Assessment of Osteogenic Exercise Efficacy via Bone Turnover Markers in Premenopausal Women: A Randomized Controlled Trial
Bibliographic record
Abstract
Assessing bone's response to physical activity interventions is challenging. This randomized controlled trial investigates if changes in bone turnover markers can offer an early evaluation of a physical activity intervention's effectiveness in improving bone mineral density (BMD) in premenopausal women. Participants in the intervention group (n = 27, with 24 completing the trial) were instructed to walk at least 10,000 steps every day on a brisk walk and to execute 60 jumps daily, each surpassing 4g of acceleration, using an accelerometer-based wearable device. Meanwhile, the control group (n = 26, with 18 completing the trial) continued with their usual lifestyle. Bone turnover markers, comprising of C-terminal telopeptide of Type I collagen, procollagen Type 1 N-terminal propeptide, and total osteocalcin (carboxylated and undercarboxylated) were measured at baseline and midway through the intervention (3 months). Dual-energy X-ray absorptiometry scans of the hip and lumbar spine were conducted at baseline and the end of the intervention (6 months) to estimate BMD. Analysis of covariance exhibited significant differences between groups in procollagen Type 1 N-terminal propeptide (-6.74 μg/L, p = .023) and C-terminal telopeptide of Type I collagen (-83 ng/L, p = .043) after 3 months, and in femoral neck BMD (+0.024 g/cm2, p = .016), total hip BMD (+0.036 g/cm2, p = .004), and lumbar spine BMD (+0.026 g/cm2, p = .020) after 6 months. A significant correlation (r = -.73; p < .001) was detected between reductions in C-terminal telopeptide of Type I collagen and increases in femoral neck BMD. In conclusion, this intervention improved BMD in premenopausal women, with bone turnover markers potentially useful for early intervention assessment, though further research is needed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".