Correlates of Moderate-to-Vigorous Physical Activity in Children With Physical Illness and Physical–Mental Multimorbidity
Bibliographic record
Abstract
This study measured physical activity (PA) and explored its correlates among children with multimorbidity (co-occurring chronic physical and mental illness; MM) versus those with chronic physical illness only (PI). This study used baseline data from the Multimorbidity in Children and Youth Across the Life Course (MY LIFE) study, an on-going cohort study following 263 children with a PI 2 to 16 years of age (mean age: 9.8 years, SD = 4.0; 47.7% female). PA was measured using accelerometry, and demographic and psychosocial variables were collected using questionnaires. Of the 55 children with MM and the 85 with PI with valid accelerometer data, 38.1% and 41.2%, respectively, met average daily PA guidelines. Correlates of moderate-to-physical PA (MVPA) among children with MM were age, ρ(53) = −0.45, p = .001, body mass index (BMI), ρ(48) = −0.28, p = .04, self-perceived behavioral conduct, ρ(24) = −0.45, p = .02, physical health-related quality of life, ρ(51) = 0.56, p < .001, and peer support, ρ(52) = 0.27, p = .04. Correlates of MVPA among children with PI were age, ρ(83) = −0.40, p < .001, sex, ρ(83) = −0.26, p = .01, self-perceived social competence, ρ(31) = 0.42, p = .02, self-perceived athletic competence, ρ(31) = 0.48, p = .005, physical health-related quality of life, ρ(83) = 0.34, p = .001, participation in community sport, ρ(31) = 0.41, p = .02, and family functioning, ρ(83) = 0.26, p = .02. These results demonstrate that children with PI and MM are insufficiently active and their PA is correlated with demographic and psychosocial factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".