how accurate is fitbit sleep

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 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 the research says about Fitbit sleep

Fitbit is a wearable wrist band that continuously monitors heart rate and body movements. More recent 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 (polysomnographies), and the ability to follow-up variability in sleep over long periods of time.3

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 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

During sleep we pass through several cycles of sleep, which consists 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

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. 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 less errors in the probability of staying in the deep sleep stage.

Fitbit Charge 2 made less 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).

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: https://www.ncbi.nlm.nih.gov/books/NBK19961/https://www.ncbi.nlm.nih.gov/books/NBK19961/#:~:text=The%20cumulative%20effects%20of%20sleep,%2C%20heart%20attack%2C%20and%20stroke.
  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/

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