(introductory text...)

Nevin S. Scrimshaw. John C. Waterlow and Beat Schürch, Editors
International Dietary Energy Consultative Group
Proceedings of an I /D /E /C /G Workshop held in London, UK
October 31 - November 4, 1994

On behalf of the UN ACC-Subcommittee on Nutrition, the International Dietary Energy Consultative Group (I/D/E/C/G) has been established for the study of dietary energy intake in relation to the health and welfare of individuals and societies by the United Nations University. Its specific objectives are:

1. The compilation and interpretation of research data on functional and other consequences of deficiency, change or excess of dietary energy.
2. The identification of related research needs and priorities, and the promotion of needed research.
3. The publication of scientific and policy statements and other information on the significance of chronic deficiencies and excesses of dietary energy.
4. The identification and promotion of appropriated and practical means of corrective action.

I/D/E/C/G Steering Committee:

- Dr. N.S. Scrimshaw, UNU, Chairman
- Dr. J.G.A.J. Hautvast, IUNS
- Dr. B. Schürch, Executive Secretary

European Journal of CLINICAL NUTRITION

Volume 50, Supplement 1, February 1996

Supplement Editors

Nevin S. Scrimshaw, John C. Waterlow, Beat Schürch,
Proceedings of an IDECG Workshop held at the London School of Hygiene and Tropical Medicine, UK
31 October-4 November 1994

Editors

J. S. Garrow
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Review Editor

B. Isaksson
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J. C. Seidell
Bead Department of Chronic Disease and Environmental Epidemiology,
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Book Review Editor

J. C. Waterlow
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Chairman of Editorial Board

A. Trichopoulou
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Editorial Board

KH Brown University of California, Davies, USA
TJ Cole Dunn Nutrition Laboratory, Cambridge, UK
EB Fern Nestlé Research Centre, Vers-Chez-les Blanc Switzerland
A Ferro-Luzzi National Institute of Nutrition, Rome, Italy
E Helsing World Health Organization, Copenhagen, Denmark
JK Huttunen National Public Health Institute, Finland
JGAJ Hautvast Wageningen Agricultural University, The Netherlands
S Inoue Yokohama City University, Kanazawa, Japan
WPT James Rowett Research Institute Aberdeen, UK
E Jéquier University of Lausanne, Switzerland
MR Law St Bartholomew's Hospital Medical College, London, UK
D Lemonnier INSERM Paris, France
J Mann University of Otago, New Zealand
JA Olson lowa State University, Ames, USA
G Schöch Förschungsinstitut für Kinderenährung, Dortmund Germany
S Truswell University of Sydney, Australia

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Foreword

The International Dietary Energy Consultative Group (IDECG) was created in 1986 to study the effects of different levels of dietary energy intake on the health and welfare of individuals and societies. Its first objective is the compilation and interpretation of relevant research data on functional and other consequences of deficiency, change or excess of dietary energy intake. This implies that virtually all of IDECG's work has directly or indirectly to do with energy requirements: current requirement figures are used to define states of energy deficiency and excess, and results of studies of consequences of energy deficiency and excess provide information on the validity of current requirement figures.

Since requirements for energy and for protein are closely interrelated and there is currently no international group concerned with protein issues, IDECG has also included protein requirements in its mission. The generally accepted current reference is the Report of a Joint FAO/WHO/UNU Expert Consultation on Energy and Protein Requirements, published as a Technical Report by WHO in 1985. Another FAO/WHO report, dealing more specifically with Protein Quality Evaluation was published by FAO in 1990.

A considerable amount of new information on human energy and protein requirements has accumulated during the past ten years, much of it documented in previous IDECG publications.

The expert committee that produced the 1985 report decided to base energy requirements, as far as possible, on information on energy expenditure. The development of the doubly labeled water method and its application in humans generated an important new data base. IDECG, in collaboration with the International Atomic Energy Agency, contributed to the validation and standardization of this new method.

When considering the energy requirements of infants, children and adolescents, the 1985 Committee stated: 'Although, in principle, it would be desirable to determine the requirements of children in the same way as for adults, from measurements of energy expenditure, this approach involves may difficulties in practice'. It concluded that the necessary information was not available to base recommendations for the energy requirements of infants and children on estimates of energy expenditure. Instead it continued to do so on the basis of information on energy intakes of infants and children growing normally. In 1989, IDECG gathered available data on activity and energy expenditure in infants and children and made a first attempt to base energy requirements on energy expenditure also in this age group.

Because of the close relationships that exist between energy and protein requirements, they have been discussed together in international meetings since 1963. IDECG devoted a workshop to the topic of protein energy interactions in 1991. At an IDECG Advisory Group meeting after this workshop, Prof. JC Waterlow, who was chairman of the committee that produced the 1985 Report, recommended that IDECG re-examine and update selected parts of the 1985 Report, providing with this work the scientific basis for a new FAO/ WHO/UNU consultation, and the representatives of the agencies concurred. Seven areas needing review were identified and scientists with expertise in these areas were asked to write position papers addressing the following three issues:

1. Do the 1985 recommendations need to be revised in your particular area? What are the main arguments for or against a revision?
2. What would your recommendations be at this particular point in time?
3. What additional work would need to be done to resolve problems that persist in this area?

The position papers were circulated widely among other scientists with expertise in each particular area.

This supplement to the European Journal of Clinical Nutrition contains the proceedings of an IDECG workshop, held at the London School of Hygiene and Tropical Medicine from 31 October to 4 November 1994, at which these position papers were presented and discussed. The discussions were then summarized and the working papers revised for this publication.

We are deeply indebted to the authors of the position papers who have devoted many hours to reviewing the literature and re-analyzing data to produce papers which should be very valuable insofar as they present in great detail the data bases as well as the assumptions and decisions that had to be made to arrive at their recommendations. They are also very valuable in defining gaps in our knowledge and identifying research that is needed to resolve problems that persist.

We are grateful to the United Nations University and the Nestle Foundation for funding this activity, to Prof. PS Shetty and his staff at the London School of Hygiene and Tropical Medicine for providing the facilities and support required for the workshop, and to Dr. E Müller for her help in preparing the manuscripts for publication.

Nevin S Scrimshaw
John C Waterlow
Beat Schürch

19 September 1995

(introductory text...)

JVGA Durnin
Department of Human Nutrition, Yorkhill Hospitals, Glasgow G3 8SJ, Scotland, UK

Descriptors: Human energy expenditure: methodology, basal metabolic rate, physical activity, body composition

Energy expenditure as the basis for estimating energy requirements

The energy requirement of an individual, in a state of desirable equilibrium, is equal to the energy expenditure. In some clinical situations, where an improvement in nutritional status may be advisable, the energy requirement may be set at a higher level than the energy expenditure in order to produce, temporarily, a positive energy balance. In certain physiological states, such as during growth in children, or in pregnancy and lactation, the energy requirement may also be higher than the energy expenditure. At the other extreme, when dealing with an obese individual or an obese population, again energy requirements would be derived from the energy expenditure, with a reduction to produce a negative energy balance; the amount and the duration of the energy imbalance would determine the rapidity and extent of the weight loss.

A more difficult situation to judge is where the energy requirements might be construed as being inadequate, because energy expenditure was less than desirable due to low levels of physical activity. In the absence of very clear-cut evidence specifically related to the health advantages of physical activity and the clinical dangers of inactivity which, for adults do not presently exist in an uncontroversial and entirely persuasive way - it is problematical to take this factor into account in calculating energy requirements. This is not to say that physical activity may not be important for physical, mental, and cognitive development and maintenance, particularly in children: it simply appears very difficult to introduce it in a quantitative way in the present context.

Methodology

Energy expenditure is therefore the key to the assessment of energy requirements. It may be measured by several different standard 'direct' techniques. 'Direct' in this context is not equivalent to the classical term 'direct calorimetry', which refers to the direct measurement of heat output in a calorimeter; in the present usage 'direct' refers to the measurement of energy expenditure from O₂ or CO₂ output. It therefore includes the various classical techniques of assessing O₂ consumption and CO₂ output, as well as the doubly-labelled water technique and the use of a whole body calorimeter.

'Indirect' methods of measuring energy expenditure comprise extrapolating from values of total energy intake in food and from heart-rate recording.

A description of the techniques, together with a brief analysis of some of the problems, is given in Durnin (1992).

1. Timed record of activities and associated energy costs

The method which has probably been used most frequently consists of a combination of a timed activity record, i.e. the average total duration of each of the 'important' activities throughout the whole 24h of the day, and an energy value (in kcal or kJ/min) for each of these activities; 'important' is defined as either occupying a significant period of time or else involving considerable physical effort. These energy values may be derived either from published data or by actual measurement of oxygen consumption.

When values from the published literature are used to apply to a specific activity, it is sometimes unclear as to whether or not 'ancillary activities' or rest pauses have been included. For example, while pushing a wheelbarrow, the worker may stop to load bricks or sand into the barrow; or during digging a ditch, the worker will have occasional rests of varying duration. It is thus uncertain whether the 'activity' which has been allocated an energy value represents that activity only, or also includes some short-term diversions.

If no precise information is provided about whether or not the activity is 'pure' or 'adulterated', it is a matter of guess-work to make the correct decision. On the whole, if the activity is spasmodic and of short-term duration perhaps of only a few minutes - it is probable that it is a 'pure' activity, e.g. planting rice, or digging a ditch. If it continues for perhaps half-an-hour or longer, it will almost certainly include some rest pauses, and will thus be 'adulterated'. In deciding what energy value to apply to an activity which has not been observed and timed (such as planting rice), if the duration of the activity is long enough to imply that it refers to a prolonged period of work, such as a whole day of 'planting rice', then the appropriate factor should be one that would include the influence of rest pauses. If the duration appears to be limited to the actual activity, excluding any pauses or diversions, the relevant factor will be higher.

Although there is a great deal of information on the energy cost of separate 'activities', it is somewhat undigested and its exact usefulness in relation to energy requirements is in a confused state. There is also a lack of clarity and logic about the relationship of actual physical activity and so-called desirable (or discretionary) activity; when people lead very inactive lives, is it sensible to consider only what they actually do, or should an allowance be made for a possibly healthier life style involving more physical activity ? This is one of the major problems to be decided in the context of energy requirements.

Also, most of the data (and indeed the outlook adopted in this paper) relate to healthy populations. The influence of malnutrition, disease, and disablement has been virtually ignored, and the importance of these conditions to energy requirements needs to be addressed in a comprehensive way, albeit at this stage in a preliminary fashion.

Useful methodological information. Even if a labour-intensive technique is used of apparently high validity, such as where activities are carefully categorized and their duration monitored, there is still the possibility of error of varying magnitude. To be able to assess the likely validity and precision of the method, certain descriptive information is needed. If a 'diary-record' is used to obtain detailed information on how the 24h of the average day is spent, it is necessary to know to what degree of exactitude this was done (e.g. in single minutes, in blocks of 5 to 10 min? etc.), and whether the diary was compiled by the subjects themselves or by observers. Who were the observers? How often was the accuracy of the diary checked, and by whom?

If indirect calorimetric measurements were made, were these done by a respirometer or using a Douglas bag, etc.? What was the duration of the measurement? Was the activity standardized or was it thought to be representative of the normal free-living situation? Some indication should also be given on the range of activities measured and the number of measurements on each activity, preferably in tabular form. How was the volume of the expired air measured? Were the instruments calibrated, and, if so, how? How was the sample of expired air collected for analysis? How long did it remain in the collecting container before analysis ? What instruments were used for analysis and how were they calibrated? If published or unpublished values of energy costs have been used, their source should be quoted and their adjustment for varying body size, etc. should be defined clearly.

This list of desirable bits of methodological information may appear somewhat excessive, but ought to have been collected in the first place, and the scientific worth of the experimental data would surely be increased by a brief description of these items.

Ancillary advantages of using this method. There are several subsidiary advantages to using this method with its attendant information on the duration and probable energy cost of the different daily activities. First, it provides a large amount of interesting physiological and social information on life styles.

Although it may be preferable actually to measure the energy expenditure of the important activities, there is no absolute necessity for this if values from the literature are used. There is therefore no need for sophisticated apparatus nor highly skilled technical assistance.

Secondly, it should allow a more accurate assessment of which overall value to attach to an activity factor that would be relevant to an extended period, perhaps at work or in leisure, or indeed over the whole 24 h day.

Thirdly, it provides information about the types and strenuousness of different forms of physical activity. Monitoring the duration and strenuousness of physical activity can also play an important role in detecting minor degrees of poor or under-nutrition, since reduced physical activity may be the first and possibly the major indication of an attempt at adaptation.

Available data on the energy cost of activities. The energy costs of many different activities were measured by Orr & Leitch (1938) and Durnin & Passmore (1955; updated 1967), and a short list, largely based on these earlier data, is given by James and Schofield (1990), but the information is still somewhat inadequate. Often, the number of individuals who supposedly supply the source of the data is ridiculously small e.g. the values for the energy cost of such simple household tasks as washing dishes, cleaning windows, and such leisure activities as playing bowls or playing golf, depend in each case in the James & Schofield (1990) tables of measurements made on only one individual. There is probably a fairly large reservoir of unpublished data of this nature, as well as some published results which have not been included in these tables. At the present time, an attempt is being made to collate them in a discriminating way, together with more information about how and where the data were obtained.

2. BMR multiplied by an appropriate activity factor

Average daily energy expenditure may also be estimated from the BMR, multiplied by an appropriate activity factor which will be dependent on the degree and duration of physical activity [BMR × A]. BMR may either be measured directly or else calculated from an equation. The activity factor may vary from about 1.2 to 1.4 for relatively inactive people, up to 2.0 or more in the case of people who are physically very active. The factor by which BMR must be multiplied may be gauged from information of different kinds; perhaps from a timed activity record, or from a questionnaire designed to provide information on habitual physical activity. (Heart-rate monitoring may also be used to calculate energy expenditure, and this will be discussed later.)

The most important constraint to the general technique of BMR × A is its basic dependence on (1) a valid value for the BMR and (2) the possibility to obtain a factor which may be applied to the BMR (perhaps 1.5 or 1.6) which will result in a calculated daily energy expenditure that is reasonably pertinent and accurate.

Variability of BMR data and validity of predictive equations. To obtain some indication of the reliability of the value obtained for BMR, we need to analyse the relevance and importance of the intra- and inter-individual variability. Measurements of BMR on an individual by indirect calorimetry, each lasting 10 to 15 min and done on two or three consecutive occasions immediately following on one another, should show a variation of no more than 2-3%. If BMR is measured on an individual on many different days over a period of weeks or months, the 'intra-individual variability' is remarkably constant with a coefficient of variation (C.V.) of about 3% (Benedict & Cathcart, 1913; Loewy & Zuntz, 1916; Lusk & Du Bois, 1924; Benedict, 1935; Berkson & Boothby, 1938; Soares & Shetty, 1987; Henry et al, 1989). The influence of measurement error, when the technique is carefully controlled, is likely to be minimal (less than 1 %) and of no practical relevance.

The variable which has probably more importance in the present context of assessing energy requirements in populations, is the inter-individual variability in BMR. This appears to have a coefficient of variation of the order of at least 8% (Harris & Benedict, 1921; Henry et al, 1989).

The extent of this C.V. is large enough to introduce all sorts of confusing sequelae. As an illustration of some practical implications, with a C.V. of 3% for intra-individual variability the 95% probability of the value for the BMR of a 70 kg man would be expected to have a variance of twice the C.V.; i.e., the values, expressed as kcal/d, or MJ, would have a range from 1590 to 1790 kcal (6.7-7.5 MJ) per day. That range is large enough to introduce uncertainty about making tenable conclusions of acceptable precision. Moreover, it refers to actually measured BMR, with the inherent probability that if BMR is derived from equations the range will be greater still. Clearly, we should be careful about making simplistic deductions about relatively small differences in BMR. When we are dealing with energy requirements, where we have to fit a hypothetically derived 'activity' factor to the BMR, the scope for error is increased, since the variability will be considerably augmented. If we are considering the inter-individual variability in a group of men whose mean body mass was 70 kg, a C.V. of 8% implies that the 95% confidence limits are from 1420 to 1960 kcal/d (6.0-8.2 MJ). A C.V. of 10% results in the range becoming 1350-2030 kcal/d (5.7-8.5 MJ).

Part of this C.V. is the result of having to make some allowance for differing body mass; a population might include adult individuals with a range of body mass from 40 to 80 kg, and various equations have been formulated to take this into account (FAO/WHO/UNU 1985; James & Schofield, 1990; DH, 1991). However, these equations assume that there is a normalisation effect produced by introducing a simple dependence of body mass into the equation, and also that the composition of the body with respect to the fat-free mass (FFM) and the fat mass (FM) has a minimal influence on energy metabolism. Neither of these assumptions is likely to be correct in all circumstances, so that an error of unknown dimension is introduced by this procedure, which might well increase the C.V. to at least 10%. (This possible error is discussed later.)

It is probably sensible to apportion most of the inter individual variability to varying body mass. Even if body composition is more or less identical in the group, a large difference in body mass will inevitably lead to a comparable variability in BMR. There are also other factors which will affect variability such as hormonal influences, perhaps especially in thyroid function; it is theoretically possible that differences in body temperature may modify energy metabolism; physical fitness is unlikely to have much relevance in this context. It is difficult to see how any allowance for these influences could be introduced, especially in field situations.

Available data on BMR. Since BMR has considerable importance in the calculation of energy requirements, and since in the great majority of cases BMR will be calculated using published data and not actually measured, in order to minimize error it is rather critical that there should be an adequate volume of BMR measurements, the use of which would allow predictive equations to be calculated. There is some doubt about whether or not this adequacy of BMR data exists. Durnin (FAO/UNU 1981) made the first comprehensive attempt to determine whether or not formulae might be derived using sex, age and body mass alone, to calculate BMR. Subsequently, Schofield et al (1985) expanded the base-line data and produced new equations. These have later been modified in a minor way (James & Schofield, 1990).

In spite of these large-scale surveys, a completely satisfactory analysis of the data has not yet been carried out. Although the C.V. due to measurement error is very small when the methodology is carefully and strictly controlled, the possibility of experimental error in measuring BMR is always present. The strict experimental requirements (10-12h post-prandial, completely relaxed, in a thermally-neutral environment, etc.) are sometimes difficult to organize, and the measurement of oxygen consumption (or energy expenditure) has not always been done with the required strict care and attention to procedure (e.g. calibration of equipment, suitable mouthpiece and valve, face-mask, or ventilated hood). Many of the published studies have not explained adequately exactly how the measurements were done, and doubt exists about the validity of some of the data. There is also an inadequate volume of information on BMR in many populations in developing countries, especially in children, adolescents and elderly people.

Derivation of the factor with which to multiply BMR. In making use of the BMR to calculate energy requirements, an overall factor denoting the physical activity level (PAL) to apply to the BMR is needed: i.e. energy expenditure or energy requirement equals

BMR × PAL.

This activity factor is usually applied only to groups or populations and not to individuals. An example might refer to a group of rural women undertaking daily work in the fields:

10 h of light agricultural work at BMR × 3.0.

Equally this approach could be used for the whole 24h of the day, for, as an example, a group of moderately active people:

BMR × 1.6.

The activity factor may also be applied to single 'activities' such as BMR × 1.2 for 'sitting quietly', BMR × 2.5 for 'household tasks', or BMR × 4.0 for 'walking', etc. This procedure makes it possible to calculate energy requirements for individuals if sufficient information is available on the life style.

The FAO/WHO/UNU (1985) Report gives values for three different levels of occupational activity: 'light', 'moderate' and 'heavy'. This sub-division is expanded a little in the DH (1991) Report, which, as well as having three categories of occupational activity, also had three levels of non-occupational activity (Table 1). In theory, this should allow a finer distinction to be made between different groups.

It should be possible to formulate some intelligent guesses about which value would be most appropriate for any particular group. The degree of concordance between the guess and reality would depend upon the accuracy of other information, such as might be gathered from activity questionnaires. In general, corroborative data should be obtained about the apparent activity levels for both occupational and non-occupational time. This should allow a reasonable estimate to be obtained, as long as the investigator making the guess has an adequate knowledge of the different relationships between the degree and duration of physical activity and energy expenditure.

3. Doubly-labelled water (DLW)

The so-called doubly-labelled water method of measuring energy expenditure makes use of the stable isotopes ¹⁸O and ²H. No attempt will be made here to discuss the likely validity of the method except to say that there may still be some reservations in accepting that the estimate of converting CO₂ production to heat production involves minimal error. Blaxter (1989) tabulated the varying heat equivalents of differing substrates (lipid, protein, carbohydrate) in relation to oxygen consumed and CO₂ produced. It is clear that while the range of oxygen consumed is relatively small (19.2-22.7 kJ/litre), that for CO₂ is much greater (17.5-27.8 kJ/litre). He concludes that, even in a state of energy equilibrium, the error involved in the conversion of CO₂ to heat could be as much as + 10%, and if the individual being measured were losing or gaining weight, the error could be much higher.

While the theoretical advantages of this method are considerable, such as the ability to provide data on free living individuals in almost any context related to age, environment, etc., there are counter-balancing drawbacks. The technicalities of the analyses of CO₂ output are such that, due to the possibility of using unsatisfactory equipment and the necessity for highly skilled and expensive technical assistance, many of the most experienced and distinguished laboratories in the field of energy balance' have been unable to obtain more than a very small amount of reliable data. The isotope ¹⁸O is also very expensive. Lastly, the data give information only on total CO₂ output during a period of several days, data which are then converted to total energy expenditure: there is no breakdown of any kind.

Table 1 Calculated physical activity level (PAL) of three adults at three levels each of occupational and non-occupational activity for men [M] and women [F]

	Occupational Activity
	Light		Moderate		Moderate/-heavy
Non-occupational	M	F	M	F	M	F
Non-active	1.4	14	1.6	1.5	1.7	1.5
Moderately active	1.5	1.5	1.7	1.6	1.8	1.6
Very active	1.6	1.6	1.8	1.7	1.9	1.7

It seems as if, unique among methods of measuring energy expenditure, the DLW technique provides no information other than on total energy expenditure over a period of several days. If we measure energy expenditure indirectly by energy intake we learn not only about energy expenditure but about diet, eating patterns, nutrient intakes, etc. If we measure energy expenditure using the timed activity and energy cost method, we obtain knowledge on life-style, activity patterns, relative strenuousness of work and leisure, etc. If we measure energy expenditure from BMR and an 'activity factor' we gather information which again includes material other than simply related to energy expenditure. Similarly with calorimetry, information on the whole pattern of the 24h of the day is also gathered. Only with the DLW method does one acquire nothing except total energy expenditure.

This quite serious drawback of the method, in conjunction with the considerable expense required, makes it desirable to assess carefully its relative advantages and disadvantages. It is not simply that we need to consider only the ability to measure energy expenditure. Because of the considerable intra- and inter-individual variability of energy expenditure, we may often need much ancillary data to help us make sensible decisions on what are frequently complex problems.

On the other hand, the DLW method could, with benefit, be used to corroborate the validity of some of the low activity factors (such as 1.4, 1.3, or even 1.2 × BMR) which are sometimes found in individuals without apparent reasonable explanation.

4. Calorimetry

Another method to be considered is the use of a respiration chamber or of a direct calorimeter. To obtain reliable data with either of these techniques involves an experimental set-up which is both expensive and technically complex. Relatively few of these chambers exist and their usefulness in the present context is restricted to specific basic problems which do not require a natural free-living environment; for example studies on relationships between energy metabolism and heart rate, on diet-induced thermogenesis, on validation of the use of DLW to measure energy expenditure in varying situations, on the influence of varying proportions of energy-supplying nutrients on energy metabolism, etc.

5. Heart rate

Extrapolating from heart rate to energy expenditure is a method which has been widely believed to be valuable and reasonably valid. The technique is fairly practicable, there are several instruments on the market which are not very expensive, and it is probably the method of choice in some population groups, such as young children, old people, and ill people. This is not the place to give a detailed critique of the methodology, other than to say that it must be used with circumspection and an awareness of the variable relationships of heart rate and energy expenditure.

Heart rate recording is also a potentially useful tool in some of the other methods of measuring energy expenditure as a means of obtaining an acceptably accurate value for the factor (A) with which to multiply BMR.

6. Energy intakes

Although the basis of calculating energy requirements depends upon obtaining values for energy expenditure, much of the assessment up to very recent times has been indirectly derived from data on energy intakes. This has often fallen out of favour in the last few years because of criticism of the likely accuracy of the energy intakes, or of the measured intakes being representative of the true intakes of the population. This is another area of energy metabolism where superficial criticisms may reflect little more than the authors' limitations.

To allow a correct interpretation of the results, it is critical to have a very precise description of how food intake was measured, what exactly was measured, and by whom, and how experienced were the observers. For example, if a 24-h recall was used, it is well known that sometimes errors can be enormous and will not be distributed in a random fashion around a more-or-less correct mean. It is helpful to know who carried out the 24h recall, where it was done, how long it took, if a standardized procedure was used, what was the experience of the observer, whether repetitions were made on other days, if the method was validated using other techniques or cross-over questioning by different observers, etc.

When food intake was measured and recorded, what was the exact way in which the measurement was done? What was the precision of the frequently quoted 'household measures'? What kinds of balances were provided? To what degree of exactitude were they read? Were they calibrated and how? Were other utensils (plates, containers, etc.) provided? What sorts of log-books were used, and how did the subjects record each item of food (e.g. in relation to composite dishes; and if sauces, etc., were taken, were they measured separately?).

Were instructions in the methodology given to the subjects in their own homes, in clinics, by writing, etc.? Was any supervision used during the study, and for how long and how often? How accurately assessed were food and drink consumed outside the home? What was the exact duration of the investigation on each individual? When this varied, it is not satisfactory to say things such as 'the investigation was carried out for periods of between 3 and 42 days on the subjects'. Was the period of the measured food intake apparently representative of the normal or not?

As far as the calculation of the intake of energy and other nutrients, which tables were used and how? For example, some well-known tables give energy values for carbohydrate, fat, and protein which do not include losses in digestion and absorption. Were allowances made for this? Sometimes duplicate samples of the diet have been measured for energy content by bomb calorimetry, but of course this can be a spuriously accurate procedure if it is not realized that the values obtained do not at all represent the available energy in the food as far as the body is concerned.

This information if indeed it were even obtained, is seldom quoted. However, where the procedure of measuring energy intake has been done in an acceptable fashion by experienced observers, it has been shown on many occasions to be non-significantly different from energy expenditure, simultaneously measured.

In the light of the occasional difficulty and expense of measuring energy expenditure, there is still a place for using energy intake, properly assessed, as the basis.

Importance of body composition

Whether or not body composition plays, not a significant but an important role in estimating energy requirements, is a disputed question. The question may perhaps be simplified to enquiring whether using body mass, or fat-free mass, as the reference makes an important difference to the estimate.

We can consider the problem at two levels of fatness.

1. Moderate levels of fatness

This includes adults aged from 20 y up to 50-60 y, with a fat mass of, for men, up to 30% of the total body mass, with the equivalent for women of up to about 35% (or a BMI of 30 or so).

If the proportionate fat mass is only moderate there is little reason to expect that it will specifically influence energy metabolism, either at rest or while physically active, in ways which are unrelated to the actual body mass. The biochemical sources of energy to the body will not differ over this sort of range of fatness nor will those levels of fatness, within the range of average and normal activity, markedly influence the mechanical efficiency and thus the energy cost of movement.

The theoretical justification for the above statements is that adipose tissue has a metabolic rate which is not grossly dissimilar from the energy metabolism of the total fat-free mass. Therefore, within the above limits of fatness, the metabolic rate of a moderately overweight person of 60 kg body mass, would not be expected to be very different from a moderately lean, or from a 'normal' individual of the same body mass. The variations in relative fatness will not influence, in an important way, the energy metabolism per kg body mass. An adequate analysis of the likely influence of varying levels of fatness on energy metabolism has still not been carried out exhaustively and this is an important area for future research.

In the case of very lean populations, there may be confusing influences. If the leanness is of semi-permanent or long-term duration, but is compatible with a level of nutritional status which does not inhibit a 'normal' lifestyle, particularly with regard to physical activity, then BMR and energy requirements are unlikely to be influenced in any important way. On the other hand, if leanness has resulted from a comparatively recent negative energy balance, BMR may be significantly lowered, and it is at least possible that such a reduction would be one of the results of seasonal deficiencies in food availability (Durnin et al, 1990; Ferro-Luzzi, 1990; Schultink et al, 1990).

2. Obesity

With greater levels of fatness, the situation is likely to become more complicated. There will certainly be an effect on the mechanical efficiency of movement but, although the biochemical energy transformations at rest may involve some basic differences from the normal, the possibility is that sources of variation both within and between individuals may mask any clear-cut effect. The considerable extent of the 95% confidence limits on inter-individual variability of BMR should always be kept in mind in relation to studies purporting to show the possible influences of body composition.

It is obviously necessary to take account of body mass, and as long as we are aware of the naively of expressing our data on BMR as energy per kg of body mass, the simplicity of the conversion and the fact that it will be unlikely to differ from energy per kg of fat-free mass (Garby et al, 1988; Henry et al, 1989) makes it a convenient and reasonable reference. It may appear that there are biologically sound reasons for making an allowance for differing body compositions and, at the simplest level, expressing data as energy per kg fat-free mass, but the probability is that this manoeuvre has, in fact, little biological justification and makes no important difference to any conclusions which may be drawn from the data. (No attempt has been made here to take into account more complex differences in body composition, i.e. related to organs or tissues, largely because of inadequate experimental information). A recent paper by Carpenter et al (1995), dealing with a meta-analysis of 13 studies using doubly-labelled water to measure total energy expenditure (TEE), seemed to show little relationship between TEE and adiposity, and a lower resting metabolic rate in women than in men, although the latter statement is made on the basis of only a very small sample of the published data. They also demonstrated the statistical dangers of using a simple ratio of TEE and body mass to compare data, but it is not clear from their paper how great an error is produced if the simple ratio is used.

In summary, in conditions of physical rest it is unlikely that body composition plays an important role in energy requirements. However, its role with regard to physical activity is more complex. Even if its influence on tissue energy metabolism is not of great practical importance, its influence on the amount of physical activity and on the energy cost of that activity might be appreciable.

3. Body mass of populations

Since obviously energy requirement, or at least BMR, is directly influenced by body mass, and populations of greater body mass will therefore have higher energy requirements, a careful appraisal of the actual or desirable body mass of all the relevant populations (infants, children, adults) becomes very important. This problem has not been considered as part of this analysis of 'general principles'.

Points of uncertainty requiring further research

1. Re-analysis of BMR

The critical importance of BMR to the estimation of energy requirements and the fact that the present equations may be less than ideal and do not include the significant addition to the literature from studies in recent years in developing countries, make it desirable that the whole literature on BMR should be critically reexamined. The reassessment should also consider the emphasis (or lack of emphasis) which can be placed on relatively small differences in energy requirements in the light of the degree of variability between individuals. Indeed, the whole interpretation which can be put on population differences in calculated energy requirements needs to be re-examined.

2. Maintenance requirement

Deciding on the precise value to be attached to the 'maintenance' requirement is a topic with some potential for confused discussion; i.e. whether multiplying BMR by 1.2, 1.3, 1.4 or even more, might be interpreted as signifying unacceptably low energy requirements.

A 'realistic' maintenance factor is one which makes a reasonable allowance for the various essentials in a normal life-style, but does not include anything more than a minimum amount of physical activity. 'Activity' would be restricted to the purchase of food, cooking, washing-up, and keeping the household in moderate order, washing and dressing. The speculative value for this level of activity is 1.4 × BMR. A lower level than this 'maintenance', which has teen termed as 'survival' value, involves only minimal movement about the house for very limited periods of time, with the suggested value of 1.2 × BMR; it is incompatible with long-term cardiovascular fitness.

If these values accurately reflect a certain type of lifestyle, the practical implications, translated into the possible duration and type of physical activity, are illustrated in the following two tables relevant to a housewife with a body mass of 50 kg.

An average woman of 50 kg will have a BMR of 1260 kcal (5.27 MJ) (Table 2) per day. If BMR × 1.4 is taken as representing the energy requirement, this gives a value of 1764 kcal (7.38 MJ), i.e. 1260 × 1.4 kcal. Diet induced thermogenesis (DIT) of about 10% will represent 176 kJ. The energy available for physical activity is thus 1764 (total energy requirement) minus 1260 (BMR) minus DIT (176 kcal), leaving a total of 328 kcal (1.3 MJ).

If it is hypothesized that a minimum amount of physical activity might require, say, (1) 3.5 h of 'standing' and (2) 2 h of 'housework', 'preparing food and cooking', 'looking after the children', etc. this would cost, in energy terms,

210 (i.e. 3.5 h) × 0.5 kcal = 105 kcal (440 kJ)^a (1)
120 (2 h) × 1.5 kcal = 180 kcal (756 kJ)^b (2)

where: ^a the value for 'standing' has been taken as 0.5 kcal/min above BMR; ^b the value for housework, etc. has been taken as 1.5 kcal/min above BMR.

Table 2 'Activity' of an individual whose energy expenditure is equivalent to BMR × 1.4

BMR for 50 kg woman = 1260 kcal/d (5.27 MJ) Therefore BMR × 1.4 = 1764 kcal/d (7.38 MJ) DIT at 10% of intake = 176 kcal/d (736 kJ) Total energy intake (1764) - BMR (1260) - DIT = 328 kcal, i.e. 328 kcal/d (1.3 MJ) for all other activities Example 210 min [approx 3.5 h] standing: at 0.5 kcal/min over BMR = 105 kcal (440 kJ) 2 h housework: at 1.5 kcal/min over BMR = 180 kcal (756 kJ) remainder is 43 kcal (180 kJ), as 15 min walking i.e. average day's activity: 2 h housework 15 min walking 3.5 h quiet standing slightly more than 18 h Iying down or sitting quietly

The energy left over for physical activity is therefore 1764-1260-176-105-180=43 kcal (180 kJ).

This quantity of energy would allow about 15 min of slowish walking. The average day for such a woman would consist therefore of 2 h housework, 3.5 h of quiet standing, 15 min walking, and 18 h of lying down or sitting quietly: there is not even any allowance for work outside the home.

The pattern certainly looks anything but typical for a 50 kg woman on an energy intake of 1.4 × BMR; yet that level of energy intake would not be unusual as far as published data are concerned.

At the lower 'survival' level of 1.2 × BMR, a similar table can be reproduced, again using a 50 kg housewife for an example (Table 3).

The BMR of an average woman of 50 kg equals 1260 kcal/d (5.27 MJ), then 1.2 × BMR equals 1512 kcal/d (6.33 MJ). The energy required for DIT is approximately 10% of the total energy intake, i.e. 10% of 1512 or 150 kcal/d (628 kJ). Therefore, the energy left over for all the physical activity of the day is the total energy intake (1512 kcal) minus BMR (1260 kcal) minus DIT (150 kcal), which comes to 102 kcal/d (427 kJ). This would be the equivalent of 204 min of an activity at an energy cost of 0.5 kcal/min over the BMR such as standing with little extraneous movement. That is, on an average day, this woman would have to remain lying down resting quietly for about 20.5 h in the day, and standing with little movement or walking around for the remaining 3.5 h. This certainly betokens a minimal existence and is not to be found in other than moribund populations (Durnin, 1990).

These illustrations of 'maintenance' and 'survival' levels of energy expenditure appear of limited applicability to normal populations. Even 1.4 × BMR which is sometimes suggested as betokening an acceptable level of energy expenditure, does not seem to be compatible with a normal healthy existence.

There seems little doubt about the comparative validity of these deductions. The BMR is a variable which, although usually calculated on the basis of a formula, is capable of being measured with a high degree of precision and reproducibility and the other values for energy expenditure - i.e. DIT, the energy of housework, standing, walking, etc.-are also reasonably valid.

It is therefore possible to estimate what the energy expenditure of an individual or a population would involve if the energy requirement were 1.4 × BMR, and we can conclude that the way of life represented by this value probably is unacceptable because of a very limited amount of physical activity.

Table 3 'Activity' of an individual whose energy expenditure is equivalent to BMR × 1.2

BMR for 50 kg woman = 1260 kcal (5.27 MJ) Therefore BMR × 1.2 = 1512 kcal (6.83 MJ) DIT at 10% of intake = (150 kcal), (628 kJ) Total energy intake (1512) - BMR (1260) - DIT = 102 kcal (427 kJ) i.e. 102 kcal available for activity Example 204 min × 0.5 kcal over BMR i.e. average day's activity: 3.5 h of standing with little movement 20.5 h of lying down or sitting quietly

Since, theoretically, populations having energy requirements equivalent to 1.4 × BMR or less appear likely to be, from the examples quoted, in a state which is physiologically abnormal, investigation of this research problem is of high importance. Studies on apparently normal people whose energy intakes are low, need to be combined with measurements of energy expenditure (perhaps most usefully employing the doubly-labelled water method.) and monitoring activity patterns in order to obtain more definitive data on this enigma.

Summary of research needs

Suggestions have been made, at different places in the text of this paper, for further analyses or studies on various aspects of energy requirements. The different items are as follows:

BMR: A critical re-assessment of all the data is highly desirable. Particular attention needs to be given to the extent of intra- and inter-individual variability.

Physical activities: There is a need for further analysis and for more investigation into the energy cost of different activities. The confusing effect of uncertainty with regard to whether or not rest-pauses have been taken into account in the estimate of the energy cost of the different activities is discussed.

Doubly-labelled water: Its role is discussed briefly and a specific proposal is made in relation to its potential use in the investigation of the 'maintenance' factor, specifically applied to BMR and the activity factor in physically inactive populations.

Maintenance factors: The validity of some of these to real life situations are discussed with specific examples of practical implications of the factors 1.4 (for 'maintenance') and 1.2 (for 'survival').

Body mass and composition: It is doubtful if, within a fairly wide range of 'fatness' (but excluding the grossly obese), there is any real benefit in field situations from taking body composition into account, bearing in mind the extent of the variability of both BMR and total daily energy expenditure. In this context, because total body mass clearly has a considerable influence on energy expenditure, further studies of the effect of actual or desirable body mass are needed.

Population sample size: Because of the considerable variability in many aspects of energy metabolism, it is critical to have a sample size which has an acceptable statistical power and is reasonably representative of the particular variables being analysed. It is not scientifically admissible to make other than highly qualified deductions from data obtained on only small sample sizes. It is particularly depressing to read statements with sweeping generalizations about energy expenditure, which contradict data obtained on very large numbers of individuals by many experienced investigators, when the statements are based on findings on nine individuals (Haggarty et al, 1994).

References

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Soares MJ & Shetty PS (1987): Long-term stability of metabolic rates in young adult males. Hum. Nutr. Clin. Nutr. 41c, 287-290.

Discussion

Since energy requirements are now preferentially derived from data on energy expenditure and expressed as multiples of BMR, most of the discussion dealt with issues related to BMR, the energy cost of physical activity, physical activity levels (PALs) and the factorial method.

Under the auspices of IDECG and with funding from the Nestle Foundation, all currently available BMR data meeting a set of stringent criteria are being reanalyzed under the supervision of Henry and Durnin. The conditions under which BMRs have to be measured were reemphasized. In some earlier studies these were as strictly observed as in more recent ones; publication date is therefore not necessarily a good screening criterion. One of the questions raised was how important it is that subjects sleep at the laboratory where their BMR is measured in the morning. Durnin cited several papers (Bullough & Melby, 1993; Soares et al, 1989; Turley et al, 1993) showing that this did not make any difference. In a group of elderly subjects, Berke et al (1992) even found a significantly higher metabolic rate when they slept in a metabolic ward, than when they slept at home before coming to the laboratory, probably because at the ward they had to sleep in an unfamiliar bed and in a foreign environment. When subjects are brought to the laboratory in the morning, the question arises as to how much resting time is required, before BMR is measured. According to Ferro-Luzzi, experiments have shown that 30 min are enough in persons who have not previously been engaged in heavy physical activity. The current analysis should provide answers to several issues discussed at the meeting and considered in need of further analysis and clarification. In particular it should enable to decide whether predictive equations can remain general or should be population-specific (taking into account ethnicity, geographic regions/climate or body composition), linear or non-linear (depending on which results in the smaller residual values), etc. The importance of taking height into consideration in predictive equations was briefly discussed. Apparently Schofield tested whether including height would make a difference in the prediction of group BMRs and found that it did not.

Even though large numbers of BMR data can be found in the literature, there are age and population groups for which an adequate data base does not yet seem to exist. Underrepresented groups are infants, children, the elderly and almost any age group from developing countries. For the establishment of predictive equations the overrepresentation of individuals with specific characteristics also needs to be avoided.

In his paper, Durnin cited several papers leading to the conclusion that the intra-individual variability of BMR in adults was in the order of 3%. Henry agreed with this figure for men, but argued that in women the menstrual cycle can increase this variability to 8-10%.

In his paper, Durnin expressed the view that, on a population basis and up to a moderate level of fatness (BMI < 30), there was nothing to be gained by expressing BMR per kg lean body mass rather than body weight. In the discussion, reference was made to Garby's work suggesting that most of the inter individual variability in BMR is probably attributable to differences in body composition and that the relative size of organs, which have a high metabolic rate can make a difference, notably in chronically undernourished individuals.

The question was raised, if there are ethnic or geographic differences in BMR. Butte et al studied BMRs of white and black adolescent girls in Houston. In absolute terms, black girls had lower BMRs, but the difference disappeared when the data were corrected for body composition. Sexual maturation is likely to affect body mass, body composition and BMR and may result in differences between adolescent girls going through menarche at different ages.

Shetty et al (1986) found that the Schofield equations overestimated BMRs in Indian subjects. Henry's equations made better predictions of BMRs of populations in tropical regions. In two recent papers (Soares et al, 1993; Piers & Shetty, 1993), Shetty's group documented that BMRs of well-nourished Indian subjects, normalized for body weight, do not differ from BMRs of American subjects, but they seem to differ from Schofield equations and BMRs of certain European populations. He imputed differences, reported in the literature, to differences in ambient temperature which may not have been taken sufficiently into account in earlier studies. Torun suggested that differences in body composition could also provide a partial explanation. On average, BMRs measured in tropical regions are 4-5% lower than BMRs measured in temperate zones.

Differences have also been observed in the same subjects, measured in temperate and tropical regions. Henry suggested that such differences could be due to weight loss or disease. Shetty referred to a study by Mason (1944) who found changes in BMR, even when the latter was normalized for body weight.

A research assistant, under the supervision of Shetty and Durnin, is re-analyzing the information that is currently available on the energy cost of various activities.

The issue as to whether and how much of an energy allowance ought to be made for discretionary activities was discussed at some length. To maintain physical fitness and promote cardiovascular health, the 1985 FAO/WHO/UNU report recommended 20 min of vigorous exercise per day to adults with a sedentary life style and included in its recommendations the energy required for this exercise. Most of the discussants agreed with Durnin that there was only very little scientific evidence relating different levels of physical activity to health, but, for various reasons, the majority felt that these earlier recommendations should nevertheless be maintained.

Prentice collected and analyzed nearly 1000 data on total energy expenditure obtained with the doubly labeled water (DLW) method. More than half of these studies included also BMR measurements, so that PAL factors could be calculated and compared with PAL values obtained with other methods. Only few, mostly institutionalized individuals have PALs below 1.4, but, on average most PAL values obtained with DLW appear somewhat higher than expected and than contained in previous reports. UK dietary reference values, for instance, had assumed that a PAL of 1.4 was representative of light, 1.6 of moderate and 1.7 of heavy occupational activity in an otherwise non-active, non-occupational life style. Average PALs of sedentary people obtained by the DLW method correspond to values that were considered representative of a moderately active life style. Some of the highest values (2.42.6) look suspect because, according to PALs obtained from calorimetry, they presuppose that people spend a considerable amount of time at 70-80% of VO_{2 max} which happens only rarely in real life. It has to be acknowledged (and perhaps provides a partial explanation) that the representativeness and generalizability of the DLW data obtained till now are limited.

The group recognized that injuries and various diseases can affect energy requirements, but was not prepared to deal with such special situations in detail. It recommended a compilation and analysis of information that is currently available in this area, but expressed a preference for presenting such information separately.

References

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Turley KR, McBride PJ, Wilmore JH (1993): Resting metabolic rate measured after subjects spent the night at home us at a clinic. Am. J. Clin. Nutr. 58, 141-144.

(introductory text...)

PS Shetty¹, CJK Henry², AE Black³ and AM Prentice³

¹ Human Nutrition Unit, Department of Public Health & Policy, London School of Hygiene and Tropical Medicine, 2 Taviton Street, London WC1H OBT; ² School of Biological & Molecular Sciences, Oxford Brookes University, Headington, Oxford OX3 OBP; ³ Dunn Clinical Nutrition Centre, Hills Road, Cambridge CB2 2DH.

Descriptors: Human energy requirements, basal metabolic rate, adaptation, total energy expenditure, physical activity level

Introduction

The FAO/WHO/UNU Expert Consultation (1985) adopted the principle of relying on estimates of energy expenditure, rather than energy intake from dietary surveys, to estimate the energy requirements of adults. Obtaining data on the energy expenditure of adult males and females has thus gained importance. Since the available reliable data on habitual energy expenditure in free-living adults has been limited hitherto, measures and/or predictions of basal metabolic rate (BMR) have also attracted more attention. The BMR of an individual can simply be defined as the minimal rate of energy expenditure compatible with life. It is measured under standard conditions of immobility, in the fasted state (12-14 h after a meal), and in an ambient environmental temperature between 26 and 30º C, to prevent activation of heat generating processes such as shivering. It can be quantified by direct or indirect calorimetric techniques, the former measuring the heat output directly, while the latter measure oxygen consumption (and carbon dioxide production), which are then appropriately converted to their energy equivalents. The BMR of adults can also be predicted with reasonable accuracy (i.e. with a coefficient of variation of 8%) from predictive equations. Since BMR constitutes between 60 and 70% of the total energy expenditure, it now forms the basis of the factorial approach for the assessment of energy requirements of adults. In this paper, several issues related to BMRs of adults, their relationship to total energy expenditure and physical activity levels and how they influence the estimation of adult energy requirements, are discussed. These include the methodology of BMR measurement, variability of BMRs and total energy expenditure (TEE), and the constancy of BMR over time in adults. A discussion on the usefulness and limitations of equations for the prediction of BMRs from anthropometric parameters, such as body weight. and the likelihood of ethnic variations in BMR and the effects of migration from one climatic zone to another are also included. The chapter also deals with metabolic adaptation and discusses the relevance of this to estimating adult energy requirements. It concludes with a discussion on the factorial approach to assessing total energy expenditure by the use of physical activity levels (PALs) and provides a review of the data to date on doubly labelled water measurements of total energy expenditure and PALs of free-living healthy adults.

Methodological implications of basal metabolic rate measurement in adults

Whether the methods or techniques used to measure the BMR of adults contribute to variability in BMRs is a question that needs to be addressed if BMRs are the basis for estimating total energy expenditure by the factorial method. BMR measurements involve, in the first instance, an estimation of the oxygen consumption of the individual which is then converted into units of heat or energy output. Comparisons of techniques using different equipment, such as Douglas bags, oxylogs, metabolators, and ventilated hoods, show no significant differences between estimates of oxygen consumption of adults obtained by two or more of these techniques in the same individual at the same time (Segal, 1987; Soares et al, 1989).

During the subsequent conversion of oxygen consumption (in ml or litres of oxygen) to energy output or expenditure (in kcals, kJ or MJ) many assumptions are made that can introduce errors into estimates of the BMR. The most important of these potential sources of error are:

(1) the value attributed to the non-protein respiratory quotient (NPRQ or RQ) when the method used does not actually measure the NPRQ from the CO₂ production;
(2) the equations used in the calculations to convert O₂ consumption and CO₂ production when measured (whether or not nitrogen excretion in the urine is estimated) into units of energy output and
(3) the corrections that are made for differences in the volumes of inspired and expired air when both CO₂ output and O₂ consumption are measured.

It is often believed that these factors do not influence the final results over the physiological range of observed RQs. This is not correct. The difference between the true NPRQ of the subject and the assumed RQ (be it 0.82 or 1.0) in the calculation can introduce an error of over 5% for the same measure of O2 consumption in a subject, if the true RQ is as low as 0.77 (Shetty et al, 1986) (Table 1). For instance, when an oxylog which assumes an RQ = 1 is used and the true RQ of the subject is less than 0.8, the difference in the estimate of energy expenditure is reported to be of the order of 4.6% (Garlick et al, 1987).

Brockway (1987) conclusively demonstrated that differences in the final estimate of BMR due to the various formulae used in the conversion of O2 consumption values, i.e. those of Weir (1949), Consolazio et al (1963), Brouwer (1965) and Passmore & Eastwood (1986), may extend over a range of about 3%.

McLean (1984) has argued that the not uncommon assumption that O₂ consumption = outlet ventilation × O₂ concentration difference, can introduce an error in the estimate of O2 consumption of the order of ± 6%. This large error emphasises the importance of correcting for differences in volume flow of inspired and expired air when measuring BMR. McLean (1984) states that this large error is fortunately cancelled out by an error in the calorific value of O₂ consumed as the RQ of the subject varies. However, this is not necessarily true. For example, the calculation of energy output from values for an O₂ deficit of, say, 5%, for a fixed volume of 5 1/min at an assumed RQ of 0.82 using Weir's formula introduces a net error of between +4.5 and -4.0% for a range of physiological RQs of between 0.71 and 1.0, respectively (Shetty et al, 1986).

The errors that arise as a result of the assumptions made in the calculation of BMR from measures of O₂ consumption, even with the tacit assumption that the measures of O₂ consumption are fairly accurate in themselves, imply that differences in BMR between individuals or groups of individuals of the order of 5% do not have biological significance unless the methodology, the assumptions made and the calculations used to arrive at the BMR values are comparable. It is, of course, assumed that certain stipulated minimal experimental prerequisites, such as absence of gross muscular activity, a post-absorptive state, thermoneutral environment, etc. are strictly met in order to ensure basal levels of metabolism, so that measurements made in different individuals, or in the same individual over time, are comparable and that biological significance can be imparted to differences that are observed under these conditions.

Table 1 Sources of error in conversion of oxygen consumption to energy output between assumed and true respiratory quotients (RQs)^a

	Error in volume of O2 consumedb			Net error in energy
True RQ	Uncorrected volume	Corrected volumec	Error caused by caloric value of the RQ used in equation (%)	Uncorrected volume	Corrected volume
Assumed RQ=0.82 in equation
(1)RQ=0.71	-7.1	-2.7	+2.6	-4.5	-0.1
(2)RQ=0.79	-5.3	-0.8	+0.7	-4.6	-0.1
(3)RQ=0.82	-4.5	0	0	-4.5	0
(4)RQ=0.94	-1.6	+3.1	-2.7	-4.3	+0.4
(5)RQ=1.00	0	+4.8	-4.0	-4.0	+0.8
Assumed RQ=1.0 in equation
(1)RQ=0.71	-7.1	-	+6.8	-0.3	-
(2)RQ=0.79	-5.3	-	+4.8	-0.5	-
(3)RQ=0.82	-4.5	-	+4.1	-0.4	-
(4)RQ=0.94	-1.6	-	+1.3	-0.3	-
(5)RQ =1.00	0	-	0	0	-

^aFor expired air volume = 51/min; O₂ deficit = 5%; and Weir's equation [calorific value of 11 of O₂ = 3.9+1.1(RQ)].
^b [(Assumed-true)/true] × 100.
^c Corrected volume indicates the value of O₂ consumed that has been corrected for difference in inspired and expired volume at the given RQ.

Variability in adult BMRs

Inter-individual variability

It is generally recognised that in a group of apparently healthy and comparable individuals there is a considerable between-individual or inter-individual variation in habitual, total daily energy expenditure. This, however, is not as large as the inter-individual variation in energy intakes. Edholm (1961) reviewed a number of studies where repeated measurements of total energy expenditure had been made and reported that the coefficient of inter-individual variability was of the order of ± 12.5% on a body weight basis. In recent studies, in which energy expenditure was measured in a respiratory chamber and both the intake and the physical activity levels were controlled, the inter-individual coefficient of variation (CV) ranged between 7.5 and 17.9% (Garby et al, 1984; De Boer, 1985). lt appeared that the CV depended upon the variations in body size; the larger the variation in body weight among subjects, the larger the CV of total energy expenditure.

Comparisons of subjects of similar body weight and body composition showed an inter-individual CV of BMR of 13% (Jéquier & Schutz, 1981). Other reports suggest that the inter-individual CV of BMR varies between 7.9 and 12.0% in both male and female subjects when measurements are made under conditions of controlled intake and physical activity (Schulz, 1984; De Boer, 1985; Daly et al, 1985). The inter-individual CV of BMR was 9.2% when intake was controlled at two levels of physical activity in males (Dallosso & James, 1984) and of the order of 11.7% in free-living males who had a CV of body weights of 15.2% (Shetty et al, 1986). In the few instances where the CVs of inter-individual variation in BMR and TEE have been simultaneously computed (in male subjects who maintained body weight) they were of the order of 10.2% and 10.3%, respectively (Dallosso et al, 1982). This last report emphasises that the inter-individual CV of TEE is reflected in the CV of BMR, since the latter makes a substantial contribution to the total energy output of an individual.

Intra-individual variability in BMR

Sukhatme & Margen (1982) argued that within individual variations in intakes are more important than between-individual variations, and that the observed inter-individual variations can largely be explained in terms of the intra-individual variations. These investigators contend that the well-documented variation in intakes observed among apparently healthy individuals indulging in similar levels of activity is evidence that different individuals operate at different levels within what they consider to be the intra-individual range of 'costless' adaptation. This has resulted in an unsubstantiated claim that intra-individual variations in energy expenditure are also large, with a high coefficient of variation even in subjects accustomed to similar levels of physical activity every day, and that this wide variation needs to be considered when assessing the energy requirements of an adult (Sukhatme & Narain, 1983). Table 2 summarises some recent data on intra-individual variations in BMR obtained from repeated measurements in the same individual when: (1) energy intake and physical activity were controlled while in a respiration chamber Jéquier & Schutz, 1981); (2) energy intake alone was controlled and BMR measurements were made on two levels of physical activity over a 24-h period (Dallosso & James, 1984); (3) physical activity was kept constant over 24 h but the energy intake was varied at two different levels (Dallosso et al, 1982) and (4) when BMR measurements were made in free-living subjects in whom neither intake nor activity were regulated (Soares & Shetty, 1986). In these studies, the CV of the measured BMR has never exceeded 5% and is frequently below 3%.

Estimation of the CV of 24-h energy expenditure measurements by whole body calorimetry also leads to similar conclusions (Table 2). Several studies (Dallosso et al, 1982; Webb & Abrams, 1983; Webb & Annis, 1983; Garby et al, 1984; De Boer, 1985) have confirmed the low CV of intra-individual measurements of 24-h energy output when both energy intake and physical activity are tightly regulated, as is usual in a calorimetry protocol. Even when energy intakes are varied at two different levels, but the activity patterns when inside the calorimeter are maintained constant, the within individual CVs do not vary by more than 2.4 or 2.6% (De Boer, 1985). When energy intakes are unaltered, but 24-h energy expenditure is varied at two different levels of activity in the same subject, a large CV (of the order of 9.8%) is seen. This is to be expected, since the 24-h energy output has been deliberately altered in these subjects. However, even in these experimental situations the CV of the measured BMR in the same subjects at the two different levels of activity while in the calorimeter is no more than 2.2% (Dallosso & James, 1984).

Table 2 Intra-individual variations in basal metabolic rate and total energy expenditure

	Sex	CV (%)
Basal metabolic rate
1. Energy intake and physical activity controlled Jéquier & Schutz (1981)	F	2.0
2. Energy intake controlled; physical activity varied Dallosso & James (1984)	M	2.2
3. Energy intake varied: physical activity controlled Dallosso et al (1982)	M	2.8
4. Energy intake and physical activity uncontrolled Soares & Shetty (1986)	M	2.9
Total energy expenditure (24 hour)
1. Energy intake and physical activity controlled
Dallosso et al (1982)	M	1.5
Webb & Abrams (1983)	F	3.3
Webb & Annis (1983)	F	6.0
Garby et al (1984)	M	2.2
De Boer (1985)	F	1.9
2. Energy intake varied; physical activity controlled
De Boer (1985)	F	2.4
De Boer (1985)	F	2.6

The intra-individual variation of total energy expenditure obtained from repeated measurements based on doubly-labelled water studies, where body weight, activity and physiological status remained unaltered, have also been recently compiled. Data from nine such studies are summarised in Table 3. They confirm that the CV is reasonably small, despite measurements being made with doubly-labelled water in the free-living state (Black et al, 1995). The mean within-subject CV of 79 individuals, in whom more than one doubly-labelled water measurement was made, was 8.9%. This includes both the methodological error and the variation in activity levels.

Recent evidence thus supports the conclusion that within-subject variations in BMR are small and insignificant, even when energy intake and physical activity are uncontrolled, (Shetty & Soares, 1988). This effectively refutes the Sukhatme-Margen hypothesis.

The constancy of BMR of adults over time

A critical analysis of the historical data on variations in BMR over long periods of time indicates that the BMR of an individual is constant over time. (Shetty & Soares, 1988; Henry, Hayter & Rees, 1989). More recent data, shown in Table 4, confirm this. BMR in 14 subjects (controls and obese), each tested on five consecutive days, had a CV of about 2% (Jéquier & Schutz, 1981); the BMRs of 166 male subjects studied on two separate occasions had a CV of less than 3% (Dallosso et al 1982). Other studies (Garby et al, 1984; Lammert et al, 1987; Soares & Shetty, 1986; 1987; Henry, Hayter & Rees,1989) support the view that the intra-individual, variations in BMR, measured over a period of days, weeks or even months or years, are small and probably not significant.

Table 3 Within-subject coefficient of variation in doubly labelled water measurement of total energy expenditure where activity, weight and physiological status are unchanged

Subjects	No. of subjects	No. of measurements	CV (%)
Adolescents confined to a metabolic facility during two periods of experimental diet. No control on activity	9	2	6.8
Twice in the calorimeter with the same imposed exercise	4	2	9.1
Mothers measured pre-pregnant and at 16 weeks of pregnancy	9	2	7.4
Mothers in weeks 4, 8 and 12 of lactation	10	3	7.9
Males living in a metabolic facility but following normal occupation. First and last measurements at same weight and activity	8	2	8.1
Males living in metabolic facility but pursuing usual sedentary occupation	7	3	7.1
Physiotherapy students. No apparent change in activity	5	2	10.5
Free-living men	17	2 or 3	11.0
Free-living men during two experimental diets	10	2	10.9
Mean of 9 studies			8.9

CV = within-subject coefficient of variation.

Table 4 Intra-individual variations in BMR (MJ/d) with time

			Coefficient of variation (%)
	Sex	n	Days	Weeks	Months	Years
Jéquier & Schutz (1981)	F	(14?)	2
Garby & Lammert (1984)	M	(22)	2.4
	M	(23)		2.2
Lammert et al (1987)	M	(7)	3.5	4.3
	M	(7)		4.8
Soares & Shetty (1986)	M	(5)		3.1
Soares & Shetty (1987)	M	(5)		2.9
	M	(10)			2.5
Henry et al (1989)	M	(9)				4.0

Table 5 Intra-individual variations in energy expenditure and body weight over time

				CV (%)
	Group	n	Time intervala (months)	EE	Body weight
BMRb Males	Entire	(10)	18.2 ± 2.3	2.5	2.5
			(7.0 33.0)
	Weight stabled	(5)	14.4 ± 2.9	3.2	0.6
			(7.0-21.0)
	Weight change	(5)	22.0 ± 3.0	1.8	4.3
			(15.0-33.0)
24-h EEc Females	Entire	(10)	9.5 ± 2.0	2.4	2.4
			(2.0-24.0)
	Weight stabled	(5)	7.2 ± 2.0	2.0	1.1
			(2.0-13.0)
	Weight change	(5)	11.8 ± 3.3	2.7	4.1
			(5 0-24.0)

^a Mean ± s.e.m.; figures in parentheses = range.
^b Soares & Shetty (1987).
^c De Boer (1985) (recalculated).
^d Considered stable if change < 2.0% of initial body weight.

A critical analysis of the variations seen in BMRs or in 24-h energy output over a period of up to 2 years, when intakes and activity patterns were not controlled over this length of time, is presented in Table 5. The BMRs of 10 male subjects measured on at least three occasions over a period of 6-36 months showed a mean CV of intra-individual differences (separated from measurement error) of the order of 2.5% (Soares & Shetty, 1987). Five of the 10 individuals who had body weight changes of > 2% had even smaller CVs (1.8%) than those who had smaller changes in body weight over the period of time. Measurements of 24-h expenditure by calorimetry in 10 females over a period of 24 months also showed small CVs of 2.4%; however, smaller CVs were seen in those five women who had <2% body weight change over this period (De Boer 1985). In these females, neither intakes nor activity patterns were controlled, except during calorimetry. These recent data confirm the conclusion that BMRs of individuals are relatively constant over a period of several years, despite reasonable fluctuations in body weight, when no attempt is made to regulate either energy intake or physical activity patterns.

Predictive equations to estimate bmrs of adults

BMR can be accurately measured by direct or indirect calorimetry, but it is simpler, in practice, to use predictive equations. By 1951, a plethora of equations to predict BMR were in existence, some easier to use than others. The predictive equations of Aub & Du Bois (1917) tended to over-estimate BMR, as the subjects measured by these authors were under thermal stress and anxious. In contrast, Robertson & Reid's equations (1952) underestimated BMR, as they were based on the lowest recorded values of metabolic rate. Finally, while Quenouille's analysis (Quenouille et al, 1951) was comprehensive, the equations were too complicated to be of routine practical use. Recently Schofield (1985), published predictive equations that were used for the FAO/WHO/UNU report (1985) and thereby became the basis for estimating energy requirements in man.

The Schofield analysis and equations, based on a database of 114 published studies of BMR, representing 7173 data points, is the largest and most comprehensive analysis of BMR to date. While the Schofield equations predict BMR accurately in many individuals from temperate climates, they seem to be less accurate in predicting BMR in populations in the tropics (Henry & Rees, 1991; Piers & Shetty, 1993) and North America (Clark & Hoffer, 1991) and appear to over-estimate BMR in many populations (Piers & Shetty, 1993; Soares, Francis & Shetty, 1993; Hayter & Henry, 1993).

Table 6 List of Italian subjects used in the database of Schofield

Study	n	Sex	Age	Subject details
Pepe & Rinaldi (1936)	217	M	6-16	None provided
	143	F	5-12	None provided
Pepe & Perrelli (1937)	257	M	5-16	None provided
	235	F	5-12	None provided
Felloni (1936)	532	M	19-25	Students of the Royal Fascist Academy
Lafralla (1937)	213	M	14-20	Students of Naples Royal Military College
Lenti (1937)	525	M	20-25	Military Servicemen
Pepe (1938)	252	M	18-24	Students of Royal Naval Academy
Occhiolo & Pepe (1939)	247	F	20-67	Various social groups
Occhiulo & Pepe (1940)	571	M	22-54	Police officers
Granall & Busca (1941-1942)	186	M	16-55	Labourers and miners
Total	3370

All references in Schofield, Schofield & James (1985).

There are several reasons to suggest that there is a need to re-analyse the worldwide data on BMR using stringent inclusion criteria in order to generate more valid equations to predict BMR in humans worldwide.

(1) Schofield collected data for his study a decade ago. Since then several laboratories have produced a large number of good quality BMR data for different age, sex and ethnic groups that also need to be included.
(2) Henry & Rees (1988) have identified over 1500 data points for Caucasian subjects in the old literature that meet all the strict criteria used by Schofield, but were not included; these values also need to be incorporated.
(3) Certain age groups (children and adults over 60) are under-represented, and these parts of the database need to be expanded in order to generate more reliable predictive equations for those age ranges.
(4) Close examination of the Schofield database (Table 6) shows that of the approximately 6000 BMR values for males between 10-60 years, over 3000 (50%) come from Italian military subjects. The validity of including such a disproportionate number of Italian military subjects may need to be queried, firstly, because the Italian group appears to have a higher BMR per kg than any other Caucasian group (Hayter & Henry, 1993), and secondly, because they may not be representative of the general Italian population. In fact Schofield (1985) noted that when Italians were isolated from the rest of the sample and compared with the derived BMR predictive equation there was a significant lack of fit. The inclusion of this disproportionately large Italian group with a higher BMR per kg may have artificially elevated the predictive equations generated by Schofield.
(5) If an analysis of the BMRs of people from the tropics and sub-tropics (Henry & Rees, 1991) points to a lower BMR than predicted by the Schofield equations, this may be due mainly to the bias imposed by the dominance of the Italian data. More recent data in fact support the view that BMRs of people in the tropics are not different from those in temperate regions (North America and Europe), provided the subjects are well nourished (Henry et al, 1987; Hayter, 1992; Soares, Francis & Shetty, 1993; Piers & Shetty, 1993).

There is thus mounting evidence to suggest that the Schofield equations may be overestimating BMR in many populations, leading to an over-estimation of their energy requirements. This has both practical and political implications. In the light of this, a critical reassessment of the worldwide data on BMR is required.

Figure 1 Linear least square regression lines of basal metabolic rates (MJ/d) on body weight of males (A) and females (B) of Indian, Chinese, North European and North American and Italian groups.

Figure 1 Linear least square regression lines of basal metabolic rates (MJ/d) on body weight of males (A) and females (B) of Indian, Chinese, North European and North American and Italian groups.

Ethnic differences in BMR

In addition to the observation that the Italian data revealed a higher BMR/kg, Quenouille et al (1951) and subsequently Schofield et al (1985) noted that Asiatic subjects (Indians and Chinese) had a BMR 10-12% lower than Europeans. Indeed such a claim for a lower BMR in Indians had first been reported by Mukerjee & Gupta (1931) and Krishnan & Vareed (1932). Extending the observations reported by Schofield et al (1985), Henry & Rees (1988; 1991) showed that the BMR was 8-10% lower in a range of other tropical residents (Filipino, Indian, Japanese, Brazilian, Chinese, Malay and Javanese) as well. In contrast to these earlier reports of low BMR in tropical peoples, recent studies have shown no difference in BMR between Indians and Europeans (Henry, Piggott & Emery 1987; Hayter 1992; Soares, Francis & Shetty, 1993).

Table 7 Recent studies of basal metabolic rate in male migrants from tropical to temperate climate

Study	Subjects	n	Age	Height (m)	Weight (kg)	kJ/kg/d
DeBoer et al (1988)	African	8	31	1.71	69.9	91	SMR
	European	7	30	1.84	78.4	87	SMR
	Chinese	7	33	1.67*	62.5*	98*	SMR
	Indian	8	26	1.72*	58.9*	98*	SMR
Henry et al (1987)	Asian	11	21	1.63	56.2	115	RMR
	British	11	25	1.68	57.4	108	RMR
Ulijaszek & Strickland (1991)	Gurkhas	17	25	1.67**	67.1	105	BMR
	British	17	23	1.73	66.8	110	BMR
Geissler & Aldouri (1985)	British	15	25	1.74	68.1	117	RMR
	Asian	15	27	1.68	63.9	107***	RMR
	African	15	28	1.71	67.1	101***	RMR
Blackwell et al (1985)	American	8	31	1.75	75.0	93	SMR
	Asian	8	25	1.66*	53.0*	108	SMR
Dieng et al (1980)	W. African	10	34	?	73.0	115	RMR
	French	10	36	?	75.0	111	RMR
Hayter & Henry (1993)	Trop 1	9	23	1.70	63.8	113	RMR
	Temp 1	9	25	1.76	67.5	114	RMR
	Trop 2	21	25	1.69	58.2	118	RMR
	Temp 2	20	23	1.77	68.3	114	RMR

Significantly lower with *P < 0.05, **P < 0.01, ***P < 0.005, by the statistical test used in the referred papers. SMR = sleeping metabolic rate, RMR = resting metabolic rate, as specified in the cited papers, expressed in kJ/kg/day. Trop (1 & 2) = two groups of tropical migrants, Temp (1 & 2) = two groups of temperate climate residents. All references in Hayter & Henry (1993).

One approach to studying this problem is to compare the BMR in different population subgroups at similar body weights, thus eliminating a major source of variance in BMR associated with body weight. BMR predictive equations, generated for different population groups over defined body weight ranges, can then be used for comparison. To achieve this, the databases of Schofield et al (1985) and Henry & Rees (1988), were combined and used for analysis (Hayter & Henry, 1993). The resultant data set contained 7737 individual measurements of body weight, height, sex, age and the geographical origin of subjects. The 18-30 year age group was considered most suitable for detailed analysis, as it had a BMR database of 2999 males and 874 females. This age range also showed a negligible effect of age on BMR. Sub samples of Indians (210 males and 137 females), Chinese (200 males and 122 females), North Americans/North Europeans (478 males and 372 females) and Italians (169 males and 135 females) were available for analysis.

The linear least square regression lines of BMR on body weight of those from ethnic subsamples for both sexes are presented in Figure 1. Italian males and females, who comprised 45% of the Schofield database, turn out to be again the most divergent group. The apparently elevated BMR of Italians and their numerical dominance in the sample appear to bias the predictive equations. This may explain why the BMR of Indians and other tropical populations is overestimated by the Schofield equations.

Effects of migration from tropical to temperate climate on BMR

An analysis of the available literature on the effects of recent migration (over 2-4 weeks) from the tropics to temperate zones, and of follow-ups till 9 months later, shows that there are no differences of any significance between the BMRs per kg body weight of tropical migrants and those of their peers born and resident in temperate zones, provided the subjects are from privileged backgrounds and well-nourished (Table 7). There is thus no reason to believe the BMRs of well-nourished tropical individuals are lower than those of European or North American subjects.

Adaptation and energy requirements

A working definition adopted by the FAO/WHO/UNU Expert Consultation (1985) on adaptation states that it is 'a process by which a new or different steady state is reached in response to a change or difference in the intake of food and nutrients'. This definition attempts to deal with both short-term and long-term adaptation; the word 'new' having relevance to short-term responses to acute changes in a subject who is in balance, while the word 'different' refers to long-term changes in individuals or groups exposed habitually to different environmental or nutritional conditions. Three general points were made by this Report in relation to both types of adaptation:

(1) The concept of a 'steady state' is relative, and the time-scale over which a state may be considered steady or stable varies for different functions.
(2) Adaptations are of different kinds: metabolic, biological/genetic, and social/behavioural.
(3) It follows from the above that adaptation must imply a range of steady states, and hence it is impossible to define a single point within the range that represents the 'norm'. Implicit in this is the understanding that different adapted states may have advantages and/or disadvantages.

The concept of a range of adapted states, each with possible advantages and disadvantages, while implying a respect and understanding for different biological and cultural situations, can also serve to condone the acceptance of double standards and the endorsement of the status quo.

An adaptive response is an inevitable consequence of a sustained perturbation in the environment and may be genetic, physiological and/or behavioural. These different types of adaptation are not mutually exclusive; they interact with each other at several levels. Every adaptation has a potential cost. Reduced physical activity in a child may reduce the interaction with the environment needed for normal development. Reduced physical activity in adults, with no apparent biological cost, may have serious economic and social consequences. At some point, adaptation will begin to have both biological and social costs. The processes and costs involved may be overt or covert, reversible or irreversible, and transient or permanent. Adaptation, both in the short-term and in the long-term, is a relatively slow process and should be distinguished from the rapid regulatory role of homeostatic mechanisms. A homeostatic response in a biological system may neither have additional costs to the organism nor lead to compromise in its function, capability, or performance, in contrast to an adaptive response which may do both in order to further the survival of the individual.

Metabolic adaptation

The suggestion that the energy metabolism of individuals is variable and adaptable, and that allowances need to be made for this when making estimates of human energy requirements, has been based on several important publications that have drawn attention to the possibility of such physiological variability in energy utilisation between individuals (Durnin et al, 1973; Edmundson, 1980) and within individuals (Sukhatme & Margen, 1982; Sukhatme & Narain, 1983). Healy (1989) criticized the validity of Sukhatme's approach based on autocorrelation. Norgan (1983) critically evaluated the following four types of evidence that have been adduced to illustrate this variation which is purported to result in adaptation in human energy metabolism:

(1) in any group of 20 or more similar individuals, energy intake can vary as much as two fold (Widdowson, 1962);
(2) large numbers of apparently healthy active adults exist on energy intakes that are lower than their estimated energy requirement (Durnin, 1979);
(3) the efficiency of work and work output is variable per unit energy intake (Edmundson, 1979); and
(4) observations based on studies of experimental or therapeutic semistarvation (Benedict et al, 1919; Keys et al, 1950; Grande, 1964; Apfelbaum, 1978) and overfeeding of humans (Sims, 1976; Norgan & Durnin, 1980).

Differences in body size, levels of physical activity and systematic errors in the estimation of energy intakes may provide explanations for most of these observations (Norgan,1983). However, what is implied or explicitly stated by the proponents of metabolic adaptation is that metabolic efficiency and mechanical work efficiency of the individual are variable and show an adjustment to variations in the levels of energy intake. The decrease in oxygen utilisation of the residual active tissue mass of an individual seen during experimental (or therapeutic) semistarvation (Keys et al, 1950) constitutes the most important biological argument for metabolic adaptation. On the basis of these observations it has been assumed that an enhanced metabolic efficiency is also a characteristic of individuals who are habitually on diets that are low in energy content.

Several physiological mechanisms, chiefly hormonal, operate to account for the changes in the metabolic activity of the tissues to enhance their metabolic efficiency, when a well nourished individual's energy intake is restricted (Shetty, 1990). The activity of the sympathetic nervous system is toned down, signalled by a decrease in energy flux, while the energy deficit lowers insulin secretion and initiates changes in peripheral thyroid metabolism. The latter are characterised by a reduction in the biologically active T₃ and an increase in the inactive reverse T₃. The reduction in the activities of these key thermogenic hormones acts possibly in a concerted manner to lower cellular metabolic rate. Changes in other hormones such as glucagon, growth hormone and glucocorticoids may influence these changes and at the same time, in association with the insulin deficiency, promote endogenous substrate mobilization which will lead to an increase in circulating free fatty acids and ketone bodies. The elevated free fatty acid levels, alterations in substrate recycling and protein catabolism will also influence this process. These hormonal and metabolic changes that accompany energy restriction aid the survival of the organism during restricted availability of exogenous calories. Hence these physiological changes that are associated with body weight and body composition changes have been considered as an indication of metabolic adaptation which is seen in a previously well nourished individual and which are aimed at increasing the 'metabolic efficiency' of the residual tissues in the body at a time of energy deficit.

It has been assumed that the physiological and metabolic responses of an adult on a low plane of habitual intake are similar to, and can be explained on the basis of, the sort of physiological changes that occur during experimental or therapeutic semi-starvation in previously well nourished adults. Ferro-Luzzi (1985) summarised the thinking at that time on ways in which an individual on habitually low intakes could respond to a sustained energy imbalance by metabolic adaptation. Metabolic adaptation was represented as a series of complex integrations of several different processes that occur during energy deficiency. These processes were expected to occur in phases which could be distinguished, and it was suggested that a new equilibrium could be established at a lower plane of energy intake. At this stage, individuals who had gone through the adaptive processes that occur during long-term energy deficiency, were expected to exhibit more or less permanent sequelae or costs of adaptation, which included a smaller stature and body mass, an altered body composition, a lower BMR, a diminished level of physical activity and possibly a modified or enhanced metabolic efficiency of energy handling by the residual tissues of the body. However, a large number of measurements made during the last decade, in subjects in environments that predispose to low energy intakes, do not confirm the existence of an enhanced metabolic efficiency (Srikantia, 1985; McNeill et al, 1987; Soares & Shetty, 1991). It would therefore appear that an increase in metabolic efficiency in the BMR component of energy expenditure, which has been hitherto considered to be the cornerstone of the beneficial, metabolic adaptation to long-term energy inadequacy, is of doubtful existence. It is more likely that a lower BMR per kg body mass in the chronically undernurished is an arte-fact attributable to the changes in body composition, more specifically the disproportionate reduction in muscle tissue with a normal or even increased non muscle or visceral organ size (Shetty, 19933, possibly enhanced by an increase in number of infective episodes in individuals living in such environments. Hence it is highly unlikely that metabolic: adaptation is of any relevance in chronic energy deficiency, as opposed to a situation where normal individuals are energy restricted.

Behavioural adaptation

The behavioural adaptations in physical activity patterns that accompany low energy intake are related to the individual's allocation of time and energy to different productive and leisure activities and to the biological as well as the economic consequences of these altered behavioural patterns. When there is both a fall in energy intakes and an increased demand for energy expenditure at work, for instance during seasonal agricultural activities, individuals adjust the time they allocate to different tasks; more time is given to work activities and less time and energy to productive tasks at home or socially desirable or pleasurable activities (Immink, 1987).

Lower energy intakes and stunting in preschool children were associated with lower levels of physical activity (Rutishauser & Whitehead, 1972). An analysis of physical activity patterns during voluntary reduction in food intake showed that the behavioural response to a deficient intake and associated weight loss was a change in the pattern of activity: lower effort discretionary activities replaced those which needed greater effort, while obligatory activities were not affected (Gorsky & Calloway, 1983). Rural Guatemalan men were able to carry out the specific agricultural task allocated to them, but took a longer time doing it (Torun et al, 1989); they also took a longer time to walk home and spent nearly 3 h resting or taking a nap or indulging in very sedentary activities during the rest of the day. Rural women in India and Africa with marginal energy intakes and low BMIs have been observed to spend fewer hours working per day and more time resting than better-off individuals in the same socio-economic milieu (Ferro Luzzi et al, 1992, Shetty & James, 1994). Waterlow (1990) computed the saving in energy that may result from doing a task (e.g. walking a certain distance) slowly rather than quickly, at the cost of having less time for other activities. He drew attention to the fact, which could be relevant, that slow muscle fibres are more efficient than fast ones in terms of ATP used per unit force developed.

Appreciable increases in both activity at work and in discretionary activities without concurrent changes in body weight were reported in male agricultural workers whose diet was supplemented (Viteri & Torun, 1975). There was also an improvement in their sense of wellbeing. Similar improvements in subjective well-being with very small body weight increases have been seen in lactating Gambian women when provided with supplementary food (Whitehead et al, 1978). All these studies support the existence of behavioural adaptation in the spontaneous, free-living physical activity of adults which may limit their work output, economic productivity and income-generating ability, at the same time restricting their socially desirable and discretionary or even their obligatory physical activity. This latter behavioural adaptation becomes an important survival strategy. Recommendations for energy requirements have to take into consideration the energy needs to cope with the cost of behavioural adaptation in adults.

Total energy expenditure (TEE) and physical activity levels (PAL) in adults: doubly-labelled water data

Over the past decade a new technique using stable isotopes has revolutionised the study of human energy expenditure. The doubly-labelled water (DLW) method permits determination of energy expenditure of free living individuals integrated over a period of, usually, between 7 and 20 days. The first data from humans were published in 1982 (Schoeller & van Santen, 1982). Since then sufficient data have accumulated to form a basis for establishing energy requirements. A database of 1614 measurements in 1123 individuals aged 2-90 years has been comprehensively analysed by Black et al (1996). Details of the methodologies employed, the database, studies included and excluded, and full references can be found in their paper.

Usage, validity and variability of the physical activity level (PAL) index

Total energy expenditure (TEE) is expressed as a multiple of BMR to determine the requirements of adults as recommended by the last FAO/WHO/UNU Expert Consultation Report (1985) on energy and protein requirements. These multiples of BMR are referred to as physical activity levels (PALs) and calculated by dividing TEE by BMR. The expression of energy expenditure (or requirements) of adults as PALs provides a convenient way of controlling for age, sex, weight and body composition and for expressing the energy needs of a wide range of people in shorthand form. The figures derived by the 1985 Consultation were based on theoretical factorial calculations, making assumptions about the energy cost and duration of day-to-day activities. The data in Table 8 on PAL values in adults are derived from actual measurements using the DLW technique. PAL provides a useful means of categorising energy requirements in a single number, taking into account differences in body size, as represented by BMR. However, the value of PAL depends both on BMR and TEE, and both have errors of measurement, so that PAL is only imprecisely estimated. The CV of BMRs, when actually measured, is very small, as described earlier, while the CV of BMRs predicted using the Schofield equations for given body weights is of the order of about 8% (Schofield, 1985). For TEE, the within-subject CV can be obtained from studies with repeated DLW measurements in persons with stable weight, activity and physiological state. Data from nine such studies, collated by Black et al (1996), have shown that the mean within-individual CV for 79 subjects was 8.9%; this includes methodological error as well as variations in activity levels. Thus, the 95% confidence limits on PALs at the individual level, assuming a measured BMR and no change in body weight or physical activity, is of the order of ± 18.5% representing about ± 0.3 PAL units on a mean PAL value of 1.65.

Table 8 also presents TEE, BMR and energy expenditure for activity (AEE) derived as TEE minus BMR. The latter expression has hitherto not been used, although a related expression, i.e. Physical Activity Ratio (PAR), has been in vogue. PAR is used as an abbreviation for multiple of BMR for an activity, and is used to provide an energy cost for a specific activity, such as sitting down, walking, etc. On the other hand, AEE represents the energy expended by an individual over and above BMR, and includes requirements for thermogenesis, including diet-induced termogenesis (DIT), and for physical activity. The usage of PAL treats it as an index of TEE adjusted for BMR. Since the PAL is a multiple of the BMR and BMR is related to body weight, it is implied that the component of TEE that represents physical activity must also be related to body weight. In theory this would only be true for those activities that involve movement of the body. The relationship of TEE to body weight suggests that most human activities do fall into this category, and the occasion on which significant physical work is done without much movement of the body, e.g. lifting heavy sacks, are relatively rare.

Table 8 Subject characteristics and energy expenditure (obtained by DLW) in different age and sex groups

		Age (y)		Height (m)		Weight (kg)		BMI (kg/m2)
Age group (y)	n	mean	s.d.	mean	s.d.	mean	s.d.	mean	s.d.
Females
18-29	89	24.4	(3.7)	1.66	(0.06)	69.2	(22.3)	25.3	(8.1)
30-39	76	33.8	(3.0)	1.64	(0.07)	67.9	(13.9)	25.2	(4.9)
40-64	47	51.6	(8.3)	1.65	(0.07)	70.0	(13.3)	25.9	(4.6)
Males
18-29	56	22.5	(3.5)	1.77	(0.07)	75.6	(18.4)	24.0	(5.3)
30-39	36	34.3	(3.3)	1.79	(0.06)	86.1	(31.4)	26.8	(8.8)
40-64	15	50.6	(8.8)	1.76	(0.06)	77.0	(10.0)	24.9	(3.0)
		TEE (MJ/d)		BMR (MJ/d)		AEE (MJ/d)		PAL
Age group (y)	n	mean	s.d.	mean	s.d.	mean	s.d.	mean	s.d.
Females
18-29	89	10.4	(2.2)	6.2	(1.1)	4.2	(1.7)	1.70	(0.28)
30-39	76	10.0	(1.7)	6.0	(0.6)	4.1	(1.5)	1.68	(0.25)
40-64	47	9.8	(1.7)	5.8	(0.7)	4.0	(1.4)	1.69	(0.23)
Males
18-29	56	13.8	(3.0)	7.5	(1.2)	6.3	(2.5)	1.85	(0.33)
30-39	36	14.3	(3.1)	8.2	(1.8)	6.1	(2.5)	1.77	(0.31)
40-64	15	11.5	(1.7)	7.0	(0.8)	4.5	(1.3)	1.64	(0.17)

The limits of human energy expenditure

Studies carried out under special conditions provide information on energy expenditure at the extremes of physical activity levels in adults and thus provide a frame of reference for evaluating values of TEE and PAL from the general population. These studies of TEE measurements using the DLW technique have been summarised by Black et al (1996). At the lower limit of physical activity, studies in non-ambulatory, chair bound subjects and in individuals confined to a calorimeter and apparently not exercising, provide a mean PAL of 1.21. This is slightly lower than the value of 1.27 suggested by FAO/WHO/UNU (1985) as the survival requirement. At the upper limit of physical activity there is a distinction to be drawn between the maximum achievable over a limited period of time and the maximum sustainable as a long-term way of life, given physical fitness and adequate food. The maximum achieved over limited periods of time was a PAL of >4.0 and TEE of 33 MJ/d in a bicycle race and a polar exploration. The maximum for a sustainable way of life may be that represented by soldiers on active service, with a mean PAL of 2.4 and TEE of 18 MJ/d. In support of this, energy intakes of 19.5 MJ/d have been recorded in colliers (Moss, 1923) and in lumberjacks (Karvonen et al, 1961). Among athletes in training, mean PALs of 2-3.5 were found, with TEE ranging from 11 to 18 MJ/d in women, and from 15 to 30 MJ/d in men. PALs greater than 2.4 were obtained in periods of 'rigorous training', which is unlikely to be a sustained lifestyle. The lower values for PAL, 2.0-2.3, were obtained in periods of apparently routine training and may well be sustained for extended periods of time. Similar values have been observed in Gambian women during the farming season (Singh et al, 1989). These data suggest a PAL range of 1.2-2.5 for sustainable lifestyles, where 1.2 is indicative of a non-ambulant life style and 2.5 represents a very physically active lifestyle (Table 9).

Energy expenditure of free-living adults with normally active daily life

A total of 319 adults (212 non-pregnant non-lactating (NPNL) females and 107 males aged 18-64 years) were identified as healthy, free-living, leading a normal daily life, not recruited as having specific and special circumstances, occupations or activities, and in whom BMR had been measured. Table 8 summarises the anthropometric characteristics of the sample, TEE, BMR, AEE and PAL by age and by sex. The data fully encompassed the PAL range from :1.2-2.5, established above as the likely range of sustainable energy expenditures (Table 9). The wide range of expenditures at any age was notable. Regression analysis of the entire data set accumulated by Black et al (1996), which included a total of 574 subjects aged 2-90 years on whom DLW data and BMR measurements were available indicated that equations based on weight, height, age and sex can explain 77% and 86% of the variance in TEE and BMR, and 41% of the variance in AEE. The latter, i.e AEE, was found to be much more sensitive to individual behavioural choices and therefore less definable using purely physiological measures. Age was a negative predictor of energy expenditure, particularly of the activity component (AEE), and remained so when TEE was expressed as PAL. Taken together with the regression analysis, the following key features seem to emerge from the analysis of Black et al (1996):

Table 9 Physical activity levels (PALs) based on doubly-labelled water (DLW) studies

Life style and level of activity	PAL
Chair-bound or bed-bound	1.2
Seated work with no option of moving around and little or no strenuous leisure activity	1.4-1.5
Seated work with discretion and requirement to move around but little or no strenuous leisure activity	1.6-1.7
Standing work (e.g. housework, shop assistant)	1.8-1.9
Significant amounts of sport or strenuous leisure activity (30-60 min four to five times per week)	+0.3 (increment)
Strenuous work or highly active leisure	2.0-2.4

(1) In early life, absolute levels of energy expenditure, whether expressed as TEE, BMR or AEE, rise with increasing body size, peak in the young adult years and decline thereafter. Adjusted for body size, TEE declines with age throughout life.
(2) Adjusted for body size, males have 11 % greater TEE than females.
(3) Expressed as PAL, differences with age remain significant. For females PAL is fairly constant during the adult years, and lower at younger and older ages. For males PAL rises to a peak at 18-29 years and declines thereafter.
(4) Differences in expenditure between the sexes are not completely removed by adjusting for body size using PAL, although the sex effect is confounded with height to some extent.
(5) As expected, mean TEE in the free-living population, however expressed, is well below that of the athletes in training and soldiers on exercises. It is important to note that this sample of 319 adults is from affluent societies and contains very few manual workers with no data from developing country individuals with a high level of physical activity.

PAL values from DLW data on free-living adults in developed societies

Figure 2 (A-D) shows the distribution of energy expenditure of adults aged 18-64 years. The distributions of PAL for both men and women have a modal value at 1.6 (encompassing 1.55-1.65). The distribution for men has a shoulder to the right suggesting the existence of two populations: active and inactive. This could be either real or an artifact of the sample. Many of the authors gave no information about the subjects beyond sex, age and body weight. Subjects designated as free living were typically recruited from among colleagues, from employees in research centres, universities or hospitals, or were volunteers responding to advertising in the local media. Occupations were typically student, housewife, white collar or professional occupation, unemployed or retired. Only three individuals were specifically identified as manual workers. This suggests a predominantly 'sedentary' population. However, some individuals had PALs at a level associated with athletes or soldiers in training, and the limited information on occupation or activity usually suggested plausible reasons for these high values. Among the twenty highest values were three manual workers, six out of thirteen 'university students and laboratory technicians' with an average of 34 min 'strenuous activity' per day and with several active sports specifically mentioned, while five 18 year old college students and two professionals were known to cycle or walk as a primary means of transport. Women were not well represented in the data set at higher PAL levels. Whether this reflects an absence of subjects recruited from more active groups or a general tendency for women to be less involved in strenuous activities is not known.

Figure 2 Data on (A) total energy expenditure (TEE) by doubly labelled water, (B) BMR, (C) energy expenditure for activity and thermogenesis (AEE) and (D) physical activity levels (PAL) of male and female subjects compared with extreme levels of physical activity.

A

B

C

D

Western lifestyle is commonly referred to as 'sedentary', and the recommendation of FAO/WHO/UNU (1985) for light activity (1.55 × BMR) is frequently interpreted as 'sedentary' and taken as applying to this whole population. However, many desk jobs involve frequent moving around. Other occupations, not necessarily strenuous, require persons to be on their feet all day (e.g. housewives, shop assistants, nurses, storekeepers). Thus a PAL of 1.55-1.65 appears to represent the average for the so-called sedentary lifestyle. There are also data to suggest that activities do not have to be obviously strenuous for relatively high PAL values to be achieved. Calorimetry studies allowing 'free activity' provide mean PALs ranging from 1.50-1.75, and individual PALs from 1.39-2.04. A factorial calculation based on 8 h sleep (PAL 0.95), 4 h sitting (PAL 1.2) and 12 h walking around (PAL 2.5) might represent the lifestyle of a housewife and yields a PAL of 1.8 × BMR.

A PAL of 1.35 has been suggested as the lowest PAL compatible with long-term weight maintenance in persons other than the completely chair- or bed-bound; this was the mean PAL in nine calorimeter studies (n = 207) with controlled, limited activity (Goldberg et al, 1991). Figure 2D shows 7.5% of men and 10.9% of women below a PAL of 1.35. However these may not represent the true long-term energy expenditure due to inaccuracies in the methods. As mentioned before, the coefficient of variation on repeat DLW measurements was 8.9% from nine studies (n=79) on subjects with no change in activity, weight or physiological status; while the CV on measured BMR can be as low as 2.5% under the rigorously controlled conditions of a calorimeter, many studies employed less rigorous conditions. The combined error for PAL is equal to at least ± 9.2%; while the FAO/WHO/UNU Report of 1985 suggested that the inter-individual variability in TEE in a specified group of individuals in whom energy expenditure measurements have been made over a week has a coefficient of variation of ± 12.5% on a body weight basis (Edholm, 1973).

The effect of moderate sport on energy expenditure can be gauged from three studies (n = 28) that imposed a programme of exercise on free-living people normally undertaking very little strenuous activity. The mean sedentary and exercising PALs were 1.63 (s.d. 0.16) and 1.99 (s.d. 0.19), respectively. The mean sedentary and exercising TEEs were 10.53 MJ (s.d. 1.67) and 12.54 MJ (s.d. 2.14) respectively. These figures lend support to the mode of 1.6 for 'sedentary' lifestyles, and show that 30-60 min of active sport, 4-5 times per week, can raise PAL by 0.3 units, but need not necessarily be reflected in a PAL above 2.0.

The relationships between lifestyle, activity and PAL suggested by a careful analysis of the measurements by DLW in adults in developed countries are summarised by Black et al (1996). The data provide little evidence to quantify the energy cost of manual occupations with fairly strenuous physical activity levels which are occupation-related, or to make recommendations about PALs. The range of PAL values which are considered as the maximum for a sustainable lifestyle appears to be between 2.0 and 2.4. The higher energy expenditures, seen in adults in the analysis by Black et al (1996), appear to be due to recourse to active means of transportation such as that resulting from cycling or walking, or to regular participation in active sports. This emphasises the importance of sport or active leisure pursuits in raising energy expenditure in sedentary Western populations, which may provide both for socially desirable activities and for increasing physical fitness and the promotion of health.

The FAO/WHO/UNU Expert Consultation (1985) suggested the average daily energy requirement of adults whose occupational work is classified as light, moderate, or heavy, expressed as a multiple of BMR, to be as follows:

	Light	Moderate	Heavy
Men	1.55	1.78	2.10
Women	1.56	1.64	1.82

It is obviously difficult to relate these categories to the data in the analysis of DLW studies (Black et al, 1996), as the information on occupations was limited and the categories do not take active leisure into account. The modal value of 1.55-1.65 for adults in the analysis falls between the light and moderate categories. The suggested range for strenuous occupation of 2.0-2.4 is compatible with the recommendation of 2.10 for heavy occupations. DLW data from adults do not appear seriously at variance with the recommendations made by the Expert Consultation.

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(introductory text...)

Nancy F Butte

Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA

Descriptors: energy requirements, energy intake, energy expenditure, energy cost of growth, infancy

The Advisory Group of IDECG recommended that select parts of the 1985 FAO/WHO/UNU Report on energy and protein requirements be reviewed for possible revision and updating. The specific questions posed were:

1. Do the 1985 recommendations need to be revised: what are the main arguments for or against a revision ?
2. What would your recommendations be at this point in time?
3. What additional work would need to be done to resolve problems that persist in this area?

Energy requirements of infants based on energy intake

'The energy requirement of an individual is the level of energy intake from food that will balance energy expenditure when the individual has body size and body composition, and level of physical activity, consistent with long-term good health; and that will allow for the maintenance of economically necessary and socially desirable physical activity. In children the energy requirement includes the energy associated with the deposition of tissues at rates consistent with good health.' (FAO/WHO/UNU, 1985). This basic tenet set forth by the 1985 FAO/WHO/UNU Expert Consultation should be upheld.

Because it was not possible to specify with any confidence the allowance for a desirable level of physical activity, the 1985 FAO/WHO/UNU energy requirements from birth to 10 years were derived from the observed intakes of healthy infants and children growing normally. For infants energy requirements were based on energy intakes compiled by Whitehead et al (1981). Estimated energy requirements were set 5% higher than observed energy intakes to compensate for underestimation of intake (Table 1). Implicit in this approach is the assumption that ad libitum intakes reflect desirable intakes for infants. Although infant intake is largely self-regulated, it can be influenced by external factors.

Correspondence: NF Butte.

Compilation of energy intakes published before and after 1980

Whitehead et al (1981) compiled energy intakes of infants from the literature predating 1940 and up to 1980. The work represented 9046 data points during infancy, weighted to account for sample size. Analysis of the energy intake data revealed a highly significant curvilinear relation between energy intake per body weight in kg and age in months:

Energy intake (kcal/kg/d) = 120 - 10.4 age + 0.76 age²
r² = 0.41 (1)

The quadratic term was significant (P = 0.001). No differences were seen between sexes. The authors attributed the sharp fall in energy intake from O to 6 months of age to the rapidly decelerating velocity of growth, a reduction in the rate of fat storage, and a decrease in energy needed for maintenance per kg body weight. The rise in energy intake from 6 to 12 months of age was ascribed to the increase in physical activity as infants begin to crawl and then walk.

Because of possible secular trends in infant feeding practices, we examined energy intakes of presumably well-nourished infants reported after 1980. An analysis was performed on the mean energy intakes from 19 longitudinal or cross-sectional studies comprising 3574 data points (Table 2, Figures 1 and 2). As noted in Table 2, dietary methodology varied across studies.

Table 1 Energy requirements of infants from birth to l year (FAO/ WHO/UNU 1985)

	Total requirement
Age (months)	(kcal/kg/d)	Boys (kcal/d)	Girls (kcal/d)
0.5	124	470	445
1-2	116	550	505
2-3	109	610	545
3-4	103	655	590
4-5	99	695	630
5-6	96.5	730	670
6-7	95	765	720
7-8	94.5	810	750
8-9	95	855	800
9-10	99	925	865
10 11	100	970	905
11-12	104.5	1050	975

FIGURE 1 Mean energy intakes (kcal/d) of formula-fed, breast-fed and mixed-fed infants reported in 1982-1994.

FIGURE 2 Mean energy intakes (kcal/kg/d) of formula-fed, breast-fed and mixed-fed infants reported in 1982-1994

Weighed dietary records, dietary recall methods, or the test-weighing method for breast milk intake were used. Food intakes were converted to metabolizable energy intakes using food composition tables, or macro nutrients were analyzed and converted to gross or metabolizable energy using Atwater factors. Bomb calorimetry was used to measure the gross energy content of breast milk and formula in a few studies. Mean energy intakes as reported were used in the present analysis. Mean total energy intakes (inclusive of solids) of breast fed and formula-fed infants were weighted by sample size at each monthly interval yielding 107 weighted mean values used in the regression analysis (BMDP1R: Dixon, 1990). The multiple regressions of energy intake per kg body weight on age and age² are summarized below.

All: Energy intake (kcal/kg/d)

= 119 - 9.9 age + 0.82 age²
r² = 0.29; n = 107 (2)

BF: Energy intake (kcal/kg/d)

= 118 - 12.8 age + 0.89 age²
r² = 0.66; n = 59 (3)

FF: Energy intake (kcal/kg/d)

= 122 - 8.5 age + 0.73 age²
r² = 0.36; n = 48 (4)

Each of these three equations was tested against the earlier curvilinear equation published by Whitehead et al (1981). We do not have evidence for a strong secular trend in energy intakes of infants before and after 1980, since the regression coefficients did not differ significantly between the Whitehead and present databases. The ~ test for equality of the regression lines across feeding groups was significant, indicating differences in the relationship of energy intake and age between breast-fed and formula-fed infants (P = 0.001) (Figure 3).

Equations (2), (3) and (4) were derived from energy intake data as reported. Two technical problems with reported data arise in the case of the breast-fed infants. Breast milk intakes measured by the test-weighing method were corrected for insensible water loss (IWL) during the course of the measurement in a few studies only (Heinig et al, 1993; Michaelsen et al, 1994). The systematic negative bias caused by not correcting for IWL during, the test-weighing is well recognized: the difficulty has been to determine the magnitude of correction necessary to fairly represent the ranges of metabolic rates, ambient temperatures, humidities, and air circulation rates likely to be encountered. Rates of IWL measured by a number of investigators were as follows: 1.5g/kg/h, Levine et al (1929); 0.83g/kg/h, Kajtar et al (1976); 0.4-0.6 g/kg/h, Doyle & Sinclair (1982); 2.5 g/kg/h, Orr-Ewing & Heywood (1982); 1.9g/kg/h, Hendrikson et al (1985); 1.14 g/kg/h, Butte et al (1990b); 3 g/kg/h, Dewey et al (1991). Most of the measurements were performed under thermoneutral conditions. Levine et al (1929) noted that rates of weight loss may increase threefold above basal levels in temperatures sufficiently high to induce visible perspiration.

Table 2 Energy intakes of infants reported in the first year of life (kcal/kg/d). Mean ± s.d. (N)

Reference	Country	Design/Subjects	N	Type of food	Dietary method
McKillop & Durnin (1982)	Scotland	Cross-sectional Low-high SESa	162	formula solids	5d weighed record FCT, ME
Hofvander et al (1982)	Sweden	Cross-sectional	150	breast milk formula solids	1 d weighed record FCT, ME 0.75 kcal/ml
Dewey & Lönnerdal (1983)	U.S.A.	Longitudinal	20	breast milk solids	2 d weighed record, FCT, ME macronutrients 0.76 kcal/ml breast milk
Butte et al (1984)	U.S.A.	Longitudinal Middle SES	45	breast milk minimal solids	1 d weighed record bomb calorimetry GE 0.66 kcal/g breast milk
Dewey et al (1984)	U.S.A.	Longitudinal	12	breast milk solids	2 d weighed record, FCT, ME macronutrients 0.65 kcal/ml breast milk
Kohler et al (1984)	Sweden	Longitudinal Suburban	59	breast milk cow's formula soy formula solids	2 d weighed record 0.70 kcal/g breast milk
Martinez et al (1985)	U.S.A.	Cross-sectional Low-middle	442	formula solids	24h recall FCT, ME
Forsum & Sadurskis (1986)	Sweden	Longitudinal Middle SES	22	breast milk	1 d weighed record 0.67 kcal/g breast milk
Hoffmans et al (1986)	The Netherlands	Longitudinal	124	formula breast milk solids	24h recall test-weighing FCT, ME
Horst et al (1987)	The Netherlands	Cross-sectional	308	breast milk formula solids	24h recall test-weighing FCT, ME
Leung et al (1988)	Hong Kong	Longitudinal Low-middle SES	174	formula weaning foods	24h recall FCT, ME
Wood et al (1988)	U.S.A.	Longitudinal	22	breast milk	1 d weighed record bomb calorimetry GE 0.60 kcal/ml breast milk
Stuff & Nichols (1989)	U.S.A.	Longitudinal Middle SES	58	breast milk solids	5 d weighed record bomb calorimetry GE 0.65 kcal/g breast milk
Butte et al (1990b)	U.S.A.	Cross-sectional Middle SES	65	breast milk formula minimal solids	3 d weighed record 0.65 kcal/g breast milk GE
Butte et al (1990a)	U.S.A.	Cross-sectional Middle SES	40	breast milk formula minimal solids	5 d weighed record bomb calorimetry GE 0.64 kcal/g breast milk
Stuff et al (1991)	U.S.A.	Longitudinal Middle SES	40	formula solids	5 d weighed record FCT, ME
Sauve and Geggie (1991)	Canada	Longitudinal Low-high SES	114	formula solids	3 d food diaries FCT ME
Michaelsen et al (1994)	Denmark	Longitudinal	60	breast milk	1 d test-weighing; IWL macronutrients GE 0.72 kcal/ml breast milk
Heinig et al (1993)	USA	Longitudinal Middle SES	119	breast milk formula solids	4 d weighed record; IWL macronutrients GE 0.70 kcal/ml breast milk

Age (months)
1	2	3	4	5	6
			97.0 (71)
B-112 (25) F-120 (25)	108 (25) 107 (25)	96 (25) 101 (25)
113 ± 19 (17)	105 ± 25 (20)	93 ± 26 (19)	93 ± 30 (19)	85 ± 20 (17)	89 ± 24 (18)
110 ± 24 (37)	83 ± 19 (40)	74 ± 20 (37)	71 ± 17 (41)
B-113 (26) F-132 (20) F-127 (13)		96 (21) 115 (19) 117 (13)		87 (13) 92 (18) 100 (13)	83 (12) 88 (18) 94 (12)
116 ± 27 (22) 114 ± 19 (22)	98 ± 26 (22) 97 ± 16 (22)	92 ± 15 (22)
			95 ± 20 (124)
			B-91 ± 13 (39) F-95 ± 19 (141)		F-97 ± 61 (96)
121 (128)	109 (150)		88 (151)		85 (153)
128 ± 37 (8) 105 ± 20 (12)	97 ± 18 (12) 99 ± 15 (14)	91 ± 18 (17) 79 ± 12 (16)	74 ± 16 (16) 74 ± 16 (17)	62 ± 12 (15)
			76 ± 13 (19) 69 ± 12 (18) 75 ± 16 (8)	70 ± 14 (19) 67 ± 17 (18) 74 ± 16 (8)	75 ± 16 (19) 65 ± 16 (18) 71 ± 12 (8)
B-99 ± 17 (17) F-108 ± 18 (17)			74 ± 12 (15) 101 ± 9 (16)
B-101 ± 16 (10) F-118 ± 17 (10)			72 ± 9 (10) 87 ± 11 (10)
		F-104 ± 17 (40)	100 ± 10 (40)	95 ± 11 (40)	90 ± 11 (40)
			110 (29)
	102 ± 20 (60)		91 ± 18 (36)
		B-86 ± 11 (71) F-99 ± 14 (46)			80 ± 13 (56) 95 ± 15 (42)

Age (months)
7	8	9	10	11	12
		96.0 (91)
79 ± 12 (8)	74 ± 7 (7)	70 ± 14 (5)	75 ± 17 (5)	72 ± 15 (6)	77 ± 5 (2)
119 ± 41 (54)	110 ± 42 (84)	126 ± 44 (103)	120 ± 44 (92)	120 ± 40 (73)	119 ± 50 (36)
		F-99 ± 25 (32)
77 ± 16 (19) 73 ± 14 (18) 65 ± 16 (8)	72 ± 21 (18) 63 ± 18 (8)	69 ± 19 (8)
86 ± 11 (23)	82 ± 11 (7)
	108 (26)				103 (31)
		84 ± 19 (46) 94 ± 18 (41)			90 ± 18 (40) 98 ± 21 (40)

^a Abbreviations: Social economic status (SES); food composition tables (FCT); metabolizable energy (ME); gross energy (GE).

To correct test-weighing values for IWL, the number and duration of breastfeedings also must be known. The systematic bias caused by IWL may be estimated for 1-4 month-old (Butte et al 1985) and 12 month-old breast-fed infants (Dewey et al 1991). Based upon the published weights, milk intakes, number of feedings, and duration of feedings (20 min was assumed for the Dewey report), and an estimated average rate of IWL of 2 g/kg/d, IWL would cause a 4 and 6% underestimation of intake in the 1-4 month-old and 12 month-old breast-fed infants, respectively.

Figure 3 Energy intake (kcal/kg/d) of infants predicted from equation (2) - (4).

Published intakes of breast-fed infants are in terms of metabolizable energy in some reports, and gross energy in others. Gross energy intake may be converted to metabolizable energy intake using Atwater factors (Watt & Merrill, 1963). Application of the Atwater factors to human milk components (Butte et al, 1984), indicates that human milk would be 96.4% metabolizable. The applicability of the Atwater factors to infants has been questioned, since the original studies were performed on adults (Schulz & Decombaz, 1987). Balance data on ten breast-fed infants fed unpasteurized human milk are available from one study (Southgate & Barrett, 1966). Metabolizable energy averaged 92%.

If not already corrected, the energy intakes of breast fed infants presented in Table 2 were corrected uniformly for IWL and metabolizable energy. A 5% correction was applied to compensate for IWL, and metabolizable energy was assumed to be 94% of gross energy intake. The energy intakes reported by Martinez et al (1985) differed substantially from those of the other formula-fed infants. These six mean values were eliminated from the database.

All: Energy intake (kcal/kg/d)
= 121 - 10.2 age + 0.72 age²
r² = 0.43; n = 101 (2a)

BF: Energy intake (kcal/kg/d)
= 116 - 12.3 age + 0.83 age²
r² = 0.66; n = 59 (3a)

FF: Energy intake (kcal/kg/d)
= 125 - 9.3 age + 0.64 age²
r² = 0.67; n = 42 (4a)

The curvilinearity of the equation of energy intake on age has important ramifications for energy requirements during infancy. The above analysis confirms White head's earlier observations of decreasing need in the first half of infancy, followed by increasing need in the latter half of infancy. However, the above analysis may be misleading because of a mathematical artifact. Energy intake standardized by body weight was regressed on age, which was highly correlated with weight (r_{age, weight} = 0.97). By dividing the ordinate (energy intake) by the abscissa value (age) or in this case a proxy (weight) for the abscissa, a curvilinear relation is created mathematically with this quadratic equation, irrespective of the actual data (Tanner, 1949). It is misleading to describe the relationship of energy intake on age, with energy intake divided by weight.

To circumvent this artifact, another model relating energy intake (kcal/d) to age with weight as a covariate was developed. Mode refers to breast-fed (coded 0) or formula-fed (coded 1). Data were weighted for sample size.

All: Energy intake (kcal/d)
= 100 - 57.7 age + 3.3 age² + 92.8 weight + 43.6 mode + 13.8 age × mode
r² = 0.81; n = 101 (5)

BF: Energy intake (kcal/d)
= 581 - 21.7 age + 1.1 age² + 24.8 weight
r² = 0.63; n = 59 (6)

FF: Energy intake (kcal/d)
= 11.8 - 71.8 age + 4.0 age² + 130 weight
r² = 0.94; n = 42 (7)

In the regression model of all cases there was both a negative linear term (age) and a positive quadratic term (age²) (P = 0.001). A significant interaction between age and feeding mode was encountered (P = 0.006). Splitting on feeding mode, the age² terms for breast-fed and formula-fed infants were significant (P = 0.04 and 0.001, respectively). A curvilinear trend in energy intake was evident. Further analysis revealed that the curvature could be explained by a significant interaction between age and weight. Energy intake (kcal/d) can best be described by the following regression equations weighted by sample size:

All: Energy intake (kcal/d)
= 210 - 59.2 age + 37.2 mode + 63.1 weight + 14.0 age × mode + 5.6 age × weight
r² = 0.80; n = 101 (8)

BF: Energy intake (kcal/d)
= 640 + 25.6 age-40.1 weight + 1.7 age × weight
r² = 0.62; n = 59 (9)

FF: Energy intake (kcal/d)
= 101 - 89.6 age + 105 weight + 7.7 age × weight
r² = 0.87; n = 42, (10)

In the overall model, weight (P = 0.001) and the interactions of age × mode and age × weight were significant (P = 0.01 and 0.002). The older the infant the greater the positive contribution of age × weight term to energy intake becomes. Energy intake of infants across the 1st year of life is best described in this multiple regression, with weight treated as a covariate.

Energy requirements of infants have been estimated from dietary intake using equations (2a), (3a), (4a) and (8)-(10) (Table 3). NCHS median weights were used to calculate energy requirements. For the estimation of the energy requirements of all infants, it was assumed that half the infants were breast-fed and half were formula fed. The current FAO/WHO/UNU energy requirements for infants are 2-15% higher than these estimates based on energy intakes recorded after 1980. The discrepancy is partially due to the 5% increment added to the 1985 FAO/WHO/UNU energy requirements to compensate for assumed underestimation of energy intakes.

Total energy expenditure of infants

The energy requirements of older children have been estimated from multiples of basal metabolic rates (BMR), reflecting various levels of physical activity (FAO/WHO/UNU, 1985). Even though information on the BMR of infants has been available, this approach was not applicable to infants because reasonable allowances for physical activity were undefined. Newly emerging data on total energy expenditure (TEE), however, may be used to derive energy requirements of infants. TEE encompasses BMR, thermoregulation, synthetic cost of growth, and physical activity.

The doubly labeled water method for the measurement of TEE has been used and validated in a number of studies in preterm infants and hospitalized term infants. Although these validation studies were not conducted under free-living conditions of term infants, the high rates of water turnover and high percentages of body water common to all infants were tested. Mean errors between the doubly labeled water method and respiration calorimetry were 0.3 ± 2.6% (Roberts et al, 1986), - 0.9 ± 6.2% (Jones et al, 1987), - 4.5 ± 6.0% (Westerterp et al, 1991), and Ä0.4 + 11.5% (Jensen et al, 1992). Although errors for individuals may be large the doubly labeled water method provides an accurate, unbiased measurement of total energy expenditure for groups and may be used for recommendations of energy intakes of infants. Available data on the TEE of infants are summarized in Table 4. The data published by Davies et al (1989, 1991) have been updated to include more infants (Davies, 1993 private communication). There are 268 data points available on presumably well nourished infants studied in Cambridge, UK and Houston, USA. The majority (90%) of the infants studied were £ 6 months of age (specific ages given in Table 4). TEE of infants living in The Gambia (n = 59) (Prentice et al, 1988; Vasquez-Velasquez, 1987, 1988), rural Mexico (n= 38) (Butte, 1993), and Peru (n= 19) (Fjeld et al, 1989) also have been studied.

Table 3 Energy requirements of infants estimated from dietary energy intake

	Energy intake
Age (months)	All (kcal/d)	BF* (kcal/d)	FF* (kcal/d)	All (kcal/kg/d)	BF* (kcal/kg/d)	FF* (kcal/kg/d)
Boys:
0-1	453	504	470	116	110	120
1-2	490	500	520	107	99	112
2-3	530	503	573	100	90	106
3-4	571	513	625	94	83	100
4 5	612	528	675	90	77	96
5-6	650	549	721	87	73	93
6 9	730	600	812	85	70	91
9-12	863	693	963	93	78	98
Girls:
0-1	440	512	448	116	110	120
1-2	461	515	474	107	99	112
2-3	487	523	504	100	90	106
3-4	517	535	540	94	83	100
4 5	554	549	585	90	77	96
5-6	594	567	632	87	73	93
6 9	675	614	726	85	70	91
9-12	784	707	842	93	78	98

* BF Breast-fed; FF Formula-fed infants.

First, we performed an analysis on the group mean values for TEE of presumably well-nourished infants (Table 4). Mean TEE was 449 ± 161 kcal/d for infants who were 4.0 ± 3.0 months old and weighed 6.1 ± 1.5 kg. Weighted for sample size, TEE was regressed on age (months), feeding mode (breast-fed, coded 0, and formula-fed, coded 1) and weight (kg) (BMDP1R: Dixon, 1990).

TEE (kcal/d) = 73.8 + 38.6 age + 40.4 mode + 35.4 weight
SEE = 25.7
r² = 0.98;
n = 14. (11)

TEE (kcal/d) was significantly affected by age (P = 0.005), feeding mode (P = 0.01), but not weight. Weight was highly correlated with age (r = 0.98). Interactions between age, mode and weight were not significant. Mean TEE for the breast-fed and formula-fed infants were 420 ± 151 and 495 ± 190 kcal/d, respectively. The high r² does not imply that the TEE of individual infants can be predicted with such a high degree of certainty. It should be remembered that the analysis was performed on group mean values. The SEE provides an indication of the error for predicting group mean values of TEE.

Table 4 Total energy expenditure of infants by doubly-labeled water method

Reference	n	Age (months)	Fxa	RQ	TEE (kcal/d)	TEE (kcal/kg/d)	Comments
Lucas et al (1987)	12BF	0.9-1.4 2.3-2.8	0.13 0.13	0.85 0.85	306 (26)b 402 (19)	66.9 (24) 71.7 (8)	BF infants, Cambridge,UK
Roberts et al (1988)	18	3	0.13	0.87	408 (28)	72 (5)	MF infants, Cambridge, UK TEE/SMR= 1.15
Vasquez-Velasquez (1987)	8 15 19 8	0-3 3-6 6-9 9-12			381 (88) 473 (106) 572 (121) 664(133)	82 (23) 78 (21) 80 (16) 85 (12)	MF Gambian infants
Fjeld et al (1989)	22FF 19FF	16 16.3			629 (84) 692 (82)	90 (12) 84 (10)	FF infants, Lima, Peru Early recovery from malnutrition Late recovery from malnutrition
Davies et al (1989)	39c 40c 37c	1.2 2.5 6.0	0.13 0.13 0.13	0.87 0.87 0.86	306 (93) 392 (96) 605 (100)	64.5 (16.7) 66.9 (14.3) 78.9 (12.0)	BF and FF infants, Cambridge, UK
Butte et al (1990a)	10BF 10FF 10BF 10FF	1 4	0.16 0.17 0.20 0.20	0.94 0.90 0.90 0.90	291 (48) 316 (42) 420 (49) 476 (58)	64 (7) 67 (8) 64 (8) 73 (9)	BF and FF infants, Houston, TX TEE/SMR = 1.28, 1,26 TEE/SMR = 1.34, 1.36
Davies et al (1991)	33c	2.8	0.13	0.86		69 (17.9)	Same infants as 1989 paper
Davies (unpublished 1993)	20BFc 29FFc 20BFc 30FFc 19BFc 18FFc 12BF 10FF	1.4 1.4 2.8 2.8 6.0 6.0 9.2 9.2			283 (80) 319 (97) 366 (73) 433 (118) 590 (119) 619 (78) 702 (124) 808 (184)	61.1 (17.8) 71.4 (19.1) 64.5 (12.6) 75.3 (19.6) 78.5 (13.7) 79.0 (11.2) 83.0 (14.8) 93.7 (21.2)	BF and FF infants, Cambridge, UK
Davies (unpublished 1993)	24	1.4				74.5 (12.1)	BF (n = 11) and FF (n = 13) infants, Cambridge, UK
Butte et al (1993)	19BF 19BF	4 6	0.23 0.24	0.88 0.85	446 (97) 542 (83)	74.1 (13.9) 76.0 (6.9)	BF infants, Capulhuac, Mexico

^a Abbreviations: Fx = isotope fractionation; RQ = respiratory quotient; TEE = total energy expenditure; BF = breast-fed; FF = formula-fed; MF = mixed-fed; SMR = sleeping metabolic rate.
^b Mean (s.d.).
^c 1993 unpublished compilation of data used.

Standardized by body weight, TEE averaged 72.6 ± 8.1 kcal/kg/d overall, and 69.2 ± 7.8 and 76.6 ± 9.3 kcal/kg/d for the breast-fed and formula-fed infants, respectively. TEE (weighted by sample size, kcal/kg/d) was significantly affected by age (P = 0.001) and feeding mode (P = 0.01); the interaction between age and feeding mode was not significant. Within studies, the TEE of breast-fed infants has been shown to be lower than that of formula-fed infants (Butte, 1990a; Davies, 1992).

TEE (kcal/kg/d) = 60.1 + 2.6 age + 6.5 mode
SEE = 3.7
r² = 0.83; n = 14. (12)

We calculated BMR according to the Schofield equation for children under the age of 3 years (1985). Mean BMR was 54.6 ± 1.6 kcal/kg/d for the boys and 52.8 ± 1.7 kcal/kg/d for the girls. The physical activity level of the infants (TEE/BMR) increased from 1.3 at 1 month to 1.7 at 12 months of age. TEE rose steadily and gradually as activity increased through infancy.

Second, we examined the TEE data from infants living under harsh environmental conditions. We compiled 88 data points on Gambian and Mexican infants under 12 months of age (Vasquez-Velasquez, 1987; Butte, 1993). The mean TEE of these infants (5.7 ± 3.1 months) was 513 ± 101 kcal/d or 79.2 ± 4.0 kcal/kg/d. The TEE (kcal/kg/d) of infants living under harsh environmental conditions was significantly higher than that of the more sheltered infants (t = 2.6, P = 0.02), but the Gambian and Mexican infants were older. The regression of TEE (kcal/kg/d) on age did not differ between the sheltered and unsheltered infants. Prentice did not find any significant differences in TEE (kcal/kg/d) between Gambian and British infants, aged 0 to 36 months (Prentice, 1993). However, we found the TEE (kcal/kg/d) of the Mexican infants to be higher than that of predominantly breast-fed infants studied in Houston (Butte et al, 1993). More data from different geographic locations are needed to resolve putative differences in TEE of infants exposed to infection and other environmental stresses.

Currently available data on TEE of infants are limited in number, age range, and geographic distribution. Nevertheless, TEE data provide strong evidence for the need to revise current recommendations for energy intake of infants. Prudently, more data should be sought, particularly in the second 6 months of life.

Energy requirement for growth

Although the energy requirement for growth relative to maintenance is small, except for the first months of life, satisfactory growth is a sensitive indicator of whether needs are being met. To determine the energy cost of growth, the energetics of growth must be understood and satisfactory growth velocities must be defined. The 1985 requirements were based on the growth reference published for international use by WHO (1983), which were derived from the United States National Center for Health Statistics growth curves (NCHS, 1977). What constitutes appropriate infant growth is a topic of controversy and is currently under debate at WHO. Because of policy implications, the findings of the WHO Expert Committee on 'Physical Status: The Use and Interpretation of Anthropometry During Infancy' should be considered if the FAO/WHO/UNU Energy and Protein Requirements are revised. Quantitatively, revision of infant growth curves will minimally impact estimated energy requirements. If growth curves were revised to reflect the growth velocities of breast-fed infants, energy requirements would decrease by 10, 16, 24 and 12 kcal/d for 0-3 months, 3-6 months, 6-9 months and 9-12 months, respectively.

In addition to the growth velocity, the energy cost of growth must be known. This cost consists of the energy content of the newly synthesized tissues and the energy expended in synthesis. In the 1985 report the energy cost of weight gain was reviewed in Annex 4 (FAO/ WHO/UNU, 1985) The value proposed for healthy term infants was 5.6 kcal/g gained. We measured the energy cost of growth in term infants and arrived at an estimate, 4.8 kcal/g (Butte et al, 1989). An additional report appeared on the energy cost of growth of infants recovering from malnutrition; the total energy cost of growth was 6-7 kcal/g (Fjeld et al, 1989). The estimated energy cost of growth is more accurate when the separate costs of protein and fat deposition are taken into account, since the components of weight gain change dramatically through the first year of life. However, the practicality of this point is significantly diminished by the fact that the energy cost of growth as a percentage of total energy requirement decreases from 35% at 1 month to 3% at 12 months.

The total energy cost of growth and its components is presented in Table 5 (Figure 4). For the present discussion, the rates of weight gain and components of weight gain, as described by Fomon et al (1982), have been used. For lack of specific information on the composition of weight gain of breast-fed and formula-fed infants, no distinction was made with respect to potential differences in the energy cost of growth between feeding groups. Median NCHS weights were used to standardize the data. The energetic efficiencies of synthesizing protein and fat were taken to be 42% (1 kcal deposited/2.38 kcal used) and 85% (1 kcal deposited/ 1.17 kcal used), respectively (Roberts & Young, 1988). Energy equivalents for fat and protein were 9.25 kcal/g and 5.65 kcal/g, respectively.

Table 5 Energy cost of growth through infancy

			Fat deposition		Protein deposition				Total energy cost growth
Age (months)	Weight (kg)	Weight gaina (g/d)	(g/d)b	(kcal/d)c	(g/d)b	(kcal/d)c	Fat synthesis (kcal/d)d	Protein synthesis (kcal/d)d	(kcal/d)	(kcal/kg/d)
Boys:
0-1	380	29	6	56	4	21	10	29	115	30
1-2	4.75	35	14	130	4	20	23	27	201	42
2-3	5.60	30	13	119	3	17	21	23	181	32
3-4	6.35	21	8	77	2	13	14	18	121	19
4 5	7.00	17	6	51	2	11	9	16	87	12
5-6	7.55	15	4	38	2	11	7	16	72	9
6 9	8.50	13	2	17	2	11	3	16	46	5
9-12	9.70	11	1	9	2	10	2	14	35	4
Girls:
0-1	3.60	26	6	52	3	19	9	26	105	29
1-2	4.35	29	13	118	3	16	21	22	177	41
2-3	5.05	24	10	93	3	15	16	20	145	29
3-4	5.70	19	7	68	2	12	12	16	108	19
4-5	6.35	16	6	55	2	11	10	15	90	14
5-6	6.95	15	5	45	2	11	8	15	79	11
6-9	7.97	11	2	16	2	10	3	14	43	5
9-12	9.05	10	1	11	2	10	2	13	36	4

^a Monthly rates of weight gain (Fomon et al, 1982).
^b Monthly rates of &t and protein deposition (Fomon et al, 1982).
^c Energy equivalents for fat and protein deposition were taken as 9.25 kcal/g and 5.65 kcal/g, respectively.
^d Energetic efficiencies of synthesizing protein and fat were taken to be 42% (1 kcal deposited/2.38 kcal used) and 85% (1 kcal deposited/1.17 kcal used), respectively (Roberts & Young, 1988).

As calculated, the energy cost of growth displays an abrupt increase at 1-2 months, followed by a gradual decline through 12 months. The abrupt increase in fat deposition may be an artifact due to interpolation of data compiled from different studies by Fomon et al (1982). Unpublished data of Southgate were used to estimate body composition at birth. Body fat was assumed to be linearly related to subscapular and infra-iliac skinfolds between the ages of 3 months and 10 years. A smoothed curve was constructed relating the percentage body fat to age from 1 month to 10 years.

Energy requirements of infants predicted from total energy expenditure and growth

We estimated energy requirements of infants from birth to 12 months of age from total energy expenditure and energy deposition as protein and fat (Table 6, Figures 5 and 6). The energy costs of protein and fat synthesis are covered in the estimate of total energy expenditure and therefore have been excluded from this estimate of energy deposition. The relatively low energy deposition at 0-1 months and high estimate at 1-2 months may be in error. Because fat deposition probably does not increase so abruptly between 1 and 2 months, the average energy deposition for the interval 0-2 months was used in calculating energy requirements. The 1985 FAO/WHO/UNU energy requirements are 9-39% higher than these estimates. These discrepancies are not trivial and could lead to overfeeding of infants.

FIGURE 4 Energy cost of fat and protein deposition in infants (kcal/d).(Boys)

FIGURE 4 Energy cost of fat and protein deposition in infants (kcal/d).(Girls)

A comparison of FAO/WHO/UNU energy requirements and estimations based on energy intakes recorded after 1980 and on TEE and growth is graphically displayed in Figures 7 and 8.

FIGURE 5 Energy requirements of infants estimated from total energy expediture and energy deposition (kcal/d).

Recommendations

The 1985 FAO/WHO/UNU recommendations for dietary energy intake of healthy infants seem too high based on reported measurements of energy intake or energy expenditure and estimates of the energy deposited for growth. Because observed energy intakes may not reflect desirable intakes, measurements of energy expenditure are preferred as the basis for estimating energy requirements. Estimated energy requirements of infants based on total energy expenditure and growth are 9-39% lower than the 1985 FAO/WHO/UNU recommendations and provide strong evidence that current estimates should be revised. However, confirmation of this observation will require expansion of the available database on total energy expenditure of healthy infants, in terms of sample size, age range and geographic distribution across the entire age range of infancy. Data are particularly scarce in the second 6 months of infancy. Estimated energy requirements should be consistent with the growth reference endorsed by WHO. To better define the energy deposited during growth, changes in body composition during infancy must be confirmed.

Given the relative uniformity of behavior, physical activity and growth of healthy infants from different geographic origins, estimates of energy requirements can be applied universally to healthy infants. It should be appreciated that energy requirements of infants are a function of age, gender, body size and feeding mode. Stipulation of estimated energy requirements by these factors will depend on the application.

More data must be sought on the energy expenditure of infants in populations at risk of high rates of infection and exposed to other environmental sources of stress to determine if energy requirements are altered under these circumstances. The energy needs for adequate catch-up growth also must be considered.

Table 6 Energy requirement estimated from total energy expenditure and energy cost of growth

	Total energy expenditure						Energy deposition
Age (months)	ALL (kcal/d)	BFa (kcal/d)	FFa (kcal/d)	ALL (kcal/d)	BFa (kcal/d)	FFa (kcal/d)	(kcal/d)	(kcal/kg/d)
Boys
0-1	248	228	268	65	61	68	113	26
1-2	320	300	340	67	64	70	113	26
2-3	389	368	409	70	67	73	136	24
3-4	454	434	474	72	69	76	90	14
4-5	516	495	536	75	72	78	62	9
5-6	574	553	594	78	74	81	49	6
6-9	684	664	705	83	80	86	28	3
9-12	843	822	863	91	87	94	19	2
Girls:
0-1	241	220	261	65	61	68	102	22.5
1-2	306	286	326	67	64	70	102	22.5
2-3	369	349	389	70	67	73	108	20
3-4	431	411	451	72	69	76	79	13
4-5	492	472	513	75	72	78	65	10
5-6	552	532	573	78	74	81	56	8
6-9	666	645	686	83	80	86	26	3
9-12	820	799	840	91	87	94	21	2

Energy requirement
Age (months)	BFa (kcal/d)	FFa (kcal/d)	ALL (kcal/kg/d)	BFa (kcal/kg/d)	FFa (kcal/kg/d)
Boys
0-1	341	381	91	87	94
1-2	413	453	93	90	96
2-3	504	545	94	91	97
3-4	524	564	86	83	90
4-5	557	598	84	81	87
5-6	602	643	84	80	87
6-9	692	733	86	83	89
9-12	841	882	93	89	96
Girls:
0-1	322	363	88	84	90
1-2	388	428	90	86	92
2-3	457	497	90	87	93
3-4	490	530	85	82	89
4-5	537	578	85	82	88
5-6	588	629	86	82	89
6-9	671	712	86	83	89
9-12	820	861	93	89	96

^a BF = breast-fed; FF = formula-fed infants.

FIGURE 6 Energy requirements if infants estimated from total energy expenditure and energy deposition (kcal/kg/d).

FIGURE 7 FAO/WHO/UNU energy requirements compared against requirements (1) based on energy intakes observed after 1980 and (2) total energy expenditure (TEE) and energy deposition during growth (kcal/d).

FIGURE 8 FAO/WHO/UNU energy requirements compared against requirements (1) based on energy intakes observed after 1980 and (2) total energy expenditure (TEE) and energy deposition during growth (kcal/kg/d).

Acknowledgements - I wish to thank Drs PSW Davies, Cambridge, UK; KG Dewey, University of California-Davis; KF Michaelsen, The Royal Veterinary and Agricultural University, Copenhagen,
Denmark; AM Prentice, Dunn Nutrition, Cambridge, UK; AS Ryan, Ross Laboratories, Columbus, Ohio, and JE Stuff, Children's Nutrition Research Center, Houston, Texas for their contribution of data used in this manuscript, as well as Dr C Garza, Cornell University, Ithaca, New York, for his thoughtful review. I would also like to thank I Tapper for manuscript preparation, and L Loddeke and R Klein for editorial review.

This work is a publication of the USDA/ARS Children's Nutrition Research Center, Department of Pediatrics Baylor College of Medicine and Texas Children's Hospital, Houston, TX. Funding has been provided from the U.S. Department of Agriculture, Agricultural Research Service under Cooperative Agreement No. 58-6250-1-003. The contents of this publication do not necessarily reflect the views or policies of the U.S. Department of Agriculture, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

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Discussion

Atwater factors indicate the average amount of energy yielded by one gram of ingested carbohydrate, fat or protein; they are used in the calculation of the metabolizable energy content of foods, for instance in food composition tables and in infant formulas. Atwater (as well as Durnin and Southgate after him) derived them from the heat of combustion, corrected for energy losses in the form of unabsorbed nutrients in feces and urine of adults. The question was raised whether the same factors were also applicable to infants. The answer to this question does not affect energy requirements per se but becomes important in a discussion of recommended dietary intakes. Several factors may influence the metabolizable energy derived from food: (1) the chemical form of the macronutrient in the food, (2) the coefficient of digestibility; (3) the extent to which the nutrients are not completely oxidized, but stored in the body; (4) gut maturation and (5) age. In growing infants nitrogen retention will be higher. Preterm infants absorb less fat than term infants, and fat is generally less well absorbed by newborn infants than by older infants. Fat digestibility is also highly dependent upon the fat source and its processing, e.g. butterfat is poorly absorbed, whereas a mixture of vegetable oils is absorbed nearly to the same extent as human milk. In a study of 10 breast-fed infants fed unpasteurized milk, Southgate found that metabolizable energy averaged 92%. Application of the Atwater factors to human milk components indicated 96% metabolizable energy. Using Atwater factors in normal infants, therefore, does not seem to entail great errors. Application of the Atwater factors in preterm or sick infants may overestimate energy availability.

In young infants the energy content of human milk is of particular importance. Since it is very variable throughout days and feeds and there is no generally agreed upon, standard method for obtaining representative milk samples and for estimating their energy density, published figures vary considerably. Butte et al, using different methods, obtained values between 0.65 and 0.67, whereas values from Sweden (0.72) and a WHO study in Hungary are considerably higher (Waterlow). In the first two figures of her paper, Butte used energy intakes as reported. Dewey pointed out that differences in fat secretion in breast milk between groups of women had been observed, even when exactly the same methods were used. Maternal body fat can affect milk fat (Prentice), as can fat intake in lean women (Dewey). Since pasteurization alters the fat, it is important to note whether pasteurized or non pasteurized milk is used. In the end, the prevailing opinion was that Dewey and Butte had made the most rigorous assessments and that their values should therefore be relied upon primarily.

Several participants were intrigued by the low level of the first two data points in the line representing energy requirements derived from TEE and growth in Butte's figures 7 and 8. Most likely this is an artifact due to an underestimate of the cost of growth in these first two time periods.

Should recommendations be the same or different for breast- and bottle-fed infants? Reeds argued that requirements and intakes should not be confused. Requirements are to be seen as a function of the organism and not of the diet, whereas recommended dietary allowances are a function of the diet and the degree to which it meets requirements. Dewey pointed out that in practice the picture was less clear and the feeding mode seemed to affect physiology. Energy expenditure is lower in breast-fed infants or, in other words, formula-fed infants appear to require more energy than breast-fed ones. These differences are most marked between 3 and 6 months of age; then they gradually disappear, probably as a consequence of the phasing out of pure breast-feeding. Butte tried to derive energy requirements from data of a mixed group of infants, 50% breast- and 50% formula-fed. Dewey advocated separate recommendations for the two feeding groups in order to avoid the :impression that breast-fed infants do not get enough energy and ought to be supplemented or the risk that formula fed infants will not get enough energy to cover their needs. Giving a wide range of requirements does not appear to be a satisfactory solution either.

Butte et al tried to determine how much of a difference in diet-induced thermogenesis (DIT) there was between breast- and formula-fed infants. During the first 4h after the meal, DIT appeared slightly lower in breast-fed infants, but the difference was not statistically significant.

Waterlow queried the validity of 42% for the energetic efficiency of protein synthesis (Table 5, footnote d), and suggested that a figure of 75% would be more in accordance with the evidence.

Do infants growing up in the more stressful environment of developing countries or urban slums have the same or higher energy requirements than infants in industrialized countries? The little information that exists on this issue shows smaller differences than expected. Total energy expenditure (TEE), expressed as kcal/kg, was for instance very similar in infants from The Gambia and the UK (Prentice). Butte compared TEE of small groups (n = 20) of 4-month-old infants from Mexico and Houston. In. Mexico it was 74 kcal/kg, in Houston 64 and 73 kcal/kg for breast- and bottle-fed infants, respectively. Several participants felt that more information was needed to decide the extent to which frequent infections and desirable catch-up growth add to energy requirements in poor environments.

(introductory text...)

B Torun¹, PSW Davies², MBE Livingstone³, M Paolisso⁴, R Sackett⁵, GB Spurr⁶ (with contribution from MPE de Guzman⁷)

¹ Health and Nutrition Unit, Institute of Nutrition of Central America and Panama, Apartado Postal 1188, Guatemala, Guatemala;
² Infant and Child Nutrition Group, MRC Dunn Nutrition Unit, Cambridge CB4 IXJ, UK;
³ Department of Biological and Biomedical Science, University of Ulster, Coleraine, Northern Ireland BT52 ISA, UK;
⁴ International Center for Research on Women, Washington, DC 20036, USA;
⁵ Departments of Anthropology, Memphis State University, Memphis, TN 38152, USA;
⁶ Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53295, USA;
⁷ Department of Science and Technology, Food and Nutrition Research Institute, Metro Manila 1604, Philippines

Descriptors: energy requirements, energy expenditure, physical activity, dietary energy, children, adolescents, time allocation

Introduction

In 1981, a group of experts was convened by FAO, WHO and UNU to evaluate the energy and protein requirements of humans, and to make appropriate dietary recommendations. Several key concepts related to energy were asserted in their report (FAO/WHO/ UNU, 1985), which included the following:

· The energy requirement is the amount of dietary energy needed to maintain health, growth, and an 'appropriate' level of physical activity.
· 'Appropriate' physical activity includes those activities that an individual must perform to survive in his/her social environment (occupational activities), and to pursue his/her physical, intellectual and social desires and wellbeing (discretionary activities). For children, this should allow the exploration of the surroundings and the interaction with other children and adults.
· Energy needs are determined by energy expenditure. Therefore, estimates of requirements should be based on measurements of energy expenditure and, for children, an additional allowance for growth.
· Energy requirements can be calculated as multiples of basal metabolic rate (BMR). In the absence of direct measurements, BMR can be estimated with mathematical equations derived from published metabolic data.

However, very little information was available on total energy expenditure (TEE) of children. Consequently, estimates of energy requirements for 1-10 year old children were based on the reported energy intakes of healthy, well nourished children, with the tacit assumption that they represented habitual intakes. These estimated requirements were derived from an extensive review of published dietary intake data on approximately 6500 children, mostly from developed countries (Ferro-Luzzi & Durnin, 1981).

Correspondence to: Dr B Torun.
Note: Participation in the preparation of this document does not imply that all contributors agree with all of the conclusions and recommendations

The FAO/WHO/UNU Expert Committee was also concerned about a perceived secular trend towards sedentary lifestyles in developed countries. Therefore, it was felt prudent to increase by 5% the reported energy intakes of children between 1 and 10 years of age to accommodate a desirable level of physical activity.

After 10 years of age, estimates of energy expenditure expressed as multiples of BMR provided the basis to calculate energy requirements, rather than energy intake data. BMR for boys and girls of a given age and weight were predicted with the mathematical equations derived by Schofield (FAO/WHO/UNU, 1985; Schofield, 1985), and the additional energy expended during the day was calculated based on the assumed energy cost of activities performed by children and adolescents in developed countries. The extra allowance for growth was assumed to be 5.6 kcal (23.4 kJ) per gram of expected weight gain. This corresponded to about 3% of the daily energy requirement at one year of age, with a gradual decrease to about 1% at 15 years.

In deriving these estimates of energy requirements for children and adolescents the FAO/WHO/UNU Expert Committee acknowledged that they exceeded the dietary energy intakes reported for these ages. It was considered that the low intakes reflected an undesirably low level of physical activity, and that dietary recommendations should include enough energy to allow an increase in activity. It should be noted that the spontaneous activity of children and hence energy expenditure can be restricted by energy intake as demonstrated by studies in Guatemala (Town, 1990b).

In the years that followed the 1981 FAO/WHO/ UNU Expert Consultation, more has been learned about the energy expenditure of children and adolescents and of the way they distribute their time in activities that demand different levels of energy expenditure, largely due to the application of the doubly-labeled water method, the improved technology and validation of heart-rate monitoring techniques, and the analysis of physiological, nutritional and anthropological studies (Schürch & Scrimshaw, 1990). Additional information on food intake and on basal and resting metabolic rates have also allowed a better appraisal of the calculation and validity of energy requirements between 1 and 18 years of age.

This document presents a critical review of that knowledge and makes recommendations for consideration by the group of experts that will revise the 1985 FAO/WHO/UNU report.

Total daily energy expenditure (TEE)

Three types of methods have been used to calculate total daily energy expenditure of free-living children and adolescents. Their advantages and limitations have been reviewed by several authors (e.g. Torun, 1984; Durnin, 1990).

(1) Doubly-labeled water. This method has two components: (a) Administration of a marker dose of ²H and ¹⁸O, and measurement of the disappearance of the isotope from the body after several days and (b) Calculation of the food quotient or estimation of the average respiratory quotient during that period of time.

The doubly-labeled water is the most accurate of the three methods. However, there are still some doubts about the appropriateness of the assumptions used for the calculation of energy expenditure. Moreover, the number of children so far studied is very small and restricted to few geographical areas due to the high cost of the isotopes and their analysis. Furthermore, it does not provide information on the patterns of physical activity throughout the day.

(2) Heart rate monitoring. This method has three components. (a) Measurement of heart rate while resting and measurement or estimation of the resting and basal metabolic rates. (b) Determination of the relationship between heart rate and oxygen consumption (or energy expenditure) with light, moderate and moderately heavy workloads. This relationship varies among individuals and must be established for every person who will be studied. (c) Minute-by-minute recording of heart rate.

Table 1 Comparison of total daily energy expenditures (MJ/d) measured with two different heart rate monitoring techniques (three-way analysis of variance with unweighted averages)

Minute-by-minute heart rate method (Spurr and Reina, 1988a)
Age (y)	n	Control children mean	s.d.	n	Mildly malnourished mean		s.d.
6-8	24	6.6	1.6	21	5.1		1.0
10-12	18	8.4	2.3	23	7.8		2.1
14-16	20	12.1	2.7	26	10.6		2.5
Daytime heart rate accumulation method (Spurr et al., 1986)
Age (y)	n	Control children mean	s.d.	n	Mildly malnourished mean		s.d.
6-8	12	6.4	1.1	9	6.3		0.7
10-12	20	9.2	1.8	19	8.0		1.8
14-16	12	11.5	2.0	16	10.2		2.1
Source			F ratio			Probability
A (Age)			109.283			<0.001
B (Nutritional status)			14.463			<0.001
C (Method)			0.413			0.521
A*B (Age*Nutrition)			0.403			0.668
A*C (Age*Method)			1.495			0.226
B*C (Method*Nutrition)			0.462			0.498
A*B*C (Age*Nutrition*Method)			1.054			0.350

Earlier studies used recorders that accumulated all heart beats over some period of time. Average heart rate over 24 h gave unacceptable results due to the poor relationship between heart rate and oxygen consumption at resting and sedentary levels of energy expenditure. However, the method yields acceptable results when the average heart rate is calculated for the period of time when children are awake, and energy expenditure calculated for the remainder of the 24 h from the resting and basal metabolic rates. As shown in Table 1, an analysis of studies by Spurr and collaborators (Spurr et al, 1986; Spurr and Reina, 1988a) indicated that the results with this heart rate accumulation method did not differ from those obtained in similar children with the minute-by-minute rate recording method.

The heart rate monitoring method has been validated with whole body calorimetry and doubly-labeled water. Comparisons varied on an individual basis, but the mean values for groups of individuals were similar to the other methods (Spurr et al, 1988; Ceesay et al, 1989; Livingstone et al, 1990a, 1992a; Emons et al, 1992). Thus, heart rate monitoring can be used to estimate the energy expenditure of groups of children. Minute-by-minute recording also allows, examining the time allocated to different intensities of physical effort.

(3) Time-motion or activity diary techniques. These have two components: (a) assessment of time allocation, which has been explored by direct observations with different timing techniques, by activity records or diaries kept by the subjects or caretakers of young children, and by recall interviews with subjects or caretakers and (b) energy costs of the activities that are observed or recorded, measured by indirect calorimetry or estimated from published values. It should be borne in mind that energy costs of activities published for adults do not apply to children under 15 (Town, 1983, 1990a). Many of the results published with the time motion or diary techniques are questionable due to the inaccuracies inherent in methods based on reporting and in the application of energy cost of activities of adults to calculate energy expenditure of children.

We have based this review and our conclusions on TEE on studies with doubly-labeled water or with appropriate techniques of heart rate monitoring. Some estimates of energy expenditure with time motion/diary techniques were selected as examples to examine the conclusions based on the other two methods.

Another relatively simple way to estimate total daily energy expenditure (TEE), and therefore requirements, of adults was proposed by the 1985 FAO/WHO/UNU Expert Consultation. Sleeping, occupational, discretional, health-promoting and other miscellaneous activities were assigned an energy cost, expressed as multiples of basal metabolic rate ( × BMR) or physical activity level (PAL)¹. A factorial calculation accounting for the time allotted to each of those activities allowed the estimation of the mean PAL in 24 h. For populations engaged in occupational activities of different intensities. TEEs of 1.55, 1.78 and 2.10 × BMR were proposed for men with light, moderate and heavy occupational activities, respectively (FAO/WHO/UNU, 1985). The corresponding factors suggested for women were 1.56, 1.64 and 1.82.

We suggest that a similar approach be used to estimate TEE of groups of children and adolescents with different lifestyles. PAL factors are proposed for those estimates in another section of this document ('Physical activity levels of children and adolescents').

Studies with doubly-labeled water

The doubly-labeled water technique is almost 40 years old, but there are still relatively few data on total energy expenditure in children, due primarily to cost. Until the mid 1970s the cost of the stable isotopes (²H and ¹⁸O) involved in the technique was restrictive. Since that time advances in technology, notably the development of highly precise isotope mass ratio spectrometers, made it possible to administer significantly less isotope, thus reducing the cost to a more manageable figure. Unfortunately, the cost of isotopes began to rise steeply in 1990 and once again fewer studies are being undertaken.

Studies that allow consideration of TEE and dietary recommendations have been done with well-nourished children and adolescents in urban centers of the United Kingdom (Prentice et al, 1988; Davies et al, 1991, 1994), Holland (Saris et al, 1989; Emons et al, 1992) and the United States (Bandini et al, 1990b; Goran et al, 1993; Fontvieille et al, 1993; Wong, 1994). Table 2 shows their age span and TEE expressed per day, per unit of body weight and PAL.

Figure 1 compares the data from Table 2, expressed as kcal/kg/day, with the FAO/WHO/UNU 1985 recommendations, with and without the allowance for growth. The values for energy expenditure shown in the table and figure do not include the small proportion of energy that should be retained for growth (between 1 and 3%, depending on age).

Current dietary energy recommendations are about 20% higher than energy expenditure of children under 7 years of age in industrialized societies. From 7 years onwards, current recommendations coincide reasonably well with the data from doubly-labeled water studies, although boys throughout adolescence and girls around puberty seem to require 5-15% more dietary energy.

¹ Total energy expenditure expressed as × BMR has been considered to reflect an individual's or population's physical activity level (PAL). This term has appeared with increasing frequency in the scientific literature. Thus, we will use it as synonym of × BMR.

FIGURE 1a Total energy expenditure estimated with doubly labeled water: boys.

FIGURE 1b Total energy expenditure estimated with doubly labeled water: girls.

Within each sex, the PALs in Table 2 show a trend towards uniformity among children between 1 and 5, 6 and 13, and 14 and more years of age. Therefore, the mean PAL values for those three age groups were calculated (Table 3). Since the studies had similar sample sizes within each age group, calculations of the mean PAL weighted for the number of children in each study gave similar results. Measured BMRs were used for calculations in three studies and BMR estimates with Schofield's equations in all others.

On the average, there were no gender differences at 1-5 and 6-13 years. Boys seemed to have higher PAL than girls after that age, but this observation was based on only three data sets from two studies (Bandini et al, 1990b; Davies et al, 1991). It should also be noted that if PALs were calculated with the equations published by FAO/WHO/UNU (1985) instead of those modified later by Schofield (1985), they would be somewhat lower for girls 1-5 and 6-13 years old than for boys of the same age ranges (1.39 vs 1.47 and 1.74 vs 1.81, respectively).

It should be kept in mind that the values shown in Tables 2 and 3 correspond to studies in a small number of well nourished children with adequate growth patterns, living in societies where food and health services are continuously and readily available.

Table 2 Groups of children, classified by sex and age, whose total daily energy expenditure has been estimated by the doubly labeled water method (does not include 1-3%, depending on age, that should be retained for growth)

			Total energy expenditure
Agea (y)	n	Weight (kg)	(kcal/d)	(kcal/kg/d)	PALb	Reference
Boys
1-1.9	8c			83.0d		Prentice et al (1988)
1.5-2.49	11	12.6 ± 1.4e	1075 ± 305	85.8 ± 26.0	1.49	Davies et al (1994)
2-2.9	6c			81.0d		Prentice et al (1988)
2.5-3.49	15	15.0 ± 1.7	1207 ± 181	81.5 ± 15.2	1.41	Davies et al (1994)
3-4	13	15.5	1300	83.9 ± 11.5	1.52	Davies et al (1991)
3-4.49	16	16.9 ± 2.3	1301 ± 211	78.2 ± 14.5	1.47	Davies et al (1994)
4-6	16	20.3 ± 4.3	1438 ± 271	71.5 ± 8.0	1.49	Goran et al, (1993)
5.4 ± 0.3	15	21.1 ± 3.9	1415 ± 252	67.1	1.44	Fontvieille et al (1993)
					(1.36)
5-6	12	18.9	1654	87.5 ± 10.0	1.77	Davies et al (1991)
7-8	10	24.6	1958	79.6 ± 9.1	1.84	Davies et al (1991)
9.3 ± 1.4	9	30.9 ± 4.3	2151	69.6	1.78	Saris et al (1989)
					(1 77)
9-10	14	29.5	2180	73.9 ± 12.2	1.86	Davies et al (1991)
12-13	8	39.7	2334	58.8 ± 9.8	1.71	Davies et al (1991)
14.5 ± 1.5	13	56.4 ± 10.2	3109 ± 506	56.3 ± 6.4	1.88	Bandini et al (1990b)
					(1.79)
15-16	12	60.1	3233	53.8 ± 7.6	1.88	Davies et al (1991)
18-19	12	71.6	3437	48.0 ± 4.3	1.86	Davies et al (1991)
Girls
1-1.9	7c			83.0d		Prentice et al (1988)
1.5-2.49	12	13.0 ± 1.9	1062 ± 212	83.0 ± 19.5	1.46	Davies et al (1994)
2-2.9	6c			81.0d		Prentice et al (1988)
2.5-3.49	16	14.9 ± 1.1	1125 ± 211	75.8 ± 15.0	1.38	Davies et al (1994)
3-4	18	14.8	1150	77.7 ± 10.3	1.46	Davies et al (1991)
3.5-4.49	11	17 ± 2.0	1263 ± 237	74.2 ± 11.0	1.52	Davies et al (1994)
4 6	14	21.0 ± 4.7	1344 ± 314	63.5 ± 5.6	1.47	Goran et al (1993)
5.5 ± 0.4	13	18.9 ± 2.5	1318 ± 189	69.7	1.51	Fontvieille et al (1993)
					(1.37)
5-6	16	18.5	1473	79.6 ± 10.5	1.71	Davies et al (1991)
7-8	15	26.0	1989	76.5 ± 17.7	1.96	Davies et al (1991)
8.1 ± 1.3	10	28.2 ± 2.6	1926	68.3	1.82	Saris et al (1989)
					(1.69)
9-10	15	29.1	1816	62.4 ± 10.5	1.69	Davies et al (1991)
12-13	10	49.3	2569	52.1 ± 7.9	1.90	Davies et al (1991)
13.2 ± 1.8	9	43.3 ± 8.9	2321 ± 281	53.6	1.82	Wong, (1994)
14.3 ± 1.0	12	55.7 ± 9.4	2385 ± 446	43.9 ± 7.7	1.66	Bandini et al (1990b)
					(1.69)
15-16	11	58.0	2453	42.3 ± 6.0	1.67	Davies et al (1991)
18-19	11	62.4	2533	40.6 ± 7.6	1.72	Davies et al (1991)

^a Range or mean ± standard deviation.
^b Physical Activity Level calculated using basal metabolic rates estimated with Schofield's equations (1985) (or, in parenthesis, measured experimentally).
^c Assuming 50% of the children studied were boys and 50% girls.
^d Assuming the same values for boys and girls.
^e Mean ± s.d.

Table 3 Mean physical activity levels of children in Table 2 grouped by age and sex. (Total energy expenditure measured with doubly labeled water; BMR's were measured or estimated with Schofield's equations)

Age (y)	Boys	Girls
1-5	1.46 ± 0.06 (6)a	1.44 ± 0.06 (6)
6-13	1.79 ± 0.06 (5)	1.80 ± 0.12 (6)
14+	1.84 ± 0.05 (3)	1.69 ± 0.03 (3)

^a Mean ± s.d. of mean values in Table 2. Number of data sets in parenthesis. Means weighted by the number of children in each study gave similar values.

Studies with heart rate monitoring

Studies to calculate TEE of children and adolescents through heart rate monitoring have also been done only in a few countries, but they include industrialized and developing societies. These studies were done with either daytime heart rate accumulation (Spady, 1980; Torun & Viteri, 1981a,b; Spurr et al, 1986; Spurr & Reina, 1987) or the minute-by-minute heart rate method (Spurr & Reina, 1988a,b, 1989a,b; Livingstone et al, 1992a; Emons et al, 1992; Torun et al, 1993; Ramirez & Torun, 1994). Table 4 shows their age span and results. All studies involved children living in urban centers. Those in Northern Ireland and Holland involved only between 3 and 6 children in each sex-and-age group, and those in Canada were clone with 11 boys and 10 girls. Most studies in Colombia and Guatemala involved 16-34 boys or girls in each age group (median sample size = 20).

Table 4 Groups of children, classified by sex and age, whose total daily energy expenditure was estimated by heart rate monitoring methods (does not include 1-3% energy, depending on age, that should be retained for growth)

			Total energy expenditure
Age (y)	n	Weight (kg)	(kcal/d)	(kcal/kg/d)	PALa	Country	Conditionc	Methodd	Source
Boys
2.5 ± 0.7	6	11.9 ± 1.0	1060	89.1 ± 9.0	1.56b	Guatemala	Stunted	Accum	Torun & Viteri (1981b)
3.1 ± 0.3	11	12.0 ± 0.8	901	74.8 ± 7.6	1.34	Guatemala	Stunted	Accum	Torun & Viteri (1981a)
6.8 ± 0.5	24	21.9 ± 1.6	1581 ± 374	72.3 ± 16.8	1.60	Colombia		M-M	Spurr & Reina (1988a)
7.0 ± 0.5	12	21.8 ± 1.4	1541 ± 255	70.2 ± 8.4	1.54b	Colombia		Accum	Spurr et al (1986)
7.0 ± 0.5	21	19.3 ± 1.7	1207 ± 243	62.7 ± 12.5	1.46	Colombia	Underweight	M-M	Spurr & Reina (1988a)
7.4 ± 0.7	9	19.4 ± 2.3	1502 ± 176	81.0 ± 9.7	1.59b	Colombia	Underweight	Accum	Spurr et al(1986)
7.5 ± 0.3	6	25.4 ± 6.6	1859 ± 388	74.4 ± 12.2	1.64	UK		M-M	Livingstone et al (1992a)
8.4	5	27.8	2414 ± 394	86.8 ± 14.2	2.13b	Holland		M-M	Emons et al(1992)
9.3 ± 0.2	5	30.2 ± 9.4	2119 ± 182	74.5 ± 17.7	1.88	UK		M-M	Livingstone et al (1992a)
9.4 ± 1.0	11	32.1 ± 4.4	2164 ± 199	66.4 ± 9.8	1.86	Canada		Accum	Spady (1980)
10.8 ± 0.5	34	33.3 ± 2.8	2051 ± 400	61.7 ± 13.0	1.75	Guatemala		M-M	Ramirez & Torun (1994)
10.9 ± 0.6	19	25.9 ± 2.6	1918 ± 425	73.6 ± 16.6	1.72b	Colombia	Underweight	Accum	Spurr et al (1986)
11.0 ± 0.6	20	32.4 ± 3.3	2209 ± 419	68.1 ± 12.7	1.79b	Colombia		Accum	Spurr et al (1986)
11.1 ± 0.6	14	33.1 ± 2.3	2009 ± 421	60.7 ± 12.7	1.67	Colombia		M-M	Spurr & Reina (1988b)
11.1 ± 0.6	23	27.2 ± 2.8	1823 ± 513	67.5 ± 17.2	1.74	Colombia	Underweight	M-M	Spurr & Reina (1988a)
11.1 ± 0.6	19	26.6 ± 3.2	1828 ± 378	68.7 ± 14.2	1.77	Colombia	Underweight	M-M	Spurr & Reina (1988b)
11.1 ± 0.5	34	28.8 ± 3.1	2015 ± 379	70.1 ± 11.5	1.83	Guatemala	Stunted	M-M	Ramirez & Torun (1994)
11.2 ± 0.5	18	33.3 ± 2.5	2020 ± 542	60.5 ± 15.2	1.74	Colombia		M-M	Spurr & Reina (1988a)
12.7 ± 0.3	5	43.8 ± 7.3	2624 ± 315	61.4 ± 12.7	1.76	UK		M-M	Livingstone et al (1992a)
14.6 ± 0.6	16	34.8 ± 5.1	2445 ± 493	71.4 ± 12.5	1.92	Colombia	Underweight	Accum	Spurr et al (1986)
14.7 ± 0.5	12	46.7 ± 3.5	2762 ± 480	58.4 ± 9.0	1.84	Colombia		Accum	Spurr et al (1986)
14.8 ± 0.6	20	49.9 ± 3.2	2896 ± 650	58.4 ± 14.4	1.94	Colombia		M-M	Spurr & Reina (1988a)
14.8 ± 0.4	26	38.9 ± 5.3	2556 ± 580	65.6 ± 13.7	1.93	Colombia	Underweight	M-M	Spurr & Reina (1988a)
15.4 ± 0.4	3	50.7 ± 6.4	2745 ± 33	54.7 ± 6.9	1.71	UK		M-M	Livingstone et al (1992a)
Girls
6.6 ± 0.5	21	21.4 ± 1.1	1386 ± 304	63.0 ± 11.5	1.53	Colombia		M-M	Spurr & Reina (1988a)
7.0 ± 0.5	16	18.2 ± 1.7	1244 ± 254	67.6 ± 13.5	1.40	Colombia	Underweight	M-M	Spurr & Reina (1988a)
7.8 ± 0.3	5	23.5 ± 2.5	1609 ± 260	68.3 ± 5.0	1.55	UK		M-M	Livingstone et al (1992a)
8.4	5	28.3	2079 ± 191	73.5 ± 6.8	1.96b	Holland		M-M	Emons et al (1992)
9.4 ± 0.5	4	33.4 ± 3.8	1729 ± 174	52.0 ± 5.2	1.63	UK		M-M	Livingstone et al (1992a)
9.4 ± 1.2	24	28.3 ± 3.4	1537 ± 340	55.2 ± 13.6	1.43	Colombia		Accum	Spurr & Reina (1987)
9.5 ± 0.8	10	31.6 ± 3 7	1716 ± 243	55.1 ± 11.6	1.52b	Canada		Accum	Spady (1980)
9.8 ± 1.0	20	23.7 ± 2.3	1640 ± 284	69.4 ± 10.3	1.70b	Colombia	Underweight	Accum	Spurr & Reina (1987)
10.8 ± 0.6	21	27.3 ± 4.0	1584 ± 369	55.1 ± 12.8	1.57	Colombia	Underweight	M-M	Spurr & Reina (1988a)
10.9 ± 0.7	11	34.2 ± 3.7	1611 ± 319	46.8 ± 8.9	1.45	Colombia		M-M	Spurr & Reina (1988a)
11.4 ± 0.5	23	29.2 ± 3.3	1867 ± 338	63.6 ± 11.6	1.72b	Guatemala	Stunted	M-M	Torun et al (1993)
11.8 ± 0.6	88e	31.1 ± 4.0	2013 ± 400	64.3 ± 11.8	1.81b	Guatemala	Stunted	M-M	Torun et al (1993)
12.2 ± 0.5	21	33.7 ± 4.4	2170 ± 441	64.5 ± 11.5	1.90b	Guatemala	Stunted	M-M	Torun et al (1993)
12.5 ± 0.4	5	45.1 ± 4.7	2232 ± 234	49.7 ± 5.4	1.60	UK		M-M	Livingstone et al (1992a)
14.9 ± 0.6	19	49.3 ± 2.7	1982 ± 452	41.7 ± 9.6	1.61	Colombia		M-M	Spurr & Reina (1988a)
15.2 ± 0.5	22	42.0 ± 4.1	1950 ± 585	48.6 ± 14.9	1.61	Colombia	Underweight	M-M	Spurr & Reina (1988a)
15.6 ± 0.4	3	55.4 ± 13.2	2365 ± 811	42.9 ± 12.3	1.88	UK		M-M	Livingstone et al (1992a)

^a Physical Activity Level calculated using BMR measured by the investigators or estimated mathematically (^b).
^b PAL calculated using BMR estimated with Schofield's equations (1985).
^c Stunted: > 1.5 s.d. below the NCHS median of height-for-age. Underweight: < 95% of weight-for-age and weight-for-height in comparison to Colombian children of upper socioeconomic groups (Rueda-Williamson et al, 1969). All others: adequate height and weight for age.
^d Accum = heart rate accumulation during daytime, and BMR while sleeping; M - M = minute-by-minute recording.
^e 22 girls measured longitudinally four times at 3-month intervals.

Table 5 Mean physical activity levels of children in Table 4 grouped by age, sex and height or weight. (Total energy expenditure estimated by heart rate monitoring; BMR's were measured or estimated with Schofield's equations)^a

	Boys			Girls
Age (years)	Adequate wt and ht	Stunted or underweight	All	Adequate wt and ht	Stunted or underweight	All
(A) Means of mean values in each studyb
2-3	-	1.45 (2)	-	-	-	-
6-13	1.72 ± 0.11 (10)b	1.68 ± 0.14 (6)	1.71 ± 0.12 (16)	1.53 ± 0.07 (7)	1.66 ± 0.19 (5)	1.58 ± 0.14 (12)
14+	1.83 ± 0.12 (3)	1.92 (2)	1.87 ± 0.10 (5)	1.74 (2)	1.61 (1)	1.70 ± 0.16 (3)
(B) Weighted meansc
2-3	-	1.42 (17)	-	-	-	-
6-13	1.65 (149)	1.71 (125)	1.68 (274)	1.50 (80)	1.67 (101)	1.60 (181)
14+	1.89 (35)	1.93 (42)	1.91 (77)	1.65 (22)	1.61 (22)	1.63 (44)

^a Data of Emons et al (1992) excluded due to their unusually high PAL's.
^b Mean ± s.d. of mean values in Table 4. Number of data sets in parenthesis.
^c Weighted by the number of children in each study (in parenthesis)

PALs in Table 4 were calculated using measured BMR in most studies; estimates with Schofield's equations (1985) were used in only six of them. Table 5 shows the mean PALs for the same age groups as in Table 3. Although there were large differences in sample sizes (3-34), the means of the mean values in each study were within 5% of the mean values weighted for the number of children in every sex-and-age group.

All Canadian, Dutch and Irish children apparently had adequate weight and height. The Colombian children were from low and low-middle socioeconomic groups of Cali. They were classified as well nourished or as marginally malnourished or underweight when their weight-for-age and weight-for-height was above or below 95% of the Colombian standards for children of upper socioeconomic groups, respectively (Rueda-Williamson et al, 1969). Most Guatemalan children were from the lower socioeconomic groups of Guatemala City. While presently well nourished, they were stunted by more than 1.5 s.d. below the NCHS/WHO median of height-for-age. One group of Guatemalan boys (Ramirez and Torun, 1994) was from the middle socioeconomic class and they had adequate height and weight.

Figure 2 compares the data in Table 4, expressed as kcal/kg/day, with the FAO/WHO/UNU 1985 recommendations. Total energy expenditure per unit of body weight was greater among the stunted and underweight children. Since the FAO/WHO/UNU values were derived from data of well nourished, non-stunted children, Figure 3 shows only the values described in Table 4 for such children. They are combined with data from doubly-labeled water in Figure 4.

The higher energy expenditure per unit of body weight often observed in stunted and mildly malnourished children, compared with those of adequate height and weight (Tables 1 and 4), could be partly due to differences in body composition. If so, the differences in TEE would be expected to decrease or disappear when expressed as multiples of BMR (i.e. PAL units). Table 6 shows the PALs of 'normal' and stunted or mildly underweight individuals within the same community. In contrast with TEE per unit of body weight, there was no consistent difference in the PAL of children and adolescents with adequate height and weight, compared with their stunted or slightly underweight counterparts (Tables 5 and 6). This supports the explanation attributing differences to body composition.

However, the differences in TEE could also be related to the children's physical activity patterns. An examination of the minute-by-minute heart rate and its energy equivalence in Guatemalan school-boys of different height and socioeconomic status, showed that during the active hours of the day, the stunted (low income) group spent less time than the taller (middle income) group in 'sedentary' activities (434 ± 160 vs 566 ± 159 min. P < 0.01) and more time in 'light' activities that demanded some degree of physical effort (213 ± 136 vs 103 ± 94 minutes/day, P < 0.01) (Ramirez and Torun, 1994). This was probably due to the different lifestyles imposed by the different socioeconomic conditions of the two groups of children.

Figure 2a Total energy expenditure estimated by heart rate monitoring: boys.

Figure 2a Total energy expenditure estimated by heart rate monitoring: girls.

Table 6 Total daily energy expenditure of well-nourished and of stunted or marginally malnourished children, measured with heart-rate monitoring techniques and expressed as multiples of basal metabolic rate

Age	n	Energy expenditure or PAL	Condition	Reference
Boys
6.9 ± 0.5	41	1.60 ± 0.35	Normal	Spurr & Reina (1989)
7.1 ± 0.6	42	1.46 ± 0.29	Underweight
11.1 ± 0.6	54	1.74 ± 0.45	Normal	Spurr & Reina (1989)
11.0 ± 0.6	82	1.77 ± 0.47	Underweight
11.1 ± 0.5	34	1.75 ± 0.35	Normal	Ramirez & Torun (1994)
10.8 ± 0.5	34	1.83 ± 0.31	Stunted
14.8 ± 0.5	34	1.84 ± 0.50	Normal	Spurr & Reina (1989)
14.8 ± 0.6	47	1.92 ± 0.43	Underweight
Girls
7.0 ± 0.7	29	1.53 ± 0.38	Normal	Spurr & Reina (1989)
7.3 ± 0.7	20	1.43 ± 0.16	Underweight
10.8 ± 0 7	24	1.45 ± 0.21	Normal	Spurr & Reina (1989)
10.8 ± 0.5	32	1.57 ± 0.38	Underweight
14.9 ± 0.6	19	1.61 ± 0.31	Normal	Spurr & Reina (1989)
15.2 ± 0.5	22	1.61 ± 0.43	Underweight

Figure 3a Total energy expenditure estimated by heart rate monitoring, excluding stunted and underweight boys.

Figure 3b Total energy expenditure estimated by heart rate monitoring, excluding stunted and underweight girls.

Figure 4a Total energy expenditure estimated with doubly labeled water or by heart rate monitoring, excluding stunted and underweight boys. Solid symbols: doubly labeled water.

Figure 4b Total energy expenditure estimated with doubly labeled water or by heart rate monitoring, excluding stunted and underweight girls. Solid symbols: doubly labeled water.

Table 7 Groups of children, classified by sex and age, whose total daily energy expenditure was estimated from time-motion observations or activity diaries^a

			Total energy expenditure
Age (y)	n	Weight (kg)	(kcal/d)	(kcal/kg/d)	PALb	Country	Condition	Methodc	Source
Boys
1.5	12d	9.3	725e,f	78.5e,f	1.39	Gambia	Mild malnutrition	O-Estimated EC	Lawrence et al (1991)
2-6	26	12.7 ± 3.2	1026f	81f	1.35	Guatemala	Stunted	O-Estimated EC	Torun (1990b)
4-6	25	17 ± 2	1130	66.5	1.27	Philippines	Normal	O-Estimated EC	Guzman et al (1991)
7-9	26	24 ± 3	1499	62.5	1.43	Philippines	Normal	O-Estimated EC	Guzman et al (1991)
10-12	25	32 ± 4	1971	61.6	1.61	Philippines	Normal	O-Estimated EC	Guzman et al (1991)
12-14	16	31.3 ± 5.6	1810	58.0	1.49	Singapore	Normal	D-Measured EC	Banerjee & Saha (1972)
13-15	24	47 ± 6	2043	43.5	1.37	Philippines	Normal	O-Estimated EC	Guzman et al (1991)
14.5 ± 0.4	102	51 ± 10	2626	51.6	1.68	UK	Normal	D-Adult EC	Durnin (1971)
14.6 ± 2.9	75d	49.3 ± 12.8	2222 ± 572f	45.1f	1.45	Canada	Normal	D-Estimated EC	Bouchard et al (1983)
16-17	65	69.4 ± 9.5	2766 ± 247	39.9	1.47	Australia	Normal, Students	D-Adult EC	McNaughton et al (1970a,b)
16-17	9	65.0 ± 9.6	2886 ± 235	444	1.60	Australia	Normal, Workers	D-Adult EC	McNaughton et al (1970a)
16-19	32	56 ± 5	2726	48.7	1.71	Philippines	Normal	O-Estimated EC	Guzman et al (1991)
18-19	12	72.3 ± 8.1	2714 ± 276	37.4	1.46	Australia	Normal, Students	D-Adult EC	McNaughton et al (1970a)
18-19	9	68.4 ± 8.4	2740 ± 268	40.1	1.52	Australia	Normal, Workers	D-Adult EC	McNaughton et al (1970a)
Girls
1.5	12d	9.3	725e,f	78.5e,f	1.43	Gambia	Mild malnutrition	O-Estimated EC	Lawrence et al (1991)
2-6	22	12.7 ± 3.2	1026f	81f	1.41	Guatemala	Stunted	O-Estimated EC	Torun (1990b)
4-6	27	17 ± 2	1058	62.2	1.28	Philippines	Normal	O-Estimated EC	Guzman et al (1991)
7-9	24	24 ± 2	1528	63.7	1.57	Philippines	Normal	O-Estimated EC	Guzman et al (1991)
13-15	24	46 ± 3	1744	37.9	1.33	Philippines	Normal	O-Estimated EC	Guzman et al (1991)
14.5 ± 0.5	90	52 ± 8	2211	42.5	1.59	UK	Normal	D-Adult EC	Durnin (1971)
14.6 ± 2.9	75d	49.3 ± 12.8	2222 ± 572f	45.1f	1.64	Canada	Normal	D-Estimated EC	Bouchard et al (1983)
16-17	6	50.9 ± 5.3	1893 ± 195	37.2	1.38	USA	Normal	D-Estimated EC	Bradfield et al (1971)
16-17	113	58.3 ± 5.4	2025 ± 167	34.7	1.37	Australia	Normal, Students	D-Adult EC	McNaughton et al (1970a)
16-17	32	54.8 ± 7.2	2139 ± 237	39.2	1.50	Australia	Normal, Workers	D-Adult EC	McNaughton et al (1970a)
16-19	32	50 ± 3	1922	38.4	1.49	Philippines	Normal	O-Estimated EC	Guzman et al (1991)
18-19	21	58.7 ± 5.4	1949 ± 195	33.2	1.38	Australia	Normal, Students	D-Adult EC	McNaughton et al (1970a,b)
18-19	24	54.3 ± 5.6	2073 ± 159	38.2	1.53	Australia	Normal, Workers	D-Adult EC	McNaughton et al (1970a,b)

^a Energy expenditure data published by the authors or calculated from their data by B. Torun.
^b Physical Activity Level calculated using BMR estimated with Schofield's equations (1985).
^c O: Observations during daytime and diary or recall interview at night. D: Activity diary. EC: Energy cost of activities.
^d Assume 50% boys and 50% girls.
^e Mean of wet (76 kal/kg) and dry (81 kcal/kg) seasons.
^f Using the same mean values for boys and girls.

Time-motion data and comparison of methods

PALs calculated from heart rate studies coincided within 5% with those calculated from doubly-labeled water studies, except for girls 6-13 years old (Tables 3 and 5). Figure 4 also indicates that the estimates of daily energy expenditure per unit of body weight calculated by heart rate monitoring coincide quite well with those based on doubly-labeled water, at least among non stunted, well nourished boys and girls.

A review of the literature allowed us to identify several studies that estimated total daily energy expenditure of children from time-motion observations or activity diaries recorded for several days, combined with indirect calorimetry measurements or estimates of the energy cost of the recorded activities. The results of those studies, listed in Table 7, were published as such by the authors or calculated by us from their data.

Figure 5a Selected values of total energy expenditure estimated with time-motion/diary methods, compared with doubly labeled water (DLW) and heart rate monitoring (HR)*: boys. *B; Bouchard et al 1983; b: Banerjee & Saha 1972; D: Durnin 1971; G: Guzman et al 1991; L Lawrence et al 1988; M: MaNaughton et al 1970a,b; T: Torun 1990

Figure 5b Selected values of total energy expenditure estimated with time-motion/diary methods, compared with doubly labeled water (DEW) and heart rate monitoring (HR)*: girls. *B; Bouchard et al 1983; b: Banerjee & Saha 1972; D: Durnin 1971; G: Guzman et al 1991; L Lawrence et al 1988; M: MaNaughton et al 1970a,b; T: Torun 1990

Figure 5 shows all the experimental data from the studies described in Tables 2, 4 and 7. Most time-motion/diary results agree reasonably well with the results from doubly-labeled water and heart rate studies, but there is a tendency to underestimate the energy expenditure of older adolescents, especially boys, with the diary method.

Conclusions

Total daily energy expenditure of free-living children has been measured by a limited number of investigators using doubly-labeled water or adequate heart rate monitoring techniques. Most of those studies have been done in industrialized countries, and none in school aged children or adolescents in rural areas of developing countries.

The experimental results suggest that current FAO/ WHO/UNU recommendations for dietary energy are too high for children under 5, and possibly under 7, years of age. By contrast, current dietary recommendations for adolescent boys and for girls around puberty seem somewhat low.

Energy expenditure per unit of body weight of stunted or mildly underweight, but otherwise healthy, school-children and adolescents in developing countries tends to be higher than among those with adequate height and weight. The causes for this must be explored further. In the meantime it seems convenient to make dietary recommendations based on the ideal weights or PALs of the general population.

The validity of these conclusions must be confirmed by other studies, as they are based on research carried out within a very narrow range of geographic and social environments, and most investigations with doubly labeled water or heart rate monitoring in industrialized countries involved small numbers of children in each age and sex group. Studies with heart-rate monitoring in developing countries included larger series of children, but they were done mainly among low income urban groups.

Studies are especially needed in rural areas of the developing world and among middle and upper socioeconomic groups of children in developing and industrialized cities. The minute-by-minute heart rate monitoring technique seems promising for this purpose, provided that the samples of children studied are of appropriate size. If finances allow it, they should be validated in the field with the doubly-labeled water method.

Time-motion/diary techniques can be useful to confirm the accuracy of the recommendations if the values used for the energy cost of activities are appropriate for children and adolescents (Town, 1983, 1990a). They also provide important information on activity patterns that will allow better estimates of the 24-h PAL, and an understanding of the behavioral determinants of physical activity in children and adolescents.

Estimates of basal metabolic rate to calculate total energy expenditure

To calculate the energy equivalent of a PAL value, it is multiplied by the BMR. The 1985 FAO/WHO/UNU Expert Consultation endorsed the use of the mathematical equations derived by Schofield, which take into account sex, age and body weight, to estimate a population's mean BMR. Although Schofield revised and modified his equations (Schofield, 1985), those initially published in the FAO/WHO/UNU report on Energy and Protein Requirements are used more often. The two sets of equations give similar values (within ± 1-2%), except for girls 3-10 years old, where the FAO/WHO/ UNU equations give BMR's 6-7% higher than the revised equations. Thus, the PAL of those girls is lower when calculated with the FAO/WHO/UNU equations. In this review we have used the revised equations (Schofield, 1985).

The PAL approach was recommended by the FAO/ WHO/UNU Experts to calculate TEE of adult populations with occupations and lifestyles that involved different PALs. It was used to estimate TEE of children and adolescents 10-18 years old with a pattern of activities that reflected the lifestyle of children in developed countries who spend several hours at school every day (FAO/WHO/UNU, 1985). No calculations were made for those with more energy-demanding lifestyles. This can be corrected, but doubts still remain about the accuracy of the Schofield-FAO/WHO/UNU equations to predict BMR in all races. This has been addressed by authors such as Henry & Rees (1988) and Elia (1992). Table 8 illustrates some of their conclusions about the possibility of over- or underestimating BMR in adults with Schofield's equations.

Accuracy of mathematical estimations of BMR

We explored the accuracy of the Schofield equations to estimate BMR of children and adolescents from various published and unpublished reports. Some studies measured BMR and others measured resting metabolic rate (RMR). The conditions for the latter varied from quasi basal conditions (supine position, 10-12 h fasting, transported by vehicle to the laboratory, resting 30-60 min prior to the measurement) to measurements done in supine, sitting and standing positions, 2-4 h after a light meal and resting for 15-45 min before the test.

The results for measured BMR are shown in Tables 9 and 10. Those results, however, must be interpreted with some caution. For example, Bandini et al (1990b) applied Weir's equation (1948) to correct for the difference in the volumes of inspired and expired air, whereas some of the others apparently did not. When only expired volume is measured and Weir's correction is not applied, BMR is underestimated by about 5%. Some systems that use a ventilated hood and compare the concentration of inhaled and exhaled O₂ and CO₂, such as the diaferometer used by Torun and Viteri (1981a), and the system used by Livingstone et al (1992a), compensate for the difference between inspired and expired air. Saris et al (1989) used a whole body indirect calorimeter that could also have compensated for that difference.

Table 8 Percentage by which Schofield equations overestimate ( + ) or underestimate ( - ) basal metabolic rate in different ethnic groups (18-60 years old)^a

	Male		Female
Ethnicity	Mean (%)	Sample size	Mean (%)	Sample size
Philippino	+9.6	82	+ 0.3	16
Indian	+ 12.7	48	+ 12.9	7
Japanese	+ 8.3	123	+ 7.9	71
Brazilian	+ 8.1	122	-	-
Chinese	+ 8.2	232	+ 3.4	156
Malay	+ 9.3	62	-	-
Javanese	+ 5.1	82	-	-
Mayan	+ 0.0	68	-	-
Chippewa Indian	- 18.5	5	- 18.5	5

^a Source: Henry & Rees 1988).

With that methodological caveat in mind, Table 9 shows the BMR of boys and girls of different age groups measured in various countries, and compares them with the BMR calculated with Schofield's equations (1985). There seems to be a difference between developed and developing countries, an age-related trend in the data from the latter, and no major effects related to stunting or mild undernutrition. This can be seen more clearly in Table 10 Except for the Colombian underweight preschool aged boys, the difference or coincidence between measured and calculated BMR was similar for boys and girls of the same age groups, either with adequate weight and height, moderately stunted or mildly underweight.

In terms of age and sex, Schofield's equations overestimated the BMR of well-nourished, stunted or underweight Guatemalan, Colombian and Chinese preschoolers by about 10-12% in boys, and by 6-9% in girls. They coincided with measured BMR in boys and girls 7-16 years old in Holland, the UK and the USA, but overestimated the BMR of Colombian boys of that age by about 5%. That overestimation was not observed in their female counterparts, nor in Chinese girls 12-15 years old. By contrast W Wong (personal communication to B Torun) found that Schofield's equations overestimated by about 6% the BMR of 9-12 year-old hispanic and oriental girls living in Houston, Texas. The equations also overestimated by 9% the BMR of Chinese girls 15-18 years old in Guangzhou, China (Table 10).

In addition to those geographic and/or ethnic differences, Henry indicated that BMR in Beninese and Indonesian children is 8-10% lower than in the U.S. and Europe (personal communication).

More evidence about the tendency of current mathematical equations to overestimate BMR of many children and adolescents is derived from measurements of resting metabolic rates that should have been between about 15 and 20% higher than BMR, considering the conditions under which RMR is measured. For example, unpublished studies by Torun and coworkers in 68 Guatemalan 10-12 year-old boys of two economic income groups and repeated measurements in 24 stunted but well nourished girls of that same age, showed that in both sexes the non-fasting mean RMR measured after 15 min in supine, sitting and standing positions was only 7% greater than their BMR calculated with Schofield's equations. This was about 10% less than expected under the prevailing RMR conditions.

Firouzbakhsh et al (1993) reported similar results in 92 boys and 107 girls, 5-16 years old, living in or near Los Angeles, California. RMR measured 2-3 h post-prandial and after resting for 15-30 min. coincided with the calculated BMR within ± 8% in all age groups and either sex.

Table 9 Comparison of measured BMR with BMR calculated from Schofield's equations (1985)

Age	n	Country	Measured (MJ/d)	Calculated (MJ/d)	Differencea (%)	Reference
Boys
2.5 - 3.8	11b	Guatemala	2.81	3.12	+ 10.9	Torun & Viteri (1981a)
2-5	22	Colombia	3.21 ± 0.27	3.59	+ 11.9	Spurr et al (1992)
2-5	17c	Colombia	2.61 ± 0.38	3.27	+ 25.2	Spurr et al (1992)
5-6	71	China	3.42 ± 0.30	3.79	+ 10.8	Ho et al (1988)
6-8	43	Colombia	4.05 ± 0.56	4.20	+ 3.7	Spurr et al(1992)
6-8	42c	Colombia	3.66 ± 0.47	3.92	+ 7.0	Spurr et al (1992)
7-7.9	6	UK	4.72 ± 0.78	4.52	- 4.2	Livingstone et al (1992a)
9-9.5	5	UK	4.75 ± 0.65	4.98	+ 4.8	Livingstone et al (1992a)
9.3 ± 1.4	9	Holland	5.08	4.94	- 2.7	Saris et al (1989)
10-12	54	Colombia	4.98 ± 0.70	5.19	+ 4.2	Spurr et al (1992)
10-12	80c	Colombia	4.37 ± 0.66	4.74	+ 8.4	Spurr et al (1992)
12-12.9	5	UK	6.30 ± 0.83	6.00	- 4.8	Livingstone et al (1992a)
14-16	34	Colombia	6.17 ± 0.74	6.35	+ 2.9	Spurr et al (1992)
14-16	47c	Colombia	5.44 ± 0.83	5.57	+ 2.5	Spurr et al (1992)
14.5 ± 1.5	14	USA	7.29 ± 0.77	6.93	- 4.9	Bandini et al (1990b)
15-15.9	3	UK	6.70 ± 0.36	6.51	- 2.9	Livingstone et al (1992a)
Girls
2-5	20	Colombia	3.10 ± 0.42	3.29	+ 6.1	Spurr et al (1992)
2-5	19c	Colombia	2.84 ± 0.38	3.09	+ 8.8	Spurr et al (1992)
5-6	85	China	3.21 ± 0.30	3.50	+ 9.1	Ho et al (1988)
6-8	29	Colombia	3.84 ± 0.51	3.92	+ 2.1	Spurr et al (1992)
6-8	25c	Colombia	3.81 ± 0.52	3.64	- 4.5	Spurr et al (1992)
7-7.9	5	UK	4.36 ± 0.86	4.03	- 7.6	Livingstone et al (1992a)
8.1 ± 1.3	10	Holland	4.80	4.69	- 2.4	Saris et al (1989)
9-9.9	4	UK	4.43 ± 0.23	4.87	+ 9.9	Livingstone et al (1992a)
10-12	29	Colombia	4.85 ± 0.57	4.74	- 2.3	Spurr et al (1992)
10-12	33c	Colombia	4.29 ± 0.82	4.39	+ 2.3	Spurr et al (1992)
12-12.9	16	China	5.26 ± 0.38	5.21	- 0.9	Min & Ho (1991)
12-12.9	5	UK	5.85 ± 0.66	5.43	- 7.2	Livingstone et al (1992a)
13-13.9	40	China	5.30 ± 0.43	5.26	- 0.8	Min & Ho (1991)
14-14.9	23	China	5.35 ± 0.36	5.48	+ 2.4	Min & Ho (1991)
14-16	15	Colombia	5.48 ± 0.58	5.69	+ 3.9	Spurr et al (1992)
14-16	19c	Colombia	5.19 ± 0.43	5.22	+ 0.5	Spurr et al (1992)
15-15.9	14	China	5.26 ± 0.24	5.57	+ 5.8	Min & Ho (1991)
16 16.9	13	China	4.99 ± 0.31	5.49	+ 10.0	Min & Ho (1991)
14.3 ± 1.0	14	USA	6.03 ± 0.56	6.02	- 0.2	Bandini et al (1990b)
15-15.9	3	UK	5.14 ± 1.00	6.00	+ 16.8	Livingstone et al (1992a)
17-17.9	20	China	4.82 ± 0.34	5.55	+ 15.2	Min & Ho (1991)

^a + indicates that Schofield's formulas give higher values, and - indicates lower values.
^b Adequate weight but previously malnourished. Height-for-age > 1.5 s.d. below the NCHS median. ^c Weight-for-age and weight-for-height < 95% of Colombian standards (Rueda-Williamson et al, 1969)

Conclusions

Even though there may be some methodological doubts about their interpretation, the preceding observations and the data shown in Tables 9 and 10 indicate that the mathematical equations endorsed in 1985 by FAO/ WHO/UNU to calculate BMR, tend to overestimate the results and, consequently, the TEE of many children and adolescents calculated from estimates of the population's PAL.

It is necessary to decide whether a single set of predictive equations for BMR should be used universally for all children and adolescents, acknowledging an error of certain magnitude in some cases, or whether specific equations must be derived and applied to certain races or to children who live in some parts of the world.

The extensive review of BMR data presently being done by CJK Henry under the auspices of IDECG and with funding from the Nestle Foundation should help to clarify this issue.

Time allocation to different activities

The habitual physical activity of children and adolescents differs among societies with different cultural characteristics and among groups of different socioeconomic conditions in the same society. For example, while many children in rural areas of developing countries partake in domestic chores or are part of their community's labor force from an early age (Rodgers and Standing, 1981), most children in industrialized countries attend school for several hours, and those in a better socioeconomic situation do not have any work obligations.

Many studies have addressed various aspects of the time allocated by children to their daily activities. These have been performed with diverse objectives by researchers whose main interests are in nutrition, physiology, anthropology, human behavior or economics. Methods have included continuous or spot observations, recall interviews with children or caretakers, subject or observer diaries, and analysis of heart rate patterns. Results have been analyzed and presented as specific activities or classified according to their purpose or physical effort.

Table 10 Mean differences between measured MBR in children of different races and BMR calculated from Schofield's equations (1985)

Country/Race	Age (y)	Conditiona	n	Differenceb	Reference
Boys
Guatemala/Mixed	2-4	Stunted	11	+ 10.9%	Torun & Viteri (1981a)
Colombia/Mixed	2-5		22	+ 11.9%	Spurr et al (1992)
Colombia/Mixed	2-5	Underweight	17	+ 25.2%	Spurr et al (1992)
China/Chinese	5-6		71	+ 10.8%	Ho et al (1988)
Colombia/Mixed	6-16		131	+ 3.7%	Spurr et al (1992)
Colombia/Mixed	6-16	Underweight	169	+ 6.4%	Spurr et al (1992)
Holland, UK,	7-16		42	- 3.0%	Saris et al (1989)
USA/Caucasian					Livingstone et al (1992a)
					Bandini et al (1990b)
Girls
Colombia/Mixed	2-5		20	+ 6.1%	Spurr et al (1992)
Colombia/Mixed	2-5	Underweight	19	+ 8.8%	Spurr et al (1992)
China/Chinese	5-6		85	+ 9.1%	Ho et al (1988)
Colombia/Mixed	6-16		73	+ 0.2%	Spurr et al (1992)
Colombia/Mixed	6-16	Underweight	77	- 1.0%	Spurr et al (1992)
Holland, UK,	7-16		41	- 0.3%	Saris et al (1989),
USA/Caucasian					Livingstone et al (1992a),
					Bandini et al (1992b)
China/Chinese	12-15		79	+ 0.1%	Min & Ho (1991)
China/Chinese	15-18		47	+ 9.1%	Min & Ho (1991)

^a Stunted: >1.5 s.d. below the NCHS median of height-for-age. Underweight: <95% of weight-for-age and weight-for-height in comparison to Colombian children of upper socioeconomic groups (Rueda-Williamson et al, 1969). All others: adequate height and weight for age.
^b + indicates that Schofield's formulas give higher values, and - indicates lower values.
^c Mixed: various degrees of mixture between caucasian and indigenous.

Quantification of total daily time distribution

The variety of methods and the lack of a standard for presenting the data make it difficult to compare across societies and to combine the results of different studies. This is further impaired by the selective nature of some studies that focus on one type of activity, and by incomplete information, such as indicating children's involvement as a percentage of activities performed without information on the time period. We, nevertheless, made an effort to compare and combine information after a critical revision of studies with time allocation data.

From a review of more than 70 studies that had some information, we identified 39 with data of sufficient quality and completeness to quantify children's total daily time allocation (Table 11).

Table 11 Studies used to evaluate and quantify children's time allocation (see 'References' for full bibliographic information)

Acharya & Bennett (1981)	Loucky (1988)
Andersen et al (1978)	MacConnie et al (1982)
Banerjee & Saha (1972)	McNaughton & Cahn (1970a,b)
Berio (1984)	Mueller (1984)
Bradfield et al (1971)	Munroe et al (1983)
Cain (1977)	Munroe & Munroe (1989)
Carbañero (1980)	Nag et al (1978)
Colfer (1981)	Niemi et al (1981)
Dresen et al (1982)	Paolisso & Sackett (1988)
Durnin (1971)	Ramirez & Torún (1994)
Franklin & Harrell (1985)	Rutenfranz et al (1974)
Gilliam et al (1981)	Saris et al (1979)
Grossman (1984)	Seliger et al (1974)
Guzmán (1991)	Shephard et al (1980)
Hart (1988)	Spady (1980)
Ho et al (1988)	Stefanik et al (1959)
Huenemann et al (1967)	Sunnegardh et al (1985)
Johnson et al (1956)	Torun et al (1993)
Johnson & Johnson (1987)	Turke (1988)

We classified activities according to two types of characteristics:

(1) Intensity of effort and energy expenditure: (a) sleep, (b) sedentary, (c) light, (d) moderate, (e) heavy. When those categories were used by the investigators, their criteria for classification were respected. When not, we allocated the time to the corresponding category according to the description of the activity or to the children's heart rate, following the criteria shown in Table 12.

(2) Nature or purpose of the activity: (a) sleep, (b) school, (c) domestic chores, (d) production (with or without wages), (e) non-work activities. Table 13 gives descriptive examples. 'Recreational activities' are mentioned in some studies. These are non sedentary leisure activities that involve more effort than the general 'non-work activities'.

Classification of activities according to their physical effort permits making estimates of total daily energy expenditure of children with different lifestyles. Most studies that describe the nature of activities, such as in Table 13, do not indicate the degree of physical effort involved. They must be assigned an energy cost, or at least an intensity of effort, to allow comparing with studies that allocate time according to the level of energy expenditure.

Table 12 Criteria to classify the physical effort of activities according to the children's heart rate

Sedentary	< 96
Light	96-120
Moderate	121-145
Heavy	> 145

Table 13 Selected examples of activities classified according to their nature or purpose

Sleep	In bed at night; napping.
School	Classroom work; recess; other school activities.
Domestic chores	Child care; cleaning house; washing dishes; laundry; food preparation and cooking; miscellaneous household crafts and tasks; fetching water; fuel collection.
Production	Agricultural activities; household manufacturing and crafts for sale; textile work; hunting, fishing and gathering; trading and selling; wage work.
Non-work activities	Eating; personal care and hygiene; resting; walking and travelling; school homework; play and leisure; social and religious activities.

Although the energy cost of some activities listed in Table 13 has been measured by indirect calorimetry, that of many others has not (see review by Torun, 1990a). Furthermore, many tasks involve a variety of specific activities with different energy demands (for example, house cleaning can involve light dusting or heavy sweeping), and pauses of different length may be interspersed with the actual physical endeavor. Consequently, we made an empirical estimation of the physical effort involved in the activity categories of Table 13, based on the energy costs that have been measured, the descriptions available in some studies, our own experience, and the assumption that domestic and productive activities in developing societies involve more physical effort than their equivalents in developed countries or urban centers. This is shown in Table 14. As with all empirical estimations, this can later be modified but it is a starting point to compare studies.

The age groups were classified as 2-5, 5-9, 10-14 and 15-19 years, as this was the age breakdown allowed by most of the reviewed studies. In addition to the overlap between the 2-5 and 5-9 groups, there was some overlap between the other categories, as some studies presented data on children aged 9-11 or 13-15.

Tables 15 and 16 show the factorial distribution of the time allocated by boys and girls, respectively, to activities with different energy demands. They are presented separately for children from industrialized countries, cities in developing countries, and rural areas in the latter, as the activities performed and the energy expenditure involved vary in each of those settings.

Table 14 Effort empirically assumed to be required by the activities listed in Table 13

	Time spent in physical effort (%) corresponding to:
Time spent in:	Sedentary	Light	Moderate	Heavy
School	67	33
Domestic chores
cities and industrialized societies		50	50
rural developing societies		33	67
Production
cities and industrialized societies		50	50
rural developing countries		33	34	33
Non-work activities	30	30	30	10
Recreational activitiesa		30	50	20

^a Described as such in some studies. They Are non-work activities. that Are not sedentary

Table 15 Weighted averages of time allocated by boys to activities that require different levels of physical effort^a

			Mean number of daily hours at:
Society	No. of studies	No. of childrenb	Sleep	Sedentary	Light	Moderate	Heavy	Mean daily energy expenditure PALc
5-9 Years			(1)d	(1.3)	(2.2)	(2.9)	(3.6)
Industrialized, urban and rural	5	225	10.5	6	4	2	1.5	1.60
Developing, urban	2	81	11	5	3	3	1	1.56
Developing, rural	13	340	10	4	4.5	4	1.5	1.75
10-14 Years			(1)	(1.3)	(2.2)	(2.9)	(3.6)
Industrialized, urban and rural	9	887	10.5	5.5	4.5	2.5	1	1.60
Developing, urban	3	133	8.5	7.5	4	3.5	0.5	1.62
Developing, rural	12	450	9	4	4.5	4.5	2	1.85
15-19 Years			(1)	(1.3)	(2.2)	(3)	(5)
Industrialized, urban and rural	5	838	9.5	5	6	3	0.5	1.70
Developing, urban	1	32	8.5	7	6	2.5	0	1.60
Developing, rural	9	200	8	3.5	5	5	2.5	2.13

^a Sources are listed in Table 11. Averages were weighted on the number of children in each study; refer to the text for explanation of procedure when the exact number of children was not known or it was too large in relation to other studies.
^b Some numbers of children are approximations, as some studies do not give exact figures.
^c Expressed as multiples of BMR or Physical Activity Level. Not calculated when time allocation was reported in only one study.
^d Energy cost of activities, in multiples of BMR, as suggested by Torun (1990^a)

Table 16 Weighted averages of time allocated by girls to activities that require different levels of physical effort^a

			Mean number of daily hours at:
Society	No. of studies	No. of childrenb	Sleep	Sedentary	Light	Moderate	Heavy	Mean daily energy expenditure PALc
5-9 Years			(1)d	(1.3)	(2.2)	(2.9)	(3.3)
Industrialized, urban and rural	4	232	10.5	6	4	2	1.5	1.58
Developing, urban	2	81	11.5	5	4	2.5	1	1.56
Developing, rural	13	310	10	4	4.5	4	1.5	1.74
10-14 Years			(1)	(1.3)	(2.2)	(2.9)	(3.3)
Industrialized, urban and rural	4	700	10	6.5	4	2.5	1	1.58
Developing, urban	2	73e	8.5	6	4.5	4.5	0.5	1.70
Developing, rural	12	400	9	3.5	4.5	5	2	1.86
15-19 Years			(1)	(1.3)	(2.2)	(3)	(4.5)
Industrialized, urban and rural	7	1023	9.5	5.5	6	2.5	0.5	1.65
Developing, urban	1	32	8	7	6.5	2.5	0	1.62
Developing, rural	9	180	8	3	5.5	5.5	2	2.06

^a Sources are listed in Table 11. Averages were weighted on the number of children in each study; refer to the text for explanation of procedure when the exact number of children was not known or it was too large in relation to other studies.
^b Some numbers of children are approximations, as some studies do not give exact figures.
^c Expressed as multiples of BMR or Physical Activity Level. Not calculated when time allocation was reported in only one study.
^d Energy cost of activities, in multiples of BMR, as suggested by Torun (1990a)
^e In one of the two studies 24 girls were studied longitudinally four times at 3-month-intervals.

Time distributions were calculated as weighted means from several studies, weighting them for the number of children involved, and rounding the time to the nearest half-hour. In studies that only presented the number of households, the number of children was assumed to be either 50 or 33% of those households, depending on other information related to the study. When the number of boys and girls was not given, equal numbers were assumed for each sex. When a study greatly outnumbered the sample size of all others for that sex and age category, only 50% of its sample size was used to calculate the weighted mean in order to reduce the bias of the results towards a single study. For example, 8 of 9 studies on boys 10-14 aged years old in industrialized countries involved between 11 and 171 children, whereas the ninth study involved 360; a weight of 180 was given to that study.

Tables 15 and 16 show that, compared with children in industrialized societies, children in developing rural areas sleep less at night, participate longer in moderate and/or heavy physical activities, and have a greater energy expenditure in relation to their basal metabolic rate. There are very few studies on children in cities from developing countries, but their physical activity falls between the other two groups, resembling more that of children in industrialized countries than that of their rural counterparts. Within the same type of society, there were no striking differences between boys (Table 15) and girls (Table 16).

In terms of the nature or purpose of the activities, children of school age in industrialized countries spend between 4.5 and 7.5 h at school during school-days. In developing countries, children in urban areas spend similar amounts of time at school, although many from the lower socioeconomic groups do not attend school at all, especially after 12 years of age. School attendance is less among their rural counterparts, who average between 0.5 and 2 h per day (Table 17).

Table 17 also shows that children in rural traditional societies of developing countries begin domestic and productive work at preschool age, and from 10 years onwards they have an important daily workload. Girls are involved in domestic work longer than boys and, after 9 years of age, boys spend more time than girls in production and wage-earning chores.

Estimations of total daily energy expenditure

Total daily energy expenditure was estimated from the time allocations in Tables 1:5 and 16, and the energy costs of sedentary, light, moderate and heavy activities suggested by Torun (1990a) as shown in those tables; the energy cost of sleep was assumed to equal basal metabolic rate. The results, expressed as PAL or multiples of BMR, are shown in the last column of those tables.

Table 17 Time allocated to school attendance, domestic work, productive work and non-work activities by children of native, traditional, rural populations from several countries^a

	Time allocated to (rounded to 0.5 h):
	School	Domestic work	Production work	Non-work and sleep
2-5 Years
Boys	<0.5	0.5	0.5	23
Girls	<0.5	1	<0.5	23
5-9 Years
Boys	1	0.5	1.5	21
Girls	1	1.5	1.5	20
10-14 Years
Boys	2	1	4	17
Girls	2	2.5	2.5	17
15-19 Years
Boys	1.5	1	6	15.5
Girls	1.5	3.5	3.5	15.5

^a Bangladesh (Cain, 1977), Borneo (Colfer, 1981), Botswana (Mueller, 1984), Guatemala (Loucky, 1988), Indonesia (Nag et al, 1978; Hart, 1988), Ivory Coast (Berio, 1984), Kenya (Munroe et al, 1983; Munroe & Munroe, 1989), Papua/New Guinea (Grossman, 1984), Panama (Franklin & Harrell, 1985), Peru (Munroe et al, 1983; Johnson & Johnson, 1987), Philippines (Carbañero, 1980), Nepal (Nag et al, 1978; Acharya & Bennett, 1981), Venezuela (Paolisso & Sackett, 1987), Western Caroline Islands (Turke, 1988).

PALs were converted into kcal/kg/day applying Schofield's equations (Schofield, 1985) to the body weight at the mid-point of the age intervals shown in Table 18 (i.e. 7.5, 12.5 and 17.5y). The NCHS/WHO median weight for age was used for children in industrialized countries, and it was assumed that the average weights for children in urban and rural developing areas corresponded to the 30th and 20th centiles of the NCHS values, respectively. The remarkable agreement with the estimates of total daily energy expenditure by the doubly-labeled water and heart rate methods (Figure 6) suggests that the criteria for classification of activities shown in Tables 13 and 14 and the factors used to assign them an energy cost (Tables 15 and 16) were good estimates.

Tables 15, 16 and 18 suggest that total energy expenditure expressed as PAL is similar for boys and girls within each age group and geographic/developmental category. In industrialized countries, it is constant between 5 and 14 years (and similar to cities in developing countries), and it increases by about 5% after that age. In rural developing societies, daily energy expenditure increases with age, as a reflection of children's increasing involvement in energy-demanding chores.

An analysis of the estimates of total daily energy expenditure shown in Table 18 indicates that, based on multiples of BMR, children of 5-9, 10-14 and 15-19 years spend about 10,15 and 25% more energy in rural developing societies than in industrialized countries. When expressed as kcal/kg, the corresponding increments in energy expenditure are about 15, 25 and 30% for the three age groups, respectively.

Table 18 Estimates of total daily energy expenditure of children based on the data shown in Tables 15 and 16, and the median weights assumed for the age span

		Estimated daily energy expenditure
Age (y)	Assumed weighta (kg)	PAL	(kcal/kg/day)b
Boys
Industrialized countries
5-9	24.0	1.60	69.9
10-14	42.3	1.60	53.2
15-19	67.8	1.70	46.6
Developing cities
5-9	22.5	1.56	70.4
10-14	38.6	1.62	56.3
Developing rural areas
5-9	21.6	1.75	80.5
10-14	36.5	1.85	66.1
15-19	60.3	2.13	60.9
Girls
Industrialized countries
5-9	23.3	1.58	65.0
10-14	43.8	1.58	46.1
15-19	56.7	1.65	42.2
Developing cities
5-9	21.6	1.56	66.8
10-14	40.0	1.70	52.2
Developing rural areas
5-9	20.7	1.74	76.2
10-14	37.6	1.86	59.2
15-19	50.4	2.06	55.9

^a Children in industrialized countries: NCHS median for mid-point of age range (i.e., 7.5, 12.5 and 17.5y); children in developing urban centers: 30th centile; children in rural societies: 20th centile.
^b Basal metabolic rate was converted to kcal/kg/day using the formulas suggested by Schofield (1985).

Figure 6a Total energy expenditure from time allocation (TA) compared with doubly labeled water (DLW) and heart rate monitoring (HR): boys.

Figure 6b Total energy expenditure from time allocation (TA) compared with doubly labeled water (DLW) and heart rate monitoring (HR): girls.

Conclusions

We believe that more insightful information on children's time allocation and its energy cost is lying unanalyzed in existing databases of nutritional, physiological and anthropological studies. Efforts must be made to retrieve, analyze and present them in a standard manner to allow making better estimates of children's energy expenditure and requirements, as well as of the behavioural and social implications of their time distribution.

The data that we were able to analyze indicates that, beginning at least at 5 years of age, children in rural areas of developing countries spend more time in activities that require more physical effort than children in cities or industrialized countries.

It seems that time allocation of physical activity is similar in urban areas of industrialized and developing countries, but more information is needed from the latter to confirm this notion. Information is also needed on the time allocated to activities by children and adolescents of different socioeconomic groups.

Table 19 Mean 24-hour physical activity levels of children and adolescents in industrialized countries and in cities of developing countries (based on data in Tables 2, 4, 7, 15 and 16)^a

Age (y)	Methodb	Boys		Girls
1-5	DLW		1.46 (86)c		1.44 (84)
	HR (St)			1.42 (17)
	TM (St)			1.36 (38)	1.42 (34)
6-13	DLW		1.79 (53)		1.80 (75)
	HR	1.71 (149)	1.71 (274)	1.50 (80)	1.59 (181)
	HR (St)	1.71 (125)	1.71 (274)	1.67 (10:1))	1.59 (181)
	TM		1.51 (67)		1.57 (24)
	TA		1.60 (1326)		1.59 (1086)
14-18	DLW		1.84 (37)		1.69 (34)
	HR		1.81 (15)	1.65 (22)	1.63 (44)
	HR (St)			1.61 (22)	1.63 (44)
	TM		1.57 (304)		1.58 (253)
	TA		1.70 (870)		1.65 (1055)

^a Excluding studies with mean PAL < 1.40 for children over 5 years, and > 1.90 for all ages.
^b DLW: doubly labeled water; HR: heart rate monitoring; TA: time allocation; TM: time-motion/diary. (St): stunted or mildly underweight; otherwise, normal.
^c Weighted mean. Number of children in parenthesis.

The conversion of time allocation data to energy expenditure gives reasonable results when activities such as those listed in Table 13 are assigned the intensity of effort shown in Table 14, and the energy equivalents shown in Tables 15 and 16 are applied to sleep, sedentary, light, moderate and heavy activities.

When time allocation is converted into energy expenditure expressed as PAL, there is practically no difference between boys and girls within the same type of society.

Physical activity levels of children and adolescents

The occupational and habitual activities of adults are classified as light, moderate and heavy, and taken into account to calculate and recommend dietary energy intakes. The data presented in this document supports the suggestion that the same approach must be applied to children from 5 years of age onwards.

To do so, estimates must be made of the 24-hour PAL of children and adolescents with different lifestyles. This is usually associated with their geographic habitat (urban or rural, industrialized or developing country) and socioeconomic conditions.

An analysis of the PALs calculated in this review for children studied with doubly-labeled water, heart rate monitoring, time-motion/diary techniques and time allocation estimates allows making practical suggestions. Table 19 summarizes those calculations for industrialized countries and cities in developing countries, calculated as weighted means for the total number of boys or girls included in all studies with a specific technique. Studies with mean PAL < 1.40 for children over 5 years old were excluded, as well as those with PAL > 1.90 at all ages, as those figures are very unlikely to represent the habitual activity level of children in cities and industrialized countries. The mean PALS of normal and stunted children calculated from heart rate monitoring methods were combined as they were derived from otherwise healthy children, and in most cases they agreed within 4%.

There is hardly any information of TEE of children and adolescents living in rural developing countries. Therefore, we only estimated their PAL from time allocation data, as described in the preceding section and shown in Tables 15, 16 and 18.

The estimates of PALs from studies on time-motion/ diary records and time allocation data involve a series of assumptions on the energy cost of activities and tasks to calculate TEE. Thus, it seems more reasonable to use the data derived from doubly-labeled water and heart rate monitoring studies to suggest PALs to estimate the energy expenditure and requirements of children and adolescents from different populations. Such PALs, based on the data in Table 19, are shown in Table 20. Assuming that those levels of physical activity correspond to children and adolescents who are neither extremely sedentary nor active and are consuming dietary energy ad libitum, we suggest that they are equivalent to a moderate PAL.

The mean coefficient of variance (CV) of the studies with doubly-labeled water and heart rate monitoring in boys and girls 1-5, 6-13 and >14 years old shown in Tables 3 and 5 is 6%. We calculated the PAL of light and heavy lifestyles by subtracting or adding twice the CV (i.e. 12%) from the moderate PAL of children and adolescents over 5 years old (Table 20). It is unlikely that infants and preschoolers have a heavy physical lifestyle. Consequently, for that age group it is suggested that the mean of the PALs shown in Table 19 (measured by DLW or HR) be applied to a 'light' lifestyle, and the additional 12% (twice the mean CV) be applied to a 'moderate' PAL.

Table 20 Physical activity levels suggested to estimate total daily energy expenditure from the mean basal metabolic rate of children and adolescents

		Habitual physical activity
Age (y)	Sex	Light	Moderate	Heavy
1-5	M, F	1.44	1.61
6-13	M	1.54	1.75	1.96
14-18	M	1.60	1.82	2.04
6-13	F	1.48	1.68	1.88
14-18	F	1.46	1.66	1.86

Table 21 Data from Table 20 rounded to the closest 0.05 PAL units

		Habitual physical activity
Age (y)	Sex	Light	Moderate	Heavy
1-5	M, F	1.45	1.60	-
6-13	M	1.55	1.75	1.95
14-18	M	1.60	1.80	2.05
6-13	F	1.50	1.70	1.90
14-18	F	1.45	1.65	1.85

To facilitate remembering those PAL factors, it is further suggested to round them to the closest 0.05 PAL units, as shown in Table 21.

As more information on TEE and BMR of boys and girls with different lifestyles becomes available and the questions related to the mathematical equations to estimate BMR are cleared, the PALs shown in Table 21 may be modified. In the meantime, their use is suggested as a first approximation to estimate energy requirements in population groups where actual data is unavailable. Table 22 shows those estimates for boys and girls with median weights-for-age corresponding to the NCHS standards. Figure 7 compares them with measurements using doubly labeled water and heart rate monitoring, expressed as kcal/kg/day.

Table 22 Estimates of total daily energy expenditure from the physical activity levels suggested in Table 21 and basal metabolic rates calculated with Schofield's equations

		Habitual physical activityb
		Light		Moderate		Heavy
Age (y)	Weighta (kg)	(kcal/d)	(kcal/kg/d)	(kcal/d)	(kcal/kg/d)	(kcal/d)	(kcal/kg/d)
Boys
1	10.4	854	82.1	942	90.6	c	c
2	12.3	1018	82.7	1123	91.3	c	c
3	14.6	1211	83.0	1337	91.6	c	c
4	16.7	1281	76.6	1413	84.6
5	18.7	1346	72.0	1486	79.4
6	20.7	1510	72.9	1704	82.3	1899	91.7
7	22.9	1587	69.3	1792	78.2	1996	87.2
8	25.3	1671	66.1	1887	74.6	2102	83.1
9	28.1	1770	63.0	1998	71.1	2227	79.2
10	31.4	1885	60.0	2126	67.7	2370	75.5
11	35.3	1988	56.3	2245	63.6	2501	70.9
12	39.8	2112	53.1	2384	59.9	2657	66.8
13	45.0	2254	50.1	2545	56.6	2836	63.0
14	50.8	2491	49.0	2803	55.2	3192	62.8
15	56.7	2659	46.9	2991	52.7	3406	60.1
16	62.1	2811	45.3	3163	50.9	3602	58.0
17	66.3	2930	44.2	3296	49.7	3755	56.6
18	68.9	3004	43.6	3379	49.1	3849	55.9
Girls
1	9.8	783	79.9	865	88.2	c	c
2	11.8	953	80.7	1051	89.1	c	c
3	14.1	1120	79.4	1236	87.6	c	c
4	16.0	1176	73.5	1297	81.1
5	17.7	1226	69.3	1352	76.4
6	19.5	1323	67.8	1499	76.9	1676	85.9
7	21.8	1393	63.9	1579	72.4	1764	80.9
8	24.8	1484	59.8	1682	67.8	1880	75.8
9	28.5	1597	56.0	1810	63.5	2023	71.0
10	32.5	1706	52.5	1933	59.4	2160	66.5
11	37.0	1783	48.2	2021	54.6	2259	61.0
12	41.5	1874	45.1	2123	51.2	2373	57.2
13	46.1	1966	42.6	2228	48.3	2490	54.0
14	50.3	1982	39.4	2256	44.8	2529	50.3
15	53.7	2048	38.1	2331	43.4	2613	48.7
16	55.9	2091	37.4	2379	42.6	2668	47.7
17	56.7	2107	37.2	2397	42.3	2688	47.0
18	56.6	2105	37.2	2395	42.3	2685	47.4

^a Median weight for age, NCHS/WHO.
^b PAL factors shown in Table 21.
^c Assume values similar to moderate physical activity in children 1-3 years old.

Dietary energy intake

The most important criteria in choosing a method for collecting food intake data in children and adolescents are: (a) the technique should not interfere with the subject's dietary pattern; (b) the data should be representative of usual or habitual intake and (c) the technique should be suitable for application in large study groups.

The methods most frequently used in childhood and adolescent population groups are similar to those applied in adult studies, namely:

(1) Retrospective or food recall methods, which depend on dietary information given from memory by the child/ adolescent and/or parent/child carer. Several specific types of data collection fall within this category, including those aimed at quantifying actual intake for a precise time (usually the previous day, or 24-h recall) and those designed to elicit information about usual consumption patterns for a longer, less precisely defined time period (diet history or food frequency methods). More than one 24-h recall should be made on different days of the week, especially when there are cultural cyclic changes in food intake (e.g. weekdays compared with weekends). Recalls of more than 24 h are sometimes performed but the accuracy with which subjects and/or parents can remember food consumption is debatable, particularly if food intake patterns are highly unstructured or unstable. In the food frequency method, subjects and/or parents/child carers report by interview or self-administered questionnaire, the frequency of consumption of particular foods during a specified time span (week, month, year). A quantitative component is added by including the size and number of portions most frequently consumed for each food.

(2) Prospective or food record methods, which require that all food items consumed be recorded at the time of consumption. Intakes are quantified by direct weighing of the food, by estimates using, household measures or by collection of duplicate diets. Quantitative assessment of usual food intake can be obtained by increasing the number of measurement days. Seven days are generally assumed to represent a good compromise between precision, subject/parental cooperation, cultural dietary patterns and investigator workload.

Each of these methods has advantages and drawbacks when applied to children and adolescents. Ultimately, all survey methods are dependent on the motivation, compliance and ability of subjects and/or parents/child carers to report accurately habitual food intake.

Food intake data must then be converted into energy equivalents. This is often done disaggregating recipes into their food components and calculating their metabolizable energy as reported in food composition tables. Care must be taken to make all necessary conversions for the proper use of food composition data. A common error is applying to 'cooked' or 'wet' weight of foods the energy values for 'raw' or 'dry' foods that appear in composition tables, without applying adequate conversion factors.

A more accurate approach is to perform chemical or calorimetric analyses of samples of foods that are ready to be eaten. This is particularly useful to calculate the energy provided by food recipes that are unlikely to appear in food composition tables or that may be subject to variations. When the energy content of food is measured by bomb calorimetry, appropriate corrections must be made to calculate metabolizable energy.

Figure 7a Energy expenditure calculated from estimates of habitual physical activity, compared with measurements using doubly labeled water and heart rate monitoring. Including data of stunted and underweight children: boys.

Figure 7b Energy expenditure calculated from estimates of habitual physical activity, compared with measurements using doubly labeled water and heart rate monitoring. Including data of stunted and underweight children: girls.

Validity of energy intakes in children and adolescents

Most dietary intake studies in children assume that the data obtained are representative of habitual food consumption, and many recent studies concluded that energy intakes (EI) have declined in industrialized countries and more privileged groups in developing countries in response to a secular trend towards lower levels of activity in children and adolescents. However, studies in adults using doubly-labeled water (DLW) measurements of total energy expenditure (TEE) to validate EI have demonstrated that intake data may underestimate habitual food intake to a greater extent than has been appreciated (Prentice et al, 1986; Livingstone et al, 1990b; Schoeller, 1990). It is conceivable, therefore, that the reportedly low intakes of children may be artifacts of dietary survey methodology, rather than indicative of a diminution in energy expenditure.

Validation studies have been reported to assess the accuracy of EI in children and adolescents, using DLW measurements of TEE. These include studies of EI by 4-day weighted dietary record (WDR) in 1.5-4.5 year olds (n = 81) (Davies et al, 1994), by 7-day WDR in 7, 9, 12, 15 and 18 year olds (n = 58), by diet history (DH) in 3, 5, 7, 9, 12, 15 and 18 year olds (Livingstone et al, 1992b) and by 14 day estimated food records in non-obese and obese adolescents (n = 55) (Bandini et al, 1990a).

Figure 8 Comparison (± s.d.) of reported habitual energy intake and energy expenditure in (a) 1.5-4.5 year old children (Davies et al, 1994) and (b) non-obese and obese adolescents (Bandini et al, 1990a).

Figure 8 Comparison (± s.d.) of reported habitual energy intake and energy expenditure in (a) 1.5-4.5 year old children (Davies et al, 1994) and (b) non-obese and obese adolescents (Bandini et al, 1990a).

Figure 9 Comparison (± s.d.) of reported habitual energy intake by diet history and weight dietary record and energy expenditure in 3-18 year old subjects (Livingstone et al, 1992b).

Figure 9 Comparison (± s.d.) of reported habitual energy intake by diet history and weight dietary record and energy expenditure in 3-18 year old subjects (Livingstone et al, 1992b).

The results shown in Figures 8-10 indicate that bias in dietary reporting does not operate uniformly across age groups and that it is influenced by the particular methodology used.

In children aged 1.5-4.5 years, mean El calculated by 4-day WDR were not significantly different from mean TEE (+3%) (Figure 8a). Similarly, the mean EI by 7-day WDR of 7 and 9 year olds were in close correspondence with simultaneous measurements of TEE ( + 2%) (Figure 10a), but in adolescents and young adults there was increasing divergence between EI and TEE as age increased: mean EI were significantly lower than TEE in 12 year olds ( - 14%) and in 15 and 18 year olds (-24%, P<0.01) (Figure 10a). Using 14-day estimated intake records, Bandini et al (1990a) also showed a substantial underestimation of EI by adolescents, with the negative bias being most apparent in obese subjects (Figure 8b). After adjustment for changes in body composition, mean estimated EI were 80 ± 23% (non-obese) and 54 ± 32% (obese) of TEE values (P < 0.001).

The age-related discrepancy differed in the study to validate EI by diet history in 3-18 year olds. There was a bias towards overestimation of EI in the younger children by this technique: as age increased, mean differences were + 12%, + 9%, + 11% and - 1% (Figures 9 and 10b)

These validation studies can be criticized because they only involved a small number of subjects in various age groups. However, all of them indicate that a bias in dietary reporting is highly probable. Thus, considerable caution needs to be applied when interpreting energy intake data sets as a basis for deriving energy requirements. Moreover, the magnitude and direction of the errors in children's EI are likely to be different from those found in adults. These biases are highly relevant to the problem of determining appropriate energy intakes for nitrogen balance studies (see Appendix).

Age is an important variable that affects compliance in dietary reporting. The results presented suggest that the mean EI assessed by weighed dietary records are more likely to represent usual food intake in younger than in older subjects. This could be due to the fact that in young children overall control of food intake and responsibility for dietary reporting are shared by parents and other adults concerned with child caring. Younger children also have less unsupervised access to food in- and out-of-home. On the other hand, by early adolescence the responsibility for reporting shifts more to the subjects themselves. Consequently, their greater food requirements in combination with unstructured eating patterns and a significant degree of out-of-home eating suggest that under-reporting (by WDR) may be partly due to forgetfulness and lack of compliance with a demanding protocol.

Obesity is another important factor. In common with obese adults (Prentice et al, 1986), obese adolescents have been found to under-report EI significantly more than their non-obese counterparts (Bandini et al, 1990a). Preoccupation with body weight and image, which may lead to real or apparent dietary restraint, seems to be well developed in girls with normal and low weight by the age of 12 years. Similar, although less marked trends, have been observed in adolescent boys (Livingstone et al, 1992b).

The method used to assess EI also may influence the results. Validation studies with various EI methods across the entire age range of childhood and adolescence are lacking. Only one study has validated simultaneously EI by WDR and DH with TEE (Livingstone et al, 1992b). Although EI by DH were biased towards overestimation in most age groups and individual measurements lacked precision, mean intakes assessed by DH seemed more representative of habitual EI across the age range than WDR. The apparent superiority of DH in overcoming an age-related bias in dietary reporting is contrary to expectations and needs to be evaluated carefully. Since DH is not a standardized instrument and it only measures memory and perception of usual diet, it is subjective and children may tend to exaggerate the intake of 'good' foods and under-estimate 'bad' foods. Accuracy in reporting is also dependent on motivation, intelligence, an adequately developed concept of time, ability to recognize foods, the complexity and stability of food patterns and the age at which children can reliably report their own food intake without control or supervision of adults.

Other factors which are likely to influence reporting accuracy and about which little is known, include social class and educational background.

In addition to the credibility of food intake reports, assessment of EI can be distorted by the use of inadequate food composition tables and/or overlooking the conversion of cooked and processed foods into their raw ingredients².

² The world-wide food composition data network being developed by INFOODS offers electronic access to information on prepared and processed foods often not available in local food composition tables (for information: http ://www.crop.cri.nz/crop/infoods/infoods.html).

Figure 10a Individual differences between energy expenditure measured by the doubly labeled water method and energy intakes as measured by 7-day weighed dietary records expressed as a percentage of energy expenditure in children aged 7 and 9 years (A), 12 years (B) and 15 and 18 years (C).

Figure 10b Individual differences between energy expenditure measured by the doubly labeled water method and energy intakes as measured by diet history expressed as a percentage of energy expenditure in children aged 3 and 5 years (A), 7 and 9 years (B), 12 years (C) and 15 and 18 years (D) (From data of Livingstone et al, 1992b).

Dietary energy intake data of children and adolescents

A selection of dietary intake studies reported in the literature from about 1980 onwards are reviewed here since earlier studies were evaluated extensively by Ferro-Luzzi & Durnin (1981), as the basis for the 1985 FAO/WHO/UNU estimated requirements. Since 1980, a vast number of dietary intake studies on children and adolescents have been reported and the studies cited in this review are by no means an exhaustive compilation. Many studies were excluded based on the following criteria:

(1) When energy intakes were reported for wide age bands (e.g. 11-16 years) and the mean age was not recorded.
(2) When energy intakes were reported combined for boys and girls over 10 years old.
(3) When data were presented in a format which could not be readily interpreted for the purposes of this review (e.g., in graphs). Unfortunately, many studies in developing countries were excluded for this reason.
(4) When the children studied were generally malnourished or obese, and their mean weight-for-height differed from the NCHS/WHO standards by more than 2 s.d. Many reports were based on representative study populations and therefore included children with a range of body weights.
(5) Only studies of healthy children were included, since many disease states are likely to affect energy intakes and requirements.

Tables 23 and 24 give details of the studies that were reviewed. Forty-eight involved children approximately 110 years old, and 41 studies included children and adolescents approximately 10-18 years old.

Tables 25-30 show the energy intakes of the children, by ascending age. Boys and girls under 5 years are listed together in Table 25, as many studies did not separate the results for each sex. The same is true of the six studies in Table 30. When body weights were not reported, median weights (NCHS) at the mid-point of the age range were assumed and, in Tables 25 and 30, averaged for boys and girls. Energy intake data are presented as absolute values, in relation to body weight, and as multiples of the estimated BMR. The latter were calculated from the mean weights using the equations proposed by Schofield to FAO/WHO/UNU (1985).

Comparison with total energy expenditure and dietary recommendations

When energy intakes are used to assess requirements or to estimate whether the mean intake satisfies a population's dietary recommendations, the possibility of bias must be acknowledged and the data should be analyzed and interpreted accordingly. Information that is incompatible with fundamental principles of energy physiology should not be accepted, as it cannot represent long-term usual intake or is due to methodological bias or inadequate reporting. Goldberg et al (1991) and Black et al (1991) suggested a screening of EI data of adult populations, calculating them as multiples of BMR. For example, a value below 1.27 × BMR, considered as the survival requirement for adults (FAO/ WHO/UNU, 1985), is unacceptable as representative of habitual intake.

Following that logic, we used the PALs shown in Table 21 to establish reasonable limits to evaluate dietary energy surveys among children and adolescents. Mean results lower than two times the coefficient of variation (i.e. 12%) below the PAL corresponding to light habitual activity, or higher than two times the CV above the PAL for heavy habitual activity were considered unlikely to represent the usual intake of healthy children. Since the PALs for boys or girls 6-13 and 14-18 years old in Table 21 are reasonably close, the acceptable limits for those age groups were averaged to simplify the evaluation of the results in Tables 25-30. Further corrections for the energy needs for growth were not made, as they are only about 3% at age 1 and less than 1 % in late adolescence.

Thus, Tables 31-33 were prepared from the data in Tables 25-30 that were between 1.28 and 1.79 × BMR for children 1-5 years, between 1.39 and 2.24 × BMR for boys 6-18, and between 1.30 and 2.10 × BMR for girls 6-18. Mean energy intakes expressed as MJ/d, kJ/kg/d and × BMR, were weighted for the number of children in each study. When a study included more than 500 or 1000 children of a given age and sex, only 30% or 20% of the number, respectively, were used to calculate the weighted means to avoid an extreme bias toward the results of that study.

As Table 31 and Figure 11 show, energy intake per unit of body weight is fairly constant for both boys and girls between 3 and 7 years of age, after which it decreases gradually until age 15 (girls) or 16 (boys).

Compared with total energy expenditure assessed with doubly-labeled water and heart rate monitoring, energy intake tends to overestimate requirements under 8-10 years and to underestimate them after that age. Those trends also apply to the 1985 FAO/WHO/UNU energy recommendations, but the overestimation is markedly higher under 6 years of age. This is partly due to the 5% additional dietary energy recommended in 1985 for children 1-10 years old to accommodate 'a desirable level of physical activity'.

The reported EI of children 1-5 years old is about 13% lower than FAO/WHO/UNU requirements (Figure 11, Table 31). Although the wide range between data sets could reflect real differences in intake, unrepresentative study samples, or artifacts in dietary survey methodology, mean intakes fell short of FAO/WHO/ UNU requirements in about 80% of the data sets.

The influence of sex on dietary energy intake is illustrated in Figure 12 and Tables 31-33. Girls have lower EI than boys, whether expressed in absolute terms or relative to their body weights or their estimated BMR, and the difference becomes greater in adolescence. These findings are consistent with their lower total energy expenditure (Tables 2-7 and 20, and Figure 5).

Conclusions

Recent trends in EI of children and adolescents suggest that if the groups studied are representative of their age and sex, and the EI data are valid measures of habitual intake, then:

(a) Habitual energy intakes of 1-6 year old children are lower than current recommendations. Increasing reported energy intakes by 5% to accommodate a 'desirable level of physical activity' may be unrealistic.
(b) Energy requirements for physical activity may be more variable in adolescent males but lower in the adolescent females, than has been assumed when deriving factorially estimated energy requirements.

For methodological and economic reasons it seems inevitable that we will continue to rely partly on reported EI data as a basis of estimating energy requirements for most populations. However, it is clear that these data can no longer be tacitly accepted as representative of usual intake. Therefore, the following recommendations need to be considered:

(a) At present there are too few studies in which energy intake and energy expenditure have been studied in the same population to know the nature and extent of bias involved in these measurements. This will require more extensive validation studies of energy and nutrient intakes that take into account differences in methodology, social status, education, age, and geographical region in both developing and industrialized countries. From these studies guidelines may emerge for detecting patterns of bias and the characteristics of individuals contributing to it.
(b) Variation among individuals within the same population can be appropriately characterized by a mean and standard deviation whose validity will depend upon the adequacy of the sample. However, the nature and extent of differences in mean values among different populations make it unlikely that they can be appropriately characterized by a single mean and standard deviation, no matter how many populations are sampled. It may be better to express a range of mean values for this purpose.
(c) Research must be done to find ways of minimizing the psychological basis of under- and over-reporting in these age groups.
(d) Appropriate 'cut-off' values based on fundamental principles of energy physiology should be used to determine the acceptance of energy intake results. This will require an extensive data base of basal and total daily energy expenditures (BMR and TEE) in association with objective measures of physical activity. In the meantime, the following estimates of multiples of BMR are suggested as provisional cut-off points: 1-5 years (boys and girls): 1.28-1.79 × BMR; 6-18 years: 1.39-2.24 × BMR (boys) and 1.30-2.10 × BMR (girls).

These recommendations will not guarantee valid data and cannot eliminate the considerable differences among populations, but may lead to the design of more effective instruments for assessing energy intake and requirements of children and adolescents.

Table 23 Dietary surveys of children aged approximately 1-10 years

Source

Country

Sex

Age (y)

No. of subjects

Methoda

Time of year

Socio economic statusb

Urban/ruralc

Race/ethnic background

Bellu et al (1991)	Italy	M & F	1	164	24-h recall	?	?	U	?
Boggio & Klepping (1981)	France	M & F	5-6, 9-11, 14-16	376	7-d weighed record	?	M	U	?
Boulton (1981)	Australia	M & F	2, 3-5, 8-18	198,486, 235	Diet history, 4-d record	12 months	M	U	Mixed
Brault-Dubuc & Mongeau (1989)	Canada	M & F	6-16	402 (L)d	7-d record	12 months	M	U	?
Catassi et al (1988)	Italy	M & F	0.5-2.5	90	3-d weighed record	?	?	?	?
Cunningham & Lee (1990)	Republic of Ireland	M & F	8-18	538	Diet history	12 months	M	U & R	Caucasian
Davies et al (1994)	United Kingdom	M & F	1.5-4.5	81	4-d weighed record	Autumn	M	U	?
Deheeger et al (1991)	France	M & F	2	323	5-d record, diet history	?	M	U	?
Duggan et al (1991)	United Kingdom	M & F	0.3-3.3	97	5-d weighed record	?	L	U	Asian
Durnin (1984)	United Kingdom	M & F	5-6, 10-11	430	5-d weighed record	?	M	U	?
Eastwood et al (1990)	Mexico	M & F	2.8-3.9, 4.0-5.0	45	1-d weighed record	?	L	R	Mixed
Griffiths et al (1987)	United Kingdom	M & F	3-4	37	7-d weighed record & duplicate analysis	?	?	?	?
Hagman et al (1986)	Sweden	M & F	2-3, 4-5, 8-9, 13-14	1020	7-d record, diet history, 24-h recalls	12 months	M	U & R	?
Hitchcock et al (1984)	Australia	M & F	1-3	205 (L)e	7-d record	12 months	M	U	?
Ho et al (1988)	China	M & F	5-6	60	7-d weighed record	?	M	U	Chinese
Hoffmans et al (1986)	Netherlands	M & F	0.3-1.5	124 (L)f	24 h-recall	Spring	M	U	?
Ikemoto et al (1989)	Japan	M & F	1-2	10	Chemical analysis	12 months	?	?	?
Jenner et al (1988)	Australia	M & F	8-10	884	Food frequency questionnaire	April - Aug	M	U	?
Knuiman et al (1983)	Finland, Netherlands, Italy, Phillipines and Ghana	M	8-9	589	7-d record or 7-d recall	Feb May	M	U & R	Mixed
Livingstone et al (1992b)	United Kingdom	M & F	3-18	78	7-d weighed record, diet history	Oct-July	M	U & R	Causasian
Leung et al (1984)	Canada	M & F	3-4	189	4-d record	?	M	U	?
Lopez-Jaramillo et al (1992)	Ecuador	M	9	114	2 × 24 h recalls	?	LU	U	Ecuadorian
Magarey & Boulton (1984)	Australia	M & F	4	178	3-d record	June Sept	M	U	Mixed

Martinez (1982)	Canada	M & F	6-7	193	3-d record	?	M	U & R	?
McKillop & Durnin (1982)	United Kingdom	M & F	1-2	143	5-d weighed record	?	M	U	?
Morgan & Zabik (1981)	USA	M & F	5-12	657	7-d record	Autumn	-	-	-
Morrison et al (1980)	USA	M & F	6-19	949	24-h recall	12 months	M	U	Black & White
Nelson et al (1990)	United Kingdom	M & F	7-12	194	7-d weighed record	April-July	?	U & R	?
Narasinga et al (1983)	India	M & F	2-6	128	Diet questionnaire	12 months	U	?	Asian
Neiderud et al (1992)	Sweden & Greece	M & F	2-8	152	24-h recall	Aug-Nov	?	U & R	Mixed
Oliveria et al (1992)	USA	M & F	3-5	91	4 × 3-d record	12 months	M	U	Caucasian
Palti et al (1979)	Israel	M & F	2.5-4	98 (L)g	24-hr recall	December - April	M	U	Mixed
Pao et al (1985)	USA	M & F	1-18	2826	24-h recall, 2-d record	Spring	M	U & R	Mixed
Parizkova et al (1986)	Czechoslovakia	M & F	3-5	22	7-d record	?	?	U	?
Paul et al (1990)	United Kingdom	M & F	1-3	48 (L)h	7-d weighed record	?	M	?	?
Payne & Belton (1992)	United Kingdom	M & F	2-5	153	7-d weighed record	May-April	M	U & R	?
Persson & Calgren (1984)	Sweden	M & F	4-5, 8-9	477	7-d record	?	M	?	?
Räsänen et al (1985)	Finland	M & F	3-18	1251	24-h recall	Autumn	M	U & R	?
Räsänen et al (1991)	Finland	M & F	9-18	1200	2 × 24-h recalls	Autumn	M	U & R	?
Räsänen & Ylonen (1992)	Finland	M & F	1.5	46	3-d record	August-November	M	U	?
Salas et al (1990)	Spain	M & F	2-9	121	2 × 24-h recall	?	M	U	Caucasian
Salz et al (1993)	USA	M & F	6-9	195	24-h recall	?	M	U & R	Caucasian
Sawaya et al (1988)	Saudi Arabia	M & F	1.1-2.0, 2.1-3.0, 3.1-4.0, 4.1-5.0	540	24-h recall	?	?	U-R1	Arab
Sunnegardh et al (1986)	Sweden	M & F	8-9, 13-14	666	24-h recall, 7-d record, diet history	?	M	U & R	?
Treiber et al (1990)	USA	M & F	3-5	55	2 × 24-h recall	?	M	U	Black and White
Vanderkooy et al (1987)	Canada	M & F	4-5	108	3-d weighed record	May-Sept	MU	U & R	Caucasian
Van Steenbergen (1984)	Kenya	M & F	1-3, 4-6	56	2-d weighed record	wet & dry	L	R	Akamba
Walker et al (1990)	Jamaica	M & F	0.75-2.0	191	4 × 24-h recall	?	L	U	Jamaican, black

^a Records = estimated (household measures) records, weighed records = weighed intake.
^b Socioeconomic status: M = mixed, L = lower, LU = lower and upper, MU = middle and upper, U = upper.
^c Urban/Rural: U = urban, R = rural.
^d L = longitudinal Brault-Dubuc & Mongeau (1984): 402 children studied in two cohorts starting at age 6 and 10 years with yearly measurements made for 7 years.
^e Hitchcock et al (1984): 205 children recruited. Measurements made at 1 year (n = 125), 1½ years (n = 142), 2 years (n = 146) and 3 years (n = 145).
^f Hoffmans et al (1986): 124 children studied. Measurements made at 16 months and 28 months.
^g Palti et al (1979): 98 children studied. Three measurements made (1st study n = 98; 2nd study n = 82; 3rd study n = 75).
^h Paul et al (1990): 48 children recruited at 2 months. Measurements made at 12 months (n = 29), 15 months (n = 25), 18 months (n = 22), 24 months (n = 22) and 36 months (n = 31).
ⁱ Described by authors as semi-rural.

Table 24 Dietary surveys of children and adolescents aged approximately 10-18 years

Source	Country	Sex	Age (y)	No. of subjects		Methoda	Time of year	Socio economic statusb	Urban/ruralc	Race/ethnic background
Adamson et al (1992)	United Kingdom	M & F	11-12	379		2 × 3-d records	January-July	M	U & R	?
Baghurst et al (1983)	Australia	M & F	14-15, 18	490		Food frequency	?	M	U	Mixed
Barber et al (1985)	Great Britain	F	15-18	448		14-d diary	?	?	U	Caucasian
Bergstrom et al (1993)	Sweden	M & F	13-16, 16-18	731		7-d record	Sept-December January-May	M	U & R	?
Boulton (1981)	Australia	M & F	2 3-5 8-18	198 486 235		Record and diet history 4-d record 4-d record	12 months 12 months 12 months	M	U	Mixed
Boggio & Klepping (1981)	France	M & F	5-6, 9-11, 14-16	376		7-d weighed record	?	M	U	?
Brault-Dubuc & Mongeau (1989)	Canada	M & F	6-17	402 (L)d		7-d record	12 months	M	U	?
Bull (1985)	United Kingdom	M & F	15-18	382		14-d record	Spring-Summer	M	U & R	?
Crawley (1993)	United Kingdom	M & F	16-17	4760		4-d record	April-July	M	U & R	?
Cunningham & Lee (1990)	Republic of Ireland	M & F	8-18	538		Diet history	12 months	M	U & R	Caucasian
Department of Health (1989)	United Kingdom	M & F	10-11, 14-15	2697		7-d weighed record	January-June	M	U & R	?
Durnin (1984)	United Kingdom	M & F	5-6, 10-11	430		5-d weighed record	?	M	U	?
Frank et al (1985)	USA	M & F	10-11, 13-14	491		24-h recall	?	?	?	Black & White
Greger et al (1978)	USA	F	12-13	184		Diet recalls, diet history	Autumn & Spring	?	?	?
Hagman et al (1986)	Sweden	M & F	2-3, 4-5, 8-9, 13-14	1020		7-d record, diet history, 24-h recalls	12 months	M	U & R	?
Hackett et al (1984)	United Kingdom	M & F	11-14	375		5 × 3-d records	?	M	U & R	?
Jenner et al (1992)	Australia	M & F	11-12	1215		2-d records	April-August	M	U	?
Johnson & Jensen (1984)	USA	M & F	10-11	60		7-d records, 24-h recalls	?	M	?	Mixed
Kaufman et al (1982)	Israel	M & F	17-18	1178		24-h recall	?	M	U	Mixed
Livingstone et al (1992b)	United Kingdom	M & F	3-18	78		7-d weighed record, diet history	October-July	M	U & R	Caucasian
McCoy et al (1984)	USA	F	12, 14, 16		1247	2 × 24-h recalls	February-May	M	U & R	Black & White
Michaud et al (1991)	France	M & F	15-19		481	1-d record	?	M	U	?
Morgan & Zabik (1981)	USA	M & F	5-12		657	7-d record	Autumn	MU	?	?
Morrison et al (l980)	USA	M & F	6-19		949	24-h recall	12 months	M	U	Black & White
Nelson et al (1990)	United Kingdom	M & F	7-12		194	7-d weighed record	April-July	?	U & R	?
Pao et al (1985)	USA	M & F	1-18		2826	24-h recall, 2-d record	Spring	M	U & R	Mixed
Perusse et al (1984)	Canada	M & F	11-17		580	3-d weighed record	?	?	U & R	?
Post et al (1987)	Netherlands	M & F	12-18		233	Diet history	Jan-April	MU	U	?
Räsänen et al (l985)	Finland	M & F	3-18		1768	2 × 24-h recalls	Autumn	M	U & R	?
Räsänen et al (1991)	Finland	M & F	9-18		1200	2 × 24-h recalls	Autumn	M	U & R	?
Rodriguez (1991)	Guatemala	M	10-11		140	3 × 24-h recalls	July-Sept	LM	U	Mixed
van den Reek et al (1986)	USA	F	12-15		8	7-d weighed duplicate method	Summer	U	U	White
Seone & Roberge (1983)	Canada	M & F	10-18		500	3-d weighed record	?	?	?	?
Skinner et al (1985)	USA	M & F	16-18		225	24-h recall	?	?	U & R	Black & White
Story (1986)	USA	M & F	13 17		277	3 × 24-h recall	?	L	R	Cherokee
Strain et al (1994)	United Kingdom	M & F	12-13, 15-16		1016	Diet history	12 months	M	U & R	Caucasian
Sunnegardh et al (1986)	Sweden	M & F	8-9, 13-14		666	24-h recall, Diet history, 7-d record	?	M	U & R	?
Tan et al (1989)	New Zealand	M & F	12-14		501	3 × 24-h recalls	Autumn	M	U	?
Tayter et al (1989)	USA	M & F	10-12		39	3 d-record	?	M	?	Caucasian
Torun et al (1993)	Guatemala	M	10-12		24(L)e	3 × 24 h recalls every 3 months	12 months	L	U	Mixed
Woodward et al (1984)	Tasmania	M & F	12-16		1055	1-d diet record	?	M	U & R	?

^a Records = estimated (household measures) records, weighed records = weighed intake.
^b Socioeconomic status: M = mixed, L = lower, LU = lower and upper, MU = middle and upper, U = upper.
^c Urban/Rural: U = urban, R = rural.
^d L-Longitudinal. Brault-Dubuc & Mongeau (1989): 402 children studied in two cohorts starting at age 6 and 10 years with yearly measurements made for 7 years.
^e Torun et al (1994): 24 girls studied four times at 3-month intervals.

Table 25 Energy intakes of children aged approximately 1-5 years

						Energy intake (El)
			Weight (kg)			(MJ/d)		(kJ/kg/d)
Source	Age (y)	N	Mean	s.d.	BMRa (MJ/d)	Mean	s.d.	Mean	s.d.	x BMRb
Bellu et al (1991)	1	164	9.81	1.28	2.26	4.15	1.29	423	131	1.84c
		(76M, 88F)
Catassi et al (1988)	1-1.25	12	10.75	1.4	2.50	4.11	0.99	382	92	1.64
	1.25-1.50	10	11.90	1.0	2.79	4.12	0.65	349	55	1.48
	1.50-2.00	18	12.30	1.7	2.90	4.21	0.93	344	76	1.45
Davies et al (1993)	1.5-2.5	23	12.85	1.67	3.04	4.20	0.63	327	49	1.38
Duggan et al (1991)	1-1.5	13	11.00	(M)d	2.57	3.89e	1.26	354	115	1.51
	1.5-2.0	9	11.00	(M)	2.57	3.71	1.06	337	96	1.44
Hitchcock et al (1984)	1	62 (M)	11.0	(M)	2.57	4.15	0.85	377	77	1.55
		63 (F)	11.0	(M)	2.57	3.98	0.83	362	75	1.93c
	1.5	72 (M)	11.0	(M)	2.57	4.96	0.89	451	81	1.74
		70 (F)	11.0	(M)	2.57	4.47	1.00	406	91	1.56
Hoffmans et al (1986)	1-2	124	11.2	-	2.62	4.08	1.06	366	107	1.56
Ikemoto et al (1989)	1-2	10	11.0	1.6	2.57	3.90	0.41	377	40	1.52
McKillop & Durnin (1982)	1-2	73 (M)	11.6	-	2.72	4.79	1.02	413	-	1.76
		70 (F)	10.9	-	2.56	4.59	0.96	420	-	1.79
Pao et al (1985)	1-2	246	11.0	(M)	2.57	4.90	1.43	445	130	1.91c
Paul et al (1990)	1.0	15 (M)	10.00	1.23	2.31	3.72	0.60	370	60	1.61
	1.0	14 (F)	9.07	0.98	2.09	3.39	0.48	370	50	1.62
	1.25	13 (M)	10.37	1.07	2.41	3.90	0.77	380	50	1.62
	1.25	12 (F)	9.70	0.68	2.25	3.63	0.46	370	50	1.73
	1.50	11 (M)	10.87	1.47	2.53	4.02	0.93	370	70	1.59
	1.50	11 (F)	10.45	1.00	2.44	3.68	0.61	350	60	1.51
Räsänen & Ylonen (1992)	1-2	23 (M)	11.0	(M)	2.57	5.20	0.83	473	75	2.02c
		23 (F)	11.0	(M)	2.57	4.57	0.92	415	84	1.78
		46 (Total)	11.0	(M)	2.57	4.89	0.93	445	85	1.90c
Sawaya et al (1988)	1.1-2	178	11.15	-	2.62	3.62	-	325	-	1.38
Van Steenbergen (1984)	1-3	22	12.25		2.90	4.16	1.74	340	142	1.44
Walker et al (1990)	0.75-2	129 stunted	8.43		1.92	3.99	1.87	473	213	2.07c
	0.75-2	62 non-stunted	11.45		2.69	4.07	1.50	356	130	1.51
Boulton (1981)	2.0	102 (M)	12.94	1.67	3.06	5.08	0.99	400	80	1.66
		95 (F)	12.65	2.91	3.00	4.73	1.03	390	90	1.58
		197 (Total)	12.78	2.69	3.02	5.02	1.86	400	140	1.66
Catassi et al (1988)	2.0 2.5	18	14.90	3.3	3.56	4.53	0.93	307	63	1.27c
Davies et al (1994)	2.5-3.5	31	14.96	1.40	3.57	4.64	0.74	310	49	1.30
Deheeger et al (1991)	2.0	131 (M)	12.1	1.6	2.85	5.51	1.34	452	110	1.93c
		192 (F)	12.2	1.9	2.87	5.85	1.08	480	89	2.04c
Duggan et al (1991)	2-3.25	10	13.5	(M)	3.20	4.35	1.62	322	120	1.36
Eastwood et al (1990)	2.75 3.9	45	15.05		3.49	6.48	1.66	430	1.85c
Hagman et al (1986)	2-3	41 (M)	15.1	-	3.61	5.80	-	384	-	1.61
		41 (F)	15.4	-	3.70	5.55	-	360	-	1.50
Hitchcock et al (1984)	2	74 (M)	13.5	(M)	3.20	5.35	1.01	396	75	1.67
		72 (F)	13.5	(M)	3.22	4.85	1.17	359	87	1.51
Hoffmans et al (1980)	2-3	124	13.8	-	3.28	4.74	1.39	344	107	1.45
Narasinga et al (1993)	2-3	9 (M)	13.4	-	3.18	5.44	-	407	-	1.71
		10 (F)	11.7	-	2.76	4.72	-	403	-	1.71
Neiderud et al (1992)	2-3	11 Greek Imm	13.5	(M)	3.20	6.08	-	450	-	1.90c
		13 Swedish	13.5	(M)	3.20	4.99	-	370	-	1.56
		20 Greek	13.5	(M)	3.20	5.63	-	417	-	1.76
Palti et al (1979)	2.5	98	13.2	-	3.12	4.58	13.38	347	105	1.47
Paul et al (1990)	2.0	13 (M)	12.20	1.20	2.87	4.22	0.78	350	60	1.47
		9 (F)	11.61	0.83	2.74	4.03	0.50	350	40	1.47
Payne & Belton (1992)	2-3	31 (M)	14.0	1.5	3.33	4.50	0.76	321	54	1.35
		42 (F)	13.5	1.4	3.22	4.39	0.83	325	61	1.36
Salas et al (1990)	2-5	61	15.0	(M)	3.48	6.68	1.39	445	93	1.92c
Sawaya et al (1988)	2.1-3	97	13.25	-	3.32	4.06	-	306	-	1.22c
Davies et al (1994)	3.5-4 5	27	16.94	2.10	3.66	5.42	0.64	320	38	1.48
Eastwood et al (1990)	2.8-3.9	45	15.05	-	3.49	6.48	1.66	430	-	1.85c
Griffiths et al (1987)	3-4	15 (M)	16.0	2.0	3.58	4.60	0.82	289	42	1.28
		22 (F)	15.4	1.5	3.52	5.48	1.07	360	71	1.56
Hitchcock et al (1984)	3-4	73 (M)	16.5	(M)	3.62	5.74	1.00	348	61	1.59
		72 (F)	16.5	(M)	3.63	5.55	0.94	336	57	1.53
Leung et al (1984)	3-4	189	16.5	(M)	3.62	5.80	1.20	352	73	1.60
Livingstone et al (1992b)	3-4	8	16.4	1.5	3.61	5.91	0.55	360	34	1.64
Narasinga et al (1983)	3 4	23 (M)	14.9	-	3.47	6.33	-	425	-	1.82c
		13 (F)	15.0	-	3.49	5.81	-	387	-	1.66
Oliveria et al (1992)	3-5	55 (M)	16.5	(M)	3.62	6.71	0.95	407	58	1.85c
		36 (F)	16.5	(M)	3.63	6.14	1.23	372	75	1.69
		91 (Total)	16.5	(M)	3.62	6.48	1.10	393	67	1.79
Palti et al (1979)	3	82	14.1	-	3.40	5.09	1.25	360	89	1.50
Pao et al (1985)	3-5	404	16.5	(M)	3.62	5.99	1.62	363	98	1.65
Paul et al (1990)	3	20 (M)	14.53	1.56	3.44	4.96	0.78	340	50	1.44
		13 (F)	14.16	1.35	3.41	4.62	0.50	330	50	1.35
Parizkova et al (1986)	3-5	22	19.3	2.50	3.89	7.25	2.03	376	105	1.86c
Payne & Belton (1992)	3-4	31 (M)	16.3	1.6	3.60	5.01	0.89	307	55	1.39
		38 (F)	15.4	1.7	3.52	4.76	0.71	309	46	1.35
Räsänen et al (1985)	3-4	153 (M)	15.7	(median)f	3.55	6.40	1.70	408	108	1.80c
		128 (F)	15.2	(median)f	3.50	5.80	1.20	382	79	1.66
Sawaya et al (1988)	3.1-4	158	15.4	-	3.53	4.62	-	300	-	1.31
Treiber et al (1990)	3-5	66	16.35	-	3.62	6.84	1.78	418	-	1.89c
Eastwood et al (1990)	4.0-5.0	22	17.25	-	3.71	6.30	1.19	365	-	1.70
Hagman et al (1986)	4-5	154 (M)	18.8	-	3.84	6.90	-	367	-	1.80c
		152 (F)	18.6	-	3.82	6.45	-	347	-	1.69
Magaray & Boulton (1984)	4-5	93 (M)	17.9	-	3.76	5.94	-	331	-	1.58
		85 (F)	17.7	-	3.74	5.44	-	307	-	1.45
Narasinga et al (1983)	4-5	17 (M)	17.3	-	3.70	6.62	-	383	-	1.79
		6 (F)	15.6	-	3.54	5.90	-	378	-	1.67
Palti et al (1979)	4	75	16.5	-	3.62	4.96	0.95	301	58	1.37
Payne & Belton (1992)	4-5	35 (M)	18.0	1.9	3.77	5.30	0.79	294	44	1.41
		30 (F)	17.6	2.2	3.73	5.06	0.89	288	51	1.36
Persson & Calgren (1984)	4-5	Total sample of 477	16.5	(M)	3.62	6.67	1.23	404	75	1.84c
		(including 8-9 y)
Sawaya et al (1988)	4.1-5	107	17.25	-	3.71	4.93	-	286	-	1.33
Vanderkooy et al (1987)	4-5	62 (M)	18.6	-	3.82	6.23	1.64	335	57	1.63
		44 (F)	18.0	-	3.77	5.38	1.47	299	53	1.43
Van Steenbergen (1984)	4-6	34	18.2	-	3.79	5.04	2.03	277	111	1.33

^a BMR = Predicted basal metabolic rate (FAO/WHO/UNU, 1985).
^b Mean energy intakes expressed as a multiple of mean predicted BMR.
^c Excluded from Table 31 because x BMR was < 1.28 or > 1.79.
^d (M) = Median (NCHS) weights at mid-year.
^e Mean energy intakes (MJ/d) calculated from recorded energy intake (kJ/kg/d) and median (NCHS) weights.
^f Median weights reported.

Table 26 Energy intakes of boys aged approximately 5-10 years

							Energy intake
			Weight (kg)			(MJ/d)		(kJ/kg/d)
Source	Age (y)	N	Mean	s.d.	BMRa (MJ/d)	Mean	s.d.	Mean	s.d.	× BMRb
Boggio & Klepping (1981)	5-6	51	19.5	2.2	3.91	6.89	1.12	353	57	1.76
Durnin (1984)	5-6	93	19.5	(Median)c	3.91	6.90	-	354	-	1.76
Livingstone et al (1992b)	5-6	6	17.9	2.5	3.76	6.57	0.83	367	46	1.75
Narasinga et al (1983)	5-6	12	17.4	-	3.71	6.63	-	381	-	1.79
Brault-Dubuc & Mongeau (1989)	6-7	102	20.6	2.52	4.01	8.91	1.73	438	99	2.22
Martinez (1982)	6 7	89	22.4	-	4.18	8.03	2.05	358	92	1.92
Morrison et al (1980)	6-9	95 (white)d	23.8	(M)e	4.31	8.10	2.51	340	105	1.88
		35 (black)	23.8	(M)	4.31	6.97	2.90	293	122	1.62
Räsänen et al (1985)	6-7	139	21.6	(M)	4.11	7.90	1.90	366	88	1.92
Salz et al (1983)	6-9	102	25.8	-	4.50	8.27	2.22	321	101	1.84
Brault-Dubuc & Mongeau (1989)	7-8	84	22.9	3.1	4.23	9.04	1.84	401	95	2.14
Livingstone et al (1992b)	7-8	6	25.4	6.6	4.46	9.75	1.93 (WDR)f	384	76	2.19
					4.46	9.41	1.50 (DH)	370	59	2.11
Nelson et al (1990)	7-10	25	27.0	(M)	4.62	7.59	1.43	281	31	1.64
Boulton (1981)	8-9	17	31.9	-	5.08	8.93	1.81	280	60	1.76
Brault-Dubuc & Mongeau (1989)	8-9	98	25.4	3.7	4.46	9.43	1.73	375	63	2.11
Hagman et al (1986)	8-9	144	27.3	-	4.64	8.90	-	326	-	1.92
Jenner et al (1988)	8-10	434	30.1	-	4.91	7.45	1.80	248	60	1.52
Knuiman et al (1983)	9	133 (Finland)	30.0	5.0	4.90	9.25	1.63	310	54	1.89
	9	117 (Netherlands)	30.0	5.0	4.90	8.75	1.38	293	46	1.79
	9	109 (Italy)	30.0	7.0	4.90	9.25	2.13	310	71	1.89
	9	114 (Philippines)	22.0	3.0	4.14	7.98	1.93	364	88	1.93
	9	116 (Ghana)	24.0	3.0	4.33	7.10	1.40	297	59	1.64
Lopez-Jaramillo et al (1992)	9	78 (LSC)g	25.5	-	4.47	5.20	1.15	204	45	1.16i
		36 (USC)	27.0	-	4.62	6.43	0.96	238	36	1.39
Sunnegardh et al (1986)	8-9	159	27.3	3.8	4.64	8.40	2.50 (24-h R)h	308	92	1.81
		142	27.3	3.8	4.64	8.90	1.20 (DR)	326	44	1.92
Boulton (1981)	9.11	23	31.6	-	5.05	8.85	1.15	280	60	1.75
Brault-Dubuc & Mongeau (1989)	9-10	103	27.8	3.9	4.69	9.74	1.91	355	77	2.08
Livingstone et al (1992b)	9-10	6	30.2	8.4	4.92	8.95	1.36 (WDR)	296	45	1.82
					4.92	9.94	1.38 (DH)	329	46	2.02
Räsänen et al (1985)	9-10	162	29.9	(M)	4.89	9.10	2.30	304	77	1.86
Räsänen et al (1991)	9-10	119	27.0	(M)	4.89	8.30	2.40	307	89	1.70

^a BMR = Predicted basal metabolic rate (FAO/WHO/UNU, 1985).
^b Mean energy intakes expressed as a multiple of mean predicted BMR.
^c Median weight report.
^d White = white children, black = black children.
^e (M) = Median (NCHS) weights at mid-year.
^f WDR = weighed dietary record, DH = diet history.
^g LSC = lower social class, USC = upper social class.
^b 24-h R = 24-h recall, DR = diet records (estimated from household measures).
ⁱ Excluded from Table 31 because × BMR was < 1.39.

Table 27 Energy intakes of girls aged approximately 5-10 years

							Energy intake
			Weight (kg)			(MJ/d)		(kJ/kg/d)
Source	Age (y)	N	Mean	s.d.	BMRa (MJ/d)	Mean	s.d.	Mean	s.d.	× BMRb
Boggio & Kleping (1981)	5-6	52	19.2	2.5	3.88	6.62	1.19	345	62	1.71
Durnin (1984)	5-6	110	18.4	(Median)c	3.80	6.00	-	326	-	1.58
Livingstone et al (1992a,b)	5-6	6	18.1	2.2	3.78	6.54	0.64	361	35	1.73
Narasinga et al (1983)	5-6	9	16.9	-	3.66	5.91	-	350	-	1.61
Brault-Dubuc & Mongeau (1989)	6-7	93	19.0	2.7	3.86	7.86	1.51	416	84	2.04
Martinez (1982)	6-7	104	21.6	-	4.10	7.28	1.38	337	64	1.78
Morrison et al (1980)	6-9	79 (white)d	23.8	(M)e	4.31	8.11	2.34	341	98	1.88
		37 (black)	23.8	(M)	4.31	6.08	2.64	255	111	1.41
Räsänen et al (1985)	6-7	145	20.5	(M)	4.00	6.80	1.30	332	63	1.70
Salz et al (1983)	6-9	93	25.6	-	4.48	7.87	2.12	308	87	1.76
Brault-Dubuc & Mongeau (1989)	7-8	73	21.0	2.81	4.05	8.25	1.43	398	82	2.04
Livingstone et al (1992a,b)	7-8	6	23.5	2.2	4.28	6.62	0.82 (WDR)f	282	35	1.55
					4.28	7.56	1.20 (DH)	322	51	1.77
Nelson et al (1990)	7-10	26	27.0	(M)	4.61	6.92	1.39	256	51	1.50
Boulton (1981)	8-9	17	29.8	-	4.87	7.74	1.12	260	30	1.59
Brault-Dubuc & Mongeau (1989)	8-9	95	23.4	4.8	4.27	8.21	1.37	358	80	1.92
Hagman et al (1986)	8-9	152	28.7	-	4.77	7.85	-	274	-	1.65
Jenner et al (1988)	8-10	450	29.3	-	4.83	6.92	1.85	236	63	1.43
Sunnegardh et al (1986)	8-9	167	28.6	6.6	4.76	7.70	2.60 (24-h R)g	269	91	1.62
		153	28.6	6.6	4.76	8.00	1.20 (DR)	280	42	1.68
Boulton (1981)	9-11	24	34.6	-	5.32	7.62	2.06	220	70	1.43
Brault-Dubuc & Mongeau (1989)	9-10	94	26.6	4.6	4.57	8.38	1.43	321	65	1.83
Livingstone et al (1992a)	9-10	6	32.2	3.6	5.10	7.95	1.26 (WDR)	247	39	1.56
					5.10	8.63	0.43 (DH)	268	13	1.69
Räsänen et al (1985)	9-10	154	30.3	(M)	4.92	7.70	-	254	-	1.57
Räsänen et al (1991)	9-10	109	27.0	(M)	4.92	7.80	2.20	289	81	1.59

^a BMR = Predicted base; metabolic rate (FAO/WHO/UNU, 1985).
^b Mean energy intakes expressed as a multiple of mean predicted BMR.
^c Median weight reported.
^d White = white children, black = black children.
^e (M) = Median (NCHS) weights at mid-year.
^f WDR = weighed dietary record, DH = diet history.
^g 24-h R = 24-h recall, DR = diet records (estimated from household measures).

Table 28 Energy intakes of boys aged approximately 10-18 years

						Energy intake (EI)
			Weight (kg)			(MJ/d)		(kJ/kg/d)
Source	Age (y)	N	Mean	s.d.	BMRa (MJ/d)	Mean	s.d.	Mean	s.d.	× BMRb
Boggio & Klepping (1981)	9-11	37	31.3	4.5	4.99	8.34	1.04	266	33	1.67
Brault-Dubuc & Mongeau (1989)	10	104	31.4	5.5	5.00	10.58	2.20	344	80	2.12
Cunningham & Lee (1990)	8-12	85	34.1	-	5.20	9.70	3.20	284	94	1.87
Department of Health (1989)	10-11	902c	36.8	7.7	5.40	8.67	1.51	236	41	1.61
Durnin (1984)	10-11	102	33.0	(Median)d	5.11	8.40	-	255	-	1.64
Frank et al (1985)	9-11	184	35.0	-	5.26	9.80	-	280	-	1.86
Morrison et al (1980)	10-12	101 (white)e	34.5	(M)f	5.23	10.15	3.68	294	107	1.94
	10-12	31 (black)	34.5	(M)	5.23	8.33	4.13	241	120	1.59
Pao et al (1985)	9-11	196	31.0	(M)	4.99	8.29	2.41	267	-	1.66
Rodriguez (1991)	10-11	140	34.2	8.0	5.22	7.38	1.92	222	59	1.41
Seone & Roberge (1983)	10-12	99	35.8	-	5.32	9.08	1.69	254	47	1.71
Tayter et al (1989)	10-12	20	35.3	-	5.30	9.18	-	260	-	1.73
Adamson et al (1992)	11-12	184	40.5	-	5.67	8.61	1.76	213	-	1.52
Hackett et al (1984)	11-12	193	39.0	5.56	8.90	-	229	-		1.60
Boulton (1981)	11-12	8	39.0	-	5.56	8.57	2.02	220	80	1.54
Brault-Dubuc & Mongeau (1989)	11-12	96	34.6	6.76	5.24	10.26	2.06	305	60	1.96
Jenner et al (1992)	11-12	626c	42.0	-	5.78	8.60	2.30	205	55	1.49
Nelson et al (1990)	11-12	76	37.0	(M)	5.41	7.74	1.67	209	45	1.43
Perusse et al (1984)	11-17	304	49.8	14.7	6.34	11.00	2.91	221	58	1.74
Boulton (1981)	12-13	15	42.7	-	5.83	10.25	1.78	240	70	1.76
Brault-Dubuc & Mongeau (1989)	12-13	79	37.6	6.8	5.45	10.63	1.87	290	50	1.95
Cunningham & Lee (1990)	12-15	93	49.3	-	6.31	11.30	3.30	229	67	1.79
Livingstone et al (1992b)	12-13	6	44.5	6.7	5.96	10.15	1.08 (WDR)g	228	24	1.70
					5.96	11.82	2.64 (DH)	266	59	1.98
Pao et al (1985)	12-14	296	44.0 (M)	5.92	9.49	2.91	216	-		1.60
Post et al (1987)	12-13	26	38.4	-	5.51	11.70	2.55	305	66	2.12
Räsänen et al (1991)	12-13	116	40.9	(M)	5.69	10.20	3.60	249	88	1.79
Strain et al (1994)	12-13	251	43.0	9.4	5.85	11.0	(Median)	256	-	1.88
Tan et al (1989)	12-14	246	44.0	(M)	5.92	10.2	2.9	232	66	1.72
Woodward et al (1984)h	12-13	132h	41.0	(Median)	5.70	9.9	(Median)	241	-	1.74
Boulton (1981)	13-14	12	52.6	-	6.55	10.0	2.54	190	30	1.53
Brault-Dubuc & Mongeau (1989)	13-14	61	42.6	7.2	5.82	10.70	2.05	257	48	1.84
Frank et al (1985)	13-14	78	49.8	-	6.34	11.03	-	221	-	1.74
Hagman et al (1986)	13-14	166	50.5	-	6.40	12.10	-	240	-	1.89
Morrison et al (1980)	13-15	94 (white)	49.8	(M)	6.34	12.06	5 55	242	111	1.90
			40 (black)	49.8 (M)	6.34	10.87	5.08	218	102	1.72
Post et al (1987)	13-14	73	43.4	-	5.88	11.60	1.71	267	39	1.97
Sunnegardh et al (1986)	13-14	171	49.8	11.8	6.34	10.8	3.9 (24-h R)i	217	78	1.70
		166			6.34	12.3	3.9 (DH)	247	78	1.94
Seone & Roberge (1983)	13-15	103	52.5	-	6.54	10.91	2.23	208	42	1.67
Story (1986)	13-17	139	66.4	-	7.58	9.57	4.94	144	75	1.26i
Woodward et al (1984)	13-14	132	48.0	(Median)	6.21	11.70	(Median)	244	-	1.88
Baghurst & Record (1983)	14-15	77	52.6	(M)	6.55	11.95	-	227	-	1.82
Bergstrom et al (1993)	14-16	155	54.3	10.2	6.67	8.90	2.20	164	41	1.33j
Boulton (1981)	14-16	25	62.3	-	7.26	11.84	3.24	190	60	1.63
Boggio & Klepping (1981)	14-16	73	56.7	12.2	6.96	10.94	2.56	193	45	1.57
Brault-Dubuc & Mongeau (1989)	14-15	49	50.0	8.8	6.36	11.60	2.46	238	49	1.82
Department of Health (1989)	14-15	513c	55.7	9.5	6.77	10.40	2.30	187	41	1.54
Post et al (1987)	14-15	95	48.9	-	6.28	12.20	1.95	249	40	1.94
Woodward et al (1984)	14-15	132	54.0	(Median)	6.65	12.10	(Median)	224	-	1.82
Brault-Dubuc & Mongeau (1989)	15-16	46	57.0	8.34	6.87	12.29	2.84	218	50	1.79
Bull (1985)	15-18	198	62.0	(M)	7.23	10.10	-	163	-	1.40
Cunningham & Lee (1990)	15-18	73	63.9	-	7.37	14.0	4.5	219	70	1.90
Livingstone et al (1992a,b)	15-16	6	56.4	9.1	6.83	11.33	1.88 (WDR)	201	33	1.66
					6.83	13.91	2.20 (DH)	247	39	2.04
Michaud et al (1991)	15-19	198	63.7	8.5	7.36	12.39	3.80	195	60	1.68
Pao et al (1985)	15-18	365	61.9	(M)	7.23	10.92	3.55	176	57	1.51
Post et al (1987)	15-16	102	55.6	-	6.77	12.5	3.03	225	54	1.85
Räsänen et al (1985)	15-16	139	58.0	(M)	6.94	11.8	3.70	203	64	1.70
Räsänen et al (1991)	15-16	118	58.0	(M)	6.94	11.8	4.30	203	74	1.70
Strain et al (1994)	15-16	252	59.0	9.4	7.01	13.10	(Median)	222	-	1.87
Woodward et al (1984)	15-16	132	60.0	(Median)	7.09	11.9	(Median)	198	-	1.68
Bergstrom et al (1993)	16-18	211	66.4	8.4	7.55	10.50	2.70	158	41	1.39
Boulton (1981)	16-17	15	65.8	-	7.51	11.84	4.35	180	60	1.50
Brault-Dubuc & Mongeau (1989)	16-17	29	59.8	8.25	7.07	11.72	3.02	198	51	1.66
Crawley (1993)	16-17	2006e	62.7	(M)	7.31	11.40	2.69	182	43	1.56
Morrison et al (1980)	16-19	82 (white)	64.0	(M)	7.37	13.20	4.25	207	67	1.79
		14 (black)	64.0	(M)	7.37	13.11	5.57	205	87	1.78
Post et al (1987)	16-17	76	61.0	-	7.16	12.80	3.49	210	57	1.79
Seone & Roberge (1983)	16-18	69	63.9	-	7.37	12.31	2.82	193	44	1.67
Skinner et al (1985)	16-18	114	64.0	(M)	7.38	12.80	5.20	200	81	1.73
Kaufman et al (1982)	17-18	627c	61.3	-	7.18	10.38	3.91	169	64	1.45
Post et al (1987)	17-18	28	63.8	-	7.36	13.00	3.17	204	50	1.77
Livingstone et al (1992b)	18-19	5	78.5	14.1	7.83	10.72	3.46 (WDR)	137	44	1.37j
					7.83	15.52	2.26 (DH)	198	29	1.98
Räsänen et al (1985)	18-19	124	65.0	(M)	7.45	12.50	3.20	192	49	1.68
Räsänen et al (1991)	18-19	93	65.0	(M)	7.45	12.50	3.80	192	58	1.68

^a BMR = Predicted basal metabolic rate (FAO/WHO/UNU, 1985).
^b Mean energy intakes expressed as a multiple of mean predicted BMR.
^c Only 30% (for n > 500) or 20% (for n > 1000) used to calculate weighted means in Table 32.
^d Median values reported.
^e White = white children, black = black children.
^f (M) = Median weight for height from Baldwin's standards (FAO/WHO/UNU, 1985).
^g WDR = Weighted dietary record, DH = diet history.
h Woodward et al (1984). Total sample size = 1055. Sample sizes for specific groups were not reported but are assumed to be evenly distributed by age group (n = 4) and sex.
ⁱ 24-hr R = 24-h recall.
^j Excluded from Table 32 because × BMR was < 1.39.

Table 29 Energy intakes of girls aged approximately 10-18 years

						Energy intake (El)
			Weight (kg)			(MJ/d)		(kJ/kg/d)
Source	Age (y)	N	Mean	s.d.	BMRa (MJ/d)	Mean	s.d.	Mean	s.d.	× BMRb
Boggio & Klepping (1981)	9-11	38	31.0	5.0	4.68	7.38	1.53	238	49	1.58
Brault-Dubuc & Mongeau (1989)	10	103	30.9	6.8	4.68	9.02	1.99	300	79	1.93
Cunningham & Lee (1990)	8-12	63	34.7	-	4.87	8.40	2.80	242	81	1.72
Department of Health (1989)	10-11	821c	37.1	7.4	4.99	7.69	1.61	207	43	1.54
Durnin (1984)	10-11	125	34.4	Mediand	4.86	7.70	-	224	-	1.58
Frank et al (1985)	9-11	159	35.0	-	4.89	8.64	-	247	-	1.77
Morrison et al (1990)	10-12	103 (white)f	36.0	(M)e	4.94	8.85	2.90	246	81	1.79
	10-12	44 (black)	36.0	(M)	4.94	7.10	4.02	197	112	1.44
Pao et al (1985)	9-11	222	32.0	(M)	5.08	7.69	2.03	240	-	1.51
Seone & Roberge (1983)	10-12	72	37.4	-	5.01	7.91	1.70	211	45	1.58
Tayter et al (1989)	10-12	19	37.0	-	5.01	7.86	-	212	-	1.57
Torun et al (1994)	10-12	72	29.59	3.55	4.63	6.42 (24-h R)g	1.56	218	53	1.39
						5.97 (FFQ)	1.76	204	61	1.29h
Adamson et al (1992)	11-12	195	41.9	-	5.24	8.25	1.95	197	-	1.57
Hackett et al (1984)	11-12	212	39.9		5.14	8.27	-	207	-	1.61
Boulton (1981)	11-12	15	41.8	-	5.23	7.53	3.02	180	40	1.44
Brault-Dubuc & Mongeau (1989)	11-12	85	34.5	7.2	4.86	9.16	1.96	274	57	1.88
Jenner et al (1992)	11-12	589c	42.9	-	5.29	7.50	2.10	175	49	1.42
Nelson et al (1990)	11-12	67	38.7	(M)	5.08	7.45	1.20	193	31	1.47
Pérusse et al (1984)	11-17	276	46.4	11.2	5.47	8.47	2.57	183	47	1.55
Boulton (1981)	12-13	7	49.9	-	5.64	6.98	1.61	140	70	1.24h
Brault-Dubuc & Mongeau (1989)	12-13	71	38.9	7.2	5.09	9.55	1.93	253	50	1.88
Cunningham & Lee (1990)	12-15	114	51.7	-	5.74	9.10	3.0	176	58	1.59
Greger et al (1978)	12-13	183 (fall)	48.0	12.0	5.55	8.46	2.45	176	51	1.52
		184 (spring)	52.0	30.0	5.75	8.08	2.35	155	45	1.41
Livingstone et al (1992a,b)	12-13	6	44.8	3.9	5.39	8.57	1.59 (WDR)i	191	35	1.59
					5.39	12.08	1.47 (DH)	270	33	2.24h
McCoy et al (1984)	12-13	441	44.0	(M)	5.35	8.43	-	192	-	1.58
Pao et al (1985)	12-14	295	46.5	(M)	5.47	7.76	2.58	167	-	1.42
Post et al (1987)	12-13	31	42.2	-	5.25	9.80	1.67	232	40	1.87
Räsänen (1985)	12-13	166	44.0	(M)	5.35	8.20	2.30	186	52	1.53
Räsänen (1991)	12-13	119	44.0	(M)	5.35	8.50	2.60	193	59	1.59
Van den Reek (1986)	12-15	8	47.0	9.0	5.50	6.20	1.94	132	41	1.13h
Strain et al (1994)	12-13	259	44.0	9.0	5.35	9.2	(Median)	209	-	1.72
Tan et al (1989)	12-14	255	46.5	(M)	5.47	7.8	2.1	168	45	1.43
Woodward et al (1984)	12-13	132j	43.0	(Median)	5.29	8.9	(Median)	207	-	1.68
Boulton (1981)	13-14	15	62.4	-	6.28	7.49	2.04	120	40	1.19h
Brault-Dubuc & Mongeau (1989)	13-14	50	44.0	7.99	5.35	9.08	1.62	213	37	1.70
Frank et al (1985)	13-14	70	48.6	-	5.58	8.35	-	172	-	1.50
Hagman et al (1986)	13-14	170	50.3	-	5.67	9.65	-	192	-	1.70
Morrison et al (1980)	13-15	78 (white)	49.3	(M)	5.61	8.55	2.68	173	54	1.52
		32 (black)	49.3	(M)	5.61	7.83	2.78	159	56	1.40
Post et al (1987)	13-14	98	48.0	-	5.55	9.60	1.98	200	41	1.73
Seone & Roberge (1983)	13-15	92	50.5	-	5.68	8.61	1.63	170	32	1.52
Sunnegardh et al (1986)	13 14	169	50.9	9.2	5.70	8.10	2.60 (24-h R)	159	51	1.42
					5.70	9.90	2.60 (DH)	194	51	1.74
Story (1986)	13-17	138	62.8	-	6.32	7.57	2.89	120	46	1.20h
Woodward et al (1984)	13-14	132	49.0	(Median)	5.60	9.00	(Median)	184	-	1.61
Baghurst et al (1983)	14-15	69	51.4	(M)	5.72	9.36	-	182	-	1.64
Bergstrom et al (1993)	14-16	189	53.7	8.2	5.84	7.10	1.60	132	30	1.22h
Boulton et al (1981)	14-16	27	57.5	-	6.03	6.90	1.84	120	30	1.14h
Boggio & Klepping (1981)	14-16	125	51.2	7.50	5.71	8.48	1.97	166	38	1.49
Brault-Dubuc & Mongeau (1989)	14-15	37	48.3	7.30	5.56	8.96	2.31	191	48	1.61
Department of Health (1989)	14-15	461	53.7	9.20	5.84	7.85	1.74	146	32	1.34
McCoy et al (1984)	14-15	440	51.4	(M)	5.72	8.40	-	163	-	1.47
Post et al (1987)	14-15	129	52.0	-	5.75	9.60	2.27	185	44	1.67
Woodward et al (1981)	14-15	132	51.0	(Median)	5.70	9.2	(Median)	180	-	1.61
Barber et al (1985)	15-18	448	56.4	-	6.00	8.6	-	152	-	1.43
Brault-Dubuc & Mongeau (1989)	15-16	32	49.9	4.98	5.64	9.16	2.17	187	43	1.62
Bull (1985)	15-18	184	53.8	(M)	5.84	7.80	-	145	-	1.34
Cunningham et al (1990)	15-18	110	57.2	-	6.02	8.90	2.50	156	44	1.48
Livingstone et al (1992b)	15-16	6	57.2	9.2	6.02	6.84	1.78 (WDR)	120	31	1.14h
					6.02	9.34	1.70 (DH)	163	30	1.55
Michaud et al (1991)	15-19	283	54.6	6.2	5.88	8.40	2.73	154	50	1.43
Pao et al (1985)	15-18	374	53.8	(M)	5.84	7.39	2.73	137	51	1.27h
Post et al (1987)	15-16	130	54.9	-	5.90	9.50	2.28	173	42	1.61
Räsänen et al (1985)	15-16	152	53.0	(M)	5.80	7.60	2.20	143	42	1.31
Räsänen et al (1991)	15-16	112	53.0	-	5.80	8.60	3.30	162	62	1.48
Strain et al (1994)	15-16	254	57.0	8.5	6.01	9.10	(Median)	160	-	1.51
Woodward et al (1984)	15-16	132	52.0	(Median)	5.75	8.50	(Median)	163	-	1.48
Bergstrom et al (1993)	16-18	176	58.4	8.7	6.08	7.10	1.90	122	33	1.17h
Boulton (1981)	16-17	12	55.9	-	5.95	6.15	1.41	110	30	1.03h
Brault-Dubuc & Mongeau (1989)	16-17	18	52.0	5.60	5.75	9.11	2.18	178	42	1.58
Crawley (1993)	16-17	2754c	54.0	(M)	5.85	8.80	2.10	163	39	1.50
Morrison et al (1980)	16-19	71 (white)	54.0	(M)	5.85	8.68	3.41	161	63	1.48
		13 (black)	54.0	(M)	5.85	8.10	5.00	150	93	1.38
Post et al (1987)	16-17	99	57.4	-	6.03	9.30	1.99	162	35	1.54
Seone & Roberge (1983)	16-18	65	54.4	-	5.87	7.96	2.18	146	40	1.36
Skinner et al (1985)	16-18	111	54.0	(M)	5.85	8.60	3.77	159	70	1.47
Kaufman et al (1992)	17-18	551c	55 7	_	5.94	6.71	2.89	120	52	1.13h
Post et al (1987)	17-18	32	57.9	-	6.05	9.80	2.83	169	49	1.62
Livingstone et al (1992b)	18-19	5	63.9	16.2	5.98	7.84	1.74 (WDR)	123	27	1.31
					5.98	10.13	1.58 (DH)	159	25	1.69
Räsänen et al (1985)	18-19	148	54.4	-	5.87	7.70	2.50	142	46	1.31
Räsänen et al (1991)	18-19	116	54.4	-	5.87	7.40	2.50	136	46	1.26h

^a BMR = Predicted basal metabolic rate (FAO WHO/UNU, 1985).
^b Mean energy intakes expressed as a multiple of mean predicted BMR.
^c Only 30% (for n > 500) or 20% (for n > 1000) used to calculate weighted means in Table 33.
^d Median values reported.
^e (M) = Median weight for height from Baldwin's standards [FAO/WHO/UNU, 1985]
^f White = white children, black = black children.
^g 24-h R = 24-hour recall, FFQ = food frequency questionnaire.
^h Excluded from Table 33 because × BMR was < 1.30 or > 2.10.
ⁱ WDR = weighed dietary record, DH = diet history.
^j Woodward et al (1981). Total sample size = 1055. Sample sizes for specific groups were not reported but are assumed to be evenly distributed by age group (n = 4) and sex.

Table 30 Combined energy intakes for male and female subjects aged 5-10 years

						Energy intake
			Weight (kg)			(MJ/d)		(kJ/kg/d)
Source	Age (y)	N	Mean	s.d.	BMRa (MJ/d)	Mean	s.d.	Mean	s.d.	× BMRb
Ho et al (1988)	5-6	60	17.5	-	3.73	5.36	-	312	-	1.44
Morgan & Zabik (1981)	5-6	162	20.5	(M)c	4.00	8.09	-	395	-	2.02
Pao et al (1985)	6-8	428	22.4	(M)	4.18	7.17	1.89	320	-	1.72
Salas et al (1990)	6-9	60	23.8	(M)	4.31	8.63	1.60	363	67	2.00
Morgan & Zabik (1981)	7-8	168	23.6	(M)	4.29	8.75	-	371	-	2.04
Persson & Calgren (1984)	8-9	(Total sample of 477 including 4-5 olds)	27.6	(M)	4.67	8.22	1.56	298	57	1.76
Morgan & Zabik (1981)	9-10	165	30.1	(M)	4.91	9.30	-	309	-	1.89

^a BMR = Predicted basal metabolic rate (FAO WHO/UNU, 1985).
^b Mean energy intakes expressed as a multiple of mean predicted BMR.
^c (M) = Median (NCHS) weights at mid-year (FAO/WHO/UNU, 1985).

Table 31 Energy intakes of subjects (sexes combined) aged 1-5 years, and of boys and girls aged 5-10 years compared with current FAO/WHO/UNU (1985) estimated requirements

			Energy intakea
			(MJ/d)		(kJ/kg/d)		(kcal/kg/d)		× BMR	FAO/WHO/UNU (1985) requirements		Percentage difference (%)b
Age (y)	Studies n	Subjects n	Mean	s.d.	Mean	s.d.	Mean	s.d.	Mean	(MJ/d)	(kJ/kg/d)	(MJ/d)	(kJ/kg/d)
Sexes combined
1-2	12	927	4.17	0.82	375	74	90	18	1.54	4.80	439	- 13.1	- 14.6
			(Range 3.39-4.96)		(Range 325-451)				(Range 1.38-1.79)
2-3	11	835	4.92	1.08	367	81	88	19	1.51	5.70	418	-13.7	-12.2
			(Range 4.03-5.80)		(Range 310-407)				(Range 1.30-1.76)
3-5	22	2460	5.76	1.15	345	67	82	16	1.53	6.50	397	-11.4	-13.1
			(Range 4.60-6.90)		(Range 277-408)				(Range 1.31-1.80)
Boys
5-6	6	273c	7.06	1.05	363	53	87	13	1.80	7.57	385	- 6.7	- 5.7
			(Range 5.36-8.09)		(Range 312-395)				(Range 1.44-2.02)
6-7	4	544	7.82	1.88	360	87	86	21	1.90	7.94	368	-1.5	-2.2
			(Range 7.17-8.91)		(Range 320-438)				(Range 1.92-2.22)
7-8	6	436	8.41	2.29	352	100	84	24	1.94	8.32	347	+ 1.1	+ 1.4
			(Range 6.97-9.58)		(Range 293-401)				(Range 1.62-2.14)
8-9	7	996	8.13	1.79	289	62	69	15	1.72	8.66	322	-6.1	- 10.2
			(Range 7.45-9.43)		(Range 248-375)				(Range 1.64-2.11)
9-10	7	1085	8.75	1.81	314	66	75	16	1.83	8.99	301	-2.7	+4.3
			(Range 7.10-9.74)		(Range 280-364)				(Range 1.64-2.08)
Girls
5-6	6	288	6.64	0.96	349	50	83	12	1.72	6.81	368	-2.5	-5.2
			(Range 5.36-8.09)		(Range 312-395)				(Range 1.44-2.02)
6 7	4	556	7.21	1.52	342	66	82	16	1.78	7.11	347	+1.4	-1.4
			(Range 6.80-7.86)		(Range 320-416)				(Range 1.70-2.04)
7-8	6	402	8.05	2.13	343	93	82	22	1.88	7.40	318	+8.8	+7.9
			(Range 6.08-8.25)		(Range 255-398)				(Range 1.41-2.04)
8-9	7	1026	7.47	1.69	266	62	64	15	1.58	7.65	268	-2.4	-0.7
			(Range 6.92-8.21)		(Range 236-358)				(Range 1.50-1.92)
9-10	6	469	8.14	1.75	283	67	68	16	1.68	7.86	259	+3 6	+9.3
			(Range 7.62-8.38)		(Range 220-321)				(Range 1.43-1.83)

^a Energy intake data (MJ/d, kJ/kg/d, × BMR) expressed as weighted means. s.d. estimated from

(ⁿ = number of studies). For studies where s.d. was not reported the mean CV of other studies in that group was assumed.

^b Percentage difference = (energy intake - FAO/WHO/UNU estimated requirement)/estimated requirement × 100.
^c Sample sizes for 5-10 year olds include studies listed in Table 30 and assume equal numbers of boys and girls.

Table 32 Energy intakes of boys aged 10-18 years compared with current FAO/WHO/UNU (1985) estimated requirements

			Energy intakea
			(MJ/d)		(kJ/kg/d)		(kcal/kg/d)		× BMR	FAO/WHO/UNU (1985) requirements			Percentage difference (%)b
Age (y)	Studies n	Subjects n	Mean	s.d.	Mean	s.d.	Mean	s.d.	Mean	(MJ/d)	(kJ/kg/d)	× BMR	(MJ/d)	(kJ/kg/d)	× BMR
10-11	10	1981	8.86	2.62	255	76	61	18	1.68	8.95	278	1.76	-1.0	-8.3	-4.5
			(Range 7.38-10.58)		(Range 222-344)				(Range 1.41-2.12)
11-12	7	1203	8.74	1.97	220	58	53	14	1.55	9.37	254	1.73	-6.7	-13.4	-10.4
			(Range 7.74-10.26)		(Range 205-305)				(Range 1.43-1.96)
12-13	9	1167	10.47	2.59	240	61	57	15	1.76	9.66	237	1.69	+8.4	+1.3	+4.1
			(Range 9.49-11.0)		(Range 216-305)				(Range 1.60-2.12)
13-14	10	1023	11.37	3.28	233	64	56	15	1.80	10.20	217	1.67	+11.5	+7.4	+7.2
			(Range 10.00-12.10)		(Range 190-267)				(Range 1.53-1.97)
14-15	8	1268	11.11	2.50	208	50	50	12	1.70	10.83	206	1.65	+2.6	+0.1	+2.4
			(Range 10.40-12.20)		(Range 187-249)				(Range 1.54-1.94)
15-16	7	795	12.34	3.25	212	57	51	14	1.75	11.29	195	1.62	+9.3	+8.7	+8.0
			(Range 11.33-13.10)		(Range 198-225)				(Range 1.66-1.87)
16-17	10	3143	11.49	3.51	184	55	44	13	1.57	11.71	187	1.60	-1.9	-1.6	-1.9
			(Range 10.10-14.00)		(Range 163-219)				(Range 1.40-1.90)
17-18	5	968	11.22	3.53	179	56	43	13	1.55	12.00	184	1.60	-6.5	-2.7	-3.1
			(Range 10.38-13.20)		(Range 169-207)				(Range 1.45-1.79)

^a Energy intake data (MJ/d, kJ/kg/d, × BMR) expressed as weighted means. s.d. estimated from

(n = number of studies). For studies where s.d. was not reported the mean CV of other studies in that group was assumed.
^b Percentage difference = (Energy intake - FAO/WHO/UNU estimated requirement)/estimated requirement × 100.

Table 33 Energy intakes of girls aged 10-18 years compared with current FAO/WHO/UNU (1985) estimated requirements

			Energy intakea
			(MJ/d)		(kJ/kg/d)		(kcal/kg/d)		× BMR	FAO/WHO/UNU (1985) requirements			Percentage difference (%)b
Age (y)	Studies n	Subjects n	Mean	s.d.	Mean	s.d	Mean	s.d.	Mean	(MJ/d)	(kJ/kg/d)	× BMR	(MJ/d)	(kJ/kg/d)	× BMR
10-11	9	1750	7.94	2.46	226	72	54	17	1.60	7.99	237	1.65	-0.6	-4.6	-3.0
			(Range 7.09-9.02)		(Range 197-300)				(Range 1.44-1.93)
11-12	8	1254	7.81	2.16	194	45	46	11	1.51	8.28	215	1.63	-5.7	-9.8	-7.4
			(Range 6.54-9.16)		(Range 175-274)				(Range 1.41-1.88)
12-13	11	2142	8.41	2.16	186	53	44	13	1.55	8.57	196	1.60	-1.9	-5.1	-3.1
			(Range 7.80-9.80)		(Range 168-253)				(Range 1.42-1.88)
13-14	9	1005	8.88	2.39	179	48	43	11	1.58	8.87	181	1.58	0.0	-1.1	0.0
			(Range 7.83-9.65)		(Range 159-213)				(Range 1.40-1.73)
14-15	8	1669	8.47	2.19	166	42	40	10	1.49	9.03	176	1.57	-6.2	-5.7	-5.1
			(Range 7.85-9.60)		(Range 146 191)				(Range 1.34-1.67)
15-16	7	818	8.72	2.39	161	45	38	11	1.48	8.95	169	1.54	-2.6	-4.7	-3.9
			(Range 7.60-9.50)		(Range 143-187)				(Range 1.31-1.62)
16-17	8	3789	8.62	2.53	156	46	37	11	1.46	8.91	166	1.53	-3.3	-6.0	-4.6
			(Range 7.80-9.30)		(Range 145-178)				(Range 1.34-1.58)
17-18	3	399	8.55	3.32	156	61	37	15	1.45	8.95	165	1.52	-4.4	-5.4	-4.6
			(Range 8.10-9.80)		(Range 150-169)				(Range 1.38-1.62)

^a Energy intake data (MJ/d, kJ/kg/d, × BMR) expressed as weighted means. s.d. estimated from

(n = number of studies). For studies where s.d. was not reported the mean CV of other studies in that group was assumed.
^b Percentage difference = (energy intake - FAO/WHO/UNU estimated requirement)/estimated requirement × 100.

Figure 11a Energy intake compared with expenditure estimated by doubly labeled water and heart rate monitoring, including stunted and underweight children, and current recommendations: boys (solid line: mean energy intake; interrupted line: FAO/WHO/UNU recommendations).

Figure 11b Energy intake compared with expenditure estimated by doubly labeled water and heart rate monitoring, including stunted and underweight children, and current recommendations: girls (solid line: mean energy intake; interrupted line: FAO/WHO/UNU recommendations).

Figure 12 Comparison of average dietary energy intakes of boys and girls.

General conclusions and recommendations

1. Dietary recommendations to satisfy the energy requirements of children and adolescents should be based on their energy expenditure and requirements for growth. Their habitual physical activity and lifestyle must be taken into account, as energy expenditure should be consistent with the attainment and maintenance of long-term good health, and the performance of economically necessary and socially desirable physical activity.

Energy for socially desirable activities is particularly important as part of the normal process of a child's development, for activities such as exploration of the surroundings, learning and behavioural adjustments to other children and adults (FAO/ WHO/UNU, 1985).

2. There is a major contrast between lifestyles of children and adolescents in rural developing societies and in developed countries. Whereas the former engage in physically-demanding obligatory or occupational activities from an early age, the latter tend to be quite sedentary (Cooper et al, 1984; Verschuur & Kemper, 1985; Atomi et al, 1986; Armstrong et al, 1990; Gortmaker et al, 1990). Discretional activities are also probably quite different in those two settings: in developing rural areas, children walk more to move around and to socialize, while those in developed countries travel in motor vehicles and spend a significant period of time sitting and watching television (Dietz and Gortmaker, 1985; Gortmaker et al, 1990).

More studies are needed in children and adolescents who live in cities of developing countries. Available evidence suggests that those in the middle and upper socioeconomic groups are relatively sedentary, with a lifestyle that resembles that of children in developed countries more than that of their rural counterparts. Habitual activities related to energy expenditure in the lower socioeconomic groups have hardly been studied.

3. Recommendations to fulfill energy requirements of children and adolescents should be made according to two or three levels of intensity of habitual physical activity, in a manner similar to that recommended for adults in the 1985 FAO/WHO/UNU Report. Provisional physical activity levels are suggested in Table 21.

4. The 1985 recommendation for 5% additional dietary energy intake to 'allow a desirable level of physical activity' among all children under 10 years of age seems unwarranted. Furthermore, scientific evidence accumulated in the last decade suggests that current FAO/WHO/UNU recommendations for dietary energy are too high for children under 5, and possibly under 7, years of age.

5. Current recommendations seem somewhat low for adolescent boys and for girls around puberty. This is more so in rural areas of the developing world, where recommendations for girls throughout adolescence and for boys and girls of school age may also be too low when expressed per unit of body weight or as multiples of BMR.

6. Healthy but stunted or slightly underweight boys and girls in developing countries seem to have a higher energy requirement per unit of body weight than their well-nourished, non-stunted counterparts. The differences in absolute terms and PAL units are less consistent. It seems reasonable to recommend for them the same total dietary energy intakes as for well-nourished, non-stunted children of the same age and sex, provided that they are encouraged and have opportunities to be physically active.

7. Dietary energy recommendations must be accompanied by strong recommendations for physical activity compatible with the achievement and maintenance of health, prevention of obesity and adequate social and psychological development. The minimum amount of exercise required by children for a healthy life has not been exactly determined. Provisional recommendations can be made, similar to those for adults, based on Simons-Morton et al's (1988) review of recommendations for physical activity for children: exercise involving dynamic movement of large muscle groups for at least 20 min, three or more times a week, at an intensity that raises and maintains heart rate at 140 or more beats per minute.
There are some contradictory and non-conclusive results on the role of physical activity for the prevention of obesity, but as Gortmaker et al (1990) point out, obesity seems to have a stronger relationship with inactivity than with vigorous physical activity.

Methodological considerations

8. The use of doubly-labeled water provides, at present, the most exact quantitative measurements of TEE of free-living children and adolescents. However, financial and technical constraints limit its application in samples large enough to represent boys and girls of all ages living in a wide variety of social and geographic settings. Minute-by-minute heart rate monitoring techniques seem promising for this purpose, especially if they are validated in the field with doubly-labeled water measurements.

9. Time-motion or activity diary techniques can provide useful information to confirm or monitor the accuracy of dietary recommendations. Sampling must be adequate in size, physiological and anthropological characteristics, and appropriate factors must be applied to quantify the energy expended in the observed/recorded/timed activities. These techniques also provide an important insight on the pattern of habitual activities of children and adolescents.

10. There is a need to obtain more information on the energy cost of activities and tasks in which children and adolescents from different societies typically engage, in order to increase and improve existing databases (e.g. Torun, 1990a). Standardized procedures must be established to define those activities and tasks and to measure their energy cost.

11. Time allocation studies can help to define the appropriate level of habitual physical activity for specific (geographic, ethnic, social) groups of children and adolescents. There is, however, a need to develop standardized procedures for the collection of time allocation data in different societies across all age groups.

12. The use of multiples of BMR, or physical activity levels (PAL), is useful in physiological and practical terms to calculate the energy expenditure and estimate the energy requirements of population groups. PALs for children and adolescents with different lifestyles have been suggested in this paper.

13. It seems that a single set of mathematical equations cannot be used across all races and geographic regions to calculate the ]BMR of boys or girls of a specific age group. To avoid making important errors in the estimation of energy requirements and recommendations, this issue must be cleared. If necessary, specific sets of mathematical equations should be derived for some races or countries.

14. Dietary energy intake studies tend to overestimate energy requirements of children under 8 and to underestimate those of children over 12 years of age. Nevertheless, they may be useful to estimate requirements of a healthy, well growing population when total energy expenditure cannot be measured or calculated. However, to accept the data as representative of habitual and appropriate intake, it is necessary that it should be: (a) derived from adequate population samples; (b) validated by studies that take into account the method used for data collection, as well as the anthropological, geographic and health characteristics of the population and (c) screened and edited to exclude information that is incompatible with fundamental principles of energy physiology in population groups (e.g. exclusion of data below or above cut-off points compatible with long-term habitual eating patterns of a healthy population). Provisional cut-off points, calculated as multiples of BMR, are suggested in this paper.

Other conclusions and recommendations

15. Other specific conclusions, including recommendations for important and much needed research, are included at the end of each section in this document.

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