Pre-sleep screen time and screen time addiction as shared determinants of poor sleep and obesity in adolescents aged 11-14 years in Scotland.

Abstract

Background: The overall quantity of screen time has been associated with short sleep duration and increasingly sedentary lifestyles, leading to adiposity. The aim of this research was to explore which components of screen time usage are shared determinants of poor sleep and higher adiposity in adolescents, using data from the Teen Sleep Well Study.

Methods: A cross-sectional study of adolescents aged 11-14 years in Fife, Scotland was conducted. Sleep was measured objectively using the Actigraph GT3X-BT and subjectively using validated questionnaires. Adiposity was assessed using body fat percentage (BF%) and obesity was measured using body mass index percentile (BMIp). Four components of screen time were addressed using questionnaires: the timing of screen time, quantity of screen time, location of screen time, and screen time addiction. Descriptive statistics and statistical tests such as Pearson correlation tables, and adjusted regression analyses were used. Mediation analyses explored wellbeing as a factor in the association between screen time and sleep and obesity.

Results: Sixty-two participants (33 female/29 male, mean age 12.2 ± 1.1 years, mean BMIp 60.3 ± 32.1) completed the study. Excessive screen time pre-sleep (30 min before sleep) and post-sleep (first 30 min after waking), excessive screen time on a weekend, and screen time addiction were shared determinants of higher adiposity, a later chronotype (evening-preference) and poor sleep outcomes: poor sleep habits, increased insomnia symptoms (IS) and increased sleep onset variability. Mediation analyses confirmed that adolescent wellbeing mediated the association between pre-sleep screen time and IS (36.3%) and BF% (21.9%), post-sleep screen time and IS (37.7%) and BF% (30.4%), videogaming addiction and IS (31.9%) and BF% (34.6%), social media addiction and IS (35.0%) and BF% (17.4%), mobile phone addiction and IS (34.0%) and BF% (10.6%), weekday screen time and IS (58.1%) and BF% (39.8%), and weekend screen time and IS (51.4%) and BF% (38.0%).

Conclusions: These screen time behaviours, alongside wellbeing should be considered in multi-component health-promoting interventions aimed at improving adolescent sleep and reducing obesity risk. Future research should employ longitudinal designs to clarify the directionality of these associations and determine the effectiveness of interventions that target both screen time behaviours and wellbeing.