a quantitative analysis of energy intake reported by young men

ORIGINAL RESEARCH

A quantitative analysis of energy intake reported byyoung men

Selma C. LIBERATO,1 Josefina BRESSAN2 and Andrew P. HILLS3

Schools of 1Public Health and 3Human Movement Studies, Queensland University of Technology, Kelvin Grove,Queensland, Australia; and 2Department of Nutrition and Health, Federal University of Vicosa, Av Ph Rolfs s/n,Vicosa, MG, Brazil

AbstractAim: To quantitatively analyse energy intake reported by young men and the accuracy of the Goldberg cut-offmethod for identifying misreporters.Methods: This was a cross-sectional study in which: food intake was assessed by a four-day food record; restingmetabolic rate was assessed by indirect calorimetry; percentage body fat was measured by dual-energy X-rayabsorptiometry; and energy expenditure was assessed by physical activity record completed simultaneously withfood intake measurements. Energy intake was analysed by direct comparison of energy intake and energy expen-diture and by the Goldberg cut-off. Subjects: 34 healthy men aged 18–25 years. Setting: Queensland University ofTechnology, Queensland, Australia. Main outcome measures: percentage of misreporters in a group of young menusing different methods. Statistical analyses: data are presented as means and standard deviations. The analyseswere conducted using Statistic for Windows 5.5 software.Results: Seven underreporters were identified by direct comparison of energy intake and energy expenditure. TheGoldberg cut-off found six out of the seven underreporters identified by direct comparison of energy intake andenergy expenditure, but wrongly identified two acceptable reporters as underreporters. The sensitivity and speci-ficity of the Goldberg cut-off method were 0.86 and 0.93, respectively.Conclusions: Seven out of 34 participants underreported their energy intake. In the absence of physical activitymeasurements, the Goldberg cut-off method identified underreporters in this group of young men with assessedresting metabolic rate.

Key words: dietary intake, food intake, nutrient intake, research methodology.

INTRODUCTION

Accurate measurements of food intake are important tostudies of the association between diet and health.1 Foodrecords have been extensively used to assess food intake.Food intake can be estimated or weighed. A disadvantage ofestimated food records is that the accuracy is highly variable,because it depends on subjects’ skills, memory and commit-ment with the study.2 Advantages compared with weighedrecords include rapid estimation of the amount of food, lowercost, higher cooperation rates and less burden on partici-pants.3 However, it is widely recognized that when peoplehave to record their food intake, they may either increase ordecrease their food intakes such that it is markedly different to

their normal intake and, therefore, food self-reports aresubject to errors and biases.4 These can be due to: a probablechange to the habitual intake influenced by the recordingprocess,5 overeating or most commonly undereating,6 under-or overestimation due to errors in the estimation of portionsize and lack of knowledge of the composition of mixeddishes7 and misreporting, including under- and overreport-ing, due to forgotten meals and failure to record because ofthe burden of recording everything that was eaten.8

Misreporting suggests that it is due only to conscious, orunconscious, omission from, or inclusion of, food items inthe report. However in this paper, misreporting also includesunder- or overeating. The errors in reported food intake mayhave implications for epidemiological studies, such as pro-ducing overestimates of deficient intakes.9 This may be moreproblematic if the under- or overreporting is related to theconsumption of selected foods or to specific participantgroups, for example, a higher discrepancy in obese than inlean participants10,11 due to social desirability bias with atendency to supply answers to dietary questions that placethe interviewee in a favourable light.11

S.C. Liberato, PhD, LecturerJ. Bressan, PhD, Associate ProfessorA.P. Hills, PhD, ProfessorCorrespondence: S.C. Liberato, School of Public Health, QueenslandUniversity of Technology, Victoria Park Road, Kelvin Grove, Brisbane,Qld 4059. Email: [email protected]

Accepted August 2008

Nutrition & Dietetics 2008; 65: 259–265 DOI: 10.1111/j.1747-0080.2008.00312.x

© 2008 The AuthorsJournal compilation © 2008 Dietitians Association of Australia

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mailto:[email protected]

Therefore, it is necessary to check the accuracy of thereported food intake of each participant in each study.Reported energy intake (EI) can be validated by compari-son of EI and energy expenditure (EE), assuming that indi-viduals are in energy balance.4 Different formulae to assessthe validity of EI from the food records have included:EI < EE,5 EI < EE - 100 kcal12 and EI < 0.8 EE.3 The Gold-berg cut-off method, which is the 95% confidence intervalfor EI/EE taking into account the number of days ofdietary assessment and daily variation in EI and EE, hasbeen considered the standard method to identifymisreporters.13

When EE is unknown, the identification of EI misre-porters can be based on the Goldberg cut-off methods,4,14,15

in which reported EI is expressed as EI/basal metabolic rate(BMR) and compared with expected physical activity level(PAL) for that population.

As the misreporting may compromise studies evaluatingdietary data, the aim of the present study was to assessunder- and/or overreporting in young men in Brisbane,Australia. The accuracy and precision of the Goldbergcut-off method to identify misreporters were alsoevaluated.

METHODS

Thirty-four healthy young men were recruited from thelocal community in the city of Brisbane, Australia throughnewspaper advertisements, flyers in clubs, schools, univer-sities and fitness centres (94% of participants were uni-versity students). Each participant read and signed anapproved written consent form. Queensland University ofTechnology Human Research Ethics Committee approvedthe participant recruitment and the data collectionprocedures.

Dietary and physical activity measurements were under-taken simultaneously over a period of four consecutive days,including two weekdays, one Saturday and one Sunday.Dietary intake was assessed by four-day food record usingestimated household measurements. The participants weregiven food intake recording sheets and instructions onhow to complete them throughout a day. The instructionsincluded a powerpoint presentation with 48 food item slideseach with three food sizes expressed on household measureson a computer screen. The four-day food record data wereentered into the Foodworks (v. 3.02) nutrient analysis soft-ware (Xyris software Pty Ltd, Brisbane, Australia, http://www.xyris.com.au) incorporating nutrient tables for use inAustralia (AUSNUT, Canberra, 2000). Macronutrients wereexpressed as absolute intake as well as source of energy(percentage of daily EI).

Resting metabolic rate (RMR) was assessed by continuousopen-circuit indirect calorimetry. A Deltatrac II metaboliccart (Datex-Ohmeda Corp., Helsinki, Finland, http://www.datex-ohmeda.com) was used to assess RMR of half theparticipants. Because of technical problems, the Moxus O2

system (AEI Technologies, Brisbane, PA, USA) was used to

assess the RMR of the remaining participants. Participantswere fitted with a Hans-Rudolf headset (WyMedical Pty Ltd,Adelaide, Australia, with two-way breathing valve and pneu-motach) and a nose clip. Both gas analysers were calibratedbefore each measurement against standard mixed referencegases. There are unlikely to be any significant differences inmeasuring RMR with two different machines, because theyare continuous open-circuit indirect calorimetry devices thatmeasure oxygen and carbon dioxide concentrations usingsimilar sensors. Participants fasted for five hours, wereinvolved in minimal physical activity before arrival andrested lying for 30 minutes in the laboratory before the mea-surement. Participants lay supine in a comfortable position,listening to a radio and being monitored to ensure that theyremained awake. Respiratory gases were collected continu-ously for 30 minutes and the data from the last 10 minuteswere used for analyses. Expired gas was analysed for oxygenconcentration via a paramagnetic O2 sensor and for carbondioxide concentration via an infrared absorption technique.The Weir equation16 was used to convert O2 and CO2 valuesto RMR values:

RMR CO O minutes day= +([ ] ×1 106 3 941 14402 2. .

Where RMR is the resting metabolic rate (kcal/day, CO2 is thecarbonic dioxide production rate (L/minute) and O2 is theoxygen consumption rate (L/minute).

The BMR was estimated by Schofied equation17 and com-pared with measured RMR.

Percentage body fat was measured by dual-energy X-rayabsorptiometry (DPX-Plus, Lunar Corporation, Madison,WI, USA; DPX-L adult software, version 1.33, Lunar Corpo-ration). Participants remained motionless in the supineposition while the scanning arm of the dual-energy X-rayabsorptiometry passed over their body from head to toe inparallel 1-cm strips.

Physical activity was recorded over four consecutivedays using nine categories of physical activity intensity (1–9)for each 15-minute period throughout the day. These cat-egories and their corresponding list of activities, as estab-lished by Bouchard et al.,18 were explained and illustrated indetail to each participant before they started to record.

Individuals expend different quantities of energy forinvolvement in the same physical activity. Moreover, indi-viduals show considerable variability in their RMR becauseof differences in genetic characteristics, fat-free mass, body-weight, age, sex and health conditions. The metabolicequivalent (MET) is used as an index of the intensity ofphysical activity to represent specific physical activities.19

The MET expresses energy cost of physical activities asmultiples of RMR (MET = EE/RMR). The four-day physicalactivity record scores 1, 2, 3, 4, 5, 6, 7, 8 and 9 corre-spond to 1, 1.5, 2.3, 2.8, 3.3, 4.8, 5.6, 6 and 7.8 METs,respectively.18 Using measured RMR, the daily EE was cal-culated for each participant after accounting for each of the96 15-minute periods of a day and multiplying the scoreby its specific MET.

The validity of the food records was determined by twomethods.

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http://www.xyris.com.au

http://www.xyris.com.au

http://www.datex-ohmeda.com

http://www.datex-ohmeda.com

Method 1: direct comparison of EIand EE

It was assumed that participants in the present study were inenergy balance, as the participants were asked to maintaina normal diet and there were no significant changes in par-ticipants’ weight before and after recording food intake.However, it should be recognized that lower or higherintakes over four days would not be enough to show achange in participant’s weight. The expected ratio of EI/EE is1.00 and the 95% confidence limits (CL) in the presentstudy were 0.62–1.38 (based on mean within-subject varia-tion on daily EI intake (CVwEI) of 23% and on within-subjectvariation in EE (CVwEE) of 15% calculated from the currentstudy’s data set. These values are similar to those proposedby Black.4

CL (EI)0.95 = EE ¥ [�2 √(CVwEI2/d + CVwEE

2)]13,15

where d is the number of days of diet assessment.

Pooled mean CVw = √(SCVi2/n),4

where CVi is the CV (CVwEI or CVwEE) calculated for eachparticipant from the number of days of dietary assessment orEE assessment available for that participant, and n is thenumber of participants.

Although EI is a continuous quantitative variable, limitswere defined to identify the underreporters (URs), accept-able reporters (ARs) and overreporters. ARs were defined ashaving the ratio EI/EE in the range 0.62–1.38, URs asEI/EE < 0.62 and overreporters as EI/EE > 1.38.

Method 2: the Goldberg cut-off wasused to identify diet reports ofpoor validity14,15

This method is supposed to be used when EE is unknown. Itwas used in the present study to evaluate its accuracy whencompared with method 1. To determine whether a value ofEI/BMR is acceptable, the following must be satisfied:

PAL ¥ exp [sdmin ¥ S/√n] < EI/BMR< PAL ¥ exp [sdmax ¥S/√n]

where PAL is the average PAL for the understudy population,sdmin and sdmax are -2 and 2 for the lower and upper 95%CLs, respectively, n is the number of participants (at indi-vidual level, n = 1), and EI/BMR is the individual ratio foreach participant in the study or the average ratio for a groupof participants. S is the overall coefficient of variance for EI,BMR and energy requirements, and it is given by

S = √(CVwEI2/d + CVtP

2 + CVwB2)

where CVwEI is the within-participant variation in EI, d is thenumber of days of diet assessment, CVwB is the coefficient ofvariation of BMR measurements and CVtP is the total varia-tion in PAL.

The values used for each factor were CVwEI = 0.23,CVtP = 0.15 (both were calculated from the current study’s

data set), and CVwB = 0.04, according to Black.4 Therefore,the overall coefficient of variance S = 0.1932. The PAL valueused to calculate the Goldberg cut-offs was 1.85 as suggestedfor men aged 18–29 years.20 Acceptable reporters weredefined as having the ratio EI/RMR, according to the methodof dietary assessment at individual and group levels(Table 1). The sensitivity of this method for detecting under-reporting was calculated as the proportion of misreporterscorrectly identified. The specificity was calculated as theproportion of non-misreporters correctly identified assuch.15

Data are presented as means and standard deviations. Theanalyses were conducted using Statistic for Windows 5.5software.

RESULTS

The characteristics of the participants are shown in Table 1.The participants had a mean age of 21.4 years, with a meanbody mass index (BMI) of 24.83 kg/m2. The direct compari-son of EI and EE found seven (20.6%) URs (Figure 1a). TheGoldberg cut-off found six out of the seven URs, but wronglyidentified two ARs as URs (Figure 1b). The sensitivity andspecificity of the Goldberg cut-off method were 0.86 and0.93, respectively.

At the group level, there was underreporting becauseaverage EI/RMR (1.45) was lower than the Goldberg cut-offlower limit (1.73) for groups (Table 1).

When BMR was estimated by equation,17 the main find-ings were: differences between RMR measured and BMRestimated ranged from -418 kcal to 480 kcal; daily EE dif-ference calculated when RMR was measured and when BMRwas estimated ranged from -834 kcal to 985 kcal; the indi-vidual (1.23 < EI/RMR < 2.78) and the group (1.72 <EI/RMR < 1.98) cut-off intervals were similar to those calcu-

Table 1 Characteristics, body mass index (BMI), body fatpercentage, daily energy intake (EI), resting metabolic rate(RMR), daily energy expenditure (EE) and physical activitylevel (PAL) and Goldberg cut-offs for EI/RMR at the indi-vidual and group levels of 34 young male participants in thestudy

Characteristics Mean � SD (minimum–maximum)

Age (years) 21.4 � 2.1 (18–25)Bodyweight (kg) 77.5 � 13.5 (54.4–103.8)Height (m) 1.76 � 0.07 (1.60–1.89)BMI (kg/m2) 24.83 � 3.49 (18.76–33.5)Body fat (%) 18.1 � 7.2 (6.0–37.4)RMR (kcal) 1904.4 � 282.1 (1348.9–2583.7)EE (kcal) 3626.4 � 613.7 (2533.3–4796.4)PAL (= EE/RMR) 1.91 � 0.18 (1.50–2.26)EI (kcal) 2726.4 � 525.8 (1781.3–3749.8)EI/RMR 1.45 � 0.32 (0.88–2.22)EI/EE 0.76 � 0.17 (0.46–1.20)Individual cut-off 1.26 < EI/RMR < 2.72Group cut-off 1.73 < EI/RMR < 1.98

A quantitative analysis of energy intake reported by young men


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lated when RMR was assessed; the specificity and sensitivityof the Goldberg cut-off method were the same as those whenRMR was assessed. However, a different UR was erroneouslyidentified as an AR and a different AR was erroneouslyidentified as a UR.

For the majority of participants, the reported daily EI wasless than the reported daily EE (Figure 2a), and there was nolinear correlation between mean daily EI and daily EE(Figure 2a), mean daily PAL (Figure 2b), body fat(Figure 2c) or RMR (Figure 2d).

DISCUSSION

Using the 95% confidence interval for EI/EE criteria, 20.6%of participants underreported their EI. The Goldberg cut-offfound six out of the seven URs identified by direct compari-son of EI and EE, but wrongly identified two ARs as URs.URs and AR can be erroneously identified when estimatedBMR by equations rather than measured RMR are used.

Estimated rather than weighed food intake records wereused in the current study to try to minimise the burden tothe participants who were already recording their physicalactivity each 15 minutes. Good agreement was observedbetween estimated food intake recorded by patients in ahospital and their observed intake for the portions of mostfoods.21

The presence of bias may not be important if it operatesequally across all members of a population so that internalcomparisons remain valid. Bias may also not be important ifsubjects are studied more than once and act as their owncontrols. However, bias becomes important if absolute levelsof intake are to be measured and the presence or absence ofdeficient intakes is to be determined. Therefore, all dietarystudies should incorporate independent measures of validity.

In the current study, using the 95% confidence interval forEI/EE criteria, 20.6% of participants underreported their EI.Black15 found 27.9% of male URs when analysing data from21 studies, where EE was measured by double-labelled water(DLW) and EI was measured by records or questionnaires.Sjoberg et al.22 found 18% and 7% URs in girls and boysaged 15–17 years, respectively. Livingstone et al.23 found14.3% URs among 14 adults with mean age of 31.1 years.

In the current study, because of the smaller age range ofparticipants (18–25 years), it was possible to choose a morespecific PAL value (1.85) for the population and, therefore,the sensitivity and specificity of the Goldberg cut-off werehigher than that found by Black.15

The individual energy requirement has been expressed asPAL, which is a multiple of BMR and enables comparisonamong different people. From analysis of 574 measurementsof EE assessed by DLW from people aged 2–95 years, Blacket al.20 concluded that for a sustainable lifestyles, the PALranges from 1.2 to 2.5 with the lower and upper limits ofmean PAL of actual energy requirements of 1.22, for non-ambulant, and 4.7, for cyclists in Tour de France.20 A meanPAL associated with men aged 18–29 years of 1.8520 issmaller than the PAL estimated in the current study (1.91)(Table 1).

Livingstone and Black9 found a mean EI/EE of 0.84 whenanalysing data from 26 studies where EI was reported and EEwas assessed by DLW, in contrast to that in the current studyof 0.76. However, an EI/EE ratio of 0.77 was observed whenonly male adolescents aged 15–18 years were considered ina study where EI was assessed by weighed records and EE byDLW.24

In the absence of physical activity measurements, theGoldberg cut-off method can be used to identify young maleURs with assessed RMR not only at the group level, but alsoat the individual level, because sensitivity and specificitywere high.

The use of self-reported physical activity records to esti-mate EE is a limitation of the current study, because indi-viduals can easily overestimate or underestimate the timespent in activity and the intensity of the activity.25 An over-estimation of EE may have occurred, which would explain ahigher PAL (1.91) compared with the mean (1.85) for menaged 18–25 years and smaller EI/EE (0.76) compared with

(a)

1500 2000 2500 3000 3500 4000

EI (kcal)

0.4

0.6

0.8

1.0

1.2

1.4E

I/E

E

(b)

1200 1500 1800 2100 2400 2700

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0.8

1.2

1.6

2.0

2.4

2.8

EI

/ RM

R

Figure 1 Energy intake (EI)/energy expenditure (EE)against EI (a) and EI/resting metabolic ratio (RMR) againstRMR (b) in 34 young men. EI was assessed by four-day foodrecord. The lines represent the 95% confidence limits ofEI/EE (a) or the Goldberg cut-offs (b). (�) Underreporters:EI/EE < 0.62 (a).



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mean (0.84) when EE was assessed by DLW. However, if EEwas overestimated, it did not appear to have jeopardized thecurrent study, for two reasons. First, the upper limit of 1.38for ARs was much higher than the highest EI/EE ratio of 1.2found for one of the participants of the current study.Second, the 95% confidence interval for EI/EE criteria iden-tified a smaller number of URs (seven) than the Goldbergcriteria (eight) and, if EE had been overestimated, a highernumber of URs would be identified by the 95% confidenceinterval for EI/EE criteria.

It is widely recognised that the advantages of self-reportquestionnaires or diaries include low cost, relative simplicityand coverage of the individual’s normal daily pattern. Thephysical activity measurement was validated,26 and no sig-nificant within- and between-individual variability in theestimated total EE and habitual physical activity was foundwhen young adults recorded their daily PA with theBouchard three-day diary in free living.27

According to Subar et al.,28 underreporting tends toincrease with increased intake, because the more respon-dents require or consume, the more difficult it is to reportconsumption accurately, perhaps because rememberingmore foods or bigger portion sizes is challenging or becauseof societal pressure to consume less. However, surprisingly,in the current study there was higher underreporting amongthose participants with lower EI (Figure 1a). This findingmay have been a chance finding related to the small numbersof the study; we cannot explain the finding in terms of smallvariation to EI, as the group did not appear homogenouswith EI ranging from 1700 to 3700 kcal. It is possible thatthose participants with lower EI were underestimating theirportion size.

More underreporting has been found in obese than innon-obese men5,12 and adolescents.29 Andersson and Ross-ner30 reported EI of 2500 � 900 kcal and 2900 � 400 kcalin obese and non-obese men aged 18–30 years, respectively,

(a)

2000 3000 4000 5000

EE (kcal)

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3000

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(kca

l)

r = 0.29 n.s.

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1.6 1.8 2.0 2.2

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2000

3000

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(kca

l)

r = 0.07 n.s.

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0 10 20 30 40

Body fat (%)

2000

3000

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r = 0.11 n.s.

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1500 1800 2100 2400 2700

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2000

3000

4000

5000E

I (k

cal)

r = 0.27 n.s.

Figure 2 Average daily energy intake (EI) assessed by four-day food record plotted against average daily energy expenditure(EE) from physical activity diaries (a), physical activity level (PAL) (b), body fat (c) and resting metabolic rate (RMR) (d).n.s P > 0.05.



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when EI was assessed by 12 telephone recalls over athree-month period. Analysing data from 840 men aged20–29 years, Briefel et al.31 reported EI/RMR of 1.83, 1.67and 1.41 in men with BMI < 20.7 kg/m2, BMI ranging from20.7–27.8 kg/m2 and BMI > 27.8 kg/m2, respectively. In thecurrent study, there was no relationship between EI andbody fat percentage (Figure 2c). Johnson et al. also did notobserve an increase in underreporting of EI as fat mass andbody fat percentage increased in men.32

In one study with lean women who had their food intakeassessed by 7-day weighed food record, the RMR and PAwere independently related to EI, each explaining 27% ofvariation in EI.6 In the current study, despite having a posi-tive tendency in association of EI with RMR (Figure 2d) andof EI with PAL (Figure 2b), the relationship was not signifi-cant. This could be due to the small sample size and largevariation for EE, RMR and PAL within the sample.

A great advantage of the current study was the use ofmeasured RMR rather than the use of equations. Accuratemeasurements of RMR were essential for the Goldberg cri-teria and allowed more confident EE estimation by four-dayphysical activity records. In addition, the current study is thefirst to show that UR and AR can be erroneously identifiedwhen estimated BMR by equations is used rather than mea-sured RMR.

In conclusion, over 20% of the participants underre-ported their EI. In the absence of physical activity measure-ments, the Goldberg cut-off method can be used to identifyyoung male URs with assessed RMR. There was no linearcorrelation between mean daily EI and the mean daily EE,PAL, body fat or RMR.

Our findings have implications for the design of dietarystudies and their interpretation. There is no consensus onhow to treat data when misreporting is identified. Somesuggestions include energy adjustment, regression-basedadjustment of nutrient intake to mean dietary EI and misre-porter exclusion or separate treatment. Identification of mis-reporters is the first stage and all dietary studies shouldincorporate independent measures of validity.

ACKNOWLEDGEMENTS

The authors thank the voluntary participants and theQueensland University of Technology for the use of its labo-ratories and facilities. S.C.L acknowledges financial supportfrom the Conselho Nacional de Desenvolvimento Científicoe Tecnológico.

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a quantitative analysis of energy intake reported by young men

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