FEATURED
Presenting Groundbreaking ANALYSES at SLEEP 2021
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SLEEP2021 PRESENTATION — SleepIQ® data to predict illness & COVID-19 symptom exacerbation
1. OBJECTIVE
This presentation illustrates a model by which symptoms for COVID-19 can be detected from the SleepIQ® technology platform.
2. HIGH LEVEL STUDY SETUP
Using an Institutional Review Board (IRB) approved survey to opted-in users, COVID-19 test results were received. Data for 82 respondents who tested positive who reported the date of symptom onset were used. Data for 1,519 respondents who tested negative who reported testing dates were used. Sleep duration, sleep quality, restful sleep duration, time to fall asleep, respiration rate, heart rate, and motion level were obtained from ballistocardiography signals stored in the cloud.
3. RESULTS
With respect to their baseline, significant increases in sleep duration, average breathing rate, average heart rate and decrease in sleep quality were associated with symptom exacerbation in COVID-19 positive respondents. In COVID-19 negative respondents, no significant sleep or cardiorespiratory metrics were observed. Accuracy of the predictive model is similar to values reported for wearable sensors. Considering additional days to confirm prediction improved the accuracy.
SLEEP 2021 PRESENTATION — LONGITUDINAL HRV CHANGES
1. OBJECTIVE
Heart rate variability (HRV) is commonly used to assess the activity of the autonomic nervous system (ANS). ANS function changes, reflected in HRV, result from factors including lifestyle, aging, cardio-respiratory illnesses, sleep state, and physiological stress. Despite broad interest in HRV, few studies have established normative overnight HRV values for a large population. To better understand population level HRV changes, real world, overnight sleep SDNN (standard deviation of all normal heartbeat intervals, lower HRV is reflected by lower SDNN) values have been analyzed for a large sample of Sleep Number® smart bed users.
2. HIGH LEVEL STUDY SETUP
Mean overnight SDNN values were obtained over the course of 18.2M sleep sessions from 379,225 sleepers (48 ± 14.7 sessions/user). 50.9% of sleepers were female. The mean age was 52.8 ± 12.7 years and age range was 21 to 84 years. Heartbeat intervals used to compute SDNN were extracted from a ballistocardiogram (BCG).
3. RESULTS
The results for this observation included three areas of interest: age, gender, and day-of-the-week. Significant SDNN changes depending on age, gender and their interaction were observed.
- For sleepers under 50, SDNN declined at a rate of about 2.1 ms/year, then leveled off for sleepers aged 50-65, and increased slightly thereafter.
- Women under 50 displayed lower, more slowly declining SDNN values than men, but this trend reversed for sleepers over 50.
- Throughout the week, SDNN values followed a U- shaped (women) or L-shaped (men) pattern, where values were highest during the weekend and lowest at mid-week.