Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study

Overview

Journal J Med Internet Res

Publisher JMIR Publications

Specialty Medical Informatics

Date 2023 Dec 13

PMID 38090797

Authors

Keita Kawai

Kunihiro Iwamoto

Seiko Miyata

Ippei Okada

Hiroshige Fujishiro

Akiko Noda

Kazuyuki Nakagome

Norio Ozaki

Masashi Ikeda

Affiliations

Soon will be listed here.

Abstract

Background: Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive.

Objective: This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders.

Methods: We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs.

Results: The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P<.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=-0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=-0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments.

Conclusions: Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders.

Citing Articles

Developing a Sleep Algxorithm to Support a Digital Medicine System: Noninterventional, Observational Sleep Study.

Cochran J JMIR Ment Health. 2024; 11:e62959.

PMID: 39727095 PMC: 11683743. DOI: 10.2196/62959.

References

Cook J, Eftekari S, Dallmann E, Sippy M, Plante D . 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. 2018; 28(4):e12789. DOI: 10.1111/jsr.12789. View

Wulff K, Dijk D, Middleton B, Foster R, Joyce E . Sleep and circadian rhythm disruption in schizophrenia. Br J Psychiatry. 2011; 200(4):308-16. PMC: 3317037. DOI: 10.1192/bjp.bp.111.096321. View

Nierenberg A, Husain M, Trivedi M, Fava M, Warden D, Wisniewski S . Residual symptoms after remission of major depressive disorder with citalopram and risk of relapse: a STAR*D report. Psychol Med. 2009; 40(1):41-50. PMC: 5886713. DOI: 10.1017/S0033291709006011. View

Byun J, Kim K, Moon H, Motamedi G, Cho Y . The first night effect during polysomnography, and patients' estimates of sleep quality. Psychiatry Res. 2019; 274:27-29. DOI: 10.1016/j.psychres.2019.02.011. View

Svensson T, Chung U, Tokuno S, Nakamura M, Svensson A . A validation study of a consumer wearable sleep tracker compared to a portable EEG system in naturalistic conditions. J Psychosom Res. 2019; 126:109822. DOI: 10.1016/j.jpsychores.2019.109822. View

Kaplan R, Wang Y, Loparo K, Kelly M, Bootzin R . Performance evaluation of an automated single-channel sleep-wake detection algorithm. Nat Sci Sleep. 2014; 6:113-22. PMC: 4206400. DOI: 10.2147/NSS.S71159. View

Harvey A, Tang N . (Mis)perception of sleep in insomnia: a puzzle and a resolution. Psychol Bull. 2011; 138(1):77-101. PMC: 3277880. DOI: 10.1037/a0025730. View

Krystal A . Psychiatric disorders and sleep. Neurol Clin. 2012; 30(4):1389-413. PMC: 3493205. DOI: 10.1016/j.ncl.2012.08.018. View

Lee X, Chee N, Ong J, Teo T, van Rijn E, Lo J . 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. PMC: 6760396. DOI: 10.5664/jcsm.7932. View

10.

Wulff K, Gatti S, Wettstein J, Foster R . Sleep and circadian rhythm disruption in psychiatric and neurodegenerative disease. Nat Rev Neurosci. 2010; 11(8):589-99. DOI: 10.1038/nrn2868. View

11.

Montgomery-Downs H, Insana S, Bond J . Movement toward a novel activity monitoring device. Sleep Breath. 2011; 16(3):913-7. DOI: 10.1007/s11325-011-0585-y. View

12.

Chinoy E, Cuellar J, Huwa K, Jameson J, Watson C, Bessman S . Performance of seven consumer sleep-tracking devices compared with polysomnography. Sleep. 2020; 44(5). PMC: 8120339. DOI: 10.1093/sleep/zsaa291. View

13.

Kang S, Kang J, Ko K, Park S, Mariani S, Weng J . Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers. J Psychosom Res. 2017; 97:38-44. DOI: 10.1016/j.jpsychores.2017.03.009. View

14.

Haghayegh S, Khoshnevis S, Smolensky M, Diller K, Castriotta R . Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis. J Med Internet Res. 2019; 21(11):e16273. PMC: 6908975. DOI: 10.2196/16273. View

15.

Cook J, Prairie M, Plante D . 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. PMC: 5509938. DOI: 10.1016/j.jad.2017.04.030. View

16.

Lee J, Byun W, Keill A, Dinkel D, Seo Y . Comparison of Wearable Trackers' Ability to Estimate Sleep. Int J Environ Res Public Health. 2018; 15(6). PMC: 6025478. DOI: 10.3390/ijerph15061265. View

17.

DiNapoli E, Gebara M, Kho T, Butters M, Gildengers A, Albert S . Subjective-Objective Sleep Discrepancy in Older Adults With MCI and Subsyndromal Depression. J Geriatr Psychiatry Neurol. 2017; 30(6):316-323. PMC: 5916761. DOI: 10.1177/0891988717731827. View

18.

Harvey A . Sleep and circadian rhythms in bipolar disorder: seeking synchrony, harmony, and regulation. Am J Psychiatry. 2008; 165(7):820-9. DOI: 10.1176/appi.ajp.2008.08010098. View

19.

Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Pollak C . The role of actigraphy in the study of sleep and circadian rhythms. Sleep. 2003; 26(3):342-92. DOI: 10.1093/sleep/26.3.342. View

20.

Miyata S, Iwamoto K, Banno M, Eguchi J, Kaneko S, Noda A . Performance of an ambulatory electroencephalogram sleep monitor in patients with psychiatric disorders. J Sleep Res. 2020; 30(4):e13273. DOI: 10.1111/jsr.13273. View