No abstract

No abstract

No abstract

Objective: Chronic conditions such as cardiovascular disease and cancer can result from a number of diet-related environmental and behavioral factors. Screening for poor diet is helpful in developing interventions to prevent chronic disease, but measuring dietary behavior can be costly and time-consuming. The purpose of this study was to test the ability of a self-rated, single-item measure for evaluating diet quality among individuals and populations. Methods: A 24-h dietary recall and single-item self-rated diet quality measure were collected for 485 adults. From dietary recalls, Healthy Eating Index-2010 (HEI) scores were computed and compared with self-rated diet quality. Data were collected in 2013 among adult (18 years and older) New York City residents. Results: The study sample was 57% female, 47% white, 56% college educated, and 45% in the highest income tertile. The mean HEI score was 56.5 out of a possible 100. Women averaged higher HEI scores compared to men (58.1 vs 54.3, p = .01). There was a modest yet significant correlation between HEI scores and self-rated diet quality (ρ = 0.29, p < .01). Overall, mean HEI score increased as self-rated diet quality improved (from 48.2 for “poor” to 63.0 for “excellent”). Conclusions: The single-item measure of self-rated diet quality may provide a simple method of identifying those with the worst diet quality. Further investigation of this measure's validity is needed with alternative measures of dietary intake and with health outcomes.

Because it is difficult to objectively measure population-level physical activity levels, self-reported measures have been used as a surveillance tool. However, little is known about their validity in populations living in dense urban areas. We aimed to assess the validity of self-reported physical activity data against accelerometer-based measurements among adults living in New York City and to apply a practical tool to adjust for measurement error in complex sample data using a regression calibration method. We used 2 components of data: 1) dual-frame random digit dialing telephone survey data from 3,806 adults in 2010-2011 and 2) accelerometer data from a subsample of 679 survey participants. Self-reported physical activity levels were measured using a version of the Global Physical Activity Questionnaire, whereas data on weekly moderate-equivalent minutes of activity were collected using accelerometers. Two self-reported health measures (obesity and diabetes) were included as outcomes. Participants with higher accelerometer values were more likely to underreport the actual levels. (Accelerometer values were considered to be the reference values.) After correcting for measurement errors, we found that associations between outcomes and physical activity levels were substantially deattenuated. Despite difficulties in accurately monitoring physical activity levels in dense urban areas using self-reported data, our findings show the importance of performing a well-designed validation study because it allows for understanding and correcting measurement errors.