Associations Between Adverse Childhood Experiences and Early Adolescent Physical Activity in the United States
Bibliographic record
Abstract
OBJECTIVE: To determine the associations between the number of adverse childhood experiences (ACEs) and objectively-measured physical activity (PA) in a population-based, demographically diverse cohort of 9-14-year-olds and to determine which subtypes of ACEs were associated with physical activity levels. METHODS: We analyzed data (n = 7046) from the Adolescent Brain Cognitive Development (ABCD) Study 4.0 release at baseline and year 2 follow-up. ACE (cumulative score and subtypes) and physical activity (average Fitbit daily steps assessed at Year 2) were analyzed using linear regression analyses. Covariates included race and ethnicity, sex, household income, parent education, body mass index, study site, twins/siblings, and data collection period. RESULTS: Adjusted models suggest an inverse association between number of ACEs and Fitbit daily steps, with ≥4 (compared to 0) ACEs associated with 567 fewer daily steps (95% CI -902.2, -232.2). Of the ACEs subtypes, emotional abuse (B = -719.3, 95% CI -1430.8, -7.9), physical neglect (B = -423.7, 95% CI -752.8, -94.6), household mental illness (B = -317.1, 95% CI -488.3, -145.9), and household divorce or separation (B = -275.4, 95% CI -521.5, -29.2) were inversely and statistically significant associated with Fitbit daily steps after adjusting for confounders. CONCLUSIONS: Our results suggest that there is an inverse, dose-dependent relationship between cumulative number of ACEs and physical activity as measured by daily steps. This work highlights the importance of screening for ACEs among young people at an early age to help identify those who could benefit from interventions or community programs that support increased physical activity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".