Estimated Sleep-Wake Patterns Obtained From a Large U.S. Sample Using a Home-Based Under-Mattress Sleep-Monitoring Device

Objectives: This study aimed to characterize sleep-wake schedules in a large U.S. sample. Methods: Descriptive analyses were performed on data from 12,507 users (46.9% female; mean age, 48.5±13.4 years) of a home-based sleep-monitoring device. To understand sleep-wake schedules and sleep regularity,...

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Published inJournal of sleep medicine Vol. 21; no. 3; pp. 140 - 149
Main Authors Zitser, Jennifer, Cotton-Clay, Andrew, Easwar, Venkat, Kinsolving, Arthur, Kahn, Philippe, Kushida, Clete A.
Format Journal Article
LanguageEnglish
Published 대한수면연구학회 01.12.2024
Subjects
정신과학
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ISSN2384-2423
2384-2431
DOI10.13078/jsm.240020

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Abstract Objectives: This study aimed to characterize sleep-wake schedules in a large U.S. sample. Methods: Descriptive analyses were performed on data from 12,507 users (46.9% female; mean age, 48.5±13.4 years) of a home-based sleep-monitoring device. To understand sleep-wake schedules and sleep regularity, the total sleep time (TST) standard deviation (SD) and sleep onset (SO) SD were included as parameters. Inferential analyses were performed on additional sleep parameters, including physiological (heart rate and respiratory rate) and sleep architecture parameters (TST, sleep efficiency, and wake after SO), in typically regular (average TST SD <60 minutes) vs. irregular (≥60 minutes) users. Results: 4,175,260 recorded nights were analyzed. The overall estimated TST SD (SD) across users mean was 66.1 (18.7) minutes, and the SO SD (SD) was 55.6 (20.5) minutes. The population was divided into 6 groups according to age: Groups 1 (20–29), 2 (30–39), 3 (40–49), 4 (50–59), 5 (60–69), and 6 (70–79). The estimated TST SD were: 70.7 (20.0), 67.2 (18.0), 66.8 (18.5), 66.1 (18.4), 63.4 (18.2), and 60.5 (18.9) minutes, respectively. The estimated SO SD were: 62.1 (21.1), 57.4 (19.4), 57.0 (20.2), 55.7 (20.0), 51.6 (20.3), and 46.7 (20.8) minutes, respectively. When participants were divided into those with regular and irregular sleep schedules, 58.4% were found to have an irregular sleep-wake schedule. Conclusions: Irregular sleep-wake schedules were prevalent across all age categories in this population. Even though this does not implicate causality, sleep habits represent a potentially modifiable behavior with health implications.
AbstractList Objectives: This study aimed to characterize sleep-wake schedules in a large U.S. sample. Methods: Descriptive analyses were performed on data from 12,507 users (46.9% female; mean age, 48.5±13.4 years) of a home-based sleep-monitoring device. To understand sleep-wake schedules and sleep regularity, the total sleep time (TST) standard deviation (SD) and sleep onset (SO) SD were included as parameters. Inferential analyses were performed on additional sleep parameters, including physiological (heart rate and respiratory rate) and sleep architecture parameters (TST, sleep efficiency, and wake after SO), in typically regular (average TST SD <60 minutes) vs. irregular (≥60 minutes) users. Results: 4,175,260 recorded nights were analyzed. The overall estimated TST SD (SD) across users mean was 66.1 (18.7) minutes, and the SO SD (SD) was 55.6 (20.5) minutes. The population was divided into 6 groups according to age: Groups 1 (20–29), 2 (30–39), 3 (40–49), 4 (50–59), 5 (60–69), and 6 (70–79). The estimated TST SD were: 70.7 (20.0), 67.2 (18.0), 66.8 (18.5), 66.1 (18.4), 63.4 (18.2), and 60.5 (18.9) minutes, respectively. The estimated SO SD were: 62.1 (21.1), 57.4 (19.4), 57.0 (20.2), 55.7 (20.0), 51.6 (20.3), and 46.7 (20.8) minutes, respectively. When participants were divided into those with regular and irregular sleep schedules, 58.4% were found to have an irregular sleep-wake schedule. Conclusions: Irregular sleep-wake schedules were prevalent across all age categories in this population. Even though this does not implicate causality, sleep habits represent a potentially modifiable behavior with health implications.
Objectives: This study aimed to characterize sleep-wake schedules in a large U.S. sample. Methods: Descriptive analyses were performed on data from 12,507 users (46.9% female; mean age, 48.5±13.4 years) of a home-based sleep-monitoring device. To understand sleep-wake schedules and sleep regularity, the total sleep time (TST) standard deviation (SD) and sleep onset (SO) SD were included as parameters. Inferential analyses were performed on additional sleep parameters, including physiological (heart rate and respiratory rate) and sleep architecture parameters (TST, sleep efficiency, and wake after SO), in typically regular (average TST SD <60 minutes) vs. irregular (≥60 minutes) users. Results: 4,175,260 recorded nights were analyzed. The overall estimated TST SD (SD) across users mean was 66.1 (18.7) minutes, and the SO SD (SD) was 55.6 (20.5) minutes. The population was divided into 6 groups according to age: Groups 1 (20–29), 2 (30–39), 3 (40–49), 4 (50–59), 5 (60–69), and 6 (70–79). The estimated TST SD were: 70.7 (20.0), 67.2 (18.0), 66.8 (18.5), 66.1 (18.4), 63.4 (18.2), and 60.5 (18.9) minutes, respectively. The estimated SO SD were: 62.1 (21.1), 57.4 (19.4), 57.0 (20.2), 55.7 (20.0), 51.6 (20.3), and 46.7 (20.8) minutes, respectively. When participants were divided into those with regular and irregular sleep schedules, 58.4% were found to have an irregular sleep-wake schedule. Conclusions: Irregular sleep-wake schedules were prevalent across all age categories in this population. Even though this does not implicate causality, sleep habits represent a potentially modifiable behavior with health implications. KCI Citation Count: 0
Author Kinsolving, Arthur
Easwar, Venkat
Kushida, Clete A.
Zitser, Jennifer
Kahn, Philippe
Cotton-Clay, Andrew
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