Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-23T00:39:59.471Z Has data issue: false hasContentIssue false

Attainment of precision in implementation of 24h dietary recalls: INTERMAP UK

Published online by Cambridge University Press:  08 March 2007

Claire Robertson*
Affiliation:
Department of Epidemiology and Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK
Rana Conway
Affiliation:
Department of Nutrition and Dietetics, King's College London, 150 Stamford Street, London SE1 8WA, UK
Barbara Dennis
Affiliation:
Department of Biostatistics, Collaborative Studies Coordinating Center, University of North Carolina at Chapel Hill, Chapel Hill NC, USA
John Yarnell
Affiliation:
Department of Epidemiology and Public Health, The Queen's University of Belfast, Mulhouse Building, Grosvenor Road, Belfast BT12 6BA, UK
Jeremiah Stamler
Affiliation:
Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago IL, USA
Paul Elliott
Affiliation:
Department of Epidemiology and Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK
*
*Corresponding author: Dr Claire Robertson, fax +44 20 7402 2150, email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Collection of complete and accurate dietary intake data is necessary to investigate the association of nutrient intakes with disease outcomes. A standardised multiple-pass 24 h dietary recall method was used in the International Collaborative Study of Macro- and Micronutrients and Blood Pressure (INTERMAP) to obtain maximally objective data. Dietary interviewers were intensively trained and recalls taped, with consent, for randomly selected evaluations by the local site nutritionist (SN) and/or country nutritionists (CN) using a twelve-criterion checklist marked on a four-point scale (1, retrain, to 4, excellent). In the Belfast centre, seven dietary interviewers collected 932 24 h recalls from 40–59-year-old men and women. Total scores from the 134 evaluated recalls ranged from thirty-four to the maximum forty-eight points. All twelve aspects of the interviews were completed satisfactorily on average whether scored by the SN (n 53, range: probing 3·25 to privacy of interview 3·98) or CN (n 19, range: probing 3·26 to pace of interview and general manner of interviewer 3·95); the CN gave significantly lower scores than the SN for recalls evaluated by both nutritionists (n 31, Wilcoxon signed rank test, P=0·001). Five evaluations of three recalls identified areas requiring retraining or work to improve performance. Reporting accuracy was estimated using BMR; energy intake estimates less than 1·2 × BMR identifying under-reporting. Mean ratios in all age, sex and body-mass groups were above this cut-off point; overall, 26·1 % were below. Experiences from the INTERMAP Belfast centre indicate that difficulties in collection of dietary information can be anticipated and contained by the systematic use of methods to prevent, detect and correct errors.

Type
Research Article
Copyright
Copyright © The Nutrition Society 2005

References

Bingham, SA (1987) The dietary assessment of individuals; methods, accuracy, new techniques and recommendations. Nutr Abst Rev 57A, 705742.Google Scholar
Bingham, SA (1991) Limitations of the various methods for collecting dietary intake data. Ann Nutr Metab 35, 117127.Google Scholar
Bingham, SA & Cummings, JH (1985) Urine nitrogen as an independent validatory measure of dietary intake: a study of nitrogen balance in individuals consuming their normal diet. Am J Clin Nutr 42, 12761289.CrossRefGoogle ScholarPubMed
Bingham, SA, Gill, C, Welch, A et al. , (1997) Validation of dietary assessment methods in the UK arm of EPIC using weighed records, and 24-hour urinary nitrogen and potassium and serum vitamin C and carotenoids as biomarkers. Int JEpidemiol 26 Suppl. 1, S137S151.Google Scholar
Bingham, SA, Gill, C, Welch, A, Day, K, Cassidy, A, Khaw, KT, Sneyd, MJ, Key, TJA, Roe, L & Day, N (1994) Comparison of dietary assessment methods in nutritional epidemiology: weighed records v. 24 h recalls, food-frequency questionnaires and estimated-diet records Br J Nutr 72, 619643.Google Scholar
Black, A (2000) 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 24, 11191130.CrossRefGoogle Scholar
Black, A, Goldberg, G, Jebb, SA, Livingstone, MBE, Cole, T & Prentice, AM (1991) Critical evaluation of energy intake data using fundamental principles of energy phisiology: 2. Evaluating the results of published surveys Eur J Clin Nutr 45, 583599.Google Scholar
Block, G (1982) A review of validations of dietary assessment methods. Am J Epidemiol 115, 492505.CrossRefGoogle ScholarPubMed
Buzzard, IM, Dennis, B, Moag-Stahlberg, A & Stamler, J (1998) Quality control of 24 hour dietary recall interview techniques: use of audiotapes for process evaluation. Eur J Clin Invest 52 Suppl. 2, S14.Google Scholar
Conway, R, Robertson, C, Dennis, B, Stamler, J & Elliott, P, (2004) Standardised coding of dietary records: experiences from INTERMAP UK. Br J Nutr 91, 765771.Google Scholar
Cook, A, Pryer, J & Shetty, P (2000) The problem of accuracy in dietary surveys. Analysis of the over 65 UK national diet and nutrition survey. J Epidemiol Community Health 54, 611616.Google Scholar
Dennis, B, Stamler, J, Buzzard, M et al. , (2003) INTERMAP: the dietary data – process and quality control. J Hum Hypertens 17, 609622.Google Scholar
Dolecek, T, Stamler, J, Caggiula, A, Tillotson, JL & Buzzard, I (1997) Methods of dietary and nutritional assessment and intervention and other methods in the Multiple Risk Factor Intervention Trial. Am J Clin Nutr 65, 196S201S.CrossRefGoogle ScholarPubMed
Dwyer, J, Picciano, MF & Raiten, DJ (2003 a) Collection of food and dietary supplement intake data: What We Eat in America-NHANES. J Nutr 133, 590S600S.CrossRefGoogle ScholarPubMed
Dwyer, JPicciano, MF & Raiten, DJ (2003 b) Food and dietary supplement databases for What We Eat in America-NHANES. J Nutr 133, 624S634S.Google Scholar
Friedman, GD, Cutter, GR, Donahue, RP, Hughes, GH, Hulley, SB, Jacobs, JRLiu, K & Savage, PJ (1988) Cardia: Study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol 41, 11051116.Google Scholar
Goldberg, G, Black, A, Jebb, SA, Cole, T, Murgatroyd, PR, Coward, W & Prentice, AM (1991) Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording Eur J Clin Nutr 45, 569581.Google Scholar
Gorder, D, Dolecek, T, Coleman, G, Tillotson, JL, Brown, H, Lenz-Litzow, K, Bartsch, G & Grandits, G (1986) Dietary intake in the Multiple Risk Factor Intervention Trial (MRFIT): nutrient and food group changes over 6 years. J Am Diet Assoc 86, 744751.Google Scholar
Grandits, GBartsch, G & Stamler, J (1997) Chapter 4. Method issues in dietary data analyses in the Multiple Risk Factor Intervention Trial. Am J Clin Nutr 65, 211S227S.CrossRefGoogle Scholar
Hajjar, I & Kotchen, T (2003) Regional variations of blood pressure in the United States are associated with regional variations in dietary intakes: the NHANES-III data. J Nutr 133, 211214.Google Scholar
Hilner, J, McDonald, A, Van Horn, L, Bragg, C, Caan, BSlattery, M, Birch, RSmoak, C & Wittes, J (1992) Quality control of dietary data collection in the CARDIA study. Control Clin Trials 13, 156169.CrossRefGoogle ScholarPubMed
Karvetti, RL & Knuts, LR (1985) Validity of the 24-hour dietary recall. J Am Diet Assoc 85, 14371442.CrossRefGoogle ScholarPubMed
Livingstone, MBE, Robson, PJ, Black, A, Coward, W, Wallace, JMW, McKinley, MC, Strain, JJ & McKenna, PG (2003) An evaluation of the senstivity and sensitivity of energy expenditure measured by heart rate and the Goldberg cut-off for energy intake: basal metabolic rate for identifying mis-reporting of energy intake by adults and children: a retrospective analysis. Eur J Clin Nutr 57, 455463.Google Scholar
Macdiarmid, J & Blundell, J (1998) Assessing dietary intake: who, what and why of under-reporting. NutrRes Rev 11, 231253.CrossRefGoogle ScholarPubMed
Morgan, KJ, Johnson, SR, Riezek, RL, Reese, R & Stampley, GL (1987) Collection of food intake data: an evaluation of methods. J Am Diet Assoc 87, 888898.Google Scholar
Phillips, ELR, Arnett, DHimes, JH, McGovern, PG, Blackburn, H & Luepker, RV (2000) Diffrences and trends in antioxidant dietary intake in smokers and non-smokers, 1980–1992: the Minnesota Heart Survey. Ann Epidemiol 10, 417423.Google Scholar
Poppitt, SD, Keogh, GF, Prentice, AM, Williams, DEM, Sonnemans, HMW, Valk, EEJ, Robinson, E & Wareham, NJ (2002) Long-term effects of ad libitum low-fat, high-carbohydrate diets on body weight and serum lipids in overweight subjects with metabolic syndrome. Am J Clin Nutr 75, 1120.CrossRefGoogle ScholarPubMed
Pryer, JA, Vrijheid, M, Nichols, R, Kiggins, M & Elliott, P (1997) Who are the ‘low energy reporters’ in the Dietary and Nutritional Survey of British adults? Int J Epidemiol 26, 146153.CrossRefGoogle ScholarPubMed
Schakel, SF (2001) Maintaining a nutrient database in a changing marketplace: keeping pace with changing food products – a research perspective. J Food Comp Anal 14, 315322.CrossRefGoogle Scholar
Sievert, YA, Schakel, SF & Buzzard, IM (1989) Maintenance of a nutrient database for clinical trials. Control Clin Trials 10, 416425.Google Scholar
Slimani, N, Ferrari, P, Ocké, M et al. (2000) Standardisation of the 24-hour diet recall calibration method used in the European Prospective Investigation into Cancer and Nutrition (EPIC): general concepts and preliminary results. Eur JClin Nutr 54, 900917.CrossRefGoogle ScholarPubMed
Smith, AF (1991) Vital and Health Statistics: Cognitive Processes in Long-term Dietary Recall. Rockville, MD: US Department of Health and Human Services.Google Scholar
Stamler, J, Elliott, P, Dennis, B, Dyer, AKesteloot, H, Liu, K, Ueshima, H & Zhou, B (2003) INTERMAP: background, aims, design, methods, and descriptive statistics (non dietary). J Hum Hypertens 17 591608.CrossRefGoogle Scholar
Stubbs, RJ, O'Reilly, L, Fuller, Z, Horgan, GMehar, C, Deary, I, Austin, E, Ritz, PMilne, E & James, WPT (2004) N08001: Detecting and Modelling Mis-reporting of Food Intake with Special Reference to Under-reporting in the Obese London: Food Standards Agency.Google Scholar
Tapsell, L, Brenninger, V & Barnard, J (2000) Applying conversational analysis to foster accurate reporting in the diet history interview. J Am Diet Assoc 100, 818824.Google Scholar
Thomas, B (1994) Manual of Dietetic Practice London: lackwell Science.Google Scholar
Tillotson, JL, Gorder, D, DuChene, A, Grambsch, P & Wenz, J (1986) Quality control in the multiple risk factor intervention trial nutrition modality. Controlled Clini Trials 7, 66S90S.CrossRefGoogle ScholarPubMed
Todd, KS, Hudes, M & Howes, Calloway D (1983) Food intake measurement: problems and approaches. Am J Clin Nutr 37, 139146.CrossRefGoogle ScholarPubMed
Van Horn, LV, Gernhofer, N, Moag-Stahlberg, A, Farris, R, Hartmuller, G, Lasser, VI, Stumbo, P, Craddick, S & Ballew, C (1990) Dietary assessment in children using electronic methods: telephones and tape recorders. J Am Diet Assoc 90, 412416.CrossRefGoogle ScholarPubMed
Willett, W (1998) Nutritional Epidemiology Oxford, UK: Oxford University Press.Google Scholar