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The Role of Non-exercise Activity Thermogenesis in Human Obesity

, M.D. and .

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Last Update: April 9, 2018.


Inactivity and positive energy balance over protracted time periods precede weight gain and promote increases in fat mass and obesity. Population-based physical activity levels have decreased in recent years paralleled by an increment of sedentary behavior. Given the technological advances in domestic, community, and working places in the last century it is not uncommon for people in industrialized countries to spend one half of the day sitting. Moreover, for the majority of subjects, regular voluntary physical exercise is limited. Energy expenditure associated with spontaneous, non-exercise physical activity is called non-exercise activity thermogenesis (NEAT). Non-exercise activity thermogenesis varies widely in a population and by up to 2000 kilocalories day-1 between two individuals of similar size. These differences are related to complex interactions of environmental and biological factors, influenced by people’s different occupations and leisure-time activities, as well as molecular and individual genetic factors. Data support the hypothesis that targeting NEAT with an intervention could be an essential tool for body weight control. In addition, manipulation of energy balance by means of food intake can exert significant effects on spontaneous physical activity. Moreover, recent evidence links NEAT with the incidence of obesity-related chronic diseases, such as diabetes. This comprehensive review systematically describes the definition, evaluation methods, and environmental and biological factors involved in the regulation of NEAT. Emphasis is placed on evidence linking NEAT with obesity and associated disorders and suggests practical approaches, relevant implications, and limitations to integrating NEAT into daily life. For complete coverage of all related areas of Endocrinology, please visit our on-line FREE web-text, WWW.ENDOTEXT.ORG.


In 2011 Finucane et al. estimated international trends of mean age-standardized body mass index (BMI) (1). Their results showed that between 1980 and 2008 the prevalence of adulthood obesity (BMI ≥ 30 kg/m2) has risen in every sub-region of the world except for Central Africa and South Asia: mean BMI increased by 0.4 kg/m2 per decade for men and 0.5 kg/m2 per decade for women (1). Recently, comparable data have been presented by the NCD Risk Factor Collaboration trial (2). According to this study, global age-standardized BMI in females rose from a mean of 22.1 kg/m2 in 1975 to 24.4 kg/m2 in 2014, while within the same time span this increase approximated 21.7 kg/m2 to 24.2 kg/m2 in men (2). The authors conclude that if the observed trend continues, global obesity prevalence will reach more than 21% in women and 18% in men by 2025 (2). With a focus on industrialized countries, the most prominent increases of weight have been observed in the United States, followed by the United Kingdom and Australia. Accordingly, data from the National Health and Nutrition Examination Survey (NHANES) point out that more than 60% of the U.S. population are overweight (i.e. BMI ≥ 25 and < 30 kg/m2) or obese and the age-adjusted prevalence of obesity is 35% among men and 40.4% among women (3, 4). Similarly, populations of European and Eastern Mediterranean countries show an overweight prevalence of up to 50% (5). Thus, in the past century the incidence and prevalence of overweight and obesity are continuously rising and have emerged as a major burden for national health care systems worldwide.

Obesity-related diseases are typically chronic and include type 2 diabetes mellitus (T2D), peripheral artery and cardiovascular disease, hypertension, obstructive sleep apnea, as well as various cancers. In the United States, seven out of the leading ten causes of death and disability are represented by chronic diseases characteristically related to obesity (6). In fact, obesity is one of the most important avoidable risk factors for significant morbidity and premature mortality, resulting in lower quality of life, elevated disability rates, and reduced expected life span by 7 years (6-9).

Excess body fat mass results from a period sustained net-positive energy balance followed by homeostatically controlled weight maintenance. The roles of diet and caloric intake have been a major focus of studies seeking to better understand the mechanisms underlying these phenomena. In countries with high obesity prevalence rates, nutritional quality is mostly poor, but controversy exists as to whether increased energy intake alone can explain the obesity epidemic. For instance, trends in improved dietary quality and reduced energy intake have been observed in recent decades in the United Kingdom and among parts of the U.S.-population even as population rates of weight gain continue to rise (10-12). Therefore, greater attention needs to be paid to the energy expenditure side of the energy balance equation.


Daily total energy expenditure (TEE) is the net amount of energy utilized by animals and humans to maintain core physiological functions and to allow locomotion. Three main components of energy balance determine TEE: Basal metabolic rate (BMR), the thermic effect of food (TEF), which is also called diet-induced thermogenesis, and the energy expended for physical activity (13). Additional components may exist (i.e. energy costs of emotion) but play a minor role with respect to energy balance (14-16). Multiple factors significantly affect our daily energy needs, including age, body composition, thyroid hormone status, sympathetic nervous system activity, ambient and body temperature, disease states, and medications (13).

Basal Metabolic Rate

Basal metabolic rate represents the minimal amount of energy expended for all of the body’s homeostatic processes. Basal metabolic rate is essentially a function of lean body mass (LBM), as about 80% of BMR is accounted for by this variable within and across species (17-22) . In free-living individuals, BMR occupies the main percentage of TEE (Figure 1). In healthy subjects with mainly sedentary occupations, BMR predicts around 60% of TEE variance (15), while it is proportionally larger in individuals with minimal physical activity (e.g., in ventilated patients in an intensive care unit) (23). Some studies suggest that BMR is reduced in subjects prone to become obese (24-26). It is, however, debatable as to whether a reduction of energy expenditure due to a decrease in BMR sufficiently explains the positive net energy balance that contributes to excess weight gain in the majority of subjects who become obese (27-29).

Of note, it is not uncommon that the term resting energy expenditure (REE) is synonymously interchanged with BMR. However, BMR is measured 12h post-absorptively, in the supine position at complete rest under quiet ambient conditions, in a thermal-neutral temperature setting and darkened lightning in the morning after 8 hours of sleep (14). By contrast, REE is equivalent to postabsorptive energy expenditure at complete rest at any time of the day and can vary as much as 10% within BMR.

Thermic Effect of Food

Thermic effect of food (TEF) is the increment of energy expenditure above REE following meal ingestion and is a relatively stable component of total energy expenditure (Figure 1). Thermic effect of food typically ranges between 8-15% of TEE and is related to food digestion, absorption and storage. The variance of TEF has been associated with nutrient composition and energy content of consumed foods (30).

Image non-exrcse-thrmo-obs_7.1-fig1.jpg

Figure 1: Model of human energy expenditure components (adapted from (31)). Exercise-related physical activity is comparable to exercise-related activity thermogenesis (EAT), while spontaneous physical activity is comparable to nonexercise activity thermogenesis (NEAT); for further explanations see text. Note that parts of spontaneous physical activity are not under voluntary control and often referred to as “fidgeting.” Arrows symbolize the multiple and varying impacts of individual factors, genetic background, and environment.

Physical Activity: EAT and NEAT

Physical activity is the second most significant contributor to TEE in most people (Figure 1). It is defined as the additional energy expenditure above REE and TEF that is required for performing daily activities. It can be categorized into exercise-related activity thermogenesis (EAT) and nonexercise activity thermogenesis (NEAT). Both vary widely within and between individuals. For the majority of subjects in industrialized countries, exercise is believed to be negligible (16). According to NHANES data, 36.1% of the studied US population was categorized as sedentary, while a further 47.6% were physically active at low levels (30, 32). Remarkably, only around 16% of subjects in NHANES met recommended guidelines for physical activity or were considered to be highly active. Even so, the latter subjects did not necessarily exercise (32-34). Thus, it is reasonable to conclude that on a population level the percentage of subjects engaging in regular, intense physical exercise is low. In those who habitually participate in purposeful physical training, EAT is believed to maximally account for 15-30% of TEE (13, 35). Other authors suggest that the majority of subjects undergoing regular physical training, defined as “bodily exertion for the sake of developing and maintaining physical fitness,” do not exercise more than two hours a week, accounting for an average energy expenditure of 100 kilocalories (kcal) per day (36). Such expenditure would contribute to only 1-2% to the variance of TEE. Taken together, for most human subjects EAT seems to be not a major contributor to TEE variance.

In contrast, NEAT represents the predominant component of daily activity-related thermogenesis, including for most subjects undergoing regular physical training. It is important to note that both NEAT and spontaneous physical activity are not interchangeable but represent complementary concepts (37): NEAT refers to energy expenditure, while spontaneous physical activity describes the types of bodily activity that are not defined as purposeful movements but still contribute to NEAT. Importantly, a certain percentage of spontaneous physical activity (and therefore NEAT) is beyond voluntary control (e.g., “fidgeting”). Accordingly, NEAT corresponds to all the energy expended with occupation, leisure time activity, sitting, standing, ambulation, toe-tapping, shoveling snow, playing the guitar, dancing, singing, washing, and more (15, 16). Some authors categorize NEAT into three main subcomponents comprising body posture, ambulation, and all other spontaneous movements, including “fidgeting” (38).

The importance of NEAT becomes apparent when considering the following: the variability in BMR between individuals of similar age, BMI and of equal gender ranges around 7-9% (39), while the contribution of TEF is maximally 15%. Thus, BMR and TEF are relatively fixed in amount and account for approximately three quarters of daily TEE variance. As EAT is believed to be negligible on a population level, NEAT consequently represents the most variable component of TEE within and across subjects. It is responsible for 6-10% of TEE in individuals with a mainly sedentary lifestyle and for 50% or more in highly active subjects (15, 19, 37).

Taken together, with respect to the modification of energy balance and regulation of body mass, NEAT must be considered as a factor with a potentially major impact. This is of particular interest in terms not only of developing therapeutic strategies for the patient with obesity, but also for designing obesity-prevention strategies for populations. The following sections will focus on these issues, including the environmental and biological modifiers of NEAT.

Measurement of NEAT

For a better understanding of the potential role of NEAT in the context of obesity it is essential to recognize the strengths and limitations of available techniques used for the quantification of NEAT. NEAT can be principally measured by two approaches: 1) By assessing total NEAT and 2) By using a factorial approach (for extensive reviews of available techniques see (40, 41)).

Total NEAT

Assuming that EAT is negligible, total NEAT can be calculated by subtracting BMR and TEF from TEE (16):

Equation 1: NEAT = TEE – (BMR + TEF)

To complete equation 1, TEE, BMR and TEF need to be measured. Under free-living conditions TEE can be reliably measured by using the doubly-labeled water method (16). This approach requires the application of stable isotopes (deuterium/2H2 and O18) containing water (2H2O18), also known as “doubly-labeled” water. The difference in the clearance rate of the two isotopes equals carbon dioxide (CO2) production generated during energy production and thus represents quantification of TEE (42) (Figure 2).

One necessary precondition of the doubly-labeled water method is that the O2 of expired CO2 is in equilibrium with the O2 in body water. Once ingested, O18 will be readily distributed in the systemic H2O, H2CO3 and CO2 pools. 2H2 will be dispensed in body water and the H2CO3 pool (equation 2).

Equation 2: CO2 + H2O ↔ H2CO3

The concentration of labeled O2 in body water will then decrease over time due to a loss of CO2 with expiration and via excreted body water, i.e. urine, respiration and perspiration. 2H2 is mainly lost through excreted body fluids, yet a small percentage can be incorporated into body fat or protein. Since body water becomes tagged with known amounts of tracers at the same time at the beginning of the measurement period, the difference of elimination rates of both tracers equals CO2 excretion. Doubly-labeled water is usually administered at baseline after samples of blood, urine and saliva have been collected (14). Subsequent sample collections occur within 7-21 days after administration, when the isotopes are completely dispersed within body compartments. Isotope-ratio mass spectroscopy is used to measure 2H2 and O18 enrichments in collected samples, which are needed to finally calculate CO2 production. Thereby, TEE can be ascertained with an error of about 6-8%. This error can be minimized by repeatedly gathering samples over the measurement period rather than relying on measurements from only two-time points.

Image non-exrcse-thrmo-obs_7.1-fig2.jpg

Figure 2: Schematic picture of the doubly labeled water method. CO2, carbon dioxide; 2H2, deuterium; O18, labeled oxygen.

It should be mentioned that the study of TEE classically relied on direct calorimetry (13, 39). Direct calorimetry, or direct measurement of heat loss from a subject, is achieved by using well-controlled environmental chambers. However, this requires participants to be confined to small rooms, which fails to capture the enormous variety of NEAT components in free-living individuals (14, 15).

By contrast, indirect calorimetry is an easily accessible and portable method commonly used to assess BMR, RMR and TEF. Indirect calorimetry is based on the principle that by measuring oxygen consumption (VO2), CO2 excretion (VCO2), or both over a defined time span, the metabolic conversion of fats, carbohydrates, and proteins into energy can be calculated using established formulae (13). The net energy released is typically expressed as kcal or Kilojoule (kJ). The small percentage of physiological protein oxidation can be estimated by nitrogen excretion in urine, or can be neglected without adding a substantial error in subjects who are in stable nitrogen balance (13). Indirect calorimetry is, accordingly, one of the most commonly used approaches in clinical investigations, either in controlled metabolic-ward studies or in field settings. For instance, Levine et al. applied indirect calorimetry to measure BMR and TEF in their study on human NEAT (43). In order to measure TEF they provided participants with a meal containing a third of the subject’s daily weight maintenance energy needs. To estimate TEF over 24 hours, the measured energy expenditure area-under-the-curve obtained when they ate the standardize meal was multiplied by three (16). Alternatively, study protocols may ignore TEF as a non-substantial variable, or simply multiply TEE by 0.1 to yield a crude estimate.

When these more sensitive techniques are not available, BMR can be alternatively estimated by using validated age-, gender- and population-specific equations. For instance, the Harris-Benedict equation is widely used in clinical settings (13). However, this equation was derived from healthy, normal-weight Caucasian adults between 1907 and 1917 and its application, therefore, has limitations (13). Substantial error could be introduced by applying it to an inappropriate collective, including subjects who are obese, which is also true for other validated equations estimating BMR (13).

The physical activity level (PAL) is frequently used in studies to provide an index of activity when measurement of total NEAT is not possible. It is calculated by expressing TEE relative to BMR (15). Sedentary subjects in industrialized countries typically have a PAL of approximately 1.5 (see Table 1). The PAL rises to values of 2.0-2.4 with strenuous work and under certain conditions it can increase to values up to 3.5-4.5 (15). Using PAL, however, can also introduce significant bias in the assessment of NEAT, as under free-living conditions the cumulative error of PAL measurements can be as high as 7% (14).

Table 1: Physical activity levels (PAL) predicted from lifestyle (from 16).
Chair or bed bound1.2
Seated work with no option of moving around and little or no strenuous leisure activity1.4 – 1.5
Seated work with discretion and requirement to move around but little or no strenuous leisure activity1.6 – 1.7
Standing work (i.e., housewife, shop assistant)1.8 – 1.9
Strenuous work or highly active leisure2.0 – 2.4

The factorial Approach to NEAT Measurement

Measuring total NEAT or using PAL provides no information regarding the individual components contributing to activity-related energy expenditure. Therefore, the factorial approach is widely used to estimate NEAT constituents. Accordingly, all physical activities of a subject of interest are recorded over a defined time span, typically seven days. The energy equivalent of each activity is determined and these equivalents are then apportioned according to the time spent by the individual in the respective activities. Finally, the data is totaled to get an estimate of the energy expenditure attributable to NEAT (14, 15). The first step when using the factorial approach is the quantification of a subject’s physical activities. Several methods are available to obtain such information, including questionnaires, interviews, and activity diaries. These approaches might be useful for estimates of particular activities, such as those related to occupational duties. Otherwise, they have substantial limitations, including inadequate or incomplete data recording, alteration of habits during assessment periods, and others (14, 15). Moreover, certain NEAT subcomponents, such as “fidgeting” might not be captured. Therefore, more technically advanced approaches have been developed. Among these are motion detectors, floor-pressure-pad displacement, cine photography, pedometers, accelerometers, and global positioning systems (GPS) (14, 15). Depending on the degree of sophistication, all of them have certain limitations. While data obtained from triaxial accelerometers typically found in wearable activity monitors is often used as an estimate of activity-related energy expenditure, they only measure actual movements (not energy expended), and a combination of methods probably yields the most appropriate estimates (14, 15). For examples, energy costs of single NEAT components are typically measured by means of indirect calorimetry, for which highly sophisticated portable systems are now available. Alternatively, tables with listed energy costs of NEAT activities may be used. This latter method is convenient and inexpensive, but for similar reasons as pointed out for PAL, substantial systematic errors and inaccuracies can be introduced (14).

Principal problems arise with the NEAT measurement per se, as little validated information is available concerning the time period necessary to representatively assess spontaneous physical activity. Many studies report extrapolation of measured results of an individual’s energy expenditure obtained during a limited time-frame to longer time intervals. In this regard, experienced investigators in this field believe that measurements of approximately seven days will likely provide a representative assessment regarding a 2-4 month time span (14).

Taken together, the difficulty of getting true estimates under free-living conditions is a major reason why available information on human NEAT physiology is limited. Representative studies are needed that are conducted over appropriate time spans (months) and utilize a combination of the factorial approach and total NEAT measurement to overcome major limitations in current estimates.


There is a close interplay between environmental factors and individual characteristics with respect to NEAT. The known and surmised issues contributing to this phenomenon will be addressed in the following sections.

Effects of Occupation, Gender, and Age

Highly physically-active individuals expend up to three times more energy in 24-hours than subjects with negligible bodily activity (44). As previously discussed, NEAT accounts for a significant proportion of the observed differences in TEE at the population level. Thereby, occupation clearly represents the key determinant of NEAT (36). Indeed, NEAT can vary by as much as 2000 kcal day-1 when comparing two adults of similar body size, lean body mass, age and gender (Figure 3).

Image non-exrcse-thrmo-obs_7.1-fig3.jpg

Figure 3: The effect of occupational intensity on NEAT (adapted from (36)). The data rely on an assumed BMR of 1600 kcal day-1.

For an average worker spending most of her/his time in a seated position, occupational NEAT is relatively low and estimated energy costs range up to a maximum of 700 kcal day-1 (Figure 3). A comparable person working mainly in a standing position can increase her/his occupational NEAT to up to 1400 kcal day-1, while an agricultural occupation would theoretically result in NEAT levels ranging around 2000 kcal day-1 and more (Figure 3) (36). Thus, occupations relying on intense physical activity can expend 1500 kcal day-1 more than a sedentary job.

This variation in daily NEAT expenditures between individuals and populations becomes even more accentuated when considering research from non-industrialized countries (45, 46). For instance, a study by Levine and colleagues included more than 5000 inhabitants of agricultural regions of the Ivory Coast of Africa (Figure 4). Each subject was studied over a time span of seven days and all of their daily tasks were recorded by a trained evaluator (46). Several lines of available evidence regarding NEAT were established from this study. First, a substantial effect of gender was observed as women apparently worked more than men in these societal constructs. Women were responsible for more than 95% of domestic and for an additional 30% of agricultural tasks. Otherwise, men exclusively worked in agricultural occupations and had more leisure time, resulting in substantially lower NEAT than women (36, 46). In contrast, men in Canada, Australia or the United Kingdom are reported to be up to three times more physically active than females, while data from the United States indicate comparable activity levels of genders (47, 48). Even less data is available on NEAT in children, but in general boys seem to be reproducibly more active than girls (49, 50).

Image non-exrcse-thrmo-obs_7.1-fig4.jpg

Figure 4: Gender- and age-dependent physical activity levels (time engaged in work activities) of people living in agricultural communities of the Ivory Coast in Africa (adapted from reference (46)).

A second important observation from this study is the decreased occupational NEAT observed with ageing (Figure 4) (46). An age-related decline of NEAT had been previously reported across species (51). The underpinnings for this age-effect on NEAT in free-living humans has been reported by Harris et al. (52), who showed that the substantial decrease in elderly as compared to younger subjects was mainly attributable to the ambulation subcomponent of NEAT. Elderly subjects walked less distance despite having a comparable number of daily walking periods compared to their younger counterparts. Indeed, elderly subjects performed 29% less nonexercise activity, corresponding to three miles less ambulation per day (52).

Industrialization and Societal Status

Industrialization and societal status are distinctive factors affecting NEAT. Urban environment and mechanization are associated with a decrease in daily physical activity. For example, it was shown that sales of labor-saving domestic machines (e.g. washing machines) and obesity rates are closely correlated in the U.S. population, while this association was not present with respect to energy intake (15, 16). Furthermore, while it is known that poverty, certain neighborhoods, and educational status are associated with increased consumption of energy-dense foods, which is thought to contribute to higher obesity rates reported in lower socioeconomic groups (53, 54), highly educated groups report more leisure-time physical activity and are three times more likely to be physically active as compared to less educated individuals (15, 16).

Seasonal Effects

Seasonal variations also play a role for NEAT. Time spent in activity is twice as likely during the summer as compared to winter months and contributions to activity from agricultural or construction work play greater roles in the summer season (16).

Together, available data indicate close relationships between environmental conditions and biological aspects of NEAT. Evidence suggests that the contribution of occupational activity to NEAT can vary by up to 2000 kcal day-1. Since the accumulation of excess body fat is a result of positive energy balance, subtle perturbations of energy balance over sustained time periods are theoretically capable of inducing obesity. Once weight is stable again following a period of weight gain, energy expenditure and energy intake need to be precisely matched to achieve a long-term persistency of body mass, as an error of 1% would lead to a gain or loss of 1 kg year-1, or some 40 kg between the ages of 20-60 years. Therefore, small alterations of energy balance by means of NEAT could theoretically result in significant effects with respect to severity of obesity in an individual and the prevalence of obesity in a population. Consequently, when considering how to influence NEAT as a strategy for the treatment or prevention of obesity, a central question arises: In what fashion does NEAT interact with another environmentally-influenced factor involved in energy balance, that of energy intake?


Alterations of energy balance are known to occur in adaptation to multiple changes in voluntary activity levels, but relatively few studies have addressed modulation of spontaneous physical activity and NEAT by alterations in nutritional energy intake. The latter is part of the concept of adaptive thermogenesis (55). Adaptive thermogenesis refers to changes in REE, EAT and NEAT that are independent of changes in LBM, but are, instead, modulated by other factors, including increased or decreased caloric intake (55). In response to caloric restriction, both REE and non-resting energy expenditure substantially contribute to changes in adaptive thermogenesis (55). Interestingly, in the majority of studies, alterations in adaptive thermogenesis in response to overfeeding are in large part explained by raised non-resting energy expenditure (i.e., NEAT), although the ability to adapt NEAT to manipulations of energy balance is highly variable between subjects. However, if an individual is able to generate a significant upregulation of NEAT in the face of positive energy balance brought about by an increase in food intake, they may be more able to prevent obesity (29).

NEAT Under Conditions of Overfeeding

Using a combination of direct calorimetry and motion detectors, Ravussin et al. have shown that the inter-person variability of TEE is considerable and remains significant even after adjustment for LBM (19). A substantial percentage of this variability is explained by differences in spontaneous physical activity including “fidgeting,” as the latter accounted for 100-800 kcal day-1 in subjects (19). Interestingly, the researchers observed elevated spontaneous physical activity levels in subjects who are obese as compared to those who are lean, indicating a positive relationship with elevated body mass. However, the latter study was performed under eucaloric conditions over a short observation period and whether acute variability in energy intake affects NEAT was not addressed. In their “weight clamping” study, Leibel et al. (56) overfed adults who were lean and overweight /obese by 10%, or approximately with 5000-8000 kcal day-1, over 4-10 weeks, followed by a weight maintenance period. Body composition, fecal caloric loss, TEE, REE, TEF and spontaneous physical activity were measured using multiple technical approaches including indirect calorimetry, the doubly labeled water method, and activity trackers in a subgroup (56). They showed that maintaining a 10% elevated body mass induced a significant increase of TEE by 9±7 kcal kg-1 LBM day-1 in subjects who had not been previously obese and by 8±4 kcal kg-1 LBM day-1 in those who had formerly been obese (56). The majority of this rise in TEE was attributable to non-resting energy expenditure, which accounted for a mean of 8-9 kcal kg-1 LBM day-1. The magnitude of this effect was comparable in in those who had previously not been obese and those who had been overweight or obese, indicating similar adaptations to overfeeding conditions (56). However, a related review pointed out the large inter-individual variability regarding the capability to adjust energy expenditure with overfeeding in the “weight clamping” experiments (31) and emphasized that in other human over-feeding studies there is a wide range of individual weight gain per unit of excessively consumed energy. Therefore, it can be proposed that some individuals show a remarkable capacity to increase energy expenditure in response to overfeeding, while others do not (31). It was further suggested that spontaneous physical activity, and accordingly NEAT, likely play an important role regarding the impact on body weight under such conditions.

Levine’s group was the first to systematically investigate the effect of overfeeding on the individual ability to adapt NEAT in free-living subjects (43). Using sophisticated methods and measuring NEAT over a representative time span, the authors overfed 16 volunteers (12 males, 4 females; age ranging from 25-36 years) by 1000 kcal day-1 in excess of their weight maintenance requirements (20% of the calories came from protein, 40% from fat, 40% from carbohydrates). The energy surplus was paralleled by a mean TEE increment of 554 kcal day-1. In this study 14% of this TEE increase was attributable to a rise in REE, approximating by 79 kcal day-1. A further 25% of the TEE increment (136 kcal day-1) corresponded to an increase in TEF, probably due to the relatively high percentage of protein intake. The most prominent effect was, however, attributable to enhanced physical activity thermogenesis, corresponding to around 336 kcal day-1. As volitional exercise of the study subjects remained at a constant low level and since the authors did not detect changes in exercise efficiency, they concluded that about 60% of the increase in TEE due to overfeeding was attributable to NEAT (43). Again, however, the change in NEAT varied remarkably between subjects, ranging from -98 to +692 kcal day-1 (57). The maximal individual increase of NEAT constituted 69% of the excessively consumed 1000 kcal day-1 in this study (57). Moreover, the change in NEAT was directly predictable of individual vulnerability or resistance to body fat accumulation (43).

Other recent several reports, however, have failed to detect compensatory increases in spontaneous physical activity during short-term overfeeding in humans (58-61). While in several cases, methodological differences make them less directly comparable to Levine’s study, (44, 59-61), a study by Siervo et al. used state-of-the-art methods and applied a highly standardized 17-week protocol with progressive overfeeding from 20 to 60% energy in excess of maintenance needs (62) in lean, healthy men. Three sequential intervals of stepwise overfeeding were each separated by one week of ad libitum energy intake. In agreement with previous findings, between-subject variability concerning weight change during ad libitum phases was high (63). However, in contrast to Levine’s study (43), Siervo et al. failed to detect a significant systematic change in spontaneous physical activity (62). The authors therefore concluded that systematic elevations in energy intake induce very limited counter-regulatory responses in energy expenditure (63).

In summary, these data underscore the highly variable individual compensatory responses with regard to adaptive thermogenesis to overfeeding. From Levine et al., supportive evidence is provided for the susceptibility to gain body mass under standardized overfeeding conditions and identifies an individual’s ability to adapt NEAT as one critical determinant (16, 29, 43). In accordance with findings of other overfeeding studies (reviewed in (57)), adaptations in thermogenesis by changes in NEAT observed between individuals may be an important explanation for why some individuals are particularly susceptible, or resistant, to weight gain.

The Impact of Caloric Deprivation on NEAT

While evidence with respect to systematic NEAT upregulation under overfeeding conditions remains somewhat controversial, TEE and spontaneous physical activity are consistently influenced with energy deprivation (55, 58). This is of importance as most subjects who are overweight or obese try to lose body mass through various low-calorie dietary strategies. The first study to quantitatively investigate adaptive thermogenesis under conditions of food restriction was the Minnesota Starvation Experiment (reviewed in (31, 39, 55)). This study showed a marked reduction in TEE that was later reproduced by numerous studies, independent of pre-starvation BMI or method used to induce weight loss (reviewed in (55)). For instance, Leibel et al. in a second arm of their “weight clamping” experiment evaluated the effect of a 10% body mass reduction, followed by weight maintenance at this level (56). While overfeeding was accompanied by a 16% increase of adjusted TEE, weight reduction from underfeeding induced a 15% decrement (57). As compared to overfeeding, it is well accepted that both REE and non-resting energy expenditure are affected by energy deprivation, which is supported by recent data from animal models (55, 58, 63). These findings are supported by results from the so-called Biosphere 2 experiment, where the authors demonstrated a major reduction of TEE and spontaneous physical activity with body mass reduction over a two-year time period, which persisted after six months of body weight regain (31, 64). Demonstrated TEE reductions by adaptive thermogenesis in weight-regainers has been reproduced in other human studies (55). Of note, when subjects recover from starvation they spontaneously overeat and, moreover, if weight loss-induced adaptive thermogenesis persists (i.e. reduced NEAT), there is a significant risk of regaining weight and “overshooting” pre-starvation body mass (55). Thus, changes in non-resting energy expenditure with underfeeding appear to occur directionally so as to conserve body energy and return subjects to their initial body mass, as recently described in humans as the so-called “thrifty phenotype” compared to a “spendthrift phenotype” (65).

Regarding this, findings from a recent study in sedentary overweight females could be of potential practical relevance (66). In this investigation, 140 pre-menopausal females reduced their body mass by an average of 25 pounds by undertaking a very low-calorie diet totaling 800 kcal day-1. Subjects were randomly assigned to one of three groups: no exercise, a structured aerobic training schedule, or an exercise program incorporating resistance training (66). Body composition, strength, TEE, REE and NEAT were measured using various approaches. One main finding was that TEE, REE and NEAT all declined with weight loss in the no-exercise group. Non-exercise activity thermogenesis was reduced by 150 kcal day-1, which was equivalent to 27% compared to baseline levels (66). However, in subjects undergoing the exercise regimens only REE was reduced, while NEAT remained unaffected (66). Thus, since caloric deprivation consistently decreases spontaneous physical activity, untrained subjects who are obese and planning to lose body mass by means of dietary intervention would be advised to engage in a concomitant exercise program to avoid negative effects on NEAT and mitigate weight cycling after reaching a lower body weight. Of note, the authors of this study emphasized that, consistent with the concept that “more is not always better,” that exercising two days a week was capable of increasing NEAT but, paradoxically, an intense three days weekly schedule was followed by a substantial decrement in NEAT (66).

An integrative view from human overfeeding studies proposes that the magnitude of adaptations regarding energy expenditure varies largely between individuals. Subjects capable of responding with increased spontaneous physical activity to overfeeding can be categorized as “compensators” and are therefore less susceptible to obesity (“spendthrift phenotype”). Subjects unable to respond to a continued energy surplus with increased NEAT (i.e. “non-compensators” or “thrifty phenotype”) seemingly represent the majority of subjects on a population level, of which a major percentage will attempt dietary weight-loss strategies at some time in their lives. However, REE and non-resting energy expenditure including NEAT are consistently reduced with underfeeding and therefore represent a risk factor for weight regain after dietary intervention. Mild voluntary exercise during and after periods of dietary weight loss could be a promising strategy to avoid decreases in NEAT, although this alone will probably not fully compensate for the obligate decline in REE. Whether the majority of dieting subjects with obesity will continuously adhere to a structured exercise program during extended periods of weight maintenance as suggested by current recommendations must be questioned (6, 32-34). Therefore, in order to advance anti-obesity strategies that systematically increase or to even conserve NEAT it is essential to evaluate the mechanisms contributing to regulation of spontaneous physical activity.


Principles of NEAT Regulation

The ability to adapt thermogenesis, and specifically NEAT, presupposes existing mechanisms in the body that sense, accumulate and integrate internal and external directionality signals with respect to energy balance (16). Figure 5 shows a proposed model of the principal regulation of spontaneous physical activity and correspondingly NEAT. In this model, physiological data regarding caloric intake, energy depots, and energy expenditure are compiled centrally by the brain. These signals converge at the data acquisition center, are rectified to a common signal, and directed to a NEAT accumulator, which is constantly summing the net amount of NEAT per unit of time. The latter continuously refers to the energy balance integrator, which, in turn, modulates spontaneous physical activity in response to changes in energy intake. Thereby, energy balance resulting from these three components is in constant flux.

Image non-exrcse-thrmo-obs_7.1-fig5.jpg

Figure 5: The NEAT hypothesis of energy balance (adapted from (16)). Continuous arrows represent energy flow through the system, broken arrows stand for putative signaling pathways. BMR, basal metabolic rate; NEAT, non-exercise activity thermogenesis; TEF, thermic effect of food.

For example, under circumstances of rising energy intake, the energy balance integrator may mediate deposition of the caloric surplus into energy stores (fat cells) or alternatively dissipate the excess energy in the form of NEAT. More likely, since BMR is essentially fixed, some energy will be spent on food digestion and absorption (increased TEF), some will be deposited as body fat, and the rest will be dissipated as NEAT (16). This model also supports the observation that although BMI and energy balance are continuously oscillating around a weight set point (or “set range”) under daily life conditions, body mass is remarkably stable in the long term. This is exemplified by data from the Framingham Study showing a mean body mass increase of only 10% over a 20-year time span in average adults (cited in (56, 67)).

Thus, it appears that the regulation of energy balance and body mass is realized by a complex network of precisely working autoregulatory systems controlling effector systems, one of which being spontaneous physical activity (NEAT). However, while ample available data detail the regulation of food intake, there is lack of information concerning the biological mechanisms driving NEAT (29). Certainly, physical activity has potential for impacting energy balance and body mass. Therefore, it is very likely that many of the biological systems involved in the regulation of energy intake are also involved in gathering and integrating information regarding NEAT and determining NEAT adaptation in humans (29).

Tissues, Organs, and Central Mechanisms Involved in NEAT Regulation

Over- and underfeeding studies in identical twins suggest an important role of genes regarding the variability underlying body mass regulation (68, 69). Specifically, regarding NEAT, it is likely that due to the polygenetic nature of obesity numerous pathways are involved in the regulation of spontaneous physical activity, as the latter has multiple environmental cues and affects a multitude of behaviors (6, 70). Indeed, Zurlo et al. have shown that spontaneous physical activity levels cluster in families and could prospectively help to explain the propensity for weight gain (71). Moreover, according to a recent review, up to 57% of the variability of spontaneous activity is thought to be familial (72).

Image non-exrcse-thrmo-obs_7.1-fig6.jpg

Figure 6: Model for the neuroendocrine regulation of NEAT in the service of energy balance (adapted from (29)). Multiple external and internal signals are sensed and integrated, whereby defined brain structures (i.e. arcuate nucleus of the hypothalamus, area postrema and nucleus of the solitary tract of the hindbrain, dopamine pathway of the mesolimbic system) interpret a multitude of sensory cues of energy availability. The involved brain systems have multiple ascending and descending projections affecting the amount of physical activity through arousal and limbic pathways and descending neural projections and endocrine signals to modulate the energy efficiency of physical activity. Thereby, the central nervous system could adapt NEAT to adjust energy balance under conditions of caloric excess or starvation. Ach, acetylcholine; AgRP, agouti-related peptide; CART, cocaine- and amphetamine-regulated transcript; CCK, cholecystokinin; LC, locus coeruleus; MCH, melanin-concentrating hormone; NPY, neuropeptide Y; POMC, pro-opiomelanocortin; PSNS, parasympathetic nervous system; SNS, sympathetic nervous system; TMN, tuberomammillary nucleus; VP, ventral pallidum.

Figure 6 shows a model suggesting how NEAT could be regulated through involvement of several well-described neuroendocrine systems interacting with peripheral tissues/organs known to affect energy balance. The associated neural mechanisms of brain regions involve a multitude of neurotransmitters and neuropeptides (for extensive review see (29) and (73)). While all are important, this section will briefly focus on the biological role of central orexin peptides as promising and potentially targeted regulators of spontaneous physical activity (for extensive review see (37)).

Experimental data indicate that injections of orexin A into defined brain regions reproducibly induce a dose-dependent increase in spontaneous physical activity along with significant increments in NEAT. Moreover, current knowledge suggests a role for the orexin system in the control of arousal and sleep, and for reward and stress reactions. This has led to extensive research showing that the orexins A and B are produced by cleavage from a single pro-peptide (37). The majority of orexin A is synthesized in the lateral hypothalamus and perifornical region. Orexin A binds to two G protein-coupled receptors, namely orexin receptor type 1 (OXR1) and 2 (OXR2). OXR1 has high affinity for orexin A, while OXR2 has equal affinity for the A and B peptide. Receptor binding of orexin A is associated with increased intracellular calcium levels and followed by enhanced neuronal firing (37). Physiologically, orexin A is capable of modulating spontaneous physical activity, food intake, and sleep as evidenced by an obesity-resistant rat model with high orexin A activity. These animals show more ambulatory and vertical movement, independent of age or food availability (29). Intriguingly, this rat model exhibits lower body weight gain when fed a high fat diet, despite consuming significantly more kcal per gram body mass. From this study, it appears that orexin A simultaneously enhances feeding behavior and induces physical activity, but the consumed calories are outweighed by those expended with spontaneous physical activity (29, 37). These data were confirmed by a mouse model with postnatal loss of orexin neurons (37). These mice exhibit hypophagia, decreased spontaneous physical activity, and develop spontaneous-onset obesity while consuming a regular chow-diet. Thus, modulation of spontaneous physical activity and resultant NEAT seems to be one of the main effects of the orexin peptide system in rodents. In humans, the orexin system has been shown to be defective in the sleep disorder narcolepsy. Since obesity is a recognized comorbidity of narcolepsy in both animal models and humans (37) regulation of this signaling system has the potential to impact clinical endpoints, including the development of overweight and obesity.

The Role of Peripheral Tissues for NEAT Regulation: Adipose Tissue and Adipokines

Peripheral tissues are also believed to contribute to NEAT regulation (Figure 6). That NEAT can be influenced by over- and underfeeding has been extensively discussed in earlier sections; but it is essential to also reconsider that longitudinal studies of fasting humans show significant decreases in adaptive thermogenesis with starvation, while refeeding may induce a variable elevation (55). These adaptations are likely the result of feedback mechanisms between thermogenesis regulation and energy depots (Figures 5 and 6). It can be further hypothesized that adaptations of thermogenesis in response to fasting or overfeeding result, at least in part, from an adipose tissue-specific impact on thermogenesis (reviewed in (31)). Indeed, studies of white adipose tissue (WAT) have led to the recognition that this energy store represents an important endocrine organ communicating with the brain through secretion of so-called “adipokines.” Accordingly, adipokines could also play a role for NEAT regulation (Figure 7) (74).

Energy availability from food and internal energy stores, ambient and body temperature, and systemic inflammation are major determinants of thermogenesis regulation. In WAT, two relevant factors converge: WAT represents the principal energy storage depot and, under conditions of significant obesity, WAT is a site of low grade inflammatory activity that contributes to systemic inflammation. These signals from WAT can induce enhanced sympathetic nervous system (SNS) activity and affect other downstream regulators of adaptive thermogenesis (Figure 7). Experimental data indicate that NEAT is one component affected by WAT signaling, including leptin. Related to the hypothesis of “adaptive thermogenesis”, Dulloo et al. in their review regarding human starvation and overfeeding suggest SNS is a key regulatory effector mechanism (31). Of note, activation of brown adipose tissue, which has recently gained a lot of interest with respect to human obesity (75-77), is suggested to be an additional site of adaptive thermogenesis.

Another representative WAT signal is leptin. Leptin is the product of the rodent obese (ob) gene and human analog and was first characterized in 1995 (78). Due to its role regarding the sensing of energy stores and regulation of food intake, it was also suggested as an important signal with respect to adaptive thermogenesis (reviewed in (29, 79)). For instance, Dauncey and Brown studied ob⁄ob mice lacking leptin production and their lean littermates when they had comparable body weights (80). Leptin-deficient mice expended less energy and showed less motor activity as compared to control animals. The energy expended relative to metabolic size was greater in lean littermates, who were also more active than the ob⁄ob mice. It was calculated that activity-related energy expenditure accounted for at least part of the body mass difference observed between ob⁄ob and wild-type mice (80, 81).

Image non-exrcse-thrmo-obs_7.1-fig7.jpg

Figure 7: Essential elements in the regulation of thermogenesis including effects of WAT (adapted from (29, 79)). BAT, brown adipose tissue; cAMP, cyclic 5” adenosine monophosphate; COX, cyclooxygenase; IL, interleukin; LPS, lipopolysaccharide; PAMPs, pathogen associated molecular pattern; PGE, prostaglandin E; PKA, protein kinase A; SNS, sympathetic nervous system; UCP, uncoupling protein; WAT, white adipose tissue.

With respect to humans, Franks et al. have shown that fasting leptin levels were significantly associated with physical activity-related energy expenditure (81). Moreover, it was pointed out in a recent review that replacement of leptin in weight-reduced subjects was capable of reversing neuroendocrine adaptations that accompany caloric reduction as evidenced by an increase in SNS activity, suggesting that this component of the non-resting energy expenditure and adaptive thermogenesis is at least in part under endocrine control (55). In response to caloric restriction, the authors therefore suggest an adaptation model where in an initial fast “first set,” reduced insulin secretion and glycogen stores are accompanied by a reduction of both REE and non-resting energy expenditure within days, while in the long term, with maintenance of reduced body weight, a “second set” mediated by a drop in leptin levels becomes active that keeps energy expenditure low so as to preserve fat depots and maintain reproductive function (55). This endocrine action of leptin with related activation of multiple pathways then enhances the risk of weight regain (55).

Overall, available data suggest that leptin’s central actions include not only reducing food intake but potentially also increasing energy expenditure mediated, in part, through increasing physical activity levels (reviewed in (29)). The potential role of leptin on NEAT in human obesity is of particular interest given the phenomenon of central leptin insensitivity under conditions of overfeeding and related obesity (82, 83). However, it must be also kept in mind that the leptin system represents only one component of peripheral tissue signaling that impacts NEAT. Other endocrine systems, such as thyroid hormone status, also have major influences on energy expenditure. For example, hyperthyroid rats have been shown to have significantly increased NEAT (84).

Skeletal Muscle as Peripheral NEAT Modulator

Skeletal muscle is a major contributor not only to REE, but also to spontaneous physical activity and has been newly recognized as an important NEAT effector. Human subjects maintaining a 10% weight loss in response to caloric restriction have increased skeletal muscle work efficiency (67, 85). This mechanism is believed to be responsible for the phenomenon that, even after adjustment for the reduced body mass, weight-loss maintaining subjects need proportionally fewer calories for performing comparable physical-activity tasks compared to before weight loss (67, 85). Therefore, adaptations of skeletal muscle work efficiency can contribute to reduced NEAT after low-calorie induced weight loss. Several recent animal models are in line with this hypothesis (63, 86). For example, a recent study in rats investigated the contribution of REE and non-resting energy expenditure to reduced TEE observed after three weeks of 50% energy deprivation (63). They reported a 42% reduction in TEE, even after correction for the loss in body weight and LBM (63). Moreover, 48% of the detected TEE reduction was explained by a significant decline in non-resting energy expenditure, i.e. spontaneous physical activity (63). Reduced baseline and activity-related muscle thermogenesis was also found in this study. Reduced skeletal muscle norepinephrine turnover as a surrogate of sympathetic nervous system drive and increased expression of the more energetically “economic” myosin heavy chain (MHC) 1 isoform were identified potential mechanisms for these changes (63, 67, 85). Thus, according to these findings, restriction of caloric intake apparently not only suppresses spontaneous physical activity, but in addition reduces related caloric demand, which is mediated by reduced sympathetic nervous system activity to skeletal muscle and altered expression of MHC isoforms. These findings are supported by data from another recent animal study investigating rodents with “high intrinsic physical activity” and counterparts with “low intrinsic running capacity” under eucaloric conditions (87). It was found that animals with high intrinsic physical activity were lean, had lower skeletal muscle economy along with increased skeletal muscle heat dissipation during activity. This resulted in higher TEE and NEAT and was related to increased activation of skeletal muscle by sympathetic nervous system (86).

Taken together, skeletal muscle is an intrinsic site for NEAT and not only of great importance quantitatively, but also represents an important effector system for NEAT mediated through modifications on the level of sympathetic nervous system activity and gene expression, particularly under conditions of altered caloric intake. Therefore, evidence indicates that NEAT is modified by a complex network regulation of central and peripheral systems. The latter involve a variety of redundantly organized immediate and delayed mechanisms, including central neuropeptides and signaling systems to and from peripheral tissues. Involvement of such biochemical signaling systems identifies spontaneous physical activity and NEAT as a potential site for pharmaceutical targeting. Pharmaceutical manipulation of such a system could be envisioned to either increase the amounts of activities people undertake or elevate activity-related energy expenditure.


The worldwide epidemic of obesity in past decades has contributed to serious health concerns. Apart from poor diet, reduced physical activity and sedentary behavior contribute to the pathogenesis of obesity and the expression of co-morbidities such as type 2 diabetes mellitus. Non-exercise activity thermogenesis is a highly variable component of daily total energy expenditure and essentially a function of environmental and individual factors. Whether caloric overfeeding systematically affects non-exercise activity thermogenesis is a matter of debate, while a negative energy balance due to voluntary caloric restriction can decrease it. As physical activity contributes to weight maintenance and prevention of body mass regain after hypocaloric dietary interventions, increasing non-exercise activity thermogenesis could represent a promising tool for body mass control.


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