Screen and nonscreen sedentary behavior and sleep in adolescents☆
Introduction
In 2006, 70% of Canadian students aged 14-18 years accumulated less than 8.5 hours of sleep per night.1 More recently, in 2014, 29% of US adolescents aged 12-14 years and 56% of those aged 15-17 years did not meet the recommended 8 hours of sleep per night.2 In studies of adolescents ranging in age from 11 to 18 years, poor sleep was associated with obesity,3, 4, 5 depressive symptoms and anxiety,6 substance abuse,7 academic performance,8 and working memory9; and it may predict poorer health in young adulthood.10 Poor sleep can also result in daytime sleepiness, which related adversely to academic performance and daytime functioning in 11- to 15-year-olds in the United States.11 In Canada, adolescents aged 14-18 years who reported daytime sleepiness missed social, sport, and work activities more often than those who did not report daytime sleepiness.1 Long sleep of ≥10 hours a night is also worrisome because it has been associated with poor mental health in adolescents aged 12-18 years12 and with risk behaviors including violence, alcohol consumption, illicit drug use, and unprotected sex in Taiwanese adolescents aged 12-18 years.13
Numerous articles identify risk factors for reduced and poor sleep in adolescents. According to a recent review, adolescents have an evening circadian phase preference for later bed and wake up times, which can result in reduced sleep on weeknights because of early school start times.14 Longer travel times to school, more time spent on homework,15 and part-time employment14 may also underpin reduced sleep in adolescents aged 12-19 years. Sedentary behavior—and in particular, screen-related sedentary behavior—is a particularly important risk factor for both poor sleep5 and short sleep16 in adolescents in grades 9 to 12. Although activities such as reading, doing homework, and talking on the telephone are usually considered to be sedentary, screen-related sedentary behaviors including media exposure17, 18, 19, 20 represent a subgroup of sedentary behaviors that are highly prevalent today and that are associated with decreased sleep in adolescents aged 14-19 years in many countries.21 In a nationally representative study of 52,000 Canadian adolescents in grades 6-12 (aged 12-18 years),22 average screen time was 7.8 hours per day, well above current Canadian guidelines for adolescents 12-17 years old of ≤2 hours a day.23 In the United States, youth aged 8-18 years reported 10.8 hours of electronic media use per day.24 Furthermore, multitasking (ie, using ≥2 screens at the same time) was frequent.15 Seventy-two percent of US adolescents aged 13-18 years use cell phones, and 71% use the Internet on a computer or laptop25 before trying to sleep. Hale and Guan26 (2014) suggested that screen types with the most impact on sleep were (in order of importance) computers, videogames, mobile devices, and TV. Therefore, rather than studying total sedentary behavior, it is critical to differentiate type and duration of different screen and nonscreen sedentary behaviors in studies of the association between sleep and sedentary behavior.
Three mechanisms may link screen time and sleep.27 First, screen time may directly displace sleep because it impedes on time that could otherwise be spent sleeping or doing other activities related to improved sleep such as physical activity.28, 29 Second, use of electronic media, particularly in the evening, increases alertness and physiological arousal in young adults, which may then impact sleep.30Third, the light emitted from light-emitting diodes (LED) screens, which are present on most electronic media, may negatively affect sleep by modifying melatonin production and thus circadian rhythm in adults, but it is unclear whether this association is operative in adolescents.31 Reduced melatonin levels were observed with ≥2 hours of use of LED screens before going to bed32 but not with ≤1 hour of use32, 33 in adolescents ranging in age from 13 to 23 years.
There are few studies on nonscreen sedentary behavior in adolescents.16 As reported by Atkin et al,34 studies on the association between sedentary behavior and sleep in adolescents often do not distinguish screen and nonscreen sedentary behavior, making it difficult to assess whether it is sedentary behavior or screens that are linked to poor sleep. A study of 9- to 16-year-old Australians reported that 60% of total sedentary time did not involve screens (ie, and instead represented time spent socializing, in passive transport, etc).35 This suggests that nonscreen sedentary behavior can occupy a considerable amount of time in an adolescent's day and that, to study the association between sedentary behavior and sleep in adolescents comprehensively, screen and nonscreen sedentary behaviors should be investigated separately.
In addition to not differentiating screen and nonscreen sedentary behavior, studies on the link between sedentary behavior and sleep in adolescents usually examine sleep duration only, excluding other relevant outcomes such as daytime sleepiness. It is critical that the risk factors for a variety of indicators of poor sleep in adolescents are identified so that optimal preventive measures can be designed for use by clinicians, parents, and adolescents. The objective of this study was to examine the association between screen (computer, videogame, TV) and nonscreen (telephone, doing homework, reading) sedentary time, and sleep duration, type of sleeper (ie, short, normal, long sleeper), and daytime sleepiness in adolescents. We hypothesized that (1) screen-related sedentary behavior is negatively associated with sleep duration and positively associated with daytime sleepiness scores (Pediatric Daytime Sleepiness Scale [PDSS]) and that (2) nonscreen sedentary behavior is not associated with sleep.
Section snippets
Participants and methods
Data were available in AdoQuest, a prospective investigation of 1859 grade 5 students aged 10-11 years at cohort inception. The objective of AdoQuest was to investigate the natural course of the co-occurrence of health-compromising behaviors in children. A stratified random sample of schools was recruited from among all French-language schools with >90 grade 5 students located in Montreal, Canada. To assure equal representation of participants of high, middle, and low socioeconomic status,
Results
More than half (57%) of participants were female. The mean (SD) age was 15.2 (0.5) (range, 12-17). Twenty-three percent of participants were employed, and these participants worked 14.0 (11.9) hours per week on average; 23% of participants had mothers who were university educated.
Most participants (77%) reported 8-10 hours of sleep per night, 9% were short sleepers, and 9% were long sleepers (5% were missing data) (Table 1). The mean (SD) number of hours of sleep per night was 9 hours 2 minutes
Discussion
This study reports associations between screen and nonscreen sedentary time and several sleep indicators in adolescents. All 3 screen activities were associated with sleep. Specifically, >2 h/d of computer use was associated with reduced sleep duration, being a short sleeper, and greater daytime sleepiness. More than 2 h/d of videogame use was associated with reduced sleep duration, and more than 2 h/d of TV use was associated with a lower probability of being a short sleeper. Among nonscreen
Conclusion
Computer, videogame, and telephone times were associated with shorter sleep duration and daytime sleepiness in adolescents. Future research differentiating screen and nonscreen sedentary behavior should focus on the timing, type, and content of sedentary behavior to provide better insight into the mechanisms linking sleep and sedentary behaviors in adolescents. Adolescents, parents, and clinicians should be made aware that decreasing time spent in these activities may improve sleep duration and
Sources of support
JOL holds a Canada Research Chair in the Early Determinants of Adult Chronic Disease. EOL is supported by a doctoral fellowship from the Fonds de Recherche du Québec-Santé and by a fellowship from CIHR in Population Intervention for Chronic Disease Prevention and was supported by Fondation CHU Sainte-Justine during this work. EC holds a Clinical Research Scholar grant (Chercheur Boursier Clinicien) from the Fonds de Recherche du Québec-Santé.
AdoQuest was funded by the Canadian Tobacco Control
Disclaimers
The views expressed in the submitted article are solely those of the authors and not an official position of the institutions or funders to which they are attached.
Acknowledgments
We thank Dr Yun Jen for her input and Marianne Dubé for technical support.
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Conflict of interest declaration: none.