The contexts within which energy compensation occur, the extent to which it occurs ^{4, 5} and the processes involved are far from resolved ^{2, 6–8}. Using the largest dataset on human energy expenditure ever assembled, by estimating the relationships between TEE, AEE, and BEE we test the mutually exclusive predictions from the three energy expenditure models (Figure 1) for individuals with unremarkable lifestyles generating natural variation in total energy expenditures over time, and without food restriction. Determining which of these energy expenditure models apply to humans under typical, free-living conditions, and quantifying its effects, will progress our understanding of the evolution and control of metabolism, and may provide key physiological information for management strategies for weight control.

We extracted paired measurements of BEE (respirometry) and TEE (doubly-labelled water (DLW) ⁹) for 1,754 adults from the International Atomic Energy Agency DLW database v.3.1.2 ¹⁰. All estimates of TEE were made using a standard calculation across all studies ¹¹. Controlling for age (y), sex, and body composition (i.e., fat free mass in kg, derived from the body water dilution spaces, and fat mass in kg, calculated as the difference between body mass and fat free mass), a multiple regression of TEE as a function of BEE revealed an overall positive and highly significant relationship between TEE and BEE, with a slope of b±se = 0.723±0.049 (Table S1A) and 95% confidence intervals (CI) that exclude both 0 and 1 [CI: 0.626; 0.820]. The positive relationship between BEE and TEE is not surprising, given that BEE represents the largest component of TEE (Figure 1B). Due to the part-whole relationship, however, the slope between BEE and TEE should be 1 unless the active and basal components of energy expenditure are positively or negatively linked (as postulated in the performance and compensation models, see Figure 1C). Because our analysis revealed that the slope is significantly <1 (Figure 2A), this indicates that a considerable degree (27.7%) of compensation occurred between the active and basal components of energy expenditure.

To further illustrate compensation, we calculated the activity energy expenditure (AEE) for each individual by subtracting BEE from 0.9*TEE (TEE adjusted to account for the thermic effect to food). A multiple regression of AEE as a function of BEE (with age, sex, and body composition as covariates) revealed an overall negative and highly significant relationship, with a slope of b±se = −0.349±0.044 (t = 7.86, p < 0.0001; Table S1A; Figure 2B) and 95% confidence interval (CI) that excludes 0 [CI: −0.436; −0.262]. These findings concur with those from the model regressing TEE as a function of BEE. Note that in principle one mechanism that does not represent energy compensation and yet could in principle create the observed patterns is that people who are more active (and have a higher AEE) have a greater proportion of muscle mass¹², which increases FFM without substantively increasing BEE ¹³ resulting in more active people having a low mass-corrected BEE. However, this possibility can be disregarded given that our analysis indicates energy compensation in people having accounted for variation in their FFM by its inclusion as a covariate (as both a main effect and as an interaction term with BEE and age).

Thus, humans living typical modern lives – not undertaking exceptional levels of activity or experiencing chronic food shortages – exhibit a fairly strong compensation between the energy they expend on activity and that expended on basal metabolic processes; over the long term more than a quarter of the extra calories burned by people during activity do not translate into extra calories expended that day. Presumably, such compensation would have been adaptive for our ancestors because it minimised food energy demands and hence reduced the time needed for foraging, the advantages of which may include reducing exposure to predation. However, it is potentially maladaptive for modern-living humans exercising to try to burn off excess food consumption, given the chain of association linking high-density foods to greater energy intake ¹⁴, obesity ¹⁵ and its related diseases ¹⁶.

Public health initiatives often include prescribed increases in activity in part to increase TEE and thereby control weight gain or promote fat loss ¹⁷. Such a prescription, however, often assumes that costs of activity are additively related to basal costs ¹⁸, which our analyses suggests is untrue. It will therefore be important when prescribing personalized exercise plans for controlling or reducing weight, and managing patient expectations, to know if the degree of long-term energy compensation changes with age and other demographic variables such as sex. It is well known that older individuals are more at risk of obesity than are younger individuals. To test if older people, and potentially one sex more than the other, exhibit greater energy compensation, we took advantage of the information on sex, age, and body composition (measured by isotope dilution) included in our dataset, which consisted of 692 men and 1,062 women aged 18 to 96 y, with fat free mass ranging from 24.3 to 97.1 (median: 47.64 kg) and body mass index (BMI) ranging from 12.5 to 61.7 (median: 25.2 kg/m²). To test if the slope (b) of the TEE-BEE and AEE-BEE relationships changes according to sex, age, and body composition, we added the interaction terms between BEE and each of these factors to the multiple regression model (in addition to other two-way interactions between sex, age, and body composition that control for sex differences and age-related changes in body composition; see Table S1B). Overall, energy compensation was not different in men vs women and did not vary with age (i.e., BEE × sex and BEE × age interactions, Table S1B). Hence, energy compensation seems to be a general phenomenon that applies equally to men and women, young and old. Note that FFM and FM were derived from isotope dilution, assuming a constant ratio for FFM hydration (73.2%) but according to published literature, FFM hydration may not be constant with adult age. However, any variation is probably small ¹⁹, and indeed unpublished analyses on data for over 1000 adults with ages ranging from 20 to over 70 indicates that the ratio of total body weight to FFM hardly changes (S. Heymsfield, pers. comm.).

Interestingly, the BEE × fat mass interaction was significant with a negative estimate (Table S1B), indicating that the slope of the TEE-BEE and AEE-BEE relationships decreases as fat mass increases. In other words, controlling for sex, age, and FFM, compensation increases with fat mass. People that are at the 10^th percentile of the BMI distribution compensate 29.7% of activity calories, whereas people at the 90^th percentile compensate 45.7% of activity calories (Figure 3). It appears then, that either individuals with greater fat levels are predisposed to increased adiposity because they are stronger energy compensators or because they become stronger compensators as they get fatter. If the former, then two people can be equally active yet one puts on fat mass while the other stays lean. If the latter, then such a positive feedback loop may imply that using exercise as a strategy to escape high adiposity becomes less and less effective. Resolving the causality of this relationship between fat mass and energy compensation might be key to better deploying exercise in the fight against the growing obesity pandemic.

The energy compensation detected in the aforementioned analysis can be the result of processes occurring at two distinct levels of covariation: between individuals and within individuals. Energy compensation at the between-individual level would indicate that people with higher-than-average AEE tend to have a lower-than-average BEE – a covariance due to genetic and/or permanent environmental factors that would cause the between-individual TEE-BEE slope (b_between) to be <1. By contrast, energy compensation at the within-individual level would indicate that, for a given individual, reversible increases in AEE are accompanied by decreases in BEE, and vice versa, which would cause the within-individual TEE-BEE slope (b_within) to be <1. To partition the relationship between TEE and BEE at the between-and within-individual levels, we re-analysed data representing paired measurements of 36 men and 32 women aged between 70 and 90 y sampled 7 y apart within the context of a longitudinal study ²⁰. This dataset provides the opportunity to estimate the extent of energy compensation occurring both between and within individuals in elderly people. Using a bivariate mixed model, we partitioned the slope of the TEE-BEE relationship (while accounting for sex, age, FFM, FM, and sex-and age-related differences in FFM and FM) at the between-and within-individual levels (Table S2A). This analysis clearly reveals that energy compensation occurs only at the within-individual level (Figure 4A). While the between-individual slope was b_between±se = 1.86±1.05, the within-individual slope was b_within±se = 0.15 ±0.17.

To further illustrate the compensation occurring at the within-individual level, we ran a second bivariate mixed model with AEE and BEE as the dependant variables. In this model, the within-individual covariance was significantly negative (Table S2B). The within-individual correlation (±se) between AEE and BEE was r = −0.58±0.08 (Figure 4B). Hence, during extended periods when the studied cohort expended more energy on activity they compensated by reducing energy expended on basal processes (but individuals with higher-than-average AEE do not necessarily have a lower-than-average BEE). The within-individual slope in these people indicates particularly strong energy compensation between AEE and BEE (Figure 4B). That is, in this sample of people, the calories they burn during bouts of activity are almost entirely compensated for by reducing energy expended on other processes such that variation in activity had little impact on TEE.

Measurements of BEE and TEE provide invaluable insights into energy management; the next step is to elucidate the proximate and ultimate mechanisms driving these observed patterns of energy compensation. One possible factor is energy intake. For example, if obese people tend to increase their food consumption in response to increased AEE less so than other demographics, they have less resources for other functions and this could encourage the body to energy compensate, reducing BEE ²¹. Another possible factor involved in energy compensation, which is relatively hard to measure and not available in our dataset, is fidgeting, or non-exercise activity thermogenesis (NEAT). In principle, NEAT can decrease in response to increases in AEE, although few studies have directly measured it ⁷ and reviews of the literature to determine whether NEAT in humans decreases to compensate or partially compensate for increases in AEE conclude that there is no evidence overall that NEAT systematically changes e.g. ⁶.

If energy compensation has an underlying genetic basis, in the future it might be possible to screen individuals to ascertain whether exercise would be a valuable fat loss intervention because they are ‘weak compensators’, or a fruitless fat loss intervention because they are strong compensators (while recognising other benefits to exercise including protecting against weight regain ^{22, 23}). Moreover, we need to understand whether there are costs to reducing BEE. If there are, such as for example a compromised immune system or slowed recovery from injury ^{24, 25}, then for some individuals the point at which exercise levels reach a detrimental level will be considerably lower than for others.

The ever growing and diversifying range of fat loss plans and fads available to the public reflects the reality, well known to researchers, that prescribed exercise programmes for weight reduction rarely result in substantive or long-term changes in body mass ²⁶. The few national guidelines that have been published converge on the recommendation of a 500-600 kcal/d deficit through exercising and dieting to instigate fat loss ²⁷. These guidelines are general for the population and do not factor in the variation in energy compensation exhibited by people with different levels of fat mass, as demonstrated in the current study. Public health strategies for fat loss should be revised to recognise energy compensation as our understanding progresses about which individuals compensate and by how much. In this vein, more research is needed on the potentially substantial diversity of energy compensation between sub-populations. In the future, personalised exercise plans targeting fat loss might be developed partly based on an individual’s genetic propensity for energy compensation.