0327 Age-Related Associations Between Chronotype and Sleep-Wake Cycles: A Big Data Analysis. (25th May 2022)

• Record Type: Journal Article
• Authors: Gottlieb, Elie; Gahan, Luke; Danoff-Burg, Sharon; Rus, Holly; Watson, Nathaniel; Raymann, Roy
• Is Part Of: Sleep. Volume 45(2022)Supplement 1
• Page Start: A147
• Page End: A147
• Publication Date: 2022-05-25
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsac079.325
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Circadian rhythms progressively delay throughout adolescence until older adulthood when they advance to become early as children. Chronotype represents a subjective assessment of when one feels their performance is optimal. Evidence suggests evening chronotype is associated with adverse health effects. Presently, it is unclear whether sleep-wake cycles and chronotype diverge across ages. Here, we examined whether self-reported chronotype was associated with the daily start of the sleep-wake cycle (indicated through objectively measured bedtime) across the lifespan using a large, ecologically-valid dataset. Methods: Data from 11, 026 users (mean age: 45.3, 54.3% female) across 1, 167, 489 nights were included in the analysis from the PSG-validated SleepScore Mobile Application, which uses a non-contact, sonar-based method to objectively capture sleep-related metrics, and questionnaires to assess self-reported lifestyle factors. Chronotype was subjectively assessed with a 5-item question ranging from definitely morning-type to definitely evening-type. Bedtime, a proxy for the daily start of the sleep-wake cycle, was captured as the time at which users started a sleep recording in the Application. Linear regressions were used for the analysis. Results: Overall, chronotypes showed a near-normal distribution with a skew toward definite evening types (n=2147) compared to definite morning (n=1560) types (21.70% versus 15.70%). As expected, average bedtime was earliest for definite morning types (mean=23:02 ± 86.4 mins) and latest for definite evening types (mean=01:10 ± 102 mins). Across all chronotypes, linear regressions revealed a significant negative association between overall age and bedtime (p<0.0001). Among definitive evening types, younger ages had later bedtimes and older ages had earlier bedtimes (ß=-0.014, SE=0.002, p<0.00001). Further, the degree of change in bedtimes across age was largest for definite morning types, whereby average bedtime decreased from 23:38 (SD=86.3 mins) at age 20 to 22:29 (SD=88.2mins) at age 80 (ß=-0.019, SE = 0.002, p<0.00001). Conclusion: The present analysis showed that, across chronotypes, younger ages had later bedtimes and older ages had earlier bedtimes, presumably driven by age-related changes in circadian rhythmicity. This association was also exemplified by morning-types showing the greatest change in bedtimes across the lifespan. Future prospective studies are warranted to examine the relationship between longitudinal changes to chronotype and endogenous circadian rhythmicity across the lifespan. Support (If Any):