Does Intervening In Childcare Settings Impact Fundamental Movement Skills Development?
Bibliographic record
Abstract
Dear Editor-in-Chief, Recently, Adamo et al. (1) published an article that highlighted the efficacy of a childcare provider preschool physical activity-based intervention (i.e., Preschool Activity Trial Intervention—Healthy Opportunities for Preschoolers) on fundamental motor skills (FMS). The authors’ concluded that the intervention was effective and increased FMS in preschoolers. Randomized controlled trials are greatly needed and require considerable time to undertake (e.g., developing rapport with childcare settings and adequately training childcare providers). However, there appear to be some inconsistencies with respect to data analyses and reporting of results that warrant additional clarification. As stated in the Analyses section, the authors used gross motor quotient and percentiles to interpret FMS development. These scores are the most reliable and provide the most meaningful interpretation (2). However, it is not clear why individual raw skill scores were then used in the analyses as the dependent variables for Figures 3A and 3B. Also, because participants in the control group demonstrated higher scores than their intervention group at baseline, did the authors consider adjusting for baseline values in their mixed models (i.e., include baseline values as a covariate)? Running statistical analyses with multiple dependent measures that are interrelated could increase the risk of a type I error. Were any adjustments made to accommodate for this? As stated in the findings, both groups’ locomotor scores increased, but only significantly in the intervention group, and the control group experienced a significant decline in object control skills, whereas no change was found in the intervention group. It is unclear how these findings align with Figures 3A and 3B. When visually examining these figures, the control participants’ locomotor and object control skill scores all appear, except for run, jump, and throw, to improve from baseline to 6 months. Additionally, some appear to have similar if not better gains than the intervention group? We hope that this letter will encourage the need for more consistency in the Test of Gross Motor Development score reporting. Although gross motor quotient and percentiles provide the most meaningful interpretation for motor skills scores, it is difficult to draw conclusions because normative data for a Canadian population has not been established. Therefore, raw scores are encouraged until these norms have been established or verified with this population. An intervention designed to promote FMS acquisition of 12 skills across 6 months is an ambitious task. It would be helpful if more information was provided on the dose of the intervention (i.e., amount of time devoted to motor skill instruction and practice) and the fidelity of implementation. This will guide future research in the area, including replication of the current study. We urge motor skills researchers to consider using the CONSORT statement (and extension to cluster trials where relevant, 3) to help with the reporting of future intervention studies (3). Findings will continue to strengthen research that focuses on the development of FMS. This is important because FMS contributes to positive health trajectories in children (4). Leah E. Robinson School of Kinesiology University of Michigan Ann Arbor, MI Anthony D. Okely Early Start Research Institute University of Wollongong Wollongong, AUSTRALIA E. Kipling Webster School of Kinesiology Louisiana State University Baton Rouge, LA Dale A. Ulrich School of Kinesiology University of Michigan Ann Arbor, MI
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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 | 0.010 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".