We are only beginning to understand the potential of wearables and health for sleep monitoring.
Sleep monitoring via wearables can provide clues about our overall sleep quality using standard metrics such as REM sleep and sleep duration by sleep stage. Still, it can also serve as a harbinger of other important elements of our health.
In this article, we explore some of the important work being done in the area of sleep science and wearables that look at early disease detection, influencing dreams, and more.
Diagnosing and detecting specific chronic conditions and comorbidities can also later help in the self-management process.
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Predicting migraine attacks before they occur
Researchers at Oulu University, Finland, used wearables and AI algorithms to evaluate whether migraines can be predicted in advance using sleep time data.
Migraine is a prevalent neurological disease affecting 39 million men, women, and children in the U.S. Early detection would help shorten the migraine attack by taking preventive medication.
There is a known relationship between sleep disturbances and migraines, and this association is especially strong between nighttime and early morning migraine attacks and sleep problems.
This study used Empatica E4 wearables and collected the following data before correlating them. Data from the electrodermal sensor, heart rate, heart rate variability (HRV), and blood volume pulse.
The study results show that algorithms designed to understand the differences in these metrics for the nights when users had migraines and for the nights when they did not, can help predict migraine accuracy with an accuracy of 84% on average.
Early Detection of Alzheimer’s disease and Parkinsons disease
One in nine people age 65 and older (11.3%) has Alzheimer’s dementia. This disease can be overwhelming for not only the person who has it but also for their caregivers.
Researchers ( Ju, McLeLand, Holtzman, et al.) studied the linkage between b-amyloid disposition, sleep-wake problems, and preclinical Alzheimer’s disease and published promising results in the JAMA Neurology Journal, “Sleep quality and preclinical Alzheimer disease.”
This novel study used a wrist-based Actigraph wearable to measure three key sleep metrics.
- Total Sleep time
- Quality of Sleep ( Total Sleep time/time in bed expressed as a percentage)
- wake time after sleep onset
Aβ42 was measured by the Alzheimer’s Disease Research Center Biomarker Core using an enzyme-linked immunosorbent assay.
This study found that a low CSF Aβ42 level is associated with poor sleep efficiency. Their findings support the hypothesis that sleep-wake abnormalities are associated with the presence of amyloid deposition in the preclinical stage of Alzheimer’s Disease.
A more recent study, “Smartwatch-based activity analysis during sleep for Early Parkinson’s disease detection,” shows how smartwatch-based sleep monitoring can aid with the early detection of Parkinson’s’ disease.
Poor sleep quality can be associated with different kinds of health-related issues that a person may experience as he or she gets older. Sleep disorders have been known to precede Parkinson’s and Alzheimer’s disease.
Leading consumer-grade wearable makers such as Apple and Samsung are investing heavily in research in this area.
Poor sleep quality and early warning for Diabetes
Sleep disorders have been known to be associated with increased mortality and morbidity in general. Numerous studies have shown that poor sleep quality is associated with a higher risk of diabetes.
As a risk factor for the development of type-2 diabetes, poor sleep quality may independently increase the incidence of diabetes.
Another study by ( D. Yadav and Kyung-Hyun Cho) examined the association between sleep duration and the onset of type-2 diabetes. It emphasized the importance of total sleep duration and sleep quality as risk markers in monitoring type 2 diabetes.
It is clinically observed that people suffering from chronic diseases such as diabetes, Chronic Kidney disease, hypertension, arthritis, and stroke usually have trouble with their sleep, like difficulty falling/staying asleep and daytime sleepiness. These correlations with sleep-wake behavior can be used to predict chronic diseases.
As new wearable sensors to measure glucose get incorporated into smartwatches, it becomes easier to correlate sleep data with glucose levels using Edge AI, and users can make more meaningful inferences about their overall health and risk of diabetes.
Today’s most consumer wearables, such as the Fitbits, Oura ring, Whoop strap, Garmin watch, Samsung Galaxy Watch, Apple Watch, and companion apps like Apple Health, Samsung Health, Garmin Connect, and Google Fit, make sense of your sleep quality and quantity.
Some watches and smart bands provide a lot of analytics, while others strive to make sure that you stop worrying about metrics and focus on getting you to sleep better.
Obsessing your sleep metrics can be a problem as well. If you worry so much about your sleep scores and quality, you may have difficulty falling asleep.
The key is to be mindful and examine trends over a longer duration (weekly or monthly). That way, if you see a bothersome trend in your sleep metrics over several months that you don’t like, you can talk to your physician and learn more.
There is a lot more to sleep on than the usual run-of-the-mill metrics. Home testing assays become more commonplace as wearables become more powerful in their sensor capabilities. Users will become more proactive with their health and overall fitness.
Do you monitor your sleep metrics regularly? How often do you monitor them, and what steps have you taken to get more restful zzzzzzs? Sound off using the comments below.