Comparison of Methods Used to Correct Self-Reported Protein Intake for Systematic Variation in Reported Energy Intake Using Quantitative Biomarkers of Dietary Intake.

BACKGROUND: Multiple methods of correcting nutrient intake for misreported energy intake have been proposed but have not been extensively compared. The availability of the Women's Health Initiative (WHI) data set, which includes several objective recovery biomarkers, offers an opportunity to compare these corrections with respect to protein intake. OBJECTIVE: We compared 5 energy-correction methods for self-reported dietary protein against urinary nitrogen-derived protein intake. METHODS: As part of the WHI Nutritional Biomarkers Study (NBS) 544 participants (50- to 80-y-old women) completed a FFQ and biomarker assessments using doubly labeled water (DLW) for total energy expenditure (TEE) and 24-h urinary nitrogen. Correction methods evaluated were as follows: 1) DLW-TEE; 2) the Institute of Medicine's (IOM's) estimated energy requirement (EER) TEE prediction equation based on sex, height, weight, and age; 3) published NBS total energy TEE prediction (WHI-NBS-TEE) using age, BMI, race, and income; 4) reported protein versus reported energy linear regression-based residual method; and 5) a Goldberg cutoff to exclude subjects reporting energy intakes <1.35 times their basal metabolic rate. Efficacy was evaluated using correlations obtained by regressing corrected protein against biomarker protein (6.25 × urinary nitrogen/0.81). RESULTS: Unadjusted self-reported protein intake from the FFQ (mean = 66.7 g) correlated weakly (r = 0.31) with biomarker protein (mean = 74.9 g). DLW-TEE-corrected self-reported protein intake (mean = 90.7 g) had the strongest correlation with biomarker protein (r = 0.47). Other energy corrections yielded lower, but still significant correlations: EER, r = 0.44 (mean = 92.1 g); WHI-NBS-TEE, r = 0.37 (mean = 90.4 g); Goldberg cutoff, r = 0.36 (mean = 88.4 g); and residual method, r = 0.35 (mean = 66.7 g). CONCLUSIONS: Our data indicate that proportional correction of reported protein intake using a measure of energy requirement from DLW-TEE or IOM-EER performed modestly better than other methods in this cohort. These energy adjustments, however, yielded corrected protein exceeding the biomarker protein, indicating that energy adjustment alone does not eliminate all self-reported protein reporting bias.

The Wisconsin Early Childhood Obesity Prevention Initiative: An Example of Statewide Collective Impact.

INTRODUCTION: The Wisconsin Early Childhood Obesity Prevention Initiative (Initiative), established in 2007, seeks to address and prevent obesity in the early care and education system through nutrition and physical activity environmental and policy changes. The collaborative includes professionals from 3 state of Wisconsin Departments, the University of Wisconsin-Extension, the University of Wisconsin-Madison, and public health and early care and education organizations. This paper explores the efforts of the Initiative to advance our understanding of collective impact in practice and its value to health promotion efforts. METHODS: Evaluators conducted a mixed methods case study to evaluate the application of collective impact principles by the Initiative. This included a survey of Initiative partners, review of archival documents, and qualitative interviews with Initiative leaders. RESULTS: Initiative partners noted progress in establishing the conditions for collective impact. Archival documents and interviews describe both formal and informal practices that helped set a common agenda, align and coordinate partner activities, and promote communication among Initiative leaders. Results also detail the important current and potential roles of "backbone" staff from healthTIDE to support the Initiative. Additionally, results suggest particularly challenging aspects of the Initiative's impact model related to shared measurement and broader stakeholder communication. While the Initiative is still setting in place the conditions for collective impact, it has achieved significant policy, systems, and environment changes since its formation. Inclusion of nutrition and physical activity criteria in the state's quality rating improvement system for child care centers is one of its outcomes. CONCLUSIONS: This case study offers several important insights about the application of collective impact in health promotion efforts, particularly in relation to the transition from previous collaborative activities, the value of establishing a clear common agenda among partners, the roles of backbone staff, and time and partner relationships in collective impact.