- Email: [email protected]
Reliability of near-infrared interactance body fat assessment relative to standard anthropometric techniques
J Clin EDidemiol Vol. 48.No. 11, PD. 1361-1367.
Pergamon
08954356(95)00052-6
1995
Copyright0 1995glhevierScienceInc. Printedin GreatBritain.All rightsreserved 0895-4356/95
$9.50 + 0.00
RELIABILITY OF NEAR-INFRARED INTERACTANCE BODY FAT ASSESSMENT RELATIVE TO STANDARD ANTHROPOMETRIC TECHNIQUES J. SCHREINER,~ JANNE PIT~~NIEMI,’ JUHA PEKKANEN,~ AND VEIKKO V. SALOMAA~~* ‘Departmentof Epidemiology, University of Minnesota,Minneapolis, Minnesota,and *Department of Epidemiology and Health promotion, and ‘Departmentof Environmental Epidemiology, National Public Health Institute, Helsinki, Finland PAMELA
(Received for publication 16 January 1995)
Abstract-We examined the repeatability of near-infrared interactance (NIR) body fat determination as compared with that of body massindex (BMI), waist-to-hip ratio (WHR), and waist girth. Thirty-nine volunteers (16 men, 23 women) had percent body fat (%BF) measurementsmade with a portable NIR device as well as the standard anthropometric indices of height, weight, waist girth, and hip circumference. Frame size and physical activity levels were also determined. For each participant, three independent measurements of each index were made by two trained readers during a 2-week period. The two readers varied significantly in their measurement of %BF and hip circumference. The variability in %BF was largely due to differences between the first and the second measurements, and only for one of the readers. Second and third measurementswere not statistically significantly different for either reader, suggestive of a training effect. Variance component calculations revealed that the reliability of NIR is 95.3%, compared with 99.9% for BMI; 93.4% for waist girth; and 82.4% for WHR, with the majority of the remaining variance accounted for by the method itself. We conclude that the NIR method has good repeatability, with low intra- and interobserver variability, provided that readers are carefully trained. However, the NIR device offers little advantage in reliability over conventional measures of adiposity such as waist girth or BMI, and requires additional input of weight, height, frame size, physical activity level, age, and gender data to calculate %BF. Associations of NIR and other anthropometric indices with cardiovascular risk factors in this population will provide additional insight into the merit of NIR body fat assessment.
NIR
Anthropometry
Reliability
Body fat
Waist-to-hip ratio
Obesity
in epidemiological research with the evidence that excess weight, particularly centrally located Measurement of both absolute and relative adi- deposition, is associated with a wide range of posity has become increasingly more important cardiovascular sequelae such as non-insulindependent diabetes mellitus, hypertension, and *Correspondenceshould be sent to: Veikko V. Salomaa, coronary heart disease [l-71. Methodology exNational public Health Institute, Departmentof Epidemiology and Health Promotion, Mannerheimintie ists for precise determination of both total adi166,FIN-00300Helsinki, Finland. posity (underwater weighing, potassium or deuINTRODUCTION
1361
PAMELA J. SCHREINERet al.
1362
terium dioxide dilution) and visceral/central obesity (computerized tomography or magnetic resonance imaging); however, many of these approaches are suitable only for clinical or laboratory environments. Because of expense, difficulty, or inconvenience to the study participant, many of these approaches are inappropriate for a population-based study. More conventional techniques-such as waist circumference, hip girth, and skinfold thicknesses-are plagued with lack of reproducibility due to the method itself or the individual performing the measurement, resulting in both imprecision and inaccuracy. The ideal solution would appear to lie in instrumentation that captures the precision of clinical methods, but is relatively inexpensive, noninvasive, easy to implement/train, and portable. Near-infrared interactance (NIR) operates because different types of tissue absorb light at different wavelengths [8]. Pure fat, for example, absorbs at 930 nm, while pure water absorbs at 970 nm. Percent body fat and lean tissue mass are then obtained by prediction equations based on area under the curve for these spectrophometric parameters. Originally, NIR technology was primarily used to assess food composition [9, lo]; later, clinical applications began to appear, but involved use of large, expensive equipment. Recently, a small, portable NIR device using the same principles has been used to measure body fat in nonclinical settings. Several studies have reported that body fat assessmentusing the portable NIR device compares favorably with “gold standard” methods such as hydrodensitometry [ll, 121;other studies report that there is little improvement in precision using this device over standard anthropometric measures[13, 141. Therefore, we examined the performance of this device among Finnish male and female volunteers. The goals of this study were (1) to compare the reliability of this technique with conventional measures (waist circumference, body mass index [BMI], and waist-to-hip ratio [WHR]) read at the same time for three distinct reading periods, and (2) to determine the interand intrareader repeatability over time for this same group of participants. MATERIALS
AND METHODS
Forty-three individuals-18 males and 25 females-volunteered to participate in a pilot study of the near-infrared interactance device
(Futrex-5000; Futrex, Gaithersburg, MD). These participants, aged 20-55 years, were recruited from a group of state employees as a pilot test of the device for the FINMONICA ‘92 risk factor survey. Each subject had a total of six readings made by two trained nurses (three readings per nurse) at three distinct time points during the period between 16 April and 30 April 1991. Of these, three participants had body fat readings at the first reading that were greater than two standard deviations away from measurementsat their second and third readings; an additional individual did not complete all three readings. These four participants were excluded, leaving a total of 39 subjects. Participants wore light clothing (without shoes and heavy outer garments), and were weighed to the nearest 100 g. Height was recorded to the nearest centimeter, with the participant standing with his/her back to the height rule. Body mass index was calculated as weight divided by height-squared, in units of kilograms per metersquared. Horizontal circumferences were measured in centimeters, with the subject in the standing position: waist girth was measured at a level midway between the lower rib margin and the iliac crest; hip girth was measuredat the widest circumference over the greater trochanters n51.
Body fat determinations were made as described in the manual of the device [16], based on optical densities detected from light emitted at 940- and 950-nm wavelengths. Percentage body fat was predicted from optical densities measured at the biceps site, adjusted for age, gender, body weight, height, body frame, and physical activity level. The biceps, as delineated in the manual, is located at the anterior aspect of the biceps halfway between the anticubital fossa and acromion. Body frame was described as small, medium, or large, determined by gender-specific cut points based on height and elbow breadth, measured to the nearest 0.1 cm using a sliding caliper. Physical activity was defined as heavy if the participant reported being a competitive athlete or aggressively exercised more than 1 hour per day on average; moderate if the individual did 30 minutes per day of brisk walking (or equivalent), but did not aggressively exercise more than 1 hour per day; light if the individual did 15 to 30 minutes per day of the equivalent of brisk walking (i.e., at least 100minutes per week of the equivalent of brisk walking); or other if the individual did less exercise than the equivalent of 100minutes per week of brisk walking. As
Reliability of NIR Body Fat Assessment
1363
mass is morely directly correlated with these measures, with lean body mass and weight almost ideally related (r = 0.916). WHR is also more strongly correlated with the other anthropometric variables than is percent body fat. Paired t testing for the difference in mean percent body fat between the two readers is significantly different (25.8 vs. 25.3%, p = 0.043); therefore, Table 3 shows the various measures by individual reader. The two readers obtain similar or identical values of height, weight, and waist circumference. Reader 1, however, measures hip circumference slightly higher (99.3 vs. 98.9 cm, respectively, p = 0.0107), on average, than reader 2, and reader 1 reports a higher average lean body mass for the participants than reader 2 (51.9 vs. 51.6 kg, respectively, p = 0.0472). Measurements made at three different times show that reader 1 reports mean percent body fat as 25.4, 25.4, and 25.1 for the first, second, and third readings, respectively; reader 2 RESULTS reports values of 24.7, 26.4, and 26.2. For reader Table 1 shows population attributes averaged 1, percent body fat means for the first reading over the three readings for the 39 participants versus the second, and for the second versus the (16 males and 23 females) in this study. Males third reading, are not statistically significantly were, on average, slightly younger, taller, different (p = 0.98 and p = 0.45, respectively); weighed more, and had a higher body massindex for reader 2, however, these same comparisons than females. Men also had greater waist and hip have p values of 0.0001 and 0.71. This suggests circumferences relative to females, resulting in that the second reader varies more between a higher WHR. In this sample, men had a lower readings than the first reader, and that after the percent body fat than women, with a higher lean first measurement the second reader has imtissue mass, and were far less likely to be ciga- proved repeatability. rette smokers. The readers also disagree on assessment of Pearson correlation coefficients for continu- frame size, particularly among individuals with ous anthropometric indices are shown by gender small frames (Pearson chi-square, p = 0.027). in Table 2. Although for both men and women, Calculation of the variance components for perpercent body fat is correlated with WHR, waist cent body fat indicates that the individual concircumference, BMI, and weight, these correla- tributes 95.3% of the variance compared with tions are stronger in men. In women, lean body 2.9% for physical activity, 1.8% for the method part of the examination, participants were also asked about their smoking habits. Means and frequencies were obtained with SAS, version 6.07 [17]. Differences between means and frequencies were assessedby r testing or analysis of variance and by Pearson chisquare, respectively. Because the covariates of interest were essentially normally distributed, Pearson correlation coefficients were calculated. The contribution of each of the independent variables (effects) to the variance of the dependent variable (either percent body fat or other anthropometric measure) was estimated by calculation of the variance components in a general linear model (PROC VARCOMP), considering the reader as a fixed effect. The effect of multiple measurementson the same subject was assessed using repeated measures analysis of variance for general linear models (REPEATED statement in PROC GLM).
Table 1. Selected population attributes by gender Males Females Covariate
Mean (SD)
Mean (SD) Pa 23 I&e (years) E.5 (9.01) 37.5 (8.86) 0.4244 Height (cm) 177.4 (5.16) 163.8 (4.97)
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et al. PAMELAJ. SCHREINER
Table 2. Pearson correlation coefficients between anthropometric indices by gender Index
% Body fat
Lean body mass (kg)
WHR
waist (cm)
BMI (kg/m*)
Height
Weight
(cm)
(kg)
- 0.242 0.526’ - 0.067 0.025 -0.166 1.00
0.762b 0.766b 0.836b 0.948b 0.9346 0.195 1.00
- 0.322 0.440* -0.046 0.008 -0.089 1.00
0.657d 0.916’ 0.7956 0.9356 0.958b 0.197 1.00
Males (n = 16)’ % Body fat Lean body mass (kg) WHR Waist (cm) BMI (kg/m*) Height (cm) Weight (kg)
1.00
0.174 1.00
0.819b 0.468 1.00
0.844'
0.842b
0.589c 0.9606 1.00
o.588c 0.851b 0.932’ 1.00
0.670b 0.831’ 0.924’ 1.00
0.752b 0.807’ 0.827b 0.958b 1.00
Females (n = 23)” % Body fat 1.oo 0.314 0.580d Lean body mass (kg) 1.00 0.6866 WHR 1.00 Waist (cm) BMI (kg/m*) Height (cm) Weight (kg) ‘n is the mean of six measurements made by two independent readers bp < 0.001.
(three measurements each).
rp < 0.05. dp < 0.01.
Abbreviations: BMI, body mass index; WHR, waist-to-hip ratio.
itself, and 0.03% for frame size-therefore, the readers’ disagreement on frame size has little effect on the measurement of body fat. Likewise, comparing the variance components for four indices of adiposity measured by the same readers reveals that for percent body fat, the reliability is 95.3% (as above), with the method (including the instrument and the assignment of exercise level and frame size) contributing to 4.6% of the variance. For BMI, the reliability is 99.9% with 0.1% of the variance due to method; for WHR, 82.4% reliability and 17.6% of the variance due Table 3. Means and frequencies associated with anthropometric and near-infrared interactance measurements for each reader Parameter measured Reader 1 Reader 2 Pa 169.4 169.4 Height (cm) 69.8 69.8 Weight (kg) 82.2 81.8 Waist (cm) 99.3 98.9 Hip (cm) % Body fat 25.3 25.8 51.9 51.6 Lean mass (kg) Frame size (%) 1 = Small 21.4 31.6 2 = Medium 72.6 66.7 3 = Large 6.0 1.7 Exercise (%) 1 = Light 7.7 2 = Moderate 53.8 49.6 3 = Heavy 42.7 35.9 “Paired t test for comparison of continuous means between readers; Pearson chi-square comparison of categorical data.
1.oo 1.oo 0.2470 0.0107 0.0428 0.0472
0.027 10.3 0.251 variable test for
to method; and for waist circumference independent of hip girth, 93.4% reliability and 6.6% of the variance due to method. Therefore, although the two readers measure most of the anthropometric indices (with the exception of BMI) slightly differently, in general the additional variance beyond interindividual variability is due to the method rather than the reader. Table 4 presents the contribution to the total variance for each of the descriptive parameters-height, weight, age, frame size, and exercise level-used in determining percent body fat by NIR. For both men and women, weight contributes the largest proportion of variance (63.7 and 56.0%, respectively), followed by height. In this select population, age and frame size combined account for
1365
Reliability of NIR Body Fat Assessment Table 4. Gender-specific variance components for descriptive parameters used to estimate percent body fat by near-infrared interactance Females Males Covariate
Type I SS”
Height (cm) Weight (kg) Age (years) Frame size Exercise level Error
1121.24 3433.50 0.16 1.64 145.31 70.51
Total: “Sum of squares.
5388.36
% variance 32.1 63.1 0.003 0.2 2.7 1.3
Type I SS’ 1953.23 2768.40 38.30 3.18 99.62 78.87 4941.60
100
70 variance 39.5 56.0 0.8 0.1 2.0 1.6 100
lower reader contributions to error with thorough, standardized training protocol. The device requires input of height, weight, gender, age, frame size, and exercise level, which are either highly precise measures or contribute very little to the variance in body fat. Therefore, training may focus on placement of the NIR probe or lighting conditions. Several previous studies have compared NIR with more conventional anthropometric indices used in population studies, but have not contrasted repeatability of these techniques. Conway et al. [ 111,using computerized spectrophotometry (rather than the hand-held device, with adjustable absorption wavelengths), found that NIR gave similar estimates of percent body fat DISCUSSION as deuterium dioxide dilution and multiple-site This study of near-infrared interactance mea- skinfold thicknesses in a group of middle-aged sured using a portable device among a volun- men and women of normal weight. Ultrasound teer sample of Finnish civil servants suggests body fat assessment,on the other hand, consisthat this method is fairly accurate and precise, tently underestimated body fat in this populawith a small amount of the variance in percent tion. Using hydrodensitometry as a reference, body fat determination contributed by the Brodie and Eston 1121compared electrical immethod itself and an even smaller portion con- pedance with the NIR method and found that tributed by the reader. In addition, this reader although the two techniques were comparable in variability decreases with time in our study, the center of the population body fat distribushowing a learning effect that may lead to even tion, the NIR device tended to overestimate being composed of the two accurately and precisely measured variables of height and weight, is completely associated with interindividual variability and not with either the reader or the method. Finally, total variance in percent body fat is also predominantly accounted for by interindividual (between group) variability, with a borderline significant portion determined by between reader variability (p = 0.067). However, the contribution of the reader to the total variance in percent body fat is explained by reader differences between the first and the third measurement, rather than the second and third, and suggeststhat there is a learning curve to the NIR method.
Table 5. Repeated measures analysis of variance for between-subjects effects and time trends F value (PI
% Body fat
BMI (kg/m*)
WHR
15.65 Subject (0.0001) 0.03 Reader (0.8739) 1.65 Time 1 vs. time 3 (0.2073) Time 2 .0.12 vs. time 3 (El 1) (0.7312) Abbreviations: BMI, body mass index; WHR, waist-to-hip ratio. 91.74 (0.0001) 3.56 (0.0669) 12.01 (0.0013)
99999.99 (0.0001) &oo,
Waist (cm) 55.04 (0.0001) 0.91 (0.3469) 3.95 (0.0545) ‘0.41 (0.5244)
1366
PAMELA J. SCHREINER et al.
body fat in lean athletic adults. McClean and Skinner [14] also reported that skinfolds and NIR were both highly correlated with underwater weighing in both genders; however, the NIR device tended to underestimate body fat in those with ~30% body fat, and to overestimate body fat in those with ~8%. They concluded that, while NIR measures did predict additional variance beyond height, weight, frame size, and physical activity, multiple-site skinfolds accounted for essentially the same amount of variance. In addition, skinfolds appeared to be more accurate at the extremes of body fat. Hortobagyi et al. [ 131reported that NIR optical density readings at 950 nm were fairly well correlated with body fat estimates derived from seven skinfolds (r = -0.67), whereas NIR measurementsat the biceps underestimated body fat in general when compared with underwater weighing. Finally, in a group of women, ages 20-72 years, Heyward et al. [8] replicated these results for a group with a less extreme range of body fat and reported that the NIR device tends to underestimate body fat/overestimate lean tissue in those women with body fat >39%, while overestimating body fat/ underestimating lean mass in those with body fat ~28% (an average body fat percentage for women). Our study was unable to compare extremes in either BMI or percent body fat due to the small sample size and the narrow distribution of these indices in our healthy volunteer population. The NIR device also requires input of descriptive data (weight, height, frame size, physical activity, age, and gender) that independently contribute substantially to estimation of body fat [12]. Elia et al. [18], looking at a young (18-40 years) population, found that NIR was not any better at predicting body fat than simple anthropometric measures (height, weight) plus either skinfolds or impedance/resistance. In this group, NIR also seriously underestimated body fat in the upper range of the population: those with BMI >50 kg/m* differed by 16%from the underwater weighing estimates. However, the percent body fat predicted by NIR from the biceps was closer to the hydrodensitometry results than either the triceps, the thigh, or any combination of the three sites; others [8, 141found that singlesite measurements at the biceps with the NIR device (versus any combination of multiple sites) corresponded most highly with their respective gold standards. None of these techniques is without difficulties. Body mass index is increased in males rela-
tive to females independent of adiposity due to their higher proportion of lean body mass. Even hydrodensitometry becomes less accurate with age as bone is lost [19] and depends on the participant having normal lung elasticity. In our study, while all the methods examined had reasonably good reader repeatability, WHR suffered from methodological variability due to the difficulties in measurement at both the waist and the hip. Waist circumference and NIR had similar reliability and method error, suggesting that since the NIR device requires weight, height, frame size, gender, and age to complete the prediction equation (McClean et al. [ 141recommend eliminating physical activity as a parameter, due to its subjective nature), standardized waist circumference measurements may be an adequate anthropometric index. In addition, because both skinfolds and NIR are capable of directly measuring only subcutaneous fat, waist circumference offers the additional advantage of capturing intra-abdominal and subcutaneous fat deposits. Skinfolds and NIR become increasingly less accurate with aging owing to the overestimation of fat free mass resulting from redistribution of fat from subcutaneous stores to visceral depots [19]. But the ultimate goal of obtaining any of these measurements is in prediction of diseaserisk factors or endpoints. Houmard et al. [20], for example, found that the predictive strength of WHR for high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, triglycerides, glucose, and insulin varies according to where waist and hip circumference are measured (with waist circumference accounting for more of the variance in these endpoints than hip girth). Spiegelman et al. [21] reported that BMI is a good predictor of both diastolic blood pressure levels and glucose, with overall body mass (either by BMI or fat mass from densitometry) being stronger predictors of blood pressure and glucose-after adjustment for age, height, and cigarette smoking-than relative fat mass or regional distribution patterns. The results obtained in the present study are expected to be better than would be found in a general population sample becausethese individuals were similar in age, nonobese, and all of Finnish (white) ethnicity. Prediction or calibration equations, whether the portable NIR device or other anthropometric determinants are used, must be both age- and gender-specific to account for real physiological variation. The NIR device
Reliability of NIR Body Fat Assessment
is a good beginning in attempts to bring laboratory technology to the field, but until the wide variation in adiposity, age, and race seen in genet-al populations can be accommodated, it has little advantage in reliability over conventional anthropometric measures of adiposity. Associations of NIR recordings and other indices of obesity with cardiovascular risk factors will be established in future analyses of the FINMONICA ‘92 risk factor survey material. Schreiner was supported by a grant from the Academy of Finland. The authors wish to acknowledge Liisa Toivanen and Taja Nuottimaki for performing the measurements.
Acknowledgmenrs-Dr.
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1. Landsberg L. Obesity and hypertension: Experimental data. J Hypertension 1992; (Suppl. 7): Sl95s201. 2. Knowler WC, Pettitt DJ, Saad MF, et al. Obesity in the Pima Indians: Its magnitude and relationship with diabetes. Am J Clin Nutr 1991; 53(Suppl. 6): 1543s-1551s. 3. McKeigue PM, Shah B, Marmot MG. Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in-South Asians. Lancet 1991: 337: 382-386. 4. Taylor R, Jalaludin B, Levy S, et al. Prevalence of diabetes, hypertension and obesity at different levels urbanization in Vanuatu. Med J Australia 1991; 155: 86-90.
Dowse G, Collins V, Zimmet P, et al. Crosssectional and longitudinal relationship between obesity, hypertension and coronary heart disease in Micronesian Nauruans. Diabetes Res Clin Prac 1990; lO(Supp1. 1): Sl79-S183. 6. Svec F. Rivera M. Huth M. Correlation of waist to hip ratio to the prevalence of diabetes and hypertension in black females. J Nat1 Med Assoc 1990; 82: 257-261. 7. Barrett-Connor E. Epidemiology, obesity, and noninsulin-dependent diabetes mellitus. Epidemiol Rev 1989; 11: 172-181. 8. Heyward VH, Jenkins KA, Cook KL, et al. Validity of single-site and multi-site models for estimating 5.
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9 teractance. Am J Hum Biol 1992; 4: 579-593. Norris KH. Reflectance spectroscopy. In: Modern ’ Methods of Food Analysis (Whitaker JR, Stewart K, eds.). Avi Publishing, New Haven, Connecticut, 1985; pp. 167-186. Lanza E. Determination of moisture, protein, fat and ’ calories in raw pork and beef by near infrared snectroscopy. J Food Sci 1983; 48: 471-474. ll. Conwav JM. Norris KH. Bodwell CE. A new anproach- for the estimation of body composition: Infrared interactance. Am J Clht Nutr 1984; 40: 1123-1130. 12. Brodie DA, Eston RG. Body fat estimations by electrical impedance and infrared interactance. Int J
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Spurts Med 1992; 13: 319-325.
13. Hortobagyi T, Israel RG, Houmard JA, et al. Comparison of body composition assessment by hydrodensitometry, skinfolds, and multiple site nearinfrared spectrophotometry. Eur J Clin Nutr 1992; 46: 205-211.
14. McLean KP, Skinner JS. Validity of Futrex-5000 for body composition determination. Med Sci Sports Exert
1992; 24: 253-258.
15. Marti B, Tuomilehto J, Salomaa V, et al. Body fat distribution in the Finnish population: Environmental determinants and predictive power for cardiovascular risk factors. J Epid Comm Health 1991; 45: l6 131-137. ’ Futrex, Inc. Futrex-5000 User’s Manual, version 6.0. l7 Futrex, Gaithersburg, Maryland, 1988. SAS Institute. SAWSTAT User’s Guide, version 6.07. SAS Institute, Gary, North Carolina, 1991. l8 Elia M, Parkinson SA, Diaz E. Evaluation of near ’ infra-red interactance as a method for predicting body composition, Eur J Clin Nutr 1990; 44: 113l9
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’ Broekhoff C, Voorrips LE, Weijenberg MP, et al. Relative validity of different methods to assess body composition in apparently healthy elderly women. Ann Nutr Metab 1992; 36: 148-156.
Houmard JA, Wheeler WS, McCammon MR, et al. An evaluation of waist to hip ratio measurement methods in relation to lipid and carbohydrate metabolism in men. Int J Obes 1991; 15: 181-188. 21. Spiegelman D, Israel RG, Bouchard C, et al. Absolute fat mass, percent body fat, and body-fat distribution: Which is the real determinant of blood pressure and serum glucose? Am J Clin Nutr 1992; 55: 20.
1033-1044.
Pergamon
08954356(95)00052-6
1995
Copyright0 1995glhevierScienceInc. Printedin GreatBritain.All rightsreserved 0895-4356/95
$9.50 + 0.00
RELIABILITY OF NEAR-INFRARED INTERACTANCE BODY FAT ASSESSMENT RELATIVE TO STANDARD ANTHROPOMETRIC TECHNIQUES J. SCHREINER,~ JANNE PIT~~NIEMI,’ JUHA PEKKANEN,~ AND VEIKKO V. SALOMAA~~* ‘Departmentof Epidemiology, University of Minnesota,Minneapolis, Minnesota,and *Department of Epidemiology and Health promotion, and ‘Departmentof Environmental Epidemiology, National Public Health Institute, Helsinki, Finland PAMELA
(Received for publication 16 January 1995)
Abstract-We examined the repeatability of near-infrared interactance (NIR) body fat determination as compared with that of body massindex (BMI), waist-to-hip ratio (WHR), and waist girth. Thirty-nine volunteers (16 men, 23 women) had percent body fat (%BF) measurementsmade with a portable NIR device as well as the standard anthropometric indices of height, weight, waist girth, and hip circumference. Frame size and physical activity levels were also determined. For each participant, three independent measurements of each index were made by two trained readers during a 2-week period. The two readers varied significantly in their measurement of %BF and hip circumference. The variability in %BF was largely due to differences between the first and the second measurements, and only for one of the readers. Second and third measurementswere not statistically significantly different for either reader, suggestive of a training effect. Variance component calculations revealed that the reliability of NIR is 95.3%, compared with 99.9% for BMI; 93.4% for waist girth; and 82.4% for WHR, with the majority of the remaining variance accounted for by the method itself. We conclude that the NIR method has good repeatability, with low intra- and interobserver variability, provided that readers are carefully trained. However, the NIR device offers little advantage in reliability over conventional measures of adiposity such as waist girth or BMI, and requires additional input of weight, height, frame size, physical activity level, age, and gender data to calculate %BF. Associations of NIR and other anthropometric indices with cardiovascular risk factors in this population will provide additional insight into the merit of NIR body fat assessment.
NIR
Anthropometry
Reliability
Body fat
Waist-to-hip ratio
Obesity
in epidemiological research with the evidence that excess weight, particularly centrally located Measurement of both absolute and relative adi- deposition, is associated with a wide range of posity has become increasingly more important cardiovascular sequelae such as non-insulindependent diabetes mellitus, hypertension, and *Correspondenceshould be sent to: Veikko V. Salomaa, coronary heart disease [l-71. Methodology exNational public Health Institute, Departmentof Epidemiology and Health Promotion, Mannerheimintie ists for precise determination of both total adi166,FIN-00300Helsinki, Finland. posity (underwater weighing, potassium or deuINTRODUCTION
1361
PAMELA J. SCHREINERet al.
1362
terium dioxide dilution) and visceral/central obesity (computerized tomography or magnetic resonance imaging); however, many of these approaches are suitable only for clinical or laboratory environments. Because of expense, difficulty, or inconvenience to the study participant, many of these approaches are inappropriate for a population-based study. More conventional techniques-such as waist circumference, hip girth, and skinfold thicknesses-are plagued with lack of reproducibility due to the method itself or the individual performing the measurement, resulting in both imprecision and inaccuracy. The ideal solution would appear to lie in instrumentation that captures the precision of clinical methods, but is relatively inexpensive, noninvasive, easy to implement/train, and portable. Near-infrared interactance (NIR) operates because different types of tissue absorb light at different wavelengths [8]. Pure fat, for example, absorbs at 930 nm, while pure water absorbs at 970 nm. Percent body fat and lean tissue mass are then obtained by prediction equations based on area under the curve for these spectrophometric parameters. Originally, NIR technology was primarily used to assess food composition [9, lo]; later, clinical applications began to appear, but involved use of large, expensive equipment. Recently, a small, portable NIR device using the same principles has been used to measure body fat in nonclinical settings. Several studies have reported that body fat assessmentusing the portable NIR device compares favorably with “gold standard” methods such as hydrodensitometry [ll, 121;other studies report that there is little improvement in precision using this device over standard anthropometric measures[13, 141. Therefore, we examined the performance of this device among Finnish male and female volunteers. The goals of this study were (1) to compare the reliability of this technique with conventional measures (waist circumference, body mass index [BMI], and waist-to-hip ratio [WHR]) read at the same time for three distinct reading periods, and (2) to determine the interand intrareader repeatability over time for this same group of participants. MATERIALS
AND METHODS
Forty-three individuals-18 males and 25 females-volunteered to participate in a pilot study of the near-infrared interactance device
(Futrex-5000; Futrex, Gaithersburg, MD). These participants, aged 20-55 years, were recruited from a group of state employees as a pilot test of the device for the FINMONICA ‘92 risk factor survey. Each subject had a total of six readings made by two trained nurses (three readings per nurse) at three distinct time points during the period between 16 April and 30 April 1991. Of these, three participants had body fat readings at the first reading that were greater than two standard deviations away from measurementsat their second and third readings; an additional individual did not complete all three readings. These four participants were excluded, leaving a total of 39 subjects. Participants wore light clothing (without shoes and heavy outer garments), and were weighed to the nearest 100 g. Height was recorded to the nearest centimeter, with the participant standing with his/her back to the height rule. Body mass index was calculated as weight divided by height-squared, in units of kilograms per metersquared. Horizontal circumferences were measured in centimeters, with the subject in the standing position: waist girth was measured at a level midway between the lower rib margin and the iliac crest; hip girth was measuredat the widest circumference over the greater trochanters n51.
Body fat determinations were made as described in the manual of the device [16], based on optical densities detected from light emitted at 940- and 950-nm wavelengths. Percentage body fat was predicted from optical densities measured at the biceps site, adjusted for age, gender, body weight, height, body frame, and physical activity level. The biceps, as delineated in the manual, is located at the anterior aspect of the biceps halfway between the anticubital fossa and acromion. Body frame was described as small, medium, or large, determined by gender-specific cut points based on height and elbow breadth, measured to the nearest 0.1 cm using a sliding caliper. Physical activity was defined as heavy if the participant reported being a competitive athlete or aggressively exercised more than 1 hour per day on average; moderate if the individual did 30 minutes per day of brisk walking (or equivalent), but did not aggressively exercise more than 1 hour per day; light if the individual did 15 to 30 minutes per day of the equivalent of brisk walking (i.e., at least 100minutes per week of the equivalent of brisk walking); or other if the individual did less exercise than the equivalent of 100minutes per week of brisk walking. As
Reliability of NIR Body Fat Assessment
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mass is morely directly correlated with these measures, with lean body mass and weight almost ideally related (r = 0.916). WHR is also more strongly correlated with the other anthropometric variables than is percent body fat. Paired t testing for the difference in mean percent body fat between the two readers is significantly different (25.8 vs. 25.3%, p = 0.043); therefore, Table 3 shows the various measures by individual reader. The two readers obtain similar or identical values of height, weight, and waist circumference. Reader 1, however, measures hip circumference slightly higher (99.3 vs. 98.9 cm, respectively, p = 0.0107), on average, than reader 2, and reader 1 reports a higher average lean body mass for the participants than reader 2 (51.9 vs. 51.6 kg, respectively, p = 0.0472). Measurements made at three different times show that reader 1 reports mean percent body fat as 25.4, 25.4, and 25.1 for the first, second, and third readings, respectively; reader 2 RESULTS reports values of 24.7, 26.4, and 26.2. For reader Table 1 shows population attributes averaged 1, percent body fat means for the first reading over the three readings for the 39 participants versus the second, and for the second versus the (16 males and 23 females) in this study. Males third reading, are not statistically significantly were, on average, slightly younger, taller, different (p = 0.98 and p = 0.45, respectively); weighed more, and had a higher body massindex for reader 2, however, these same comparisons than females. Men also had greater waist and hip have p values of 0.0001 and 0.71. This suggests circumferences relative to females, resulting in that the second reader varies more between a higher WHR. In this sample, men had a lower readings than the first reader, and that after the percent body fat than women, with a higher lean first measurement the second reader has imtissue mass, and were far less likely to be ciga- proved repeatability. rette smokers. The readers also disagree on assessment of Pearson correlation coefficients for continu- frame size, particularly among individuals with ous anthropometric indices are shown by gender small frames (Pearson chi-square, p = 0.027). in Table 2. Although for both men and women, Calculation of the variance components for perpercent body fat is correlated with WHR, waist cent body fat indicates that the individual concircumference, BMI, and weight, these correla- tributes 95.3% of the variance compared with tions are stronger in men. In women, lean body 2.9% for physical activity, 1.8% for the method part of the examination, participants were also asked about their smoking habits. Means and frequencies were obtained with SAS, version 6.07 [17]. Differences between means and frequencies were assessedby r testing or analysis of variance and by Pearson chisquare, respectively. Because the covariates of interest were essentially normally distributed, Pearson correlation coefficients were calculated. The contribution of each of the independent variables (effects) to the variance of the dependent variable (either percent body fat or other anthropometric measure) was estimated by calculation of the variance components in a general linear model (PROC VARCOMP), considering the reader as a fixed effect. The effect of multiple measurementson the same subject was assessed using repeated measures analysis of variance for general linear models (REPEATED statement in PROC GLM).
Table 1. Selected population attributes by gender Males Females Covariate
Mean (SD)
Mean (SD) Pa 23 I&e (years) E.5 (9.01) 37.5 (8.86) 0.4244 Height (cm) 177.4 (5.16) 163.8 (4.97)
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et al. PAMELAJ. SCHREINER
Table 2. Pearson correlation coefficients between anthropometric indices by gender Index
% Body fat
Lean body mass (kg)
WHR
waist (cm)
BMI (kg/m*)
Height
Weight
(cm)
(kg)
- 0.242 0.526’ - 0.067 0.025 -0.166 1.00
0.762b 0.766b 0.836b 0.948b 0.9346 0.195 1.00
- 0.322 0.440* -0.046 0.008 -0.089 1.00
0.657d 0.916’ 0.7956 0.9356 0.958b 0.197 1.00
Males (n = 16)’ % Body fat Lean body mass (kg) WHR Waist (cm) BMI (kg/m*) Height (cm) Weight (kg)
1.00
0.174 1.00
0.819b 0.468 1.00
0.844'
0.842b
0.589c 0.9606 1.00
o.588c 0.851b 0.932’ 1.00
0.670b 0.831’ 0.924’ 1.00
0.752b 0.807’ 0.827b 0.958b 1.00
Females (n = 23)” % Body fat 1.oo 0.314 0.580d Lean body mass (kg) 1.00 0.6866 WHR 1.00 Waist (cm) BMI (kg/m*) Height (cm) Weight (kg) ‘n is the mean of six measurements made by two independent readers bp < 0.001.
(three measurements each).
rp < 0.05. dp < 0.01.
Abbreviations: BMI, body mass index; WHR, waist-to-hip ratio.
itself, and 0.03% for frame size-therefore, the readers’ disagreement on frame size has little effect on the measurement of body fat. Likewise, comparing the variance components for four indices of adiposity measured by the same readers reveals that for percent body fat, the reliability is 95.3% (as above), with the method (including the instrument and the assignment of exercise level and frame size) contributing to 4.6% of the variance. For BMI, the reliability is 99.9% with 0.1% of the variance due to method; for WHR, 82.4% reliability and 17.6% of the variance due Table 3. Means and frequencies associated with anthropometric and near-infrared interactance measurements for each reader Parameter measured Reader 1 Reader 2 Pa 169.4 169.4 Height (cm) 69.8 69.8 Weight (kg) 82.2 81.8 Waist (cm) 99.3 98.9 Hip (cm) % Body fat 25.3 25.8 51.9 51.6 Lean mass (kg) Frame size (%) 1 = Small 21.4 31.6 2 = Medium 72.6 66.7 3 = Large 6.0 1.7 Exercise (%) 1 = Light 7.7 2 = Moderate 53.8 49.6 3 = Heavy 42.7 35.9 “Paired t test for comparison of continuous means between readers; Pearson chi-square comparison of categorical data.
1.oo 1.oo 0.2470 0.0107 0.0428 0.0472
0.027 10.3 0.251 variable test for
to method; and for waist circumference independent of hip girth, 93.4% reliability and 6.6% of the variance due to method. Therefore, although the two readers measure most of the anthropometric indices (with the exception of BMI) slightly differently, in general the additional variance beyond interindividual variability is due to the method rather than the reader. Table 4 presents the contribution to the total variance for each of the descriptive parameters-height, weight, age, frame size, and exercise level-used in determining percent body fat by NIR. For both men and women, weight contributes the largest proportion of variance (63.7 and 56.0%, respectively), followed by height. In this select population, age and frame size combined account for
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Reliability of NIR Body Fat Assessment Table 4. Gender-specific variance components for descriptive parameters used to estimate percent body fat by near-infrared interactance Females Males Covariate
Type I SS”
Height (cm) Weight (kg) Age (years) Frame size Exercise level Error
1121.24 3433.50 0.16 1.64 145.31 70.51
Total: “Sum of squares.
5388.36
% variance 32.1 63.1 0.003 0.2 2.7 1.3
Type I SS’ 1953.23 2768.40 38.30 3.18 99.62 78.87 4941.60
100
70 variance 39.5 56.0 0.8 0.1 2.0 1.6 100
lower reader contributions to error with thorough, standardized training protocol. The device requires input of height, weight, gender, age, frame size, and exercise level, which are either highly precise measures or contribute very little to the variance in body fat. Therefore, training may focus on placement of the NIR probe or lighting conditions. Several previous studies have compared NIR with more conventional anthropometric indices used in population studies, but have not contrasted repeatability of these techniques. Conway et al. [ 111,using computerized spectrophotometry (rather than the hand-held device, with adjustable absorption wavelengths), found that NIR gave similar estimates of percent body fat DISCUSSION as deuterium dioxide dilution and multiple-site This study of near-infrared interactance mea- skinfold thicknesses in a group of middle-aged sured using a portable device among a volun- men and women of normal weight. Ultrasound teer sample of Finnish civil servants suggests body fat assessment,on the other hand, consisthat this method is fairly accurate and precise, tently underestimated body fat in this populawith a small amount of the variance in percent tion. Using hydrodensitometry as a reference, body fat determination contributed by the Brodie and Eston 1121compared electrical immethod itself and an even smaller portion con- pedance with the NIR method and found that tributed by the reader. In addition, this reader although the two techniques were comparable in variability decreases with time in our study, the center of the population body fat distribushowing a learning effect that may lead to even tion, the NIR device tended to overestimate being composed of the two accurately and precisely measured variables of height and weight, is completely associated with interindividual variability and not with either the reader or the method. Finally, total variance in percent body fat is also predominantly accounted for by interindividual (between group) variability, with a borderline significant portion determined by between reader variability (p = 0.067). However, the contribution of the reader to the total variance in percent body fat is explained by reader differences between the first and the third measurement, rather than the second and third, and suggeststhat there is a learning curve to the NIR method.
Table 5. Repeated measures analysis of variance for between-subjects effects and time trends F value (PI
% Body fat
BMI (kg/m*)
WHR
15.65 Subject (0.0001) 0.03 Reader (0.8739) 1.65 Time 1 vs. time 3 (0.2073) Time 2 .0.12 vs. time 3 (El 1) (0.7312) Abbreviations: BMI, body mass index; WHR, waist-to-hip ratio. 91.74 (0.0001) 3.56 (0.0669) 12.01 (0.0013)
99999.99 (0.0001) &oo,
Waist (cm) 55.04 (0.0001) 0.91 (0.3469) 3.95 (0.0545) ‘0.41 (0.5244)
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PAMELA J. SCHREINER et al.
body fat in lean athletic adults. McClean and Skinner [14] also reported that skinfolds and NIR were both highly correlated with underwater weighing in both genders; however, the NIR device tended to underestimate body fat in those with ~30% body fat, and to overestimate body fat in those with ~8%. They concluded that, while NIR measures did predict additional variance beyond height, weight, frame size, and physical activity, multiple-site skinfolds accounted for essentially the same amount of variance. In addition, skinfolds appeared to be more accurate at the extremes of body fat. Hortobagyi et al. [ 131reported that NIR optical density readings at 950 nm were fairly well correlated with body fat estimates derived from seven skinfolds (r = -0.67), whereas NIR measurementsat the biceps underestimated body fat in general when compared with underwater weighing. Finally, in a group of women, ages 20-72 years, Heyward et al. [8] replicated these results for a group with a less extreme range of body fat and reported that the NIR device tends to underestimate body fat/overestimate lean tissue in those women with body fat >39%, while overestimating body fat/ underestimating lean mass in those with body fat ~28% (an average body fat percentage for women). Our study was unable to compare extremes in either BMI or percent body fat due to the small sample size and the narrow distribution of these indices in our healthy volunteer population. The NIR device also requires input of descriptive data (weight, height, frame size, physical activity, age, and gender) that independently contribute substantially to estimation of body fat [12]. Elia et al. [18], looking at a young (18-40 years) population, found that NIR was not any better at predicting body fat than simple anthropometric measures (height, weight) plus either skinfolds or impedance/resistance. In this group, NIR also seriously underestimated body fat in the upper range of the population: those with BMI >50 kg/m* differed by 16%from the underwater weighing estimates. However, the percent body fat predicted by NIR from the biceps was closer to the hydrodensitometry results than either the triceps, the thigh, or any combination of the three sites; others [8, 141found that singlesite measurements at the biceps with the NIR device (versus any combination of multiple sites) corresponded most highly with their respective gold standards. None of these techniques is without difficulties. Body mass index is increased in males rela-
tive to females independent of adiposity due to their higher proportion of lean body mass. Even hydrodensitometry becomes less accurate with age as bone is lost [19] and depends on the participant having normal lung elasticity. In our study, while all the methods examined had reasonably good reader repeatability, WHR suffered from methodological variability due to the difficulties in measurement at both the waist and the hip. Waist circumference and NIR had similar reliability and method error, suggesting that since the NIR device requires weight, height, frame size, gender, and age to complete the prediction equation (McClean et al. [ 141recommend eliminating physical activity as a parameter, due to its subjective nature), standardized waist circumference measurements may be an adequate anthropometric index. In addition, because both skinfolds and NIR are capable of directly measuring only subcutaneous fat, waist circumference offers the additional advantage of capturing intra-abdominal and subcutaneous fat deposits. Skinfolds and NIR become increasingly less accurate with aging owing to the overestimation of fat free mass resulting from redistribution of fat from subcutaneous stores to visceral depots [19]. But the ultimate goal of obtaining any of these measurements is in prediction of diseaserisk factors or endpoints. Houmard et al. [20], for example, found that the predictive strength of WHR for high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, triglycerides, glucose, and insulin varies according to where waist and hip circumference are measured (with waist circumference accounting for more of the variance in these endpoints than hip girth). Spiegelman et al. [21] reported that BMI is a good predictor of both diastolic blood pressure levels and glucose, with overall body mass (either by BMI or fat mass from densitometry) being stronger predictors of blood pressure and glucose-after adjustment for age, height, and cigarette smoking-than relative fat mass or regional distribution patterns. The results obtained in the present study are expected to be better than would be found in a general population sample becausethese individuals were similar in age, nonobese, and all of Finnish (white) ethnicity. Prediction or calibration equations, whether the portable NIR device or other anthropometric determinants are used, must be both age- and gender-specific to account for real physiological variation. The NIR device
Reliability of NIR Body Fat Assessment
is a good beginning in attempts to bring laboratory technology to the field, but until the wide variation in adiposity, age, and race seen in genet-al populations can be accommodated, it has little advantage in reliability over conventional anthropometric measures of adiposity. Associations of NIR recordings and other indices of obesity with cardiovascular risk factors will be established in future analyses of the FINMONICA ‘92 risk factor survey material. Schreiner was supported by a grant from the Academy of Finland. The authors wish to acknowledge Liisa Toivanen and Taja Nuottimaki for performing the measurements.
Acknowledgmenrs-Dr.
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