ABSTRACT
OBJECTIVES: To determine the extent to which time spent with digital devices predicts meaningful variability in pediatric sleep. STUDY DESIGN: Following a preregistered analysis plan, data from a sample of American children (n = 50 212) derived from the 2016 National Survey of Children's Health were analyzed. Models adjusted for child-, caregiver-, household-, and community-level covariates to estimate the potential effects of digital screen use. RESULTS: Each hour devoted to digital screens was associated with 3-8 fewer minutes of nightly sleep and significantly lower levels of sleep consistency. Furthermore, those children who complied with 2010 and 2016 American Academy of Pediatrics guidance on screen time limits reported between 20 and 26 more minutes, respectively, of nightly sleep. However, links between digital screen time and pediatric sleep outcomes were modest, accounting for less than 1.9% of observed variability in sleep outcomes. CONCLUSIONS: Digital screen time, on its own, has little practical effect on pediatric sleep. Contextual factors surrounding screen time exert a more pronounced influence on pediatric sleep compared to screen time itself. These findings provide an empirically robust template for those investigating the digital displacement hypothesis as well as informing policy-making.
Subject(s)
Pediatric Obesity/epidemiology , Screen Time , Self Report , Sleep Deprivation/epidemiology , Sleep/physiology , Television/statistics & numerical data , Video Games/statistics & numerical data , Adolescent , Child , Child, Preschool , Family Characteristics , Female , Follow-Up Studies , Humans , Infant , Male , Motor Activity/physiology , Pediatric Obesity/etiology , Pediatric Obesity/physiopathology , Prevalence , Retrospective Studies , Sedentary Behavior , Sleep Deprivation/etiology , Time Factors , United States/epidemiologyABSTRACT
OBJECTIVES: To evaluate the effectiveness of Internet filtering tools designed to shield adolescents from aversive experiences online. STUDY DESIGN: A total of 1030 in-home interviews were conducted with early adolescents aged from 12 to 15 years (M = 13.50, SD = 1.18) and their caregivers. Caregivers were asked about their use of Internet filtering and adolescent participants were interviewed about their recent online experiences. RESULTS: Contrary to our hypotheses, policy, and industry advice regarding the assumed benefits of filtering we found convincing evidence that Internet filters were not effective at shielding early adolescents from aversive online experiences. CONCLUSIONS: Preregistered prospective and randomised controlled trials are needed to determine the extent to which Internet filtering technology supports vs thwarts young people online and if their widespread use justifies their financial and informational costs.