Due to copyright issues, an electronic copy of the thesis must be ordered from the faculty. For the faculty to have time to process the order, the order must be received by the faculty at the latest 2 days before the public defence. Orders received later than 2 days before the defence will not be processed. After the public defence, please address any inquiries regarding the thesis to the candidate.
Trial Lecture – time and place
See Trial Lecture.
Adjudication committee
- First opponent: Professor Tormod Bøe, University of Bergen, Norway
- Second opponent: Associate Professor Jenny Van Dongen, Vrije Universiteit Amsterdam, Netherlands
- Third member and chair of the evaluation committee: Professor II Simon Rayner, University of Oslo
Chair of the Defence
Professor II Sara Salehi Hammerstad, University of Oslo
Principal Supervisor
Associate Professor Olav Bjerkehagen Smeland, University of Oslo
Summary
This thesis examined genetic, environmental, and behavioral factors underlying adolescent mental health, with a focus on screen use as a widespread environmental exposure. Data from the Norwegian Mother, Father, and Child Cohort Study (MoBa) were used, combining questionnaire, genetic, and diagnostic information.
The genetic architecture of screen behaviors (TV watching, gaming, social media use) was characterized through genome-wide association studies and post-GWAS analyses. Screen behaviors showed modest heritability, with negative genetic correlations with educational attainment and significant genetic overlaps with psychiatric disorders.
Phenotypic analyses revealed that screen use was linked to elevated odds of psychiatric diagnoses such as depression, anxiety, and neurodevelopmental disorders. Very low social media use was also associated with higher risk, challenging a simple "more is worse" assumption. Genetic sensitivity analyses indicated that associations between high screen use and psychiatric diagnoses are confounded by shared genetic factors.
Data-driven approaches integrated these factors within a predictive framework for adolescent mental health risk. Machine learning models performed best using self-reported symptoms and psychosocial variables, while screen use and genetic factors contributed little. Simplified models using three questionnaire items – e.g. mental distress, sleep problems, and weekly exercise – achieved discrimination comparable to larger models.
Overall, the findings show that screen use and mental health associations are neither uniform nor unidirectional, vary across outcomes and screen behaviors, and are partly shaped by shared genetic factors. This challenges simplistic narratives around screen use and has implications for prevention policy. The results further suggest that brief symptom-based questionnaires can support identification of adolescents at elevated psychiatric risk in practical settings.
Additional information
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