0330 Self-Reported Exercise and Objectively Measured Sleep: A Big Data Analysis Using Consumer Sleep Technology. (25th May 2022)

• Record Type: Journal Article
• Authors: Gahan, Luke; Gottlieb, Elie; Aman, Aman; Watson, Nathaniel; Raymann, Roy
• Is Part Of: Sleep. Volume 45(2022)Supplement 1
• Page Start: A148
• Page End: A149
• 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/zsac079.328
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Exercise is bidirectionally associated with sleep, whereby exercise can be an efficacious element of behavioural therapy for sleep, and longer sleep duration has been associated with increased physical activity. Given poor sleep and physical inactivity are each widely recognized as critical public health priorities, further research into the relationship between objective sleep and indices of exercise using ecologically-valid sleep measurement tools is warranted. Here, we examined the association between self-reported exercise intensity and duration, and objectively measured sleep using consumer sleep technology. Methods: Data from 2, 662 users (mean age: 47.4, 36.5% female) across 343, 308 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 capture self-reported data. Exercise intensity ( "At what level of intensity do you work out?", 3 point scale) and exercise frequency ("How many times a week do you exercise for at least 20 mins?", 5 point scale) were gathered used self-report questionnaires. Linear regression modelling was used for analysis, with age and gender used as confounding variables. Results: Greater reported exercise frequency was associated with an increase in TST (ß=3.3 mins, SE=0.838, p<0.001) and sleep efficiency (ß=0.5%, SE=0.116, p<0.001). Exercise frequency was also associated with reductions in WASO (ß=-1.153mins, SE=0.429, p<0.01) and SOL (ß=-0.425mins, SE=0.163, p<0.01). Greater reported exercise intensity was associated with an increase in TST (ß=4.908 mins, SE=1.886, p<0.01) and sleep efficiency (ß=1.16%, SE=0.255, p<0.001). Exercise intensity was also associated with reductions in WASO (ß=-3.282mins, SE=0.965, p<0.01) and SOL (ß=-0.852mins, SE=0.272, p<0.01). Conclusion: Self-reported exercise frequency and intensity were associated with improved objective sleep metrics across the board. This big data finding using ecologically-valid consumer sleep technology can further contribute to public health recommendations regarding the positive impact of exercise on sleep. Support (If Any):