Dataset Information

Testing an online measure of portion size selection: a pilot study concerned with the measurement of ideal portion size.

ABSTRACT:

Background

Portion size is known to be a key driver of food intake. As consumed portions are often pre-planned, 'ideal portion size'-an individual's preferred meal size selected prior to eating-has been identified as a strong predictor of actual consumption. However, assessments of ideal portion size have predominantly relied on laboratory-based computer tasks, limiting use online. Therefore, this cross-sectional study sought to pilot test the validity of a web-based tool to measure ideal portion size.

Methods

In an online study (N = 48), participants responded to images of a range of foods. Each food was photographed in a series of different portions and loaded into an 'image carousel' that would allow participants to change the size of the displayed portion by moving a slider left-to-right. Using this image carousel, participants selected their ideal portion size. They also completed measures of expected satiety and expected satiation and self-reported their age and body mass index (BMI). A non-parametric correlation matrix was used to explore associations between ideal portion size and identified predictors of food intake.

Results

Supporting convergent validity of this measure, ideal portion size was significantly correlated with expected satiety (r_s = .480) and expected satiation (r_s = -.310) after controlling for effects of baseline hunger and fullness, consistent with past research. Similarly, supporting divergent validity of this measure, ideal portion size was not significantly correlated with age (r_s = -.032) or BMI (r_s = -.111,).

Conclusions

Pilot results support the validity of this web-based portion size selection tool used to measure ideal portion size, though further research is needed to validate use with comparisons to actual food intake.

SUBMITTER: Embling R

PROVIDER: S-EPMC8446476 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background Structural racism is a complex system of inequities working in tandem to cause poor health for communities of color, especially for Black people. However, the multidimensional nature of structural racism is not captured by existing measures used by population health scholars to study health inequities. Multidimensional measures can be made using complex analytical techniques. Whether or not the multidimensional measure of structural racism provides more insight than the existing unidimensional measures is unknown. Methods We derived measures of Black-White residential segregation, inequities in education, employment, income, and homeownership, evaluated for 2,338 Public Use Microdata Areas (PUMAs) in the United States (US), and consolidated them into a multidimensional measure of structural racism using a latent class model. We compared the median COVID-19 vaccination rates observed across 54 New York City (NYC) PUMAs by levels (high/low) of structural racism and the multidimensional class using the Kruskal-Wallis test. This study was conducted in March 2021. Findings Our latent class model identified three structural racism classes in the US, all of which can be found in NYC. We observed intricate interactions between the five dimensions of structural racism of interest that cannot be simply classified as “high” (i.e., high on all dimensions of structural racism), “medium,” or “low.” Compared to Class A PUMAs with the median rate of two-dose completion of 6·9%, significantly lower rates were observed for Class B PUMAs (5·5%, p = 0·04) and Class C PUMAs (5·2%, p = 0·01). When the vaccination rates were evaluated based on each dimension of structural racism, significant differences were observed between PUMAs with high and low Black-White income inequity only (7·2% vs. 5·3%, p = 0·001). Interpretation Our analysis suggests that measuring structural racism as a multidimensional determinant of health provides additional insight into the mechanisms underlying population health inequity vis-à-vis using multiple unidimensional measures without capturing their joint effects. Funding This project is funded by the Robert J. Jones Urban Research and Outreach-Engagement Center, University of Minnesota. Additional support is provided by the Minnesota Population Center, which is funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant P2C HD041023).