Associations of Motor Competence, Cardiorespiratory Fitness, and Physical Activity: The Mediating Role of Cardiorespiratory Fitness
Bibliographic record
Abstract
Purpose: Supporting children’s physical activity (PA) behavior is imperative in order to safeguard their health. In an attempt to gain a deeper understanding about children’s PA behavior, the aim of this study was to investigate the associations among motor competence (MC), cardiorespiratory fitness (CRF) and ambulatory PA during middle and late childhood. Method: A cross-sectional design was adopted and a total of 576 8–12-year-old children (Mage = 10.2 years, SD = 1.3) were examined. MC was assessed by the Canadian Agility and Movement Skill Assessment; daily PA (steps/day) was obtained by pedometers; CRF was measured by the Progressive Aerobic Cardiovascular Endurance Run. The associations among the key study variables were investigated by correlation and mediation analyses. Using a bootstrap method, two mediation models were tested: (a) MC predicting PA through CRF, (b) PA predicting MC through CRF. Results: MC, CRF, and PA present significant and positive associations both in boys and girls (p < .05). CRF fully mediates the relationship between MC and PA in both directions [Model 1: b = .138, 95% CI (.0952, .1869), Model 2: b = .108, 95% CI (.0752, .1445)]. The pathway leading from PA to MC (R2 = .375, p < .0001) has stronger predictive utility than the reverse pathway (R2 = .124, p < .0001). Conclusion: MC and CRF are important predictors of children’s PA participation; therefore, systematic and targeted interventions focused on the enhancement of these two factors should be used as a mechanism to reinforce children’s PA behavior.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".