Modulators of Energy Expenditure Accuracy in Adults with Overweight or Obesity: E-MECHANIC Secondary Analyses

Abstract

Purpose: American College of Sports Medicine (ACSM) metabolic equations are used to estimate energy expenditure (EE) of physical activity and prescribe aerobic exercise to meet EE requirements. Limited evidence supports their accuracy in sedentary adults with overweight or obesity during controlled exercise interventions. The purpose of this study was to compare EE estimated by the ACSM walking equation versus EE measured by indirect calorimetry during a 24-wk aerobic exercise intervention, and identify potential modulators for their accuracy.

Methods: Data from the exercising groups (8 or 20 kcal·kg body weight -1 ·wk -1 ) of the E-MECHANIC study were utilized in this ancillary analysis ( N = 103). Every 2 wk for the initial 8 wk and monthly thereafter, EE was measured via indirect calorimetry during absolute (2 mph, 0% grade) and relative (65%-85% peak oxygen uptake (V̇O 2peak )) workload exercise. Resting metabolic rate, V̇O 2peak , and body composition were assessed at baseline and follow-up. An EE offset factor (EOF) was calculated to express measured EE as a percentage of the estimated EE at each workload (EOF < 100% represents an overestimation of ACSM estimated EE).

Results: The accuracy of the equation decreased with increasing exercise workload (0.44%, 9.2%, and 20.3% overestimation at absolute, relative, and maximal workloads, respectively, at baseline) and overestimation of EE was greater after the exercise intervention. Furthermore, race, sex, age, fat mass, and V̇O 2peak were identified as modulators for equation accuracy. Greater overestimation of EE was observed in Black compared with White females, particularly at lower exercise workloads.

Conclusions: These findings support future efforts to improve the accuracy of metabolic equations, especially in diverse populations. Researchers should account for exercise efficiency adaptations when using metabolic equations to prescribe exercise precisely.

FIGURE 1

Measured and estimated EE at rest, absolute workload (2.0 mph, 0% grade), relative workload (65%–85% V̇O_2peak), and maximal workload (maximum EE achieved during graded V̇O_2peak test). Measured EE collected from indirect calorimetry and estimated EE calculated using the ACSM’s equation for treadmill walking. ● baseline; ● follow-up. *Significantly different from baseline, P < 0.05. #Significantly different from estimated EE within a time point, P < 0.05. Bar chart presents mean with error bars indicating SD.

FIGURE 2

Changes in estimated (A and B) and measured (C and D) EE, RER (E and F), V̇O₂ (G and H), and EOF (I and J) during absolute (2.0 mph, 0% grade) and relative workload (65%–85% V̇O_2peak) exercise across the 24-wk intervention. Measured EE collected via indirect calorimetry. Estimated EE calculated using the ACSM’s metabolic equation for treadmill walking. Black females; White females. Solid lines represent females; dotted lines represent males. Data presented as means with error bars indicating 95% confidence interval.

FIGURE 3

Gross and net EOF at an absolute workload (2.0 mph, 0% grade), relative workload (65%–85% V̇O_2peak), and maximal workload (maximum EE achieved during graded V̇O_2peak test). EOF represents the accuracy of EE estimation equations by expressing measured EE (via indirect calorimetry) as a percent of estimated EE (calculated using the ACSM’s equation for treadmill walking). Net EOF was calculated by removing the resting components from measured and estimated EE.