Influence of Childhood and Adolescent Fat Development on Fat Mass Accrual During Emerging Adulthood: A 20‐Year Longitudinal Study
Bibliographic record
Abstract
OBJECTIVE: Fat mass and the prevalence of overweight/obesity (OWO) increase during emerging adulthood (EA; 18-25 years). The factors that contribute to the transition from having healthy weight to having OWO during EA are understudied. This study aimed to identify the independent effect of concurrent physical activity (PA) and energy intake (EI) and childhood/adolescent fat accrual, PA, and EI on EA fat accrual. METHODS: One hundred twenty-six participants (59 male) were measured serially between 1991 and 2011. Measures included age, height, weight, total body and trunk fat mass (TBF and TrF, in grams) derived from dual-energy x-ray absorptiometry, and PA and EI. Composite childhood/adolescent z scores were calculated for each participant (average mean z score) for TBF, TrF, PA, and EI. Multilevel random-effects models were developed. RESULTS: EA fat accrual was predicted by childhood and adolescent TBF and TrF z score (0.30 ± 0.05, P < 0.05), respectively, in both sexes. Concurrent PA (-0.06 ± 0.02, P < 0.05) was significant in males only. CONCLUSIONS: These results underscore the importance of maintaining a lower TBF and TrF during childhood and adolescence, and a higher level of PA in order to mitigate TBF and TrF accrual and prevent the transition from having healthy weight to having OWO during EA.
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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 | 0.000 | 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".