Validation of Fitbit Inspire 2TM Against Polysomnography in Adults Considering Adaptation for Use

Su Eun Lim, Ho Seok Kim, Si Woo Lee, Kwang-Ho Bae, Young Hwa Baek, Su Eun Lim, Ho Seok Kim, Si Woo Lee, Kwang-Ho Bae, Young Hwa Baek

Abstract

Purpose: The commercialization of sleep activity tracking devices has made it possible to manage sleep quality at home. However, it is necessary to verify the reliability and accuracy of wearable devices through comparison with polysomnography (PSG), which is the standard for tracking sleep activity. This study aimed to monitor overall sleep activity using Fitbit Inspire 2™ (FBI2) and to evaluate its performance and effectiveness through PSG under the same conditions.

Patients and methods: We compared the FBI2 and PSG data of nine participants (four male and five female participants; average age, 39 years) without severe sleeping problems. The participants wore FBI2 continuously for 14 days, considering the period of adaptation to the device. FBI2 and PSG sleep data were compared using paired t-tests, Bland-Altman plots, and epoch-by-epoch analysis for 18 samples by pooling data from two replicates.

Results: The average values for each sleep stage obtained from FBI2 and PSG showed significant differences in the total sleep time (TST), deep sleep, and rapid eye motion (REM). In the Bland-Altman analysis, TST (P = 0.02), deep sleep (P = 0.05), and REM (P = 0.03) were significantly overstated in FBI2 compared to PSG. In addition, time in bed, sleep efficiency, and wake after sleep onset were overestimated, while light sleep was underestimated. However, these differences were not statistically significant. FBI2 showed a high sensitivity (93.9%) and low specificity (13.1%), with an accuracy of 76%. The sensitivity and specificity of each sleep stage was 54.3% and 62.3%, respectively, for light sleep, 84.8% and 50.1%, respectively, for deep sleep, and 86.4% and 59.1%, respectively for REM sleep.

Conclusion: The use of FBI2 as an objective tool for measuring sleep in daily life can be considered appropriate. However, further research is warranted on its application in participants with sleep-wake problems.

Keywords: polysomnography; sleep; tracking; validation study; wearable.

Conflict of interest statement

The authors report no conflicts of interest in this work.

© 2023 Lim et al.

Figures

Figure 1
Figure 1
Flow diagram for selection of study participants. PSG, polysomnography.
Figure 2
Figure 2
Bland–Altman plots of the FBI2 versus PSG. Bland–Altman plots presenting the different values of the FBI2 and PSG on the y-axis against PSG values on the x-axis across TIB, TST, SE, WASO, light sleep (Stage 1+2), deep sleep (Stage 3) and REM. The horizontal solid blue line denotes the average mean difference, while the dashed lines represent the 95% confidence interval (or lower-upper agreement limit). FBI2, Fitbit Inspire 2™.

References

    1. Cappuccio FP, Cooper D, D’Elia L, Strazzullo P, Miller MA. Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies. Eur Heart J. 2011;32(12):1484–1492. doi:10.1093/eurheartj/ehr007
    1. Xie J, Li Y, Zhang Y, et al. Sleep duration and metabolic syndrome: an updated systematic review and meta-analysis. Sleep Med Rev. 2021;59:101451. doi:10.1016/j.smrv.2021.101451
    1. Shin D, Hur J, Cho KH, Cho EH. Trends of self-reported sleep duration in Korean adults: results from the Korea National Health and Nutrition Examination Survey 2007–2015. Sleep Med. 2018;52:103–106. doi:10.1016/j.sleep.2018.08.008
    1. National Health Insurance Service. Useful Health Life Statistics Information to Know [Internet]. Wonju: National Health Insurance Sevice; 2021. Available from: . Accessed February 21, 2023.
    1. Lee YJ, Kim DJ, Lee H. A study on sleep-wake assessment for substantiation of sleep products. Sleep Med Psychophysiol. 2020;27(2):51–55.
    1. Depner CM, Cheng PC, Devine JK, et al. Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions. Sleep. 2020;43(2):zsz254. doi:10.1093/sleep/zsz254
    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;20(1 Pt 2):183–200. doi:10.1111/j.1365-2869.2009.00814.x
    1. FDA. Device software functions including mobile medical applications U.S. food & drug administration website; 2022. Available from: . Accessed September 28, 2022.
    1. Fitbit. What should I know about sleep stages? [homepage on the Internet]. Available from: . Accessed July 26, 2022.
    1. Cook JD, Prairie ML, Plante DT. Utility of the Fitbit Flex to evaluate sleep in major depressive disorder: a comparison against polysomnography and wrist-worn actigraphy. J Affect Disord. 2017;217:299–305. doi:10.1016/j.jad.2017.04.030
    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;15(11):1645–1653. doi:10.5664/jcsm.8032
    1. Scott H, Lack L, Lovato N. A systematic review of the accuracy of sleep wearable devices for estimating sleep onset. Sleep Med Rev. 2020;49:101227. doi:10.1016/j.smrv.2019.101227
    1. Haghayegh S, Khoshnevis S, Smolensky MH, Diller KR, Castriotta RJ. Accuracy of wristband fitbit models in assessing sleep: systematic review and meta-analysis. J Med Internet Res. 2019;21(11):e16273. doi:10.2196/16273
    1. Munos B, Baker PC, Bot BM, et al. Mobile health: the power of wearables, sensors, and apps to transform clinical trials. Ann N Y Acad Sci. 2016;1375(1):3–18. doi:10.1111/nyas.13117
    1. Cook JD, Eftekari SC, Dallmann E, Sippy M, Plante DT. Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: a comparison against polysomnography. J Sleep Res. 2019;28(4):e12789. doi:10.1111/jsr.12789
    1. Lunsford-Avery JR, Keller C, Kollins SH, Krystal AD, Jackson L, Engelhard MM. Feasibility and acceptability of wearable sleep electroencephalogram device use in adolescents: observational study. JMIR Mhealth Uhealth. 2020;8(10):e20590. doi:10.2196/20590
    1. Meltzer LJ, Hiruma LS, Avis K, Montgomery-Downs H, Valentin J. Comparison of a commercial accelerometer with polysomnography and actigraphy in children and adolescents. Sleep. 2015;38(8):1323–1330. doi:10.5665/sleep.4918
    1. de Zambotti M, Baker FC, Willoughby AR, et al. Measures of sleep and cardiac functioning during sleep using a multi-sensory commercially-available wristband in adolescents. Physiol Behav. 2016;158:143–149. doi:10.1016/j.physbeh.2016.03.006
    1. Bastien CH, Vallières A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med. 2001;2(4):297–307. doi:10.1016/S1389-9457(00)00065-4
    1. Hori T, Sugita Y, Koga E, Shirakawa S, Inoue K. Proposed supplements and amendments to ‘A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects’, the Rechtschaffen & Kales (1968) standard. Psychiatry Clin Neurosci. 2001;55:305–310.
    1. Sleep API variable: sleep [homepage on the Internet]. Available from: . Accessed July 26, 2022.
    1. Menghini L, Cellini N, Goldstone A, Baker FC, de Zambotti M. A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code. Sleep. 2021;44(2):zsaa170. doi:10.1093/sleep/zsaa170
    1. Kosmadopoulos A, Sargent C, Darwent D, Zhou X, Roach GD. Alternatives to polysomnography (PSG): a validation of wrist actigraphy and a partial-PSG system. Behav Res Methods. 2014;46(4):1032–1041. doi:10.3758/s13428-013-0438-7
    1. Peters B. Why WASO has a negative effect on sleep quality; 2022. Available from: . Accessed February 21, 2023.
    1. Scott H, Lovato N, Lack L. The development and accuracy of the THIM wearable device for estimating sleep and wakefulness. Nat Sci Sleep. 2021;13:39. doi:10.2147/NSS.S287048
    1. Kim E-J, Ahn Y-M, Shin H-B, Kim J-W. Detrended fluctuation analysis of sleep electroencephalogram between obstructive sleep apnea syndrome and normal children. Sleep Med Psychophysiol. 2010;17(1):41–49.
    1. 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;29(1):e12931. doi:10.1111/jsr.12931
    1. Mantua J, Gravel N, Spencer RM. Reliability of sleep measures from four personal health monitoring devices compared to research-based actigraphy and polysomnography. Sensors. 2016;16(5):646. doi:10.3390/s16050646
    1. Chinoy ED, Cuellar JA, Huwa KE, et al. Performance of seven consumer sleep-tracking devices compared with polysomnography. Sleep. 2021;44(5):zsaa291. doi:10.1093/sleep/zsaa291
    1. Le Bon O, Staner L, Hoffmann G, et al. The first-night effect may last more than one night. J Psychiatric Res. 2001;35(3):165–172. doi:10.1016/S0022-3956(01)00019-X
    1. Newell J, Mairesse O, Verbanck P, Neu D. Is a one-night stay in the lab really enough to conclude? First-night effect and night-to-night variability in polysomnographic recordings among different clinical population samples. Psychiatr Res. 2012;200(2–3):795–801. doi:10.1016/j.psychres.2012.07.045
    1. Stucky B, Clark I, Azza Y, et al. Validation of Fitbit Charge 2 sleep and heart rate estimates against polysomnographic measures in shift workers: naturalistic study. J Med Internet Res. 2021;23(10):e26476. doi:10.2196/26476
    1. Kawasaki Y, Kasai T, Sakurama Y, et al. Evaluation of sleep parameters and sleep staging (slow wave sleep) in athletes by fitbit alta HR, a consumer sleep tracking device. Nat Sci Sleep. 2022;14:819–827. doi:10.2147/NSS.S351274
    1. Leung W, Case L, Sung MC, Jung J. A meta-analysis of Fitbit devices: same company, different models, different validity evidence. J Med Eng Technol. 2022;46(2):102–115. doi:10.1080/03091902.2021.2006350

Source: PubMed

3
구독하다