Recent studies have investigated the usefulness of Fitbit Charge 2 for clinical sleep tracking.
The polysomnography has long been the gold standard in sleep tracking for assessing sleep-wake state patterns and at a deeper level, the composition of sleep stages, recent research has investigated the use of Fitbit Charge 2 for clinical sleep tracking.
A typical sleep progression begins with two stages of light sleep moving into a deep sleep, followed by rapid-eye-movement (REM) sleep in which dreams and memories form. Healthy adults will cycle through non-REM sleep (i.e., light sleep and deep sleep) and REM sleep multiple times a night.
Each sleep stage has specific brain wave, muscle and eye activity that allows researchers to detect when a person has entered each stage of sleep and to measure for how long they are in each stage. Polysomnography’s sleep data is obtained through the use of multiple technologies including electroencephalographic (EEG) to measure brain waves, electromyographic (EMG) to measure muscle activity, and electrooculographic (EOG) to measure eye activity which ensures its validity.
However, consumer wearable devices such as the Fitbit device are gaining traction as a less expensive and less intrusive option to collect sleep data from patients. Already commercially available for the general population, the adoption of Fitbit devices to automatically detect and store sleep data via the Fitbit app would allow for sleep analyses to occur outside of traditional sleep laboratories. In this way, Fitbit devices would reduce costs and increase accessibility of sleep tracking for patients.
Despite the apparent benefits of using wearable devices such as the Fitbit Charge 2, a multisensory device, to gain insights on patients’ sleeping patterns, questions have been raised over the validity and reliability of its sleep data.
Recently, a study demonstrated promising results from a validity comparison of the sleep data collected from a Fitbit Charge 2 and polysomnography. Participants included 44 healthy adults with no medical conditions or drug use that could interfere with their sleep stages. Nine participants who had periodic limb movement during an initial sleep test were separated from the main sample analysis due to the interference with their sleep quality. Participants were monitored overnight in a sleep laboratory using both the Fitbit Charge 2 and the gold standard, polysomnography.
Sleep data gathered from the Fitbit device was compared to that of the polysomnography to determine how accurate the Fitbit was in detecting sleep stages and estimating the time spent in each sleep stage. Researchers also assessed the Fitbit Charge 2’s sensitivity in detecting when a participant was in a wake state and when they were in a sleep state as recorded by the polysomnography. Data was compared between the two measuring devices to evaluate the accuracy of the Fitbit device in recording sleep patterns across the night; that is, whether the Fitbit’s sleep cycle data overlapped with that of the polysomnography.
The study found that while the Fitbit Charge 2 accurately estimated the amount of time a participant spent in REM sleep as recorded by the polysomnography, the device significantly overestimated the time spent in light sleep and significantly underestimated the time spent in deep sleep. The Fitbit Charge 2 also showed strong accuracy in detecting when a participant was in each of the sleep stages, except for deep sleep in which the Fitbit device only had an accuracy of .49 for detecting deep sleep as recorded by the polysomnography. Further, the Fitbit device had a low ability to specify when a participant was in a wake state compared to the polysomnography. In terms of sleep cycles across the night, the Fitbit Charge 2 correctly identified 82% of polysomnography cycles, which was not a significant difference.
Overall, the Fitbit Charge 2 measured well against gold standard, except in detecting deep sleep; however, the authors of the study point out that sleep tracking in populations with known sleep variances (e.g., adolescents, elderly) and in different settings may prove difficult for the Fitbit device.
This study has been followed by a comparative study of the validity of the Fitbit devices, Fitbit Charge 2 and Fitbit Alta HR, against polysomnography in patients with obstructive sleep apnea. The study tackles some of the limitations found within the previous study through comparing the sleep data gathered by Fitbit devices and in a population with a diagnosed sleeping disorder. Out of an initial sample of 65 participants, 55 were confirmed to meet the qualifications for obstructive sleep apnea.
Similar to the previous study, participants were monitored overnight in a sleep laboratory using both the polysomnography and a Fitbit device. Researchers reported that only the Fitbit devices’ measure of REM sleep matched that of the polysomnography; all other sleep outcomes differed significantly. Measures such as wakefulness after sleep onset were underestimated while total sleep time was overestimated indicating the Fitbit devices do not yet provide sufficient sleep tracking data in a clinical population who require precise and accurate sleep assessments for proper diagnoses.
Fitbit devices appear to have acceptable detection and monitoring of sleep stages and cycles for the mass consumer population to use for their personal knowledge; however, the limitations of these devices in accurately detecting wake states and deep sleep stages in particular proves to be insufficient for use in a clinical setting.
Whereas an error in estimating wake states or failure to detect deep sleep in the general population may not be harmful, the failure to accurately assess clinical patients can lead to misdiagnoses. The benefits of completing sleep assessments outside of a laboratory setting with a much cheaper device do not outweigh the need to obtain valid and reliable sleep tracking. While Fitbit devices show promise in changing the way sleep assessments are performed, further updates to its technology are required before it matches the gold standard polysomnography.
de Zambotti M, Goldstone A, Claudatos S, Colrain IM, Baker FC. A validation study of Fitbit Charge 2™ compared with polysomnography in adults. Chronobiol Int. 2018 Apr;35(4):465-476. doi: 10.1080/07420528.2017.1413578. Epub 2017 Dec 13. PMID: 29235907.
Moreno-Pino F, Porras-Segovia A, López-Esteban P, Artés A, Baca-García E. Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea. J Clin Sleep Med. 2019 Nov 15;15(11):1645-1653. doi: 10.5664/jcsm.8032. PMID: 31739855; PMCID: PMC6853383.
Image by Wokandapix from Pixabay