0186 The Relationships between General Technology Use, Technology Use at Bedtime, and Sleep. (12th April 2019)

• Record Type: Journal Article
• Authors: Peszka, Jennifer; Sestir, Marc A; Kennedy, Lindsay A; Mastin, David F
• Is Part Of: Sleep. Volume 42(2019)Supplement 1
• Page Start: A76
• Page End: A76
• Publication Date: 2019-04-12
• 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/zsz067.185
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Research shows associations between technology use and disrupted and shortened sleep, leading to the suggested intervention of reducing technology use with the intention of improving sleep. However, whether all technology use or only that associated with bedtime is related to disrupted sleep has not yet been established. We examined the relationship between inadequate sleep and both bedtime social technology use and typical weekly technology use to determine if these utilizations of technology were differentially related to sleep disruption. Methods: Students ( N =383) and faculty/staff ( N =141) from the same institution completed a 45-minute well-being survey for chances to win prizes. Participants rated their sleep quality during the past month, frequency of symptoms of inadequate sleep, frequency of bedtime social technology use (active use and passive monitoring), and reported typical weekly hours spent using non-school or work technology (e.g., gaming, Internet use, social networking) as a measure of general technology use. Passive bedtime social technology use, active bedtime social technology use, and general technology use were analyzed separately and correlated with sleep quality and symptoms of inadequate sleep. Results: For students, active bedtime social technology use significantly predicted daytime sleepiness ( r (247)=.33; p <.05), increased preoccupation with sleep ( r (246)=.25; p <.05), mood disturbance ( r (247)=.19; p <.05), avolition ( r (248)=.17; p <.05), reduced cognition ( r (247)=.17; p <.05), and poorer sleep quality ( r (248)=-.24, p <.05). Whereas faculty/staff active bedtime social technology use significantly predicted daytime sleepiness ( r (120)=.26, p <.01) and poorer sleep quality ( r (120)=-.21, p <.05). Neither passive bedtime nor general technology use significantly predicted sleep variables for students (all p s>.08 and .19, respectively) or faculty/staff (all p s>.16 and .15, respectively). Conclusion: For students and faculty/staff, active bedtime social technology use was associated with indicators of poor sleep. Whereas passive bedtime social technology and typical time spent using recreational technology were not. These findings suggest reduction of active social technology use during designated sleep time as a possible intervention for poor sleep in college students and working adults, but that technology use in general and passive bedtime use may not require curtailment. Support (If Any): Nancy and Craig Wood Odyssey Professorship