Non-Exercise Activity Thermogenesis (NEAT)
Levine, James A
Non-exercise activity thermogenesis (NEAT) is the energy expended for everything we do that is not sleeping, eating or sports-like exercise. NEAT can be measured by one of two approaches. The first approach is to measure or estimate total NEAT. Here, total daily energy expenditure is measured and from it, the basal metabolic rate-plus-thermic effect of food is subtracted. The second approach is the factoral approach whereby the components of NEAT are quantified and total NEAT calculated by summing these components. The amount of NEAT that humans perform represents the product of the amount and types of physical activities and the thermogenic cost of each activity. The factors that impact a human’s NEAT are readily divisible in biological factors such as weight, gender and body composition and environmental factors such occupation or dwelling within a “concrete jungle.” The impact of these factors combined explains the substantial variance in human NEAT. The variability in NEAT might be viewed as random and unprogrammed but human data contradict this thesis. It appears that changes in NEAT accompany experimentally induced changes in energy balance and may be important in the physiology of weight change. NEAT and a sedentary lifestyle may thus be of profound importance in obesity.
Key words: energy expenditure, physical activity, obesity, malnutrition
© 2004 International Life Sciences Institute
Biological entities obey physical laws so in this regard, humans and mammals obey the laws of thermodynamics. Human energy stores can only increase and obesity can only occur when food intake exceeds energy expenditure (or metabolic rate). Similarly, energy stores can only be depleted when energy expenditure exceeds food intake. Thus, the balance between food intake and energy expenditure determines the body’s energy stores. The quantity of energy stored by the human body is impressive; lean individuals store at least two to three months of their energy needs in adipose tissue whereas obese persons can carry a year’s worth of their energy needs. It is the cumulative impact of energy imbalance over months and years that results in the development of obesity or undernutrition.
There are three principal components of human energy balance (Figure 1): basal metabolic rate (BMR) thermic effect of food (TEF) and activity thermogenesis. There are also other small components of energy expenditure that may contribute the whole, such as the energetic costs of medications and emotion.
BMR is the energy expended when an individual is lying at complete rest, in the morning, after sleep, in the postabsorptive state. In individuals with sedentary occupations BMR accounts for approximately 60% of total daily energy expenditure. Three-quarters of the variability in BMR is predicted by lean body mass within and across species.1,2 Resting energy expenditure is the energy expenditure at complete rest in the postabsorptive state and in general is within 10% of BMR.
TEF3-6 is the increase in energy expenditure associated with the digestion, absorption, and storage of food and accounts for approximately 10-15% of total daily energy expenditure (Figure 2). Many believe there to be facultative as well as fixed components.
Activity thermogenesis can be separated into two components: exercise-related activity thermogenesis and non-exercise activity thermogenesis (NEAT, Figure 1). The role of exercise in human energy balance will not be reviewed here, but it should be noted that for the vast majority of dwellers in developed countries, exercise-related activity thermogenesis is negligible or zero. NEAT, even in avid exercisers, is the predominant component of activity thermogenesis and is the energy expenditure associated with all the activities we undertake as vibrant, independent beings. NEAT includes the energy expenditure of occupation, leisure, sitting, standing, walking, talking, toe-tapping, playing guitar, dancing, and shopping. The enormous variety of components has made NEAT challenging to study and its role in human energy balance difficult to define.
Regardless of the difficulties in measuring NEAT and its components, it has long been recognized that NEAT is likely to contribute substantially to the interand intrapersonal variability in energy expenditure. For example, if three-quarters of the variance of BMR is accounted for by variance in lean body mass and if TEF represents 10-15% of total energy expenditure, then the majority of the variance in total energy expenditure that occurs independent of body weight must be accounted for by variance in physical activity.
NEAT is the most variable component of energy expenditure, both within and between subjects, and ranges from about 15% of total daily energy expenditure in very sedentary individuals to 50% or more of total daily energy expenditure in highly active individuals.7-9 Hence its potential role in body weight regulation justifies our scrutiny.
The Amount of NEAT Performed by Humans
To systematically understand the amount of NEAT that subjects expend, we need to first quantify and define the energy efficiency of the non-exercise activity (Figure 3). The limited volume of data on total NEAT in humans can then be placed in perspective. It is difficult to gain true estimates of free-living individuals’ non-exercise activities because the variety of non-exercise activities is so enormous and the ability to quantify them so poor. What transpires from reviewing available data is, not surprisingly, that a variety of factors affect NEAT.
The impact of occupation on non-exercise activity can be overt. For example, compare the non-exercise activity of a laborer versus a civil servant.10-12 Here, activity levels vary severalfold. There are more subtle occupational effects on physical activity. For example, in many populations where women work both in home and out of the home, their cumulative work burden exceeds that of male cohabitants by several hours per day.13
The Concrete/Urban Environment
The importance of population sedentariness is well illustrated by studies of physical activity levels for individuals moving from agricultural communities to urban environments or of the effects of industrialization.14 In many populations where this has occurred, urbanization has been associated with decreased physical activity. Sedentary cues are unmistakable in developed countries, often through services designed to optimize convenience and throughput at the expense of necessitating locomotion. Examples include drive-through restaurants and banks, televisions, escalators, motorized walkways and clothes’ washing machines. In the United States, schools may be built out of walking reach of the community served, suburbs built without pavements, city streets felt to be unsafe for leisure walking or, playgrounds that are unsafe for children to play in.
Genetics may play a role in determining the amount of non-exercise physical activity performed.15 Based on twin and family studies, the heritability for physical activity level is estimated to be between 29 and 62%. Analysis of self-reports of physical activity from the Finnish Twin Registry, consisting of 1537 monozygotic and 3057 dizygotic twins, estimated a 62% heritability level for age-adjusted physical activity.16 Analyses of self-reported physical activity from the Quebec Family Study, consisting of 1610 members of 375 families, showed a heritability level of 29% for habitual physical activity.17 It is recognized that the boundaries between non-exercise and exercise-related energy expenditure may be poorly defined. Nonetheless, it is intriguing to speculate that genetics directly impact NEAT. Speculate that the twin of a laborer chooses to be a lumberjack rather than an office worker.
Studies have consistently shown a decline in physical activity with aging in men and women.18-20 Some data suggest that the “aging gap” is closing. During the period form 1986-1990, activity levels increased more in elderly subjects than in young adults.21
Overall, adult men and women in the U.S. report similar levels of total physical activity, although women are becoming more active.21,22 In other countries, such as Canada, England and Australia, men report being 1.5 to 3 times more active than women.22,23 In children, there are consistent gender differences with boys being more active than girls.24,25 Gender may influence physical activity in more subtle ways. For example, society and culture may dictate that women work both in the public domain and in the home. Where this occurs in agricultural communities, women’s energy needs were found to be 30% greater that predicted.15
There are substantial data to suggest that overweight individuals show lower activity levels than their lean counterparts.26,27 This appears to be true across all ages, for both genders, and for all ethnic groups. It is not possible to ascertain whether effects of body composition on non-exercise activities occur independent of weight.
Groups with more education consistently report more leisure time physical activity than groups with less education. In the United States, higher education groups are 2 to 3 times more likely to be active than low education groups.22,23,25 This contrasts with low-income countries, where child labor is commonplace. Here, poverty is predictive of greater child labor and the most impoverished children thereby have greatest NEAT levels.28,29
Seasonal Variations in Physical Activity
Limited data are available regarding differences in the amount of physical activity performed during different seasons. Who volitionally walks to work in the rain? Data from Canada suggest wide differences in time spent in physical activity due to season. Time spent in activity was twice as high during the summer months compared with winter months.30 Common sense dictates and data confirm that occupational-NEAT is greatly seasonally dependent in agricultural communities where workloads vary cyclically.31-33
Thus, although little objective data exist with respect to how much and what types of non-exercise activities people perform, it is clear that there are a variety of cultural/environmental factors that have an impact on physical activity. It is difficult, from existing data, to quantify the impact of NEAT versus exercise. However, what is clear is that NEAT is highly variable and dramatically affected by factors such as those discussed above and by a variety of other variables that are yet to be clearly defined. Also, genetics may subtly affect self-selected physical activity perhaps through job and/or leisure time choices.
The Energy Costs of Non-Exercise Activities
The other major determinant of NEAT is the energetic efficiency with which non-exercise activities are performed (Figures 3 and 4). It is recognized that even trivial movement is associated with substantial deviation in energy expenditure above resting values. For example, mastication is associated with deviations in energy expenditure of 20% above resting.34 Very low levels of physical activity such as fidgeting can increase energy expenditure above resting levels by 20 to 40%.35 It is not surprising then that ambulation, whereby body weight is supported and translocated, is associated with substantial excursions in energy expenditure.36 Even ambling or browsing in a store (walking at 1 mph) doubles energy expenditure while purposeful walking (2-3 mph) is associated with doubling or tripling of energy expenditure. When body translocation was logged using a triaxial accelerometer, the output from this unit correlated with nonresting energy expenditure.37 This implies that ambulation may be a key component of NEAT. The energy costs of a multitude of occupational and nonoccupational physical activities have been charted and tabulated.38,39 What is noteworthy is the manifold variance in the energy costs of occupation-dictated activities ranging from
Several Factors Affect the Energetic Efficiency of Physical Activity
Body weight. It requires more energy to move a larger body than a smaller one. Several investigators have demonstrated that the energy expended in physical activity during weight-bearing physical activity increases with increasing body weight.40,41 It is less clear whether work efficiency varies with body composition, independently of body weight. Some studies42-44 have found no differences in weight-corrected work efficiency between obese and non-obese subjects, while others45,46 have found a greater work efficiency in the obese.
Effects of changes in body weight. There is controversy as to whether work efficiency changes with weight loss. Several studies have reported that energetic efficiency is reduced following weight reduction. Foster et al.47 measured the energy cost of walking in 11 obese women before weight loss and at 9 and 22 weeks post weight loss. They determined that the energy cost of walking (after controlling for loss of body weight) decreased substantially by 22 weeks after weight loss. They estimated that with a 20% loss of body weight, subjects would expend about 427 kJ/hr less during walking than before weight loss. Geissler et al.48 compared energy expenditure during different physical activities and found that energy expenditure was about 15% lower in the post-obese compared with controls. DeBoer et al.49 found that sleeping metabolic rate declined appropriately for the decline in fat-free mass when obese subjects lost weight but that total energy expenditure declined more than expected for the change in fat-free mass. Similar results were obtained by Leibel et al.50 who speculated that increased work efficiency may be partially responsible for weight regain following weight loss.
Alternatively, Froidevaux et al.51 measured the energy cost of walking in 10 moderately obese women before and after weight loss and during refeeding. Total energy expended during treadmill walking declined with weight loss but was entirely explained by the decline in body mass. Net efficiency of walking did not change. Poole & Henson also found no change in efficiency of cycling after caloric restriction in moderately obese women.45 Weigle & Brunzell demonstrated that about 50% of the decline in energy expenditure with weight loss was eliminated when they replaced weight lost by energy restriction with external weight worn in a specially constructed vest.52
Thus, while it is clear that total energy expenditure declines with weight loss, the extent to which changes in work efficiency contribute to this decline is controversial. It is an important question because if work efficiency truly changes, it implies that a mechanism may exist to define the work efficiency of NEAT activities and may have an impact on energy balance.
Role of skeletal muscle metabolism in determining work efficiency. Differences in skeletal muscle morphology/metabolism may play a role in differences in work efficiency. Henriksson53 suggested that changes in muscle morphology in response to energy restriction leads to changes in the relative proportion of Type I vs. Type II fibers in human subjects. Some studies suggest that Type II fibers have a greater fuel economy than Type I fibers.54,55 Since Type II fibers appear to be better preserved during starvation than Type I fibers,55 overall fuel economy and work efficiency may increase following energy restriction and loss of body mass. However, a recent study on muscle fiber type before and after an 11-kg weight loss in obese females did not show any changes in the fiber type distribution.56
The potential contribution of skeletal muscle differences to differences in work efficiency between weight stable lean and obese subjects is more controversial. Data suggest that obese subjects oxidize proportionally more carbohydrate and less fat than lean subjects in response to perturbations in energy balance57-59 and that differences in morphology/metabolism of skeletal muscle and sympathetic nervous system activity60 may underlie some of the whole body differences.61 However, it is not clear to what extent such differences contribute to differences in work efficiency. Further, such differences may arise from genetic and environmental causes.
Genetic contributions to work efficiency. Very little information is available to allow estimation of the genetic contribution to differences in work efficiency. When the energy costs associated with common body postures (sitting, standing) and low-intensity activities (walking, stair climbing, etc.) were measured in 22 pairs of dizygotic and 31 pairs of monozygotic, sedentary twins there was a significant genetic effect for energy expenditure for low intensity activities (from 50 to 150 watts) even after correction for differences in body weight.62 No genetic effect was seen for activities requiring energy expenditure greater than six METs. These observations hint at an intriguing possibility, namely that the efficiency of NEAT activities may be genetically programmed.
Age and work efficiency. Work efficiency for NEAT activities may vary with age. For example,63 children are about 10% more energy efficient during squatting exercises than adults. However, there is little information available to evaluate the effects of aging on work efficiency. Skeletal muscle mass is often lost as a subject ages and if the loss involves a greater proportion of Type I vs. Type II fibers, work efficiency could increase with age.
Exercise training and work efficiency. If the work efficiency of NEAT activities varies as a function of muscle morphology, exercised-induced effects in skeletal muscle could be important for NEAT. Alterations in exercise can alter the fiber type proportions of skeletal muscle as well as induce significant changes in enzyme activities. Aerobic exercise training results primarily in the transformation of Type IIb into Type IIa fibers, while transformation of Type II fibers into Type I fibers is not common unless the exercise training has been extremely intense over a long period of time. Type I fibers have a greater mitochondrial density, are more oxidative and more fatigue-resistant than Type IIb fibers. Type IIb fibers are glycolytic in nature with lower mitochondrial content and are more prone to fatigue. Type IIa fibers are intermediate in their mitochondrial content and, in humans, closely resemble Type I fibers in oxidative capacity. However, an overlap of oxidative capacity exists between fiber types groups. Type I and Type IIa fibers are more energy efficient than Type IIb fibers and the proportions of these fiber types will vary according to the type of exercise training performed. It has been shown that even independently of fiber type alterations, the activities of important enzymes in oxidative and glycolytic pathways can be modified as a result of exercise training, and can lead to improvements in metabolic efficiency. Training may increase work efficiency, as elite runners and cyclists average lower energy expenditures (15% for running and up to 50% for swimming) at specified velocities compared with untrained individuals,64-66 possibly because exercise directly effects NEAT through changed work efficiency.
Gender and work efficiency. There are several reports that female athletes, unlike male athletes, are more energy efficient than their sedentary counterparts.67-69 Reports in the literature have shown increased energy efficiency in female runners,69 dancers70 and swimmers71 as compared with sedentary females. Most reports make conclusions regarding energetic efficiency based on indirect rather than direct measurements of energy intake and/or expenditure. For example, Mulligan & Butterfield69 concluded that female runners had increased energy efficiency because their self-reported energy intake was less than their estimate energy expenditure. However, in the few studies in which both intake and expenditure were measured directly, no evidence of increased energy efficiency was seen in female runners70 or cyclists.71 Thus, the question of whether female athletes show a different energy efficiency than sedentary females is controversial. Whether there are inherent gender differences for the efficiency of non-exercise activities is open to speculation but could readily be studied.
Total NEAT in Humans and its Variability
Data readily demonstrate the marked variance in NEAT. Black et al.72 reviewed physical activity level (PAL) values from 574 measurements of total energy expenditure made using doubly labeled water in individuals from affluent societies. It was clear that PAL values varied two- to threefold. Lifestyle and cultural milieu were implicated as major predictors of NEAT and its variability (Table 1).
Impressively, these figures echo those derived from lower income societies (Table 2).33,73 Further insight into total NEAT comes from Westerterp’s observation that in free-living individuals, the cumulative impact of low-intensity activities over greater duration is of greater energetic impact than short bursts of high-intensity physical activities.74 Thus, for a given individual, NEAT is defined by sum energetic cost of occupational plus non-occupational activities, which in turn is influenced by the sedentariness of the individual’s microenvironment (e.g., workplace) and macroenvironment (e.g., country). It is fascinating to speculate that a person with a “high programmed NEAT” might select an active job (e.g., car washing or ambulatory mail delivery) despite living in a sedentary country such as the United States.
The Role of NEAT in Physiology
It is not surprising that NEAT should play a role in human energy homeostasis. NEAT is the key predictor of non-BMR energy expenditure and BMR is largely predicted by body size or lean body mass. NEAT then becomes the crucial component of energy expenditure that is most variable and least predictable. Consider the energy expenditure of a person who works as a road layer but then becomes a secretary. For this example, it is self-evident that variations in NEAT can result in severalfold variations in total energy expenditure independent of body size. What is not self-evident is whether changes in NEAT contribute to the mechanism by which adipose tissue accumulates.
Changes in NEAT with Positive Energy Balance
Several studies have employed an overfeeding paradigm to determine whether energy expenditure changes during forcible overfeeding. On balance, these studies have demonstrated that as over-feeding occurs, NEAT increases.75 In one such study, 12 pairs of twins were overfed by 1000 kcal/day above estimated resting needs. There was a fourfold variation in weight gain, which had to represent substantial variance in energy expenditure modulation because food intake was clamped. This variance in energy expenditure response could not be accounted for by changes in resting energy expenditure alone and so indirectly, NEAT is implicated. It is fascinating to note that twinness accounted for some of the interindividual variance in weight gain. This suggests that NEAT responsiveness with overfeeding is in part genetically determined. NEAT was directly implicated in the physiology of weight gain when 16 sedentary, lean individuals were carefully overfed by 1000 kcal/day.76 All components of energy expenditure and body composition were carefully determined. There was a tenfold variation in fat gain and an eightfold variation in changes in NEAT. Those individuals who increased their NEAT the most gained the least fat with overfeeding, and those individuals who failed to increase their NEAT with overfeeding gained the most fat (Figure 5). Studies are too sparse to define how changes in the amount of non-exercise activity interplay with changes in energy efficiency; the bulk of evidence suggests that increases in the amount of physical activity predominate. Changes in BMR and TEF were not predictive of changes in fat gain. These data strongly imply that NEAT may counterbalance fat gain with positive energy balance when appetite is clamped.
Changes if NEAT with Negative Energy Balance
With underfeeding, physical activity and NEAT decrease. Chronic starvation is known to be associated with decreased physical activity.77-79 Whether those individuals who are susceptible to ready fat loss are those who fail to decrease NEAT with underfeeding has not been established. However, let us argue that with a prolonged energy deficit of 500 to 600 kcal/day, BMR decreases by ~10% (i.e., ~200 kcal/day, assuming a sustained decrease in LBM that may not actually occur),80-87 and TEF decreases by ~0 to 50 kcal/day.88,89 Hence, NEAT has to decrease by ~200 to 300 kcal/day once fat loss reaches a plateau. In one study with severe energy reduction (800 kcal/day),52 decreases in NEAT likely accounted for 33% of the decrease in total daily energy expenditure (TDEE) in lean subjects, 46% in obese subjects with 10% weight loss and 51% in obese subjects with 20% weight loss.52 If NEAT decreases with negative energy balance, is it because the quantity of physical activities decrease, or that energy efficiency decreases-or both? Studies to date have not definitively answered this question. With severe energy reduction (420 kcal/day) in obesity, VO^sub 2max^ and energy expenditure at submaximal loads may decrease,90 although with less restrictive energy restriction, VO^sub 2max^ appears unchanged.91 Thus, the balance of information suggests that NEAT decreases with negative energy balance. It is unclear whether the effect is through decreased amounts of activity, altered energetic efficiency, or both.
Overall, it appears that NEAT increases with weight gain and decrease with weight loss. This creates an intriguing scenario whereby NEAT might act to counterbalance shifts in energy balance. It could be that these changes in NEAT, along with those that affect BMR and TEF, are small and swamped by changes in energy intake. However, consider that some subjects overfed by 1000 kcal/day increased NEAT by >600 kcal/day. This argues that changes in energy expenditure and NEAT may be quantitatively important in the physiology of body weight regulation. This in turn raises the question as to whether NEAT contributes to pathologic perturbations in energy balance.
The Role of NEAT in Disease: Does NEAT Contribute to Obesity and/or Undernutrition?
NEAT and Obesity
The role of low levels of NEAT in the pathogenesis of obesity in individuals is difficult to support with direct data. The evidence that links low levels of NEAT with obesity genesis is indirect and primarily derived from population sources. Epidemiologic studies have consistently shown a negative relationship between measures of physical activity (usually self-reports) and indices of obesity (usually, body mass index [BMI]).92 This relationship is present in most data sets obtained from the U.S. population.93 The relationship appears to be similar in men and women, and across all ages.94-97 Further, there is evidence for a similar relationship in AfricanAmericans,98 Hispanics,99 and Native Americans.100 This inverse relationship between physical activity and BMI has been seen using both self-reports of amount of physical activity95 and actual measurements made using doubly labeled water.101-103
Despite the lack of a definite prospective longitudinal study, there is evidence that low levels of physical activity may be contributing to the increasing incidence of obesity in the U.S. population. In several cohort studies where indices of obesity over time were assessed without intervention, associations between low levels of physical activity and indices of obesity were found.104-106 In these studies, baseline measures of physical activity were inversely related to BMI. In some, low levels of physical activity predicted high weight gain over the follow-up period and in some, decreases in physical activity over time were associated with greater weight gain.106-108
As physical activity declines in both affluent and nonaffluent populations, obesity increases. It is unclear whether declines in NEAT, volitional exercise or both is/are to blame. Let us divide total physical activity into that activity performed intentionally during leisure time (leisure time physical activity [LTPA]) or activity performed in daily living (lifestyle physical activity [LSPA]), which is akin to NEAT. Are there clear secular trends in LTPA and LSPA?
While there are ample data indicating that the vast majority of Americans get little or no LTPA, there is a scarcity of evidence that this has changed dramatically over the past two decades.107-110 Attractive sedentary activities such as television watching, video games and home-computer use now compete for LTPA. However, it is not possible with existing data to conclude that substantial decreases in LTPA have occurred simultaneously with the onset of the obesity epidemic. Thus, it is likely that declines in LSPA (or NEAT) have contributed to the increased prevalence of obesity, but unfortunately it is difficult to quantify this contribution. While most obesity experts accept that technological advances have reduced the amount of LSPA required, this decline has not been documented well enough to allow quantification of the changes.
In fact, it is only in recent years that attempts have begun to measure LSPA. All indications are that work-related physical activity has declined. However, the only prospective data available come from Finland, where a 225 kJ/day decline in work-related physical activity has occurred over 10 years.113 Similarly, there is reason to believe that other forms of energy expended in activities of daily living have declined rapidly over the past two or three decades. One can, for example, estimate the energy savings due to proliferation of televisions and computers, remote control devices, microwave ovens and increased use of prepared foods. While each may reduce physical activity only slightly, together these energy savings accumulate and can have a significant impact upon total energy expenditure. Declines in energy expended for transportation has also likely declined in recent years. This can best be illustrated with data from the National Personal Transportation Survey.111 In the United States, the number of annual walking trips between 1990 and 1995 had declined 12%, while the number of daily car trips had increased by nearly an identical amount. To better understand the role of NEAT in obesity, it is helpful to appreciate that the sum of a multitude of small changes76 in physical activity all have an impact on NEAT, energy balance and weight gain.
The World Health Organization (WHO) has declared obesity as a world epidemic.112 In many developed countries one third, or even more of the population, are obese-using the definition of obesity as being a body mass index of > or =30 kg/m^sup 2^. In the United States, for example, more than half of the population is either overweight or obese and the problem is worsening.95 Other countries, such as the United Kingdom, are not far behind.113 Obesity carries with it enormous health implications associated with the metabolic comorbiditics, mechanical complications and cancer.114,115 Metabolic comorbidities include diabetes, hypertension, hyperlipidemia and coronary artery disease. Mechanical complications include arthritis, sleep disorders, carpal tunnel syndrome, edema and varicose veins. Obesity-related cancers include colon and breast cancer. Not only are there enormous health consequences for nations where obesity is commonplace, but there are also devastating financial consequences. It has been crudely estimated that obesity costs the United States more than 65 billion dollars per year. There are enormous health and financial implications of an obesity epidemic, and once obesity is established in a population, it has proven impossible to eradicate it or even significantly affect it. For the overwhelming majority (>95%) of obese patients who seek help from their doctors, obesity is not cured.116 Thus, it behooves us as a scientific and healthcare community to prevent obesity from taking hold in populations where it is relatively rare at present. Many South American and Asian countries fall under this category. Of concern is that sparse objective data suggest that obesity is becoming more common and this is of even greater concern in populations particularly prone to diabetes, such as Singapore. In these countries, one can perform crude modeling to predict that if obesity rates start to increase sharply, diabetes could soon have an impact on more than a quarter of the population. Thus, in countries where obesity is starting to emerge and prevalence increase, it is time to act. In many South American and Asian countries, now is that time.
NEAT in Undernutrition
Having established that NEAT decreases during negative energy balance, how does NEAT influence chronic starvation?
In disease-associated starvation, such as restrictive-type anorexia nervosa, patients have been noted to use several maneuvers to accentuate negative energy balance through increased NEAT.117 Examples of such activities include fidgetiness, excessive gum chewing and carrying heavy book bags.
There are 800 million persons at fear of starvation worldwide.118 One wonders how NEAT affects this global energy balance issue and thus, a population’s energy needs. Measured PAL values75 have provided information regarding the energy expenditure associated with different occupations and the impacts of culture, gender and physical states such as pregnancy and lactation.74,104 This information has proven invaluable in terms of understanding the energy requirements of individuals and has, in part, guided, how statutory agencies such as the UN and WHO have defined population energy needs.119 What is becoming clear is that the greater the quality of data on NEAT for a given population, the better the estimates will be of that population’s energy needs.
An example of this concept is illustrated by studies conducted in Côte D’Ivoire agricultural communities. NEAT was meticulously measured using the factoral method in 3352 women and men. It was determined that the women worked 2 to 3 hours more per day than men did because of the combined work burden of domestic and agricultural tasks that the women performed. This translated to the women’s energy needs being 30% greater than UNAVHO predictions. In Gambia, doubly labeled water was used to measure NEAT in rural Gambian women who worked both in the home and in agriculture during the peak agricultural season.33 These women also show greater NEAT than anticipated and to a similar degree. They also proved to be in negative energy balance and showed weight loss. These studies illustrate how culture, gender, occupation and energy supply interplay to impact NEAT and, in turn, energy balance in starvation-threatened communities.
It has been repeatedly and consistently acknowledged that data on NEAT are crucial for better understanding the energy needs of starvation-threatened individuals120 With judicious use of doubly labeled water, indirect calorimetry and kinematic sensors, enormous advance is feasible in this arena.
The Measurement of NEAT
To understand the potential role of NEAT in human energy balance, one must first appreciate the strengths and limitations of available techniques.
First, little information is available regarding the time period of measurement needed to gain a representative assessment of NEAT. Approximately 7 days120 of measurement is likely to provide a representative assessment of activity thermogenesis for a given 3 or 4 month block of time. Such 7-day measurements can potentially be repeated to understand the importance of variables such as season or changing occupational roles.
Broadly, NEAT can be measured by one of two approaches. The first approach is to measure or estimate total NEAT. Here, total daily energy expenditure is measured and from it, BMR-plus-TEF is subtracted. The second approach is the factoral approach whereby the components of NEAT are quantified and total NEAT calculated by summing these components. Each approach will now be discussed.
Measurement or Estimation of Total NEAT
To measure or estimate total NEAT in a sedentary person, the following formula is applied:
NEAT = total daily energy expenditure
– (basal metabolic rate + thermic effect of food)
To complete this calculation, one needs data on total daily energy expenditure (TDEE), BMR and TEF. TDEE can be directly measured using a room calorimeter whereby either gas exchange and/or heat loss are measured in a person confined to a small (e.g., 12 m^sup 2^) room for a day. These measurements of TDEE are enormously limited because subjects are confined within the room/chamber for the measurement duration and so cannot perform their normal daily activities.11,121
Thus, if the O2 in body water is tagged with the tracer O^sup 18^, the label will distribute not only in body water but also in circulating H^sub 2^CO^sub 3^ and expired CO2. Over time, the concentration of the O2 label in body water will decrease as CO2 is expired and body water is lost in urine, perspiration and respiration. If the hydrogen molecule in body water is tagged with deuterium (D), the label will distribute solely in the circulating H2O and H^sub 2^CO^sub 3^. Over time, the concentration of the D^sub 2^ label will decrease, as body water is lost (some of the hydrogen can become portioned into body protein or fat, however). Thus, if both the O2 and H2 in body water are tagged with known amounts of tracers at the same time, the differences in the elimination rates of the O2 and H2 tracers will represent the elimination rate of CO2. Subjects are usually given doubly labeled water orally after baseline samples of urine, saliva or blood have been collected. Time is allowed for complete mixing of isotopes to occur within the body water space and then samples of urine, saliva or blood are collected over 7 to 21 days. These samples are used for measurements of D^sub 2^ and O^sup 18^ enrichments using mass spectroscopy. Changes in D^sub 2^ and O^sup 18^ concentrations in body water are then calculated over time and CO2 production and energy expenditure thereby determined. Energy expenditure can be measured over 7 to 21 days using doubly labeled water with an error of ~6 to 8%. This error can be decreased to a small degree, by collecting samples repeatedly over the measurement period rather than by collecting samples only before and after the measurement period.
Having measured TDEE, determinations of BMR and TEF are needed to calculate NEAT. BMR is invariably measured using an indirect calorimeter, whereby oxygen consumption, carbon dioxide production or both of these variables are measured. Energy expenditure is then calculated by means of established formulae.126,127 Indirect calorimeters vary in sophistication and cost.128 In the laboratory, ventilated open circuit calorimeters are most often used. Here expired air is collected by means of a mouthpiece, mask, hood or from a sealed chamher.37,124 The air is then mixed, the rate of flow is measured and oxygen and/or carbon dioxide concentrations are determined. Measurements to within 1% of chemical standards can be achieved using these devices. In the field, Douglas bags or portable expiratory open circuit calorimeters can be used. The Douglas bag129-132 comprises a polyvinyl chloride (or other leak proof material) bag of ~100 to 150 liter.133 After collection of the expired air, the volume of expired air in the bag is measured and a sample is analyzed to determine oxygen and/or carbon dioxide concentrations. The technique is highly operator dependent and under optimal conditions the error of energy expenditure measurements undertaken with Douglas Bags can be small (
Where calorimeters are not available, BMR can be estimated by calculation as it tracks well to body size. A variety of age-, gender- and population-specific formulae have been published specifically for this.14 Caution is advised when such formulae are applied because BMR is a numerically large component of NEAT and body weight only accounts for about three-quarters of the variance in BMR. Hence, numerically important errors may be introduced by such an approach.
TEF is often not measured but rather estimated or ignored when determining NEAT. TEF can be measured by providing a subject with a meal (we use a third of the subject’s daily weight-maintenance needs). The energy expenditure in response to meal is calculated from the area under the time versus energy expenditure curve (Figure 2). The area-under-the-curve for this meal is then multiplied by three (in this laboratory) to give total TEF/day. Other investigators multiple TDEE by 0.10 to provide a crude estimate of NEAT. Alternatively, NEAT is ignored and assumed to be a non-crucial variable because it is numerically small.
Thus, it is possible to directly measure total NEAT. TDEE can be measured even in free-living individuals using doubly labeled water. BMR + TEF optimally should be measured using an indirect calorimeter, but can be estimated if necessary. When TEF has not been measured, TDEE is frequently expressed relative to BMR to provide an index of physical activity. The physical activity level (PAL) is TDEE divided by BMR. PAL values (and similar indices) are important, as they are often used to compare physical activity between population and population subgroups. It should be noted that PAL corrects TDEE for body size because of the aforementioned relationship of BMR to body weight. For sedentary subjects, the PAL is approximately 1.5, but this can increase to around 3.5 to 4.5 under conditions of extreme NEAT. It is impressive that the cumulative error for measurements of PAL can be ~7%, considering that the measurements are performed in free-living, unrestrained individuals. The major limitation of measuring total NEAT or PAL is that no information is obtained regarding the components of NEAT. It is therefore difficult to implicate specific mechanisms from one total NEAT or PAL.
The Factoral Approach to Measuring NEAT
This is a frequently used approach for estimating NEAT in free-living individuals. First, a subject’s physical activities are logged over the time period of interest (e.g., 7 days). The energy equivalent of each of these activities is determined. The time spent in each activity is then multiplied by the energy equivalent for that activity. These values are then totaled to derive an estimate of NEAT. The advantage of this approach is that the components of NEAT can be defined.
There are two pivotal issues when using the factoral method to measure NEAT. First, how accurate are the activity logs? Second, how accurate/representative are the determinations of the energy costs of the activities?
Quantifying physical activities. Nonspecific information about habitual and occupational activity can be obtained using questionnaires, interviews or time-and-motion studies. Predictably, substantial errors are introduced through inaccurate recall and inadequate data recording. These approaches can be applied for following trends in certain activities particularly with relation to occupational practices.138
Activity diaries are often used to record the nature and amount of time spent performing activities over the period of interest.35 This has several limitations. For example, subjects may show variable literary and/or innumeracy, they may report their activities inaccurately or incompletely, and they may alter their normal activity patterns during periods of assessment. To limit these sources of error, one approach is to have trained enumerators follow subjects and objectively record subjects’ activities. This is time consuming and expensive but potentially a valuable source of accurate and objective data.
Alternatively, instrumentation can be used to log or quantify human activities. A variety of such instruments are available. Some kinematic techniques are specific for use in confined spaces such as radar tracking, floor pressure-pad displacement and cine photography.139,140 These instruments are precise but subjects are confined and so normal daily activities are impossible. Other kinematic techniques have been used in free-living individuals and generally focus on pedometers and accelerometers of varying sophistication. Pedometers typically detect the displacement of a subject with each stride. However, pedometers tend to lack sensitivity because they do not quantify stride length or total body displacement. Overall, pedometer output is poorly predictive of NEAT141 but potentially of value for quantifying walking (e.g., to determine compliance with a walking program). Accelerometers detect body displacement electronically with varying degrees of sensitivity; uniaxial accelerometers in one axis and triaxial accelerometers in three axes. Portable uniaxial accelerometer units have been widely used to quantify non-exercise activity. 142-144 However, these instruments are not sufficiently sensitive to quantify the physical activity of a given free-living individual but rather are of potential value for comparing activity levels between groups. Greater precision has been obtained using triaxial accelerometers.145-147 In free-living subjects, data from these devices correlate reasonably well with total daily energy expenditure, measured using doubly labeled water and divided by BMR (i.e., PAL values).39 Kinematic approaches can be more sophisticated still. Data on movement-gathered using a triaxial accelerometer-and body position-determined using inclinometers-can be combined in free-living people to further characterize human activities every half-second and capture >80% of NEAT.148
A host of newer technologies are under development that may aid in quantifying NEAT. For example, the utility of motion tracking using a Global Positioning System (GPS) has not been fully defined for human studies although is limited because GPS does not work indoors and has a precision of about 3 m. GPS will require further evaluation and validation before its role in measuring NEAT is defined.
Thus, it is possible to quantify physical activity using one or several tools of variable precision and sophistication. What is essential is that the appropriate and feasible tool be used to address the hypothesis of interest in the population under study.
Measuring the energy cost of non-exercise activities. The energy costs of non-exercise activities are readily measurable, although published tables that list the energetic costs of physical activities are often used instead.
To measure the energy cost of a given physical activity, an indirect calorimeter is most often used, as described above. The configurations most apt are the ventilated hood, Douglas Bag and portable calorimeter systems. Instead of the measurement being performed at rest, it is performed during the activity of interest. Reliable and precise measurements both in the laboratory and field settings are thereby obtainable. At best, the energy costs for each of a subject’s activities would be measured. This is rarely practical except in studies with very few subjects.
The tables that list the energetic costs of physical activities are inexpensive and convenient.40,41 However, substantial, albeit systematic, errors can be introduced. First, the tables may not include the precise activity the subject performed. Second, the energy cost for a given activity may be highly variable between subjects even independent of gender and weight. Third, calorimeter methods for measuring the energy costs of activities have not been standardized between investigators so that precision and accuracy of data in the activity tables cannot always be assured. To limit the errors introduced by activity tables, population-gender-age specific group means for the energy costs of the majority of the studied subjects’ activities should be available.
Because of the difficulty measuring total NEAT and/or its components, o little information is available regarding the role of NEAT in physiology or human health. While it would be possible to combine the total and factoral approaches to better clarify the impact of NEAT on human energetics and health, engaging in such endeavors necessitates investigator and capitol investment.
The Mechanism of NEAT
Very little is known about the mechanism by which NEAT is regulated, for several reasons. First, very little data are available regarding the physiologic modulation of NEAT. Second, despite the evidence that NEAT is altered with changing energy balance, no information is available with respect to which components of NEAT are specifically altered. It is not known which components of NEAT predominate, nor which components predominantly change during fluxes of energy balance. In the absence of this information it has not been possible to further elucidate the mechanism by which NEAT has an impact on energy expenditure and energy balance. Third, there is ample evidence to demonstrate the impact of environment and culture on NEAT. Hence, in the minds of some, effort may not be warranted to define the biological mechanism by which NEAT is modulated. Fourth, understanding that NEAT represents the energy expenditure associated with spontaneous physical activity, the concept of a unifying mechanism by which NEAT is driven is difficult to grasp. Thus, for a host of reasons, very little is known about the mechanism driving NEAT.
Is there sufficient evidence that NEAT is modulated in physiology to warrant resource allocation to better understand its mechanism? On balance, it appears that NEAT is modulated during shifts in energy balance. The strong negative correlation between increases in NEAT and fat gain during overfeeding supports this contention, as do the consistent studies demonstrating that physical activity and NEAT decrease during negative energy balance.
How might one begin to investigate the mechanism by which NEAT is modulated? A simple starting point might be to understand its components. For example, if future studies showed that during positive energy balance, ambulation energy expenditure increases and accounted for the vast majority of the changes in NEAT, this might suggest that the mechanism that drives ambulation energy expenditure is pivotal for understanding NEAT. One might then want to define whether it is the amount or the energy efficiency of walking that is crucial; both may occur together. Thus, by systematically evaluating NEAT and its components, the mechanism of NEAT may become clearer.
The next question is one of concept. Is it conceivable that there are any putative moderators of NEAT? Here one might divide potential mechanisms into (a) central mediators of NEAT, (b) hormonal mediators of NEAT, and (c) peripheral signals of NEAT. An example of a central mediator of NEAT are the orexins.149,150 These are neuromediators associated with arousal. Injecting Orexin A into rodent hypothalamic nuclei increases NEAT in the short term. Interestingly, leptin was reported to increase physical activity (by observation) when injected in Ob/Ob mice.151,152 However, this more likely stems from rendering completely immobile mice-mobile though decreasing adiposity. In humans, increasing NEAT levels do not mirror increasing leptin levels.153 Hormonal mediators of NEAT have been identified. Thyroxin hormone excess in humans is associated with increased spontaneous physical activity.154 Also, the sympathetic nervous system155 has the potential to affect NEAT. Peripheral signals of NEAT are hard to identify. Experience dictates in many though, that a long bout of exercise is often associated with hunger and tiredness. Direct evidence of a peripheral signal of NEAT is lacking at this time, however. Thus, albeit crudely, examples do exist whereby central and noncentral mediators of spontaneous physical activity have been identified.
To date, little is known regarding the mechanism by which NEAT is integrated. This is due to the paucity of data regarding how NEAT and its components are modulated in physiology. However, as information becomes available, hypothesis-driven research will allow a further elucidation of the mechanism of NEAT. It is intriguing to speculate that there are specific neuromediators of NEAT, and that NEAT may be a carefully regulated ‘tank’ of physical activity that is crucial for weight control.
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James A. Levine, M.D.
Dr. Levine is with the Endocrine Research Unit, Mayo Clinic, Rochester, MN, 55905, USA.
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