Normalizing Fitness Data

The following is an exclusive excerpt from the book NSCA's Guide to Tests and Assessments, published by Human Kinetics. All text and images provided by Human Kinetics.

Field tests have become popular in applied exercise science and sport performance enhancement programs because of their simplicity and ability to generalize results. However, numerous confounding factors may influence the validity of test data from such evaluations. In addition to gender, age, level of physical fitness, and skill, body size is well recognized as a factor that influences both muscle fitness and the outcome of a number of functional performance tests (e.g., strength testing, vertical jump, sprint speed). Therefore, adjusting for body mass appears to be necessary when assessing these functional characteristics, especially when comparing to a norm-referenced standard (i.e., peer group).

For muscular strength capacity, the simplest way to normalize data is to divide strength by body mass. This ratio method provides a straightforward index of relative muscular strength abilities and is often considered superior to measuring absolute strength, especially when determining the contribution(s) to explosive movement performance (Peterson, Alvar, and Rhea 2006). It is important to note that this method is based on the assumption that the relationship between strength and body mass is linear.

However, some research has demonstrated that the relationship between strength and body mass tests may not be linear, but is, instead, curvilinear. Other ways to normalize strength are used in powerlifting (Wilks Formula) and Olympic-style weightlifting (Sinclair Formula), and allow the identification of a strength composite index relative to body mass. These formulas minimize the risk of handicapping or recompensing the bigger athlete and smaller athlete, respectively, and provide for an equitable competitive environment.

However, as in strength and conditioning for large team sports, it is often necessary to compare numerous people of differing body masses. Research pertaining to dimensional scaling suggests that such comparisons of muscular strength attributes among people of variable body sizes should be expressed relative to body mass, raised to the power of 0.67—for example, (kg lifted) / (kg body weight)0.67 (Jaric, Mirkov, and Markovic 2005). Known as allometric scaling, this statistical transformation of the raw data is used to provide the appropriate relationship between body mass and the strength outcome of interest.

Allometric scaling is derived from the theory of geometric similarity and assumes that humans have the same basic shape, yet may still differ in size. Other investigations have demonstrated different requisite scaling exponents for performance in activities not related to maximal force production (e.g., aerobic power). Regardless of the performance outcome being assessed, allometric scaling is based on several assumptions, including the following:

• The relationship between body dimension (usually body mass, lean body mass, or muscle cross-sectional area) and performance is curvilinear.
• The relationship between performance (P) and body size (S) may be assessed by the equation: P = aSb, where a and b are the constant multipliers and scaling exponent, respectively (Nevill, Ramsbottom, and Williams 1992).
• The curvilinear relationship must pass through the origin of both variables (e.g., an athlete with no lean body mass would have a strength score of 0).

Solving for the scaling exponent (b) allows for the removal of individual differences in the scaling factor (S) (i.e., body size) on the performance outcome (P) (e.g., strength).

Allometric scaling is necessary for any outcome in which body dimension and respective performance do not share a linear relationship. If strength and body mass shared a linear association, the scaling exponent would be equal to 1 (b = 1), and the aforementioned ratio method would sufficiently characterize relative strength— that is, (kg lifted) / (kg body weight). However, because this is not the case, a correction factor must be applied to accurately report or examine body mass–adjusted strength. Ultimately, using the correct scaling equation for a specific sample population for a particular performance-based test minimizes the confounding influence of body dimension.

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