Physical activity is prospectively associated with spinal pain in children (CHAMPS Study-DK)
Bibliographic record
Abstract
ABSTARCT: Spinal pain and physical inactivity are critical public health issues. We investigated the prospective associations of physical activity intensity with spinal pain in children. Physical activity was quantified with accelerometry in a cohort of primary school students. Over 19 months, parents of primary school students reported children's spinal pain status each week via text-messaging (self-reported spinal pain). Spinal pain reports were followed-up by trained clinicians who diagnosed each child's complaint and classified the pain as non-traumatic or traumatic. Associations were examined with logistic regression modeling using robust standard errors and reported with odds ratios (OR). Children (n = 1205, 53.0% female) with mean ± SD age of 9.4 ± 1.4 years, participated in 75,180 weeks of the study. Nearly one-third (31%) of children reported spinal pain, and 14% were diagnosed with a spinal problem. Moderate intensity physical activity was protectively associated with self-reported [OR(95%CI) = 0.84(0.74, 0.95)], diagnosed [OR(95%CI) = 0.79(0.67, 0.94)] and traumatic [OR(95%CI) = 0.77(0.61, 0.96)] spinal pain. Vigorous intensity physical activity was associated with increased self-reported [OR(95%CI) = 1.13(1.00, 1.27)], diagnosed [OR(95%CI) = 1.25(1.07, 1.45)] and traumatic [OR(95%CI) = 1.28(1.05, 1.57)] spinal pain. The inclusion of age and sex covariates weakened these associations. Physical activity intensity may be a key consideration in the relationship between physical activity behavior and spinal pain in children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".