1980's - Development of Empirical Equations
Empirical data is knowledge acquired by means of observation or experimentation. By collecting data from a sample population deemed to represent the expected characteristics of the entire population, researchers can derive equations that may be used to predict outcomes. In body composition, researchers have identified trends in muscle and fat mass and have used this data to predict body composition based on specific variables.
In 1986, research was published in which the impedance index was combined with factors such as body weight and gender into empirical equations. Over time, numerous other equations were developed based on additional factors such as age, ethnicity, and body type.
Although empirical estimations have the potential to provide an accurate estimate of a healthy individual’s body composition, significant problems arise when they are used for medical purposes in which accurate and precise assessments are a requirement.
For instance, age is a common factor in empirical equations used for body composition. In general, most individuals tend to lose lean body mass with age due to a sedentary lifestyle. Based on this trend, empirical equations often skew lean body mass up for younger individuals and down for older individuals. However, such data manipulation can cause inaccuracies and significant misassessments regarding health risks in population outliers such as obese youth or fit older adults.
Suppose a device that relies on empirical equations to estimate body composition is used on two people who have the same amount of lean body mass, but one person is 30 years old and the other is 40 years old. Because most individuals tend to lose lean body mass with age, even though the two individuals have the same amount of lean body mass, the empirical equations will skew the 40 year old’s lean body mass down, resulting in a higher percent body fat.