Dietary assessment for sports dietetics

© 2001 British Nutrition Foundation Nutrition Bulletin, 26, 29–42

Correspondence: Dr Alison E Black DPhil SRD, 9 Birch Close,

Cambridge CB4 1XN, UK.

Based on a talk to the Sport, Exercise and Nutrition Alliance

Conference, Loughborough, UK, 19 April 2000.

Summary The different techniques for assessing energy and nutrient intakes are described.

Work showing that energy intake is frequently underestimated is summarised. The

validity of reported energy intake can be evaluated by comparing reported intake

with expected energy expenditure. To do this, information on activity patterns must

also be obtained.

Keywords: dietary assessment, validity

REVIEW

Dietary assessment for sports dietetics

Alison E. BlackMRC Dunn Nutrition Centre, Cambridge, UK

Introduction

There are three situations in which it might be desirable

to measure food intake, each of which has differing

requirements for the quality of data. Small-scale

(n < 100) research studies are the most demanding of

quality data. Here, the ability of the technique to

measure the intake of each individual with adequate

precision is important. Investigations into the effect of

dietary composition on sports performance might be

such a study. Interest may well be focused on intake

during a specific period of study rather than on the

average intake over a long time-period.

In epidemiological studies with a sample size in the

hundreds or thousands, analysis focuses on comparisons

between groups of subjects, or on correlations between

intake and outcome measures. Good precision of data

at the individual level is less important, as statistical

techniques are available that can allow for large random

errors. Such studies are primarily interested in measur-

ing the intake averaged over long periods of time, com-

monly referred to as ‘habitual’ or ‘usual’ intake.

The assessment of food intake for the purpose of

offering practical dietetic advice on a one-to-one basis

can be both the least and the most demanding situation.

On the one hand, it may be sufficient to have a purely

qualitative record of the foods eaten. On the other hand,

obtaining a precise measure of the nutrient intake of one

individual is particularly demanding.

Fundamental concepts

There are two concepts that are fundamental to under-

standing the limitations of dietary assessment.

Precision (repeatability, reproducibility, reliability)

Does the method give the same answer on repeated

applications? Precision is low when there are large

random errors. Poor precision does not affect the esti-

mate of the mean, but militates against correct ranking

of individuals. The precision of the measurement is

defined by the 95% confidence limits of repeated

measurements.

Validity (accuracy)

Does the method measure what you believe you are

measuring? Does it give an observed estimate of intake

that is close to the true intake? A valid data set is one

that is not subject to systematic errors. Systematic errors

are errors that operate in one direction and thus, intro-

duce bias into the results. They distort the data and may

lead, for example, to estimates of mean intake that are

either too high or too low. A valid diet record is one in

29

which the subject records exactly what he/she ate during

the period of study and this is what he/she would have

eaten if no investigator had intervened. A valid diet

report is one in which the subject reports past food

intake without conscious or subconscious distortion.

Illustrations of concepts

These concepts are illustrated in Figure 1. In each

graph, the solid line represents the true mean and the

dotted line, the observed mean. The dots represent re-

peated measurements expressed as deviations from the

observed mean. In graphs 1 and 2 the measurements

deviate very little from the observed mean, i.e. the

random errors are small and the precision is good.

However, in graph 2, systematic error has biased the

result to an under-estimation of the true mean. In a

laboratory situation, this could arise from a mistake in

making up a standard solution. Graph 3 demonstrates

a valid measurement with no systematic bias in the esti-

mate of the mean, but with large random errors and

poor precision. In a dietary survey, this implies a valid

measure of the group mean, but poor ranking of indi-

viduals. Graph 4 shows data that is both imprecise and

invalid. Unfortunately, this represents the situation in

many dietary surveys. It has been known for decades

that dietary intake data lack precision. Research since

1982 has shown that dietary data are frequently

invalid (biased). Under-estimation of mean intake is

widespread.

30 Alison E. Black


Poor precision is a function of the variability of

food intake

Nutritionists are usually primarily interested in nutrient

intake, but this is a derived measurement. The primary

measurement is of the foods eaten. There are thousands

of foods and food products in the supermarket. The

choices made by individuals vary widely, both in terms

of foods eaten and the daily pattern of meals and snacks.

Foods chosen vary in kind and quantity from day to day,

week to week and season to season. Consequently, the

energy and nutrient intakes also vary widely.

Figure 2 shows the day-by-day energy intake for one

subject, studied on 63 days. The straight line at zero

indicates the average intake over the whole 63 days, the

so-called ‘habitual’ intake. Intakes over shorter periods

are plotted as the difference from the average. The

closely dotted line shows the daily intakes. These varied

widely. The highest and lowest daily intakes were 6 MJ

greater and less than the average. Clearly, a record of

intake on a single day gives little information about that

individual and certainly does not characterise his or her

long-term intake. The average coefficient of variation

for daily energy intake is 23% (Bingham 1987; Nelson

et al. 1989). The dashed line shows the energy intake

averaged over 3 successive days (the number of days

often used in larger dietary surveys). The variation is

substantially reduced, but 3-day measurements do not

necessarily characterise an individual. The average coef-

ficient of variation for 3-day records is about 13%. The

solid line shows intakes averaged over 7 successive days.

Figure 1 Visual definition of accuracy (validity) and precision (repeatability)(from Black 1999).

635649423528211470-8

-6

-4

-2

0

2

4

6

8

?(1d mean-average intake)

?(3d mean-average intake)?(7d mean-average intake)

long term average intake Days

Inta

ke-m

ean

inta

ke, M

J

Figure 2 Energy intake of one individual undertaking a weighed diet recordevery sixth day for 1 year. (Mean ± SD 11.00 ± 2.15 MJ; Cvw 19.2%) (fromBlack 1999).

Dietary assessment for sports dietetics 31


Variation is further reduced such that the extreme

intakes deviate away from the average by about 1 MJ.

The average coefficient of variation of 7-day records is

about 8.5%. Thus the 95% confidence limits are ±17%,

i.e. a 7-day record obtains a measure that is within

±17% of the true mean on 95 out of 100 measurements.

How many days?

The number of days needed to obtain a good measure

of ‘habitual’ intake in an individual depends on the level

of precision required.

Figure 3 shows the relationship between the intra-

subject coefficient of variation on daily nutrient intake

(CVw), the number of days (d) studied and the precision

of the measurement. The dotted lines show where the

combination of CVw and d give a standard error of the

mean (SEM) of 5, 7.5, 10 or 12.5%, the corresponding

95% confidence limits being ±10%, ±15%, ±20% and

±25%. If the CVw of daily energy intake is 23%, then

21 days of records are needed to measure energy intake

of an individual to ±10%. Alternatively, a 7-day record

gives a precision of between ±15 and ±20%. The CVw

of daily intake for the macronutrients is similar to that

for energy lying between 20 and 26%. For micronutri-

ents, it is between 30 and 40% for those such as iron,

magnesium and the B vitamins, which are widely dis-

tributed in foods, about 60% for Vitamin C and very

much greater for nutrients such as Vitamin A that are

in found large amounts in few foods (Black 1986).

Methods of dietary assessment

There are four basic approaches to the measurement of

food intake: diet records, diet recall, diet history and

food frequency questionnaire (Cameron & van Staveren

1988; Margetts & Nelson 1991).

Diet records

In this method, the subject is asked to keep a detailed

record, on specified days, of all items of food and drink

at the time of consumption. The number of days

recorded classically is 7, but is often less and may be

more.

‘Diet record’ is a blanket term. In American literature

it is often used without qualification but with ‘quan-

tified in household measures’ understood. Weighed

records are favoured where kitchen scales are a common

item of household equipment and recipes are custom-

arily quoted by weight, e.g. in the UK. Estimated records

are favoured where standard cups and spoons are

normal equipment and recipes are quoted in these vol-

umetric measures, e.g. in the USA.

There are several variants of the method.

Weighed record The subject is required to weigh each

item of food and drink at the time of consumption

(Table 1). Food is weighed ‘as served’ and plate waste

is also weighed. This is the gold-standard method, but

it is demanding of subject co-operation. It may have

poor compliance and subjects may alter food patterns

to simplify the recording, thereby introducing bias. It

is the method of choice for small-scale research stud-

ies, although estimated records (see below) are also

acceptable.

Estimated record The procedure is the same as for the

weighed record but portions are described in terms of

household measures (e.g. cups, spoons), in dimensions,

or number of items of predetermined size. Diagrams or

photographs may be used as aids to quantifying por-

tions. Investigators then have to assign weights to the

described portion before calculating nutrient intake

from food tables. Estimating weights means that errors

are greater than for the weighed records. The errors may

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5

10

15

20

25

30

35

40

45

50

sem 5%

sem 7.5%

sem 10 %sem 12. 5%

Days of records or recalls

CV

w

Figure 3 Nomogram to calculate the number of days of diet recordsrequired to estimate nutrient intake of an individual with given precision(from Black 1986).

Table 1 Weighed records

Advantages Disadvantages

Good information on individuals Substantial subject burdenCan categorise meal patterns Lower compliance than with other Good information on individual foods methodsErrors due to day to day variation in Covers a limited time period

intake are well understoodCan vary the number of days studied

be random, but could also be biased. However, the

respondent burden is less than for the weighed record

and compliance may be better.

Menu Record This is a qualitative record only of foods

eaten, without any attempt to quantify the portion sizes.

The record may be analysed in terms of frequencies of

consumption of different foods or by assigning ‘average’

weights to portions. Assigning average portion weights

should not bias the observed mean, but will reduce pre-

cision at the individual level. The method has potential

for gathering information on foods eaten and meal pat-

terns over a prolonged period of time.

Diet recall

The subject is asked to recall the actual food and drink

consumed in the past on specified days, usually the

immediate past 24 h (24-h recall), but sometimes the

previous 48 h (Table 2). Portions are quantified as in

estimated records. Additional error is introduced by the

dependence on memory. The main advantage is that

24-h recalls can be conducted by a single short inter-

view or by telephone, and large numbers can be studied.

It also has potential in the sports context when face-

to-face meetings with sports persons are difficult to

arrange, because 24-h recalls can be repeated at inter-

vals over time to improve precision at the individual

level. However, the days must be carefully chosen to rep-

resent the different kinds of day in the sports person’s

lifestyle and appropriately weighted before calculating

their mean intake. For example, 24-h recalls might be

conducted for equal numbers of rest, training and com-

petition days, but the athletes’ lifestyle might contain

them in a proportion of 2 : 7 : 1.

Diet history

The diet history is a face-to-face interview in which

the investigator attempts to construct a typical 7 days’

eating pattern by questioning the subject about past

food intake (Table 3). The respondent may be referred

to a preceding period of 3, 6 or even 12 months. A 24-

h recall is first taken and then, each meal and intermeal

32 Alison E. Black


period is considered in turn to identify and quantify pos-

sible alternative menus. The interview may be open-

ended or fully structured. A checklist of foods is usually

used to probe for missing items. The method requires

an experienced interviewer who is very knowledgeable

about local foods and meal patterns and customary

portion sizes. The interview lasts 60–90 min and

demands high levels of concentration and communica-

tion skills from both interviewer and respondent. The

supposed advantage is that it can be referred to an

extended period of time and is presumed to measure

‘habitual’ intake. This, however, is doubtful. Evidence

suggests that the diet history may obtain more valid

group mean intakes than weighed diet records, but that

there are greater discrepancies at the individual level

(Livingstone et al. 1992; Black et al. 2000). There is also

evidence that the near past is better remembered than

the distant past. The disadvantages are that the diet

history depends on memory and on complex cognitive

tasks such as the respondent’s perception of their dietary

pattern (which may be distorted) and their ability

to conceptualise portions (which may be poor). The

method is of little use when eating patterns are irregu-

lar, as is often the case among sports persons.

Food frequency (and amount) questionnaires (FFQ)

The respondent is presented with a preprinted list of

foods with options to indicate how often each is eaten.

An element to quantify portions may be included. An

example is shown in Figure 4.

The form is for self-completion and may be sent by

post. The list of foods may be short or long and depends

on the aims of the study. The technique is designed for,

and only suitable for, epidemiological scale studies and

each questionnaire must be designed for the specific

study aims and study population. The advantages are

that an FFQ can be used with very large numbers, and

can be designed for computer scanning for data entry.

The disadvantages are the dependence on memory and

ability to do the exceedingly complex cognitive task of

converting very variable dietary patterns into frequen-

Table 2 24 h recall: single


Minimal subject burden (15–30 min) No information on individualsCan be conducted by telephone Depends on memoryCan study large numbers Estimated portions

Table 3 Diet history


Single interview (60–90 min) Expert interviewer needed?Measures habitual intake Requires complex cognitive skills?Can refer to distant past Large random errors

Estimated portions Unsuitable for erratic meal patterns



cies of consumption over a usually extended time period

(Table 4). The errors of this method have been poorly

quantified, but precision at the individual level is cer-

tainly poor.

Validity of different dietary assessmentmethods

Until 1980 there was no method available by which the

‘truth’ of diet reports could be checked. One method

could be compared with another, but without knowing

which, if any, was valid. In 1980, Isaksson suggested

using urinary nitrogen excretion to validate protein

intake. The technique was developed and refined by

Bingham et al. (1983; 1985). In 1982, Schoeller and van

Santen published the technique for measuring total free-

living energy expenditure using doubly-labelled water.

This has now been widely used to check the validity of

reported energy intake. Energy intake is the keystone of

dietary planning. Energy intake must be correct in order

to maintain energy balance and stable body weight. All

other nutrients have to be provided within the alloca-

tion for energy, and in general, since the more food that

is eaten, the greater the intake of any nutrient. If the

energy intake is under-reported other nutrients are also

under-reported.

The doubly-labelled water technique

The doubly-labelled water (DLW) technique measures

free-living energy expenditure. The subject is given a

dose of water enriched with the stable isotopes deu-

terium (2H) and oxygen-18 (18O). Small urine samples

are collected at baseline before administration of the

dose and subsequently either daily (multi-point method;

Coward 1988) or at the beginning and end of the mea-

surement period (two-point method; Schoeller et al.1986). The urine samples are analysed by isotope ratio

mass spectrometry to determine the rate of disappear-

ance of each isotope from the body. Deuterium is lost

in water only, whereas oxygen-18 is lost in both water

and CO2. The rates of disappearance measure the body’s

water and water-plus-CO2 turnover rates, from which

CO2 production can be calculated by difference. The

total energy expenditure is calculated from CO2 pro-

duction by applying classical indirect calorimetric equa-

tions. The measurement period is most usually 14 days

in adults and 10 in children, but periods from 7 to

21 days have been used. The principle of the method,

experimental protocol, details of mass spectrometric

analysis, methods of calculation, fractionation and res-

piratory quotient assumptions and sources of errors

have been fully documented elsewhere (IDECG 1990;

Speakman 1997).

Validation of energy intake

The validation of reported energy intake against mea-

sured energy expenditure rests on the fundamental phy-

siological equation:

At the group level and in the time scale of a dietary

assessment, body weight can be regarded as constant

and therefore, mean energy intake (EI) must equal mean

energy expenditure (EE). Validation is by direct com-

parison of EI with EE.

Figure 5 shows data from early studies conducted at

the Dunn Nutrition Centre, Cambridge and the Univer-

sity of Ulster at Coleraine. Each graph represents a

EI EE= ± Changes in body stores

Figure 4 Example layout of a food frequency questionnaire (adapted fromNelson et al. 1997).

Table 4 Food frequency questionnaire


Moderate subject burden (30–60 min) Must be study- and population-Postal survey possible specificLarge numbers can be studied Poor precision?Measures habitual diet Depends on memory?Can refer to distant past Requires complex cognitive skills

Errors unknown

different group of subjects. Each bar represents one

DLW measurement compared with one energy intake

measurement expressed as the percentage difference

between intake and expenditure (EI-EE)*100/EE.

Where bars rise above the zero line, reported intake

exceeded expenditure and vice versa. The horizontal

lines show the 95% confidence limits of agreement

between EI and EE.

Graph 1 shows subjects in which food intake was

recorded by observers (Diaz Bustos 1989; Prentice et al.1989). The majority of individual differences fell within

the 95% confidence limits and the mean difference

was +2%, indicating that the assumption of agreement

between EE and valid reports of EI at the group level is

justified. Graph 2 shows data accumulated from studies

of normal-weight women who volunteered for intensive

studies (Goldberg et al. 1991; 1993). The mean dif-

34 Alison E. Black


ference was small (-4%), the individual differences were

both positive and negative and the majority fell within

the 95% confidence limits, all indicating valid diet

records from this group. Graph 3 shows results from

postobese (Black et al. 1995) and obese (Prentice et al.1986) women. The mean differences were -27 and

-39%, respectively, indicating marked bias towards

underestimation of energy intake. All individual differ-

ences were negative and a high proportion was outside

the confidence limits of agreement, indicating invalid

individual records. Graph 4 shows adults randomly

selected from participants in a community-based dietary

survey (Livingstone et al. 1990). The mean difference

was -19%. Although few of the individual results were

outside the confidence limits of agreement (and there-

fore could not be detected as invalid records), there was

an excess of subjects with negative differences. This

1. Intake observed, mean +2%

2. Normal weight wolunteers, mean –4%

604020

0–20–40–60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24604020

0–20–40–60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 286040200

–20–40–60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2160

40

20

0

–20

–40

–601 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Subjects

3. Post obese mean –27%

4. Random sample of adults, mean –19%

Per

cent

Per

cent

Per

cent

Per

cent

Figure 5 Doubly-labelled water validations of reported energy intake from studies conducted at the Dunn Nutrition Unit, Cambridge and the University of Ulster at Coleraine. (1) Energy intake observed by researchers (Diaz-Bustos 1989; Prentice et al. 1989); (2) studies in normal weight female volunteers (Goldberg et al. 1991; 1993); (3) studies in women volunteering for studies of postobese and obese persons (Prentice et al. 1986; Black et al. 1995); (4) studyin a randomly selected group of adults (Livingstone et al. 1990).



seminal study had profound implications for all dietary

surveys. It had always been recognised that dietary

surveys reported energy intakes at the lowest end of the

distribution that could not maintain bodyweight in the

long term. However, it had been assumed that these low

intakes were balanced by over-high intakes at the other

extreme, and that a valid measure of mean intake would

be obtained. In the study by Livingstone et al. (1990),

contrary to expectation, good agreement between EI

and EE was found in the third of subjects reporting the

highest EI and poor agreement among those in both the

middle and lowest thirds. This study seriously chal-

lenged the assumption of validity in randomly selected

samples and raised a large question mark over all

community-based studies.

Since those early studies, many further studies report-

ing both energy intake and DLW EE have entered the

literature. Figure 6 shows the distribution of mean

EI : EE from 43 studies of adults comprising 77 sub-

groups (men and women separately). Mean EI : EE was

0.83 (SD 0.14). In 22 (29%) subgroups, EI and EE

agreed to within ±10%, but 53 (69%) subgroups had

a reported mean EI more than 10% below mean EE.

Only two groups had a mean EI more than 10% above

mean EE. It is clear therefore, that dietary studies have

a widespread bias towards the underestimation of

energy intake, and that men are no less prone to under-

reporting than are women.

Unfortunately very few DLW studies have used

methods other than weighed or estimated diet records.

Table 5 shows the mean EI : EE by the dietary assess-

ment method. There were no significant differences

between methods for self-reported intake, but there

were too few studies to enable clear conclusions to

be drawn about the relative validity of the different

methods. Furthermore, the numbers in each study were

usually small and the subjects highly selected, e.g. vol-

unteers coming forward in response to local publicity,

recruited from university staff/students or from a special

group such as athletes. DLW validation has therefore

uncovered bias to the under-estimation of energy intake,

but has been unable to unravel the relative validity

of different methods or to determine the precision of

each.

Subject-specific bias

Figure 7 illustrates another serious problem in dietary

data, namely subject-specific bias. The top graph shows

the individual EI : EE ratios for 31 adults studied by

weighed records on two occasions 2 years apart

(Livingstone et al. 1990). Those who under-reported on

the first occasion also under-reported on the second oc-

casion. At the other end of the scale, one subject over-

reported on both occasions. The lower graph shows the

individual EI : EE ratios for 56 schoolchildren and ado-

lescents studied by weighed records and diet history

(Livingstone et al. 1992). It demonstrates differential

bias between the methods, and also that those who

reported low intakes by one method tended to report

low intakes by the other method. Thus, in any dietary

dataset, correct ranking of subjects is not only handi-

capped by poor precision due to random errors, but

also by differential systematic bias between individuals

that cannot be eliminated by repeating the dietary

assessment.

Figure 6 Frequency distribution of EI : EE by sex in 43 DLW studies of adultscomprising 77 subgroups (men and women separately).

Table 5 Mean EI : EE by dietary assessment method and sex in43 DLW studies comprising 77 subgroups

Dietary method Sex n Mean SD

Observation 5 1.06 0.09Weighed records F 17 0.83 0.11Weighed records M 5 0.85 0.11Estimated records F 16 0.85 0.10Estimated records M 9 0.83 0.11Diet history 4 0.84 0.1424 h recall (single or multiple) 6 0.84 0.08FFQ 6 0.87 0.12All 77 0.86 0.13

Self-reported energy intake in sports persons

Table 6 summarises results from DLW studies on ath-

letes in which EI was measured. Although some studies

reported good agreement between intake and expendi-

ture, a substantial number showed under-reporting.

Only one study reported both EI and DLW EE in young

sports persons (Chinese gymnasts) (Davies et al. 1997).

However, Table 7 shows the energy expenditure of nor-

36 Alison E. Black


mally active children as determined by DLW studies

(Black et al. 1996), expressed as EE : BMR (BMR, basal

metabolic rate). It also shows reported energy intakes

expressed as EI : BMR obtained from several studies of

young sports persons reviewed by Thompson (1998).

These reported energy intakes nearly all fall below the

energy expenditures measured in normally active chil-

dren recruited from the community and not selected as

being competitive athletes. This suggests that only the

15–18-year-old swimmers reported energy intakes that

might reasonably reflect their energy expenditure.

Evaluating the quality of individual diet reports

Clearly, with widespread and substantial bias to the

under-estimation of food intake, it is essential to ex-

amine the results of dietary assessments very critically.

It is all too easy to put the reported food intake through

a nutrient-analysis programme, obtain reported nutrient

intakes calculated to several decimal places, and assume

that the data are valid and provide a true record of

intake. Validation by doubly-labelled water, urinary N,

or other biochemical markers are unlikely to be avail-

able except in a research context. Therefore, the dietary

reports can only be compared with expected energy

expenditure. By expressing energy requirements as a

multiple of BMR (by physical activity level [PAL]), some

of the variation due to age, sex and weight can be elimi-

nated and a single figure can be assigned to each given

broad activity category. Energy intake can be expressed

as EI : BMR for comparison.

Figure 8 shows the frequency distribution of PAL

values in free-living adults from predominantly white-

collar occupations (Black et al. 1996). The mode was

1.6. This represents the activity level of normally active

but sedentary persons. The value of 1.4, given as the

recommended intake for light activity by the UK Dietary

Reference Values (DH 1991) represents a lifestyle of

couch-potato leisure and seated work, e.g. receptionist

or VDU operator. At the other extreme, the PAL value

of 2.4 found in soldiers on active service probably rep-

resents the maximum energy expenditure for a sustain-

able lifestyle, although values as high as 4–5 PAL may

be achieved over short periods of time in activities such

as the Tour de France or Nordic skiing.

Table 8 summarises results from studies that have

imposed exercise on sedentary individuals on 4–5 days/

week. The mean cost of the imposed energy expenditure

was 2 MJ. The mean increase in PAL value was +0.3

units. The mean PAL for the sedentary arm of the

studies was 1.63, supporting the previous estimate of

A

B

Figure 7 Subject-specific bias in dietary assessment demonstrated byrepeat measurements of EI validated by DLW EE. (A) Thirty-one adults inwhom EI was measured by 7-day weighed record in 1987 and 1989 andDLW EE in 1989. From Black (2001), data of Livingstone et al. (1990). (B)Fifty-eight children and adolescents aged 7–18 years in which EI was meas-ured by 7-day weighed records and by diet history within a 4-week periodand DLW EE concurrently with the weighed records. From Black (2001), dataof Livingstone et al. (1992).



Table 6 Dietary reporting in athletes: Mean EI : EE as reported in DLW studies on athletes

Subjects n Sex Mean age (years) EI : EE Reference

Yachtsmen, round the world race 6 M 44 1.03 (Branth et al. 1996)Swimmers 8 M/F 20 1.01 ( Jones & Leitch1993)Nordic skiers, in rigorous training 4 F 25 0.99 (Sjödin et al. 1994)Nordic skiers, in rigorous training 4 M 26 1.00 (Sjödin et al. 1994)Body builders 10 M/F 29 0.91 (Quevedo et al. 1991)Sedentary non runners 5 F 31 0.97 (Mulligan et al. 1990)Moderately active runners 9 F 37 0.90 (Mulligan et al. 1990)Very active runners 7 F 30 0.75 (Mulligan et al. 1990)Chinese gymnasts 6 F 7 0.77 (Davies et al. 1997)Chinese gymnasts 6 M 8 0.96 (Davies et al. 1997)Runners 9 F 26 0.87 (Schulz et al. 1992)Classical ballet dancers 11 F – 0.79 (Hill & Davies 1999)Cross country runners in training 9 F – 0.68 (Edwards et al. 1993)Elite swimmers, in rigorous training 5 F – 0.56 (Trappe et al. 1997)Cyclists in Tour de France 4 M 24 0.83 (Westerterp et al. 1986)Cyclists in Tour de France 4 M 24 0.73 (Westerterp et al. 1986)Cyclists in Tour de France 4 M 24 0.65 (Westerterp et al. 1986)

Table 7 DLW energy expenditure measured in normally active children recruited from the community compared with energy intakes asself-reported by young athletes (from Black et al. 1996)

Energy expenditure of normally active children

Sex Age (years) n EE : BMR

Females 7–10 17 1.6711–14 28 1.7015–18 11 1.81

Males 7–10 27 1.7311–14 34 1.7415–18 11 1.97

Self-reported energy intakes of young athletes

Sex and activity Age (years) n EI : BMR Reference

FemalesDance 12–17 92 1.43 (Benson et al. 1985)Gymnastics 7–10 29 1.57 (Benardot et al. 1989)

11–14 240 1.51 (Zonderland et al. 1985; Loosli et al. 1997; Benardot et al. 1989; van Erp-Baart et al. 1989;Benson et al. 1990; Ersoy 1991)

15–18 56 1.30 (Moffat 1984; van Erp-Baart et al. 1985; Hickson et al. 1986; van Erp-Baart et al. 1989;Fogelholm et al. 1995)

Swimming 11–14 100 1.56 (Zonderland et al. 1985; van Erp-Baart et al. 1989; Benson et al. 1990; Hawley et al. 1991)15–18 22 2.43 (Berning et al. 1991)

Males 11–14 46 1.45 (Hickson et al. 1987)Football 15–18 88 1.68 (Hickson et al. 1987)

11–14 9 1.85 (Hickson et al. 1987)Swimming 15–18 42 2.28 (van Erp-Baart et al. 1989; Berning et al. 1991)

1.6 as a mean for normally active persons. The mean

PAL during the exercising arm of the studies was 1.99,

suggesting that the mean PAL of sports persons in

serious training is unlikely to be less than 2.0.

The relationship between lifestyle, activity and PAL

suggested by the data is summarised in Table 9. Sum-

marising data from the general population, Black et al.(1996) concluded ‘The data provide little evidence to

quantify the energy cost of manual occupations, but the

range 2.0–2.4 is suggested as the maximum for a sus-

tainable lifestyle.’

Table 10 summarises EE from DLW studies of ath-

letes. Unfortunately, very limited information was given

in these studies about the duration and intensity of phy-

sical exercise, nor the general lifestyle. The data show

38 Alison E. Black


PAL values between 4 and 5 for extreme activity,

2.5–4.0 for athletes in rigorous training, and values

around 1.8–2.2 for normal training. Two studies

confirm the value of 1.5–1.6 for a sedentary lifestyle.

These studies give guidance on the mean level of energy

expenditure by athletes, but Figure 9 shows that aver-

ages may conceal large individual variation. In order to

evaluate the reported energy intake of any one athlete

therefore, it is essential to obtain as much information

as possible about their lifestyle and physical activity.

If physical activity is determined from a full activity

diary, then it is possible to estimate the time spent on

different activities and do a factorial calculation of

energy expenditure. This can be expressed either in

absolute MJ or as PAL. Table 11 gives a theoretical

example. A simplified calculation is shown in Table 12.

The latter assumes a general activity level of 1.6 ¥ BMR

for time not spent in exercise and adds the cost of the

exercise for the remaining part of the 24 h.

Practical application

The estimates of energy and nutrient intake obtained

from subjects should be examined critically to evaluate

Figure 8 Frequency distribution of energy expenditure in free-living adults aged 18–64 years expressed as the physical activity level (EE : BMR)(smoothed curves, three point running average). Dotted line, males; solid line,females. From Black et al. (1996).

Table 8 Energy cost of imposed exercise

TEE (MJ/day) BMR (MJ/day) AEE (MJ/day) PAL

Subjects and protocol n Period 1 Period 2 Period 1 Period 2 Period 1 Period 2 Period 1 Period 2 Reference

Adult women* 2 8.1 9.0 5.1 5.3 3.0 3.7 1.59 1.69 (Bingham et al. 1989)Adult men* 3 10.6 14.6 6.7 6.6 3.8 7.9 1.57 2.19 ≤Obese schoolboys† 10 10.4 11.6 6.1 6.0 4.2 5.6 1.69 1.951 (Blaak et al. 1991)Adult women‡ 5 9.5 11.3 6.2 6.0 3.3 5.3 1.53 1.87 (Westerterp et al. 1992b)Adult men‡ 8 12.0 14.6 7.3 6.9 4.7 7.6 1.65 2.11 ≤All subjects 28 10.5 12.5 6.5 6.3 4.1 6.3 1.63 1.99 ≤

*Period 1, restricted activity; period 2, 5–10 min isometric exercises plus 60 min jogging on 5 days for 2 weeks. †Period 1, normal activity; period 2, 45 min oncycle ergometer at 55–67% of VO2max on 5 days for 10 weeks. ‡Period 1, normal activity, no running or jogging, other sport <1 h/week; period 2, 30–90 minrunning on 4 days/week as training for half-marathon. TEE, total energy expenditure; AEE, activity energy expenditure (TEE-BMR); PAL, physical activity level(TEE/BMR).

Table 9 Typical PAL values for a range of lifestyles

Chair- or bed-bound 1.2Seated work with no option of moving around and little or 1.4–1.5

no strenuous leisure activitySeated work with discretion and requirement to move around 1.6–1.7

but little or no strenuous leisure activityStanding work (e.g. housewife, shop assistant) 1.8–1.9Significant amounts of sport or strenuous leisure activity +0.3

(30–60 min 4–5 times per week)Strenuous work or highly active leisure 2.0–2.4



the probable validity of the dietary report. If the esti-

mated energy intake is significantly lower than prob-

able energy expenditure, as judged by knowledge of the

lifestyle and physical activity programme of the subject,

then food intake has been under-reported. Energy is,

of course, a surrogate measure for the total quantity

of food eaten. Because the amount of any nutrient

obtained is related to the quantity of food eaten (the

greater the total quantity of food, the greater the intake

of any nutrient), evaluating the validity of the reported

energy intake provides a check on the overall quality of

the data.

Table 10 Mean DLW energy expenditure in studies of athletes

Subjects n Sex Age EE (MJ) EE : BMR Reference

Cyclists in Tour de France 4 M 24 29.4 4.08 (Westerterp et al. 1986)Cyclists in Tour de France 4 M 24 36.0 5.01 (Westerterp et al. 1986)Cyclists in Tour de France 4 M 24 35.7 4.99 (Westerterp et al. 1986)Arctic explorer 1 M 35 32.0 4.50 (Stroud et al. 1993)Arctic explorer 1 M 47 34.2 4.42 (Stroud et al. 1993)Elite swimmers, rigorous training 5 F – 23.4 – (Trappe et al. 1997)Nordic skiers, rigorous training 4 M 26 30.2 4.00 (Sjödin et al. 1994)Nordic skiers, rigorous training 4 F 25 18.3 3.40 (Sjödin et al. 1994)Athletes, rigorous training 4 F – 14.6 2.79 (Haggarty & McGaw 1988)Yachtsmen, round world 6 M 44 19.3 2.51 (Branth et al. 1996)Climbers at altitude 6 M/F 35 11.8 1.82 (Westerterp et al. 1994)Climbers at altitude 5 M/F – 13.6 2.20 (Westerterp et al. 1992a)Climbers at altitude 6 M/F 27 19.4 2.80 (Pulfrey & Jones 1996)Swimmers 8 M/F 20 14.5 – ( Jones et al. 1993)Cross country runners, training 9 F – 12.5 2.24 (Edwards et al. 1993)Chinese gymnasts 12 M/F 7 8.4 1.98 (Davies et al. 1997)Runners 9 F 26 11.8 2.03 (Schulz et al. 1992)Classical ballet dancers 11 F – 13.0 – (Hill & Davies 1999)Body builders 10 M/F 29 14.1 1.95 (Quevedo et al. 1991)Sedentary 5 M 31 12.5 1.61 (Seale et al. 1996)Endurance training 5 M 29 14.9 1.92 (Seale et al. 1996)Strength training 5 M 32 16.9 2.09 (Seale et al. 1996)Sedentary non runners 5 F 31 7.5 1.50 (Mulligan et al. 1990)Moderately active runners 9 F 37 9.3 1.84 (Mulligan et al. 1990)Very active runners 7 F 30 11.0 1.98 (Mulligan et al. 1990)

Figure 9 Total energy expenditure of individual athletes expressed as physi-cal activity level (PAL) (Westerterp et al. 1986; Schulz et al. 1992; Edwardset al. 1993; Jones et al. 1993; Stroud et al. 1993; Sjödin et al. 1994).

Table 11 Factorial calculation of energy expenditure. Clinicaldietitian, 28 years, 55 kg, estimated BMR 5.45 MJ

Activity Duration (h) PAR1 Cost (MJ)

Bed 8.0 1.0 1.82Dressing/undressing 1.0 2.8 0.64Chores morning/evening 2.25 2.1 1.31Driving to/from work/club 1.0 1.6 0.36Walking at work 2.0 2.8 1.27Standing, ward round 2.0 2.1 0.95Sitting, office work 3.5 1.6 1.27Sitting, meal breaks 1.0 1.2 0.27Exercise, squash 0.5 6.9 0.78Standing, socialising 0.5 1.6 0.18Watching TV 1.75 1.2 0.40Total 24.0 9.21PAL 9.21/5.45 = 1.69

1PAR, physical activity ratio, i.e. the energy cost of specific activities expressedas EE : BMR.

Conclusions and recommendations

1 Self-reported energy intake frequently substantially

underestimates true energy intake. In such cases, intake

of other nutrients will also be underestimated. Dietary

reports should therefore be examined very critically.

2 Reported energy intake can be evaluated by compari-

son with expected energy requirements. It is most easily

done by expressing both as multiples of the BMR, which

can be estimated from weight, or weight and height,

using equations such as those of Schofield (1985).

3 Energy expenditures in groups of athletes have been

reported as around 2.0–2.5 during normal training, and

2.5–4.0 during rigorous training. Values greater than

4.0. have been reported in periods of extreme physical

endurance.

4 Expenditure in individual athletes may deviate sub-

stantially from the group average. Therefore, for the

energy intake reported by a given athlete, that individ-

ual’s personal physical activity should be measured and

used for comparison.

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