名古屋学芸大学健康･栄養研究所年報

第4号

2010年

《Original Article》

Estimating Stature from Knee Height for Elderly females
aged 60-80 years old in Aichi Prefecture, Japan
Katsumi Yamanaka＊, Shizuno Ishida＊, Hisashi Susaki＊, Chiaki Kojima＊,
Natsuyo Omine＊, Yuuki Itoh＊, Yoshihiro Hirata＊, Kiwako Okada＊,
Aya Sumiya＊ and Mika Toba＊

Abstract
Purpose: The aim of this study was to develop predictive equations to estimate stature from knee height inelderly female in Nagoya. Design: Cross sectional study, convenient sample. Methods: Height, weight, andknee height were measured (knee height was taken from the top of the patella with the knee flexed at 90°).
The predictive equations were fixed according to a stepwise linear regression analysis. Results: Knee height
was highly correlated with stature. A linear regression analysis revealed the equation (Predicted stature(cm)=69.8+2.0×knee height(cm)-0.13×Age. R2=0.669)
Conclusion: Knee height is useful for estimating stature when height measurement is not possible.
Key words: height, knee height, elderly, linear regression analysis

height and weight measured. In order to avoid these

Introductiondifficulties, alternative methods of estimating height

Height and weight are important anthropometricfrom other anthropometric measurements have beenmeasurements for the assessment of health condi-

developed. Generally, the knee height method istions in people. Ideal body weight is based on height.

most common now. At present, the formula devised

Body mass index (BMI), which is calculated fromby Miyazawa 1) and Chumlea 2) are often used inheight and weight (expressed in kg/m2), is used toJapan. However, whereas Miyazawa’s formula wasjudge the level of obesity. Energy expenditure isdeveloped from the data of people aged from 21 tomeasured from basal energy expenditure (BEE),

97 from various locations of Japan and Chumlea’s

which is estimated from the Harris-Benedict’s

formula was developed from the data of people inequation (for male; BEE=664.7+(13.75×weight)+

the US. The ratio of knee height to height changes in(5.0×height)+(6.76×age)). Creatinine height index,

time, and varies across age, races and regions In thiswhich shows protein nutrition, also requires a mea-

paper, we attempt to create a height estimated for-surement of height. However, the measurement ofmula to concentrating on the elderly living in Aichi

height in the elderly who are confined to bed or in

Prefecture. We also try to assess the formula.

wheelchairs or who have spinal deformities can bedifficult. Miyazawa 1) reported that there were 76% of admitted patients who could not have their＊ School of Nutritional Sciences, Nagoya University of Arts and Sciences, Nisshin, Aichi, Japan1

52.1±8.0. The mean knee height was 44.2±2.2 as

Methods

shown in Table 1.

1, Participants
2, Actual height, knee height and weight

All participants were female and were over 60
years old. They were healthy people who lived in

The scatter diagram for actual height and knee

the residential area in Nagoya city and participatedheight was shown in Fig. 1. People who were tallerin this study with informed consent.

in statue had a higher knee height. The Pearson’s
correlation coefficient was 0.85 (p<0.01). The scatter diagram for actual height and age was shown

2, Measurements
The participants were requested to stand straight,

in Fig. 2. The Pearson’s correlation coefficient waswith heels together, and stature was measured with

-0.23 (p<0.01). The scatter diagram for actual heighta stature meter with a handle moving (Yagami Co.).

and weight was shown in Fig. 3. The Pearson’s cor-

Weight was measured on scales (weight meter).

relation coefficient was 0.53 (p<0.01).

Knee height was measured with a knee height caliper
3, Habit of exercise(Ross Co.). Knee height is the distance from the soleof the foot to the anterior surface of the thigh with

The average height of participants who had a habit

ankle and knee each flexed to a 90 degree angle.

of exercise was 151.2±5.2. The average height ofparticipants who did not have a habit of exercise
was 148.8±5.7.

3, Statistics
Linear regression analysis was used to derive

4, A model for the prediction of statureequations on knee height (independent variable)

An estimation equation for stature was devel-to predict height (dependent variable), using SPSS

oped according to a multiple regression analysis.

(Version 15.0J for Window).

yi=ai+b1x1+b2x2+b3x3. In this equation, yi represented
stature (dependent) and xn represented independent

Results

factors, x1: knee height, x2: age, x3: weight, and ai

1, Participants’ characteristics

was the intercept, and b1, b2 b3 represented the re-

There were 133 female participants. The averagegression coefficients (slops) of knee height, age andyear was 71.5, and the number of people was 55 inweight. R2 is called the coefficient of determination

the age group of 60-69 years, 65 in the age groupwhich is interpreted as the proportion of the totalof 70-79 years, and 18 in the age group of 80 years

variation in height accounted for by factors (factors

old and over.

“explains” R2 of the variability of stature). A model

The mean height and its standard deviation (ST)for the prediction of stature using parameters knee

was 150.6±5.4. The mean weight and its ST washeight, age and weight was shown as follow:

Table 1 Actual height and predicted height
Mean

S.D.

Mini.

Max.

Actual stature (cm)

150.6

5.4

139.5

171.0

Predicted stature 1) (cm)

150.9

4.5

141.1

162.5

Difference-0.3

Weight (kg)

52.1

8.0

34.2

-1.6
75.0

Knee height (cm)

44.2

2.2

39.8

50.5

1) Stature was predicted from knee height and age

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Estimating Stature from Knee Height for Elderly females aged 60–80 years old in Aichi Prefecture, Japan

Fig. 1 Scatter diagram for actual height and knee height. n=133
The Pearson’s correlation coefficient=0.85, p<0.001

55

actual height(cm)

50

45

40

35
50

60

70

80

90

age(years)
Fig. 2 Scatter diagram for actual hight and age. n=133
The Pearson’s correlation coefficient=-0.23, p<0.01

2) Predicted stature from knee height and age

1) Predicted stature from knee height

Predicted stature(cm)Predicted stature(cm)=59.1+2.1×knee height(cm)

2=69.8+2.0×knee height(cm)-0.13×Age

R =0.648R2=0.669

The inclusion of age into the equation above 1)
improved the value of R2

3

Fig. 3 Scatter diagram for actual height and weight
The Pearson’s correlation coefficient=0.53, p<0.01

3) Predicted stature from knee height, age, and(2) 50 kg and overweightPredicted stature(cm)Predicted stature(cm)   =63.0+2.0×knee height(cm)-0.04×Age=59.1+2.1×knee height(cm)-0.13×Age+0.08×weight(kg)

R2=0.68

R2=0.68
5) Predicted stature from knee height, age, andweight according to the group of physical exercise

The addition of weight to the regression above 2)
2

habit

reduced only 0.01 of R .

(1) Habit of physical exercise
Predicted stature(cm)For the weight group, and the group with a habit   =68.3+1.8×knee height(cm)-0.06×Ageof physical exercise, a model for the prediction of+0.11×weight(kg)

stature using the parameters of knee height, age and

R2=0.70

weight was shown as follows:(2) No habit of physical exercise
4) Predicted stature from knee height and age ac-

Predicted stature(cm)cording to the weight group (less than 50 kg, and   =83.7+1.9×knee height(cm)-0.26×Age

50 kg and over)+0.02×weight(kg)

(1) Less than 50 kg,

6) Predicted stature from knee height and age ac-

Predicted stature(cm)cording to weight and habit of physical exercise.

   =82.3+1.9×knee height(cm)-0.26×Age

R2=0.70R2=0.59

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Estimating Stature from Knee Height for Elderly females aged 60–80 years old in Aichi Prefecture, Japan

Fig. 4 Scatter diagram for actual height and predicted height
The Pearson’s correlation coefficient=0.99, p<0.001
Predicted height was calculated from the equation
Predicted stature(cm)=69.8+2.0×knee height(cm)-0.13×Age.

(1) Less than 50 kg and habit of physical exer-

(4) 50 kg and over, and No habit of physical ex-

cise

ercisePredicted stature(cm)Predicted stature(cm)   =68.8+2.0×knee height(cm)-1.5×Age   =79.1+1.9×knee height(cm)-0.17×Age2

R =0.64R2=0.81

The R2 value in the regressions of stature on knee(2) Less than 50 kg and no habit of physical ex-height and age was the highest in the group 50 kg anderciseover, and habit of physical exercise. However, The

Predicted stature(cm)value of R2 did not improved in the other group.

   =95.8+2.0×knee height(cm)-0.50×Age
R2=0.54



5, Actual stature and predicted stature
Difference between actual stature and calculated(3) 50 kg and over, and habit of physical exer-

stature was very small (less than 2 cm) as shown in

cise

Table 1. The scatter diagram for actual height andPredicted stature(cm)predicted height was shown in Fig 4. The Pearson’s   =63.0+2.0×knee height(cm)-0.02×Age

2correlation coefficient was 0.84 (p<0.001).

R =0.66

Discussion
The estimation of stature from the measurementof the long bones (humerus, tibia, knee height) was5carried out in practicing. Within these long bone

We propose that the estimation of the height formeasurements, the estimation from knee height isfemales, as “Predicted stature=69.8+2.0×knee

higher correlated to the stature than other long boneheight-0.13×Age”.

measurements. There are many equations for estimating stature from knee height, which have been

Reference

reported by Chumlea 2), Lera 3), Chumlea 4), Han 5),

1)

Chumlea 6), Palloni 7), Donini 8), Zhang 9), Myers 10),

Miyazawa Y, Chikamori M, Uchiyama S, Iwatani S,
Nakabe N, Kamimura F. Knee-height hou no houhou to

Shahar 11), Cockram 12), Knous 13) in various countries

mondaiten, Clin Nutr. 2005; 107: 411-416(Appendix 1). In Japan, the equations by Miyazawa

2)

Chumlea WC, Roche AF, Steinbaugh ML. Estimating

and also by Chumlea are popular when estimating

stature from knee height for persons 60 to 90 years ofa person’s height from knee height.

age. J Am Geriatr Soc. 1985; 33: 116-120
3)

To evaluate the relationship between stature

Lera L, Luis Santos J, García C, Arroyo P, Albala C.
Predictive equations for stature in the elderly: A study

and knee height or other factors, all authors whoin three Latin American cities. Annals Human Biology.

mentioned above used the simple or multiple linear

2005; 32: 773-781

regression models 1-12). In both models, the slopes for4)knee height were approximately 1.8 and the slopes

Chumlea WC, Guo SS, Steinbaugh ML. Prediction of
stature from knee height for black and white adults andfor age ranged from -0.07 to -0.26. A negative as-

children with application to mobility-impaired or handi-sociation between height and age was observed incapped persons. J Am Diet Assoc. 1994; 94: 1385-1388,
1391

all authors. Concerning the decline of height, there
is one report

14)

5)

in which the decline of height withHan TS, Lean MEJ. Lower leg length as an index of
stature in adults. Int J Obes. 1996; 20: 21-27

age was seen amongst individuals 45 years and over,6)

and the mean decline of height per year became

Chumlea WC, Guo SS, Wholihan K Cockram D, Kuczmarski RJ, Johnson CL. Stature prediction equations forlarger according to age. In addition, another reportelderly non-Hispanic white, non-Hispanic black, andby Chumlea 15) showed that the mean annual rate of

Mexican-American persons developed from MHANES

decline was approximately the same (-0.5 cm/year)

III data. J Am Diet Assoc. 1998; 98: 137-142
7)regardless of an individual’s age. Changes in body

Palloni A, Guend A. Stature prediction equations for
elderly Hispanics in Latin American countries by sex

posture or thinning of vertebrae disc can contributeand ethnic background. J Gerontol Med Sci. 2005; 60:

to a reduction in height in elderly females. In the804-810model for simple linear regression, in which y1 is

8)height and b1 is knee height, R2 ranged from 0.56 toDonini LM, de Felice MR, de Bernardini L, Ferrari G,
Rosano A, dé Medici M, Cannella C. Prediction of stat-

0.71. The addition of age to the regression (multiple

ure in the Italian elderly. J Nutrition, Health & Aging.

2

linear regression) did improve the value of R , which

2000; 4: 72-76
9)ranged around 0.60-0.80.

Zhang H, Hsu-Hage BHH, Wahlqvist ML. The use of

There is a report that estimation from arm lengthknee height to estimate maximum stature in elderly

was less correlated to the stature than from knee

Chinese. J Nutrition, Health & Aging.1998; 2: 84-87
10) Myers SA, Takiguchi S, Yu M. Stature estimated fromheight. We did not assess the estimation of theknee height in elderly Japanese Americans. J Am Geri-

height from arm length. Arm length is easy to mea-

atr Soc. 1994; 42: 157-160.

sure in the ambulatory patients, and changes little

11) Shahar S, Pooy NS. Predictive equations for estimation

with age. Arm length may be used as a substituteof stature in Malaysian elderly people. Asia Pac J Clinfor measurement of stature. However, at present,

Nutr. 2003; 12: 80-84

the measurement of knee height provides a simple,

12) Cockram DB, Baumgartner RN. Evaluation of accuracy

quick, and accurate means estimating stature forand reliability of calipers for measuring recumbent kneeheight in elderly people. Am J Clin Nutr. 1990; 52: 397-

the elderly, whose height cannot be measured.

400.
6Estimating Stature from Knee Height for Elderly females aged 60–80 years old in Aichi Prefecture, Japan13) Knous BL, Arisawa M. Estimation of height in elderly
Japanese using region-specific knee height equations.
Am J. Human Biology. 2002; 14: 300-307.
14) Cline MG, Meredith KE. Boyer JT, Burrows B. Declineof height with age in adults in a general population
sample: Estimating maximum height and distinguishingbirth cohort effects from actual loss of stature with aging. Hum Biology. 1989; 61: 415-425.
15) Chumlea WC, Garry PJ, Hunt WC, Rhyne RL. Distributions serial changes in stature and weight in a healthyelderly population. Hum Biology. 1988; 60: 917-925.
16) Muncie HL, Sobal J, Hoopes M, Tenney JH, Warren JW.
A practical method of estimating stature of bedridden
female nursing home patients. J Am Geriatr Soc. 1987;
35: 285-289.

7

Appendix 1. Various equations (linear multiple regression model) developed to predict stature using knee height and age
Author, reference number, date of publishing
Characteristics of participants, ethics, age
Number of participants, gender, equations
Chumlea WC 2) 1985
Participants: American, age range 65-104 years old
N=106 meny=64.19+2.02×KH-0.04×age R2=0.67
N=130 women y=84.88+1.83×KH-0.24×age R2=0.65
Cockram DB 12) 1990
Participants: ambulatory patients, age range 64-83 years old in OH, USA
N=15 mean age 71.8 men
y=47.65+2.51×KH-0.14×age R2=0.85
N=25 mean age
women y=95.0+1.79×KH-0.32×age
R2=0.83
Chumlea WC 4) 1994
Participants: American, the data from National Health Examination Survey (1960-1970), age range 18-60 years old
N=2537 mean age 38.6 white women y=70.25+1.87×KH-0.06×age R2=0.66
N=402
mean age 41.4 black women y=68.10+1.86×KH-0.06×age R2=0.67
Myers SA 10) 1994
Participants: Japanese-American in USA, mean age 72 years old (62-86)
N=16 meny=53.69+2.57×KH-0.23×age R2=0.70
N=16 women y=69.10+2.11×KH-0.22×age R2=0.78
Han TS 5) 1996
Participants: in England, age range 17-70 years old
N=78 mean age 43.9 men
y=54.9+2.30×KH-0.06×age
N=82 mean age 43.1 women y=71.3+1.91×KH-0.10×age

R2=0.79
R2=0.72

Chumlea WC 6) 1998
Participants: non-hispanic black, white and Mexican-American in USA, age 60 years old and over
N=1,369 mean age 70.6 Non-hispanic white men
y=78.31+1.94×KH-0.14×age
R2=0.69
N=1,472 mean age 69.9 Non-hispanic black men
y=79.69+1.85×KH-0.14×age
R2=0.70N=474
mean age 68.9 Mexican-American men
y=82.77+1.83×KH-0.16×age
R2=0.67
N=481
mean age 71.8 Non-hispanic white women y=82.21+1.85×KH-0.21×age
R2=0.67
N=497
mean age 70.5 Non-hispanic black women y=89.58 +1.61×KH-0.17×age R2=0.67
N=457
mean age 68.3 Mexican-American women y=84.25+1.82×KH-0.26×age
R2=0.67
Zhang H 9) 1998
Participants: Chinese in Melbourne, Australia, age range 30-90 years old
N=130 mean age 53.8 men
y=71.70+1.98×KH-0.04×age R2=0.69
N=117 mean age 42.0 women y=78.46+1.79×KH-0.07×age R2=0.72
Donini LM 8) 2000
Participants: Italian, age 61 years and over
N=113 mean age 72.8 men
y=99.67+1.58×KH-0.23×age
N=172 mean age 73.4 women y=94.87+1.58×KH-0.23×age

R2=0.75
R2=0.75

Knous BL 13) 2002
Participants: Japanese in Joetsu city, JapanAge range, 65-74 years old in men, 65-75 years old in women
N=40 mean age 68.0 men
y=71.16+2.61×KH-0.56×age R2=0.84
N=39 mean age 68.0 women y=64.19+2.02×KH-0.04×age R2=0.73

8Estimating Stature from Knee Height for Elderly females aged 60–80 years old in Aichi Prefecture, Japan

Palloni A 7) 2005
Participants: Hispanics in Latin American countries, age 60 years old and over
N=1363 Black men
y=117.99+1.10×KH-0.12×age
N=2135 Black womeny=112.6+1.09×KH-0.17×age
N=85
Mestizo men
y=73.17+1.89×KH-0.08×age
N=160
Mestizo women y=97.97+1.43×KH-0.21×age
N=206
Mexican men
y=67.00+1.92×KH-0.10×age
N=315
Mexican women y=75.57+1.78×KH-0.11×age
N=136
Mulatto men
y=123.4+1.02×KH-0.15×age
N=256
Mulatto womeny=110.33+1.00×KH-0.09×age
Miyazawa Y 1) 2005
Participants: Japanese in nursing home
Age range, 21-88 years old in men, 21-97 years old in women
N=245 meny=64.02+2.12×KH-0.07×age
N=505 women y=77.88+1.77×KH-0.10×age
Lera L 3) 2005
Participants: people in San Paulo, Brazil, Santiago, Chile, Mexico city. Age: 60 years and over
San Paulo, Brazil
N=713 mean age 73.3 men
y=67.2+1.96×KH-0.08×age
R2=0.69
N=944 mean age 72.1 women y=69.87+1.85×KH-0.11×age R2=0.58
Santiago, Chile
N=389 mean age 70.6
N=615 mean age 71.3

men
women

y=64.88+2.09×KH-0.10×age
y=75.17+1.78×KH-0.10×age

R2=0.70R2=0.54

Mexico city
N=388 mean age 69.5
N=607 mean age 70.0

men
women

y=63.88+1.99×KH-0.06×age
y=73.09+1.87×KH-0.19×age

R2=0.67
R2=0.59

In the equations, y means predictive height in cm, KH means knee height in cm, and age means age in years, and R2 means themultiple correlation coefficient of determination.

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和文抄録

高齢者女性の下肢長から身長推定式について
＊

＊

山中 克己
＊
大峰 夏世

＊

石田 静乃
＊
伊藤 勇貴
＊
角谷 亜矢

須崎
尚
＊
平田 芳浩
＊
鳥羽 美香

＊

小島 千明
＊
岡田希和子

背景：高齢者の栄養評価をする場合、身長や体重の値が必要になる。しかし、高齢者では寝たきり、脊椎の変形などにより身長の測定が出来ないことが多い。
目的：女性高齢者について、下肢長から身長を推定する式（重回帰式）を作る。
方法：名古屋市内に在住する健康な女性高齢者（60歳以上）133名について、身長、下肢長を測定して重回帰分析を行った。
結果：分析の結果、重回帰式

推定身長（cm）＝69.8＋2.0×下肢長（cm）－0.13×年齢（年）を得た。その寄与割合は R ＝0.669であった。
2

結論：名古屋市在住の女性高齢者の身長を下肢長、年齢から推定できる。
キーワード：女性高齢者、身長、下肢長、重回帰式＊名古屋学芸大学管理栄養学部

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