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HomeLive WellLifestyleDo wearable sleep trackers work?

Do wearable sleep trackers work?

Are wearable devices like Fitbit really reliable for recording your sleep patterns? Explore the strengths and limitations of using wearable technology for sleep monitoring.

How well does your Fitbit track your sleep? Find out the truth about its accuracy and how it compares to clinical sleep studies.

Quick Facts

  • Fitbit uses machine learning algorithms to obtain personalized sleep information.
  • New Fitbit models show improved accuracy in detecting sleep phases.
  • There are some limitations compared to baseline sleep studies.
  • Most studies focus on young, healthy participants; the results may not apply to everyone.
  • More research is needed to validate the results in various groups.

Millions of adults worldwide suffer from sleep disorders,1 which are associated with hypertension, depression, obesity, diabetes, and stroke.2 Wearable tracking devices like Fitbit can help people learn more about their sleep habits and improve their quality of sleep.

Although it was first designed as a fitness tracker, Fitbit soon developed into a device used for tracking a variety of health-related measurements, including sleep-related data.

Just how accurate is Fitbit sleep when used for sleep tracking?

What a wearable sleep tracker does while you snooze

Fitbit is a wearable wristband that continuously monitors heart rate and body movements.

Some models use “sleep-staging” – a machine learning algorithm that incorporates measurement data and provides a detailed personalized report on sleep patterns.3

The device has become popular among adult sleepers and researchers alike because of the ease of its use, relative inexpensiveness compared to standard clinical sleep studies (polysomnography), and the ability to follow-up variability in sleep over long periods of time.3

So what does the latest research say about Fitbit Sleep?

A 2024 review of the evidence by researchers at Soonchunhyang University College of Medicine, South Korea analysed 24 peer reviewed research studies to find out whether consumer wearable sleep monitors really work.6 Doctors collected data from over 700 subjects using 798 patient using Fitbit, Jawbone, myCadian watch, WHOOP strap, Garmin, Basis B1, Zulu Watch, Huami Arc, E4 wristband, Fatigue Science Readiband, Apple Watch, or Xiaomi Mi Band 5 to track their sleep.

They found that none of the sleep trackers gave statistically similar sleep results as a standard sleep study (polysomnography). In real terms it means that if a doctor did a round of sleep study tests on you, your wearable is unlikely to give the same result, or one consistently close to it.

Two exceptions were that fitbit was as good as a sleep study at detecting how many times subjects woke in the night, and wearables gave similar results to sleep study as to how long it took to fall asleep.

So overall, they kind of work, and Fitbit might be slightly better than some of the others.

Researchers aimed to test Fitbit’s sleep accuracy

Researchers set out to test how accurate Fitbit sleep is as a sleep tracker in a study published in the Journal of Medical Internet Research and found that the newer models of Fitbit (that could detect stages of sleep) showed better accuracy of sleep detection than older models.3

The newer models of Fitbit were also better than older models at calculating overall sleep and wake times because they don’t rely solely on the detection of motion to indicate sleep.

However, their results were less specific (had more false positives) compared to a standard sleep study offered in a sleep lab.

The researchers evaluated all published studies on the accuracy of Fitbit, and 22 papers met their selection criteria.

Ten studies assessed the older models of Fitbit and five studies assessed the more recent models with only three of them comparing the results to gold standard polysomnography.

The researchers analyzed data from eight of the studies and found that newer Fitbit models were more accurate at estimating several sleep parameters like total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) with results that were similar to the measurements provided by polysomnography.

Three studies assessed the consistency of the readings from Fitbit and found that the device showed little variability, so it could be used to assess trends in sleep quality.

However, more than half of these studies were done in labs and participants were mostly young or middle-aged sleepers, so the results haven’t been replicated in older adults or those who have a sleep disorder.

Accuracy of Fitbit Charge 2 in measuring sleep

During sleep, we pass through several cycles of sleep, which consist of two stages of light sleep followed by a stage of deeper sleep, and finally REM sleep.

Each stage is important and changes in the percentage and duration of these stages can point to sleep disorders or the effects of certain medications.4

Measuring sleep stage transitions between light sleep

A 2019 study by researchers in Japan evaluated the accuracy of Fitbit Charge 2 in measuring sleep stage transitions between light sleep, deep sleep, and REM sleep stages (rapid eye movement).5

They also investigated the factors associated with measurement errors by the device.

Findings

The researchers found that Fitbit Charge 2 underestimated transitions from one sleep stage to another, compared to a reference medical device called Sleep Scope.

They suggested that the accuracy of Fitbit decreases when there are more transitions between sleep stages.

The researchers did not find Fitbit Charge 2 suitable for studies related to sleep-stage transitions.

The study was published in JMIR mHealth and uHealth and involved the data of one night of sleep from 23 eligible participants using Fitbit Charge 2 and Sleep Scope simultaneously.

Eight of the participants reported a Pittsburgh Sleep Quality Index (PSQI) >5, which means that they weren’t satisfied with their sleep quality.

When participants reported better sleep quality (PSQI)<5, Fitbit sleep showed fewer errors in the probability of staying in the deep sleep stage.

Fitbit Charge 2 made fewer measurement errors in the probability of sleep transition between different stages when there was less fragmentation in sleep.

This was assessed by the number of times of waking during sleep using a measurement called WASO (wake after sleep onset).

Drawbacks

The main drawback of this study was the short duration (one night) and the participation of only young and healthy individuals. This makes the results limited and unsuitable for generalizing to the general population.

Studies have shown that sleep-tracking devices have a positive impact on health.3

Fitbit wristbands are useful for the public to track their sleep patterns and improve their quality of sleep.

Although they are practical and easy to use, the data they provide is not as accurate as the data provided from a clinically performed sleep study.3,5

However, they offer a feasible method for conducting population-based research so their efficiency requires further evaluation.3,5

References

  1. Berhanu H, Mossie A, Tadesse S, Geleta D. Prevalence and Associated Factors of Sleep Quality among Adults in Jimma Town, Southwest Ethiopia: A Community-Based Cross-Sectional Study. Sleep Disord. 2018 Apr 22;2018:8342328. doi: 10.1155/2018/8342328. PMID: 29850261; PMCID: PMC5937373.https://www.hindawi.com/journals/sd/2018/8342328/#results
  2. Institute of Medicine (US) Committee on Sleep Medicine and Research; Colten HR, Altevogt BM, editors. Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem. Washington (DC): National Academies Press (US); 2006. 3, Extent and Health Consequences of Chronic Sleep Loss and Sleep Disorders. Available from here
  3. Haghayegh S, Khoshnevis S, Smolensky MH, Diller KR, Castriotta RJ. Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis. J Med Internet Res. 2019;21(11):e16273. Published 2019 Nov 28. doi:10.2196/16273 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6908975/
  4. Shrivastava D, Jung S, Saadat M, Sirohi R, Crewson K. How to interpret the results of a sleep study. J Community Hosp Intern Med Perspect. 2014 Nov 25;4(5):24983. doi: 10.3402/jchimp.v4.24983. PMID: 25432643; PMCID: PMC4246141. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4246141/pdf/JCHIMP-4-24983.pdf
  5. Liang Z, Chapa-Martell MA. Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors. JMIR Mhealth Uhealth. 2019;7(6):e13384. Published 2019 Jun 6. doi:10.2196/13384 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592508/
  6. Lee YJ, Lee JY, Cho JH, Kang YJ, Choi JH. Performance of consumer wrist-worn type sleep tracking devices compared to polysomnography: a meta-analysis. J Clin Sleep Med. Published online November 1, 2024. doi:10.5664/jcsm.11460
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