Reducing Socioeconomic Inequalities in Adult Cardiovascular Disease Risk by Targeting Unhealthy Movement Behaviours During Adolescence: A Protocol
Bibliographic record
Abstract
Populations with lower socioeconomic position (SEP) are at increased risk of developing cardiovascular disease (CVD). Movement behaviours, including physical activity, sedentary behaviour, and sleep, contribute to socioeconomic gradients in CVD risk, as low-SEP populations are less likely to meet evidence-informed recommendations for these behaviours. Adolescence represents a sensitive period for establishing lifelong health behaviours, with CVD risk beginning to accumulate before adulthood. This study will model the potential effect of adolescent movement behaviour interventions on socioeconomic inequalities in adult CVD risk. We will conduct a population-based cohort study of adolescents from the Add Health study, recruited in 1994-1995 from the US and followed into adulthood. Unhealthy movement behaviours, including a low level of moderate-to-vigorous physical activity, a high level of recreational screen time, and short sleep duration, will be operationalized based on the 24-hour Movement Guidelines and measured twice during adolescence (ages 12-24 years). Parental educational attainment and family financial hardship will be used to capture SEP in adolescence. The outcome will be the 30-year risk of CVD, assessed in adulthood (ages 33-41 years) using a validated risk score that incorporates objectively measured biomarkers, demographic information, and self-reported health indicators. We will perform causal decompositions to quantify the change of socioeconomic inequalities in adult CVD risk under 2 interventional scenarios: (i) elimination (unhealthy movement behaviours are eliminated in the whole population of adolescents); and (ii) equalization (the distributions of unhealthy movement behaviours for low-SEP adolescents are equalized to those of high-SEP adolescents). This study will provide insights into how modifying adolescent movement behaviours may contribute to reducing socioeconomic inequalities in CVD risk.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".