Sleep Number Corporation announced new data from its 360 smart bed at SLEEP 2022, the 36thannual meeting of the Associated Professional Sleep Societies, LLC from June 4-8 in Charlotte, NC. Data presented showed results of a study to measure skin temperature using sensors to be deployed in 360 smart beds, and a study to measure daytime alertness using Sleep Number's proprietary SleepIQ® technology, which is embedded in every 360 smart bed. Temperature meaningfully impacts sleep quality, and alertness is an indicator of sleep's impact on daytime performance.

These studies further demonstrate Sleep Number's ability to provide unparalleled quality sleep, as well as the 360 smart bed's potential research capabilities to accurately assess and monitor sleep using non-invasive, longitudinal methodology. To date, Sleep Number has leveraged and learned from over 15 billion hours of sleep data gathered from over 1.8 billion real-world sleep sessions. Body and ambient temperature significantly influence sleep patterns, both in falling asleep and staying asleep.

As the body prepares for sleep, temperature increases in distal (hands and feet) and proximal (abdomen) areas, while core body temperature decreases. The ratio of distal to proximal temperature can be highly predictive of sleep onset, which can offer insight into one's sleep quality. However, there are few devices that can measure these temperatures unobtrusively.

Sleep Number's study sought to unobtrusively estimate distal skin temperature during sleep using a temperature sensor array on a bed. Three participants' skin temperatures were measured using five equally spaced temperature sensors set laterally across the bed to align with the torso. Sleep Number compared their distal skin temperature data to that of a research-grade, FDA-approved smart watch and then used the sleep data from the 360 smart bed to build predictive models estimating distal skin temperature.

Results of the study showed that a temperature sensor system, coupled with an optimized decision-tree model to predict distal skin temperature for each minute, can predict a mean distal sleep temperature for each sleep session with reasonable accuracy. This study also showed the 360 smart bed platform with the sensor array enabled unobtrusive, real-world collection of distal skin temperatures during sleep and may be useful for future studies measuring overnight temperature. Poor quality sleep is associated with a broad range of health risks such as chronic illnesses, impaired vigilance and cognitive issues, including alertness.

Understanding and predicting alertness trends throughout the day can better inform daytime performance. Previously, the two-process model (TPM) of sleep regulation combining sleep homeostasis and circadian rhythm has been used to derive a daytime alertness curve. The TPM model has been used to model the effects of sleep deprivation on memory, circadian misalignment, temperature regulation and brain function; however, the TPM-derived alertness curve comes largely from small-scale, controlled studies.

Sleep Number's study showed that a similar, three-parameter alertness measure, which included sleep homeostasis, circadian rhythm and self-rated alertness responses from 360 smart bed sleepers can scale to a large group of people under real-world conditions. Human sleep is regulated by two components: sleep homeostasis and circadian rhythms. Sleep homeostasis, or pressure to sleep, builds up in the body as time awake increases and decreases during sleep. Circadian rhythms are natural, internal cycles of biological and behavioral processes that rise and fall across the 24-hour day.

Circadian rhythms promote sleepiness before usual bedtime, help initiate sleep and begin promoting wakefulness before usual wake-up time in the morning. Sleep Number 360 smart bed sleepers rated their subjective alertness on a scale from one to 10 through the SleepIQ technology app. The data was analyzed by age group (18-40, 41-65 and 66-90) and compared to a three-parameter version of the TPM-derived alertness curve.

In total, more than 65,500 sleep sessions were gathered over 95 days. The study showed that: Overall, subjective alertness followed a similar trend to previous studies: mean hourly alertness increased in the morning, dipped slightly in the afternoon, increased during the evening and decreased again during the night. Unlike previous studies, however, this study saw a greater increase in alertness from the afternoon to evening.

Based upon the demographic makeup of the sleeper population, Sleep Number was also able to analyze hourly trends in mean alertness scores by age group. Younger sleepers had the most stable alertness scores throughout the day, but lower levels of alertness compared to older age groups. In contrast, middle-aged and older sleepers had higher levels of alertness, but their score varied more widely throughout the day compared to younger sleepers.

These results show that TPM-derived alertness can effectively predict daily alertness trends in a large group of people under real-world conditions. Quality sleep is associated with overall health and wellbeing, which is why all Sleep Number 360 smart beds are informed by the RU-SATED model, six scientific factors proven by Dr. Dan Buysse, Professor of Psychiatry and Clinical and Translational Science, University of Pittsburgh, to provide quality sleep: Regularity, Satisfaction, Alertness, Timing, Duration and Efficiency.