Muscle-building behaviors from adolescence to emerging adulthood: A prospective cohort study
Bibliographic record
Abstract
This study aimed to identify patterns of change in muscle-building behaviors from adolescence to emerging adulthood and determine what adolescent factors predict new-onset muscle-building behavior in emerging adulthood. Prospective cohort data from a diverse sample of 1,535 participants followed from adolescence (baseline, Mage = 14.4 ± 2.0 years) to emerging adulthood (follow-up, Mage = 22.1 ± 2.0 years) from the population-based EAT 2010–2018 (Eating and Activity over Time) study were analyzed. Changes in muscle-building behavior were identified (starting, stopping, persistent use, or never use). Log-binomial regression models examined adolescent predictors of starting (i.e., new-onset) muscle-building behaviors in emerging adulthood. Prevalence of any use in adolescence (EAT 2010) and/or emerging adulthood (EAT 2018) was 55.1% (males) and 33.0% (females) for protein powder/shakes, 6.7% (males) and 5.4% (females) for steroids, and 19.4% (males) and 6.5% (females) for other muscle-building substances (e.g., creatine, amino acids). In particular, 22.6% (males) and 13.7% (females) started protein powder/shakes, 2.2% (males) and 1.0% (females) started steroid use, and 9.0% (males) and 2.0% (females) started other muscle-building substances during emerging adulthood. Adolescent protein powder/shake consumption was associated with starting steroids/other muscle-building substances use in emerging adulthood in males (adjusted risk ratio [ARR] 2.09, 95% confidence interval [CI] 1.29–3.39) and females (ARR 4.81, 95% CI 2.01–11.48). Adolescent use of protein powders/shakes may lead to a two- to five-fold higher risk of new use of steroids and other muscle-building products in emerging adulthood. Clinicians, parents, and coaches should assess for use of muscle-building behaviors in adolescents and emerging adults and discourage use of harmful products.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".