--- title: "Wearable Sleep Tracking Accuracy Against a Sleep Lab | KnowYourPrime Evidence Registry" description: "Finding KYP-ER-0007, rated Uncertain. For whether you are asleep, yes, closely. For how long you were awake and which stage you were in, much less so, and accuracy differs by device. Across 24 studies, wrist trackers differed from the lab by about a quarter of an hour of total sleep." url: https://knowyourprime.com/evidence-wearable-sleep-tracking-accuracy --- KnowYourPrime Evidence Registry · Finding KYP-ER-0007 # Wearable Sleep Tracking Accuracy Against a Sleep Lab Question assessedDo consumer wearables measure sleep accurately compared with polysomnography in a sleep lab? KnowYourPrime evidence rating Uncertain Evidence is conflicting, inconsistent, or difficult to interpret. For whether you are asleep, yes, closely. For how long you were awake and which stage you were in, much less so, and accuracy differs by device. Across 24 studies, wrist trackers differed from the lab by about a quarter of an hour of total sleep. Finding KYP-ER-0007 Version 1.0 Reviewed October 24, 2026 Rating rule 1.4 [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ## Why this is rated Uncertain Polysomnography measures brain waves, eye movement and muscle tone; a wearable infers sleep from movement and heart signals. Telling sleep from wake is the easy part, and nearly every device does it well. Detecting waking moments and staging sleep are harder. One multi-device study and a large Oura validation affirm good agreement, but meta-analyses and reviews find significant differences from polysomnography and low precision for stages, and an algorithm study found wrist pulse data stages sleep worse than ECG. The rule rates the claim Uncertain. How the rating rule reads this evidence - Anchor-grade evidence affirms the core relationship. - Multiple supporting studies are consistent with the core relationship. - An anchor study is present but its own reported finding does not cleanly affirm this claim, so it does not count toward the Established threshold. - A clean anchor supports this claim, but an unresolved conflicting study contradicts it on the core relationship, so the evidence is treated as in genuine dispute. - Some evidence conflicts on secondary points that do not change the core direction. ## Key results - Across 24 studies and 798 participants, wrist-worn trackers differed from polysomnography by about 17 minutes of total sleep time (95 percent CI 7 to 26 minutes), 4.7 points of sleep efficiency and about 13 minutes of wake after sleep onset. (Lee et al., Journal of Clinical Sleep Medicine, 2025) - In 35 healthy adults, Oura Gen 3, Fitbit Sense 2 and Apple Watch Series 8 each detected sleep versus wake with at least 95 percent sensitivity; Oura did not differ from polysomnography on any stage, while Apple Watch underestimated deep sleep by 43 minutes and overestimated light sleep by 45. (Robbins et al., Sensors, 2024) - In 96 adults over multiple lab nights, Oura Gen 3 did not differ from polysomnography for total sleep time, light or deep sleep, with 91.7 percent epoch accuracy and 73.0 to 74.6 percent specificity for wake. (Svensson et al., Sleep Medicine, 2024) - An ECG-trained staging algorithm agreed less well with polysomnography when run on wrist pulse data than on ECG across 389 recordings (kappa 0.56 against 0.60). (Gilst et al., BMC Research Notes, 2021) ## What this finding covers **Applies to:** Consumer wrist and ring sleep trackers compared with polysomnography, mostly in healthy adults, for total sleep, wake and stages. **Does not cover:** Sleep disorder diagnosis; under-mattress and bedside sensors, covered in a separate finding; models released after the studies. ## Limitations - Most validation is in young or middle-aged healthy sleepers, where trackers perform best. - Single lab nights do not show night-to-night consistency at home. - Results for one model and algorithm version do not carry to later versions. ## Evidence base 30 studies scored · 8 anchor · 1 conflicting · 21 supporting · 1 letters or comments not counted · Evidence last evaluated Oct 17, 2026 ### Anchor studies The studies the rating rests on. 1. Real-World Use of Consumer Sleep Devices: A Rapid Review.Landvatter et al., Chest, 2026. [doi.org/10.1016/j.chest.2025.10.039](https://doi.org/10.1016/j.chest.2025.10.039) 2. Performance of consumer wrist-worn sleep tracking devices compared to polysomnography: a meta-analysis.Lee et al., Journal of Clinical Sleep Medicine, 2025. [doi.org/10.5664/jcsm.11460](https://doi.org/10.5664/jcsm.11460) 3. Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review.Schyvens et al., JMIR mHealth and uHealth, 2024. [doi.org/10.2196/52192](https://doi.org/10.2196/52192) 4. Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults.Robbins et al., Sensors, 2024. [doi.org/10.3390/s24206532](https://doi.org/10.3390/s24206532) \[funding listed from Oura\] 5. Keeping Pace with Wearables: A Living Umbrella Review of Systematic Reviews Evaluating the Accuracy of Consumer Wearable Technologies in Health Measurement.Doherty et al., Sports Medicine, 2024. [doi.org/10.1007/s40279-024-02077-2](https://doi.org/10.1007/s40279-024-02077-2) 6. Validity and reliability of the Oura Ring Generation 3 (Gen3) with Oura sleep staging algorithm 2.0 (OSSA 2.0) when compared to multi-night ambulatory polysomnography: A validation study of 96 participants and 421,045 epochs.Svensson et al., Sleep Medicine, 2024. [doi.org/10.1016/j.sleep.2024.01.020](https://doi.org/10.1016/j.sleep.2024.01.020) \[conflict statement: three authors report financial support from Oura\] 7. Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study.Lee et al., JMIR mHealth and uHealth, 2023. [doi.org/10.2196/50983](https://doi.org/10.2196/50983) \[authors work for and hold stock in Asleep, maker of the SleepRoutine app it tested\] 8. Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative DataFeehan et al., JMIR Mhealth and Uhealth, 2018. [doi.org/10.2196/10527](https://doi.org/10.2196/10527) ### Conflicting studies Studies whose findings run against the claim. 1. Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance.Gilst et al., BMC Research Notes, 2021. [doi.org/10.1186/s13104-020-05355-0](https://doi.org/10.1186/s13104-020-05355-0) \[author affiliations include Philips\] ### Supporting studies Studies consistent with the claim that do not carry the rating on their own. **Show all 21 supporting studies** 1. Evaluating the Feasibility of Low-Cost, Contactless Consumer Sleep-Tracking Devices as Measurement Tools for Preliminary Sleep Research.Zhai et al., Sensors, 2026. [doi.org/10.3390/s26082371](https://doi.org/10.3390/s26082371) 2. Evaluation of a Contactless Sleep Monitoring Device for Sleep Stage Detection at Home in a Healthy Population: Prospective Study in Free-Living Conditions.Stefanos et al., JMIR Human Factors, 2026. [doi.org/10.2196/77033](https://doi.org/10.2196/77033) \[author affiliations include Withings\] 3. A Comprehensive Review of Home Sleep Monitoring Technologies: Smartphone Apps, Smartwatches, and Smart Mattresses.Mathunjwa et al., Sensors, 2025. [doi.org/10.3390/s25061771](https://doi.org/10.3390/s25061771) 4. Automated REM vs NREM sleep staging using single overnight heart rate and accelerometer data.Mohan et al., Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2025. [doi.org/10.1109/EMBC58623.2025.11252902](https://doi.org/10.1109/EMBC58623.2025.11252902) 5. Diagnostic value of wearable and contactless technologies for detecting obstructive sleep apnea: a systematic review and meta-analysis.Hasanain et al., Sleep & Breathing, 2025. [doi.org/10.1007/s11325-025-03544-4](https://doi.org/10.1007/s11325-025-03544-4) 6. Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology.Nunes et al., Sensors, 2025. [doi.org/10.3390/s24247982](https://doi.org/10.3390/s24247982) \[conflict statement: five authors are ActiGraph employees\] 7. Performance evaluation of an under-mattress sleep sensor versus polysomnography in > 400 nights with healthy and unhealthy sleep.Manners et al., Journal of Sleep Research, 2025. [doi.org/10.1111/jsr.14480](https://doi.org/10.1111/jsr.14480) \[conflict statement: two authors had a Withings-supported research grant\] 8. Performance of wearable finger ring trackers for diagnostic sleep measurement in the clinical context.Herberger et al., Scientific Reports, 2025. [doi.org/10.1038/s41598-025-93774-z](https://doi.org/10.1038/s41598-025-93774-z) 9. Validation of nocturnal resting heart rate and heart rate variability in consumer wearables.Dial et al., Physiological Reports, 2025. [doi.org/10.14814/phy2.70527](https://doi.org/10.14814/phy2.70527) 10. Validity and clinical utility of a wrist-worn device against polysomnography.Guo et al., PloS One, 2025. [doi.org/10.1371/journal.pone.0330774](https://doi.org/10.1371/journal.pone.0330774) 11. A systematic review of the performance of actigraphy in measuring sleep stages.Yuan et al., Journal of Sleep Research, 2024. [doi.org/10.1111/jsr.14143](https://doi.org/10.1111/jsr.14143) 12. Overnight Sleep Staging Using Chest-Worn Accelerometry.Schipper et al., Sensors, 2024. [doi.org/10.3390/s24175717](https://doi.org/10.3390/s24175717) \[conflict statement: authors were employed by Philips\] 13. Sleep assessment using EEG-based wearables - A systematic review.Gans et al., Sleep Medicine Reviews, 2024. [doi.org/10.1016/j.smrv.2024.101951](https://doi.org/10.1016/j.smrv.2024.101951) 14. The Combination of Topological Data Analysis and Mathematical Modeling Improves Sleep Stage Prediction From Consumer-Grade Wearables.Lee et al., Journal of Biological Rhythms, 2024. [doi.org/10.1177/07487304241288607](https://doi.org/10.1177/07487304241288607) 15. A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy AdultsMiller et al., Sensors, 2022. [doi.org/10.3390/s22166317](https://doi.org/10.3390/s22166317) \[conflict statement: the authors' lab receives funding and equipment from WHOOP\] 16. Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep.Rentz et al., Sensors, 2021. [doi.org/10.3390/s21155071](https://doi.org/10.3390/s21155071) 17. Validation of Zulu Watch against Polysomnography and Actigraphy for On-Wrist Sleep-Wake Determination and Sleep-Depth Estimation.Devine et al., Sensors, 2021. [doi.org/10.3390/s21010076](https://doi.org/10.3390/s21010076) 18. Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis.Haghayegh et al., Journal of Medical Internet Research, 2020. [doi.org/10.2196/16273](https://doi.org/10.2196/16273) 19. PSG Validation of minute-to-minute scoring for sleep and wake periods in a consumer wearable device.Cheung et al., PloS One, 2020. [doi.org/10.1371/journal.pone.0238464](https://doi.org/10.1371/journal.pone.0238464) 20. Improving Sleep Quality Assessment Using Wearable Sensors by Including Information From Postural/Sleep Position Changes and Body Acceleration: A Comparison of Chest-Worn Sensors, Wrist Actigraphy, and Polysomnography.Razjouyan et al., Journal of Clinical Sleep Medicine, 2018. [doi.org/10.5664/jcsm.6802](https://doi.org/10.5664/jcsm.6802) 21. Processed EEG in natural sleep.Nieuwenhuijs et al., Best Practice & Research. Clinical Anaesthesiology, 2006. [doi.org/10.1016/j.bpa.2005.08.006](https://doi.org/10.1016/j.bpa.2005.08.006) ## Version history | Version | Date | Rating | Change | | --- | --- | --- | --- | | 1.0 | October 24, 2026 | Uncertain | First published. | Rating history on the research desk: Established (July 9, 2026), then Emerging (September 4, 2026), then Uncertain (September 17, 2026). ## Cite this finding KnowYourPrime Evidence Registry, Finding KYP-ER-0007, version 1.0, reviewed 2026-10-24. https://knowyourprime.com/evidence-wearable-sleep-tracking-accuracy Machine-readable: [JSON](https://knowyourprime.com/evidence-wearable-sleep-tracking-accuracy.json) · [Markdown](https://knowyourprime.com/evidence-wearable-sleep-tracking-accuracy.md) · [All findings](https://knowyourprime.com/evidence) ## Related - Research desk: [the sleep research desk entry for this question](https://knowyourprime.com/research-desk-sleep#wearable_accuracy), with every scored study and its role - Related finding: [Under-Mattress Sleep Sensor Accuracy](https://knowyourprime.com/evidence-under-mattress-sleep-sensor-accuracy) (KYP-ER-0008) - On this site: [What Is the Most Accurate Sleep Tracker? What Sleep-Lab Studies Found](https://knowyourprime.com/most-accurate-sleep-tracker) - On this site: [Is Fitbit Sleep Tracking Accurate? Garmin, WHOOP Too](https://knowyourprime.com/how-accurate-is-fitbit-garmin-whoop-sleep-tracking) - On this site: [Watches Are Only So-So at Guessing Sleep Stages](https://knowyourprime.com/how-wearables-track-sleep-stages) This is KnowYourPrime's own assessment of the published evidence, rated under the [KnowYourPrime evidence methodology](https://knowyourprime.com/methodology). The rating is set by a fixed rule from the roles of the studies above and moves only when the evidence or the rule changes. Every change to the rating or the text is published as a new dated version. To report an error, write to [corrections@knowyourprime.com](mailto:corrections@knowyourprime.com). ```json { "@context": "https://schema.org", "@type": "Article", "@id": "https://knowyourprime.com/evidence-wearable-sleep-tracking-accuracy#finding", "headline": "Wearable Sleep Tracking Accuracy Against a Sleep Lab", "name": "KnowYourPrime Evidence Registry, Finding KYP-ER-0007: Wearable Sleep Tracking Accuracy Against a Sleep Lab", "license": "https://creativecommons.org/licenses/by/4.0/", "description": "For whether you are asleep, yes, closely. For how long you were awake and which stage you were in, much less so, and accuracy differs by device. Across 24 studies, wrist trackers differed from the lab by about a quarter of an hour of total sleep.", "url": "https://knowyourprime.com/evidence-wearable-sleep-tracking-accuracy", "identifier": { "@type": "PropertyValue", "propertyID": "KnowYourPrime Evidence Registry finding ID", "value": "KYP-ER-0007" }, "version": "1.0", "datePublished": "2026-10-24", "dateModified": "2026-10-28", "inLanguage": "en", "isPartOf": { "@type": "CreativeWorkSeries", "name": "KnowYourPrime Evidence Registry", "url": "https://knowyourprime.com/evidence" }, "author": { "@type": "Organization", "name": "KnowYourPrime", "url": "https://knowyourprime.com" }, "publisher": { "@type": "Organization", "@id": "https://knowyourprime.com/#organization", "name": "KnowYourPrime", "url": "https://knowyourprime.com" }, "about": "Do consumer wearables measure sleep accurately compared with polysomnography in a sleep lab?", "citation": [ { "@type": "ScholarlyArticle", "name": "Real-World Use of Consumer Sleep Devices: A Rapid Review.", "url": "https://doi.org/10.1016/j.chest.2025.10.039", "datePublished": "2026", "isPartOf": { "@type": "Periodical", "name": "Chest" } }, { "@type": "ScholarlyArticle", "name": "Performance of consumer wrist-worn sleep tracking devices compared to polysomnography: a meta-analysis.", "url": "https://doi.org/10.5664/jcsm.11460", "datePublished": "2025", "isPartOf": { "@type": "Periodical", "name": "Journal of Clinical Sleep Medicine" } }, { "@type": "ScholarlyArticle", "name": "Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review.", "url": "https://doi.org/10.2196/52192", "datePublished": "2024", "isPartOf": { "@type": "Periodical", "name": "JMIR mHealth and uHealth" } }, { "@type": "ScholarlyArticle", "name": "Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults.", "url": "https://doi.org/10.3390/s24206532", "datePublished": "2024", "isPartOf": { "@type": "Periodical", "name": "Sensors" } }, { "@type": "ScholarlyArticle", "name": "Keeping Pace with Wearables: A Living Umbrella Review of Systematic Reviews Evaluating the Accuracy of Consumer Wearable Technologies in Health Measurement.", "url": "https://doi.org/10.1007/s40279-024-02077-2", "datePublished": "2024", "isPartOf": { "@type": "Periodical", "name": "Sports Medicine" } }, { "@type": "ScholarlyArticle", "name": "Validity and reliability of the Oura Ring Generation 3 (Gen3) with Oura sleep staging algorithm 2.0 (OSSA 2.0) when compared to multi-night ambulatory polysomnography: A validation study of 96 participants and 421,045 epochs.", "url": "https://doi.org/10.1016/j.sleep.2024.01.020", "datePublished": "2024", "isPartOf": { "@type": "Periodical", "name": "Sleep Medicine" } }, { "@type": "ScholarlyArticle", "name": "Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study.", "url": "https://doi.org/10.2196/50983", "datePublished": "2023", "isPartOf": { "@type": "Periodical", "name": "JMIR mHealth and uHealth" } }, { "@type": "ScholarlyArticle", "name": "Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data", "url": "https://doi.org/10.2196/10527", "datePublished": "2018", "isPartOf": { "@type": "Periodical", "name": "JMIR Mhealth and Uhealth" } }, { "@type": "ScholarlyArticle", "name": "Evaluating the Feasibility of Low-Cost, Contactless Consumer Sleep-Tracking Devices as Measurement Tools for Preliminary Sleep Research.", "url": "https://doi.org/10.3390/s26082371", "datePublished": "2026", "isPartOf": { "@type": "Periodical", "name": "Sensors" } }, { "@type": "ScholarlyArticle", "name": "Evaluation of a Contactless Sleep Monitoring Device for Sleep Stage Detection at Home in a Healthy Population: Prospective Study in Free-Living Conditions.", "url": "https://doi.org/10.2196/77033", "datePublished": "2026", "isPartOf": { "@type": "Periodical", "name": "JMIR Human Factors" } }, { "@type": "ScholarlyArticle", "name": "A Comprehensive Review of Home Sleep Monitoring Technologies: Smartphone Apps, Smartwatches, and Smart Mattresses.", "url": "https://doi.org/10.3390/s25061771", "datePublished": "2025", "isPartOf": { "@type": "Periodical", "name": "Sensors" } }, { "@type": "ScholarlyArticle", "name": "Automated REM vs NREM sleep staging using single overnight heart rate and accelerometer data.", "url": "https://doi.org/10.1109/EMBC58623.2025.11252902", "datePublished": "2025", "isPartOf": { "@type": "Periodical", "name": "Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference" } }, { "@type": "ScholarlyArticle", "name": "Diagnostic value of wearable and contactless technologies for detecting obstructive sleep apnea: a systematic review and meta-analysis.", "url": "https://doi.org/10.1007/s11325-025-03544-4", "datePublished": "2025", "isPartOf": { "@type": "Periodical", "name": "Sleep & Breathing" } }, { "@type": "ScholarlyArticle", "name": "Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology.", "url": "https://doi.org/10.3390/s24247982", "datePublished": "2025", "isPartOf": { "@type": "Periodical", "name": "Sensors" } }, { "@type": "ScholarlyArticle", "name": "Performance evaluation of an under-mattress sleep sensor versus polysomnography in > 400 nights with healthy and unhealthy sleep.", "url": "https://doi.org/10.1111/jsr.14480", "datePublished": "2025", "isPartOf": { "@type": "Periodical", "name": "Journal of Sleep Research" } }, { "@type": "ScholarlyArticle", "name": "Performance of wearable finger ring trackers for diagnostic sleep measurement in the clinical context.", "url": "https://doi.org/10.1038/s41598-025-93774-z", "datePublished": "2025", "isPartOf": { "@type": "Periodical", "name": "Scientific Reports" } }, { "@type": "ScholarlyArticle", "name": "Validation of nocturnal resting heart rate and heart rate variability in consumer wearables.", "url": "https://doi.org/10.14814/phy2.70527", "datePublished": "2025", "isPartOf": { "@type": "Periodical", "name": "Physiological Reports" } }, { "@type": "ScholarlyArticle", "name": "Validity and clinical utility of a wrist-worn device against polysomnography.", "url": "https://doi.org/10.1371/journal.pone.0330774", "datePublished": "2025", "isPartOf": { "@type": "Periodical", "name": "PloS One" } }, { "@type": "ScholarlyArticle", "name": "A systematic review of the performance of actigraphy in measuring sleep stages.", "url": "https://doi.org/10.1111/jsr.14143", "datePublished": "2024", "isPartOf": { "@type": "Periodical", "name": "Journal of Sleep Research" } }, { "@type": "ScholarlyArticle", "name": "Overnight Sleep Staging Using Chest-Worn Accelerometry.", "url": "https://doi.org/10.3390/s24175717", "datePublished": "2024", "isPartOf": { "@type": "Periodical", "name": "Sensors" } }, { "@type": "ScholarlyArticle", "name": "Sleep assessment using EEG-based wearables - A systematic review.", "url": "https://doi.org/10.1016/j.smrv.2024.101951", "datePublished": "2024", "isPartOf": { "@type": "Periodical", "name": "Sleep Medicine Reviews" } }, { "@type": "ScholarlyArticle", "name": "The Combination of Topological Data Analysis and Mathematical Modeling Improves Sleep Stage Prediction From Consumer-Grade Wearables.", "url": "https://doi.org/10.1177/07487304241288607", "datePublished": "2024", "isPartOf": { "@type": "Periodical", "name": "Journal of Biological Rhythms" } }, { "@type": "ScholarlyArticle", "name": "A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults", "url": "https://doi.org/10.3390/s22166317", "datePublished": "2022", "isPartOf": { "@type": "Periodical", "name": "Sensors" } }, { "@type": "ScholarlyArticle", "name": "Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep.", "url": "https://doi.org/10.3390/s21155071", "datePublished": "2021", "isPartOf": { "@type": "Periodical", "name": "Sensors" } }, { "@type": "ScholarlyArticle", "name": "Validation of Zulu Watch against Polysomnography and Actigraphy for On-Wrist Sleep-Wake Determination and Sleep-Depth Estimation.", "url": "https://doi.org/10.3390/s21010076", "datePublished": "2021", "isPartOf": { "@type": "Periodical", "name": "Sensors" } }, { "@type": "ScholarlyArticle", "name": "Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis.", "url": "https://doi.org/10.2196/16273", "datePublished": "2020", "isPartOf": { "@type": "Periodical", "name": "Journal of Medical Internet Research" } }, { "@type": "ScholarlyArticle", "name": "PSG Validation of minute-to-minute scoring for sleep and wake periods in a consumer wearable device.", "url": "https://doi.org/10.1371/journal.pone.0238464", "datePublished": "2020", "isPartOf": { "@type": "Periodical", "name": "PloS One" } }, { "@type": "ScholarlyArticle", "name": "Improving Sleep Quality Assessment Using Wearable Sensors by Including Information From Postural/Sleep Position Changes and Body Acceleration: A Comparison of Chest-Worn Sensors, Wrist Actigraphy, and Polysomnography.", "url": "https://doi.org/10.5664/jcsm.6802", "datePublished": "2018", "isPartOf": { "@type": "Periodical", "name": "Journal of Clinical Sleep Medicine" } }, { "@type": "ScholarlyArticle", "name": "Processed EEG in natural sleep.", "url": "https://doi.org/10.1016/j.bpa.2005.08.006", "datePublished": "2006", "isPartOf": { "@type": "Periodical", "name": "Best Practice & Research. Clinical Anaesthesiology" } }, { "@type": "ScholarlyArticle", "name": "Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance.", "url": "https://doi.org/10.1186/s13104-020-05355-0", "datePublished": "2021", "isPartOf": { "@type": "Periodical", "name": "BMC Research Notes" } } ], "encoding": [ { "@type": "MediaObject", "encodingFormat": "application/json", "contentUrl": "https://knowyourprime.com/evidence-wearable-sleep-tracking-accuracy.json" }, { "@type": "MediaObject", "encodingFormat": "text/markdown", "contentUrl": "https://knowyourprime.com/evidence-wearable-sleep-tracking-accuracy.md" } ] } ``` ```json {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://knowyourprime.com/"},{"@type":"ListItem","position":2,"name":"Evidence Registry","item":"https://knowyourprime.com/evidence"},{"@type":"ListItem","position":3,"name":"Wearable Sleep Tracking Accuracy Against a Sleep Lab"}]} ```