The go-to study to analyze sleep has long been polysomnography, which analyzes a person’s oxygen level, heart rate and breathing, eye and leg movements, simultaneously with an electroencephalogram, or EEG while they sleep. The latter is the one where they stick 14 electrodes to you that detect the electrical activity in your brain, your heart rate, and muscle movement.
An EEG can be used to study your brainwaves as you navigate through different stages and cycles in your sleep. A wearable smartwatch or fitness tracker on your wrist, however, is far from your brain.
The most popular wearables like the Fitbit, Apple Watch, Garmin watch, Galaxy Watch, etc, use a combination of tracking heart rate and heart rate variability (HRV), movement, breaths per minute, and, on occasion, skin temperature. The ones that track sleep cycles then use an algorithm to make an educated guess on what your sleep cycles look like, based on your body’s response.
A 2017 study compared polysomnography with the performance of a Fitbit Charge 2 in 35 adults and found that the Fitbit detected sleep onset with a 96% accuracy, but overshot time spent asleep by 9 minutes.
With regards to sleep cycle and stages detection, the study revealed the Fitbit detected light sleep with an 81% accuracy, deep sleep with only a 49% accuracy, and REM sleep with a 74% accuracy.
A newer study published in April of 2022 compared the performance of the Fitbit Alta HR with the results of an EEG both worn simultaneously by 40 college athletes while sleeping. This work found that the Fitbit is a useful tool for athletes’ sleep management as it resulted in satisfactory concurrence with the EEG in tracking sleep onset, time spent asleep, and sleep cycles.
While not 100% accurate, the Fitbit was found to have a strong correlation in total sleep time and between the percentage of deep sleep in between sleep onset and initial REM sleep.
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