Does Intervening In Childcare Settings Impact Fundamental Movement Skills Development?
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,113 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,009 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».