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Anthropometric Measures are Associated with Canadian Agility and Movement Skill Assessment Scores

2017· article· en· W2618414561 on OpenAlexaffabout
Kevin Belanger, Mark S. Tremblay, Patricia E. Longmuir, Joel D. Barnes, Dwayne P. Sheehan, Jennifer L. Copeland, Sarah J. Woodruff, Brenda Bruner, Barbi Law, Luc J. Martin, Angela M. Kolen, Michelle Stone, Kristal D. Anderson, Kirstin N. Lane, Nathan Hall, Melanie Gregg, Travis J. Saunders, Dany J. MacDonald, François Trudeau, Claude Dugas

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2017
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of Prince Edward IslandUniversity of WinnipegDalhousie UniversityQueen's UniversityCamosun CollegeChildren's Hospital of Eastern OntarioNipissing UniversityUniversity of WindsorSt. Francis Xavier UniversityUniversity of LethbridgeUniversité du Québec à Trois-RivièresMount Royal University
Fundersnot available
KeywordsAnthropometryWaistBody mass indexGross motor skillCompetence (human resources)Physical therapyPsychologyMovement assessmentMotor skillMedicineDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

PURPOSE: Recent literature suggests that anthropometric measures are correlates of gross motor competence in children. The purpose of this study was to determine if body mass index (BMI) or waist circumference (WC) are associated with children’s scores on the Canadian Agility and Movement Skill Assessment (CAMSA). METHODS: Children aged 8-12 years (n = 7,773), with parental consent, from 7 Canadian provinces had their physical literacy level measured using the Canadian Assessment of Physical Literacy (CAPL). CAPL testing was completed between 2012-2016 and administered by trained research staff. As part of the CAPL tests, movement competence was measured using the CAMSA which evaluates fundamental, combined, and complex movement and motor skills. Children were scored on time to complete the CAMSA (range 1-14 points) and ability to demonstrate the movement skill criteria (range 0-14 points) for a combined score out of 28, with the best of two trials used for analyses. BMI was calculated from measured height and weight and converted to BMI z-score using the World Health Organization’s (WHO) BMI-for-age charts and formulae based on the LMS method. WC was measured in duplicate using an elastic tape measure at the level of the iliac crest and recorded in centimeters, with the average of the two measures used for analyses. Children were grouped for analysis based on those meeting (≥ -2.0 to ≤ 1.0) and not meeting (< -2.0 or > 1.0) the WHO’s recommended level of BMI z-score. Separate multiple linear regression models were used to predict CAMSA score for BMI z-score and WC, with both models adjusting for age and sex. RESULTS: The difference in CAMSA scores between BMI z-score groups was significant (p < 0.001, Cohen’s d = 0.3). In the BMI z-score model, results of the regression (F [3,7455] = 353, p <0.0001, R2 = 0.12) indicated that CAMSA scores were lower by 0.3 units for every 1 unit increase in BMI z-score. In the WC model, results of the regression (F [3,7455] = 402.2, p <0.0001, R2 = 0.14) found lower CAMSA scores of 0.1 units for each 1 centimeter increase in WC. Age and sex were strongly associated with CAMSA score in both models, as expected. CONCLUSIONS: These results align with previously reported findings suggesting that anthropometric measures have a moderate relationship with children’s performance on movement competence assessments.

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)0.005
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 score0.194
Threshold uncertainty score0.390

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.022
GPT teacher head0.314
Teacher spread0.292 · 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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Citations0
Published2017
Admission routes2
Has abstractyes

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