Examining the Influence of Adolescent Physical Activity on Adult Bone Mineral Density
Bibliographic record
Abstract
2020 Adolescence represents a critical time for bone mineralization and the amount of bone accrued during the growing years and the subsequent loss of bone determined adult bone mineral status. Work by our research group has shown that the growing skeleton responds to increased physical activity (PA) by increasing bone mineral accrual. Using 6 years of longitudinal data, we have previously shown that active boys and girls had significantly greater total body bone mineral content (TB BMC) compared to their inactive peers (Bailey et al., 1999). However, it is unclear whether the increased TB BMC accrual rates seen in adolescence translate into greater bone mineral density (BMD) at adulthood. PURPOSE: The purpose of this study was to investigate the influence of adolescent PA on adult BMD. METHODS: Physical activity and dualenergy X-ray absorptiometry (DEXA) scans of the total body (TB) were collected annually from 1991–1997 on 70 girls and 68 boys (ages 8–14) at study entry. Using an annual composite PA score based on 2–4 annual activity assessments (PAC-Q), subjects were categorized into activity groupings where the top 25%, middle 50%, and bottom 25% were classified as active, average, and inactive respectively. For our purposes we choose to analyze data from the top and bottom quartiles which represented 34 boys and 34 girls. Of these 68 subjects, 51 (26 females and 25 males) returned as adults to undergo the same tests in 2002–2003. RESULTS: A one-way analysis of variance (ANOVA) demonstrated that those males who were classified as active during adolescence had significantly greater adult BMD than their inactive peers (F(1,24) = 5.43, P = 0.03). In contrast, no significant difference in adult BMD between active and inactive adolescent females was shown (F(1,23) = 0.31, P = 0.59). CONCLUSIONS: In this group, male adolescent PA is more predictive of adult BMD compared to female adolescent PA. However, this gender difference warrants further investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".