Longitudinal Use Patterns of Technology Subtypes During the Transition Into Early Adolescence: Results From the Adolescent Brain Cognitive Development Study

• Published in: Journal of adolescent health Vol. 75; no. 5; pp. 809 - 818
• Main Authors: Borodovsky, Jacob T., Squeglia, Lindsay M., Mewton, Louise, Marsch, Lisa A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2024
• Subjects: ABCD study; Adolescent; Adolescent Behavior - psychology; Adolescent Development; Child; Cross-Sectional Studies; Epidemiology; Female; Humans; Longitudinal; Longitudinal Studies; Male; Self Report; Social media; Social Media - statistics & numerical data; Technology; Television - statistics & numerical data; Text Messaging; Texting; Video games; Video Games - statistics & numerical data; Video games; Longitudinal; Texting; Technology; Social media; Adolescent; ABCD study; Epidemiology
• DOI: 10.1016/j.jadohealth.2024.06.020
• ISSN: 1054-139X; 1879-1972

Adolescents encounter a complex digital environment, yet existing data on youth technology use rarely differentiates technology subtypes. This study maps the evolution and intricacies of youth engagement with technology subtypes. N = 11,868 participants in the Adolescent Brain Cognitive Development study followed from ages ∼9/10 to ∼13/14. We examined youths' self-reported hours per day (hr/day) of technology subtypes: TV/Movies, video games, YouTube, social media, video chat, and texting. We used descriptive statistics and multilevel logistic regression to assess cross-sectional and longitudinal use patterns of technology subtypes, agreement between child and parent reports on the child's technology use, and associations between each technology subtype and sociodemographics (child's biological sex, parent education, income, and marital status). At age 9/10, ∼75% of youth reported minimal (<30 min/day) social technology use (social media, video chat, texting) and up to ∼1.5 hr/day of TV, video games, and YouTube. By age 13/14, TV trajectories were converging to >2 hr/day, but social technology trajectories “fanned out” into a wide range of usage rates. Child and parent reports were weakly correlated (rs range: 0.13–0.29). Using child-reported hours of technology use, increases in the subject-specific odds of using a technology >2 hr/day ranged from 25% (YouTube; 95% CI: 1.16–1.35) to 234% (social media; 95% CI: 3.14–3.55). Compared with males, females had ∼100–200% greater odds of >2 hr/day of social technologies, but ∼40–80% reduced odds of >2 hr/day of video games and YouTube. Higher parent education and income predicted significantly lower odds of >2 hr/day of use – regardless of technology subtype. Distributions of youths' self-reported technology engagement are highly contingent on technology subtype, age, and biological sex. Future research on youth development and technology may benefit from considering youths' varied digital experiences.