HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Kant, A. K. Search for Related Content PubMed PubMed Citation Articles by Kant, A. K.

Nature of Dietary Reporting by Adults in the Third National Health and Nutrition Examination Survey, 1988–1994

Department of Family, Nutrition, and Exercise Sciences, Queens College of the City University of New York, Flushing, New York

Address reprint requests to: Ashima K. Kant, PhD, Associate Professor, Department of Family, Nutrition, and Exercise Sciences, Queens College of the City University of New York, Flushing, NY 11367. E-mail: ashima_kant{at}qc1.qc.edu

	ABSTRACT

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS ACKNOWLEDGMENTS REFERENCES

 
Objective: Low reporting of food intake is an acknowledged problem in dietary assessments; however, differences in food intake relative to reporting status are poorly understood. This study examined the relation of a measure of dietary reporting status with the nature of food intake reported by adults in the third National Health and Nutrition Examination Survey.

Methods: Subjects were 6948 women and 6452 men, 20 years of age or older, with a complete and reliable 24-hour dietary recall. The ratio of reported energy intake to estimated basal energy expenditure (EI/BEE) was computed as a measure of dietary reporting status. The independent relation of EI/BEE ratio with 1) the amount, number, and energy density of nutrient-dense and low-nutrient-dense foods, 2) the number of reported eating occasions, 3) macro- and micronutrient intake and 4) serum concentrations of folate, ascorbate and carotenoids were examined using gender-specific multiple regression models.

Results: The EI/BEE ratio related positively with the amount, number and energy density of both nutrient-dense and low-nutrient-dense foods, and grams of alcoholic beverages. The EI/BEE ratio was an independent negative predictor of serum folate, ascorbate and alpha-carotene concentrations confirming the underreporting of food sources of these nutrients. The relative odds of reporting 30% of energy as fat or < 10% of energy as saturated fat decreased with ratio of EI/BEE; however, the odds of reporting all five food groups or meeting the recommended intake of selected micronutrients increased with EI/BEE.

Conclusions: The quantity and the quality of food intake reported in the 24-hour recall in NHANES III differed in relation to the ratio of EI/BEE.

Key words: NHANES III, dietary intake, energy intake, dietary assessment, energy underreporting, biomarkers, nutrition surveys, food group intake, dietary patterns

	INTRODUCTION

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS ACKNOWLEDGMENTS REFERENCES

 
Misreporting of self-reported food intake is an acknowledged source of measurement error in prospective or retrospective methods of dietary assessment [1–4]. The most prevalent type of misreporting is energy underreporting, with subjects reporting implausibly low energy intakes inconsistent with biological viability [2–4]. Small well controlled studies as well as national nutrition surveys in the US and several other countries have shown discrepancies between respondent-reported energy intake assessed from a variety of dietary assessment methods relative to measured or estimated energy expenditure [5–15]. For instance, nearly 28% of women and 18% of men in the first phase of the third National Health and Nutrition Examination Survey (NHANES III) reported energy intakes that were less than 90% of the energy required for basal requirements.

However, the differences in the nature of reported food intake in relation to reporting status are not well understood. A number of studies exploring the nature of dietary reporting have examined intakes of macro- and micronutrients in relation to reporting status or have profiled subject characteristics associated with underreporting [7,10,11,13,15–19]. Food intake in relation to dietary reporting status has been examined less frequently and the results have been equivocal [12–16,20]. Few studies have systematically evaluated eating occasions, number or amount of nutritionally desirable or undesirable foods/foodgroups reported in relation to reporting status. Such information is important for improving dietary assessment methods and for interpretation of dietary data. Finally, although it has been suggested that reported dietary intake be validated using biological markers, few studies have examined biomarkers in relation to reporting status [1,20].

The purpose of this study was to elucidate the nature of dietary reporting. Specifically, we examined the relation of a commonly used measure of reporting status [21]—the ratio of reported energy intake to calculated basal energy expenditure (EI/BEE)—with 1) the number and amount of foods reported from nutrient-dense and the low-nutrient-dense food groups, 2) the energy density of foods reported from the nutrient-dense and the low-nutrient-dense food group, 3) the number of reported eating occasions on the recall day, 4) macro- and micronutrient intake and, lastly, 5) serum concentration of selected nutrients as biomarkers of reported intake.

	METHODS

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS ACKNOWLEDGMENTS REFERENCES

 
This study used data from the third National Health and Nutrition Examination Survey (NHANES III), 1988–1994. The NHANES III is a multistage stratified probability sample of the non-institutionalized, civilian US population, two months of age or more. The survey included administration of a questionnaire at home and a full medical exam along with a battery of tests in a special mobile examination center (MEC) [22]. Demographic and medical history information was obtained during the household interview. The MEC exam included a physical and dental exam, dietary interview, body measurements and collection of blood and urine samples. Body weight and height were measured using standardized procedures in the MEC [22].

Dietary Assessment Method
A 24-hour dietary recall was collected by a trained dietary interviewer in a MEC interview using an automated, micro-computer based interview and coding system [22]. Respondents recalled type and amount of food consumed using recall aids such as abstract food models, special charts, measuring cups and rulers to help in quantifying the amounts consumed. Special probes were used to help the recall of commonly forgotten items such as condiments, accompaniments, fast foods and alcoholic beverages and so on.

Analytic Sample
All adults 20 years of age or older were eligible for inclusion (n = 17,030) in this study. From this eligible sample a complete and reliable dietary recall (as defined by the NCHS) was not available for 1051 respondents, leaving 15,979 eligible for inclusion. We further excluded respondents stating that food intake on the recalled day was "much less" or "much more" than usual (n = 2245), women who were pregnant (n = 282) or nursing (n = 91) and those missing information on body weight (n = 33) or height (n = 16). The final analytic sample comprised 13,400 respondents (6452 men and 6948 women). Many respondents were in more than one exclusion category.

Assessment of Intake of the Nutrient-Dense and Low-Nutrient-Dense Foods Groups
As a first step, the 4152 foods reported by the eligible adult survey respondents in the 24-hour dietary recall were classified as belonging to one or more of the five nutrient-dense food groups (dairy, fruit, grain, meat and vegetable) or the low-nutrient-dense foods group using methods we have described previously [23–25]. Briefly, the assignment of foods into the various groups was dependent on their nutrient content and uses in the diet. The dairy group included milk, yogurt, cheese and buttermilk, but excluded butter, cream, cream cheese and dairy desserts. The fruit group included all fresh, frozen, dried and canned fruits and fruit juices, but excluded fruit drinks. The grain group included all breads, cereals, pastas, and rice, but excluded pastries. The meat group included meat, poultry, fish, eggs and meat alternates such as dried beans, nuts and seeds. The vegetable group included all raw or cooked fresh, frozen and canned vegetables and juices. Mixed dishes containing foods from several groups were assigned to all the relevant groups. Foods excluded from these major food groups were assigned to the low-nutrient-dense foods group. The low-nutrient-dense foods were further categorized into five sub-groups as follows: 1) visible fat—butter, oil, dressings, and gravies, etc., 2) sweeteners—sugar, syrup, candy, preserves, carbonated and non-carbonated sweetened drinks, etc., 3) baked and dairy desserts—cookies, cakes, pies, pastries, ice cream, puddings, and cheese cakes, etc., 4) salted snacks—potato, corn, and tortilla chips, etc., and, lastly, 5) miscellaneous—coffee, tea, spices, etc. We calculated the grams and number of all foods in each nutrient-dense and low-nutrient-dense subgroup.

To determine the number of eating occasions reported in each 24-hour recall, all foods and beverages reported at any one reporting time—irrespective of the kind or quantity of food(s) consumed—were assumed to constitute one eating occasion. Details of the methods for determining frequency of eating occasions have been previously described [26]. Energy content (kcal)/100 g of all foods, nutrient-dense foods and low-nutrient-dense foods was also computed to determine energy density of reported foods.

The NHANES III nutrient data base for individual foods, which is derived from the US Department of Agriculture’s Survey Nutrient Data base [27], was used for determining energy, macronutrient and micronutrient content of all foods. The micronutrients examined included those considered to be of current or potential public health significance—vitamins A, E, B-6, B-12, C, folate and the minerals calcium and iron [28].

We also examined the relation of the serum concentrations of selected vitamers and analytes with EI/BEE using laboratory data from the NCHS public release CD-ROM [29,30]. The serum analytes selected have been shown to correlate well with dietary intake of respective nutrients and included serum folate, ascorbate and the carotenoids beta-carotene, alpha-carotene, beta-cryptoxanthin, lutein/zeaxanthin and lycopene [31,32]. The analytical methods used for determining serum nutrient concentrations have been described [29,30].

Assessment of Dietary Reporting Status
Basal energy expenditure (BEE) was calculated using gender-age-weight specific equations computed by Schofield [21]. This is the most frequently used method in the published literature for estimating basal energy expenditure. Dietary reporting status was assessed by computing a ratio of reported energy intake (EI) to calculated basal energy expenditure (BEE). Instead of using preestablished cuts of EI/BEE ratio to identify low or adequate reporters, in this study, we have described food intake by weighted gender-specific tertiles of ratio to identify subjects with a low, medium or high reporting status.

Statistical analyses
Because published data suggest gender and body weight related differences in the likelihood of dietary misreporting, all descriptive data are presented stratified by body mass index (BMI) (kg/m²) and gender. The mean number of eating occasions, the amount of food per eating occasion and the grams and number of foods reported from the nutrient-dense and the low-nutrient-dense food groups were obtained by tertiles of EI/BEE, by two BMI categories ( 24.9 and > 24.9) separately for men and women and were adjusted for a number of variables reported in the literature to correlate with the ratio of EI/BEE [10–15]. These covariates included age (years), race (non-Hispanic white, non-Hispanic black, Mexican American, other), smoking status (never, former, current, unknown), education (years), level of weekly physical activity (none, 1–2 times/week, > 2 times/week, and unknown) and response to question about trying to lose weight (yes, no, unknown). The procedure used for obtaining adjusted estimates and standard errors from survey data is based on Taylor Linearization methods as reported by Graubard and Korn [33]. All statistical analyses were performed using SAS [34] and software (SUDAAN) designed for analysis of survey data [35]. This software generates variance estimates that are corrected for multi-stage stratified probability design of complex surveys. Sample weights provided by the NCHS to correct for differential probabilities of selection, non-coverage and non-response were used in all analyses to obtain point estimates [27].

The independent association of the ratio of EI/BEE with food group intake and eating occasions was examined using linear regression procedures to adjust for multiple covariates. The gender-specific regression models included age, ethnicity, smoking status, education, level of physical activity, response to question about trying to lose weight and BMI as covariates along with the EI/BEE ratio variable.

Food Consumption Correlates of Dietary Reporting
Gender-specific linear regression models were used for identifying significant independent food consumption correlates of the ratio of EI/BEE. Food consumption variables in regression models included the amount and number of nutrient-dense and low-nutrient-dense foods, number of eating occasions, grams of alcohol and energy content/100 g of nutrient-dense and low-nutrient-dense foods and number of eating occasions.

Nutrient Intake in Relation to EI/BEE
Mean intake of energy and percent energy from macronutrients were obtained by tertiles of the ratio of EI/BEE and BMI categories adjusted for age and ethnicity. Adjusted estimates of percentage of the population meeting the age-gender-specific recommended dietary allowances (RDAs) of protein, vitamin A, iron, and dietary reference intakes (DRIs) of folate, vitamin B-12, vitamin B-6, vitamin E, ascorbate and calcium, by tertiles of EI/BEE ratio and BMI categories, were also obtained [31,32,36,37]. The independent association of each nutrient variable with EI/BEE was examined using multiple regression models with age, ethnicity, education and BMI as covariates. For continuous outcomes (intakes of energy, % energy from macronutrients and dietary fiber), we used linear regression models; for dichotomous outcomes, such as whether or not recommended intake of a nutrient was reported, logistic regression models were used.

Serum Vitamer/Analyte Concentrations in Relation to EI/BEE
Mean serum concentrations of selected vitamers were obtained by tertiles of the ratio of EI/BEE and BMI categories adjusted for age, race, hours of fasting before phlebotomy, supplement use in the 24 hours before phlebotomy, supplement use in the last month, dietary nutrient intake and smoking status (except folate). The independent association of each serum analyte with the ratio of EI/BEE was examined using multiple regression models with age, race, hours of fasting before phlebotomy, supplement use in the 24 hours before phlebotomy, supplement use in the last month, smoking status (except folate), dietary nutrient intake and BMI as covariates.

	RESULTS

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS ACKNOWLEDGMENTS REFERENCES

 
Table 1 presents the percentage of men and women in various socio-demographic and life-style categories by tertiles of EI/BEE ratio and by two BMI categories. More of those in the first tertile of ratio were older, reported no physical activity per week, had less than 12 years of education and were currently trying to lose weight.

Table 5 shows the mean ± SE of macronutrient intake by tertiles of EI/BEE ratio, by 2 BMI cuts, by gender. The EI/BEE ratio was a significant, independent, positive predictor of percent energy from total and saturated fat (p < 0.0000), dietary fiber (p < 0.0000), percent energy from alcohol (p 0.01) and percent energy from all low-nutrient-dense foods combined (p 0.006). Percent energy from carbohydrate and protein related inversely (p < 0.0000) with the ratio of EI/BEE.

The multicovariate-adjusted mean ± SE of the serum concentrations of selected vitamers and analytes by tertiles of EI/BEE ratio, by 2 BMI cuts, by gender, are presented in Table 7. In gender-specific regression models, a significantly inverse association of EI/BEE with RBC folate (p 0.02), serum folate (p 0.0005), vitamin C (p 0.0002) and alpha carotene (p < 0.05) concentrations was noted. The EI/BEE was a significant positive predictor of serum lycopene concentration. In these models, dietary folate, dietary ascorbate and dietary carotenoids were positive predictors, respectively, of serum concentrations of folate, ascorbate, and the carotenoids alpha and beta carotene, beta cryptoxanthin and lutein/zeaxanthin (p < 0.000).

	DISCUSSION

The reported amounts of all nutrient-dense and low-nutrient-dense foods and their subgroups increased with the EI/BEE ratio, but to a differing extent (Tables 2 and 3). The differences among EI/BEE tertiles in the reported amounts of dairy and meat appeared greater relative to other nutrient-dense subgroups. And the differences among EI/BEE tertiles in the reported amounts of desserts, visible fats and salty snacks appeared larger relative to sweeteners and miscellaneous foods. These observations suggest that quantitative differences in reported amounts of individual nutrient-dense and low-nutrient-dense subgroups in relation to EI/BEE status were not similar in magnitude. Further, differences among tertiles in number of most reported foods as well as number of eating occasions were smaller than amount of foods reported. These observations suggest that those with low EI/BEE ratios were more likely to report lower amounts of foods rather than omit food items or eating occasions.

In accord with published findings [31,32], in the present study, dietary intakes of ascorbate, folate and carotenoids were significant independent predictors, respectively, of serum concentrations of ascorbate, folate and the carotenoids (except lycopene). Notably, however, after adjusting for dietary nutrient intake along with other covariates in multiple regression models, EI/BEE ratio was also a significant negative predictor of serum ascorbate, serum/RBC folate and -carotene (Table 7). This observation confirms that food sources of ascorbate, folate and -carotene were underreported. Conversely, the ratio of EI/BEE was a significant independent positive predictor of serum lycopene concentration suggesting overreporting of the dietary sources of lycopene (tomato and tomato products) in those with low ratios. The serum concentrations of beta carotene, beta cryptoxanthin and lutein/zeaxanthin were not related with the ratio of EI/BEE, suggesting that food sources of these carotenoids were less likely to be misreported by those with low ratios. While there is some overlap in food sources of ascorbate, folate and the carotenoids, the major sources for these nutrients in the US population, however, are not identical. In the US population, leading sources of folate include ready-to-eat cereals, yeast breads, orange/grapefruit juice, dried lentils and beans; leading sources of ascorbate included orange/grapefruit juice, tomatoes, fruit drinks, potatoes, broccoli and ready-to-eat cereals; leading sources of carotenoids included carrots, tomatoes, spinach/greens, sweet potatoes and cantaloupe [38,39].

Some of the reasons for a low EI/BEE ratio from a 24-hour recall may be 1) day-to-day (within-individual) variation, 2) intentional misreporting of nutritionally undesirable and desirable foods, 3) inadvertent misreporting due to memory lapses, 4) inadvertent misreporting due to poor quantitative estimation of foods consumed, 5) inadvertent misreporting due to ignorance of ingredients composing complex dishes, 6) reasonably accurate intake reporting, but possibly low because individuals were attempting to create a negative energy balance or, in reporting low energy intake, were more likely to report foods with lower energy-density. It is likely that all of these reasons were prevalent in this population, but the available data do not permit a delineation. The analytic sample in this study excluded subjects stating that their intake on the recalled day was "much more" or "much less" than usual. One possible result of these exclusions in the present study may be to narrow the range of reported EI/BEE ratios. How the respondents perceived this question is unknown; therefore, whether these exclusions managed to exclude only those with intakes not encompassed by the within-individual variation is unknown. The NHANES III dietary recall interview included a review of a list of commonly forgotten foods (e.g., alcoholic and non-alcoholic beverages, fruit, candy and snack items) to decrease inadvertent omissions due to memory lapses. Improvement of dietary recall methods using multiple-pass techniques developed by the USDA should help to decrease further inadvertent low reporting due to memory lapses. Some studies suggest different reasons for the discrepancy between reported and measured intake in lean and overweight individuals [40,41]. In a well controlled study, the lean subjects underate, while the obese subjects both underate and underrecorded their food intake. Because the NHANES III utilized a 24-hour dietary recall, the recalled intake may not reflect deliberate undereating. In the present study, BMI was associated positively with reported amounts of both nutrient-dense and low-nutrient-dense foods, but not with number of unique foods from most of these food groups (BMI specific data not shown). Also, respondents with low or high BMI reported similar general patterns of food intake across tertiles of EI/BEE ratio. This study does support the notion that those reporting low EI/BEE ratio, irrespective of BMI, reported more of foods with lower energy-density. It is also possible that at least some subjects with a high BMI but low EI/BEE ratio were on energy restricted diets and may have reported their actual food intake.

Consistent with the positive association of energy-density of foods with EI/BEE ratio, the nutrient-density of protein, carbohydrate, dietary fiber and most examined micronutrients related inversely with EI/BEE. A similar direction of macronutrient intake in relation to EI/BEE has been previously reported [14,15,42]. However, the likelihood of meeting the standard of intake of protein and micronutrients increased with EI/BEE. A higher proportion of those in the lowest tertile of EI/BEE ratio reported 30% of energy from fat and had lowest fat intake (g/1000 kcals), but a lower proportion mentioned all five food groups. Differences among EI/BEE ratio tertiles in fat intake were greater than for other macronutrients, suggesting some selective low reporting of fat intake in accord with another recent report [40]. Various micronutrients examined were at varying levels of risk among tertiles of EI/BEE ratio—vitamin E and calcium intake differed greatly among tertiles, whereas the differences though present were smaller in ascorbate and vitamin B-6 intake. This may relate to differences in reporting of the individual nutrient-dense food groups among tertiles. Pryer et al. [13] also noted differential reporting of various foods and therefore nutrients by low-energy reporters. The implication of these observations is that a single correction factor such as energy adjustment may be of limited value in correcting estimates of different food groups and nutrients for reporting status.

Similarly to other studies of this kind, we used EI/BEE as a measure of reporting status. However, in the absence of independent validation of reported food intake, the extent to which the EI/BEE ratio is able to classify individuals into categories of reporting accuracy remains unknown. To describe reporting status, cut-offs of EI/BEE, especially those suggested by Goldberg et al. [43], have been used by some researchers [11–13,18], but different cut-offs or quantiles of distribution have been used by others [14,16,42]. Notably, however, the use of cut-offs of EI/BEE to define reporting status also provides no information about true reporting accuracy and requires a number of assumptions. Black [44] has recently examined the usefulness and identified several limitations of the Goldberg cut-offs for identifying diets of poor validity.

One assumption inherent in the methods used in this study is that the estimate of basal energy expenditure is unbiased. Logistic and budgetary constraints make it impossible to use the doubly labeled water method for measuring energy expenditure for large surveys such as the NHANES and necessitate reliance on estimates obtained from age, weight and gender-specific equations. Energy expenditure predicted from such equations generally compares well with measured energy expenditure [21]. It must, however, be acknowledged that for subjects in extremes of body weight, age and physical fitness, data on measured basal and total energy expenditure are sparse. Therefore, it is likely that in such cases the calculated energy expenditures approximate actual basal expenditures with limited reliability.

It is also possible that some individuals overreported their food intake. In the present study, however, the eating patterns of this group are indistinguishable from those of respondents in the highest tertile of the ratio of EI/BEE. Another limitation is lack of available information on whether respondents were weight stable at the time of the survey which may potentially relate to reporting status. Furthermore, this study examined broad food groups to provide a comprehensive picture of the nature of dietary reporting and is not sensitive to differences in intake of individual foods that compose the broad food groups. Our approach is more useful for help in interpreting dietary data, but of less value for identifying specific foods items that may be targeted in dietary assessment. Lastly, all methods of assessing dietary intake, including the 24-hour dietary recall used in the NHANES III, have a number of unique and common sources of measurement error, which make the recall method unsuitable for describing intakes of individuals, but acceptable for describing group means [1]. Because a single 24-hour dietary recall for assessing intake provides no information about within-individual variability in food intake, some respondents with a low EI/BEE may have reported actual intakes; however, we cannot identify these respondents from the available survey information. Therefore, this study makes no claims about assessment of reporting accuracy, but merely confines itself to interpreting reported dietary data in relation to reported EI/BEE.

Application
The findings of this study may be useful in improving assessment and interpretation of dietary data from a single 24-hour dietary recall. Among tertiles of ratio, the magnitude of differences in the amounts both nutrient-dense and low-nutrient-dense foods reported were greater than the number of reported foods and eating occasions. Therefore, while efforts for improving recall of amounts and number of foods and number of eating occasions will jointly contribute to improved reporting, efforts towards subject training and aids in quantifying foods reported may be of relatively greater importance. However, qualitative differences in reported intake of both nutrient-dense and low-nutrient foods as reflected in energy density of reported foods across the range of EI/BEE are not so amenable to ascertainment. A low EI/BEE is likely to be associated with higher estimates of inadequacy of micronutrient intake in those meeting the dietary goals for fat intake. Lastly, confirming a previous report [13], because of differential reporting of individual nutrient-dense or low-nutrient-dense food groups (and consequently differential estimates of prevalence of marginal intakes of various micronutrients), across the range of EI/BEE, a single correction factor to adjust for low-reporting may be of limited use.

	ACKNOWLEDGMENTS

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS ACKNOWLEDGMENTS REFERENCES

 
I thank Daniel F. Heitjan, PhD, Columbia University School of Public Health, for statistical consultation, and Lisa L. Kahle for programming assistance.

	FOOTNOTES

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS ACKNOWLEDGMENTS REFERENCES

 
Supported in part by an NIH research grant (R03 CA81604).

Received August 29, 2001. Accepted December 7, 2001.

	REFERENCES

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS ACKNOWLEDGMENTS REFERENCES

 

1. Bingham SA: The dietary assessment of individuals; methods, accuracy, new techniques and recommendations. Nutr Abstr Rev (Series A)57 :705 –742,1987 .
2. Lissner L, Heitman BL, Lindroos AK: Measuring intake in free-living humans: A question of bias. Proc Nutr Soc57 :333 –339,1998 .[Medline]
3. Livingstone MBE: Assessment of food intake: Are we measuring what people eat? Br J Biomed Sci52 :58 –67,1995 .[Medline]
4. Scholler DA: How accurate is self-reported dietary energy intake. Nutr Rev48 :373 –379,1990 .[Medline]
5. Black AE, Cole TJ, Goldberg GR, Jebb SA, Livingstone MBE, Prentice AM: Critical evaluation of energy intake data using fundamental principles of physiology: 2. Evaluating the results of published surveys. Eur J Clin Nutr45 :583 –599,1991 .[Medline]
6. Kaczkowski CH, Jones PJH, Feng J, Bayley HS: Four-day multimedia diet records underestimate energy needs in middle-aged and elderly women as determined by doubly labeled water. J Nutr13 :802 –805,2000 .
7. Lichtman SW, Pisarka K, Berman ER, Pestone M, Dowling H, Offenbacher E, Weisel M, Heshka S, Matthews DE, Heymsfield SB: Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. N Engl J Med327 :1893 –1898,1992 .[Abstract]
8. Buhl KM, Gallagher D, Hoy K, Matthews DE, Heymsfield S: Unexplained disturbance in body weight regulation: a diagnostic outcome assessed by doubly labeled water and body composition analyses in obese patients reporting low energy intakes. J Am Diet Assoc95 :1393 –1400,1995 .[Medline]
9. Devries JHM, Zock PL, Mensink RP, Katan MB: Underestimation of energy intake by 3-d diet records compared with energy intake to maintain body weight of 269 nonobese adults. Am J Clin Nutr59 :227S –231S,1994 .[Abstract/Free Full Text]
10. Johnson RK, Goran MI, Poehlman ET: Correlates of over- and underreporting of energy intake in healthy older men and women. Am J Clin Nutr59 :1286 –1290,1994 .[Abstract/Free Full Text]
11. Briefel RR, Sempos CT, McDowell MA, Chien S, Alaimo K: Dietary methods research in the third National Health and Nutrition Examination Survey: Underreporting of energy intake. Am J Clin Nutr65 :1203S –1209S,1997 .[Abstract/Free Full Text]
12. Krebs-Smith SM, Graubard BI, Kahle LL, Subar AF, Cleveland L, Ballard-Barbash R: Low energy reporters versus others: a comparison of reported food intakes. Eur J Clin Nutr54 :281 –287,2000 .[Medline]
13. Pryer JA, Vrijheld M, Nichols R, Riggins M, Elliott P: Who are the ‘low energy reporters’ in the dietary and nutritional survey of British adults. Int J Epidemiol26 :146 –154,1997 .[Abstract/Free Full Text]
14. Becker W, Foley S, Shelley E, Gibney M: Energy under-reporting in Swedish and Irish dietary surveys: implications for food based dietary guidelines. Br J Nutr81 :S127 –S131,1999 .
15. Johansson L, Solvell K, Bjorneboe G, Drevon CA: Under- and overreporting of energy intake related to weight status and lifestyle in a nationwide sample. Am J Clin Nutr68 :266 –274,1998 .[Abstract]
16. Pomerleau J, Ostbye T, Bright-See E: Potential underreporting of energy intake in the Ontario Health Survey and its relationship with nutrient and food intakes. Eur J Epidemiol18 :553 –557,1999 .
17. Lissner L, Lindroos AK: Is dietary underreporting dietary macronutrient specific? Eur J Clin Nutr48 :453 –454,1994 .[Medline]
18. Smith WT, Webb KL, Heywood PF: The implications of underreporting in dietary studies. Aust J Pub Health18 :311 –314,1994 .
19. Lafay L, Basdevant A, Charles MA, Vray M, Balkau B, Borys J-M, Eschwage F, Romon M: Determinants and nature of dietary underreporting in a free-living population: the Fleurbaix Lavaentie Ville Sant (FLVS) Study. Int J Obes Relat Metab Disord21 :567 –573,1997 .[Medline]
20. Bingham SA, Cassidy A, Cole TJ, Welch A, Runswick SA, Black AE, Thurnham D, Bates C, Khaw KT, Key TJA, Day NE: Validation of weighed records and other methods of dietary assessment using the 24-h urine nitrogen technique and other biological markers. Br J Nutr73 :531 –550,1995 .[Medline]
21. Schofield WN: Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr39(Suppl 1) :5S –41S,1985 .
22. Plan and operation of the Third National Health and Nutrition Examination Survey, 1988–94. Series 1: programs and collection procedures. Vital Health Stat 132 :1 –407,1994 .
23. Kant AK, Block G, Schatzkin A, Ziegler RG, Nestle M: Dietary diversity in the US population. J Am Diet Assoc91 :1526 –1531,1991 .[Medline]
24. Kant AK, Schatzkin A, Block G, Ziegler RG, Nestle M: Food group intake patterns and associated nutrient profiles of the US population. J Am Diet Assoc91 :1532 –1537,1991 .[Medline]
25. Kant AK: Consumption of energy-dense, nutrient-poor foods in the US population: effect on nutrient profiles. Am J Clin Nutr72 :929 –936,2000 .[Abstract/Free Full Text]
26. Kant AK, Schatzkin A, Ballard-Barbash R, Graubard BG: Frequency of eating occasions and subsequent weight change in the NHANES I Epidemiologic Follow-up Study. Int J Obes Relat Metab Disord19 :468 –474,1995 .[Medline]
27. U.S. Department of Health and Human Services (DHHS), National Center for Health Statistics: "Third National Health and Nutrition Examination Survey, 1988–1994, NHANES III, (CD-ROM. Series 11, No. 1A)." Hyattsville, MD: Centers for Disease Control and Prevention,1997 .
28. Interagency Board for Nutrition Monitoring and Related Research: "Third Report on Nutrition Monitoring in the United States: Volume 1." Washington, DC: US Government Printing Office,1995 .
29. U.S. Department of Health and Human Services (DHHS), National Center for Health Statistics: "Third National Health and Nutrition Examination Survey, 1988–1994, NHANES III Laboratory Data File (CD-ROM. Series 11, No. 1A)." Hyattsville, MD: Centers for Disease Control and Prevention,1996 .
30. U.S. Department of Health and Human Services (DHHS), National Center for Health Statistics. Third National Health and Nutrition Examination Survey, 1988–1994, NHANES III Second Laboratory Data File (CD-ROM. Series 11, No. 2A). Hyattsville, MD: Centers for Disease Control and Prevention,1997 .
31. Panel on Dietary Antioxidants and Related Compounds, Subcommittee on Upper Reference Levels of Nutrients and Interpretation and Uses of Dietary Reference Intakes, and the Standing Committee on the Scientific Evaluation of Dietary Reference Intakes: "Dietary Reference Intakes for Vitamin C, Vitamin E, Selenium, and Carotenoids." Washington, DC: National Academy Press,2000 .
32. Standing Committee on the Scientific Evaluation of Dietary Reference Intakes and its panel on Folate, Other B Vitamins, and Choline and Subcommittee on Upper Reference Levels of Nutrients. Food and Nutrition Board, Institute of Medicine: "Dietary Reference Intakes for Thiamin, Riboflavin, Vitamin B-6, Folate, Vitamin B-12, Pantothenic Acid, Biotin, and Choline." Washington, DC: National Academy Press,1998 .
33. Graubard BI, Korn EL: Predictive margins with survey data. Biometrics55 :652 –659,1999 .[Medline]
34. SAS Institute Inc: "SAS User’s Guide, Release 8." Cary, NC: SAS Institute Inc,2000 .
35. Shah BV, Barnwell BG, Bieler GS: "SUDAAN User’s Manual, Release 7.5." Research Triangle Park, NC: Research Triangle Institute,1997 .
36. National Research Council. Food and Nutrition Board: "Recommended Dietary Allowances," 10th ed. Washington, DC: National Academy Press,1989 .
37. Standing Committee on the Scientific Evaluation of Dietary Reference Intakes. Food and Nutrition Board: "Dietary Reference Intakes for Calcium, Phosphorus, Magnesium, Vitamin D, and Fluoride." Washington, DC: National Academy Press,1997 .
38. Subar AF, Krebs-Smith SM, Cook A, Kahle LL: Dietary sources of nutrients among US adults, 1989–91. J Am Diet Assoc98 :537 –547,1998 .[Medline]
39. Mangels AR, Holden JM, Beecher GR, Forman MR, Lanza E: Carotenoid content of fruits and vegetables: An evaluation of analytic data. J Am Diet Assoc93 :284 –296,1993 .[Medline]
40. Goris AHC, Westerterp-Plantenga MS, Westerterp KR: Undereating and underrecording of food habitual intake in obese men: selective underreporting of dietary fat. Am J Clin Nutr71 :130 –134,2000 .[Abstract/Free Full Text]
41. Goris AHC, Westerterp KR: Underreporting of habitual food intake is explained by undereating in highly motivated lean women. J Nutr29 :878 –882,1999 .
42. Voss S, Kroke A, Klipstein-Grobusch K, Boeing H: Is macronutrient composition of dietary intake data affected by underreporting? Results from the EPIC-Potsdam study. Eur J Clin Nutr52 :119 –126,1998 .[Medline]
43. Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA, Prentice AM: Critical evaluation of energy intake data using fundamental principles of physiology. 1. Derivation of cut-off values to identify underrecording. Eur J Clin Nutr45 :569 –581,1991 .[Medline]
44. Black AE: Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int J Obes Relat Metab Disord24 :1119 –1130,2000 .[Medline]

This article has been cited by other articles:

				A. K Kant, M. B Andon, T. J Angelopoulos, and J. M Rippe Association of breakfast energy density with diet quality and body mass index in American adults: National Health and Nutrition Examination Surveys, 1999-2004 Am. J. Clinical Nutrition, November 1, 2008; 88(5): 1396 - 1404. [Abstract] [Full Text] [PDF]

				A. K Kant and B. I Graubard Ethnic and socioeconomic differences in variability in nutritional biomarkers Am. J. Clinical Nutrition, May 1, 2008; 87(5): 1464 - 1471. [Abstract] [Full Text] [PDF]

				U. Nothlings, S. P Murphy, L. R Wilkens, B. E Henderson, and L. N Kolonel Dietary glycemic load, added sugars, and carbohydrates as risk factors for pancreatic cancer: the Multiethnic Cohort Study Am. J. Clinical Nutrition, November 1, 2007; 86(5): 1495 - 1501. [Abstract] [Full Text] [PDF]

				A. K. Kant and B. I. Graubard Ethnicity Is an Independent Correlate of Biomarkers of Micronutrient Intake and Status in American Adults J. Nutr., November 1, 2007; 137(11): 2456 - 2463. [Abstract] [Full Text] [PDF]

				J. A. Tooze, M. Z. Vitolins, S. L. Smith, T. A. Arcury, C. C. Davis, R. A. Bell, R. F. DeVellis, and S. A. Quandt High Levels of Low Energy Reporting on 24-Hour Recalls and Three Questionnaires in an Elderly Low-Socioeconomic Status Population J. Nutr., May 1, 2007; 137(5): 1286 - 1293. [Abstract] [Full Text] [PDF]

				A. K Kant and B. I Graubard Secular trends in patterns of self-reported food consumption of adult Americans: NHANES 1971-1975 to NHANES 1999-2002. Am. J. Clinical Nutrition, November 1, 2006; 84(5): 1215 - 1223. [Abstract] [Full Text] [PDF]

				J. A Tooze, A. F Subar, F. E Thompson, R. Troiano, A. Schatzkin, and V. Kipnis Psychosocial predictors of energy underreporting in a large doubly labeled water study Am. J. Clinical Nutrition, May 1, 2004; 79(5): 795 - 804. [Abstract] [Full Text] [PDF]

				S. Blanc, D. A Schoeller, D. Bauer, M. E Danielson, F. Tylavsky, E. M Simonsick, T. B Harris, S. B Kritchevsky, and J. E Everhart Energy requirements in the eighth decade of life Am. J. Clinical Nutrition, February 1, 2004; 79(2): 303 - 310. [Abstract] [Full Text] [PDF]

				A. K. Kant Reported Consumption of Low-Nutrient-Density Foods by American Children and Adolescents: Nutritional and Health Correlates, NHANES III, 1988 to 1994 Arch Pediatr Adolesc Med, August 1, 2003; 157(8): 789 - 796. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Kant, A. K. Search for Related Content PubMed PubMed Citation Articles by Kant, A. K.