|Year : 2015 | Volume
| Issue : 3 | Page : 154-159
Estimation of stature from hand length and hand breadth in medical students of Maharashtra, India
MS Supare1, SV Pandit1, AS Bagul2
1 Department of Anatomy, SVN Government Medical College, Yavatmal, India
2 Department of Pediatrics, Government Medical College, Nagpur, Maharashtra, India
|Date of Web Publication||16-Jul-2015|
A S Bagul
Department of Pediatrics, Government Medical College, Nagpur - 440 003, Maharashtra
Source of Support: None, Conflict of Interest: None
Background: For identification, stature can be estimated from body parameters using regression equation or multiplication factor. However, regression equations and multiplication factors are specific for the region only and same formulae cannot be used in all population. Aim: To formulate regression equation and multiplication factor for estimation of stature from hand length (HL) and hand breadth (HB) for a region in Maharashtra, India. Materials and Methods: It was a cross-sectional study and 400 students of three Government Medical Colleges of Maharashtra, aged 18-24 years were enrolled for the study during 2011-2013. Stature, HL, and HB were measured and subjected to statistical analysis. Unpaired t-test and simple linear regression were used. Results: Stature, HL, and HB of 400 medical students (219 males and 181 females) were measured. Subjects were divided into six groups depending upon age. Simple regression equation and multiplication factor for male and female, and for each age group were derived for estimation of stature. We found correlation coefficient (R) of 0.74 for right HL (RHL), 0.75 for left HL (LHL), 0.45 for right HB and 0.46 for left HB in male, and 0.75 for RHL, 0.74 for LHL, 0.56 for right HB and 0.55 for left HB in female by using simple regression, which showed strong correlation between stature and HL, and HB. Conclusion: Stature can be accurately estimated from HL and HB by using simple regression equation or multiplication factor.
Keywords: Hand breadth, hand length, simple regression, stature
|How to cite this article:|
Supare M S, Pandit S V, Bagul A S. Estimation of stature from hand length and hand breadth in medical students of Maharashtra, India. Int J Health Allied Sci 2015;4:154-9
|How to cite this URL:|
Supare M S, Pandit S V, Bagul A S. Estimation of stature from hand length and hand breadth in medical students of Maharashtra, India. Int J Health Allied Sci [serial online] 2015 [cited 2022 Sep 30];4:154-9. Available from: https://www.ijhas.in/text.asp?2015/4/3/154/160875
| Introduction|| |
Identification is more important in dead unknown bodies and in mass disaster where only parts of bodies might be available. Apart from identification, stature measurement is required for assessment of children's growth,  calculation of nutritional indices,  for prediction and standardization of physiological parameters such as lung volumes,  muscle strength,  glomerular filtration rate,  and resting metabolic rate  and for adjustment of drug dosage.  However, in some cases, measurement of stature is difficult or impossible due to deformities of the trunk or legs, lower limb amputation, or in patients who are unable to stand. 
The dimensional relationship between body segments and stature has been the focus of scientist, anatomist and anthropologist for many years.  For this purpose, many sets of regression equation have been developed, and the better known are Karl Pearson from Western countries and Singh and Sohal (1952) from India.  Previous studies have reported the effectiveness of using hand length (HL) and hand breadth (HB) in estimating stature. , However, estimation of stature from these formulae in all population are not appropriate; as climate, heredity, nutritional status of the population has been reported to have an effect on stature. This explains the difference in anthropometric measurements in different geographical areas. Racial and ethnic variations are well-known in India and, therefore, region-wise study seems to be necessary.
Hence, this study was planned to formulate regression equation and multiplication factor for estimation of stature from HL and HB in medical students of Vidarbha region of Maharashtra.
| Materials and methods|| |
It was a cross-sectional study. Four hundred students (219 males and 181 females) of three Government Medical Colleges of Maharashtra, India, aged 18-24 years were enrolled in the study during 2011-2013. The students who are born and brought up in this region with ancestral origin from this region were enrolled. Students with deformity of limb or vertebral column were excluded. Approval of the Institutional Ethical Committee was sought for before the start of the study.
Stature (standing height)
The subject was asked to stand barefoot on the base of a standard stadiometer in a standard standing position. The individual was instructed to stand barefoot, heels are slightly separated, and weight is borne evenly on both feet. Heels, buttocks, and back are brought in contact with the vertical surface. The head is so positioned that the subject looks directly forward with the Frankfurt plane (the line joining the floor of the external auditory meatus to the lower margin of orbit) and biauricular plane being horizontal. The headpiece of stadiometer is kept firmly over the vertex to compress the hair and height is then measured. 
The subject was asked to place their hand supine on a flat hard table, and measurements were taken using vernier calipers. The length is measured from distal transverse crease of wrist to tip of middle finger. 
The subject was asked to place their hand prone on the flat hard table, and measurements were taken using vernier calipers. The hand will be placed on a table with the fingers together and the thumb out to the side, with a sliding caliper the breadth of the hand will be measured at the level of the knuckles. The HB was measured as a distance between the radial side of 2 nd metacarpophalangeal joint to the ulnar side of 5 th metacarpophalangeal joint. 
Observations were recorded in a predesigned pretested proforma. All these measurements were taken by the single author to avoid inter observer bias and were measured at a fixed time to avoid diurnal variation.
As there was no Indian study to guide about sample size, assumption is made on a pilot study conducted on 50 subjects to calculate sample size. Required sample size for male is 195 and for female 174. The power of the study is 80%.
The measurements obtained were statistically analyzed using Stata version 10.0 (Texas, USA). All measurements were presented as mean ± standard deviation (SD). Analysis of the differences in stature, HL, and HB, between male and female subjects, and right and left side was done by unpaired t-test. Karl Pearson's correlation coefficients are derived between variables and stature, and standard error (SE) and coefficient of determination (R2 ) were calculated. Single linear regressions were done to estimate stature from HL and HB. Multiplication factor is calculated. Multiplication factor is the ratio of the stature to the respective physical measurements. All the tests were two-sided, and P < 0.05 was considered significant.
| Results|| |
Of 400 enrolled subjects, males were 54.74% (219/400), and females were 45.26% (181/400). All subjects were divided in six groups according to age as 18-19, 19-20, 20-21, 21-22, 22-23, 23-24 years. Stature in male was 170.75 ± 9.47 and in female was 159.46 ± 7.66. It is observed that males have greater stature than females, and it was statistically significant (P < 0.001, 95% confidence interval [CI] = –9.576-13.003).
In males, the right HL (RHL) was 18.46 ± 1.13 cm and left HL (LHL) was 18.42 ± 1.14 cm. It is observed that RHL was more than LHL, but it was not statistically significant (P = 0.712, 95% CI = –0.173-0.253). In females, the RHL was 17.25 ± 1.05 cm and LHL was 17.22 ± 1.06 cm. It is observed that RHL is more than LHL, and it was not statistically significant (P < 0.787, 95% CI = –0.188-0.248).
In males, the right HB (RHB) was 7.96 ± 0.55 cm and left HB (LHB) was 7.94 ± 0.56 cm. It is observed that RHB is more than LHB, and it was not statistically significant (P = 0.724, 95% CI = –0.084-0.124). In females, the RHB was 7.44 ± 0.53 cm and LHB was 7.43 ± 0.52 cm. It is observed that RHB is more than LHB, and it was not statistically significant (P < 0.8563, 95% CI = –0.098-0.118).
Linear regression equations were derived for each age group separately in male and female. In male, HL shows high positive value (right-0.74, left-0.75) of correlation coefficient [Figure 1]. SE is ± 5.63 for right and ± 5.62 for LHL. The equation derived for total males can be used within the predictive range (R2 ) of ± 0.55 for RHL and ± 0.56 for LHL [Table 1]. In females, HL shows high positive value (right-0.75, left-0.74) of correlation coefficient [Figure 2]. The equation derived for total females can be used within the predictive range (R2 ) of ± 0.56 for RHL and ± 0.55 for LHL [Table 2].
|Figure 1: Scatter diagram and regression line showing the relationship between stature and hand length in male. RHL: Right hand length, LHL: Left hand length, cm: Centimeter|
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|Figure 2: Scatter diagram and regression line showing the relationship between stature and hand length in female. RHL: Right hand length, LHL: Left hand length, cm: Centimeter|
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|Table 1: Regression equation for estimating height from HL in males in various age groups|
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|Table 2: Regression equation for estimating height from HL in females in various age groups|
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In males, HB shows the high positive value of correlation coefficient (right-0.45, left-0.46) which suggests a strong positive correlation between stature and HB [Figure 3]. SE is ± 7.53 for RHB and ± 7.51 for LHB. The equation derived for total males can be used within the predictive range (R2 ) of ± 0.20 for RHB and ± 0.21 for LHB [Table 3]. In females, HB shows the high positive value of correlation coefficient (right-0.56, left-0.55) [Figure 4]. SE is ± 6.34 for RHB and ± 6.43 for LHB. The equation derived for total females can be used within the predictive range (R2 ) of ± 0.32 for RHB and ± 0.30 for LHB [Table 4]. Correlation of coefficient of HL is more than that of HB in both, male and female.
|Figure 3: Scatter diagram and regression line showing the relationship between stature and hand breadth in male. RHB: Right hand breadth, LHB: Left hand breadth, cm: Centimeter|
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|Figure 4: Scatter diagram and regression line showing the relationship between hand breadth and stature in female. RHB: Right hand breadth, LHB: Left hand breadth, cm: Centimeter|
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|Table 3: Regression equation for estimating height from HB in males in various age groups|
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|Table 4: Regression equation for estimating height from HB in females in various age groups|
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Multiplication factor was derived for estimation of stature from HL and HB [Table 5]. If these measurements are multiplied by the respective multiplication factor, the approximate stature of an individual can be obtained. In both, male and female, the SD is more for HB than HL.
| Discussion|| |
The interest in the estimation of stature for identification already existed in antiquity. The skeleton is one part of the body that resists all environmental insult for maximum time and thus, can be a valuable tool in identification. Stature is a parameter that can be estimated even in mutilated and dismembered bodies, as well as in fragmentary remains.
This study aimed at the estimation of stature from HL and HB by formulating linear regression equation and multiplication factors. It is not always possible to measure all variables, so it is useful to have separate regression equation available for each variable.
In the present study, a total of 400 (219 males and 181 females) healthy medical students between the age of 18 and 24 years were enrolled. Since the maximum height of an individual is attained between 18 and 24 years, these individuals were selected for the study. Recently published studies ,, also enrolled same age group subjects. However, Pandhare et al.  and Ibegbu et al.  enrolled children for the study. Although, a published study  found the contribution of age is insignificant in the estimation of stature from arm span, we have considered age, and regression equations are derived for different age groups.
The stature estimated in this study in male was 170.75 ± 9.43 cm and in female was 159.46 ± 7.66 cm. It is observed that males have greater stature than females. This can be explained by the genetic constitution of males. Age of puberty being 2 years later in males as compared to females give them additional time for growth. This suggests that the formula for one sex cannot be applied to estimate stature for other sex. The stature found by different authors in India in different region or state is slightly different , and this can be explained by the different genetic constitution, environmental factors, and nutrition in different population groups.
The human hand which is the most used and versatile part of the body and is of great importance to investigators in the field of anthropometry. Rastogi et al.,  Jakhar et al.,  Pawar and Dadhich  and Pandhare et al.  from India used HL for estimation of stature.
In males, the RHL was 18.46 ± 1.13 cm and LHL was 18.42 ± 1.14 cm. The published study by Sunil et al.  reported RHL is 19.6 ± 1.3 and LHL is 19.5 ± 1.2, which is more than the present study. In females, we found RHB was 17.25 ± 1.05 cm and LHB was 17.22 ± 1.06 cm. The published study by Sunil et al.  reported RHL is 18.2 ± 1.0 and LHL is 18.1 ± 1.0, which is more than the present study. Thus, variations are there in HL and HB in different regions of India.
In both, males and females, HL showed high positive value of correlation coefficient, which suggests a strong positive correlation between stature, HL, and HB. Regression equations for estimation of stature in male from RHL is S = 68.69 + 5.52 RHL and from LHL is S = 69.09 + 5.51 LHL, and in female from RHL is S = 65.22 + 5.46 RHL and from LHL is S = 66.90 + 5.37 LHL.
Other studies also showed similar results of positive correlation coefficient using HL. A study done by the Sunil et al.  using HL showed a correlation coefficient of 0.6 in male and 0.7 in female, and a study by Kaur et al.  reported 0.58 in male and 0.55 in female.
In males, the RHB was 7.96 ± 0.55 cm and LHB was 7.94 ± 0.56 cm. Chawla and Rajkumar  reported RHB of 8.36 ± 5.7 and LHB of 8.26 ± 7.6, which is more than the present study. In females, the RHB was 7.44 ± 0.53 cm and LHB was 7.43 ± 0.52 cm.
In both, males and females, HB showed high positive correlation coefficient, which suggests a strong positive correlation between stature and HB. Regression equations for estimation of stature in male from RHB is S = 115.32 + 6.96 RHB and from LHB is S = 115.92 + 6.90 LHB, and in female from RHB is S = 98.48 + 8.18 RHB and from LHB is S = 100 + 7.99 LHB. Similar results were obtained by other published studies using HB. Chawla and Rajkumar  found a correlation coefficient of 0.587 for RHB and 0.575 for LHB and Krishan et al.  reported 0.514 and 0.537 for RHB and LHB, respectively. However, Ozaslan et al.  found a correlation coefficient of 0.173 for male and 0.257 for female, which is less than the present study.
Although we found RHL and RHB were more than LHL and LHB in male and female, it was not statistically significant. Similar findings were reported by Ibeachu et al.,  Sunil et al.,  and Bhatnagar et al.,  however published data  suggest right-hand dimensions are generally greater than left-hand dimensions. This shows that if sex and race are known and side of the hand is known then stature can be estimated more precisely using any of the mentioned formulas.
In both, male and female, we found that HL showed greater correlation coefficient (R = 0.89) than HB. Earlier studies , also reported arm span as most reliable body parameter to predict stature among all other studied variables. When arm span not used, HL showed a better correlation with stature than other body parameters as shown by studies. , Similar to present study, Ozaslan et al.  also found HB has the weakest correlation with stature among studied variables such as HL, HB, wrist breadth, foot length, foot breadth, and ankle breadth.
When multiplication factors are used to estimate stature in males and females, we found greater SD for HB than HL. Although, multiplication factor is frequently used by authors  to estimate stature from body parameter, the error of estimate is usually large whenever multiplication factor method is used and it is well-explained by previous study.  Chhabra  estimated the height by three methods and found regression equation as the best method to predict height.
In our study, we have formulated both regression equation and multiplication factor for estimation of stature. It may also be emphasized that all measurements exhibit the high value of correlation in both sexes and thus offer a reliable estimate of stature for both males and females of this region. Therefore, depending upon the availability of body parts, stature may be estimated using linear regression equations and multiplication factors with reasonable accuracy. The stature can be accurately and satisfactorily estimated for medico-legal and forensic purposes since bilateral and bisexual differences have been taken into account while devising the linear regression equation and multiplication factor. However, if the age of the person is known, then better result can be obtained using the different linear regression equation as per age.
This study is done on medical students of Government Medical College, which are from middle to higher socioeconomic class. Hence, the anthropometric measurements can be on higher side, when compared to general population. The ideal study to formulate regression equation for all population should be a community-based study. A range is the only thing that can be accurately determined from a formula. Moreover, these equations cannot be applied for giants and dwarfs. A similar study should be done in all regions to formulate simple regression equation specific for each region.
| Conclusions|| |
Stature is more in male than female with statistical significance. Stature is estimated from HL and HB with accuracy using simple regression equation or multiplication factor. HL shows greater correlation with stature than HB.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]