How Should Youth Handgrip Strength be Normalized? New Insights Using 3-D Allometry with “Generalizable” Norm-Referenced Values, Data from NHANES
Bibliographic record
Abstract
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 = $$HGS/({HT}^{2}*{M}^{0.333})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:mi>G</mml:mi> <mml:mi>S</mml:mi> <mml:mo>/</mml:mo> <mml:mo>(</mml:mo> <mml:msup> <mml:mrow> <mml:mi>HT</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> <mml:mrow/> <mml:mo>∗</mml:mo> <mml:msup> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>0.333</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . If only a single body size dimension were available, then HGS should be normalized using height 2 (i.e., $$HGS/{HT}^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:mi>G</mml:mi> <mml:mi>S</mml:mi> <mml:mo>/</mml:mo> <mml:msup> <mml:mrow> <mml:mi>HT</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> ) because height was identified as the strongest single body size dimension associated with HGS. Sex- and age-specific norms for $$HGS/({HT}^{2}*{M}^{0.333})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:mi>G</mml:mi> <mml:mi>S</mml:mi> <mml:mo>/</mml:mo> <mml:mo>(</mml:mo> <mml:msup> <mml:mrow> <mml:mi>HT</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> <mml:mrow/> <mml:mo>∗</mml:mo> <mml:msup> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>0.333</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> declined from age 6–8 years and progressively increased thereafter. Conclusions Allometrically scaling HGS by $$({HT}^{2}*{M}^{0.333})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mrow> <mml:mi>HT</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> <mml:mrow/> <mml:mo>∗</mml:mo> <mml:msup> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>0.333</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> helps normalize strength for body size in population-based youth research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".