A multi-compartment models is the most accurate way to measure muscle and fat because it allows us to actually measure each tissue in the body, rather than estimate it. The problem is, this usually requires a expensive equipment, and a lot of time.

In 2018, our team developed a new way to measure body composition for coaches, trainers, and athletes that don’t have access to the fancy laboratory equipment we do for research. We called it our “Field 3-compartment Model.” The take home message? You don’t need Underwater Weighing. You don’t need a fancy method to measure Total Body Water. All you need are skinfold calipers and a simple bioimpedance device. Check it out here: https://pubmed.ncbi.nlm.nih.gov/29348622/

We took it a step further with the MADE app. Could we create another version of the “Field 3-compartment model” using the MADE app to reduce potential measurement error between people?

We applied our algorithms to images of our research participants and combined those values with a simple bioimpedance measure of Total Body Water. BOOM! Data collection for each person was done in about 60 seconds, versus about 60 minutes for Underwater Weighing, or 10-15 minutes for a skinfold assessment.

Here are the results. In our sample of 24 female athletes, Body Fat % with the MADE app was 17.90±5.12 versus 18.65±5.63 when measured using the skinfold assessments after accounting for Total Body Water.

Figure 1. Differences in percent body fat between 3-compartment models derived from the MADE app and skinfold assessment.

After we found no differences, we wanted to see how strongly the two measures were correlated. Correlations range from -1.0 to +1.0, with values close to zero indicating no relationship. We found excellent agreement between methods, with a correlation equal to 0.96.

Figure 1. Bivariate correlation between percent body fat using 3-compartment models derived from the MADE app or skinfold assessment.

These results were presented at the 2020 American College of Sports Medicine Annual Meeting. Check us out.