Body Composition
Narrative Review
2011
Body composition assessment: An evidence-based review
By Scott B. Going and Timothy G. Lohman
Journal of Strength and Conditioning Research, 25, pp. S48-S59
Abstract
Abstract
Accurate assessment of body composition is a fundamental requirement for monitoring training adaptations, guiding dietary prescriptions, and evaluating the health implications of body weight changes. Yet the methodologies available to clinicians and athletes for estimating body fat percentage and lean mass vary enormously in their accuracy, cost, accessibility, and sensitivity to change. This evidence-based review by Going and Lohman (2011) provides a comprehensive evaluation of the principal body composition assessment methods available to exercise science practitioners, from laboratory reference methods to field-accessible tools.
The review identifies dual-energy X-ray absorptiometry (DEXA) as the closest available surrogate to the gold standard of four-compartment (4C) model analysis, providing valid and reliable estimates of bone mineral content, fat mass, and lean mass in a single scan with acceptable radiation exposure [1]. Hydrostatic weighing (underwater weighing) remains a valid laboratory reference, but practical limitations restrict its use. Bioelectrical impedance analysis (BIA) offers accessibility and low cost but demonstrates significant variability related to hydration status, food intake, and technical parameters — producing measurement errors of 3-5% body fat in unfavorable conditions [2].
Skinfold caliper measurements retain relevance for tracking relative changes in trained populations when standardized protocols and experienced practitioners are used, though their accuracy for absolute body fat estimation depends heavily on the validity of population-specific equations and assessor skill [3]. The review concludes that method selection should be driven by the intended purpose of assessment — population screening, absolute body fat estimation, or within-individual change tracking — and that consistent, standardized conditions are as important as method selection for reliable monitoring.
Introduction
<h2>Introduction</h2> <p>Body composition — the distribution of body mass between fat tissue and fat-free tissue (principally muscle, bone, and water) — is one of the most important physiological variables in exercise science, nutrition research, and clinical health evaluation. Unlike body weight alone, which cannot distinguish between changes in fat mass, muscle mass, and hydration, body composition measurements provide meaningful insights into the nature and direction of physiological change in response to diet, exercise, and other interventions.</p> <p>For athletes, body composition tracking serves multiple purposes. It allows quantification of hypertrophic responses to resistance training programs, assessment of fat loss during caloric restriction phases, monitoring of seasonal fluctuations that may affect performance, and evaluation of recomposition (<a href="/terms/body-recomposition/" class="term-link" data-slug="body-recomposition" title="simultaneous muscle gain and fat loss">simultaneous muscle gain and fat loss</a>) outcomes. For the general population, body fat percentage is a more meaningful health indicator than body mass index (BMI) for identifying metabolic risk, as two individuals with identical BMI may have dramatically different body fat distributions [4].</p> <p>Despite the clear utility of body composition assessment, no single widely accessible method provides perfectly accurate measurements. All commonly used techniques rest on assumptions that may be violated in specific populations or circumstances — assumptions about body water content, tissue density, mineral content, and the proportionality of body compartments. Understanding these assumptions and their potential sources of error is essential for selecting the appropriate method and interpreting its results correctly [5].</p> <p>Going and Lohman's (2011) review is particularly valuable for its simultaneous consideration of accuracy, precision, cost, and practical feasibility — qualities that must be weighed against each other when selecting a body composition method for clinical or research use. By providing a systematic framework for comparing methods across multiple criteria, this review enables practitioners to make informed decisions appropriate to their specific context and resources.</p>Evidence Review
<h2>Evidence Review</h2> <h3>Reference Methods: The Four-Compartment Model</h3> <p>The four-compartment (4C) model — which divides body mass into fat, water, protein, and mineral components measured by multiple independent techniques — represents the most valid body composition assessment approach currently available for in vivo human measurement. By separately quantifying each major body compartment, the 4C model avoids the assumption violations that affect simpler methods and provides a body fat percentage estimate with a technical error of approximately 1-1.5% [6].</p> <p>However, the 4C model requires isotope dilution for total body water, DEXA for bone mineral content, and hydrostatic weighing or air plethysmography for body volume — a combination that is logistically complex, expensive, and time-consuming, restricting its use to specialized research contexts.</p> <h3>DEXA: The Practical Reference Standard</h3> <p>Dual-energy X-ray absorptiometry (DEXA) uses differential X-ray attenuation at two energies to distinguish bone mineral, lean soft tissue, and fat mass across body regions. When compared against the 4C model, DEXA demonstrates validity coefficients of r = 0.95-0.98 and standard errors of the estimate of approximately 1.5-2.5% body fat in healthy adults [1].</p> <p>Key advantages of DEXA include: - Regional body composition data (visceral adiposity, limb lean mass) - High reproducibility (coefficient of variation less than 2%) - No hydration-dependent variation (unlike BIA) - Ability to detect small changes (sensitivity approximately 0.5 kg fat mass) - Radiation dose equivalent to 1-2 days of natural background exposure</p> <p>DEXA's primary limitations are cost (typically $75-200 per scan in clinical settings), requirement for specialized equipment, and the fact that body fat estimates can vary between scanner manufacturers due to software differences, limiting cross-site comparisons.</p> <h3>Hydrostatic Weighing</h3> <p>Based on Archimedes' principle, hydrostatic weighing calculates body density from the difference between body weight in air and body weight submerged in water. Body density is then converted to body fat percentage using established population equations. Hydrostatic weighing demonstrates validity comparable to DEXA against 4C model reference, with standard errors of approximately 2-3% body fat [7].</p> <p>Practical limitations — requirement for full submersion, maximal expiration, and multiple trials — reduce its accessibility and reproducibility compared with DEXA, particularly in clinical populations.</p> <h3>Bioelectrical Impedance Analysis (BIA)</h3> <p>BIA measures the resistance and reactance of the body to a small alternating electrical current, from which body water and <a href="/terms/lean-body-mass/" class="term-link" data-slug="lean-body-mass" title="fat-free mass">fat-free mass</a> are estimated using population-specific regression equations. Consumer-grade BIA devices (bathroom scales, handheld analyzers) are widely available but demonstrate measurement errors of 3-8% body fat under real-world conditions [2].</p> <p>The primary sources of BIA variability include:</p> <table> <thead> <tr> <th>Source of Variability</th> <th>Magnitude of Error</th> <th>Mitigation</th> </tr> </thead> <tbody> <tr> <td>Hydration status</td> <td>2-4% body fat</td> <td>Standardize testing conditions</td> </tr> <tr> <td>Food intake (2-4h prior)</td> <td>1-2% body fat</td> <td>Fast 2-4 hours before testing</td> </tr> <tr> <td>Exercise (prior 12h)</td> <td>1-2% body fat</td> <td>Test rested</td> </tr> <tr> <td>Menstrual phase (females)</td> <td>1-3% body fat</td> <td>Test at consistent cycle phase</td> </tr> <tr> <td>Electrode placement</td> <td>1-2% body fat</td> <td>Use standardized protocol</td> </tr> </tbody> </table> <p>Multi-frequency BIA and segmental BIA devices offer improved accuracy over single-frequency consumer models, with standard errors of approximately 2-3% in controlled conditions [8].</p> <h3>Skinfold Caliper Measurements</h3> <p>Skinfold measurements estimate subcutaneous fat thickness at standardized anatomical sites, from which body fat percentage is estimated via population-specific equations. In experienced hands with standardized protocols and appropriate prediction equations, skinfold measurements demonstrate validity of r = 0.90-0.95 against hydrostatic weighing reference, with standard errors of 2.5-4% body fat [3].</p> <p>Intra-assessor reliability (same person measuring the same subject) is generally high (CV approximately 3-5%), while inter-assessor reliability is substantially lower, making cross-practitioner comparisons unreliable. The Durnin-Womersley (4-site), Jackson-Pollock (7-site), and ISAK protocols are the most rigorously validated skinfold methodologies.</p>Discussion
<h2>Discussion</h2> <h3>Method Selection Framework</h3> <p>The appropriate body composition assessment method depends critically on the intended purpose. Three distinct purposes warrant different method hierarchies:</p> <p>Purpose 1 (absolute body fat percentage estimation): For determining <a href="/terms/intermittent-fasting/" class="term-link" data-slug="intermittent-fasting" title="if">if</a> an athlete is within health risk categories, or establishing a precise baseline before a competition preparation, DEXA is the method of choice for most practitioners with access, providing clinically meaningful absolute estimates with acceptable measurement error.</p> <p>Purpose 2 (tracking relative changes over time in an individual): For this purpose, the consistency of measurement conditions is more important than the absolute accuracy of the method. Well-administered BIA or skinfold measurements taken under identical conditions (same time of day, hydration status, time since last meal, and time since last exercise bout) can provide reliable relative change data sufficient for monitoring training responses [9].</p> <p>Purpose 3 (population screening for health risk stratification): For this purpose, simple, low-cost, high-throughput methods are appropriate. Waist circumference is an evidence-based alternative to body fat percentage for cardiometabolic risk stratification, requires no specialized equipment, and demonstrates robust associations with metabolic disease risk independent of total body fat [10].</p> <h3>The Measurement Error Problem in Practice</h3> <p>A fundamental practical challenge is that athletes and coaches frequently interpret single-measurement changes in body composition as meaningful without accounting for the inherent measurement error of the method used. If DEXA has a standard error of 2% body fat, and an athlete measures at 18% and 16% body fat at two time points, the apparent 2% reduction is within the measurement error range — the true change could be anywhere from -4% to 0%.</p> <p>This underscores the importance of interpreting body composition changes relative to method-specific measurement error, using multiple sequential measurements to establish trends, and avoiding over-interpretation of single measurements. Practical cutpoints for meaningful change — requiring the observed change to exceed 1.5-2 times the method's standard error — provide a more rigorous basis for clinical decision-making [11].</p> <h3>Lean Mass vs. Fat Mass vs. Body Fat Percentage</h3> <p>An often-overlooked consideration is which body composition variable is most relevant for a given purpose. Body fat percentage can change without any change in absolute fat mass, simply due to changes in lean mass — an athlete who gains 2 kg of muscle while maintaining constant fat mass will appear to have "lost" body fat percentage. Tracking absolute fat mass and absolute lean mass separately, rather than relying solely on body fat percentage, provides a more complete picture of compositional change, particularly informative during resistance training and recomposition phases [12].</p> <h3>Accessibility and Practical Hierarchy</h3> <p>For the majority of athletes without access to DEXA or research laboratory methods, the most practical body composition monitoring approach combines multiple simple metrics:</p> <ol> <li>Scale weight (daily, averaged weekly to smooth fluctuations)</li> <li>Waist circumference (weekly, morning, consistent conditions)</li> <li>Progress photographs (monthly, consistent lighting and angles)</li> <li>Performance metrics (<a href="/terms/training-volume/" class="term-link" data-slug="training-volume" title="training volume">training volume</a>, strength levels, endurance performance)</li> </ol> <p>This multi-metric approach captures both objective compositional changes and functional performance indicators, providing a more comprehensive picture than any single measurement and being resilient to the limitations of individual methods.</p>Practical Recommendations
<h2>Practical Recommendations</h2> <p>Based on the reviewed evidence, the following framework guides practical body composition assessment decisions.</p> <h3>Method Selection by Context</h3> <table> <thead> <tr> <th>Context</th> <th>Primary Method</th> <th>Supplementary Methods</th> </tr> </thead> <tbody> <tr> <td>Research / clinical reference</td> <td>DEXA (or 4C model <a href="/terms/intermittent-fasting/" class="term-link" data-slug="intermittent-fasting" title="if">if</a> available)</td> <td>Hydrostatic weighing</td> </tr> <tr> <td>Athlete monitoring (DEXA available)</td> <td>DEXA every 3-6 months</td> <td>Scale weight + waist circumference weekly</td> </tr> <tr> <td>Athlete monitoring (no DEXA)</td> <td>Skinfold (standardized protocol)</td> <td>Scale weight + waist circumference weekly</td> </tr> <tr> <td>General population health screening</td> <td>Waist circumference</td> <td>Scale weight, BMI</td> </tr> <tr> <td>Daily / frequent tracking</td> <td>Scale weight trend</td> <td>Progress photographs monthly</td> </tr> </tbody> </table> <h3>Standardization Requirements</h3> <p>Regardless of method, body composition measurements should be conducted under standardized conditions to minimize measurement variability:</p> <ul> <li>Time of day: same time each measurement (morning, post-void, pre-exercise is preferred)</li> <li>Hydration: consistent hydration status (not immediately following intense exercise or excessive fluid intake)</li> <li>Food intake: same relative time since last meal (minimum 2 hours for BIA, optimal 8-12 hours for any method)</li> <li>Exercise: minimum 12 hours since last strenuous exercise session</li> <li>Menstrual cycle phase (females): note cycle phase at each measurement and compare same-phase measurements</li> </ul> <h3>Interpreting Change Data</h3> <p>When interpreting body composition changes: - Require changes to exceed 1.5 times the method's standard error before concluding meaningful change has occurred - Use 3 or more sequential measurements to establish trend rather than comparing only two data points - Track fat mass and lean mass separately rather than relying solely on body fat percentage - Contextualize body composition data with performance metrics to distinguish meaningful adaptation from measurement noise [13]</p> <h3>DEXA Access and Frequency</h3> <p>For athletes with access to DEXA, the recommended monitoring frequency is: - Competition preparation: every 4-6 weeks during active fat loss phase - Off-season building phase: every 6-12 weeks - General monitoring: every 3-6 months</p> <p>More frequent DEXA scanning (more than monthly) is unnecessary for most purposes, as the time between scans must be sufficient for the expected magnitude of change to exceed the method's measurement error.</p> <h3>The Scale as Primary Daily Tool</h3> <p>Despite its limitations, body weight measured on a calibrated bathroom scale remains the most practical daily body composition monitoring tool. Interpreting weekly average scale weight trends (averaging 7 daily measurements) removes the day-to-day fluctuations due to hydration, glycogen, and food volume, revealing the underlying fat and lean mass trend with reasonable reliability [14]. For most athletes, this approach, combined with periodic DEXA or skinfold assessments and visual progress photographs, provides adequate monitoring resolution for practical decision-making.</p>관련 논문
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