Stature estimation using per-cutaneous tibial length in Malwa population of Punjab

Rajiv, Joshi; Bhardwaj, Monika; Singh, Ramandeep

doi:10.5958/0974-0848.2016.00111.1

Cited by 1 publication

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“…16 A positive correlation exists between long bone length and stature in tibial length and ulna. [16][17][18] The differences in bone association could be attributed to environmental, nutritional and genetic factors. 5 In contrast, the calculated and actual estimation of stature from long bones showed a deviation and lower R 2 values.…”

Section: Discussionmentioning

confidence: 99%

Prediction of stature from long bones versus hand and foot measurements: A comparative study of the Kolhapur population

Karmalkar

Nikam

2021

NMJI

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Background Estimation of stature is usually done by measurement of the long bones. Although hand and foot dimensions are useful in predicting stature, they are population-specific. Methods We compared the accuracy of predicting stature by hand and foot dimensions, with long bone (tibia and ulna) lengths, and developed a stature predictive regression formula from the parameters used for the sample population in Kolhapur. We recorded hand and foot measurements and long bone measurements of 1000 consenting participants 18–50 years of age using a stadiometer for height and an anthropometric rod compass for other measurements. Correlation between the variables and stature was determined using Pearson’s correlation analysis (p<0.05). A multiple linear regression formula was derived for the prediction of stature. Results A positive correlation was observed between mean stature and foot length (r=0.67, p<0.05), tibia (r=0.66, p<0.05), ulna (r= 0.75, p<0.05) and hand length (r=0.69 left, r=0.72 right, p<0.05). There was no correlation between foot breadth and stature. Multiple linear regression analysis of hand and foot dimensions returned R2=62.96 and standard error of estimate 4.689 with comparable computed and experimental measurements. Conclusion The dimensions of the hand and foot can be used to predict stature. The formula derived from the multiple regression analysis incorporating hand and foot dimensions is a good fit to estimate stature in the study population.

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Section: Discussionmentioning

confidence: 99%