Hoda S Abdel Magid1, Carly E Milliren2, Kelley Pettee Gabriel3, Jason M Nagata4. 1. Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA. Electronic address: hmagid@stanford.edu. 2. Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, MA, USA. 3. Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL, USA. 4. Division of Adolescent and Young Adult Medicine, Department of Pediatrics, University of California, San Francisco, CA, USA.
Abstract
OBJECTIVES: To examine the association between individual, neighborhood, and school-level influences on individual screen time among adolescents and young adults (AYAs) in the National Longitudinal Study of Adolescent to Adult Health. METHODS: We classified screen time continuously as self-reported total hours per week of television, videos, and video/computer games at baseline and categorical as extended screen time (≥14 h per week). We fit cross-classified multilevel models (CCMM) to examine to examine the individual-, school- and neighborhood-level demographic and socioeconomic factors associated with screen time. Models were fit using MLwiN with Bayesian estimation procedures. RESULTS: AYAs reported an average of 22.8 (SD = 19.4) and 21.9 (SD = 20.3) hours of screen time, respectively. At the individual level, younger age, male sex, Black/multiracial race, receipt of public assistance, and lower parental education were associated with higher screen time. At the school level, being out of session (i.e., school and national holidays including summer), having a higher proportion of non-White students, and having a lower proportion of parents with a college education were associated with higher individual screen time. CONCLUSIONS: We found that individual-level factors most influence youth screen time, with smaller contributions from school factors. Published by Elsevier Inc.
OBJECTIVES: To examine the association between individual, neighborhood, and school-level influences on individual screen time among adolescents and young adults (AYAs) in the National Longitudinal Study of Adolescent to Adult Health. METHODS: We classified screen time continuously as self-reported total hours per week of television, videos, and video/computer games at baseline and categorical as extended screen time (≥14 h per week). We fit cross-classified multilevel models (CCMM) to examine to examine the individual-, school- and neighborhood-level demographic and socioeconomic factors associated with screen time. Models were fit using MLwiN with Bayesian estimation procedures. RESULTS: AYAs reported an average of 22.8 (SD = 19.4) and 21.9 (SD = 20.3) hours of screen time, respectively. At the individual level, younger age, male sex, Black/multiracial race, receipt of public assistance, and lower parental education were associated with higher screen time. At the school level, being out of session (i.e., school and national holidays including summer), having a higher proportion of non-White students, and having a lower proportion of parents with a college education were associated with higher individual screen time. CONCLUSIONS: We found that individual-level factors most influence youth screen time, with smaller contributions from school factors. Published by Elsevier Inc.
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