Factors relating to eating style, social desirability, body image and eating meals at home increase the precision of calibration equations correcting self-report measures of diet using recovery biomarkers: Findings from the Women's Health Initiative

Background: The extent to which psychosocial and diet behavior factors affect dietary self-report remains unclear. We examine the contribution of these factors to measurement error of self-report. Methods. In 450 postmenopausal women in the Women's Health Initiative Observational Study doubly labeled water and urinary nitrogen were used as biomarkers of objective measures of total energy expenditure and protein. Self-report was captured from food frequency questionnaire (FFQ), four day food record (4DFR) and 24 hr. dietary recall (24HR). Using regression calibration we estimated bias of self-reported dietary instruments including psychosocial factors from the Stunkard-Sorenson Body Silhouettes for body image perception, the Crowne-Marlowe Social Desirability Scale, and the Three Factor Eating Questionnaire (R-18) for cognitive restraint for eating, uncontrolled eating, and emotional eating. We included a diet behavior factor on number of meals eaten at home using the 4DFR. Results: Three categories were defined for each of the six psychosocial and diet behavior variables (low, medium, high). Participants with high social desirability scores were more likely to under-report on the FFQ for energy (β = -0.174, SE = 0.054, p < 0.05) and protein intake (β = -0.142, SE = 0.062, p < 0.05) compared to participants with low social desirability scores. Participants consuming a high percentage of meals at home were less likely to under-report on the FFQ for energy (β = 0.181, SE = 0.053, p < 0.05) and protein (β = 0.127, SE = 0.06, p < 0.05) compared to participants consuming a low percentage of meals at home. In the calibration equations combining FFQ, 4DFR, 24HR with age, body mass index, race, and the psychosocial and diet behavior variables, the six psychosocial and diet variables explained 1.98%, 2.24%, and 2.15% of biomarker variation for energy, protein, and protein density respectively. The variations explained are significantly different between the calibration equations with or without the six psychosocial and diet variables for protein density (p = 0.02), but not for energy (p = 0.119) or protein intake (p = 0.077). Conclusions: The addition of psychosocial and diet behavior factors to calibration equations significantly increases the amount of total variance explained for protein density and their inclusion would be expected to strengthen the precision of calibration equations correcting self-report for measurement error.