Influence Of Sports And Energy Intake On BMD In Female Athletes Compared To Sedentary Controls
Bibliographic record
Abstract
Physical activity, particularly percussive activities with adequate energy intake may influence bone mineral density (BMD) in young female adults. Unfortunately, it is common to see energy deficiencies in this population that can put them at risk of health issues including lower BMD PURPOSE: To determine the influence of the type of sport and energy intake on BMD in female athletes compared to sedentary students. METHOD: Seventy-three female students (age 20.8± 1.9 y, height 167.4± 8.8 cm, weight 62.3± 9.2) from McGill University were evaluated (44 from McGill Varsity Teams: basketball (BB n=13), volleyball (VB n=11), figure skating (FS n =13), and synchronized swimming (SS n=7); and 29 sedentary healthy women (controls). Dietary intake (kcal/day) was assessed using a 3-day Food Log and analysed with the Food ProcessorTM Software. Lumbar spine (LS) (L1-L4) and femoral neck (FN) BMD were assessed by DXA scanning. A one-way ANOVA explored between-group differences and an ANCOVA examined the influence of energy intake on BMD. RESULTS: A significant difference in BMD at the LS and FN sites was observed between the type of sports (F(4,68) = 8.6, p < .001, η2 = .335; F(4,68) = 6.3, p < .001, η2 = .272, respectively). Also, BB (LS = 1.7± 1.53, p < .001; FN = 1.7± 1.13, p = .001) and VB (LS = 1.5 ± 1.55, p = .001; FN = 1.7 ± 1.66, p = .002) players had a significantly higher BMD in both sites compared to their non-athletic counterparts (LS = -0.3 ± 1.19; FN = 0.1 ± 1.04). The FS and SS athlete’s bone densities were not different from the control group (p = .719; p = .246). No significant association was observed between BMD at both sites and total energy intake/day across all groups (F(1,67) = .496, p = .484 , η2 = .007; F(1,67) = .035, p = .852, η2 = .001). There was a significant difference between the delta energy intake (recommended intake minus actual intake) in both BB and SS groups compared to the control group (p = .003 and p = 0.02, respectively). CONCLUSION: The type of sport revealed an influence on BMD. However, no significant relationship was observed between energy intake and BMD. A significant discrepancy was found between the required versus actual energy intake in some athletes. These data suggest that female varsity athletes should work closely with sports dieticians to promote healthy eating and optimize bone health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".