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Estimation of energy expenditure rates from time-allocation data


As previously discussed, although indirect calorimetry or heart-rate monitoring can provide relatively precise determination of energy-expenditure rates, they are high cost, time-intensive techniques. Hence, it becomes difficult to use these techniques to examine all activities or large segments of a population. At the other extreme, the self-assessment method of estimating energy costs allows for only very gross categorization of the cost of activities (e.g. "light," "moderate," and "heavy") with respect to the effort involved in their performance.

Table 9. A comparison of energy expended in herding between a 12-year-old Nuñoa boy and a man

Activity

Energy expenditure rate (kcal/min)

Time expenditure (mini/day)

Energy cost (kcal/day)

12-year-old boy (30 kg)  
Lying

0.9

2

1.8

Sitting

1.0

343

343.0

Standing

1.0

20

20.0

Squatting with arm motion

1.5

2

3.0

Walking slowly

2.3

59

135.7

Walking moderately

2.7

11

29.7

Walking with light load

3.3

18

59.4

Walking up and down hills

3.5

20

70.0

Running

4.5

5

22.5

Total  

480

685.1

Man (54 kg)  
Lying

1.2

2

2.4

Sitting

1.3

343

445.9

Standing

1.5

20

30.0

Squatting with arm motion

1.9

2

3.8

Walking slowly

3.3

59

194.7

Walking moderately

4.5

11

49.5

Walking with light load

5.5

18

99.0

Walking up and down hills

6.0

20

120.0

Running

7.5

5

37.5

Total  

480

982.2

Source: Thomas, 1973a, p 141.

Fortunately, there are two techniques with which one can build on the limited data obtainable through indirect calorimetry while avoiding the imprecision of self-assessment methods. One is the use of standardized published values of similar activities, and the other is the interpolation of estimates from activities for which energy cost has been derived through indirect calorimetry (or heart-rate monitoring) on the same group.

The use of published values is a common technique for estimating the energy cost of habitual activities in energy flow studies (Rappaport, 1968; Lee, 1969; Kemp, 1971; Gross and Underwood, 1971; Johnson, 1978; Morren, 1977; Winterhalder, 1977; Smith, 1980). Here the purpose is to describe major flows of energy through the human population and to compare the energetic consequences of rather different activities. Thus, errors introduced by this estimating technique are usually unimportant.

Standarized values for a wide variety of activities and occupations can be found in Passmore and Durnin (1955), Durnin and Passmore (1967), Godin (1972), FAO/ WHO/UNU (1985), and Astrand and Rodahl (1986). The principal limitation in using published values is that these cannot account for population differences in expenditure rates for local conditions (e.g. high altitude or disease that may cause rate variations) Furthermore, determination of values is heavily biased to activities and rates of young men so that is is difficult to estimate the expenditure rates of children through simple corrections for weight; the most recent FAO/WHO/UNU report (1985) provides techniques for addressing this problem. As we have stated before, when internal population comparisons between sex-age groups and body types are desired, more precise methods have to be used. These are most informative when presented in terms of kcal/min/kg body weight, so that the expenditure rates of individuals of different weights can be compared. As Montgomery and Johnson (1976) have argued, presentation of the degree of variation around the mean energy cost for a group may provide valuable data which is too often omitted in energy cost tables.

In figure 5, Astrand and Rodahl (1986) show the range of variability that can exist for some activities. When this range is very broad, such as working with an axe (see the last entry in the table), it suggests that more specific definitions of that activity are needed. One particularly important aspect of knowing the range of variability lies in being able analytically to overestimate or underestimate costs. Thus, in testing a hypothesis where a high energetic efficiency ratio (input to output) is expected, it would be appropriate to consistently overestimate inputs. Following this procedure, the investigator could confidently state that the energetic ratio is at least at the stated level, and most probably well above this. Frequently when one has low confidence in the accuracy of central tendency measures, or when a large range of variability exists, it is best to perform the analysis from one end of the range. Then, as we have just suggested, one can assume more accurate values would fall consistently above or below those stated.

Under some circumstances, relying upon published values can be justified. For example, in Winterhalder's study of the food-acquisition activities of Cree Indians in the boreal forest, the value of the more precise information that could have been obtained using indirect calorimetry merited neither inconveniencing the subjects nor the additional research efforts that would have been necessary. The use of more elaborate techniques of estimating energy expenditure would have precluded the simultaneous collection of other equally important data and might have deterred the study population from participating. Winterhalder states that since "the patterns and levels of various activities that go into foraging are highly variable . . . precise information on expenditure for specific acts is of reduced value" and that the increased accuracy would have been "illusory when placed in the broader context" (Winterhalder, 1977, p. 602).

Winterhalder's assessment of his own research situation appears valid, and similar realistic assessments are always necessary in deciding for or against any methodological strategy. However, strategies such as Winterhalder's must be weighed against the fact that the energy values of a specific activity may vary quite considerably among populations and under different conditions. In many studies, a combination of strategies has been utilized such that published tables are deemed adequate for some activities, while more precise indirect calorimetry techniques are adopted for measuring "critical" activities.

Fig. 5. Energy expenditure of different activities (after Astrand and Rodahl, 1986, p. 439).

Besides the use of published values, a second method of estimating non-measured energy-expenditure values can be used in conjunction with limited indirect calorimetry. This estimation involves the interpolation of results of measured activities to those not measured. Such a method may be preferable to using published values, in that the non-measured activity values are derived from information gathered from the same population and under the same general environmental conditions. In this way, at least some of the inaccuracies inherent in the use of standardized tables can be overcome.

Results derived from the use of such methods can be seen in table 6, which lists activities performed by the Nuñoa population in producing potatoes (Thomas, 1973a). Comparing this table with table 3, which lists activities actually measured, it can be noted that the values for many specific activities could be estimated with some confidence through interpolation. Ultimately, these figures were used to analyse seasonal variations in the energy costs of activities and to provide energetic efficiency ratios for various productive activities. Comparisons of heart rate or self-assessment of effort among measured activities and those to be estimated provide a basis for interpolation.


Assessment of endurance capacity


Up to this point we have been concerned solely with determining the energy costs of activities, without consideration of the actual physiological strain imposed on the individual. This factor becomes important in evaluating how long an activity can be maintained and may be particularly critical for analysis involving strenuous, high-endurance tasks. Long-distance load-carrying serves as an example. While a 12-year-old boy could carry a 25 kg load at an energyexpenditure rate considerably below that of a young man, the former's endurance capacity is expected to be substantially less.

Christensen (1953) has considered various indicators for grading physical effort. Categories of light, moderate, heavy, and very heavy have been proposed based on caloric expenditure, heart rate, body temperature, and sweating rate during work. Ability to predict an individual's endurance capacity depends not so much on the absolute values of these measures, but the degree to which they approach a maximal limit.

This is particularly so for oxygen consumption and heart rate. Thus, in Nuñoa where maximal working capacity or oxygen consumption rate for men is equivalent to approximately 10.6 kcal/min., the cost of planting guiñoa seed is 5.2 kcal/min or approximately 50 per cent of the maximal value (see table 3) (Thomas, 1973a). Had we measured instead a young man who had had tuberculosis, such that his maximal working capacity was reduced by 12 per cent (Rode and Shepard, 1971), he would then be working at 56 per cent of maximal instead of 50 per cent. In the case of planting quiñoa, normal well-conditioned young men could perform this activity for considerably longer than those who had had tuberculosis. Measures of physiological strain can, therefore, provide a basis for deciding which individuals or segments of a population are best suited for strenuous-endurance activities.

Determination of maximal working capacity values is described in most texts on work physiology (see Weiner and Lourie, 1969; Lange-Anderson et al., 1971; Shephard, 1978, 1985; Astrand and Rodahl, 1986). Two types of tests are recommended for the field: the bicycle ergometer and the step test. Because of problems in transporting the bike ergometer, high cost, and lack of familiarity of many subjects with riding a bicycle, we would recommend an adjustable step test. This can be con

structed in the field and made in a manner that allows it to be broken down for transporting. Step height is set at 40 per cent of leg length and the subject steps at a rate of 30 steps per minute carrying a pack of 25 per cent of body weight. A high-flow respiratory valve, several 200-litre Douglas bags and accompanying three-way valves, and a gas meter are the basic equipment needed to collect expired air. Otherwise, equipment and analytical procedures are the same as those referred to under "indirect calorimetry. "


Summary


The relative strengths and weaknesses of methods used to collect time-allocation and energyexpenditure rates are presented in table 10. As is apparent, techniques that are most accurate tend to have low feasibility ratings and vice versa. By "feasibility" we refer to a combination of practical considerations including sample size, investigator time input, need for highly skilled personnel and costly equipment, and acceptability to a population.

The choice of an appropriate technique is to a great extent determined by the degree of accuracy required and the practical limitations of a specific research situation. In general, assessments at the population level are probably best carried out using methods that allow the largest number of subjects to be sampled without an undue loss of accuracy. The researcher's own experience and knowledge of his/her particular research goals ultimately provide the only real guidelines in deciding how much accuracy is required. These guidelines apply not only to considerations of the actual techniques to be used, but also to the number of subjects, the length of time they are to be studied, and the need for repeating observations at seasonal or other intervals. Whatever methodologies are devised, however, the investigator should be aware of the extent of possible errors and of the fact that it is virtually impossible, regardless of the methods used, to measure daily energy expenditure with an accuracy of much better than 10 per cent. In order to reduce the weaknesses or biases attendant upon all of these approaches, we recommend that several techniques be utilized simultaneously and that frequent comparisons be made on the same data set.

Table 10. Accuracy and feasibility of research methods

 

Scorea

 

Scorea

Time allocation

A

B

Energy expenditure

A

B

Time-motion study

1

4

Indirect calorimetry

1

5

Randomized visit time-motion

1

2

Heart-rate monitoring

2

4

Diary cards

3

3

Interpolation from tested activities

3

3

Recall questionnaires

(24-hour)

4

1

Published tables

4

1

     

Self-assessment questionnaires

5

2

A = accuracy; B = feasibility; 1 = best; 5 = worst.


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