Accelerometer-derived daily impact score is positively associated with bone strength accrual during adolescence
Bibliographic record
Abstract
Bone adapts to its loading environment throughout the lifespan, with rapid gains during adolescence. Accelerometers are commonly used to measure physical activity (PA) and examine its associations with bone outcomes; however, common approaches reduce high-resolution data to a few metrics (e.g., minutes per day (min/d) in various activity intensities). In this observational cohort study, we investigated whether using new accelerometry metrics to synthesize count-based accelerometry data would explain relationships between PA and estimated bone strength accrual. Four years of longitudinal data from over 300 children and adolescents in the Healthy Bones Study III were used to examine associations between the accelerometer-derived intensity gradient (IG) and daily impact score (DIS) with estimated bone strength (failure load, N; 8 % site of the distal tibia) as measured with high-resolution peripheral quantitative computed tomography. We also explored whether associations were independent of a traditional metric of min/d of vigorous physical activity (VPA). In linear mixed effects models, IG was not associated with bone strength independent of VPA ( β IG = −515.2 (−1302.1, 270.4), p = 0.20; β VPA = 25.5 (14.0, 37.0), p < 0.001), but DIS was positively associated with bone strength independent of VPA ( β DIS = 25.2 (7.0, 43.6), p = 0.007; β VPA = 3.2 (−6.1, 1.4), p = 0.67). Our findings suggest that the DIS may be a more appropriate metric for evaluating accelerometry data in relation to adolescent bone strength accrual compared with traditional duration (minutes of PA per day) metrics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".