{"id":5041,"date":"2018-11-05T16:09:47","date_gmt":"2018-11-06T00:09:47","guid":{"rendered":"https:\/\/www.sleepscore.com\/?post_type=news_media&p=5041"},"modified":"2025-09-22T09:57:32","modified_gmt":"2025-09-22T16:57:32","slug":"sleepscore-labs-big-data-analyses-reveal-eight-distinct-sleep-profiles","status":"publish","type":"news_media","link":"https:\/\/www.sleep.ai\/de\/news\/sleepscore-labs-big-data-analyses-reveal-eight-distinct-sleep-profiles\/","title":{"rendered":"SleepScore Labs Big Data Analyses Reveal Eight Distinct Sleep Profiles"},"content":{"rendered":"

Findings of Objective and Self-Report Big Data Analysis Presented at ESRS 2018 in Basel, Switzerland<\/em><\/p>\n

\u00a0<\/em><\/p>\n

CARLSBAD, Calif. \u2014 September 28, 2018<\/strong> \u2014 SleepScore Labs<\/a><\/u>, the company behind the innovative non-contact sleep improvement system for consumers, has identified eight distinct sub-groups of sleepers based on the analysis of sleep data coupled with demographic, lifestyle and basic health information. In order to identify these sleeper profiles, sleep scientists from SleepScore Labs analyzed self-report data, and combined this with nearly two million nights of objectively measured sleep data collected unobtrusively in real home environments.<\/p>\n

Roy Raymann, Ph.D., Vice President of Sleep Science and Scientific Affairs at SleepScore Labs recently presented the findings of this study, \u201cTwo Million Nights to Characterize Sleep Heterogeneity: What Objective and Self Report Big Data Tell Us,\u201d at ESRS 2018, the 24th<\/sup> Congress of the European Sleep Research Society, in Basel, Switzerland.<\/p>\n

Demographic details, overall health and lifestyle characteristics all affect sleep. However, little is known about the complexities associated with these factors as they relate to sleep outcomes in real life settings, and the assessment of sleep is mostly limited to a single self-report sleep measure in many cases.<\/p>\n

\u201cAnalyzing sleep and environmental data objectively collected by our devices helps us uncover more information about sleep diversity in the population in real life. It can help us define and target specific groups with different issues in a more personalized way\u201d said Roy Raymann, Ph.D., Vice President of Sleep Science and Scientific Affairs, SleepScore Labs. \u201cThese findings will aid in the development of more personalized behavior change guidelines, which in turn might make it easier to be compliant to the suggested behavioral change. The ultimate end goal is to help people achieve better sleep and spur the development of new interventions to help improve sleep.\u201d<\/p>\n

Various categorical variables reflecting demographic, health, and lifestyle factors (e.g., age, BMI, smoking, exercise, blood pressure, stress) were analyzed using Multiple Correspondence Analyses (MCA). This analysis was complemented with data stemming from almost two million nights of S+ by ResMed sleep recordings. As a result, eight distinct clusters of sleepers were revealed including (1) active and healthy; (2) older, with healthy lifestyle; (3) stressed, tired but active patients without sleep issues; (4) active and healthy but with restless sleep and sleep breathing issues; (5) otherwise healthy with sleep disorders; (6) stressed smokers; (7) overweight and older with health issues and restless sleep; and (8) overweight older patients with health issues and sleep disorders. Notably, the two clusters containing mostly smokers and individuals with high BMI (indicating obesity), experienced the poorest sleep, thus showing a strong association between poor sleep and unhealthy lifestyle factors.<\/p>\n

Also at ESRS 2018, representatives from ResMed and SleepScore Labs presented, \u201cAge Related Sleep Stage Trends as Measured Using Remote Sleep Sensing Hardware.\u201d Findings demonstrate that non-contact bio-motion sleep sensing technology is capable of capturing the well-known age-related sleep stage trends. This lays the foundations for further improvement in the performance of sleep monitoring technologies and shows their validity for use beyond consumer sleep tracking.<\/p>\n

SleepScore Labs is backed by 12 years of comprehensive research and development, including more than 44 million hours of objective sleep data across seven million nights. SleepScore\u2019s sleep measurement technology is powered by ResMed and has been validated extensively against polysomnography (PSG) \u2013 the gold standard in sleep measurement.<\/p>\n

Visit SleepScore Labs\u2019 SleepShop<\/a> for more information about its products and how to improve sleep quality. Connect with SleepScore Labs on Facebook<\/a><\/u>, X<\/a> and Instagram<\/a><\/u>. The SleepScore app is free to download for Android via Google Play<\/a><\/u> and iOS devices from the Apple App Store<\/a><\/u>.<\/p>\n

About SleepScore Labs<\/strong><\/p>\n

SleepScore Labs\u2122 was created to accurately measure sleep while connecting consumers to solutions that help people sleep, feel and live better. The company is a joint venture between ResMed, Dr. Mehmet Oz, and Pegasus Capital Advisors L.P. and is committed to advancing the science and technologies around sleep and overall health. Together with the foremost medical, health, and scientific experts from around the world, the company utilizes SleepScore by ResMed\u2122 technology to provide the most accurate and advanced sleep improvement system for consumers together with data insights, product evaluation tools, and technology licensing opportunities for companies developing products and solutions in the sleep industry. SleepScore Labs was created in 2016 and is headquartered in Carlsbad, California, with an office in Dublin, Ireland.<\/p>\n

# # #<\/p>\n

Media Contacts<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

PR for SleepScore Labs<\/p>\n

Ariel Rothbard<\/p>\n

209-345-6726<\/p>\n

ariel@thebrandamp.com<\/u><\/p>\n

SleepScore Labs<\/p>\n

Ben Mar<\/p>\n

858-264-5853<\/p>\n

ben.mar@sleepscorelabs.com<\/a><\/u><\/p>\n","protected":false},"excerpt":{"rendered":"

Findings of Objective and Self-Report Big Data Analysis Presented at ESRS 2018 in Basel, Switzerland \u00a0 CARLSBAD, Calif. \u2014 September 28, 2018 \u2014 SleepScore Labs, the company behind the innovative non-contact sleep improvement system for…<\/p>\n","protected":false},"featured_media":0,"template":"","news_media_categories":[1904],"class_list":["post-5041","news_media","type-news_media","status-publish","hentry","news_media_categories-press-releases"],"acf":[],"yoast_head":"\nSleepScore Big Data Analyses Reveal Eight Distinct Sleep Profiles<\/title>\n<meta name=\"description\" content=\"Read about the findings of Objective and Self-Report Big Data Analysis Presented at ESRS 2018 in Basel, Switzerland.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.sleep.ai\/de\/news\/sleepscore-labs-big-data-analyses-reveal-eight-distinct-sleep-profiles\/\" \/>\n<meta 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