TitleUse of recovery biomarkers to calibrate nutrient consumption self-reports in the Women's Health Initiative.
Publication TypeJournal Article
Year of Publication2008
AuthorsNeuhouser, ML, Tinker, L, Shaw, PA, Schoeller, D, Bingham, SA, Van Horn, L, Beresford, SAA, Caan, B, Thomson, C, Satterfield, S, Kuller, L, Heiss, G, Smit, E, Sarto, G, Ockene, J, Stefanick, ML, Assaf, A, Runswick, S, Prentice, RL
JournalAmerican journal of epidemiology
Date Published2008 May 15
KeywordsWomen's Health

Underreporting of energy consumption by self-report is well-recognized, but previous studies using recovery biomarkers have not been sufficiently large to establish whether participant characteristics predict misreporting. In 2004-2005, 544 participants in the Women's Health Initiative Dietary Modification Trial completed a doubly labeled water protocol (energy biomarker), 24-hour urine collection (protein biomarker), and self-reports of diet (assessed by food frequency questionnaire (FFQ)), exercise, and lifestyle habits; 111 women repeated all procedures after 6 months. Using linear regression, the authors estimated associations of participant characteristics with misreporting, defined as the extent to which the log ratio (self-reported FFQ/nutritional biomarker) was less than zero. Intervention women in the trial underreported energy intake by 32% (vs. 27% in the comparison arm) and protein intake by 15% (vs. 10%). Younger women had more underreporting of energy (p = 0.02) and protein (p = 0.001), while increasing body mass index predicted increased underreporting of energy and overreporting of percentage of energy derived from protein (p = 0.001 and p = 0.004, respectively). Blacks and Hispanics underreported more than did Caucasians. Correlations of initial measures with repeat measures (n = 111) were 0.72, 0.70, 0.46, and 0.64 for biomarker energy, FFQ energy, biomarker protein, and FFQ protein, respectively. Recovery biomarker data were used in regression equations to calibrate self-reports; the potential application of these equations to disease risk modeling is presented. The authors confirm the existence of systematic bias in dietary self-reports and provide methods of correcting for measurement error.