Biomarkers of Nutritional Exposure and Nutritional Status Markers of the Validity of Reported Energy Intake1 M. Barbara E. Livingstone2 and Alison E. Black3,4 School of Biomedical Sciences, University of Ulster at Coleraine, Coleraine BT52 1SA, Northern Ireland, U.K.
KEY WORDS: biomarkers diet assessment energy intake epidemiology nutrition
Energy intake (EI)5 is the foundation of the diet, because all other nutrients must be provided within the quantity of food needed to fulfill the energy requirement. Reported EI is therefore a surrogate measure of the total quantity of food intake. If total EI is underestimated, then the intakes of nutrients correlated with EI (the macronutrients, most minerals and the B vitamins) are also likely to be underestimated. This
may, for example, lead to overestimation of the proportion of the population with deficient intake or distortion of the associations between nutrient intake and disease outcome. Evaluating the validity of reported EI provides a valuable check on the general quality of the dietary data in any study. Three concepts are fundamental to understanding the limitations of dietary assessment: habitual intake, validity and precision. The habitual intake of an individual is the person’s intake averaged over a prolonged period of time (weeks or months rather than days). For energy it is the intake that maintains weight stability. For other nutrients, it may be thought of as the intake required to produce a steady physiological state and hence to influence nutritional status and health in both the short and the long terms. Habitual intake is the value that studies of diet and health would ideally measure; however it is a largely hypothetical concept, because intake varies widely from day to day. Weekly or monthly variation can also be significant (1). A valid (or accurate) report is one that measures the true intake during the period of study. A valid diet record is a complete and accurate record of all food consumed on specified days, and where the choice of food and drink consumed has not been influenced by the act of recording, i.e., a subject ate and drank exactly what he/she would have eaten and drunk if he/she had not been involved in a research study. A valid diet recall is a complete and accurate recall of all
1 Published as part of The Journal of Nutrition supplement publication ‘‘Biomarkers of Nutritional Exposure and Nutritional Status.’’ This series of articles was commissioned and financially supported by International Life Sciences Institute, North America’s Technical Committee on Food Components for Health Promotion. For more information about the committee or ILSI N.A., call 202-6590074 or E-mail
[email protected]. The opinions expressed herein are those of the authors and do not necessarily represent the views of ILSI N.A. The guest editor for this supplement publication was Jo Freudenheim, University at Buffalo, State University of New York, Buffalo, NY 14214. 2 To whom correspondence should be addressed. E-mail: mbe.livingstone@ ulster.ac.uk. 3 Previous address: Dunn Nutrition Centre, Downham’s Lane, Milton Road, Cambridge CB4 1XJ, U.K. 4 Present address: Little Danes, Frinstead Road, Millstead, Siltingbourne Kent ME9 OSA U.K. 5 Abbreviations used: ADMR, average daily metabolic rate; BMI, body mass index; BMR, basal metabolic rate; CVwEE, within-subject coefficient of variation in energy expenditure; CVwEI, within-subject coefficient of variation in daily energy intake; DEBQ, Dutch Eating Behavior Questionnaire; DLW, doubly labeled water; EE, energy expenditure; EI, energy intake; EI:BMR, energy intake/basal metabolic rate; EI:EE, energy intake/energy expenditure; FFQ, food frequency questionnaire; LER, low-energy reporter; PAL, physical activity level; SE, socioeconomic status; TFEQ, Three Factor Eating Questionnaire.
0022-3166/03 $3.00 Ó 2003 American Society for Nutritional Sciences.
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ABSTRACT Energy intake (EI) is the foundation of the diet, because all other nutrients must be provided within the quantity of food needed to fulfill the energy requirement. Thus if total EI is underestimated, it is probable that the intakes of other nutrients are also underestimated. Under conditions of weight stability, EI equals energy expenditure (EE). Because at the group level weight may be regarded as stable in the timescale of a dietary assessment, the validity of reported EI can be evaluated by comparing it with either measured EE or an estimate of the energy requirement of the population. This paper provides the first comprehensive review of studies in which EI was reported and EE was measured using the doubly labeled water technique. These conclusively demonstrate widespread bias to the underestimation of EI. Because energy requirements of populations or individuals can be conveniently expressed as multiples of the basal metabolic rate (BMR), EE:BMR, reported EI may also be expressed as EI:BMR for comparison. Values of EI:BMR falling below the 95% confidence limit of agreement between these two measures signify the presence of underreporting. A formula for calculating the lower 95% confidence limit was proposed by Goldberg et al. (the Goldberg cutoff). It has been used by numerous authors to identify individual underreporters in different dietary databases to explore the variables associated with underreporting. These studies are also comprehensively reviewed. They explore the characteristics of underreporters and the biases in estimating nutrient intake and in describing meal patterns associated with underreporting. This review also examines some of the problems for the interpretation of data introduced by underreporting and particularly by variable underreporting across subjects. Future directions for research are identified. J. Nutr. 133: 895S–920S, 2003.
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validation. Only for the 24-h recall technique or for individual meals (where direct observations are possible) have any true studies of validity been undertaken. In early studies in which a subject’s intake was covertly observed and then assessed by recall, intake was usually underestimated, the assumption being that this was primarily due to faulty memory (6). Nevertheless until recently, dietary intakes were reported as if valid, and the interpretation of links between intake and health were based, often erroneously, on the assumption of validity. Until the advent of biomarkers, the so-called validation studies simply compared the results of one method with another. The weighed dietary record was often assumed to be the gold standard, and the validity of other methods was evaluated by comparison. These studies are actually studies of ‘‘relative validity.’’ Recently the term ‘‘calibration’’ studies was introduced to describe studies of relative validity and to distinguish them from studies of validity that use external markers of intake. Only the advent of external markers of intake has made it possible to test assumptions about validity. In contrast to the micronutrients, there are no biochemical biomarkers of EI. All three methods of validation rest on the assumption that EI must equal energy expenditure (EE) when weight is stable. These methods are as follows: 1) comparison of self-reported EI with the EI required to maintain weight; 2) direct comparison of reported EI and measured EE; and 3) comparison of reported EI with presumed energy requirements, both expressed as multiples of basal metabolic rate (BMR). The technique for the validation of protein intake, 24-h urinary nitrogen excretion, also has some limited value as a marker of EI. Energy intake for weight maintenance The earliest indicator of bias to the underestimation in selfreported EI came from an 18-wk metabolic study in which the EI reported by the subjects before entry into the study was 27% less than that required to maintain weight during the study (9). The finding has been confirmed in other similar studies (10– 12). Clearly this technique cannot be used to validate reported EI in studies of free-living subjects, although combined residential and free-living studies have been used to examine characteristics of underreporters (12), psychological and behavioral correlates of underreporting (13) and the accuracy of the multiple-pass 24-h recall (14). However, because in the time scale of a diet record the group mean weight should remain unchanged, the monitoring of body weight at the beginning and end of a diet record can indicate whether, on average, undereating has occurred. Substantial undereating such as temporary dieting may be detectable at the individual level. Direct comparison of energy intake and measured energy expenditure
FIGURE 1 Visual representation of accuracy (validity) and precision (repeatability). From Black, 1999 (200). True average (solid lines), repeat measurements (d) and measured average (dashed lines) are shown.
The doubly labeled water technique. The doubly labeled water (DLW) technique is the gold standard for measuring EE under free-living conditions. The subject is given a dose of water enriched with the stable isotopes deuterium (2H) and oxygen 18 (18O). Urine samples are collected at baseline before administration of the dose and subsequently either daily [multipoint method (15)] or at the beginning and end of the measurement period [two-point method (16)]. The urine samples are analyzed by isotope ratio mass spectrometry to determine the rate of disappearance of each isotope from the body. Deuterium is lost in water only, whereas oxygen 18 is lost in both water and carbon dioxide. The rates of disappearance
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food and drink consumed on specified day(s). A valid diet history or food-frequency questionnaire (FFQ) accurately reflects typical food consumption over a designated period of time, undistorted by behavioral patterns or false memory. Poor validity derives from systematic errors (bias) in the reporting of food intake. A precise technique is one that yields the same answer on repeat administrations. Precision may be expressed in various ways; for example, 1) mean absolute difference, 2) mean difference as a percentage of overall mean intake, 3) coefficient of variation of the differences within individuals, 4) correlation coefficient, or 5) percentage of individuals classified in the same quantile on both occasions. As noted above, food intake varies widely with time. Therefore precision of dietary assessment at the individual level is poor even when repeat surveys show good agreement for mean intake. Some published values for the coefficient of variation of the differences within individuals are 16.5% for 3-d food records (2), 18.6% for a dietary history (3) and 28.5% for an FFQ (4). Poor precision derives from large random errors in the techniques of dietary assessment. It reduces the sensitivity for identifying invalid reports (Fig. 1). It is not the purpose of this paper to review the older literature on repeatability of dietary assessment or the relative validity of different methods, which has been comprehensively reviewed elsewhere (5–8). This paper reviews the new literature in which the validity of reported EI has been tested using external methods of validation. This paper differs from the others in this series in that the discussion here is of a biomarker in relation to reports of dietary intake rather than the use of a biomarker as an indicator of intake. As detailed below, there are few biomarkers of EI, and because of their nature, they do not lend themselves to large-scale studies. This makes an understanding of the limitations of reported EI particularly important. Understanding the limitations of dietary assessment techniques and the quantification of the errors involved has been handicapped for decades by a lack of independent methods for
NUTRITIONAL BIOMARKERS: REPORTED ENERGY INTAKE
EI 5 EE 6 changes in body stores At the group level and in the time scale of a dietary assessment, body weight can be regarded as constant, and therefore mean EI must equal mean EE. Validation is by direct comparison of EI with EE. Various modes of expressing this comparison have been used, including the ratio EI:EE (as a proportion of 1.0 or as a percentage), the percentage difference (EI 2 EE/EE) 3 100 (i.e., the reciprocal of EI:EE) and the difference EI 2 EE. Alternatively a number of authors have presented data as a Bland-Altman plot [EE 2 EI against the means of both measurements (28)] and shown 62 SD of the difference. However these define the confidence limits of the data including any invalid (biased) data. To identify invalid reports, the confidence limits of a valid data set must be defined. For individual subjects in energy balance, habitual EI must equal habitual EE. The expected ratio of EI:EE is 1.00, but variation in both measurements means that absolute agreement cannot necessarily be expected even for valid data. The limitations of EI:EE for identifying individual underreporters
were explored in data from 22 studies (249 subjects) with measurements of both EI and DLW-EE (27). Values falling above or below the 95% confidence limits of the ratio indicate over- or underreporting, respectively. The 95% confidence limits of the ratio define the range within which the differences between EI and EE could have arisen by chance in a valid data set. The 95% confidence limits were calculated as 95% CL52 3 ½ðCVwEI2 =dÞ1CVwEE2 22 r ðCVwEI =dÞ CVwEE where CVwEI is the within-subject coefficient of variation for daily EI, d is the number of days of diet records, CVwEE is the within-subject coefficient of variation for repeat DLW-EE and r is the correlation between EI and EE. Within-subject CVwEI for daily EI ranges from 15 to 45% with an average of 23% (6). For the purpose of assigning confidence limits to the diet history and food-frequency questionnaire, CVwEI based on weighed records assuming a 28-d record was used. The CVwEE derived from analysis of studies with repeat DLW measurements is 8.2% assuming EE was measured concurrently with EI (27) and the correlation between EI and EE from accumulated individual DLW data was r 5 0.425 (29). Substituting these values into the equation and assuming a 7-d record yields a figure of 618%. Thus subjects with EI:EE , 0.82 or . 1.18 would be deemed under- or overreporters, respectively. A value of ;15% is the lowest that could be expected, and shorter records have substantially wider confidence limits of approximately 640% for a 1-d 24-h recall (27). When individual data from accumulated DLW studies were examined and values between 618% were excluded, mean EI:EE was 0.97. Thus not all invalid records could be identified, and even under the best conditions a bias of 23% in mean intake remained. Other direct measures of energy expenditure. Reported EI can also be compared directly with estimates of EE derived from heart-rate monitoring, accelerometers or physical activity questionnaires. Four validation studies have done so, deriving EE from leisure-time activity and BMR using an equation derived from earlier DLW studies (30), an activity diary (31), the EI for weight maintenance (12) and heart-rate monitoring (32). These other methods have their own sources of errors and bias. Their precision and validity is certainly poorer and the sensitivity and specificity for detecting invalid reports are worse than when DLW-EE is used. However, if the characteristics of underreporters are to be explored, techniques such as these must be used (see EI:BMR: The Goldberg cutoff technique). A detailed discussion of other methods for measuring EE are beyond the scope of this review. Doubly labeled water validation studies of reported energy intake. Since 1986, many studies have been conducted in which DLW-EE and reported EI have been measured concurrently in the same individuals. Figure 2 shows EI:EE from 43 studies of adults comprising 77 subgroups (men and women separately). Mean 6 SD EI:EE was 0.83 6 0.14. In 22 (29%) subgroups, EI and EE agreed to within 610%, but 53 (69%) subgroups had a reported mean EI . 10% below mean EE, whereas only two groups had a mean EI . 10% above mean EE. Unfortunately most of these studies were carried out on small numbers of highly selected subjects. Nevertheless the totality of the data from a variety of subjects, countries and lifestyles indicates that although some well-motivated and trained volunteers can reliably report habitual intakes, the majority of self-reported dietary intakes are systematically biased toward underestimation of EI. The demonstration of substantial but individually variable underreporting (mean, 220%; range, 255 to 140%) in randomly selected free-living
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measure the body’s water and water-plus-carbon dioxide turnover rates, from which carbon dioxide production can be calculated by difference. The total EE is calculated from carbon dioxide production by applying the classical indirect calorimetric equations. The measurement period is most usually 14 d in adults, but periods from 7 to 21 d have been used. The principle of the method, experimental protocol, details of mass spectrometric analysis, methods of calculation, fractionation and respiratory quotient assumptions and sources of errors have been fully documented elsewhere (17,18). The DLW technique has been validated against concurrent measurements of EE by respiratory gas exchange in a wide variety of subjects and metabolic circumstances including sedentary adults, adults exercising to exhaustion and subjects in states of energy balance and imbalance (16,19–25). Under well-controlled experimental conditions, accuracy is on the order of 1–3% and precision is 2–8%. A review of 14 studies with repeat DLW measurements in the field (26) estimated analytical variation in the method to be ;4% and withinsubject physiological variation to be ;7%. Because the DLW measurement is integrated over 10–14 d, it accounts for daily and weekly fluctuations in EE. It does not account for monthly or seasonal fluctuations and is not necessarily a measure of habitual EE. In 32 studies with repeat DLW measurements, the mean within-subject coefficient of variation in EE (CVwEE), including analytical and physiological variation and that due to changed activity, ranged from 6.5 to 22.6%. Multiple regression showed a positive association between mean CVwEE and the time span covered by the DLW measurements. There were small associations with weight change and reproduction, but neither accounted for additional variance above that accounted for by time span. There were no associations with age, sex, mean age or mean total EE. From the regression equation, CVwEE ranged from 8.2% at zero time span to 25.4% at 52 wk (27). Doubly labeled water provides an independent and objective measure of EE and it is easy to use in the field because it places minimal burden on the subjects and no restriction on the subjects’ activities. Unfortunately it requires sophisticated laboratory and analytical backup and is extremely expensive; therefore it cannot be a routine tool for validating EI data. Limitations of validating energy intake using doubly labeled water energy expenditure. The validation of reported EI against measured EE rests on the fundamental physiological equation
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adults (33) raised serious issues for the interpretation of epidemiological studies. Subject-specific bias to dietary assessment. Dietary surveys usually report a range of EI that at the extremes of the distribution cannot represent habitual intake. It was customary to assume that these extreme values were obtained by chance due to day-to-day variation in food intake, and that repeat measurements would eventually obtain a measure of the habitual intake of individuals. However, this assumption does not necessarily hold. An analysis of seven studies conducted in Cambridge and Belfast, U.K. provides evidence for subjectspecific bias in repeated weighed records and also in assessment by different methods (34). Two studies are shown in Figure 3 in which expressing misreporting as the ratio EI:EE controls for real differences in EE. Figure 3A (adults) demonstrates variable bias across individuals but similar bias within individuals in weighed records obtained 2 y apart. Figure 3B demonstrates differential mean bias between weighed records and diet history and variable bias across individuals with a tendency to rank similarly by both methods. Further evidence of subject-specific bias comes from three national surveys. Price et al. (35) found that the strongest indicator of underreporting in 1989 was being an underreporter in 1982. Kroke et al. (36) reported a high correlation (50.74; n 5 28) between the degree of underreporting (EI 2 EE) from two different assessments (the German European Prospective Study on Nutrition, Cancer and Health (EPIC) FFQ versus 12 3 24-h recalls). In the third National Health and Nutrition Examination Survey (NHANES III) (37), a subsample of 311 men and 312 women provided 2 3 24-h recalls 1 mo apart: Fifty-five percent of men and 58% of women who underreported on the first occasion also underreported on the second occasion. Subject-specific bias has important implications for the analysis of dietary surveys, because under- and overreported intakes distort the ranking of subjects and are likely to extend the range of reported intakes. In a data set of 574 DLW measurements, the total between-subject variation in EE for 16 age-sex groups ranged from 9.5 to 22.8% with a mean 6 SD of
FIGURE 3 Subject-specific bias in dietary assessment demonstrated by repeat measurements of EI validated by DLW-EE. Thirty-one adults in whom EI was measured by 7-d weighed record in 1987 and 1989 and DLW-EE was measured in 1989 [from (34), data of Livingstone et al. 1990 (33)] (upper panel). Fifty-eight children and adolescents aged 7–18 y in whom EI was measured by 7-d weighed records and by diet history within a 4-wk period and DLW-EE was concurrently measured with the weighed records [from (34), data of Livingstone et al. 1992 (48)] (lower panel).
15.4 6 3.6% (38), whereas studies with repeated DLW measurements suggested that the true between-subject variation in EE may be on the order of 11–13% (27). If this figure is correct, then it must also represent the between-subject variation in habitual EI. However, between-subject variation in reported EI is substantially greater, for example, 35% in the USDA Continuing Surveys of Food Intake of Individuals (CSFII) 85–86 (39), 25% in the U.K. survey of adults 1986 (40), and 26% in the Dutch Monitoring Project on Risk Factors for Chronic Diseases in the Netherlands (MORGEN) survey 1995 (41), which gives a false impression of ability to rank individuals. Furthermore it cannot be assumed that repeating measurements on the same individuals will overcome the problem. The tendency to maintain consistency in dietary
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FIGURE 2 Frequency distribution of the ratio of energy intake to energy expenditure (EI:EE) by sex in 43 doubly labeled water (DLW) energy-expenditure (DLW-EE) studies of adults comprising 77 subgroups (men and women separately).
NUTRITIONAL BIOMARKERS: REPORTED ENERGY INTAKE
TABLE 1 Comparisons of reported energy intake with energy expenditure measured by doubly labeled water in studies with both men and women Men
Women
Study
n1
Mean
SD
n
Mean
SD
Reference
1 2 3 4 5 6 7 8 9 10 11 Mean of means (11 studies)
7 16 27 17 9 81 11 4 8 39 28
0.88 0.81 0.88 0.93 0.87 0.88 0.70 0.99 1.02 0.77 0.86 0.87
0.11 0.22 0.18 0.15 0.13 — — 0.03 0.15 — —
6 15 18 11 12 56 11 4 5 43 36
0.68 0.82 0.89 0.93 0.87 0.76 0.82 1.01 0.87 0.82 0.90 0.85
0.18 0.29 0.17 0.1 0.2 — — 0.17 0.12 — —
(60) (33) (58) (181) (182) (30) (183) (184) (185) (186) (104)
0.09
SD
1
n, No. of participants.
0.10
studies employed methods other than diet records. The multiple-pass 24-h recall obtained a good estimate of EI in young children (46) but overestimated EI in adolescents (47), even though the latter authors had excluded from their analysis five subjects in which the gap between EI and EE was . 3 SD. They speculated that inappropriate portion sizes were reported. The diet history obtained good mean intakes (48), but poor agreement at the individual level was noted. The Willett FFQ was demonstrated to be unsuitable for use with young children, probably due to inappropriate portion sizes built into the design (49). At present, however, there have been too few validation studies in pediatric groups to justify advocating one particular method over another. Different dietary assessment methods. The imposition of a particular survey technique may induce method-specific behavioral alterations in actual and reported intakes. The nature and extent of these constraints are difficult to quantify, and thus the true validity of different dietary assessment methods is unknown. Distinction must be made between validity at the group level (the mean value) and validity at the individual level (correct ranking). A method may provide a valid mean but rank poorly due to poor precision or variable bias across subjects. Alternatively a method may give an invalid mean but nevertheless rank subjects correctly if precision is good and bias is similar across subjects. Figure 4 summarizes EI:EE from 43 studies comprising 77 groups of men and women by dietary assessment method (33,50–60). Table 3 summarizes mean EI:EE by dietary assessment method. In studies where food intake was observed, mean EI:EE was 1.06, which justifies the assumption that EI should equal EE at the group level. There were no significant differences between other methods. However, the majority of DLW validation studies have used either weighed or estimated records, and there are few validations of other methods. The few studies that have compared the mean intake from several methods simultaneously in the same subjects (Table 4) give inconsistent results (48,57,61–64). For example, in some studies, the diet history has provided more accurate mean estimates of EI than can be found in diet records (62), but the reverse has also been observed (57). In children and adolescents, the diet history apparently overcomes the observed agerelated bias in reported mean EI by weighed records (48). However, although this may be taken as establishment of proof of primacy for the diet history over diet records, the diet history data lacked precision at the individual level with 35% of the results by diet history residing outside the 95% confidence limit that assumes a valid measure of habitual intake. Similar conclusions have been reached with older subjects (62,65). Inconsistencies in validation have been observed with the 24-h recall and FFQ. The 24-h recall has performed both better (61) and worse (63,64) than weighed records. In the study by Sawaya et al. (61), the Willett FFQ (66) yielded intakes close to those expected and performed better than the Fred Hutchinson Cancer Research Center/Block FFQ (67,68). However, the mean intakes from large-scale studies that used these instruments have frequently not approached expected energy requirements, which suggests that the subjects in the study by Sawaya et al. (61) were more motivated and compliant than is usual in large-scale studies. Overall, the small numbers and highly selected samples in most DLW validation studies mitigate against firm conclusions about the comparative validity of different survey techniques. DLW studies have concentrated on validation of mean intake and have not evaluated the ability of any method to rank individuals. In the data from accumulated DLW studies (Fig.
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reporting highlights the need to initiate research into the behavioral and psychological correlates of dietary reporting. It also emphasizes the need for statistical techniques that can account for systematic bias as well as random error (see Can data be adjusted to allow for both random and systematic error?). Men versus women. Table 1 summarizes mean EI:EE values from studies that included both sexes. None of the tested differences between men and women were significant. The overall mean 6 SD values for men and women, respectively, were 0.87 6 0.09 and 0.85 6 0.09. In an alternative analysis of individual data from 21 studies comprising 429 adults (29), underreporters, acceptable reporters and overreporters were defined as having EI:EE values , 0.76, 0.76–1.24 and . 1.24, respectively. For men and women, respectively, the proportion of underreporters, valid reporters and overreporters were 28, 67 and 5% and 38, 59 and 4%. These differences between men and women were not significant (x2 5 4.25; df 5 2; P 5 0.2). Children and adolescents. The validation of dietary assessments in children has been reviewed elsewhere (42). Table 2 summarizes EI:EE in studies of children and adolescents. There was a trend to increasing underreporting with age. In children aged 1–6 and 7–12 y, reporting was adequate on average with means 6 SD for EI:EE of 0.93 6 0.11 and 0.94 6 0.17, respectively, although very variable for the 7–12-y-old children. For the younger children, reporting is the responsibility of a parent or caregiver, and there is likely to be less access to unsupervised eating. For 7–12-y-olds, the novelty of recording food intake may help to sustain enthusiasm. In adolescents, reporting was generally poor, with a mean 6 SD for EI:EE of 0.81 6 0.14. Increased energy requirements, unstructured eating, concerns with self image and rebellion against authority may all contribute to poor compliance. In studies of both sexes presented separately, females underreported more than males with means 6 SD for EI:EE of 0.85 6 0.15 and 0.92 6 0.15 (P 5 0.03), respectively. As with obese adults, obese children also underreported to a greater extent than did normal-weight children, and this reporting bias was also age related: 214% in 6-y-olds (Livingstone, unpublished data), 225% in 10-y-olds (43) and 240% in adolescents (44). Even in normal-weight adolescents, an association between body mass index (BMI) and reporting bias has been demonstrated (45). Only four
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TABLE 2 Comparisons of reported energy intake with energy expenditure measured by doubly labeled water in children and adolescents aged 1–18 y EI:EE Study
1 2 3 4 5 6
Subject & region Young children England Northern Ireland U.S.A. U.S.A. England Northern Ireland Low risk of obesity High risk of obesity Obese Prepubertal children Northern Ireland
5
England
7
U.S.A African American Caucasian U.S.A.
8
9 10
Scotland U.S.A. African American Caucasian
2
11 5 12
Northern Ireland
Adolescents U.S.A. African American Caucasian England
8 2
U.S.A. Lean Obese USA Northern Ireland
13
Sweden
5
England
14
U.S.A. Controls Exercising individuals Controls Exercising individuals Mean, 1–6 y Mean, 7–12 y Mean, 13–18 y Males, 7–18 y Females, 7–18 y
Sex
81 20 45 24 8 6
M, M, M, M, M F
Diet method
Mean
SD
Reference
4d WR DH FFQ 3 3 MP24hR 7d WR 7d WR
0.97 1.12 1.59 0.97 0.82 0.79
— 0.19 — — 0.21 0.22
(187) (48) (49) (46) (188)
50 50 14
M, F M, F M, F
6 6 6
7d WR 7d WR 7d WR
0.98 0.95 0.86
0.17 0.19 0.16
Livingstone (unpublished data)
11 12 11 12 13 13
M F M F M F
7–9 7–9 7–9 7–9 7–10 7–10
DH DH 7d WR 7d WR 7d WR 7d WR
1.13 1.07 1.18 0.96 0.93 0.80
0.23 0.18 0.22 0.16 0.13 0.14
(48)
45 21 14 40 33 19 20
M, F M, F F F F F M, F
2 3 MP24hR 2 3 MP24hR 7d ER 7d ER 7d ER 7d ER 3d ER
1.14 1.24 0.97 0.65 0.84 0.81 0.84
— — — — — — —
27 29 31 31 6 6 6 6
F M F M M F M F
9–12 9–12 9–12 9–12 12 12 12 12
8d ER 8d ER 8d ER 8d ER DH DH 7d WR 7d WR
0.74 0.72 0.76 0.83 1.07 1.20 0.92 0.85
— — — — 0.19 0.14 0.10 0.12
41 40 12 9
F F M F
13 13 11–14 11–14
3d 3d 7d 7d
ER ER WR WR
0.72 0.64 0.71 0.76
— — 0.24 0.13
28 27 14 11 11 11 11 25 25 5 10
MF MF F M F M F M F M F
12–18 12–18 12–16 15–18 15–18 15–18 15–18 15 15 15–17 15–17
14d ER 14d ER 7d ER DH DH 7d WR 7d WR 7d WR 7d WR 7d WR 7d WR
0.81 0.59 0.78 1.03 0.96 0.77 0.72 0.82 0.78 0.97 0.63
0.19 0.24 — 0.21 0.21 0.23 0.20 0.18 0.16 0.09 0.18
10 10 6 6
M M F F
15–17 15–17 15–17 15–17
3d 3d 3d 3d
1.04 0.89 1.01 0.79 0.93 0.94 0.81 0.92 0.85
— — — — 0.112 0.17 0.143 0.154 0.154,5
F F F F
Age (y)
1.5–4.5 3–5 4–7 4–7 4–6 4–6
8 8 8 9 10 11 10.7 6 3
ER ER ER ER
(188) (47) (189)
(190) (43)
(48)
(191) (188) (44) (189) (48)
(45) (188) (82)
1 Diet methods: d, day; WR, weighed record; ER, record in household measures/estimated weights; FFQ, food frequency questionnaire; DH, diet history; MP24hR, multiple-pass 24-h recall. 2 Excluding study by Kaskoun et al. 1994 (49), which demonstrated that the Willett FFQ was inappropriate for use in young children. 3 Student’s t test: significantly different from ages 1–6 and 7–12 y; P ¼ 0.02. 4 From studies that include both males and females, separately identified. 5 Paired t test: females significantly different from males; P ¼ 0.03.
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2
n
1
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6), the correlation between EI and EE for all reports was r5 0.425. If misreporters were excluded (EI:EE , 0.76 and . 1.24), r 5 0.83. If more stringent criteria were applied (EI:EE , 0.82 or . 1.18), r 5 0.89. However the majority of these data were derived from diet records, either weighed or estimated. Overreporting. There exists a small element of overreporting. In accumulated data from DLW studies (429 adults), 4% of women and 5% of men were overreporters defined as having an EI:EE ratio . 1.24 (29). Subjects who persistently reported large or small EI values have been studied by several authors who are searching for metabolic differences in energy expenditure (69–72). Few significant differences in energy metabolism were found, but it was subsequently demonstrated in one study (73) that used DLW that large eaters overreported (EI, 10.49 MJ/d; EE, 8.49 MJ/d) and small eaters underreported (EI, 5.89 MJ/d; EE, 11.27 MJ/d). Undereating versus underrecording. Underrecording is the discrepancy between EI and measured EE where there is no TABLE 3 Comparison of reported energy intake by dietary assessment method with energy expenditure measured by doubly labeled water Dietary method
n
Mean
SD
Observation Weighed records1 Estimated records1 Diet history 24-h recall (single or multiple) Food-frequency questionnaire
5 22 25 4 6 6
1.06 0.84 0.84 0.84 0.84 0.87
0.09 0.11 0.10 0.14 0.08 0.12
All
68
0.86
0.13
1 Excluding studies on subjects recruited as obese or as large and small eaters.
change in body mass. Undereating, on the other hand, is accompanied by a decline in body mass over the food-recording interval (74). When reported EI is adjusted to account for changes in body mass, some of the bias can be attributed to undereating (44,54,74,75). Underrecording is then inferred from the difference between total bias and the amount attributable to undereating. However, underrecording was ingeniously quantified using water balance (76,77). Total water intake was calculated from reported food and water intakes and the calculated amount of metabolic water. Water loss was determined from deuterium elimination over the period of study. Because true water intake must equal water loss, underrecording was defined as [(water intake 2 water loss)/ (water loss)] 3 100%. Undereating was defined as [(body mass change in recording week 3 30 MJ/7 d)/(EE)] 3 100%. In lean women, the water intake and loss values were equivalent, but body weight declined and the totality of the reporting bias (216%) could be attributed to undereating (76). In contrast, in obese men, calculated underrecording bias was 226%, calculated undereating bias was 212% and together they accounted for the total reporting bias of 237% (77). In both studies, weight was monitored during nonrecording and recording weeks. The respective mean 6 SD weight changes were 0.07 6 0.59 and 20.57 6 0.77 kg for lean women and 0.0 6 1.0 and 21.0 6 1.3 kg for obese men. These figures are uncomfortable reminders of the effect that recording food intake has on modifying behavior in the direction of reduced intake. Body weight status. Profound underreporting was found in obese subjects recruited in response to advertising or through obesity clinics (54,75,78). A negative association between the extent of underreporting and measures of weight status (body weight, percentage body fat or BMI) has also been found in studies that have encompassed a range of body sizes (30,79). The fact that this association is found in samples that include lean, overweight and obese subjects is understandable. However, this association has also been found in groups of subjects not including obese persons (13,45), although the association has not always been significant (80). The probability of underreporting increases as BMI increases, but not all obese persons underreport, and not all normal-weight persons provide valid reports. Figure 5 shows the association between EI:EE and BMI in subjects of DLW studies recruited from the community (i.e., not specifically obese subjects). Both underand overreporters appear across the full range of BMI values from lean to grossly obese. Relationship between underreporting and energy expenditure/ intake. There is some evidence that underreporting is greater as EE increases (80–82) whether expressed as EI-EE or EI:EE, although this may be an artifact of the way that the misreporting is expressed. If true, one can speculate that because higher EE means that more food is eaten (more items, more frequent meals, larger portions), then the recording burden will be greater as will the scope for omitting items or altering intake for convenience. Overall, the DLW validation studies provide substantial evidence of bias to underestimation by diet records. However, because the sample sizes were often small and the subjects were generally highly selected, they cannot be regarded as representative of randomly selected populations. Thus the true size of the bias remains unquantified. Unfortunately there have been relatively few DLW studies that validate the diet history, recall or FFQ, and the results are even less conclusive. Nevertheless, DLW-EE could be extremely valuable in validating the ability of different methods for ranking subjects as well as measuring mean intake in a randomly selected subsample from a large-
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FIGURE 4 Frequency distribution of EI:EE according to dietary assessment method in 43 studies of adults comprising 77 subgroups (men and women separately).
901S
SUPPLEMENT
902S
TABLE 4 Comparisons of reported energy intake with energy expenditure measured by doubly labeled water in studies using more than one dietary assessment method EI:EE3 Study 1
Subjects 24 children, 7 & 9 y 12 children, 12 y 11 males, 15 & 18 y 11 females, 15 &18 y
2
16 females, 45–65 y
3
10 females, 25 6 3.5 y (nonobese)
10 females, 74 6 4.4 y (nonobese)
44 females, 18–50 y (restrained eaters and disinhibitors)
5
10 females & 1 male (postobese)
6
26 females, 60 y (unrestrained eaters) 34 females, 60 y (restrained eaters)
Mean
SD
Reference
7d WR DH 7d WR DH 7d WR DH 7d WR DH 4 3 4d WR DH 7d WR 2 3 24hR FFQ1 FFQ2 7d WR 2 3 24hR FFQ1 FFQ2 14d ER 2 3 24hR 21d WR DH1 DH2 7d WR 3 3 24hR FFQ2 7d WR 3 3 24hR FFQ2
1.03 1.10 0.89 1.14 0.77 1.03 0.96 0.72 0.89 0.98 0.814 0.974 0.825 0.725 0.78 0.85 1.065 0.855 0.79 0.75 0.766 0.646 0.846 1.077 0.987 0.927 0.948 0.87 0.868
0.22 0.16 0.12 0.17 0.23 0.21 0.21 0.20 0.17 0.27 — — — — — — — — — — 0.18 0.20 0.21 0.08 0.08 0.06 0.05 0.05 0.08
(48)
(62) (61)
(63) (57) (64)
1 FFQ1, Willett food-frequency questionniare; FFQ2, Fred Hutchinson Cancer Center/Block food-frequency questionniare; OxFFQ, Oxford foodfrequency questionnaire; 24hR, 24-h recall. 2 DH1 and DH2 indicate different observers. 3 EI:EE, energy intake/energy expenditure. 4 Mean EI significantly different from older subjects (unpaired t test). 5 Mean EI significantly different between methods. 6 Mean EI:EE significantly different (paired t test). 7 Unrestrained eaters significantly different from restrained eaters; P , 0.01. 8 FFQ2 significantly different from 7d WR; P , 0.01.
population study, particularly if it were used alongside other biomarkers. Such a definitive validation study remains to be done. Validation against presumed energy requirements EI:BMR: The Goldberg cutoff technique. Unfortunately, DLW is too expensive and technically challenging to be used for routine validation of EI. However, reported EI can also be evaluated against presumed energy requirements (83,84). In this technique, mean EI is expressed as a multiple of the mean BMR estimated from equations (85) and is compared with the presumed mean EE of the population, which is also expressed as a multiple of the BMR. The ratio EE:BMR is here referred to as the physical activity level (PAL), although other authors refer to it as the average daily metabolic rate (ADMR). The equation of Goldberg et al. (83) calculates the lower 95% confidence limit of EI:BMR assuming a given PAL requirement, below which it is unlikely that the mean intake represents either habitual intake for weight maintenance or a random low intake (the Goldberg cutoff). It makes allowance for the errors associated with the number of subjects (n), the length of the
dietary assessment (d days) and variation in each of food intake, BMR and physical activity. In the original publication, the cutoff was calculated assuming an energy requirement of 1.55 3 BMR, and it was demonstrated conclusively that underreporting was widespread (86). In two-thirds of 37 community-based and epidemiological surveys from 10 countries, the mean EI:BMR was below the study-specific Goldberg cutoff (86). A PAL of 1.55 3 BMR was selected as the basis for comparison because it is the value defined by FAO/WHO/ UNU (87) as that which represents a sedentary level of energy expenditure. However, subsequent analysis of nearly 600 DLW measurements has shown that this is a conservative figure (38). In 16 age-sex groups, except those over 75-y-old, the mean PAL in free-living people was . 1.55. Thus the extent of underreporting based on this figure was almost certainly underestimated. The Goldberg equation was devised to evaluate the overall bias to underreporting at the group level. In theory, the cutoff value calculated for a sample size of n 5 1 can be used to identify underreporters at the individual level. Since publication of the Goldberg equation, numerous investigators have
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4
Diet method1,2
NUTRITIONAL BIOMARKERS: REPORTED ENERGY INTAKE
used a cutoff value based on a PAL of 1.55 and n 5 1 to identify a group of ‘‘low-energy reporters’’ (LER) (31,35,37,39,88–95). These studies are summarized in Table 5 and discussed below (see Factors associated with low-energy reporting). It has been demonstrated subsequently that this approach identifies only ;50% of underreporters (29). It excludes those who have underreported from a higher intake such that EI:BMR does not fall below the cutoff for a PAL of 1.55 3 BMR. This is illustrated in Figure 6 based on individual data from DLW studies. In this figure, separate symbols identify underreporters, acceptable reporters and overreporters as defined by EI:EE values of , 0.76, 0.76–1.24 and . 1.24, respectively. The lower and upper horizontal lines show the cutoff values for n 5 1 based on PAL values of 1.55 and 1.95, respectively. The sensitivity (proportion of underreporters correctly identified) improved if the cutoff for the higher mean PAL value was used, but this was at the cost of reduced specificity (proportion of non-underreporters correctly classified). Using cutoffs for agesex specific mean PAL values derived from DLW studies (38)
did not improve the sensitivity; the total percentage of subjects misclassified remained ;20%. However, when subjects were assigned to low, moderate and high activity levels and cutoff values were calculated using the three FAO/WHO/UNU activity levels (87), the proportion misclassified fell to 13% of men and 17% of women. Nevertheless, the ability of the Goldberg cutoff to identify individual underreporters remained limited (29). Undoubtedly, the Goldberg cutoff has been useful in raising awareness of the issue of misreporting, but it has considerable limitations, and the concepts have not always been either fully understood or applied correctly. Common misinterpretations of the concepts include applying the cutoff calculated for groups to individuals, confusing the cutoff for habitual intake with that for low intake obtained by chance and interpreting the exemplar tables of cutoffs based on a PAL value of 1.55 as recommendations for universal application. The effect of the latter misapplication on estimates of the proportion of underreporters has been examined in young adults (90), where true PAL values were higher (1.92 and 1.77), and in children aged 1–18 y (96), where age-specific cutoff values based on sedentary PAL values in children (1.45–1.6) (97) were compared with the blanket cutoff based on 1.55. The principles of the technique have recently been restated and the use and limitations of the technique fully discussed (84). At the group level, the Goldberg cutoff can be used to assess the overall bias in a study provided that a PAL value appropriate to the population is used for comparison. At the individual level, the cutoff is limited by low sensitivity and poor specificity. To improve sensitivity, methods must be found to account for differing levels of physical activity. Simple questionnaires have their errors and raise the problem of choosing appropriate PAL values for differing levels of activity. If more sophisticated methods of assessing EE such as heart-rate monitoring are used, then EE can be assessed in absolute terms and EI can be compared directly with EE, in which case the Goldberg cutoff becomes redundant. Five studies exemplify these approaches. Four have compared EI directly with EE, the latter being derived respectively from leisure-time activity and BMR using an equation based on earlier DLW studies (30), an activity diary (31), the EI for weight maintenance (12) and heart-rate monitoring (32). Samaras et al. (98) used a physical activity questionnaire to assign subjects to low, medium and high activity categories, but did not calculate Goldberg cutoffs; they merely defined acceptable reporters and underreporters as those above or below the FAO/WHO/UNU (87) recommended activity level. None of these five studies compared the technique used with any other, and because individual data were not presented, it is impossible to determine how these techniques might compare with a blanket EI:BMR cutoff in identifying misreporters or whether the conclusions about association between misreporting and subject characteristics would have been different. The identification of individual misreporters is clearly complex, but until this is achieved, the characteristics of the bias cannot be fully explored or its effects minimized in the analysis of dietary data. Factors associated with low-energy reporting. The use of EI:BMR to check the validity of EI data has increased awareness of the prevalence of underreporting in dietary surveys. Since 1991 a substantial but by no means definitive body of evidence has been generated examining the factors associated with underreporting. Results from 10 studies based on national dietary surveys have been reviewed and extensively discussed by Macdiarmid & Blundell (99). This section briefly summarizes the results from a larger body of data.
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FIGURE 5 Relationship between EI:EE and body mass index (BMI) in studies of adult volunteers (n ¼ 243 women,165 men) recruited from the community (excluding obese subjects recruited specifically for studies of obesity).
903S
Australia 1983 Germany Potsdam EPIC
The Netherlands MORGEN EPIC Finland MONICA 1982 1992
USA CSFII 85–86 U.K. NSHD 1989 U.K. DNSBA France FLVS Goteborg, Sweden Sydney, Australia WSDS Denmark MONICA 1987–1988 The Netherlands
4 5
6
8
Geelong, Australia
Cambridge,U.K.
North Tyneside, U.K.
Vermont, U.S.A.
The Netherlands
Kiel, Germany
Ontario, Canada
Norway NORKOST
16
17
18
19
20
21
22
23
15
14
13
9 10 11 12
7
3
2
U.S.A. NHANES II U.S.A. NHANES III Greece EPIC
1
Location
16–79 y
Turkish immigrants, 18–57 y Students (nonobese), 22–38 y 18–651 y
56–81 y
20–60 y, high & low socio economic area Females 50–65 y NIDDM patients 45–79 y
26 6 10 y
35, 45, 55 & 65 y
Adults
Females, 19–50 y 36 & 43 y 18–64 y 25–55 y 70 y
25–64 y
20–59 y
25–64 y 35–64 y
30–82 y
20–601 y
181 y
Subject age
1,461/1,559
14,586/15,662
17/33
30/39
81/56
100/85
160
98/113
119/187
152/171
204/308
977/1,020 983/873 529/504 369/440
1,854
1,304/1,432
2,079/2,467
2,632/2,813 2,356/2,862
3,884/5,378
3,956/3,813
11,663
n (M/F)
t Tests Mann-Whitney linear regression Multiple linear and logistic regression ANOVA, multiple regression
t Tests
Multiple regression
Comparison of quintiles t Tests
Multiple regression
Paired t tests
ANOVA
Logistic regression
Logistic regression x 2 LER vs. non-LER — Pitman test for trend
Multiple regression
—
ANOVA, multiple regression
— Test for trend across quintiles
Logistic regression
Multiple regression
Logistic regression
Statistical analysis
FFQ
FFQ
7d ER DH
2 3 24hR
3d WR
2 3 3d ER
4 3 4d WR
3 3 4d WR
3d WR or 3d ER
DH
FFQ
48hR15d ER 7d WR 3d ER DH
3 24hR over 1 y
3d ER
FFQ
24hR FFQ
1 3 24hR week days only 1 3 24hR all days of the week FFQ
Diet method
,1.1 ,1.2 ,1.05 ,1.20
EI:BMR ,1.14 1.14–1.35 1.35–2.39 .2.4
EI:BMR ,1.2
EI:EE ,0.8
EI:BMR ,1.08 vs .1.45
Age specific cutoff for PAL from Black et al. 1996 (38) D EI EE
UN/DN .1.0
D EIrep EImain EI:BMR ,0.96
DUPr DPr
EI:BMR ,1.14
EI:BMR EI:BMR EI:BMR EI:BMR
EI:BMR ,1.06
EI:BMR ,1.28
Mean EI:BMR compared
EI:BMR ,0.9 Quintiles
EI:BMR ,1.14
EI:BMR ,0.92 .1.20 EI:BMR ,0.9
LER criterion
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Study
Characteristics of low energy reporters1,2
TABLE 5
1.62 M 1.72 F 1.51 M 1.39 F 1.50 M 1.51 F
14/11 12/18 One-fifth ,1.17 (lowest quintile) —
% BF 23.1 6 6.0 M 32.1 6 7.2 F 22.1 6 2.4
1.39 M 1.36 F 1.39 M 1.22 F 1.41 M 1.44 F — 1.47 M 1.49 F 1.40 M 1.27 F % diff 8.0% M 12.2% F 1.38 M 1.20 F 1.40
27/30 — — 17 20
—
1.39 M 1.44 F 20/25 18/20 55/50 7/5
24.6 6 3.1 M 23.4 6 3.8 F
22.5 (19–30)
—
48 (ER) 48 (DH) 43/38
1.58 M 1.48 F
25.3 6 3.3 M 24.3 6 3.2 F 27.8 6 4.1 M 27.9 6 4.1 F
% diff 12% M 24% F 1.37 M 1.34 F — —
26.9 6 3.6 M 29.5 6 5.1 F
1.14
42/59
23.9 6 1.6
25.0 6 4.3
—
— 24.4 6 4.1 — —
24.4 6 5.3
26.0 6 4.9
19/23 27/39 16/16 22/28
52
1992 42/47
26.6 6 3.8
1982 1.57 M, 1.39 F 1992 1.48 M, 1.33 F 1.09
25.5 6 3.6 M 24.6 6 4.2 F
1,982 26/34
1.45
—
1.43 M 1.26 F
BMI
27.8 6 3.7 M 27.8 6 5.2 F — —
—
—
SD
31 , 0.92 46 . 1.20 22/31
Mean 6
Mean EI:BMR
% LER (M/F)
904S SUPPLEMENT
Size/fatness
Higher BMI
Higher BMI
Higher BMI — Higher BMI
Higher BMI
Higher BMI
Higher BMI
Higher BMI; other sizerelated variables
1
2
3 4 5
6
7
8
9
Dortmund, Germany
25
Study
U.K. PHENOBASE twin study
ns
—
F
—
M F —
F
F
Sex
Age
—
ns
Older
Older
Older Older —
Older
Younger
Males, 1–5 y Females, 1–5 y Males, 6–13 y Females, 6–13 y Males, 14–18 y Females, 14–18 y
Females, 39–70 y
292 316 96 94 73 161
436
Lower ed (F); low SE origin (F); unemployed (F)
Lower ed
Lower ed
Higher ed (M)
Lower ed — ns
—
LER in 1982; extroversion scores at age 26 y; recent emotional troubles
— — More h/wk in domestic work activity; more h/wk in sport activity. Dieting during past year; not smoking; trying to lose or maintain wt Year of study; geographical region Poorer health; dieting
No. of foods reported; nonsmoking; trying to lose weight
Not Caucasian
Other significant variables
Education or socioeconomic status Lower ed
3d ER
FFQ
—
ANOVA
1.4 0.6 3.1 2.1 12.3 19.9
48
6 28
Ht; birth wt; current SE; whether living alone or with others; geographical region; smoking in 1989; on a special diet; vegetarianism; change in eating habits; exercise habits
Occupational activity; leisure activity; smoking
—
Activity level
Significant but not adding to the variance accounted for: day of the week, physical activity, education, less/ usual/more than usual intake, race — — Occupational status; marital status
—
Other nonsignificant variables
EI BMR EI (BMR 31.35) EI (BMR 3PAL) EI:BMR ,0.97 EI:BMR ,0.97 EI:BMR ,1.04 EI:BMR ,1.01 EI:BMR ,1.07 EI:BMR ,0.97
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24
(92, 128)
—
(35)
(39)
(41)
—
Excluded pregnant; lactating; ill; missing wt or ht —
(95) (192) (193)
(37)
(91)
Reference
—
24.3 6 4.6
— — —
Dieters not excluded. Excluded: pregnant
Notes
1.53 1.50 1.59 1.59 1.44 1.25
—
NUTRITIONAL BIOMARKERS: REPORTED ENERGY INTAKE 905S
Size/fatness
Higher BMI
Higher BMI
Higher BMI Higher BMI
Higher % BF
ns
Higher BMI
Higher BMI; wt
Higher BMI
Higher% BF (F)
Higher BMI
ns
Higher BMI
10
11
12 13
14
15
16
17
18
19
20
21
22
—
—
ns
F
F
—
—
F
ns
F F
ns
F
Sex
35–49 y
ns
ns
ns
Younger
Older (M)
Older
ns
ns
— ns
Older
ns
Age
Higher income
—
—
—
—
Lower SE(M); higher occ. (M); lower ed (F) —
—
—
— Ed. ns
Higher SE
Lower SE (F)
Education or socioeconomic status
Lower RMR; higher cost of physical activity; higher PAL Asian born; urban living
—
—
Higher restraint score DEBQ & TFEQ —
—
—
5-y wt change
Non smoker; non drinkers; Caucasian (M); not married (M) Dietary restraint; feel too heavy; dieted at least once — —
Other significant variables
(127) (140) (103)
(12)
—
EIrep = selfreported intake before trial using controlled intake —
Sex; marital status; education; place of birth other than Asia
Household size; years of residence; employment situation —
—
% Snack energy; time since diagnosis; smoking; NIDDM treatment type BMI; ht; wt; WHR; FM; FFM; VO2max
—
—
EE from DLW regression equation —
—
TFEQ hunger; TFEQ disinhibition
Education (M); socioeconomic area; age (F).
— Exercising .1 session/wk; On a special diet Smoking; fat distribution; social group; education; activity; dieting attempts; subjective wt problems; wt stable since age 25 y D body wt during dietary trial.
Excluded pregnant & recent weight loss — —
Play sport regularly
(102)
(31)
(194)
(30)
(100)
(113)
(89)
(93)
(94)
Excluded unwell & dieting during survey
State benefits (M); geographical region (M)
Reference
Notes
Other nonsignificant variables
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Study
(Continued)
Characteristics of low energy reporters1,2
TABLE 5
906S SUPPLEMENT
— Poorer record keeping (M); study influenced intake (M) — Older F Higher BMI (F) 25
1 Columns 1–11 identify the study and the characteristics of the population. Columns 12–18 indicate the factors examined for association with low-energy reporting (LER) in each study. Factors listed are those that were significantly associated with low energy reporting (ns, factor examined but no significant association was found). 2 Abbreviations used: EI:BMR, energy intake: basal metabolic rate; EIrep, reported energy intake; EImain, energy intake to maintain body weight; PAL, physical activity level; Upr, protein intake estimated from urinary N excretion; Dpr, dietary protein intake; UN/DN, urinary N excretion/dietary N intake; SE, socioeconomic status; Ed, educational level; Occ, occupational level; DEBQ, Dutch Eating Behavior Questionnaire; TFEQ, Three-Factor Eating Questionnaire; NIDDM, non-insulin-dependent diabetes mellitus; RMR, resting metabolic rate; BMI, body mass index; WHR, waist/hip ratio; BF, body fat; FM, fat mass; FFM, fat free mass; VO2max, maximal aerobic power.
(196)
(98)
BMR estimated (195); PAQ used to assign subjects to low, medium or high PAL (87) — % BF — Higher BMI; wt; FM; FFM 24
—
ns
Lower physical activity score; wish to reduce wt — — Older F Higher BMI 23
907S
FIGURE 6 Ratio of EI to estimated basal metabolic rate (EI:BMRest) against physical activity level (PAL) (ratio of EE to measured basal metabolic rate, BMRmeas) in female subjects (n ¼ 264) from 21 studies with measured EI, BMR and DLW-EE. Symbols designate acceptable reporters, over- or underreporters (AR, OR and UR, respectively) by the direct comparison of EI:DLW-EE. Lower Goldberg cutoff for PAL ¼ 1.55 or 1.95, d ¼ 7 and n ¼ 1 are shown (horizontal lines) [from Black, 2000 (29)]. Underreporters, EI:DLW-EE , 0.76; acceptable reporters, EI:DLWEE ¼ 0.76–1.24; overreporters, EI:DLW-EE . 1.24.
Table 5 summarizes 25 adult studies that have examined the characteristics of LER. The majority used a single Goldberg cutoff for EI:BMR to define LER and compared these with either the rest of the study population or a subgroup from the top end of the distribution or across quintiles. The cutoff values for EI:BMR ranged from , 0.9 to , 1.28 depending on the criteria set by the authors. A limited number of studies compared EI directly with EE or assigned subjects to low-, medium- or high-activity levels. One study assigned age-sexspecific PAL values and calculated subject-specific cutoffs. Two studies used 24-h urinary nitrogen excretion as the validation tool. Sample size ranged from 50 to 11,000; age from 18 to 64 y; mean study BMI from 22.1 to 27.8 kg/m2 and mean study EI:BMR from 1.09 to 1.57. The number of variables examined for their association with low-energy reporting varied from two or three anthropometric measures to a comprehensive range of anthropometric, sociodemographic and sociopsychological
Downloaded from jn.nutrition.org by guest on May 20, 2013
—
—
(101)
NUTRITIONAL BIOMARKERS: REPORTED ENERGY INTAKE
908S
SUPPLEMENT
(103), self perceptions of feeling too heavy and having dieted at least once (93). Cultural effects. Just as there are cultural differences in attitudes toward food, so it is likely that there are cultural differences in dietary reporting behaviors. The majority of studies in Table 5 found an association between LER and low education or SE status. Exceptionally, in France, LER was associated with high SE status (93). In the U.S.A., an association with non-Caucasian compared with Caucasian ethnic groups has been found (91). The less pronounced degree of misreporting that has been observed in older African Americans has been attributed to their more relaxed attitudes toward body image and body weight (104). In a collaborative study in five European cities (Cambridge, U.K.; Maastricht, Netherlands; Potsdam, Germany; Copenhagen, Denmark; Barcelona, Spain), the mean reported EI was lowest in Cambridge and relatively high in Potsdam (our unpublished data). Cultural patterns and attitudes regarding food and body weight in each country may account for the reporting differences. However differences in study samples, recruitment procedures and field workers’ attitudes and behaviors cannot be ruled out as contributory factors. Nevertheless, Table 5 includes data from 10 different countries, and the inescapable conclusion is that underreporting seems to be a universal phenomenon in Western cultures. Behavioral effects. Subject response to recording food intakes has been little explored but merits much more attention in the future. Subjects questioned hypothetically about what they might do if asked to keep a dietary record openly admitted the possibility of misreporting (105). Postsurvey focus groups have reported their food records to be an accurate account of food eaten but admitted to simplifying food choice to ease the reporting burden. In another postsurvey interview, 46% of subjects admitted altering their eating pattern during a 7-d record. Those indicating inconvenience as the reason for changed behavior had a mean EI:BMR of 1.53; those indicating embarrassment or guilt had a mean EI:BMR of 1.1. However those who claimed not to have changed their eating pattern had an EI:BMR of 1.23 (106). These findings highlight some key issues for future research. Are people too guilty to admit to changed eating habits, or do they truly not perceive that they have altered their eating behavior? A person may eat a series of meals that is within their normal dietary pattern and perceived as normal, but is not what they would have eaten had the survey not intervened. Exhortations by researchers such as ‘‘please don’t change what you eat’’ may be falling on deaf ears. A plausible influence on reporting behavior may be the need to achieve a self-presentation goal through socially desirable responding. Worsley et al. (107) found a significant positive correlation between a 12-item scale for social desirability specifically related to food and the intake of vegetables and fresh fruit, but negative correlation with snack foods such as cakes, cookies, chocolates, candies, pies and pastries. On the other hand, no such association was observed with the nonspecific Marlow-Crowne score for social desirability responding (108). This suggests that it is social desirability behaviors specifically related to food intake that need to be studied in the context of misreporting. Hebert et al. (109,110) distinguished between social desirability—the defensive tendency of individuals to respond in a manner consistent with societal norms and to avoid criticism, and social approval—the tendency for an individual to seek a positive response (praise) in testing situations. They hypothesized that the FFQ, because it involves complex cognitive tasks, may be more prone to both biases than are short-term records that are focused on specific foods eaten on specific days. For men, the social approval score
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factors. Consequently it is highly probable that the many differences between studies and the variations in statistical analysis may have influenced the conclusions drawn from each. Weight status. The most robust finding in 22 out of 25 studies was a positive association between low-energy reporting and a high BMI. Furthermore, weight status was the single most significant variable associated with underreporting in those studies that examined a range of variables. However, the association between obesity and low-energy reporting is not absolute. The probability that a subject will underreport increases as BMI increases (35,101), but there are obese subjects who do not underreport and nonobese subjects who do underreport. Johansson et al. (101) noted that 52% of underreporters had a BMI , 25 kg/m2 and, although the proportion of underreporters was highest among obese subjects (BMI . 30), only 5% of the total sample was obese. In the study by Samaras et al. (98), the proportion of underreporters increased from 28% using a blanket cutoff of 1.35 3 BMR to 48% when three cutoffs based on three activity levels were used. However, the conclusions about associations between underreporting and parameters of body size and composition remained unaltered. Age-sex effects. Most studies found a higher proportion of LER among women and older subjects. It is unclear, however, whether this is a true finding or an artifact of the application of a single cutoff for EI:BMR. Doubly labeled water data suggest that men and younger subjects have higher EE (38) and thus higher EI values. If they underreport to the same degree as women and older subjects, then a single cutoff applied to all would inevitably identify more women and older persons as LER. In those studies with information on EE, Adams (100) found an association of underreporting with female sex and younger age. On the other hand, Johnson et al. (30) and de Vries et al. (12) found an association with female sex but none with age in relatively young groups. The DLW studies reviewed (see Men versus women) did not find significant differences in reporting between men and women. The inconsistencies in agesex associations require further investigation in representative population samples that have identified underreporters at all levels of energy requirement. Socioeconomic effects. The effects of education and/or socioeconomic (SE) status on reporting accuracy are less predictable. On the one hand, poor literacy skills in the less well educated might be expected to result in underreporting. On the other hand, health or diet consciousness in the better educated or those of higher SE status might prompt the same response. Of the 11 studies in which this variable was examined, the findings were inconsistent. For example, one study (89) observed that LER in men was associated with living in a low-SE area but having a higher occupation. In lowincome women, poor literacy scores along with percentage body fat were the best predictors of misreporting of EI (79). On the other hand, in two studies, LER was associated with higher SE status (93,102). Health consciousness. Although various aspects of perceived health status may be associated with reporting accuracy, only smoking and facets of dieting behavior have been significantly associated with LER. Significant associations have been found between LER and not smoking (37,41,94). The positive association between underreporting and obesity, weight consciousness and dieting is the most securely based. Factors that have been associated with misreporting include reporting of fewer foods and trying to lose weight (37), dieting in the recent past and efforts to maintain weight stability (41), dieting during the survey period (39), weight change over 5 y
NUTRITIONAL BIOMARKERS: REPORTED ENERGY INTAKE
;30% of the variance in EI:BMR, were the total number of foods reported, weight status, age, trying to lose weight and smoking. Urinary nitrogen excretion in relation to reported energy intake Because validation by urinary nitrogen excretion (123,124) indicates underreporting of protein intake, by definition there will also be underreporting of EI. However, the converse may not be true. There is evidence that protein is better reported than total EI (discussed in Bias in estimating nutrient intake). In double validations that used urinary nitrogen excretion and DLW-EE (57,58,62), mean reporting bias was 22% for protein and 214% for energy. For individuals there was a significant correlation between the validation ratios EI:EE and the urinary nitrogen/dietary nitrogen ratio, but the proportion of subjects identified as underreporters was greater by DLW than urinary nitrogen excretion. It was unclear whether this was a real phenomenon or instead dependent on the criteria for each validation. In another double validation, however, Kroke et al. (36) found similar mean underreporting of protein (23%; n 5 134) and energy (22%; n 5 34). If protein or nutrients highly correlated with protein intake are of primary interest, then validation by urinary nitrogen excretion must be the method of choice. If energy or macronutrients are of primary interest, then validation of EI together with critical examination of the macronutrient intake are essential. Several studies have used urinary nitrogen excretion to validate the diet-history technique. These have been reviewed recently (62,201). Urinary nitrogen has also been used to validate the FFQ as discussed by Bingham (201). Urinary nitrogen excretion has scope for the validation of ranking of individuals. However, it should be noted that eight collections are needed to obtain a precise measure of urinary nitrogen excretion in individuals (123), and that validation of the collection itself is also essential. As many as 25% (Bingham, personal communication) and 17% (125) of collections have been either not received or rejected as incomplete as judged by para-aminobenzoic acid recovery (126). Consequences of underreporting Bias in estimating nutrient intake. Validation against EE identifies only the bias in reporting EI. Tables 6 and 7 list studies that have examined whether this reflects underreporting of the diet as a whole or whether there is bias in estimating nutrient intakes through altered food choices and/or selective reporting of foods. With the caveat that these data were derived from studies using different dietary methods and different criteria were used to define LER, the summary (Table 7) shows that the percentage of energy from protein was significantly higher (P , 0.000) in the LER, whereas fat energy (percent) tended to be lower (P , 0.004) and total carbohydrate energy (percent) was variable (not significant). In six studies (nine subgroups) where data on the percentage of energy derived from starches and sugars were available, starch energy tended to be higher in LER (P , 0.002) but sugars energy was lower (P , 0.03). It is evident that bias in reporting total EI is associated with variable bias in estimated macronutrient intake. In studies comparing the micronutrient intakes between LER and non-LER, the absolute intakes of all micronutrients by LER were significantly lower. This is to be expected given the generally high correlation between micronutrient and total EI, but has implications for estimating the proportion of the
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was significantly associated with greater fat and energy intake reported by FFQ, whereas in women a higher social desirability score was associated with lower reported fat and energy intake by FFQ. Psychological effects. Various psychological instruments have been used to identify traits associated with underreporting, but the results have been conflicting and have made only a limited contribution to understanding underreporting behavior. Questionnaires derived from the area of eating disorders, including the Dutch Eating Behavior Questionnaire (DEBQ) (111) with scales for restraint, emotional and external eating, and the Three Factor Eating Questionnaire (TFEQ) (112) with scales for restraint, disinhibition and hunger, have been used. Bingham et al. (113) found underreporters to be significantly different from others on both restraint scales but not on disinhibition or hunger scales. One study (64) found a modest degree of underreporting in restrained compared to unrestrained eaters, and two studies (59,114) found no significant difference. Lindroos et al. (115) observed no associations between EI and any TFEQ score in normal weight women but identified a positive association between EI and both disinhibition and hunger in the obese group. There was no significant association between EI and restraint across the obese group as a whole, but those in the highest quartile for restraint score did report the lowest EI. Other cognitive, perceptual and emotional components of dietary reporting behavior have been explored. Significant associations have been found between LER at age 43 y, extroversion scores at age 26 y and recent emotional problems (35). No associations were found with neuroticism scores at age 26 y, other depressive symptom scores relating to the past year or symptoms of a clinically recognizable mental disorder (35). Kretsch et al. (13) reported an association between underreporting and the Beck depression inventory in obese but not lean women. Taren et al. (116) explored the most comprehensive battery of tests. They examined the associations between reporting accuracy and social desirability (108), two restraint items (117,118), eight items from the Eating Disorder Inventory (119,120), two items from the Weinberger Adjustment Inventory (121,122) and two elements of the SorensonStunkard Silhouettes. However, they found that age and percentage body fat were the most important predictors of underreporting. After correcting for these variables, there were associations between underreporting and only three variables: social desirability score, body dissatisfaction and perception that a smaller body image is healthy. Overall it appears that questionnaires designed for other purposes may not help substantially in investigating the issue of misreporting. These limited findings suggest that research needs to be focused very specifically on behaviors connected with food and the process of dietary assessment. The foregoing discussion highlights the complexity of the phenomenon of misreporting and the difficulty of drawing meaningful conclusions from existing work. In an analysis of the most comprehensive set of variables (anthropometric, social and psychological) studied in the UK 1946 birth cohort (35), it is notable how few factors were significantly associated with LER apart from low energy reporting at an earlier phase of the study (1982) and parameters of body weight. The first association was the stronger (see Subject-specific bias to dietary assessment). When low EI:BMR in 1982 was included in the logistic regression model, only previous underreporting, ln(BMI) and, in women, social class at age 4 y and ‘‘currently in paid work’’ remained significant predictors of underreporting. In NHANES III, which also investigated a wide range of variables (37), the five main factors, which accounted for
909S
FFQ
7d WR
48hR 1 5d ER Males & females
FFQ
3d ER
4 3 4dWR
DH
7d ER
2
3
4
5
6
7
8
9
Females
Males
Females (70 y)
Males (70 y)
Females
Males (16–17 y)
Females
Males
Females
Males
Females
Males
Females
Males
1 3 24hR
Subjects
1
Method
35 35 37 37
126 33 30 79 81 121
39
Lowest third EI; top third EI; lowest third EI; top third EI
Q5 EI:BMR Q1 EI:BMR ‘‘Not dieting’’ & EI:BMR . 1.35 ‘‘dieting’’ & EI:BMR ,1.35 UN/DN ,1.0 UN/DN . 1.0 EI:BMR . 2.10 EI:BMR ,1.20 EI:BMR . 1.82 EI:BMR ,1.20
Q1 EI:BMR
2356
Total 2,862 322
Non-LER LER non-LER LER Non-LER LER Non-LER LER Non-LER LER Non-LER LER Non-LER LER Non-LER LER Q5 EI:BMR
Group
3,082 874 2,624 1,189 3,358 526 4,814 564 719 264 529 344 782 178 725 213 Total
n
13.3 14.71 15.7 12.83 15.2 13.53 16.1
12.23 13.6 12.43 14.7
8.143 6.65 15.593 7.28 11.133 5.77
14.433 9.07 10.113 6.17
12.83 13.8 12.03
12.23 5.8 12.833 9.16
13.3
15.12 16.9 15.02 17.3 11.85 12.3 11.95 12.3 13.53 15.0 13.93 15.8 143 16 153 17 12.63
12.12 5.41 8.462 4.10 12.32 7.19 10.22 5.62 — — — — 10.953 7.10 8.443 5.52 15.23 7.6
Protein
EI (MJ/d)
39.5 39.2 41.0 41.9
37.11 35.3 38.63 34.5 37.23 34.5
42.5
35.23 32.0 41.8
31.7
35.02 30.9 35.02 30.4 52.85 51.6 55.65 52.8 37.4 37.9 39.7 39.5 393 37 403 37 35.23
Fat
43.1 44 44.1 41.3
44.6 45.2 47.1 48.4 48.5 48.5
42.5
40.63 42.4 44.6
40.4
47.02 50.3 49.22 52.9 32.75 34.4 33.65 36.2 42.12 40.8 43.51 42.5 44 44 46 45 38.83
Total CHO
—
18.23 15.7 19.83 17.4 —
—
—
23.63 24.8 23.43 24.8 —
—
12.87
29.76 22.2 24.1 —
22.7 25.4 23.3 23.1
1.8 3.2 3.6 —
5.0 2.9 2.2 1.8
20.5 18.5 20.8 18.3
22.6 21.2 —
15.01,7
29.66
2.72 3.0 1.2
6.3
—
—
4.3 3.4 2.5 1.12 4.95 3.9 1.25 1.0 7.3 6.5 3.01 2.3 5.9 5.2 2.7 2.9 5.31
Sugars
Starch
Alcohol
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Study
Energy, % Definition of LER
‘‘Not dieting’’ 1 EI:BMR . 1.35 ‘‘dieting’’ 1 EI:BMR ,1.35 UrineN:DietN . 1.0 EI:BMR . 2.10 (M) or EI:BMR . 1,82 (F) vs. EI:BMR ,1.20; tested for trend across 3 EI:BMR groups Lowest third EI vs. top third EI
(198)
(127)
(113)
(197)
(193)
(35)
EI:BMR ,1.1
Q5 EI:BMR . 1.81 vs. Q1 EI:BMR ,1.17 Tested for significance of trend across five quintiles
(94)
(95)
(37)
Reference
EI:BMR ,1.20
EI:BMR ,1.14
EI:BMR ,0.9
Macronutrient intake of low energy reporters and non-low energy reporters expressed as percentage of total energy
TABLE 6 910S SUPPLEMENT
non-LER LER
2 3 24hR
FFQ
FFQ
FFQ
7dER
FFQ
Mean Mean t P
15
16
17
18
19
20
8
Irish males
7d ER DH
14
Significantly different from paired value: nonalcohol energy. Q¼ Quintile.
Swedish males
Females
Females
Males
Females
Males
Males & females
Students (sex not stated)
Males & females
Males & females
1
non-LER LER Non-LER LER
Non-LER LER non-LER LER Non-LER LER
P ,0.05,
2
P ,0.01,
3
P ,0.001 ,
228 non-LER 208 LER Not stated Non-LER LER 1.10–1.34 LER ,1.10 Not stated Non-LER LER 20 studies —
785 386 316 120
8,755 5,831 10,076 5,586 808 286
LER EI:BMR .1.45 EI:BMR ,1.08
3 3 4d WR
13
24 22 22
3d ER
12
Females
5d ER
11
LER non-LER LER Large eaters; small eaters Non-LER LER EI:BMR .0.96 EI:BMR ,0.96 Non-LER LER non-LER
42 M 35 F 50 F 9 9 865 165 191 34 26 24 26
45–79 y
Non-LER
58 M
Patients with NIDDM,
2 3 3d ER
311 28 34.04 30.7
38.94 37.4 36.64 35.4 321 29
33.4 36 35
37.3 40.13 34.6 37.81 29.7 42.31 41.3 36.32 38.5 37.11 32.7 36.6
39.3
48.8 48.6
48.1 47.9 50.23 49.8 —
47.9 50 50
42.4 42.73 45 45.7 51.4 41.53 37.9 42.62 39.8 43.91 49.3 45.01
42.4
1 1 2.11 2.8
—
25.4 26.2
—
—
2 2
—
—
—
20.8 16.2 —
3
—
2
21.8 24.6 —
—
—
21.4 23.4 —
23.9 26.8 —
—
5.1 —
5.2 4.3 4.4
4.9 2.3 3.83 6.2 —
—
4
P ,0.0001;
5
significance of difference not tested;
6
extrinsic sugars;
—
—
7
(132)
(132)
(98)
(101)
(102)
(194)
(31)
(89)
(93)
(199)
(100)
EI:BMR ,1.10, 1.10–1.34, .1.34 EI: BMR ,1.27
EI (BMR 3 1.35) EI (BMR 3 physical activity factor)
EI:BMR 1.35– 2.39 vs. EI:BMR ,1.14
EI:BMR . 1.45 vs. EI:BMR ,1.08 EI:BMR ,1.2
EI:EE ,0.8 EE estimated from activity diary
EI:BMR ,0.96
Large eaters vs. small eaters EI:BMR ,1.05
Subject specific cutoff using PAL values from (38)
intrinsic sugars, milk sugars and starch;
16.44 34.13 49.2 1.91 25.5 10.22 25.7 6.5 18.7 31.9 48.4 2.6 3 3 37.3 46.2 — — — 12.0 14.4 9.2 15.0 35.8 46.0 7.0 15.8 34.8 45.8 40.33 45.2 — — — 11.7 14.63 7.9 16.5 39.4 44.1 — 14.3 6 1.8 38.3 6 5.3 44.3 6 4.2 3.10 6 1.8 24.4 6 3.0 20.4 6 3.6 16.1 6 2.4 36.4 6 5.7 44.7 6 4.5 2.91 6 1.6 25.9 6 2.8 19.2 6 4.8 2.05 2.04 2.05 2.07 2.31 2.23 0 0.0001 0.33 0.33 0.002 0.07
16.74 19.4
15.44 17.0 16.04 17.4 —
12.908 6.908 9.858 5.458 12.81 7.3 9.61 5.5 9.54 5.8
13.7 14 15
20.2 17.73 20.3 13.21 16.4 16.23 20.8 15.53 19.0 14.10 13.7 14.01
6.88 7.23,8 5.38 11.96 5.29 9.53 5.8 8.933 5.19 10.731 7.41 12.851 6.97 —
18.33
9.13,8
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10
NUTRITIONAL BIOMARKERS: REPORTED ENERGY INTAKE 911S
F
1.113 1.02 1.083 1.052 1.052 1.133 1.062 1.02 — 1.04 — 1.00 0.98 — 1.083 1.072 1.113 1.143 1.08 1.10 1.113 1.04 1.083 — —
M
1.063 1.00 1.043 1.02 1.01 1.093 1.05 0.98 — 0.96 — 0.931 1.00 — 1.041 1.02 1.102 1.093 1.11 1.07 1.051 1.03 1.082 — —
— 1.051 — 1.153 — — — — — — — — — — — — — — 1.343 — — — — 1.153 —
M — 1.082 — 1.143 — — — — — — — — — — — — — — 1.293 — — — — 1.092 —
F
Study 2 Price et al. 1997 (35)
1.133 1.01 — 1.051 1.073 1.041 — — 1.103 1.30 — 1.13 1.041 1.333 1.063 1.09 — — 1.243 — 1.083 — — — —
M 1.173 1.092 — 1.093 1.163 1.083 — — 1.063 1.21 — 1.10 0.65 1.372 1.09 1.04 — — 1.273 — 1.173 — — — —
F
Study 3 Hirvonen et al. 1997 (128)
— 1.10 — 1.18 — — — — — — 1.29 — — — — — — — 1.42 — — — — 1.23 1.08
M — 1.12 — 1.19 — — — — — — 1.42 — — — — — — — 1.41 — — — — 1.24 1.03
F
Study 4 Briefel et al. 1997 (37) F 1.05 0.98 1.00 1.07 1.05 1.01 — — — 0.80 — — — 0.93 1.03 1.00 1.02 1.06 1.00 — — — — — —
M 1.05 1.02 1.02 1.05 1.04 1.02 — — — 0.87 — — — 0.95 1.04 1.02 1.03 1.05 1.09 — — — — — —
Study 5 Gnardellis et al. 1998 (95)
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— 0.99 — — 1.341 — — — — — — — — — — — — — 2.141 — 1.461 — — — —
M1F
Study 6 Clark et al. 1995 (199)
— 0.862 ns ns ns 1.173 — — — ns — — — ns ns ns — 1.112 ns — — — — — —
M1F
Study 7 Rutishauser et al. 1994 (89)
Difference in nutrient density in diets of LER and non-LER subjects is expressed as the ratio (LER intake per MJ)/(non-LER intake per MJ). Significance of differences in intake per MJ between LERs and non-LERs given in the original paper: ns, not significant; 1 P , 0.05; 2 P , 0.01; 3 P , 0.0001.
Potassium Calcium Phosphorus Iron Magnesium Zinc Copper Iodine Selenium Retinol Vitamin A Vitamin D Vitamin E b-Carotene Thiamin Riboflavin Pyridoxine Nicotinic acid Ascorbic acid Vitamin B-12 Folate Biotin Pantothenate Fiber Cholesterol
Nutrient
Study 1 Pryer et al. 1997 (94)
Comparison of nutrient densities in diets of low energy reporters and non-low energy reporters1
TABLE 7
912S SUPPLEMENT
NUTRITIONAL BIOMARKERS: REPORTED ENERGY INTAKE
complex and operate in different ways in different people. Possible factors include the general climate of knowledge about food and health, perceived reasons for the study, personal image management and the unconscious messages conveyed by the researchers themselves. The latter has not been researched at present. Undoubtedly a major advance has been the growing recognition that cultural, behavioral and psychological factors underpin dietary reporting behavior. If nutritionists are to fully understand the impact of these factors on the dietary reporting process, collaboration with behavioral scientists is essential in the future. Some issues of interpretation raised by misreporting Obesity, meal patterns and macronutrient intake: an example of interpretative problems. Obesity-related issues provide good illustrations of the interpretative problems associated with misreporting. The hypothesis that obese people were energy thrifty derived from studies in the 1960s that found the EI of obese subjects to be similar to or lower than their lean counterparts. This led to nearly two decades of research to elucidate the hypothesized defect in the EE of obese persons. None was found. With the advent of DLW, it became clear that the hypothesis had been based on invalid reports of food intake by obese persons recruited for studies of obesity (44,54,75). The study of links between obesity and diet composition has run into similar problems. For example, the Scottish Heart and Health Study (133) reported the highest prevalence of obesity in subjects in the top fifth of fat energy (by percentage) and the lowest prevalence in the top fifth of sugars energy (by percentage). However, this conclusion is confounded by three facts. First, because the percentage energy derived from protein and starch is relatively constant, there is a reciprocal relationship between fat and sugars when expressed as percentage energy (134). Second, there is an association between obesity and low energy reporting (see EI:BMR: The Goldberg cutoff technique) and third, there is a possible association between LER and underreporting of sugars (see Bias in estimating nutrient intake). Flynn et al. (134) found a positive association between BMI and fat intake as assessed by diet history when dieters were excluded from the analysis. On the other hand, Macdiarmid et al. (135) found their conclusions differed depending on which subjects were included or excluded from the analysis. Conclusions about obesity and meal patterns may also be confounded by associations between obesity/high BMI, low meal frequency and underreporting (136). Bellisle et al. (137) used data from Kant et al. (138) to demonstrate the probable artifactual nature of the association between low meal frequency and BMI. As reported meal frequency decreased, the gap between EI and a presumed energy requirement of 1.4 3 BMR increased. In adolescents (139), a negative association between BMI and feeding frequency also disappeared when dieters, underreporters and normal-weight subjects who considered themselves to be overweight were excluded. Estimating the proportion of a population that has deficient nutrient intake. Extremes of the nutrient-intake distribution, particularly those derived from short records or 24-h recalls, do not represent habitual intake. Consequently, even a valid data set is likely to overestimate the number of subjects with deficient intakes. Underreporting may produce serious overestimates of deficient intakes. Smith et al. (140) have shown that the proportion of subjects with intakes less than the Recommended Daily Allowance for iron, zinc, calcium and potassium decreases significantly when subjects with an
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population with suboptimal intakes (see Estimating the proportion of a population that has deficient nutrient intakes). If lower intakes are the result of underreporting of the diet as a whole, then micronutrient density (intake per megajoule) should not differ between LER and non-LER. Table 7 summarizes findings for micronutrient density by expressing intakes as a ratio: (intake per megajoule by LER) / (intake per megajoule by nonLER). The significant differences in micronutrient density between LER and non-LER as tested in the original papers are also shown. In 52 (58%) out of 89 tests, the differences were significant. Out of 119 ratios, 105 (88%) were . 1.0, which indicates that the diet of LER had a higher nutrient density than that of the non-LER. The mean 6 SD percentage difference (excluding the exceptionally high value for ascorbic acid in Study 6) was 8.8 6 12.1%. Thus bias in reporting total EI is associated with variable bias in reporting nutrient intake. Bias in reporting meal patterns and foods eaten. Differential reporting of nutrients must be a consequence of differential reporting of foods, but fewer studies have examined this issue. Table 8 summarizes these studies (35,101,102,113,127–129). There was a general tendency for LER to report more ‘‘good’’ foods such as meat, fish, vegetables, salad and fruit and less ‘‘bad’’ foods such as cakes, cookies, sugar, candies and fats. However, this could simply reflect the fact that main food items tend to be remembered better than ancillary items as demonstrated by Poppitt et al. (114) in a direct validation of food intakes reported by 24-h recall. Studies that have reported on meal patterns are few and are difficult to interpret given the problems of defining meals, snacks and eating frequencies. In general, underreporting seems to be primarily a consequence of the omission of complete items or meals. In the NHANES III study, LER reported fewer meals, snacks and foods (37). Poppitt et al. (114) observed that snacks were omitted from a 24-h recall, although meals were well reported. Goris et al. (77) however noted snacks to be accurately reported and underreporting to occur at meals. Drummond et al. (130) found lower eating frequency in underreporters. Andersson et al. (131) found the proportion of energy from snacks to increase, from fruit to decrease and the portion sizes for different kinds of meals to increase when underreporters were excluded. Whether differences in portion sizes contribute to bias does not appear to have been addressed by other authors. Livingstone et al. (33) noted a difference in alcohol consumption between low- and high-energy reporters. Because alcohol is notoriously underreported, this may contribute to the bias in reports from alcohol consumers. Better reporting of meals rather than snacks is consistent with the differences in macronutrient intakes between LER and nonLER. Drummond et al. (130) found that snack foods provided 50% of energy as carbohydrate compared with only 41% in meals. Snacks also contained more sugar and less protein, although the percentage of fat energy was similar to that from meals. When underreporters were excluded from their analysis, significant correlations between eating frequency and sugar (in grams) and total carbohydrate (in grams) intake were observed. Becker et al. (132) showed lower fruit and vegetable consumption by underreporters whether expressed as grams per day, occasions per day, percentage of consumers or grams per eating occasion. Thus underreporting may include denial of consumption and underreporting of both the number of occasions and the quantity per occasion. In conclusion, underreporting of food intake is a selective rather than a general phenomenon. The evidence points to differences in reporting for macro- and micronutrients, foods and meal patterns. The reasons for such misreporting are clearly
913S
Pasta, rice, beef, veal, fried white fish, fried potatoes White bread, refined cereals, lamb, pork —
Males
Females Males & females
Males & females
Finland; g/MJ
Norway; g/10 MJ
Ontario, Canada; servings/MJ
Males Males & females
Cheese, bread Potatoes, meat, fish, nonalcoholic beverages
Males Females Males & females
Norway; correlation between EI:BMR and energy-adjusted intakes
Potatoes, rice, pasta — Vegetables, fish, potato, meat, fruit
Males & females
Denmark; % energy
Added fats, grain products Rice and pasta, oils; low fat mayonnaise, sour cream, ice cream, potato chips, cakes and cookies, chocolate, nuts
Desserts, candy
— Cakes, potato chips, edible fats, candy, chocolate, soft drinks with sugar, whole milk —
Fats
— —
Breakfast cereals, cakes, milk, milk products, eggs, fats, sugar, confectionery Sugar, candy, soft drinks, spread, cooking fats
Butter, liqueurs, spirits, wines
— Skim and whole-fat cheese, skim milk, fruit, breads and cereals, margarine, fatty meats
—
—
Bread, meat, fish, total vegetables, fruit, nuts, beverages, alcoholic drinks Bread, oatmeal, muesli, buns, cakes, milk, yogurt, cheeses, alcohol, other — Potatoes, rice, pasta Sausages, milk, soft drinks, candy, ice cream — Fruits & berries, beer, wine
—
Whole milk, cheese, low fat milk, yogurt, low fat spread, other fat spreads, other fish, fruit juice, soft drinks, beer, cider, perry, coffee, tea —
Cookies, pastries, puddings, sugar, confectionery
Fruit, nuts
No difference
LER eat less
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Skim milk, soft drinks (no sugar), vegetables, breads, cereals Fruit, vegetables, meats, meat alternates, milk products, coffee, tea — Low fat meats, fish, vegetables, potatoes
Fruit, juice, vegetables, meat, fish
Females
Females
Eggs, bacon, ham, poultry, prepared meat products, vegetables, salad
LER eat more
Males & females
Sex
Cambridge, U.K.; g/d
U.K.; % energy
Study
Foods reported by low energy reporters compared with non-low energy reporters
TABLE 8
(129)
(102)
(101)
(128)
(127)
(113)
(94)
Reference
914S SUPPLEMENT
NUTRITIONAL BIOMARKERS: REPORTED ENERGY INTAKE
EI:BMR , 1.14 are excluded. Haraldsdo´ttir et al. (141) have demonstrated the same effect in five large European dietary surveys. Excluding subjects with implausibly low intakes increased the 5th percentile values by 10–25% and reduced the proportion of the sample with intakes below the reference values by 1–14 percentage points. W. Becker (personal communication) has also shown that the effect on the proportion with low intakes is greatest if the reference value is close to the mean intake and smaller if the reference value is similar to extreme intakes. Statistical methods of adjusting data to take random low intakes into account have been addressed by the National Research Council (142) and the Iowa State University (143,144). New techniques are needed to address the problem of bias. Deriving food-based dietary guidelines. Research that shows differential reporting of foods is summarized (see Bias in reporting meal patterns and foods eaten). Becker et al. (132) have explored various ways of presenting food patterns and show that defining patterns of food intake for the derivation of food-based dietary guidelines is seriously hampered by the selective reporting of foods.
Random error and the poor precision of dietary assessment make the correct ranking of individuals difficult and attenuate the correlation between nutrient intake and outcome parameters. The statistical handling of this problem has been intensively studied over the past two decades (145–158). However, this work has been based on the assumption of valid reports of food intake. A second problem is how to separate the effect of total EI from the effect of nutrient intake in assessing the associations between diet and disease outcomes. This is difficult, because macronutrient intakes are directly related to total EI and many micronutrient intakes are strongly correlated with total EI. Absolute EI is influenced by body size and physical activity; thus associations between nutrient intake and disease that are independent of these factors will be weak. On the other hand, when EI is associated with disease, specific nutrients will also tend to be associated simply on the basis of their correlation with EI. Four models have been proposed for accounting for total EI when one is examining the effects of nutrients on disease outcomes (159): the standard multivariate model, the energy-partition model (160), the nutrient-density model (161) and the residuals model (162). However, energy adjustment cannot eliminate differential biases in the reporting of macronutrient intake and under some conditions may even exacerbate the problem (163). Excluding underreporters from the data set is a solution that has been adopted by some (130,164–166). This introduces unknown bias into the data set and may also eliminate those subjects of greatest interest to the scientific questions posed. Stallone et al. (167) examined three approaches to data analysis: including all subjects using raw data, including all subjects using regression-based energy adjustments and excluding LER (those with EI:BMR , 1.2). They concluded that including all subjects and using energy adjustment was preferable to excluding LER. They noted also that ‘‘this approach cannot eliminate bias due to selective underreporting of foods. . .nor does it provide corrected estimates of absolute nutrient intake. ’’ The relative merits of the different methods for energy adjustment have been vigorously debated (168– 174).
Bias in dietary assessment has been addressed by several workers. A covariance model has been proposed for evaluating the relative validity of different dietary assessment methods (155,156). This model allows for mean bias in all methods and does not assume that one method is a gold standard. Variable bias in measurements from individuals has been included in a model that allows all measurement-error parameters to depend on body mass index and incorporates a random underreporting quantity that applies to each dietary self-report instrument (175). More recently another more complex model has been proposed (176–179). Future directions Advances in the past decade using energy benchmarks have led to the universal recognition that underreporting in dietary surveys is pervasive. Nevertheless, although a substantial body of research has been generated in a short period of time, knowledge may not have advanced as much as is currently thought. Validation studies using DLW-EE have been limited by small and usually highly selected samples of subjects, whereas studies using the single Goldberg cutoff have identified only a proportion of underreporters. This review has highlighted several directions for future research aimed at better study design and improved strategies for the interpretation of dietary data. Our inability to obtain good information on food intake has been succinctly described by Blundell (180) as ‘‘. . ..a dilemma for nutrition. . .but an enigma for psychology.’’ It must be remembered that nutrient intake, which is usually the desired endpoint of a dietary assessment, is only a derived measurement. The primary measure is food intake. Differential reporting of nutrients is the inevitable end result of differential reporting of foods and meals. If dietary assessments are to be improved, we must understand which foods and meals are misreported. More fundamentally still, we need to understand why people misreport food intake. Nutritionists are trained mainly in the biological sciences and thus it is likely that they have failed to fully appreciate that dietary assessments depend on a complex interplay of cognitive and behavioral processes such as language, interpersonal communication and mutual understanding between subject and researcher. How do people think about and describe foods? How do people process information about the foods they eat? Is it possible to evaluate the comprehension and reporting abilities of the population? If the nature and the sources of bias in dietary reporting are to be identified, nutritionists must go beyond the narrowly mechanistic focus of measurement of intake and examine the social, cultural and psychological context of such reporting. For example, a hitherto unresearched area is the role that the investigators themselves play in enhancing or hindering the reporting process. General conduct toward subjects, unconscious body language, phraseology, voice intonation, unconscious attitudes and many other aspects of behavior may unwittingly contribute to the problem. When better understanding of the how, why and what of dietary reporting has been achieved, then perhaps improved techniques for reporting food intake can be developed. The identification of underreporters at the individual level is pivotal in studies that assess the causes and consequences of misreporting. Given that the DLW-EE for EI validations is available to only a few research centers, alternative techniques for measuring expenditure such as heart-rate monitoring, accelerometers or physical activity questionnaires need to be evaluated for their sensitivity and specificity for detecting underreporting.
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Can data be adjusted to allow for both random and systematic error?
915S
916S
SUPPLEMENT
LITERATURE CITED 1. Tarasuk, V. & Beaton, G. H. (1991) The nature and individuality of within-subject variation in energy intake. Am. J. Clin. Nutr. 54: 464–470. 2. Toeller, M., Buyken, A., Heitkamp, G., Milne, R., Klischan, A. & Gries, F. A. (1997) Repeatability of three-day dietary records in the EURODIAB IDDM Complications Study. Eur. J. Clin. Nutr. 51: 74–80. 3. van Staveren, W. A., de Boer, J. O. & Burema, J. (1985) Validity and reproducibility of a dietary history method estimating the usual food intake during one month. Am. J. Clin. Nutr. 42: 554–559. 4. Wheeler, C., Rutishauser, I., Conn, J. & O’Dea, K. (1994) Reproducibility of a meal-based food frequency questionnaire. The influence of format and time interval between questionnaires. Eur. J. Clin. Nutr. 48: 795–809. 5. Marr, J. W. (1971) Individual dietary surveys: purposes and methods. World Rev. Nutr. Diet. 13: 105–164. 6. Bingham, S. (1987) The dietary assessment of individuals: methods, accuracy, new techniques and recommendations. Nutr. Abst. Rev. 57: 705–742. 7. Block, G. (1982) A review of validations of dietary assessment methods. Am. J. Epidemiol. 115: 492–505. 8. Willett, W. (1990) Nutritional Epidemiology. Oxford University Press, New York, NY. 9. Hallfrisch, J. J., Steele, P. & Cohen, L. (1982) Comparison of sevenday diet record with measured food intake of twenty-four subjects. Nutr. Res. 2: 263–273. 10. Mertz, W., Tsui, J. C., Judd, J. T., Reiser, S., Hallfrisch, J., Morris, E. R., Steele, P. D. & Lashley, E. (1991) What are people really eating? The relation between energy intake derived from estimated diet records and intake determined to maintain body weight. Am. J. Clin. Nutr. 54: 291–295. 11. Lissner, L., Habicht, J.-P., Strupp, B. J., Levitsky, D. A., Haas, J. D. & Roe, D. A. (1989) Body composition and energy intake: do overweight women overeat and underreport? Am. J. Clin. Nutr. 49: 320–325. 12. de Vries, J. H. M., Zock, P. L., Mensink, R. P. & Katan, M. B. (1994) Underestimation of energy intake by 3-d records compared with energy intake to maintain body weight in 269 nonobese adults. Am. J. Clin. Nutr. 60: 855–860. 13. Kretsch, M. J., Fong, A. K. H. & Green, M. W. (1999) Behavioral and body size correlates of energy intake underreporting by obese and normal-weight women. J. Am. Diet. Assoc. 99: 300–306. 14. Jonnalagadda, S. S., Mitchell, D. C., Smiciklas-Wright, H., Meaker, K. B., Van Heel, N., Karmally, W., Ershow, A. G. & Kris-Etherton, P. M. (2000) Accuracy of energy intake data estimated by a multiple-pass, 24-hour dietary recall technique. J. Am. Diet. Assoc. 100: 303–311. 15. Coward, W. A. (1988) The doubly-labeled-water (2H218O) method: principles and practice. Proc. Nutr. Soc. 47: 209–218. 16. Schoeller, D. A., Ravussin, E., Schutz, Y., Acheson, K. J., Baertschi, P. & Jequier, E. (1986) Energy expenditure by doubly labeled water: validation in humans and proposed calculation. Am. J. Physiol. 250: R823–R830. 17. International Dietary Energy Consultative Group (IDECG) (1990) The Doubly-Labeled Water Method for Measuring Energy Expenditure. Technical Recommendations for Use in Humans (Prentice, A. M., ed.). International Atomic Energy Authority, NAHRES-4, Vienna, Austria. 18. Speakman, J. R. (1997) Doubly Labeled Water: Theory and Practice. Chapman & Hall, London. 19. Schoeller, D. A. & van Santen, E. (1982) Measurement of energy expenditure in humans by doubly labeled water method. J. Appl. Physiol. 53: 955– 959. 20. Prentice, A. M., Coward, W. A., Murgatroyd, P. R., Davies, H. L., Cole, T. J., Sawyer, M., Goldberg, G. R., Halliday, D. & McNamara, J. P. (1985) Validation of the doubly-labeled water method for measurement of energy expenditure by continuous whole-body calorimetry over 12-day periods in man. In: Substrate and Energy Metabolism (Garrow, E. J. S. & Halliday, D., eds.), p. 18. John Libbey, London. 21. Coward, W. A. & Prentice, A. M. (1985) Isotope method for the measurement of carbon-dioxide production rate in man. Am. J. Clin. Nutr. 41: 659– 661. 22. Westerterp, K. R., deBoer, J. O., Saris, W. H. M., Schoffelen, P. F. M. & Ten Hoor, F. (1984) Measurement of energy expenditure using doubly labeled water. Int. J. Sports Med. 5: 74–75. 23. Schoeller, D. A., Kushner, R. F. & Jones, P. J. H. (1986) Validation of doubly labeled water for measuring energy expenditure during parenteral nutrition. Am. J. Clin. Nutr. 44: 291–298. 24. Westerterp, K. R., Brouns, F., Saris, W. H. M. & Ten Hoor, F. (1988) Comparison of doubly labeled water with respirometry at low-activity and high-activity levels. J. Appl. Physiol. 65: 53–56. 25. DeLany, J. P., Schoeller, D. A., Hoyt, R. W., Askew, E. W. & Sharp, M. A. (1989) Field use of D218O to measure energy expenditure of soldiers at different energy intakes. J. Appl. Physiol. 67: 1922– 1929. 26. Schoeller, D. A. & Hnilicka, J. M. (1996) Reliability of the doubly labeled water method for the measurement of total daily energy expenditure in free-living subjects. J. Nutr. 126: 348S–354S. 27. Black, A. E. & Cole, T. J. (2000) Within- and between-subject variation in energy expenditure measured by the doubly-labeled water technique: implications for validating reported dietary energy intake. Eur. J. Clin. Nutr. 54: 386–394. 28. Bland, J. M. & Altman, D. G. (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1: 307–310.
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The Goldberg cutoff is best used for identifying the presence of bias at the group level, but even for this purpose, information on the activity profiles of the population is needed to choose a suitable PAL value as the basis for the calculation of appropriate cutoffs. For identifying underreporters at the individual level, the Goldberg cutoff technique could be refined by using physical activity questionnaires to allocate subjects to appropriate PAL levels. Is this feasible? Can the physical activity questionnaire, which is the only feasible option in epidemiological surveys, adequately classify subjects into low, medium or high levels of activity? For identifying individual underreporters, detailed information on occupation and leisure activity will be required to permit the derivation of a subject-specific PAL to evaluate the EI of each subject individually. In epidemiological studies the desired measure is the longterm habitual intake of the nutrient under study. Which dietary assessment method best measures group mean intakes? Which dietary assessment method most reliably ranks subjects according to their habitual intake? What is the pattern and magnitude of misreporting? Is it a continuous variable ranging from gross overreporting through valid reporting to gross underreporting and including infinite gradations of misreporting in between? Can a data set be adequately corrected by excluding an identifiable group of gross underreporters? If these questions are to be resolved, large-scale studies in randomly selected populations are required, in which a proportion of the participants is randomly selected for intensive validation studies. The ideal studies should include many different methods of dietary assessment and several biomarkers and should also incorporate repeatability studies to account separately for both random error and systematic bias. At the same time, it must be recognized that at the individual level, it is very difficult to separate low values due to poor repeatability from low values due to underreporting. When the behavioral and psychological issues of dietary assessment are better understood, it may be possible to devise techniques of dietary assessment that are less prone to bias. Even then, human nature may be such that the ultimate goal of totally valid dietary assessments is probably not achievable. If not, then the confidence limits of acceptable self-reporting and ground rules for the correction of data sets need to be established. Alternatively, statistical techniques for analyzing the data that will account for systematic bias as well as random error are needed. In conclusion, the uncovering of the problems associated with systematic bias in dietary assessments does not mean that dietary studies should be abandoned. The study of nutrition cannot be isolated from the reality of food intake. The interdisciplinary nature of nutrition is admirably summed up in the definition of the science of nutrition as promulgated by The Nutrition Society of the U.K.: ‘‘The science of nutrition explores the capacity of diets [emphasis ours] to nourish man and animals by defining both supply and requirements in terms of a common currency, the nutrients. The practice of nutrition is to advance, apply and promote understanding of effects of dietary nutrients on growth, development, reproductive capacity, health and well-being in man and animals.’’ Nutritional health is ultimately dependent on the availability and composition of food and the choices made by the individual in the selection of his or her diet. Future cross-disciplinary research should establish the limits of dietary data such that valid conclusions about the relationships between nutrition and health can be drawn from a complete understanding of that data.
NUTRITIONAL BIOMARKERS: REPORTED ENERGY INTAKE
(1986) High levels of energy expenditure in obese women. Br. Med. J. 292: 983–987. 55. Black, A. E., Goldberg, G. R., Jebb, S. A., Bingham, S. A., Livingstone, M. B. E. & Prentice, A. M. (1992) Validations of dietary assessment using doubly labeled water. Proc. Nutr. Soc. 51: 72A. 56. Schulz, S., Westerterp, K. R. & Bru¨ck, K. (1989) Comparison of energy expenditure by the doubly labeled water technique with energy intake, heart rate and activity recording in man. Am. J. Clin. Nutr. 49: 1146–1154. 57. Black, A. E., Jebb, S. A., Bingham, S. A., Runswick, S. A. & Poppitt, S. D. (1995) The validation of energy and protein intakes by doubly labeled water and 24-hour urinary nitrogen excretion in post-obese subjects. J. Hum. Nutr. Diet. 8: 51–64. 58. Black, A. E., Bingham, S. A., Johansson, G. & Coward, W. A. (1997) Validation of dietary intakes of protein and energy against 24 hour urinary N and DLW energy expenditure in middle-aged women, retired men and postobese subjects: comparisons with validation against presumed energy requirements. Eur. J. Clin. Nutr. 51: 405–413. 59. Tuschl, R. J., Platte, P., Laessle, R. G., Stichler, W. & Pirke, K.-M. (1990) Energy expenditure and everyday eating behavior in healthy young women. Am. J. Clin. Nutr. 52: 81–86. 60. Goran, M. I. & Poehlman, E. T. (1992) Total energy expenditure and energy requirements in healthy elderly persons. Metabolism 41: 744–753. 61. Sawaya, A. L., Tucker, K., Tsay, R., Willett, W., Saltzman, E., Dallal, G. E. & Roberts, S. B. (1996) Evaluation of four methods for determining energy intake in young and older women: comparison with doubly labeled water measurements of total energy expenditure. Am. J. Clin. Nutr. 63: 491–499. 62. Black, A. E., Welch, A. A. & Bingham, S. A. (2000) Validation of dietary intakes measured by diet history against 24 h urinary nitrogen excretion and energy expenditure measured by the doubly-labeled water method in middle-aged women. Br. J. Nutr. 83: 341–354. 63. Howat, P. M., Mohan, R., Champagne, C., Monlezun, C., Wozniak, P. & Bray, G. A. (1994) Validity and reliability of reported dietary intake data. J. Am. Diet. Assoc. 94: 169–173. 64. Bathalon, G. P., Tucker, K. L., Hays, N. P., Vinken, A. G., Greenberg, A. S., McCrory, M. A. & Roberts, S. B. (2000) Psychological measures of eating behaviour and the accuracy of 3 common dietary assessment methods in healthy postmenopausal women. Am. J. Clin. Nutr. 71: 739–745. 65. Rothenberg, E., Bosaeus, I., Lernfelt, B., Landahl, S. & Steen, B. (1998) Energy intake and expenditure: validation of a diet history by heart rate monitoring, activity diary and doubly labeled water. Eur. J. Clin. Nutr. 52: 832–838. 66. Willett, W. C., Sampson, L., Stampfer, M. J., Rosner, B., Bain, C., Witschi, J., Hennekens, C. H. & Speizer, F. E. (1985) Reproducibility and validity of a semiquantitative food frequency questionnaire. Am. J. Epidemiol. 122: 51–65. 67. Kristal, A. R., Shattuck, A. L., Henry, H. J. & Fowley, A. S. (1990) Rapid assessment of dietary intake of fat, fiber and saturated fat: validity of an instrument suitable for community intervention research and nutritional surveillance. Health Promot. 4: 288–295. 68. Block, G., Woods, M., Potosky, A. & Clifford, C. (1990) Validation of a self-administered diet history questionnaire using multiple diet records. J. Clin. Epidemiol. 43: 1327–1335. 69. Rose, G. A. & Williams, R. T. (1961) Metabolic balance studies on large and small eaters. Br. J. Nutr. 15: 1–9. 70. McNeill, G., McBride, A., Smith, J. S. & James, W. P. T. (1989) Energy expenditure in large and small eaters. Nutr. Res. 9: 363–372. 71. Fricker, J., Baelde, D., Igoin-Apfelbaum, L. & Apfelbaum, M. (1992) Underreporting of food intake in obese ‘‘small eaters.’’ Appetite 19: 273–283. 72. Clark, D., Tomas, F., Withers, R. T., Brinkman, M., Chandler, C., Phillips, J., Ballard, F. J., Berry, M. N. & Nestel, P. (1992) Differences in energy metabolism between normal weight ‘large-eating’ and ‘small-eating’ women. Br. J. Nutr. 68: 31–44. 73. Clarke, D., Tomas, F., Withers, R. T., Chandler, C., Brinkman, M., Phillips, J., Berry, M., Ballard, F. J. & Nestel, P. (1994) Energy metabolism in free-living, ‘large-eating’ and ‘small-eating’ women: studies using 2H218O. Br. J. Nutr. 72: 21–31. 74. Milne, A. C., McNeill, G. & Zakary, A. (1991) Weight change as an indicator of energy imbalance during 7d weighed food intake studies. Ecol. Food Nutr. 26: 281–289. 75. Lichtman, S. W., Pisarska, K., Berman, E. R., Pestone, M., Dowling, H., Offenbacher, E., Weisel, H., Heshka, S., Matthews, D. E. & Heymsfield, S. B. 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