Garmin announced Advanced Sleep Monitoring, an enhanced capability to more accurately identify sleep stages, last June. Developed against truth data generated by a clinical device, the feature is the result of a sleep study by Garmin Health conducted under the supervision of Dr. Suzanne Stevens, Director of the University of Kansas Medical Center (KUMC) Sleep Medicine Clinic with certifications by the American Board of Psychiatry and Neurology and the American Board of Sleep Medicine. 

Dr. Stevens presented the details of the study on May 7^th at the Annual Meeting of the American Academy of Neurology. The poster presentation covered study design, results, and the conclusion that Garmin wearables that support Advanced Sleep Monitoring present a valid method to estimate sleep stages. For consumers this results in valuable insights regarding their sleep hygiene and how their sleep patterns may be affecting their overall health. For researchers the results indicate Garmin wearables may be suitable for longitudinal studies where monitoring participant activity and sleep patterns are desired endpoints.

In contrast to similar studies, the Garmin Health and KUMC study was conducted outside a sleep clinic in real-world conditions typical for most users. Study participants used a clinical reference device at home in their own beds, providing data representative of their normal sleeping patterns. Only participants using CPAP machines or with tattoos on their wrist were excluded, meaning data was collected from participants with a variety of known and unknown sleeping conditions, as would generally be the case for users of consumer wearables. 

Outside of the study, algorithm performance from 3,200 nights of sleep in a production environment collected during beta testing was cross-checked with clinical reference data regarding average distribution of sleep stages by age group. The results aligned with expectations, including a decrease of deep sleep and total sleep duration as a function of age.

Garmin Health provides enterprise solutions that leverage Garmin wearables and the high-quality sensor data they produce for use in the corporate wellness, population health and patient monitoring markets.  Contact us to request more information about how to build your own innovative experience or research study.

Commercially Available Wearable
Provides Valid Estimate of Sleep Stages

Scott Burgett¹_,Robert Blair¹, Darrell Lightfoot², Catherine Siengsukon³, Adam Reetz¹, Suzanne Stevens^2
1Garmin International, ²Neurology, University of Kansas Health Systems, ³University of Kansas Medical Center

Introduction

In the
last several years there has been an explosion of smart wearable devices that
are capable of measuring steps, activity, heart rate, and other biometric data
about the wearer. An estimated 117 million smart wearables will be sold in 2019,
with that number rising to 233 million in 2022 [1].  Actigraphy has been the gold standard for many
years to estimate sleep stages using wearables. Actigraphy uses accelerometer
information to estimate wake/sleep periods of the user [2]. Many modern
wearables also measure heart rate and heart rate variability by using optical photoplethysmography
(PPG). PPG uses light emitted into the skin to measure pulse rate by observing
small changes in the intensity of the reflected light due to capillary blood volume
changes as the pulse pressure wave transits the sensor field of view. The use
of actigraphy, heart rate, and heart rate variability together provides the
opportunity to estimate more than just wake/sleep, such as light (N1 and N2),
deep, and REM stages of sleep.

Although many wearables purport to accurately distinguish between light and deep sleep [3], little information is available on the validity of using wearables in this manner. A previous study by Fitbit described a non-production method to estimate sleep stages in a healthy adult population using PPG and actigraphy in a wearable device [4]. This study describes the method and results of using optical PPG and actigraphy in a wearable device (a vivosmart 3 manufactured by Garmin International) to estimate sleep stages in a population of adults.

Method

This study involved recruiting
subjects to wear a Garmin vívosmart 3 and a reference device so that the
accuracy of the Garmin device in estimating sleep stages could be assessed. The
most accurate method to determine sleep stages involves the use of in-laboratory
polysomnography (PSG), which includes the use of EEG, EOG, and EMG sensors. However,
the use of an in-laboratory PSG is cumbersome for the subjects, and many times
does not yield the same quality and quantity of sleep as a subject sleeping in
familiar surroundings in their own bed. EEG systems that are take home devices
such as the Sleep Profiler have been developed and yield comparable sleep
architecture estimates to PSG [5]. In this study, the Sleep Profiler was used
as the reference device, reducing burden on…