Examining the Impact of Different Types of Front-of-package Warning Labels for Sugar-sweetened Beverages on Perceived Weight Stigmatization Among a Sample of US Adults
Phase: NA
Conditions: Weight Stigma
Sponsor: University of North Carolina, Chapel Hill

Title: Impact of Front-of-Package Labels on Perceived Weight Stigmatization 
Protocol Date: November 30th 2023 
NCT #: NCT06179043 
 
 
Introduction 
The goal of the analysis described in this document is to use data to be collected 
through an online experiment to examine the impact of different front-of-package label 
types and contents on outcomes related to weight stigma and label effectiveness. This 
document pre-specifies our planned analytic approach prior to data collection. 
Study Protocol 
Participants will complete an online randomized experiment programmed in Qualtrics. 
After providing informed consent and completing an initial parent experiment on alcohol 
messages, participants will be randomly assigned to view 1 of 4 types of front-of-
package labels for sugar-sweetened beverages: neutral control labels, nutrient warning 
labels, text-only health warning labels, or graphic warning labels. In random order, they 
will view and rate two versions of their assigned label type on perceived weight 
stigmatization (primary outcome) and perceived message effectiveness (secondary 
outcome). The two versions will differ only on whether their content references calories 
or obesity (i.e., the modified version will not reference either). After rating each version 
of the label, participants will complete measures of weight bias (secondary outcome) 
and demographic characteristics. 
Hypotheses 
Primary outcome: We hypothesize that graphic health warnings will be perceived as 
most stigmatizing, followed respectively by text-only health warnings, nutrient warnings, 
and control labels (H1). We also hypothesize that, for all label types, the modified 
version will be perceived as less stigmatizing than the regular version of the same label 
(H2). 
Secondary outcomes: We hypothesize that graphic health warnings will be perceived as 
most effective, followed respectively by text-only health warnings, nutrient warnings, 
and control labels (H3). We also hypothesize that graphic health warnings will elicit the 
most weight bias, followed respectively by text-only health warnings, nutrient warnings, 
and control labels (H4). 
Main Analyses 
We will use a two-sided critical alpha of 0.05 to conduct all statistical tests. All 
confidence intervals presented will use a 95% confidence level. Analyses of the primary 
and secondary outcomes will include all participants according to the trial arm to which 
they were randomized. We will use complete case analysis to handle any missing data. 
To prepare the data, we will verify that Cronbach’s alpha for all scales is sufficient 
(≥0.7) and, if so, we will average items to create a mean score on each scale for each 

participant. If Cronbach’s alpha is not sufficient (<0.7), we will drop items as necessary 
to improve reliability or treat items as separate constructs. 
We will first descriptively report unadjusted means for the primary and secondary 
outcomes for each experimental arm. To test H1 and H2 (model 1) and H3 (model 2), 
we will fit separate mixed-effects models with random intercepts to account for repeated 
measures within participants. These models will include indicator variables for label type 
(i.e., nutrients, text-only, and graphic - the control label type will be omitted as the 
referent group), an indicator variable for label version (i.e., regular vs. modified), and 
interaction terms between label type and label version (i.e., nutrients*modified, text-
only*modified, and graphic*modified). To test H4 (model 3), we will fit a linear model 
including only indicators for label type. 
If the interaction terms in models 1 and 2 are not significant, after fitting models 1-3, we 
will use these models to estimate the main average differential effects of label types 
(i.e., differences in predicted means between label types averaging between the two 
version of each label). We will obtain the effect of each label type vs. control, as well as 
of nutrient vs. text-only, text-only vs. graphic, and nutrient vs. graphic label types. If, in 
turn, the interaction terms in models 1 and 2 are significant, after fitting models 1 and 2, 
we will estimate the average differential effects of label types and report them 
separately for each version of the labels (i.e., we will present differences by label type 
separately for regular labels and for modified labels). We will not adjust the p-value for 
each label type compared to control, but will adjust for multiple tests for the three 
additional pairwise comparisons using the Bonferroni-Holm correction. 
Exploratory Analyses 
We will examine whether participant characteristics (i.e., gender, age, race/ethnicity, 
and perceived weight status) moderate the effect of label type (compared to control) on 
the primary outcome. If the main analysis (model 1) reveals that there is no significant 
interaction by label type and label version, we will fit a series of mixed-effects models 
with random intercepts (one for each potential moderator) including label type, label 
version, the moderator, two-way interactions between label type and label version, and 
two-way interactions between label type and the moderator. If, in turn, the main analysis 
(model 1) reveals a significant interaction by label type and label version, we will run 
separate moderation models for regular labels and for modified labels. We will consider 
moderation to be supported if the interaction terms between label types and the 
moderator (in either scenario) are statistically significant, and we will probe such 
significant interactions by calculating the marginal effect of label types on the outcome 
at different levels of the moderating variables. We will use Wald tests for categorical 
moderators with more than 2 categories. 
Sample Size and Power 
This study will occur in a survey that will follow a parent experiment about alcohol 
messages. The total sample size (~2,500 participants) was calculated based on the 
primary outcomes of the parent experiment. 
Using G*Power3.1 and specifying a between-within subjects ANOVA (which should 
most closely approximate the proposed mixed linear model), we determined the 

minimum effect size we would be able to detect on our primary outcome with this pre-
determined sample size. With 80% power, a critical alpha of 0.05, four conditions, and 
two repeated measures, we would be able to detect an effect of f=0.057 (equivalent to 
d=0.11) or larger for the between-subjects factor, and f=0.028 (equivalent to d=0.06) or 
larger for the within-subjects factor. Given that a previous study to analyze the effect of 
FOPLs on perceived weight stigmatization obtained an effect size of d=0.25 comparing 
nutrient warnings to control, this study’s sample should be sufficiently large to detect an 
even smaller effect when comparing between nutrient warnings and text-only health 
warnings, which we expect to be the smallest effect. 
Interim Analysis 
No interim analyses are planned.