Validation of a Consumer Sleep Wearable Device With Actigraphy and Polysomnography in Adolescents Across Sleep Opportunity Manipulations

Xuan Kai Lee, Nicholas I Y N Chee, Ju Lynn Ong, Teck Boon Teo, Elaine van Rijn, June C Lo, Michael W L Chee, Xuan Kai Lee, Nicholas I Y N Chee, Ju Lynn Ong, Teck Boon Teo, Elaine van Rijn, June C Lo, Michael W L Chee

Abstract

Study objectives: To compare the quality and consistency in sleep measurement of a consumer wearable device and a research-grade actigraph with polysomnography (PSG) in adolescents.

Methods: Fifty-eight healthy adolescents (aged 15-19 years; 30 males) underwent overnight PSG while wearing both a Fitbit Alta HR and a Philips Respironics Actiwatch 2 (AW2) for 5 nights, with either 5 hours or 6.5 hours time in bed (TIB) and for 4 nights with 9 hours TIB. AW2 data were evaluated using two different wake and immobility thresholds. Discrepancies in estimated total sleep time (TST) and wake after sleep onset (WASO) between devices and PSG, as well as epoch-by-epoch agreements in sleep/wake classification, were assessed. Fitbit-generated sleep staging was compared to PSG.

Results: Fitbit and AW2 under default settings similarly underestimated TST and overestimated WASO (TST: medium setting (M10) ≤ 38 minutes, Fitbit ≤ 47 minutes; WASO: M10 ≤ 38 minutes; Fitbit ≤ 42 minutes). AW2 at the high motion threshold setting provided readings closest to PSG (TST: ≤ 12 minutes; WASO: ≤ 18 minutes). Sensitivity for detecting sleep was ≥ 90% for both wearable devices and further improved to 95% by using the high threshold (H5) setting for the AW2 (0.95). Wake detection specificity was highest in Fitbit (≥ 0.88), followed by the AW2 at M10 (≥ 0.80) and H5 thresholds (≤ 0.73). In addition, Fitbit inconsistently estimated stage N1 + N2 sleep depending on TIB, underestimated stage N3 sleep (21-46 min), but was comparable to PSG for rapid eye movement sleep. Fitbit sensitivity values for the detection of N1 + N2, N3 and rapid eye movement sleep were ≥ 0.68, ≥ 0.50, and ≥ 0.72, respectively.

Conclusions: A consumer-grade wearable device can measure sleep duration as well as a research actigraph. However, sleep staging would benefit from further refinement before these methods can be reliably used for adolescents.

Clinical trial registration: Registry: ClinicalTrials.gov; Title: The Cognitive and Metabolic Effects of Sleep Restriction in Adolescents; Identifier: NCT03333512; URL: https://ichgcp.net/clinical-trials-registry/NCT03333512.

Citation: Lee XK, Chee NIYN, Ong JL, Teo TB, van Rijn E, Lo JC, Chee MWL. Validation of a consumer sleep wearable device with actigraphy and polysomnography in adolescents across sleep opportunity manipulations. J Clin Sleep Med. 2019;15(9):1337-1346.

Keywords: Fitbit; actigraphy; adolescent sleep; polysomnography.

© 2019 American Academy of Sleep Medicine.

Figures

Figure 1. Bland-Altman plots for total sleep…
Figure 1. Bland-Altman plots for total sleep time and wake after sleep onset.
Bland-Altman plots, in minutes, of (A) total sleep time and (B) wake after sleep onset. Red, green, and blue points represent data collected from the 5-hour, 6.5-hour, and 9-hour time in bed conditions respectively. Solid lines and bolded numbers represent the mean biases of each recording, whereas dashed lines and regular numbers represent 1.96 standard deviation limits of agreement. H5 = Actiwatch 2 high wake threshold with 5 immobile minutes for sleep onset and end, M10 = Actiwatch 2 medium wake threshold with 10 immobile minutes for sleep onset and end, PSG = polysomnography.
Figure 2. Bland-Altman plots for sleep stages.
Figure 2. Bland-Altman plots for sleep stages.
Bland-Altman plots, in minutes, of (A) stage N1 + N2 sleep (light sleep), (B) stage N3 sleep (deep sleep), and (C) REM sleep. Red, green, and blue points represent data collected from the 5-hour, 6.5-hour, and 9-hour time in bed conditions, respectively. Solid lines and bold numbers represent the mean biases of each recording, whereas dashed lines and regular numbers represent 1.96 standard deviation limits of agreement. H5 = Actiwatch 2 high wake threshold with 5 immobile minutes for sleep onset and end, M10 = Actiwatch 2 medium wake threshold with 10 immobile minutes for sleep onset and end, PSG = polysomnography, REM = rapid eye movement.

References

    1. International Data Corporation New wearables forecast from IDC shows smartwatches continuing their ascendance while wristbands face flat growth. . Accessed August 28, 2018.
    1. Dickinson DL, Cazier J, Cech T. A practical validation study of a commercial accelerometer using good and poor sleepers. Health Psychol Open. 2016;3(2):2055102916679012.
    1. Liang Z, Chapa Martell MA. Validity of consumer activity wristbands and wearable EEG for measuring overall sleep parameters and sleep structure in free-living conditions. J Healthc Inform Res. 2018;2(1-2):152–178.
    1. Wright SP, Hall Brown TS, Collier SR, Sandberg K. How consumer physical activity monitors could transform human physiology research. Am J Physiol Regul Integr Com Physiol. 2017;312(3):R358–R367.
    1. Sadeh A, Acebo C. The role of actigraphy in sleep medicine. Sleep Med Rev. 2002;6(2):113–124.
    1. Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Pollak CP. The role of actigraphy in the study of sleep and circadian rhythms. Sleep. 2003;26(3):342–392.
    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.
    1. de Zambotti M, Baker FC, Colrain IM. Validation of sleep-rracking technology compared with polysomnography in adolescents. Sleep. 2015;38(9):1461–1468.
    1. Montgomery-Downs HE, Insana SP, Bond JA. Movement toward a novel activity monitoring device. Sleep Breath. 2012;16(3):913–917.
    1. Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015;12:159.
    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;35(4):465–476.
    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.
    1. Takeda T, Mizuno O, Tanaka T. Time-dependent sleep stage transition model based on heart rate variability. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:2343–2346.
    1. Aktaruzzaman M, Migliorini M, Tenhunen M, Himanen SL, Bianchi AM, Sassi R. The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability. Med Biol Eng Comput. 2015;53(5):415–425.
    1. Werner H, Molinari L, Guyer C, Jenni OG. Agreement rates between actigraphy, diary, and questionnaire for children’s sleep patterns. Arch Pediatr Adolesc Med. 2008;162(4):350–358.
    1. Meltzer LJ, Walsh CM, Traylor J, Westin AM. Direct comparison of two new actigraphs and polysomnography in children and adolescents. Sleep. 2012;35(1):159–166.
    1. Lo JC, Ong JL, Leong RL, Gooley JJ, Chee MW. Cognitive performance, sleepiness, and mood in partially sleep deprived adolescents: The Need for Sleep Study. Sleep. 2016;39(3):687–698.
    1. Johnson NL, Kirchner HL, Rosen CL, et al. Sleep estimation using wrist actigraphy in adolescents with and without sleep disordered breathing: a comparison of three data modes. Sleep. 2007;30(7):899–905.
    1. Pesonen AK, Kuula L. The validity of a new consumer-targeted wrist device in sleep measurement: an overnight comparison against polysomnography in children and adolescents. J Clin Sleep Med. 2018;14(4):585–591.
    1. Patanaik A, Ong JL, Gooley JJ, Ancoli-Israel S, Chee MWL. An end-to-end framework for real-time automatic sleep stage classification. Sleep. 2018;41(5)
    1. Iber C, Ancoli-Israel S, Chesson AL, Jr, Quan SF. for the American Academy of Sleep Medicine. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. 1st ed. Westchester, IL: American Academy of Sleep Medicine; 2007.
    1. Short MA, Gradisar M, Lack LC, Wright H, Carskadon MA. The discrepancy between actigraphic and sleep diary measures of sleep in adolescents. Sleep Med. 2012;13(4):378–384.
    1. Fitbit Inc Start sleeping better with Fitbit. . Accessed July 24, 2018.
    1. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–310.
    1. Short MA, Gradisar M, Lack LC, Wright HR, Chatburn A. Estimating adolescent sleep patterns: parent reports versus adolescent self-report surveys, sleep diaries, and actigraphy. Nat Sci Sleep. 2013;5:23–26.
    1. Dettoni JL, Consolim-Colombo FM, Drager LF, et al. Cardiovascular effects of partial sleep deprivation in healthy volunteers. J Appl Physiol (1985) 2012;113(2):232–236.

Source: PubMed

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