Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review
Abstract BackgroundDespite being the gold-standard method for objectively assessing sleep, polysomnography (PSG) faces several limitations as it is expensive, time-consuming, and labor-intensive; requires various equipment and technical expertise; and is impractical for long-t...
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Language: | English |
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JMIR Publications
2024-03-01
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Series: | JMIR mHealth and uHealth |
Online Access: | https://mhealth.jmir.org/2024/1/e52192 |
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author | An-Marie Schyvens Nina Catharina Van Oost Jean-Marie Aerts Federica Masci Brent Peters An Neven Hélène Dirix Geert Wets Veerle Ross Johan Verbraecken |
author_facet | An-Marie Schyvens Nina Catharina Van Oost Jean-Marie Aerts Federica Masci Brent Peters An Neven Hélène Dirix Geert Wets Veerle Ross Johan Verbraecken |
author_sort | An-Marie Schyvens |
collection | DOAJ |
description |
Abstract
BackgroundDespite being the gold-standard method for objectively assessing sleep, polysomnography (PSG) faces several limitations as it is expensive, time-consuming, and labor-intensive; requires various equipment and technical expertise; and is impractical for long-term or in-home use. Consumer wrist-worn wearables are able to monitor sleep parameters and thus could be used as an alternative for PSG. Consequently, wearables gained immense popularity over the past few years, but their accuracy has been a major concern.
ObjectiveA systematic review of the literature was conducted to appraise the performance of 3 recent-generation wearable devices (Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP) in determining sleep parameters and sleep stages.
MethodsPer the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, a comprehensive search was conducted using the PubMed, Web of Science, Google Scholar, Scopus, and Embase databases. Eligible publications were those that (1) involved the validity of sleep data of any marketed model of the candidate wearables and (2) used PSG or an ambulatory electroencephalogram monitor as a reference sleep monitoring device. Exclusion criteria were as follows: (1) incorporated a sleep diary or survey method as a reference, (2) review paper, (3) children as participants, and (4) duplicate publication of the same data and findings.
ResultsThe search yielded 504 candidate articles. After eliminating duplicates and applying the eligibility criteria, 8 articles were included. WHOOP showed the least disagreement relative to PSG and Sleep Profiler for total sleep time (−1.4 min), light sleep (−9.6 min), and deep sleep (−9.3 min) but showed the largest disagreement for rapid eye movement (REM) sleep (21.0 min). Fitbit Charge 4 and Garmin Vivosmart 4 both showed moderate accuracy in assessing sleep stages and total sleep time compared to PSG. Fitbit Charge 4 showed the least disagreement for REM sleep (4.0 min) relative to PSG. Additionally, Fitbit Charge 4 showed higher sensitivities to deep sleep (75%) and REM sleep (86.5%) compared to Garmin Vivosmart 4 and WHOOP.
ConclusionsThe findings of this systematic literature review indicate that the devices with higher relative agreement and sensitivities to multistate sleep (ie, Fitbit Charge 4 and WHOOP) seem appropriate for deriving suitable estimates of sleep parameters. However, analyses regarding the multistate categorization of sleep indicate that all devices can benefit from further improvement in the assessment of specific sleep stages. Although providers are continuously developing new versions and variants of wearables, the scientific research on these wearables remains considerably limited. This scarcity in literature not only reduces our ability to draw definitive conclusions but also highlights the need for more targeted research in this domain. Additionally, future research endeavors should strive for standardized protocols including larger sample sizes to enhance the comparability and power of the results across studies. |
first_indexed | 2024-04-24T11:34:39Z |
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language | English |
last_indexed | 2024-04-24T11:34:39Z |
publishDate | 2024-03-01 |
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series | JMIR mHealth and uHealth |
spelling | doaj.art-e789b4be175943e5838b3e10cd73b7852024-04-10T07:12:53ZengJMIR PublicationsJMIR mHealth and uHealth2291-52222024-03-0112e52192e5219210.2196/52192Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic ReviewAn-Marie Schyvenshttp://orcid.org/0000-0001-6030-475XNina Catharina Van Oosthttp://orcid.org/0009-0003-1046-0190Jean-Marie Aertshttp://orcid.org/0000-0001-5548-9163Federica Mascihttp://orcid.org/0000-0002-0059-1440Brent Petershttp://orcid.org/0000-0002-3234-5607An Nevenhttp://orcid.org/0000-0003-0165-1799Hélène Dirixhttp://orcid.org/0000-0001-7652-1070Geert Wetshttp://orcid.org/0000-0002-5485-9705Veerle Rosshttp://orcid.org/0000-0002-7830-7892Johan Verbraeckenhttp://orcid.org/0000-0002-6087-678X Abstract BackgroundDespite being the gold-standard method for objectively assessing sleep, polysomnography (PSG) faces several limitations as it is expensive, time-consuming, and labor-intensive; requires various equipment and technical expertise; and is impractical for long-term or in-home use. Consumer wrist-worn wearables are able to monitor sleep parameters and thus could be used as an alternative for PSG. Consequently, wearables gained immense popularity over the past few years, but their accuracy has been a major concern. ObjectiveA systematic review of the literature was conducted to appraise the performance of 3 recent-generation wearable devices (Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP) in determining sleep parameters and sleep stages. MethodsPer the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, a comprehensive search was conducted using the PubMed, Web of Science, Google Scholar, Scopus, and Embase databases. Eligible publications were those that (1) involved the validity of sleep data of any marketed model of the candidate wearables and (2) used PSG or an ambulatory electroencephalogram monitor as a reference sleep monitoring device. Exclusion criteria were as follows: (1) incorporated a sleep diary or survey method as a reference, (2) review paper, (3) children as participants, and (4) duplicate publication of the same data and findings. ResultsThe search yielded 504 candidate articles. After eliminating duplicates and applying the eligibility criteria, 8 articles were included. WHOOP showed the least disagreement relative to PSG and Sleep Profiler for total sleep time (−1.4 min), light sleep (−9.6 min), and deep sleep (−9.3 min) but showed the largest disagreement for rapid eye movement (REM) sleep (21.0 min). Fitbit Charge 4 and Garmin Vivosmart 4 both showed moderate accuracy in assessing sleep stages and total sleep time compared to PSG. Fitbit Charge 4 showed the least disagreement for REM sleep (4.0 min) relative to PSG. Additionally, Fitbit Charge 4 showed higher sensitivities to deep sleep (75%) and REM sleep (86.5%) compared to Garmin Vivosmart 4 and WHOOP. ConclusionsThe findings of this systematic literature review indicate that the devices with higher relative agreement and sensitivities to multistate sleep (ie, Fitbit Charge 4 and WHOOP) seem appropriate for deriving suitable estimates of sleep parameters. However, analyses regarding the multistate categorization of sleep indicate that all devices can benefit from further improvement in the assessment of specific sleep stages. Although providers are continuously developing new versions and variants of wearables, the scientific research on these wearables remains considerably limited. This scarcity in literature not only reduces our ability to draw definitive conclusions but also highlights the need for more targeted research in this domain. Additionally, future research endeavors should strive for standardized protocols including larger sample sizes to enhance the comparability and power of the results across studies.https://mhealth.jmir.org/2024/1/e52192 |
spellingShingle | An-Marie Schyvens Nina Catharina Van Oost Jean-Marie Aerts Federica Masci Brent Peters An Neven Hélène Dirix Geert Wets Veerle Ross Johan Verbraecken Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review JMIR mHealth and uHealth |
title | Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review |
title_full | Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review |
title_fullStr | Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review |
title_full_unstemmed | Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review |
title_short | Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review |
title_sort | accuracy of fitbit charge 4 garmin vivosmart 4 and whoop versus polysomnography systematic review |
url | https://mhealth.jmir.org/2024/1/e52192 |
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