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Record W4410896752 · doi:10.1007/s40279-025-02235-0

How Should Youth Handgrip Strength be Normalized? New Insights Using 3-D Allometry with “Generalizable” Norm-Referenced Values, Data from NHANES

2025· article· en· W4410896752 on OpenAlexaff
Alan Nevill, Justin J. Lang, Mark Niemz, Grant R. Tomkinson

Bibliographic record

VenueSports Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsPublic Health Agency of CanadaUniversity of Ottawa
FundersUniversity of South Australia
KeywordsAllometryPercentileNational Health and Nutrition Examination SurveyWaistAnthropometryCollinearityCircumferenceStatisticsDemographyMathematicsPopulationSample size determinationMedicineBody mass indexBiologyEnvironmental healthInternal medicineEcology

Abstract

fetched live from OpenAlex

Abstract Background Handgrip strength (HGS) is an important marker of health. Using allometric scaling, we previously identified that adult HGS should be normalized by a cross-sectional or surface area measure of body size, although it is unclear whether scaling youth HGS by the same body size dimension is appropriate. We therefore aimed to (1) identify the optimal body size dimension(s) to normalize youth HGS for differences in body size and (2) generate norm-referenced values for HGS using the identified body size dimension(s). Methods Data were from the National Health and Nutrition Examination Survey (NHANES), a representative sample of the US non-institutionalized civilian population. Exclusions resulted in a final sample of 4816 youth (51.2% male) aged 6–19 years. Handgrip strength was measured using electronic hand dynamometry. Body size dimensions included body mass, height, and waist circumference. Allometry was used to identify the most appropriate dimension(s) associated with HGS. Population-weighted, sex-stratified generalized additive models for location, scale, and shape were used to develop norms by sex and age. Norms were tabulated as percentile values (3rd to 97th) and visualized as smoothed percentile curves. Results Predicting HGS using all three body size dimensions (three-dimensional) resulted in collinearity predominantly owing to the presence of waist circumference, prohibiting the use of all three body size dimensions as normalizers. However, collinearity was not an issue when two of the three dimensions (body mass and height) were adopted. Allometry identified a “generalizable” normalizing ratio as HGS n = $$HGS/({HT}^{2}*{M}^{0.333})$$ H G S / ( HT 2 ∗ M 0.333 ) . If only a single body size dimension were available, then HGS should be normalized using height 2 (i.e., $$HGS/{HT}^{2}$$ H G S / HT 2 ) because height was identified as the strongest single body size dimension associated with HGS. Sex- and age-specific norms for $$HGS/({HT}^{2}*{M}^{0.333})$$ H G S / ( HT 2 ∗ M 0.333 ) declined from age 6–8 years and progressively increased thereafter. Conclusions Allometrically scaling HGS by $$({HT}^{2}*{M}^{0.333})$$ ( HT 2 ∗ M 0.333 ) helps normalize strength for body size in population-based youth research.

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)0.021
metaresearch head score (Gemma)0.053
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 score0.021
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.053
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.195
GPT teacher head0.374
Teacher spread0.179 · 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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Citations6
Published2025
Admission routes1
Has abstractyes

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