OBJECTIVES: To re-state the principles underlying the Goldberg cut-off for identifying under-reporters of energy intake, re-examine the physiological principles and update the values to be substituted into the equation for calculating the cut-off, and to examine its use and limitations. RESULTS: New values are suggested for each element of the Goldberg equation. The physical activity level (PAL) for comparison with energy intake : basal metabolic rate (EI:BMR) should be selected to re¯ect the population under study; the PAL value of 1.55 x BMR is not necessarily the value of choice. The suggested value for average withinsubject variation in energy intake is 23% (unchanged), but other sources of variation are increased in the light of new data. For within-subject variation in measured and estimated BMR, 4% and 8.5% respectively are suggested (previously 2.5% and 8%), and for total between-subject variation in PAL, the suggested value is 15% (previously 12.5%). The effect of these changes is to widen the con®dence limits and reduce the sensitivity of the cut-off. CONCLUSIONS: The Goldberg cut-off can be used to evaluate the mean population bias in reported energy intake, but information on the activity or lifestyle of the population is needed to choose a suitable PAL energy requirement for comparison. Sensitivity for identifying under-reporters at the individual level is limited. In epidemiological studies information on home, leisure and occupational activity is essential in order to assign subjects to low, medium or high PAL levels before calculating the cut-offs. In small studies, it is desirable to measure energy expenditure, or to calculate individual energy requirements, and to compare energy intake directly with energy expenditure.
Objective: To explore the speci®city and sensitivity of the Goldberg cut-off for EI : BMR for identifying diet reports of poor validity as compared with the direct comparison of energy intake with energy expenditure measured by doubly-labelled water. Design: Twenty-two studies with measurements of total energy expenditure by doubly-labelled water (EE), basal metabolic rate (BMR) and energy intake (EI) provided the database (n 429). The ratio EI : EE provided the baseline de®nition of under-(UR), acceptable-(AR) and over-reporters (OR), respectively EI : EE`0.76, 0.76 ± 1.24 and b 1.24. Four strategies for identifying under-and over-reporters using the Goldberg cut-off were explored. Sensitivity of the cut-off was calculated as the proportion of UR correctly identi®ed and speci®city as the proportion of non-UR correctly identi®ed. Results: UR, AR and OR (by EI : EE) were 34, 62 and 4% respectively of all subjects. When a single Goldberg cut-off for the physical activity level (PAL) of 1.55 was used, for men and women respectively the sensitivity was 0.50 and 0.52 and the speci®city 1.00 and 0.99. Using a cut-off for higher PAL traded speci®city for sensitivity. Using the cut-off for a PAL of 1.95, sensitivity was 0.76 and 0.85 and the speci®city 0.87 and 0.78 for men and women respectively. Using cut-offs for mean age ± sex speci®c PAL did not improve sensitivity. When subjects were assigned to low, medium and high activity levels and cut-offs for three different PALs used, sensitivity improved to 0.74 and 0.67 without loss of speci®city (0.97 and 0.98), for men and women respectively. If activity levels for men were applied to the womens' data, sensitivity improved to 0.72. Conclusion: To identify diet reports of poor validity using the Goldberg cut-off for EI : BMR, information is needed on each subject's activity level.
OBJECTIVE: To investigate the degree of bias in under-reporting of food intake in obese and non-obese subjects, hypothesising that under-reporting may be selective for either macronutrient content (carbohydrate (CHO), fat, protein, alcohol), speci®c food types or eating occasions (meals, snacks). DESIGN: Thirty-three women (18 obese, 15 non-obese) were recruited to a long-stay metabolic facility for 24 h. Ad libitum food intake was covertly measured throughout the study and a reported food intake completed at the end of 24 h. RESULTS: Reported total daily energy intake was signi®cantly lower than measured intake. Whilst meals were accurately reported, energy from snack foods eaten between meals was signi®cantly under-reported. (P`0.001) Reported total carbohydrate and added sugar intakes were signi®cantly lower than measured, whilst reported protein and fat intakes were not signi®cantly different from measured. Reported alcohol intake was also considerably lower than measured, but high variability prevented signi®cance. CONCLUSIONS: In both obese and non-obese women the major cause of under-reporting, as assessed by covert study design in subjects restricted within a metabolic facility, is the failure to report between-meal snack foods. There is some evidence for increased under-reporting in high CHO, but no evidence of a bias in under-reporting towards high fat or high protein foods.
Objectives: To estimate the total (CV t ), within-subject (CV w ) and between-subject (CV b ) variation in free-living energy expenditure as measured by the doubly-labelled water (DLW) technique. To examine the limitation of the DLW measurement of energy expenditure for evaluating reported energy intake. To estimate the probable minimum and maximum`habitual' energy expenditures for a sustainable lifestyle. Design: Review and analysis of individual data from 25 studies with repeat DLW measurements of energy expenditure (EE). Results: Pooled mean CV w derived from 21 studies was 11.8% for EE and 12.3% for physical activity level (PAL). Multiple regression analysis of CV w in 25 studies found a positive association with time span between measurements. At zero time CV w for EE was 8.2% rising to 9.6% at 13 weeks and 15.4% at 52 weeks. At the same time points CV w for PAL was 9.1%, 10.0% and 13.4% respectively. Pooled mean CV t was 13.0% for EE and 10.7% for PAL. CV b calculated from pooled mean CV t and CV w was 20.6% for EE and 7.2% for PAL. 95% con®dence limits of PAL in 11 age ± sex groups averaged 1.2 to 2.2. Conclusions: The analysis supported previous estimates of 8% for within-subject variation in DLW measurements including analytic plus inherent biologic variation. Variation that included changes in weight, season and activity increased with increased time between measurements to about 15% at a time span of 12 months. Con®dence limits of agreement between EE and reported energy intake were estimated to range from AE 15% to AE 32%. Estimates of the range of usual EE for normally active persons ranged from 1.3 to 2.2. Descriptors: energy expenditure; doubly-labelled water; within-subject variation European Journal of Clinical Nutrition (2000) 54, 386±394 IntroductionThe doubly-labelled water (DLW) technique for measuring free-living energy expenditure (EE) over a period of typically 7 ± 14 days incorporates short term day to day variation in activity, including differences between weekdays and weekends. However, a 14-day period cannot account for seasonal variation, variable leisure activity, intermittent employment or other situations that change EE with time. A random sample of individuals will inevitably select some in a period of relatively low activity and some in a period of relatively high activity, and thus single measurements may not represent`habitual' EE. Withinsubject variation in DLW EE has been investigated in freeliving young men (Goran et al, 1993), in young men with imposed totally sedentary conditions (Goran et al, 1994), and in female nutritionists (Schoeller & Hnilicka, 1996). Schoeller & Hnilicka (1996) also reviewed data from 15 other studies. However, all these authors were primarily interested in the experimental reliability of the DLW technique and in factoring out the variation due to changes in activity. The aim of the present paper was to determine the within-subject variation under ®eld conditions including altered activity. It reviews 25 DLW studies with repeat measures of DLW EE ...
Twelve women were studied before pregnancy and at 6-wk intervals from 6 to 36 wk gestation. Total energy expenditure (TEE) by the doubly labeled water method, basal metabolic rate (BMR), energy intake, and body composition were assessed on each occasion. There was substantial interindividual variation in the response to pregnancy. Mean total energy costs were as follows: delta BMR 112 +/- 104 MJ (range -53 to 273), delta TEE 243 +/- 279 MJ (range -61 to 869 MJ), and fat deposition 132 +/- 127 MJ (range -99 to 280 MJ). The mean total cost of pregnancy (cumulative TEE above baseline+energy deposited as fat and as products of conception) was 418 +/- 348 MJ (range 34-1192 MJ). This was much higher than current recommendations for incremental energy intakes. Self-recorded incremental intakes (208 +/- 272 MJ) seriously underestimated the additional costs. The variability in response emphasizes the problems in making prescriptive recommendations for individual women, because there is no way of predicting metabolic or behavioral responses to pregnancy.
Objectives: To compare validation of reported dietary intakes from weighed records against urinary nitrogen excretion and energy expenditure measured by DLW, and to examine the utility of the Goldberg cut-off for EI:BMR in the identi®cation of under-reporters. Design: Energy (EI) and nitrogen (protein) intake (NI) were measured by 16 d of weighed diet records collected over 1 y. They were validated against urinary nitrogen excretion in 5±8 (mean 6.0) 24 h urine collections and total energy expenditure (EE) measured by doubly labelled water (DLW). Basal metabolic rate (BMR) as measured by whole body calorimetry in women or bedside ventilated hood (Deltatrac) in men. Individual subjects were identi®ed as under-reporters if Urine N:NI was b 1.00 or if EI:EE was`0.79. The agreement between the two ratios in detecting under-reporting was examined. The results from the direct validation by DLW were also compared with validation using the Goldberg cut-off for EI:BMR . Subjects: Eighteen women aged 50±65 y and 27 men aged 55±87 y were selected from participants in two larger dietary surveys as representing the full range of dietary reporting as measured by Urine N:NI. Data from a previous study of 11 post-obese subjects were also included. Results: The two ratios, Urine N:NI and EI:EE, were signi®cantly related (r 70.48, P`0.01). Using the above cut-offs, seven (4F, 3M) subjects were identi®ed as under-reporters by both methods, one (1M) by Urine N:NI only and 8 (3F, 5M) by EI:EE only. There was close agreement in post-obese subjects where 6 subjects showed a substantial degree of under-reporting by both methods (r 70.87, P`0.001). The correlation between direct validation by DLW and EI:BMR est was 0.65 (P`0.001). Some limitations of the Goldberg cutoff for identifying individual under-reporters were demonstrated. Conclusions: EI:EE provides an estimate of the degree of under-reporting of energy at the group and individual level. Urine N:NI identi®es under-reporting of protein intake and the most obvious under-reporters of energy, but is probably of lesser value in estimating the overall degree of under-reporting of energy at group level. Good validation by EI:BMR depends on knowledge of physical activity at both group and individual level. However, the correlation of 0.65 between EI:EE and EI:BMR est suggests that EI:BMR could be usefully incorporated into analysis of data from epidemiological studies. Validation measures consisting of at least predicted EI:BMR ratios and urinary measures should be incorporated into dietary surveys.
Alterations in energy balance must be accommodated by adjustments in the net storage of the major energy-yielding macronutrients: carbohydrate, protein, and fat. This study used continuous whole-body calorimetry to measure changes in energy expenditure and substrate oxidation during a 12-d imposed energy imbalance in six lean men on mixed diets (overfeeding: 16.5 MJ/d, +33%, n = 3; underfeeding: 3.5 MJ/d, -67%, n = 3). Changes in total energy expenditure (TEE) and its components were modest; TEE changed by +6.2% (overfeeding) and -10.5% (underfeeding). In consequence, body weight changed by +2.90 and -3.18 kg. Marked changes in metabolic fuel selection occurred over the course of the study. Carbohydrate intake (540 and 83 g/d for overfeeding and underfeeding, respectively) exerted direct autoregulatory feedback on carbohydrate oxidation (551 and 106 g/d at day 12 for overfeeding and underfeeding, respectively). Subjects were close to balance by day 5. Changes in protein oxidation were small and not sufficient to prevent the oxidation of body protein mass, or its accretion, in response to energy deficit or surplus. Fat oxidation (59 and 177 g/d for overfeeding and underfeeding, respectively) was not sensitive to dietary fat intake (150 and 20 g/d, for overfeeding and underfeeding, respectively), rather, its oxidation was inversely related to the oxidation of other substrates. Changes in fat balance accounted for 74.1% and 84.0% of the energy imbalance during overfeeding and underfeeding, respectively. This study shows a clear oxidative hierarchy for the macronutrients. Metabolic fuel selection is dominated by the need to maintain carbohydrate balance. This induces inappropriate counterregulatory alterations in fat oxidation during energy surplus.
Objective: To identify adults and children as under-(UR), acceptable (AR), or over-reporters (OR) of energy intake (EI) using energy expenditure measured by doubly labelled water (DLW) (EE DLW ), and to use this as a reference to determine the sensitivity and specificity of (i) EE measured by heart rate (EE HR ), and (ii) the Goldberg cut-off technique for classifying subjects into the same categories. Design: Retrospective analysis of a dataset comprising concurrent measurements of EE DLW , EE HR , basal metabolic rate (BMR), and EI by weighed record (EI WR ) on 14 adults and 36 children. EI by diet history (EI DH ) was also measured in the children only. EI WR :EE DLW provided the reference definition of subjects as UR, AR or OR. Three strategies for classifying mis-reporters based on EE HR and Goldberg cut-offs were then explored. Sensitivity and specificity were calculated respectively as the proportion of UR and non-UR correctly identified.Results: Approximately 80% of all subjects were AR. For EI WR and EI DH respectively, the sensitivity of EE HR was 0.50 and 1.00, and specificity was 0.98 and 1.00. Although designating subjects as having low, medium or high activity levels (EE HR :BMR meas ) and calculating cut-offs based on appropriate WHO physical activity level PALs did not change sensitivity, specificity dropped to 0.98 (EI WR ) and 0.97 (EI DH ). Cut-offs based on a PAL of 1.55 reduced sensitivity to 0.33 (EI WR ) and 0.00 (EI DH ), but specificity remained unchanged. The sensitivity of all cut-offs based on physical activity level (PALs) for EI WR was 0.50 (adults) and 0.25 (children). Conclusions: If the precision of EE HR was improved, it may be useful for identifying mis-reporters of EI.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.