Paul A. Estabrooks

The Validity of Self-reported Dietary Intake Data: Focus on the “What We Eat In America” Component of the National Health and Nutrition Examination Survey Research Initiative

See also page XXX T he article by Archer et al 1 in the current issue of Mayo Clinic Proceedings questions the validity and value of memory-based dietary assessment tools when investigating the relationship between dietary intake and health outcomes. In doing so, the authors cast doubt on the evidence base used by the US government’s 2015 Dietary Guidelines Advisory Committee (DGAC) and its actions to improve public health. Archer et al advocate that dietary recall cannot be used to establish relationships between dietary intake and health. They suggest Popper’s criteria for scientific inquirydindependently observable, measurable, falsifiable, valid, and reliabledas the standard by which to judge recall-based assessments. In this editorial, we provide empirical examples that suggest that, by Popper’s criteria, recall measures can be scientifically sound. We also highlight a different perspective that values multiple forms of evidence to determine the scientific appropriateness of measurement instruments, including predictive validity, sensitivity to change, feasibility, and actionability, as well as the goal and design of a given study. Attempting to develop recommendations to improve health is a complex enterprise because of the interactive nature of genetics, environmental factors, and individual behavior; however, one thing is cleardbehaviors matter. Tobacco use, physical inactivity, excessive alcohol consumption, and a poor diet are the leading preventable causes of death in the United States. The body of research that contributed to these findings includes a variety of scientific approaches that range from retrospective and prospective epidemiological studies to randomized controlled trials. One consistency across scientific inquiry and behavioral domains is that participant recall has been used as a representation of behavior, by Archer et al and others (for examples, see references 7 through 10).

Group-based lifestyle sessions for gestational weight gain management: a mixed method approach.

OBJECTIVE To integrate group-based lifestyle sessions (GBLS) within prenatal care for gestational weight gain (GWG) management. METHODS In Study 1, participants attended GBLS during prenatal care visits. Participants in Study 2 attended off-site GBLS whereby care providers were asked to discuss the program with patients. Process and outcome evaluation were conducted through a mixed-methods approach. RESULTS In both pre-experimental feasibility studies, data provide preliminary support for GBLS (eg, positive care provider and patient feedback, weight gain patterns) as well as highlight areas for future research (eg, lack of GWG management discussions, preference for GBLS location). CONCLUSIONS GBLS represents a promising approach to GWG management. Future research should assess the generalizability, sustainably, and compatibility of GBLS within prenatal care.

Beginning A Patient-Centered Approach in the Design of A Diabetes Prevention Program

Objective: The purpose of this study was to identify patient preferences for different components of a local diabetes prevention program that would improve reach.A secondary purpose was to determine if patient characteristics were related to program preferences. Methods: Participants were identified through electronic medical records from two family medicine clinics in Virginia. Participants completed a mailed survey addressing demographics, economic status, risk factors for diabetes, and preferences regarding diabetes prevention interventions—delivery mode, program length, and duration. Results: Twenty-nine percent of eligible participants responded (n = 142); 83% of participants were at risk for diabetes and 82% had a household income <

The association between worksite physical environment and employee nutrition, and physical activity behavior and weight status

20,000.When presented with the choice between a class-based vs. a technology-based program, 83% preferred a technology-based program. Whites were less likely to choose the technology-based program, with no significant differences based on age, education, income, or gender. Conclusions: Contrary to beliefs that lower income individuals may not use technology-based interventions, lower socioeconomic patients indicated a preference for a technology- and telephone-supported diabetes prevention program over in-person class approaches. Findings provide formative data to support the design of a patient-centered, technology-enhanced diabetes prevention program in a real-world setting, thereby increasing potential participation and reach.