Ecological Momentary Assessment of Weight Stigma and Eating Behavior in Everyday Life.

BACKGROUND: Weight stigma is widespread, but the existing literature on its harmful consequences remains largely limited to lab-based experiments and large-scale longitudinal designs. PURPOSE: The purpose of this study was to understand how weight stigma unfolds in everyday life, and whether it predicts increased eating behavior. METHODS: In this event-contingent ecological momentary assessment study, 91 participants reported every time they experienced weight stigma and documented whether they ate, how much they ate, and what they ate. These reports were compared against a timepoint when they did not experience stigma. RESULTS: Participants reported a wide variety of stigmatizing events from a variety of sources, with the most common ones being the self, strangers, the media, and family. Multilevel models showed that participants were no more likely to eat post-stigma (vs. the comparison point), but if they did eat, they ate more servings of food (on average consuming 1.45 more servings, or 45% more). Moderation analyses indicated that this effect was amplified for men versus women. CONCLUSION: Experiencing weight stigma appears to beget behavioral changes, potentially driving future weight gain, placing individuals at ever more risk for further stigmatization.

This study looked at how weight stigma in everyday life impacts eating. People reported on episodes of weight stigma and their eating in the next 30 min. Weight stigma came from many different places, including family, strangers, media, and even themselves. Even though people did not necessarily eat more after weight stigma episodes, if they did eat, they ate significantly more food­about 45% more. This relationship was stronger in men than in women. The study also explored whether different kinds of people react differently to weight stigma. Weight stigma experiences led to even more food eaten among people who tended to have high buy-in about negative stereotypes of heavier people, as well as people who thought weight was an important part of their identity. The opposite was seen among people who worried the most about experiencing weight stigma in the future. These findings suggest that experiencing weight stigma may not always prompt people to eat immediately, but when they do eat, they tend to eat more, challenging the idea that weight stigma motivates people to eat less.

Response Category Functioning on the Health Care Engagement Measure Using the Nominal Response Model.

As part of a scale development project, we fit a nominal response item response theory model to responses to the Health Care Engagement Measure (HEM). When using the original 5-point response format, categories were not ordered as intended for six of the 23 items. For the remaining, the category boundary discrimination between Categories 0 (not at all true) and 1 (a little bit true) was only weakly discriminating, suggesting uninformative categories. When the lowest two categories were collapsed, psychometric properties improved greatly. Category boundary discriminations within items, however, varied significantly. Specifically, higher response category distinctions, such as responding 3 (very true) versus 2 (mostly true) were considerably more discriminating than lower response category distinctions. Implications for HEM scoring and for improving measurement precision at lower levels of the construct are presented as is the unique role of the nominal response model in category analysis.

Matching IRT Models to Patient-Reported Outcomes Constructs: The Graded Response and Log-Logistic Models for Scaling Depression.

Item response theory (IRT) model applications extend well beyond cognitive ability testing, and various patient-reported outcomes (PRO) measures are among the more prominent examples. PRO (and like) constructs differ from cognitive ability constructs in many ways, and these differences have model fitting implications. With a few notable exceptions, however, most IRT applications to PRO constructs rely on traditional IRT models, such as the graded response model. We review some notable differences between cognitive and PRO constructs and how these differences can present challenges for traditional IRT model applications. We then apply two models (the traditional graded response model and an alternative log-logistic model) to depression measure data drawn from the Patient-Reported Outcomes Measurement Information System project. We do not claim that one model is "a better fit" or more "valid" than the other; rather, we show that the log-logistic model may be more consistent with the construct of depression as a unipolar phenomenon. Clearly, the graded response and log-logistic models can lead to different conclusions about the psychometrics of an instrument and the scaling of individual differences. We underscore, too, that, in general, explorations of which model may be more appropriate cannot be decided only by fit index comparisons; these decisions may require the integration of psychometrics with theory and research findings on the construct of interest.