Association between Functional Movements Skills and Health Indicators in Children Aged between 9 and 12 Years Old
Bibliographic record
Abstract
Background: Children’s health is a current concern and data suggests that poor fundamental movement skills (FMS) could be associated with poor health, which may or may not be mediated by low physical activity level. However, tools to assess FMS have not been standardized, and could consequently lead to different associations between FMS and health indicators. Objective: The primary objective of this study was to evaluate the associations between FMS and health indicators using two different FMS measurement tools often used in Canada. Methods: A total of 145 children between the ages of 9 to 12 were recruited from schools, after school programs, and summer camps in 2016. FMS were evaluated using the Passport for Life (bound, plank, run, kick, throw) and the PLAYbasic (run, hop, throw, kick, and balance). The association between each test and an average score for each tool were tests with health indicators including anthropometric measures, grip strength, cardiorespiratory fitness, and percent body fat. Results: Participants were composed of 54.2% boys aged 10.4 ± 1.2 years with an average body mass index of 18.8 ± 3.8 kg/m2. The association between the average score of both tools was 0.77 (p < 0.01), body mass index was significantly associated with 67% of FMS elements using the Passport for Life (r ranging from −0.18 to −0.32; p < 0.05), and 60% of FMS using the PLAYbasic (r ranging from −0.15 to −0.30; p < 0.05). There were no significant differences between the associations of the health indicators with FMS and either FMS assessment tool (Passport for Life and PLAYbasic) (p = 0.05). Average score of FMS was significantly associated with all health indicators using both PLAYbasic and Passport for Life (all p < 0.05). Conclusions: Health indicators in children are associated with FMS regardless of whether the Passport for Life or the PLAYbasic was used as the assessment tool. It is worth investigating if interventions that improve FMS lead to improvements in these health indicators.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".