Hostname: page-component-89b8bd64d-9prln Total loading time: 0 Render date: 2026-05-11T02:07:56.876Z Has data issue: false hasContentIssue false
  • English
  • Français

Misreporting of energy: prevalence, characteristics of misreporters and influence on observed risk estimates in the Malmö Diet and Cancer cohort

Published online by Cambridge University Press:  08 March 2007

Irene Mattisson ,
Elisabet Wirfält ,
Carin Andrén Aronsson ,
Peter Wallström ,
Emily Sonestedt ,
Bo Gullberg  and
Göran Berglund
Irene Mattisson*
Affiliation:
Department of Clinical Sciences, Malmö, Lund University, Malmö University Hospital, SE-205 02, Malmö, Sweden
Elisabet Wirfält
Affiliation:
Department of Clinical Sciences, Malmö, Lund University, Malmö University Hospital, SE-205 02, Malmö, Sweden
Carin Andrén Aronsson
Affiliation:
Department of Clinical Sciences, Malmö, Lund University, Malmö University Hospital, SE-205 02, Malmö, Sweden
Peter Wallström
Affiliation:
Department of Clinical Sciences, Malmö, Lund University, Malmö University Hospital, SE-205 02, Malmö, Sweden
Emily Sonestedt
Affiliation:
Department of Clinical Sciences, Malmö, Lund University, Malmö University Hospital, SE-205 02, Malmö, Sweden
Bo Gullberg
Affiliation:
Department of Clinical Sciences, Malmö, Lund University, Malmö University Hospital, SE-205 02, Malmö, Sweden
Göran Berglund
Affiliation:
Department of Clinical Sciences, Malmö, Lund University, Malmö University Hospital, SE-205 02, Malmö, Sweden
*
*Corresponding author: Dr Irene Mattisson, Malmö Diet and Cancer Study, UMAS, entrance 59, SE-205 02 Malmö, Sweden, fax +46 40 336215, email irene.mattisson@med.lu.se
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.

The present study investigates the prevalence of misreporting of energy in the Malmö Diet and Cancer cohort, and examines anthropometric, socio-economic and lifestyle characteristics of the misreporters. Further, the influence of excluding misreporters on risk estimates of post-menopausal breast cancer was examined. Information of reported energy intake (EI) was obtained from a modified diet history method. A questionnaire provided information on lifestyle and socio-economic characteristics. Individual physical activity level (PAL) was calculated from self-reported information on physical activity at work, leisure time physical activity and household work, and from estimates of hours of sleeping, self-care and passive time. Energy misreporting was defined as having a ratio of EI to BMR outside the 95% CI limits of the calculated PAL. Logistic regression analysed the risk of being a low-energy reporter or a high-energy reporter. Almost 18% of the women and 12% of the men were classified as low-energy reporters, 2·8% of the women and 3·5% of the men were classified as high-energy reporters. In both genders high BMI, large waist circumference, short education and being a blue-collar worker were significantly associated with low-energy reporting. High-energy reporting was significantly associated with low BMI, living alone and current smoking. The results add support to the practice of energy adjustment as a means to reduce the influence of errors in risk assessment.

Keywords

EnergyMisreportingPhysical activity level

Information

Type
Research Article
Information
British Journal of Nutrition , Volume 94 , Issue 5 , November 2005 , pp. 832 - 842
Copyright
Copyright © The Nutrition Society 2005

References

Ainsworth, BE, Haskell, WL, Leon, AS, Jacobs, DR, Montage, HJ, Sallis, JF & Paffenbarger, RS (1993) Compendium of physical activity: classification of energy costs of human physical activities. Med Sci Sports Exerc 25, 71–80.Google Scholar
Becker, W & Welten, D (2001) Under-reporting in dietary surveys – implications for development of food-based dietary guidelines. Public Health Nutr 4, 683–687.CrossRefGoogle ScholarPubMed
Bingham, SA, Day, NE & Luben, R (2003) Dietary fibre in food and protection against colorectal cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC): an observational study. Lancet 361, 1496–1501.Google Scholar
Black, AE (2000a) 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, 1119–1130.Google Scholar
Black, AE (2000b) The sensitivity and specificity of the Goldberg cut-off for EI:BMR for identifying diet reports of poor validity. Eur J Clin Nutr 54, 395–404.Google Scholar
FAO/WHO/UNU. (1985) Energy and Protein Requirements. Report of a Joint FAO/WHO/UNU Expert Consultation. Technical Reports Series no. 724. Geneva: WHO.Google Scholar
Goldberg, GR & Black, AE (1998) Assessment of the validity of reported energy intakes – review and recent development. Scand J Nutr 42, 6–9.Google Scholar
Goldberg, GR, Black, AE, Jebb, SA, Cole, TJ, Murgatroyd, PR, Coward, WA & Prentice, AM (1991) Critical evaluation of energy intake using fundamental principles of energy physiology: 1 Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr 45, 569–581.Google Scholar
Haftenberger, M, Schuit, AJ, Tormo, MJ, Boeing, H, Wareham, N, Bueno-de-Mesgiuta Bas, H, Kumle, M, Hjartåker, , Chirlaque, MD & Ardanaz, E (2002) Physical activity of subjects aged 50–64 years involved in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 5, 1163–1177.CrossRefGoogle ScholarPubMed
Hofstetter, A, Schutz, Y, Jequier, E & Wahren, J (1986) Increased 24-hour energy expenditure in cigarette smokers. N Engl J Med 314, 79–82.CrossRefGoogle ScholarPubMed
Holmes, MD, Hunter, DJ, Colditz, GA, Stampfer, MJ, Harkinsson, SE, Speizer, FE, Rosner, BA & Willett, WC (1999) Association of dietary intake of fat and fatty acids with risk of breast cancer. JAMA 281, 914–920.Google Scholar
Hu, FB, Stampfer, MJ, Manson, JE, Rimm, E, Colditz, GA, Rosner, BA, Hennekens, CH & Willett, WC (1997) Dietary fat intake and the risk of coronary heart disease in women. N Engl J Med 337, 1491–1499.CrossRefGoogle ScholarPubMed
Johansson, L, Solvoll, K, Bjorneboe, G-Eaa & Drevon, CA (1998) Under- and overreporting of energy intake related to weight status and lifestyle in a nationwide sample. Am JClin Nutr 68 ,266–274.Google Scholar
Kipnis, V, Carroll, RJ & Freedman, LS (1999) Implications of a new dietary measurement error model for estimation of relative risk: application to four calibration studies. Am J Epidemiol 150, 642–651.CrossRefGoogle ScholarPubMed
Kipnis, V, Subar, AF, Midthune, DN, Freedman, LS, Ballard-Barbash, R, Troiano, RP, Bingham, SA, Schoeller, DA & Schatzkin, Carroll RJ (2003) Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol 158, 14–21.CrossRefGoogle ScholarPubMed
Lilienthal, Heitmann B & Lissner, L, (1995) Dietary underreporting by obese individuals – is it specific or non-specific?. Br Med J 311, 986–989.Google Scholar
Livingstone, MBE & Black, AE (2003) Markers of the validity of reported energy intake. J Nutr 133, 895S–920S.Google Scholar
Löf, M, Hannestad, U & Forsum, E (2003) Comparison of commonly used procedures, including the doubly-labelled water technique, in the estimation of total energy expenditure of women with special reference to the significance of body fatness. Br J Cancer 90, 961–968.Google Scholar
Macdiarmid, JI, Vail, A, Cade, JE & Blundell, JE (1998) The sugar–fat relationship revisited: differences in consumption between men and women of varying BMI. IntJ Obes 22, 1053–1061.CrossRefGoogle ScholarPubMed
Manjer, J, Carlsson, S, Elmståhl, S, Gullberg, B, Janzon, L, Lindström, M, Mattisson, I & Bergland, G (2001) The Malmö Diet and Cancer Study: representativity, cancer incidence and mortality in participants and non-participants. Eur J Cancer P rev 10, 489–499.Google Scholar
Mattisson, I, Wirfält, E, Johansson, U, Gullberg, B, Olsson, H & Berglund, G (2004a) Intakes of plant foods and fat and risk of breast cancer in postmenopausal women – a prospective study in the Malmö Diet and Cancer cohort. Br J Cancer 94, 122–127.CrossRefGoogle Scholar
Mattisson, I, Wirfält, E, Wallström, P, Gullberg, B, Olsson, H & Berglund, G (2004b) High fat and alcohol intakes are risk factors of post-menopausal breast cancer: a prospective study in the Malmö Diet and Cancer Cohort. IntJ Cancer 110, 589–597.CrossRefGoogle Scholar
National Bureau of Statistics. (1989) Occupations in Population and Housing Census 1985 (FoB 85) According to Nordic Standard Occupational Classification (Nordisk Yrkesklassificering, NYK) and Swedish Socio-economic Classification (Socioekonomisk Indelning SEI). Reports on Statistical Co-ordination no. 1989:5. Stockholm: Statistics Sweden (in Swedish).Google Scholar
Price, GM, Paul, AA, Cole, TJ & Wadsworth, MEJ (1997) Characteristics of the low-energy reporters in a longitudinal national dietary survey. Br J Nutr 77, 833–851.CrossRefGoogle Scholar
Riboli, E, Elmståhl, S, Saracci, R, Gullberg, B & Lindgärde, F (1997) The Malmö Food Study: validity of two dietary assessment methods for measuring nutrient intake. int J Epidemiol 26, S161–S173.CrossRefGoogle ScholarPubMed
Richardsson, MT, Leon, AS, Jacobs, DR Jr, Ainsworth, BE & Serfass, R (1994) Comprehensive evaluation of the Minnesota Leisure Time Physical Activity Questionnaire. J Clin Epidemiol 47, 271–281.Google Scholar
Rosell, MS, Hellénius, M-L, de Faire, UH & Johansson, GK (2003) Associations between diet and the metabolic syndrome vary with the validity of dietary intake data. Am J Clin Nutr 78, 84–90.CrossRefGoogle ScholarPubMed
Salmerón, J, Ascherio, A, Rimm, EB, Colditz, GA, Spiegelman, D, Jenkins, DJ, Stampfer, MJ, Wing, AL & Willett, WC (1997) Dietary fiber, glycemic load, and risk of NIDDM in men. Diabetes Care 20, 545–550.CrossRefGoogle ScholarPubMed
Seidell, JC (1998) Dietary fat and obesity: an epidemiological perspective. Am J Clin Nutr 67, Suppl. 546S–550S.CrossRefGoogle Scholar
Stallone, DD, Brunner, E, Bingham, SA & Marmot, M (1997) Dietary assessment in Whitehall II: the influence of reporting bias on apparent socioeconomic variation in nutrient intakes. Eur J Clin Nutr 51, 815–825.CrossRefGoogle ScholarPubMed
Subar, AF, Kipnis, V & Troiano, RP (2003) Using intake biomarker to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN Study. Am J Epidemiol 158, 1–13.CrossRefGoogle Scholar
Taylor, HL, Jacobs, DR Jr, Schucker, B, Knudsen, J, Leon, AS & Debacker, G (1978) A questionnaire for the assessment of leisure time physical activities. J Chronic Dis 31, 741–755.CrossRefGoogle ScholarPubMed
Tooze, JA, Subar, AF, Thompson, FE, Troiano, RP, Schatzkin, A & Kipnis, V (2004) Psychosocial predictors of energy underreporting in a large doubly labeled water study. Am J Clin Nutr 79, 795–804.Google Scholar
Warwick, PM & Busby, R (2004) Prediction of twenty-four-hour energy expenditure in a respiration chamber in smokers and non-smokers. Eur J Clin Nutr 47, 600–603.Google Scholar
Willett, WC, Howe, GR & Kushi, LH, (1997) Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr 65 Suppl. S1220–S1228.Google Scholar
Wirfält, E, Mattisson, I, Gullberg, B & Berglund, G (2000) Food patterns defined by cluster analysis and their utility as dietary exposure variables: a report from the Malmö Diet and Cancer Study. Public Health Nutr 3, 159–173.CrossRefGoogle Scholar
Wirfält, E, Mattisson, I, Johansson, U, Gullberg, B, Wallström, P & Berglund, G (2002) A methodological report from the Malmö Diet and Cancer study: development and evaluation of altered routines in dietary data processing. Nutr J 1 3, http://www.nutritionj.com.CrossRefGoogle Scholar
World Health Organization Study Group. (2000) Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation. WHO Technical Report Series no. 894 Geneva: WHO.Google Scholar
Young-Hwan, J, Talmage, DA & Role, LW, (2002) Nicotinic receptor-mediated effects on appetite and food intake. Int J Neurobiol 53, 618–632.Google Scholar