Between 1991 and 1998, the prevalence of obesity, defined as body mass index (BMI) ≥ 30 kg/m2, increased in the United States by nearly 50% (1), and 55% of adults are now classified as overweight or obese (BMI ≥ 25 kg/m2) (2). The etiology of the rising prevalence of obesity is unclear, although there is increasing evidence to suggest that reduced physical activity may play a major role (3, 4, 5, 6, 7). Accordingly, the Surgeon General has strongly emphasized the importance of and the need to increase the proportion of the general U. S. population who spontaneously engage in physical activity (8). Part of the problem in understanding the role of physical activity in the rising prevalence of obesity is that longitudinal trends survey data provide only self-reported leisure-time physical activity, rather than total daily physical activity (8).
Measures Based on Energy Expenditure or Oxygen Uptake
Activity energy expenditure (AEE) is a direct measure of the energy cost of physical activity, which is particularly useful in studies of energy balance. AEE may be used to measure the energy cost of a specific exercise task (as kcal/min or kJ/min), or it may be used to estimate a subject's average EE during nonresting periods, using the equation: AEE (kcal/d) = total daily EE (TEE; kcal/d) − resting EE (REE; kcal/d). AEE can also be estimated taking into account an average thermic effect of food of 10%, which is considered in the formula by some investigators: AEE (kcal/d) = 0.9 · TEE (kcal/d) − REE (kcal/d).
AEE is influenced by body weight (impacting the energy cost of moving one's body mass) and by the economy or efficiency of performing exercise tasks. Thus, AEE does not necessarily reflect the intensity of activity performed, and it does not permit comparison of the amounts or duration of activity performed by different individuals.
Because it is difficult to compare values of AEE between individuals of different body mass, in some analyses AEE has been adjusted for body weight (13). A recent refinement of this approach is activity-related time equivalent (ARTEEE), which corrects AEE not only for body weight but also for the economy of performing physical activities (14). ARTEEE is an index of the amount of time a person spends at a level of EE equivalent to that of a reference activity or set of activities, using the equation: ARTEEE index (min/d) = (TEE [kcal/d] × 0.9 − REE [kcal/d])/(reference activity EE [kcal/min] − REE [kcal/min]). The numerator is AEE per day, except that TEE is multiplied by 0.9 to adjust for an average thermic effect of food of 10%. TEE can be assessed using doubly labeled water (DLW) or chamber calorimetry. The denominator is AEE per minute for the reference activity task(s), i.e., the average, above-rest energy cost of performing standardized exercises. Because the energy cost of exercise is measured on each subject, the denominator takes into account the contribution of the subject's weight and economy of movement to the energy cost of performing the tasks. The exercise tasks are performed in the laboratory and are selected to reflect typical activities of subjects in free-living conditions, e.g., walking, stair climbing, walking carrying a small load, etc. ARTEEE is particularly useful for expressing the amount of time each day one expends an amount of energy equivalent to that of the reference activity and enables comparisons among subjects with different body weights and different exercise energy economies, as has been found in black vs. white women (14). The index is expensive to assess and, hence, its usefulness is limited to studies of small groups.
A commonly used alternative to AEE is to express, as a ratio, the energy cost of a sustained exercise task relative to REE. Examples of such EE-based indices include physical activity level (PALEE), metabolic equivalent (METEE), and physical activity ratio (PAREE). PALEE expresses total daily EE relative to basal or REE, thereby providing an index of the average relative excess energy output consequent to physical activity (i.e., intensity × duration) over a typical 24-hour period. TEE is commonly derived using the DLW technique (15). Based on this objective measurement of EE, the validity of PALEE has been tested and confirmed in a large subject sample (16). When derived from DLW data, the index is relatively accurate but expensive to assess and, hence, is best suited for small-group studies dependent on objective measures of EE. A less expensive approach is to estimate TEE using HR data (discussed in the next section). Individual variability in the thermic effect of food and in exercise energy economy is not taken into account in the PALEE index. Furthermore, the denominator, REE, is generally measured over a period of 30 minutes or less, whereas the numerator, TEE, is measured over periods of either 24 hours (using chamber calorimetry) or 1 to 2 weeks (using DLW). Hence, small day-to-day variations in REE will result in disproportionately large variations in the estimates of physical activity level.
For the purposes of large population studies, PALEE has been calculated by dividing total energy intake by an estimate of REE (17). In this approach, energy intake serves as a surrogate of TEE and, hence, assumes that the study subjects are in energy balance when energy intake is assessed. REE is predicted from subject characteristics, including sex, weight, height, and age. PALEE can also be used to estimate total daily energy requirements of a population by assuming an average physical activity level for the group under study (18). For example, a light physical activity level equivalent to a PALEE of 1.56 would predict an average energy requirement of ∼2000 kcal/d for women weighing 55 kg (18). In a related manner, the index has been used to verify the accuracy of self-reported energy intake (19), i.e., below a certain threshold (physical activity level × 1.2), the index suggests that energy intake is underestimated.
Although previously unreported in the literature, a potentially useful variation of the PALEE index is daytime physical activity level (PALEEday), which provides a description of the average intensity of physical activity over the nonsleeping period. PALEEday is based on assessment of EE during daytime awake hours only, excluding the EE of sleep, because the duration and quality of sleep may vary among subjects. PALEE and PALEEday provide potentially useful but different information. Consider an example of an individual with the same level of daytime physical activity but different periods of sleep (case 1 vs. case 2; Figure 1). With the shorter daytime period of activity and the longer duration of sleep, the value of PALEE drops from 1.5 to 1.4. Although accurately reflecting the lower 24-hour average activity level, the reduced PALEE value makes the individual seem to have been less physically active while awake. By contrast, PALEEday gives an activity level that is unchanged at 1.7. For an individual with a longer period of sleep, but who is more active for a shorter daytime period (case 1 vs. case 3), PALEE remains unchanged at 1.5, whereas PALEEday is increased from 1.7 to 1.8, reflecting the more active daytime period. An obvious limitation of the PALEEday index is obtaining data on daytime EE. This would be best derived from chamber calorimetry, using only the daytime period, or from HR monitoring (see PALHRday below). However, whole-body calorimeters are not suited for assessing free-living EE and small portable indirect calorimeters using a face mask to collect expired gas impose a restriction for EE measurements of several hours duration.
Figure 1. Examples of how PALEE can provide different types of information than PALEEday. In a situation in which the period of sleep is longer but daytime physical activity is unchanged (case 1 vs. case 2), PALEE indicates a lower activity level, whereas PALEEday remains unchanged. In a situation in which the period of sleep is longer but daytime physical activity is increased (case 1 vs. case 3), PALEE indicates no change in activity level, whereas PALEEday is increased.
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The METEE is defined as the ratio of work metabolic rate to a standard resting metabolic rate and describes the relative intensity of exercise tasks performed in the steady state. One METEE is typically considered the resting metabolic rate of a person while sitting quietly (20). The index is generally used by exercise physiologists to express the oxygen uptake or intensity of various activities as multiples of the resting METEE level. It is useful for describing and prescribing exercises of different intensities (21). The Compendium of Physical Activities (22) describes various daily physical activities according to their METEE levels, with activity intensities ranging from 0.9 METEE (sleeping) to 18 METEE (running at 10.9 mph). Such a classification system is particularly useful for epidemiological studies, wherein METEE scores can be ascribed to individuals according to their self-reported physical activity levels and then related to particular health risk outcomes (23). The actual energy cost of an activity will vary between individuals due to a number of factors, such as body mass, adiposity, age, sex, and environmental conditions (22). To allow for variations in body weight, the METEE is generally expressed in terms of oxygen uptake per unit body mass, with 1 METEE equivalent to ∼3.5 mL O2/kg × min (21). Because 5 kcal is approximately equal to 1 L of oxygen consumed, 1 METEE is equivalent to ∼1 kcal/kg × h or 4.184 kJ/kg × h. A more accurate estimate of oxygen uptake can be obtained by indirect calorimetry.
The index PAREE is defined as the per-minute energy cost of performing a physical activity relative to the person's per-minute REE (18). Thus, it is essentially identical to METEE, except that REE is usually measured rather than estimated. Typically, PAREE is used in nutrition studies to compare the energy cost of various physical activities (24).
To demonstrate the types of information and typical values obtained from various physical activity measures, we have ascribed values to each measure based on two hypothetical subjects, one lean and one obese (Table 2). Although the subjects are hypothetical, their physiological patterns are based on data obtained from never-obese and overweight subjects with comparable characteristics studied in our laboratory (14, 25). Among the EE-based physical activity indexes, several results are noteworthy. Unadjusted for body weight, AEE was found to be similar in the two subjects despite the fact that subject 2 is less physically active. In fact, it seems that the majority of obese subjects are moderately active and that an increase in the activity level of obese subjects is limited by the ability to perform exercise of higher intensity.
Table 2. Representative values obtained for currently available and proposed new measures to assess physical activity: values are based on two hypothetical subjects of the same sex, age, and height, but different weights and activity levels*
| || ||Subject 1 (lighter, more active)||Subject 2 (heavier, less active)|
|Parameter (units)|| ||Female, age = 40 yr BMI = 25 kg/m2 (67 kg, 1.63 m) REE = 1480 kcal/d (1.0 kcal/min) 24-hr EE = 2150 kcal/d||Female, age = 40 yr BMI = 30 kg/m2 (80 kg, 1.63 m) REE = 1550 kcal/d (1.1 kcal/min) 24-hr EE = 2200 kcal/d|
|Parameters based on EE or O2 uptake|| || || |
|AEE||Activity EE (kcal/d or kcal/kg · d)||450 kcal/d||450 kcal/d|
| || ||6.7 kcal/kg/d||5.6 kcal/kg/d|
|PALee||Physical activity level (24-hr EE/REE) (ratio)||1.45||1.42|
| || ||(2150/1480)||(2200/1550)|
|PALeeday||Daytime physical activity level (daytime EE/REE) (ratio)||1.65||1.58|
| || ||(2450/1480)||(2450/1550)|
|METee||Metabolic equivalent [exercise O2 uptake (O2/kg · min)/standard resting O2 uptake (O2/kg · min)] (ratio)||3.4 (12.0./3.5)||3.4 (12.0/3.5)|
|PARee||Physical activity ratio (reference exercise EE [kcal/min]/REE [kcal/min]) (ratio)||4.0||4.5|
| || ||(4.0/1.0)||(5.0/1.1)|
|ARTEee||Activity-related time equivalent (min/d) (24-hr EE [kcal/d] · 0.9− REE [kcal/d])/(reference exercise EE [kcal/min]− REE [kcal/min])||152 min/d||110 min/d|
| || ||(2150 · 0.9− 1480)/(4.0− 1.0)||(2200 · 0.9− 1550)/(5.0− 1.1)|
|Parameters based on HR|| || || |
|HRnet||Net HR (beats/d) (average 24-hr HR [beats/min]− resting HR [beats/min]) · 1440 min/d||21,600 beats/d (80− 65) · 1440||21,660 beats/d (85− 70) · 1440|
|PALhr||Physical activity level (24-hr HR/resting HR) (ratio)||1.23||1.21|
| || ||(80/65)||(85/70)|
|PALhrday||Daytime physical activity level (daytime HR/resting HR) (ratio)||1.31||1.29|
| || ||(85/65)||(90/70)|
|PARhr (METhr)||Physical activity ratio (exercise HR/resting HR) (ratio)||1.85||1.86|
| || ||(120/65)||(130/70)|
|ARTEhr||Activity-related time equivalent (min/d) (24-hr HR [beats/d)− resting HR [beats/d])/(reference exercise HR [beats/min]− resting HR [beats/min])||393 min/d||360 min/d|
| || ||(115,200− 93,600)/(120− 65)||(122,400− 100,800)/(130− 70)|
The reason for the finding that the AEE is similar in the two subjects is that subject 2, although less active, has a greater body mass. Although this limitation of AEE may be readily apparent, it is a reminder that AEE is best used to compare activity-related EE rather than physical activity levels. Other parameters, such as PALEE and PALEEday, provide a better comparison of activity levels and, as shown in Table 2, they reflected a greater activity level in the more active subject.
METEE and PAREE were used to compare the responses of the hypothetical subjects to the standardized cycle ergometer exercise task. Estimated METEE levels, expressed relative to body weight, were comparable at 3.4 times the standard resting oxygen uptake. By contrast, PAREE levels, which were based on actual measured responses to the cycle ergometry task performed in the laboratory but unadjusted for body weight, indicated that the heavier subject had a greater energy cost of performing the exercise task than the lighter subject and, hence, had a higher PAREE value. Finally, the ARTEEE index indicated that the lighter more active subject spent an average of 42 more minutes each day (152 vs. 110 min/d) at an EE level equivalent to riding the cycle ergometer in the laboratory (∼3 METEE). This type of information may be useful if an investigator wishes to determine how much time a person spends on an average day maintaining an EE of physical activity comparable to that prescribed by an investigator or health agency.
Measures Based on HR
Although variable from one person to the next, within individuals HR and oxygen uptake tend to be linearly related throughout a wide range of aerobic exercise tasks (26). When this relationship is known for a particular subject, recordings of HR can be used to estimate the person's oxygen consumption and, in turn, EE in free-living conditions (27, 28). HR and oxygen uptake have been shown to be moderately correlated during field and laboratory activities, with HR accounting for nearly 50% of the variability in oxygen uptake (29). Use of HR monitors to estimate EE is relatively inexpensive, convenient, noninvasive, and versatile. HR monitoring has been used with increased frequency in recent years, facilitated by availability of low-cost, portable monitors that are capable of measuring and storing minute-by-minute data over several hours and averaged data over days or weeks. An advantage of minute-by-minute HR monitoring is that it permits preselection of a threshold HR above the sedentary level, obviating a well-recognized limitation of HR data, i.e., that HR is not a good predictor of EE at low levels of physical activity (26). In addition, minute-by-minute HR data provide information about the frequency, intensity, and duration of free-living physical activities (28, 30).
Estimates of PALEE can be obtained relatively inexpensively by obtaining total daily EE derived from HR monitoring data in free-living conditions, based on the individual regression of HR against oxygen uptake measured in the laboratory (30, 31). Differences in levels of fitness can substantially affect the slope of the regression between HR and EE among individuals. For example, a trained person can be expected to have a lower HR for the same level of oxygen consumption. However, such differences should not preclude use of this measure to estimate physical activity-related EE. Perhaps the major limitation of assessing physical activity based on HR data is the lack of established relationships between HR and energy cost of the wide variety of activities encountered in daily living. An additional limitation is the confounding effect of factors other than energy demand on the HR response to exercise. Confounding factors include ambient conditions, time of day, emotional state, hydration status, food and caffeine intake, smoking, previous activity, body position, muscle groups used, and the static vs. dynamic use of limbs (26, 32, 33, 34). As a result, HR data obtained within individuals may yield large variability and unreliable estimates of EE (35, 36, 37, 38). However, when applied to groups of individuals, the HR-monitoring technique provides an acceptable estimate of TEE and associated patterns of physical activity (26).
Another simple and relatively accurate HR-based method to assess EE in free-living conditions is net HR (HRnet) (29). HRnet parallels AEE in that resting HR is subtracted from total daily average HR using an equation such as: HRnet (beats/d) = (daily average HR [beats/min] − resting HR [beats/min]) × 1440 (min/d). Obtained using a HR monitor, HRnet provides a means to compare individuals’ average, above-rest HR level during routine daily activities. HRnet would be expected to have the same limitations as AEE. That is, HRnet will be influenced by individual differences in HR responses to the same activities and will not necessarily reflect the duration or intensity of the activities performed.
The physical activity ratio based on HR data (PARHR) is an index of a subject's HR response to a selected physical activity divided by the resting HR and enables comparisons among subjects of the intensities of their responses to specific exercise tasks. Other indices based on HR responses, which, to our knowledge, have not yet been reported in the literature but which are potentially useful and worthy of additional study, are physical activity level (PALHR), daytime physical activity level (PALHRday), and activity-related time equivalent (ARTEHR). As a parallel measure to PALEE, which is based on EE data, PALHR is a ratio of a person's average daily HR and resting HR. Similarly, PALHRday is a measure comparable to PALEEday, i.e., a person's average HR is measured during daytime awake hours and expressed relative to resting HR. To assess physical activity during the daytime, Wareham et al. (30) used an approach somewhat similar to PALHRday by expressing the percentage of daytime hours in which the physical activity level was at a predetermined level above basal EE ARTEHR is a measure parallel to ARTEEE and, potentially, can be used as an index of the amount of time a person sustains a HR equivalent to that of a reference exercise task. ARTEHR refines the HRnet parameter by comparing one's average daily above-rest HR to the above-rest HR response to a standard reference exercise task. That is:
where total daily HR = the subject's average HR throughout a 24-hour period and exercise HR = the subject's mean, steady-state HR response to a standardized exercise task performed in the laboratory (e.g., walking on a treadmill).
Shown in Table 2 are values for the two hypothetical subjects for parameters based on HR data. Several findings are noteworthy. As in the case of AEE, HRnet was found to be similar in the two subjects, despite the fact that one is more physically active. Although a measure of each subject's above-rest average daily HR pattern in free-living conditions, this parameter does not necessarily reflect the level of physical activity because it is influenced by nonactivity-related factors such as body mass. PALHR and PALHRday described average 24-hour activity levels in the range of 1.23 to 1.31, which were considerably below the levels of 1.42 to 1.65 described by their EE-based counterparts PALEE and PALEEday. This is most likely explained by the fact that there is a considerably lower ceiling for relative intensity parameters based on HR data than those based on EE data, because HR cannot vary as much as EE. For example, in a young individual the range of HR responses to an exercise task is a maximum of ∼4–5-fold that of resting HR. The range of EE responses to the same task in a very fit individual may be ∼20-fold that of basal EE. In part, this is reflected in the responses of the hypothetical subjects to the cycle ergometry task, wherein the PAREE value was more than twice the value obtained using PARHR.
Measures Based on Accelerometry
Accelerometry devices measure the rate (i.e., intensity) of body movement in up to three planes (i.e., anterior–posterior, lateral, and vertical). When the devices provide raw data, investigators can estimate the relative intensities as well as duration of various physical activities. Because laboratory studies have demonstrated linear relationships between accelerometry counts and EE during physical activities such as walking and running (39, 40), the energy cost of such activities can be obtained from accelerometry data. Various activities then can be classified according to their intensity and can be expressed using METEE levels (40). To estimate TEE of individuals in free-living situations, many manufacturers incorporate into their devices computer programs that convert into EE the sum of the measured accelerometry counts and predict REE from standard regression equations based on the subject's characteristics of age, height, weight, and sex.
Advantages of accelerometry devices include their small size (permitting subjects to wear the monitors without interfering with normal movement) and their ability to record data continuously for periods of days, weeks, and even months (41). Models with internal real-time clocks also help discriminate activity patterns (42). A notable limitation of most currently available accelerometers is their inability to detect the additional energy cost of upper body movement (unless sensors are placed on the upper limbs), load carriage (static work), or moving on soft or graded terrain (39, 43, 44). Basically, their accuracy is limited to assessing the energy cost of dynamic work, such as locomotion on the level. Not surprisingly, then, accelerometry data demonstrate a better relationship with walking than with other common household activities, such as house cleaning and yard work, or recreational activities, such as playing golf (43). Finally, because the relationship of accelerometry data to EE is dependent on the type of activity performed, estimates of the energy cost of tasks performed in the laboratory may not apply to activities performed under free-living conditions (43).
In summary, ambulatory accelerometers enable objective assessment of total physical activity and can well distinguish differences in activity levels (even of low magnitude) among individuals and within given individuals. In addition, it can assess the effect of lifestyle interventions on physical activity. Clinical prescriptions for increasing physical activity level and/or duration can also benefit from inconspicuous assessment of physical activity using accelerometers. Finally, a change in AEE (or TEE) can be also picked up quantitatively, provided the acceleration signals can be properly converted by an adequate algorithm into EE.
As a new index that has not yet been described in the literature, ARTEaccel is a potentially useful extrapolation of activity-related time equivalent (ARTE)EE, the difference being that ARTEaccel is based on accelerometry-derived data. ARTEaccel is an index of the amount of time a person spends with accelerometric patterns equivalent to that of a reference exercise task. That is:
where total daily ACCL = the subject's average number of accelerometry counts during a 24-hour period and exercise ACCL = the subject's mean, steady-state accelerometry count response to a standardized exercise task in the laboratory (e.g., level treadmill walking).
The ARTEaccel index has the potential advantage over an unadjusted accelerometry reading in that it would enable comparison of physical activity duration among subjects who differ in their biomechanical efficiency of movement. The index may be worthy of investigation to demonstrate whether it appropriately and usefully corrects for interindividual variations in accelerometric responses to specified activity tasks.