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Record W4401462372 · doi:10.7717/peerj.17865

Correlation between gross motor coordination and basic coordination capacities in normal-weight and overweight/obese children aged 9–10 years

2024· article· en· W4401462372 on OpenAlexaboutno aff
Yuan Sui, Lin Cui, Binbin Jia, Xiangyang Ding, Min He, Yingen Da, Yue Shi, Fei Li, Pan Li

Bibliographic record

VenuePeerJ · 2024
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
FundersShanghai University of Sport
KeywordsOverweightGross motor skillMotor coordinationCorrelationObesityOrientation (vector space)Motor skillPsychologyNormal weightRhythmDevelopmental psychologyPhysical medicine and rehabilitationMedicineEndocrinologyInternal medicineNeuroscienceMathematics

Abstract

fetched live from OpenAlex

Background Gross motor coordination (GMC) plays a crucial factor in children’s motor development and daily activities. It encompasses various sub-capacities, such as spatial orientation, rhythm, and motor reaction, collectively referred to as basic coordination capacities (BCC). However, children who are overweight and obese (OW/OB) often display poorer GMC. This study aims to examine the impact of gender and weight status (BMI categories) on children’s GMC and BCC. It also seeks to investigate the impact of BCC and BMI on GMC. Method The study involved 266 participants, 135 in the NW group (boys: n = 75; girls: n = 60) and 131 in the OW/OB group (boys: n = 68; girls: n = 63). An NW status is defined by a BMI z-score between ≥−2SD to ≤1SD, while an OW/OB status corresponds to a BMI z-score > 1SD. Physical activity was assessed using the Physical Activity Questionnaire for Children, developed by the University of Saskatchewan, Canada. We used six field tests to evaluate BCC, including single leg standing test (static balance), YBT (dynamic balance), rhythmic sprint test (rhythm), reaction time test (motor reaction), target standing broad test (kinesthetic differentiation), and numbered medicine ball running test (spatial orientation). GMC was evaluated with Kiphard-Schilling’s Body Coordination Test (KTK). Result The motor quotient (MQ) was primarily affected by weight status (F = 516.599, p < 0.001; gender: F = 6.694, p = 0.01), with no significant interaction effect (F = 0.062, p = 0.803). In BCC, gender had a significant main effect on rhythm capacity (F = 29.611, p < 0.001) and static balance (F = 11.257, p = 0.001) but did not significant influence other sub-capacities ( p > 0.05). Weight status impacted dynamic balance (F = 11.164, p = 0.001). The interaction of gender and weight status significantly impacted motor reaction (F = 1.471, p = 0.024) and kinesthetic differentiation (F = 5.454, p = 0.02), but did not affect other sub-capacities ( p > 0.05). The physical activity was not significant affected by gender (F = 0.099, p = 0.753), weight status (F = 0.171, p = 0.679) and the interactions of two variables (F = 0.06, p = 0.806). In the regression analysis, except motor reaction ( p > 0.05), other BCC sub-capacities influenced GMC to varying extents (β = −0.103–0.189, p < 0.05). Nonetheless, only two types of balance significantly mediated the relationship between BMI and GMC (BMI→MQ: β = −0.543, p < 0.001; BMI→YBT: β = −0.315, p < 0.001; BMI→SLS: β = −0.282, p < 0.001; SLS→MQ: β = 0.189, p < 0.001; YBT→MQ: β = 0.182, p < 0.001). Conclusion Compared to gender, the main effect of weight status on most GMC and BCC’s sub-capacities was more pronounced. OW/OB children exhibited poorer GMC, which is related to their reduced static and dynamic balance due to excess weight. Kinesthetic differentiation, spatial orientation, and rhythm capacity are not significantly associated with BMI, but these sub-capacities positively influence gross motor coordination (GMC), except for hand-eye motor reaction.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.010
GPT teacher head0.248
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations4
Published2024
Admission routes1
Has abstractyes

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