Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jul;38(7):1010-1022.
doi: 10.1080/07420528.2021.1903481. Epub 2021 Apr 1.

Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls

Luca Menghini  1   2 , Dilara Yuksel  1 , Aimee Goldstone  1 , Fiona C Baker  1   3 , Massimiliano de Zambotti  1
Affiliations

Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls

Luca Menghini et al. Chronobiol Int. 2021 Jul.

Abstract

We evaluated the performance of Fitbit Charge 3™ (FC3), a multi-sensor commercial sleep-tracker, for measuring sleep in adolescents against gold-standard laboratory polysomnography (PSG). Single-night PSG and FC3 sleep outcomes were compared in thirty-nine adolescents (22 girls; 16-19 years), 12 of whom presented with clinical/subclinical DSM-5 insomnia symptoms (7 girls). Discrepancy analysis, Bland-Altman plots, and epoch-by-epoch analyses were used to evaluate FC3 performance. The influence of several factors potentially affecting FC3 performance (e.g., sex, age, body mass index, firmware version, and magnitude of heart rate changes between consecutive PSG epochs) was also tested. In the sample of healthy adolescents, FC3 systematically underestimated PSG total sleep time by about 11 min and sleep efficiency by 2.5%, and overestimated wake after sleep onset by 9 min. Proportional biases were detected for "light" and "deep" sleep duration, resulting in significant underestimation of these parameters for those participants having longer PSG N1+ N2 and N3 durations, respectively. No significant systematic bias was detected for sleep efficiency and sleep onset latency. Epoch-by-epoch analysis showed sleep-stage sensitivity (average proportion of PSG epochs correctly classified by the device for a given sleep stage) of 68% for wake, 78% for "light" sleep, 59% for "deep" sleep, and 69% for rapid eye movement (REM) sleep in healthy sleepers. Similar results were found in the sample of adolescents with insomnia symptoms. Body mass index was positively associated with FC3-PSG discrepancies in wake after sleep onset (R2 = .16, p = .048). The magnitude of the heart rate acceleration/deceleration between consecutive PSG epochs was an important factor affecting FC3 classifications of sleep stages. Our results are in line with a general trend in the literature, suggesting better performance for the recently introduced multi-sensor devices compared to motion-only devices, although further developments are needed to improve accuracy in sleep stage classification and wake detection. Further insight is needed to determine factors potentially affecting device performance, such as accuracy and reliability (consistency of performance over time), in different samples and conditions.

Keywords: Fitbit; Wearable sleep trackers; accuracy; adolescence; consumer sleep technology; insomnia.

PubMed Disclaimer

Conflict of interest statement

Disclosure of interest. The authors report no conflict of interest related to the current work. MdZ and FCB have received research funding unrelated to this work from Ebb Therapeutics Inc., Fitbit Inc., International Flavors & Fragrances Inc., and Noctrix Health, Inc. The authors declared no agreements with Fitbit Inc. that could bias the results of this research in any way.

Figures

Figure 1.
Figure 1.
Bland-Altman plots of sleep measures in the sample of healthy sleepers (black dots) and adolescents with insomnia symptoms (blue triangles). PSG, polysomnography; REM, Rapid-Eye-Movement. Red solid lines indicate bias, whereas gray solid lines indicate the 95% limits of agreement (LOAs), both with their 95% confidence intervals (dotted lines), computed from the group of healthy sleepers. Density diagram on the right side of each plot represents the distribution of FC3-PSG differences among healthy sleepers.
Figure 2.
Figure 2.
Fitbit Charge 3™ heart rate by polysomnographic sleep stage (a) and predicted probability of epoch-by-epoch (EBE) transition agreement by absolute Fitbit Charge 3™ heart rate changes to polysomnographic EBE transitions (b), in the sample of healthy sleepers. FC3, Fitbit Charge 3™, PSG, polysomnography, HR, heart rate; bpm, beats per minute; “light”, PSG-based N1 + N2 sleep; “deep”, PSG-based N3 sleep; REM, Rapid-Eye-Movement sleep. Figure (a) shows the distribution of FC3 HR values averaged by participant (gray dots) for each PSG sleep stage (error bars indicate 95% confidence intervals). Figure (b) shows the predicted probabilities (with 95% confidence intervals) of EBE transition agreement depending on absolute percent FC3 HR change to transitions between consecutive epochs classified with the same stage (solid red line) or with different stages (solid blue line) by the PSG.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. American Psychiatric Association. 2013. Diagnostic and statistical manual of mental disorders: DSM 5. 5th. Arlington (VA): American Psychiatric Publishing.
    1. Beattie Z, Oyang Y, Statan A, Ghoreyshi A, Pantelopoulos A, Russell A, Heneghan C. 2017. Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals. Physiol Meas. 38:1968–1979. doi:10.1088/1361-6579/aa9047. - DOI - PubMed
    1. Berry RB, Brooks R, Gamaldo CE, Harding SM, Lloyd RM, Marcus CL, Vaughn B V. 2020. The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications version 2.6. Darien (IL): American Academy of Sleep Medicine.
    1. Bland JM, Altman DG. 1999. Measuring agreement in method comparison studies. Stat Methods Med Res. 8:135–160. doi:10.1177/096228029900800204. - DOI - PubMed
    1. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. 1989. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Res. 28:193–213. doi:10.1016/0165-1781(89)90047-4. - DOI - PubMed

Publication types