Evaluating Chile's Proposed Non-nutritive Sweeteners Warning Label Regulation
Phase: NA
Conditions: Diet, Healthy
Sponsor: University of North Carolina, Chapel Hill

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Testing Non-Nutritive Sweetener Front-of-Package Labels in Chile 
NCT06842693 
April 18th, 2025 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Study Protocol and Analytic Plan 
 
Introduction 
This study aims to examine the effects of 2 types of non-nutritive sweetener (NNS) 
warning labels on a sample of Chilean parents’ product selection and perceptions. 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, participants will be randomly assigned to view 1 of 3 
types of labels: a neutral control label, a text NNS warning label (i.e., Chilean NNS 
label), or an octagonal NNS warning label (i.e., Colombian NNS label). 
First, participants will view their assigned label on four similar products (one 
unsweetened, one sweetened with sugar, one sweetened with NNS, and one 
sweetened with both sugar and NNS) displayed in random order. They will then 
complete a task in which they select one of the products to purchase for their child and 
identify which of the products contain NNS. During this task, in all experimental arms, 
products will also display Chile’s current "high in sugar" and "high in calories" labels 
when applicable. This task will be repeated 3 times, each time with a different product 
category (i.e., fruit drinks, yogurts, breakfast cereals), with categories displayed in 
random order. 
Next, participants will view their assigned label on two products containing NNS (i.e., 
flavored milk and cereal bar). For each product, participants will rate the perceived 
message effectiveness of the label, perceived product healthfulness, and perceived 
relative product healthfulness. 
Lastly, after all product exposures, participants will rate their intentions to limit their 
child's NNS consumption. 
 
Hypotheses 
Correct identification of NNS-containing products (co-primary outcome): We predict that, 
compared to the control arm, correct identification of NNS-containing products will be 
higher for participants assigned to the Chilean NNS label arm or to the Colombian NNS 
label arm (H1). 
 
Selection of NNS-sweetened product (co-primary outcome): We predict that, compared 
to the control arm, selection of the NNS-sweetened product will be lower for participants 
assigned to the Chilean NNS label arm or to the Colombian NNS label arm (H2). 
 
Selection of unsweetened product (co-primary outcome): We predict that, compared to 
the control arm, selection of the unsweetened product will be higher for participants 
assigned to the Chilean NNS label arm or to the Colombian NNS label arm (H3). 
 

Selection of sugar-sweetened product (secondary outcome): We predict that, compared 
to the control arm, selection of the sugar-sweetened product will not differ for 
participants assigned to the Chilean NNS label arm or to the Colombian NNS label arm 
(H4). 
 
Perceived message effectiveness (secondary outcome): We predict that, compared to 
the control arm, the perceived message effectiveness (PME) of the labels will be higher 
for participants assigned to the Chilean NNS label arm or to the Colombian NNS label 
arm (H5). 
 
Perceived product healthfulness (secondary outcome): We predict that, compared to the 
control arm, perceived product healthfulness will be lower for participants assigned to 
the Chilean NNS label arm or to the Colombian NNS label arm (H6). 
 
Perceived relative product healthfulness (secondary outcome): We predict that, 
compared to the control arm, perceived relative product healthfulness will be lower for 
participants assigned to the Chilean NNS label arm or to the Colombian NNS label arm 
(H7). 
 
Intentions to limit NNS consumption (secondary outcome): We predict that, compared to 
the control arm, intentions to limit their child’s NNS consumption will be higher for 
participants assigned to the Chilean NNS label arm or to the Colombian NNS label arm 
(H8). 
 
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. 
For all selection/identification tasks, we will code outcomes dichotomously. For correct 
identification of NNS-containing products, we will dichotomize the outcome to correct 
identification of NNS-containing products (coded as 1) vs. incorrect identification (coded 
as 0). Correct identification will be defined as having identified the two NNS-containing 
products in the set and no other products, while incorrect identification will be defined as 
any other scenario. For selection of NNS-sweetened product, we will dichotomize the 
outcome to selection of one of two NNS-sweetened products (coded as 1) vs. selection 
of either of the two non-NNS-sweetened products (coded as 0). For selection of 
unsweetened product, we will dichotomize the outcome to selection of the unsweetened 
product (coded as 1) vs. selection of any of the other three products (coded as 0). For 
selection of sugar-sweetened product outcome, we will dichotomize the outcome to 
selection of one of the two sugar-sweetened products (coded as 1) vs. selection of 
either of the other two non-sugar-sweetened products (coded as 0).  
For dichotomous outcomes, we will first descriptively report unadjusted proportions in 
each experimental arm, including proportions for each product category and for all 
product categories simultaneously (e.g., whether the participant correctly identified the 

NNS-containing products in all three product categories). Next, to test H1-H4, we will 
use logistic mixed-effects regression models, treating the intercept as random to 
account for repeated measures within participants. Models will regress each outcome 
on indicator variables for the experimental arms, product categories, and their 
interactions. After model estimation, we will test the joint statistical significance of the 
interaction terms, and, if not significant, we will drop the interactions from the models. 
Regardless of the presence of the interaction terms in the final model, we will obtain the 
predicted probabilities of each outcome by experimental arm for each separate product 
category, which we will use to conduct a joint test of statistical significance of the 
differences in predicted probabilities across product categories. If such differences are 
jointly significant, we will report average differential effects (ADEs) of each type of NNS 
label (i.e., differences in predicted probabilities between each NNS label type and the 
control label) separately for each product category. Alternatively, if differences are not 
jointly significant, we will report ADEs of each type of NNS label across product 
categories. 
For the PME outcome, we will verify that Cronbach’s alpha is sufficient (>0.7) and 
average each participant’s scores across the three PME items. If Cronbach’s alpha is 
not sufficient (<0.7), we will treat each item as a separate construct instead. Next, for all 
continuous outcomes, we will descriptively report unadjusted means in each 
experimental arm, including means for each product category and averaged across all 
product categories. To test H5-H8, we will use linear mixed-effects regression models. 
Models will regress the outcomes on indicator variables for experimental arms, product 
category, and their interactions. After model estimation, we will test the joint statistical 
significance of the interaction terms. If the interactions are jointly significant, we will 
keep them in the models and report ADEs of each type of NNS label (i.e., differences in 
predicted means between each NNS label type and the control label) separately for 
each product category. Alternatively, if the interactions are not jointly significant, we will 
drop them from the models and report ADEs of each type of NNS label across product 
categories. 
We will use complete case analysis to handle any missing data. 
 
Exploratory Analyses 
Post estimation of each main analytic model, we will obtain exploratory comparisons 
between the outcomes in the two NNS label arms. 
Additionally, we will examine whether gender and/or educational attainment moderates 
the effects of the NNS labels (compared to control) on the co-primary outcomes. If there 
are no significant differences in ADEs by product category in the main analyses, 
moderation analyses will use logistic mixed-effects models with random intercepts 
including indicator variables for experimental arms, the moderator, and their 
interactions. If, alternatively, there are significant differences in ADEs by product 
category in the main analyses, we will use separate regression models for each product 
category. 
 
 

Sample Size and Power 
According to our calculations, among the co-primary outcomes, selection of sugar-
sweetened products requires the largest sample size. Therefore, we powered the study 
to detect differences in selection of sugar-sweetened products between the control arm 
and each of the NNS label arms. Our calculations are based on the assumption that a 
certain proportion of consumers will prefer an unlabeled product over a comparable 
product with any warning label, but will be indifferent between two labeled products 
regardless of the number of labels. The higher the preference for unlabeled products, 
the larger the sample size required to detect differences in selection of sugar-
sweetened products, as demonstrated by the examples below: 
 
 
Assumption: 60% preference for unlabeled products 
 
 
Unsweetened 
NNS-
sweetened 
Sugar-
sweetened 
NNS- and sugar- 
sweetened 
Total sugar-
sweetened 
Control 
0.3* 
0.3* 
0.2 
0.2 
0.4 
Chile NNS 
label 
0.6* 
0.133 
0.133 
0.133 
0.267 
Colombia 
NNS label 
0.6* 
0.133 
0.133 
0.133 
0.267 
* Unlabeled options 
Sample size required (80% power, two-sided two-sample proportions tests): 197 per 
group. 
 
 
Assumption: 90% preference for unlabeled products 
 
 
Unsweetened 
NNS-
sweetened 
Sugar-
sweetened 
NNS- and sugar- 
sweetened 
Total sugar-
sweetened 
Control 
0.45* 
0.45* 
0.05 
0.05 
0.1 
Chile NNS 
label 
0.90* 
0.033 
0.033 
0.033 
0.067 
Colombia 
NNS label 
0.90* 
0.033 
0.033 
0.033 
0.067 
* Unlabeled options 
Sample size required (80% power, two-sided two-sample proportions tests): 1,102 per 
group. 
 
 
If consumers are not indifferent between labeled products with different numbers of 
labels, but instead always prefer fewer warning labels (i.e., the proportions choosing the 
NNS- and sugar-sweetened product, which carries two labels, fall to zero in the NNS 
label arms, with a corresponding increase in the proportions choosing the NNS-

sweetened or the sugar-sweetened products, which both carry one label), the sample 
size required for each of these two examples would be lower (82 and 435 per group, 
respectively). However, since the presence of an NNS warning label along with a sugar 
warning label may indicate lower levels of sugar (compensated for by NNSs), it is not 
clear a priori that consumers would always prefer the product with fewer labels. 
Therefore, to be conservative, we assumed that 90% of participants would prefer 
unlabeled products over comparable products with warning labels, and the remaining 
10% would be indifferent between comparable products with different numbers of 
warning labels. Based on these assumptions, a sample of 1,102 participants per arm 
(3,306 total) provides 80% power to detect differences between the control arm and 
each NNS label arm, using two-sided two-sample proportions tests with a critical alpha 
of 0.05. 
Given the use of repeated measures, this required sample size further assumes that 
respondents will not choose differently across product categories – i.e., that 1,102 is the 
effective sample size. This is a conservative assumption, as within-participant variation 
would increase the effective sample size, thereby reducing the number of participants 
required. 
 
Exclusions and Outliers 
We will exclude participants who complete the survey implausibly quickly (defined as 
<1/3 of the median completion time) and those who completed less than 90% of the 
survey. 
 
Interim Analyses 
No interim analyses are planned.