Why do individuals not lose more weight from an exercise intervention at a defined dose? An energy balance analysis

Abstract

Weight loss resulting from an exercise intervention tends to be lower than predicted. Modest weight loss can arise from an increase in energy intake, physiological reductions in resting energy expenditure, an increase in lean tissue or a decrease in non-exercise activity. Lower than expected, weight loss could also arise from weak and invalidated assumptions within predictive models. To investigate these causes, we systematically reviewed studies that monitored compliance to exercise prescriptions and measured exercise-induced change in body composition. Changed body energy stores were calculated to determine the deficit between total daily energy intake and energy expenditures. This information combined with available measurements was used to critically evaluate explanations for low exercise-induced weight loss. We conclude that the small magnitude of weight loss observed from the majority of evaluated exercise interventions is primarily due to low doses of prescribed exercise energy expenditures compounded by a concomitant increase in caloric intake.

Figure 1

Diagram that maps the progression of study analysis begins with the exercise intervention which increases exercise energy expenditure. The increase will lead to an energy imbalance which may translate to an energy deficit or an energy surplus. The mechanistic influences on energy imbalance that are examined are changes to resting metabolic rate (RMR), non-exercise activity (NEAT), fat-free mass (FFM) and energy intake (EI). The impact of these changes on energy imbalance translates to changed body weight (W).

Figure 2

Top panel: Linear regression at baseline between resting metabolic rate (RMR) and fat-free mass (FFM; kg) (RMR = 17.3 FFM + 156.4, R² = 0.66) and observed RMR values (closed circles) post intervention in the study of Bouchard et al. (13). The observed values falling below the regression line, particularly at lower levels of FFM, indicate metabolic adaptation. Bottom panel: Bland–Altman plot comparing expected vs. post-intervention-observed RMR. The Bland–Altman analysis revealed a bias of −80.9 kcal d⁻¹ with 95% confidence intervals of −311 and 149, although this bias was not consistent over levels of RMR (slope = 0.45 intercept = −608.6, R² = 0.39), indicating that metabolic adaptation primarily occurs for individuals with lower RMRs or body mass.

Figure 3

The Forbes curve does not predict ΔFFM during aerobic exercise. Bland–Altman plots depicting Forbes curve predictions of ΔFFM (kg) for caloric restriction alone from Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) Phase I (40) (first row), caloric restriction combined with aerobic exercise from CALERIE Phase I (40) (second row) and aerobic exercise alone from the Bouchard trial (13) (third row). The Bland–Altman indicates good agreement for caloric restriction alone (R² = 0.52, P = 0.02 [significant correlation] with a low bias [0.3 kg]). The bias increases for caloric restriction combined with exercise (1.2 kg) and the correlation is not significant (R² = 0.13, P = 0.30). The bias increases further for aerobic exercise alone (3.6 kg) and the correlation is once again not significant (R² = 0.01, P = 0.727). FFM, fat-free mass.

Figure 4

Plot of a proposed modified piecewise Forbes curve (dotted and solid_ modelling body composition during aerobic exercise superimposed over the original Forbes curve [dashed] describing body composition changes during caloric restriction). The top curve models the observation of less fat-free mass (FFM) and more fat mass (FM) during weight loss and assumes greater FFM and less FM during weight gain. Extrapolation of the model beyond available data is indicated by the dotted portion of the top curve.