 10.1080/15402002.2017.1300587.
    1. Asgari Mehrabadi M, Azimi I, Sarhaddi F, Axelin A, Niela-Vilén H, Myllyntausta S, Stenholm S, Dutt N, Liljeberg P, Rahmani AM. Sleep tracking of a commercially available smart ring and smartwatch against medical-grade actigraphy in everyday settings: instrument validation study. JMIR Mhealth Uhealth. 2020 Nov 02;8(10):e20465. doi: 10.2196/20465. v8i10e20465
    1. Ohayon M, Wickwire EM, Hirshkowitz M, Albert SM, Avidan A, Daly FJ, Dauvilliers Y, Ferri R, Fung C, Gozal D, Hazen N, Krystal A, Lichstein K, Mallampalli M, Plazzi G, Rawding R, Scheer FA, Somers V, Vitiello MV. National sleep foundation's sleep quality recommendations: first report. Sleep Health. 2017 Feb;3(1):6–19. doi: 10.1016/j.sleh.2016.11.006. S2352-7218(16)30130-9
    1. Wulterkens BM, Fonseca P, Hermans LW, Ross M, Cerny A, Anderer P, Long X, van Dijk JP, Vandenbussche N, Pillen S, van Gilst MM, Overeem S. It is all in the wrist: wearable sleep staging in a clinical population versus reference polysomnography. Nat Sci Sleep. 2021 Jun 28;13:885–97. doi: 10.2147/NSS.S306808. 306808
    1. Pino-Ortega J, Gómez-Carmona CD, Rico-González M. Accuracy of Xiaomi Mi band 2.0, 3.0 and 4.0 to measure step count and distance for physical activity and healthcare in adults over 65 years. Gait Posture. 2021 Jun;87:6–10. doi: 10.1016/j.gaitpost.2021.04.015.S0966-6362(21)00138-7
    1. de la Casa Pérez A, Latorre Román PÁ, Muñoz Jiménez M, Lucena Zurita M, Laredo Aguilera JA, Párraga Montilla JA, Cabrera Linares JC. Is the Xiaomi Mi band 4 an accuracy tool for measuring health-related parameters in adults and older people? An original validation study. Int J Environ Res Public Health. 2022 Jan 30;19(3):1593. doi: 10.3390/ijerph19031593. ijerph19031593
    1. Miranda-Duro MD, Nieto-Riveiro L, Concheiro-Moscoso P, Groba B, Pousada T, Canosa N, Pereira J. Analysis of older adults in Spanish care facilities, risk of falling and daily activity using Xiaomi Mi band 2. Sensors (Basel) 2021 May 11;21(10):3341. doi: 10.3390/s21103341. s21103341
    1. Concheiro-Moscoso P, Groba B, Martínez-Martínez FJ, Miranda-Duro MD, Nieto-Riveiro L, Pousada T, Queirós C, Pereira J. Study for the design of a protocol to assess the impact of stress in the quality of life of workers. Int J Environ Res Public Health. 2021 Feb 03;18(4):1413. doi: 10.3390/ijerph18041413. ijerph18041413
    1. Queirós C, Oliveira S, Monteiro Fonseca S, Marques AJ. Stress at work and physiological indicators: a study with wearable sensors. Psicol Saúde Doença. 2020 Mar;21(01):183–90. doi: 10.15309/20psd210127.
    1. Concheiro-Moscoso P, Martínez-Martínez FJ, Miranda-Duro MD, Pousada T, Nieto-Riveiro L, Groba B, Mejuto-Muiño FJ, Pereira J. Study protocol on the validation of the quality of sleep data from Xiaomi domestic wristbands. Int J Environ Res Public Health. 2021 Jan 27;18(3):1106. doi: 10.3390/ijerph18031106. ijerph18031106
    1. Berry RB, Brooks R, Gamaldo CE, Harding SM, Lloyd RM, Marcus CL, Vaughn BV. American Academy of Sleep Medicine. Darien, IL, USA: American Academy of Sleep Medicine; 2015. [2022-06-29]. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.2.
    1. Mi Smart Band 5. Xiaomi Inc. [2022-03-17].
    1. Kemp B, Olivan J. European data format 'plus' (EDF+), an EDF alike standard format for the exchange of physiological data. Clin Neurophysiol. 2003 Sep;114(9):1755–61. doi: 10.1016/s1388-2457(03)00123-8.S1388245703001238
    1. Lee-Messer C. PyEDFlib -EDF/BDF toolbox in Python: PYEDFlib documentation. PyEDFlib. 2021. [2022-07-29].
    1. Berry RB, Budhiraja R, Gottlieb DJ, Gozal D, Iber C, Kapur VK, Marcus CL, Mehra R, Parthasarathy S, Quan SF, Redline S, Strohl KP, Davidson Ward SL, Tangredi MM, American Academy of Sleep Medicine Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. Deliberations of the sleep apnea definitions task force of the American Academy of Sleep Medicine. J Clin Sleep Med. 2012 Oct 15;8(5):597–619. doi: 10.5664/jcsm.2172.
    1. Martin Bland J, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327:307–10.
    1. 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.S0002-9394(08)00773-3
    1. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977 Mar;33(1):159–74. doi: 10.2307/2529310.
    1. Lee JM, Byun W, Keill A, Dinkel D, Seo Y. Comparison of wearable trackers' ability to estimate sleep. Int J Environ Res Public Health. 2018 Jun 15;15(6):1265. doi: 10.3390/ijerph15061265. ijerph15061265
    1. Markwald RR, Bessman SC, Reini SA, Drummond SP. Performance of a portable sleep monitoring device in individuals with high versus low sleep efficiency. J Clin Sleep Med. 2016 Jan;12(1):95–103. doi: 10.5664/jcsm.5404. jc-00051-15
    1. Inbal-Shamir T, Kali Y. The relation between schoolteachers' perceptions about collaborative learning and their employment of online instruction. Proceedings of the 8th International Conference on Computer Supported Collaborative Learning; CSCL'07; July 16-21, 2007; New Brunswick, NJ, USA. 2007. pp. 295–303.
    1. 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. v6i4e94
    1. Lee XK, Chee NI, Ong JL, Teo TB, van Rijn E, Lo JC, Chee MW. Validation of a consumer sleep wearable device with actigraphy and polysomnography in adolescents across sleep opportunity manipulations. J Clin Sleep Med. 2019 Sep 15;15(9):1337–46. doi: 10.5664/jcsm.7932.
    1. Czeisler MÉ, Capodilupo ER, Weaver MD, Czeisler CA, Howard ME, Rajaratnam SM. Prior sleep-wake behaviors are associated with mental health outcomes during the COVID-19 pandemic among adult users of a wearable device in the United States. Sleep Health. 2022 Jun;8(3):311–21. doi: 10.1016/j.sleh.2022.03.001. S2352-7218(22)00018-3

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