Associations Between Adverse Childhood Experiences, Adolescent Screen Time and Physical Activity During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: To determine the associations between Adverse Childhood Experiences (ACEs), adolescent screen time, and physical activity during the early COVID-19 pandemic. METHODS: Data (2016-2020) from the Adolescent Brain Cognitive Development (ABCD) study were analyzed. Linear regression analyses estimated associations between ACE score and screen time and physical activity in May 2020, adjusting for potential confounders. RESULTS: Of the 6749 adolescent respondents primarily aged 12-13, 81.6% reported a history of one ACE or more. In adjusted models, a higher ACE score was significantly associated with greater hours per day of screen time, with youth with ≥4 ACEs associated with 2.3 more hours of screen time per day compared to youth with 0 ACEs. In addition, the adjusted models found that a higher ACE score was associated with lower physical activity; youth with ≥4 ACEs averaged 0.8 fewer hours per week of physical activity and 0.5 fewer days per week of 60 minutes of physical activity compared to youth with 0 ACEs. Gender and race were also significantly associated with changes in screen time and physical activity. CONCLUSIONS: ACEs are associated with higher adolescent sedentary behaviors, particularly greater screen time, during the early COVID-19 pandemic (May 2020). Clinicians caring for youth exposed to trauma in the postpandemic environment should explore screen time and physical activity behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".