OBJECTIVE: Investigation of relative bias in diet history measurement during dietary intervention trials. DESIGN: Retrospective analysis of human dietary data from two randomised controlled trials examining modified fat diets in the prevention and treatment of type II diabetes mellitus. SETTING: Wollongong, Australia. SUBJECTS: Thirty-five overweight, otherwise healthy subjects in trial 1 and 56 subjects with diabetes in trial 2. INTERVENTIONS: Diet history interviews and three-day weighed food records administered at one-month intervals in trial 1 and three-month intervals in trial 2. RESULTS: In a cross-sectional bias analysis, graphs of the association between bias and mean dietary intake showed that bias decreased in higher carbohydrate consumers in trial 1 (r = -0.344, P < 0.05). No other significant associations were found. In a longitudinal analysis, bias did not change over time in either trial. There were no significant differences in bias magnitudes between the trials, with the exception of monounsaturated fat measurement where bias was significantly greater and more positive in trial 2, indicating overestimation of monounsaturated fat intake with the diet history. Subjects in control and intervention groups underestimated energy, fat, saturated fat and alcohol intakes with the diet history in both trials. Overweight and obese individuals appeared to make the greatest contribution to the overall underestimation of saturated fat intake by the diet history regardless of whether they were in the control or intervention group and whether they were healthy or had diabetes. CONCLUSION: Bias in diet history measurement appears to be macronutrient-specific, with energy, fat and saturated fat consistently underreported in the interview by subjects with and without diabetes and in both intervention and control groups in a dietary intervention trial. Relative bias analysis appears to be an informative tool in quality control for dietary intervention trials when biochemical markers are unavailable.
OBJECTIVE: Investigation of relative bias in diet history measurement during dietary intervention trials. DESIGN: Retrospective analysis of human dietary data from two randomised controlled trials examining modified fat diets in the prevention and treatment of type II diabetes mellitus. SETTING: Wollongong, Australia. SUBJECTS: Thirty-five overweight, otherwise healthy subjects in trial 1 and 56 subjects with diabetes in trial 2. INTERVENTIONS: Diet history interviews and three-day weighed food records administered at one-month intervals in trial 1 and three-month intervals in trial 2. RESULTS: In a cross-sectional bias analysis, graphs of the association between bias and mean dietary intake showed that bias decreased in higher carbohydrate consumers in trial 1 (r = -0.344, P < 0.05). No other significant associations were found. In a longitudinal analysis, bias did not change over time in either trial. There were no significant differences in bias magnitudes between the trials, with the exception of monounsaturated fat measurement where bias was significantly greater and more positive in trial 2, indicating overestimation of monounsaturated fat intake with the diet history. Subjects in control and intervention groups underestimated energy, fat, saturated fat and alcohol intakes with the diet history in both trials. Overweight and obese individuals appeared to make the greatest contribution to the overall underestimation of saturated fat intake by the diet history regardless of whether they were in the control or intervention group and whether they were healthy or had diabetes. CONCLUSION: Bias in diet history measurement appears to be macronutrient-specific, with energy, fat and saturated fat consistently underreported in the interview by subjects with and without diabetes and in both intervention and control groups in a dietary intervention trial. Relative bias analysis appears to be an informative tool in quality control for dietary intervention trials when biochemical markers are unavailable.
Authors: Tom E Nightingale; Jean-Philippe Walhin; James E Turner; Dylan Thompson; James L J Bilzon Journal: Trials Date: 2016-06-08 Impact factor: 2.279
Authors: Richard B Lopez; Pin-Hao A Chen; Jeremy F Huckins; Wilhelm Hofmann; William M Kelley; Todd F Heatherton Journal: Soc Cogn Affect Neurosci Date: 2017-05-01 Impact factor: 3.436