Abstract
Objectives. To test whether countermarketing messages for sugary drinks lead to lower intentions to consume sugary drinks and less perceived weight stigma than health education messages.
Methods. In August 2023, we conducted an online randomized controlled trial with US adults (n = 2169). We assessed the effect of countermarketing messages, health education messages, and neutral control messages on intentions to consume sugary drinks and perceived weight stigma.
Results. Both countermarketing messages (Cohen d = −0.20) and health education messages (d = −0.35) led to lower intentions to consume sugary drinks than control messages (Ps < .001). However, both types of messages elicited more perceived weight stigma than control messages (ds = 0.87 and 1.29, respectively; Ps < .001). Countermarketing messages were less effective than health education messages at lowering intentions to consume sugary drinks (d for countermarketing vs health education = 0.14) but also elicited less perceived weight stigma than health education messages (d = −0.39; Ps < .01).
Conclusions. Countermarketing messages show promise for reducing sugary drink consumption while eliciting less weight stigma than health education messages, though they may need to be refined further to minimize weight stigma and maximize effectiveness.
Clinical Trial Number. ClinicalTrials.gov NCT05953194. (Am J Public Health. 2024;114(12):1354–1364. https://doi.org/10.2105/AJPH.2024.307853)
Consuming sugary drinks increases risk of heart disease, high blood pressure, type 2 diabetes, and tooth decay.1 Despite some declines in sugary drink consumption over the last decade, nearly two thirds of adults in the United States consume at least 1 sugary drink every day,1 suggesting that new strategies are needed to reduce sugary drink consumption. Media campaigns (including those delivered through print, broadcast, and social media) are a highly scalable strategy for increasing knowledge, shifting attitudes and norms, and ultimately encouraging people to adopt healthier behaviors,2 including limiting sugary drink consumption.3–5
Most sugary drink media campaigns are health education campaigns that contain messages seeking to reduce sugary drink consumption by educating consumers about the health harms of sugary drinks.3 Recently, however, researchers and health departments have begun to develop a new approach to sugary drink media campaigns borrowed from tobacco control: countermarketing.3,6 Countermarketing campaigns seek to reduce consumption of unhealthy products by exposing and undermining deceptive, manipulative, misleading, or harmful marketing activities used by the companies that make them.6 Countermarketing campaigns have been successfully used to reduce smoking,6 prompting researchers to begin applying countermarketing to discourage people from consuming unhealthy foods and beverages.7–10 Although few real-world countermarketing campaigns for sugary drinks have been evaluated, 2 studies found that parents exposed to these campaigns had lower intentions to serve sugary drinks and were less likely to select sugary drinks for their children compared with parents exposed to control campaigns not about beverages.11,12
As interest grows in using countermarketing campaigns to reduce sugary drink consumption, it is important to compare messages for countermarketing campaigns to messages for health education campaigns on both effectiveness and unintended consequences. One potential advantage of countermarketing messages is that they are designed to counteract the effects of sugary drink marketing,6 whereas health education messages typically do not address marketing. Sugary drink marketing uses positive, emotional messaging to encourage consumers to develop lasting positive attitudes toward marketed brands7 and purchase more sugary drinks.8 Countermarketing messages aim to undermine the effects of marketing by exposing marketing practices that are deceptive, manipulative, misleading, or harmful. For example, the Hawaii Department of Health’s “Sweet Lies” countermarketing campaign exposes how companies put marketing claims like “natural” and “100% vitamin C” on fruit-flavored drinks to mislead consumers into thinking these drinks are healthier than they are.9 By exposing deceptive, manipulative, misleading, and harmful marketing practices, countermarketing messages increase negative attitudes toward sugary drink companies and, in turn, reduce sugary drink consumption. Moreover, by exposing these marketing practices, countermarketing messages may be especially motivating to young adults, who tend to be more motivated by proximal outcomes (like not wanting to be manipulated by sugary drink marketing practices) than distal outcomes (like diabetes or heart disease).10,13
A second potential advantage of countermarketing messages is that they emphasize industry accountability for body weight, whereas some health education campaigns (implicitly or explicitly) emphasize individual responsibility for body weight. Emphasizing individual responsibility for body weight can lead people to devalue or reject people with higher body weight (i.e., can increase weight stigma).14 By focusing on industry accountability rather than individual responsibility for body weight, countermarketing messages could elicit less weight stigma than health education campaigns. Minimizing weight stigma is a critical goal for public health campaigns given that weight stigma reduces diet quality and worsens mental and physical health.15,16
Despite the potential benefits of countermarketing over health education, few studies have compared adults’ responses to countermarketing and health education messages.3 To address this gap, we examined whether countermarketing messages for sugary drinks lead to lower intentions to consume sugary drinks and less perceived weight stigma than health education messages. We also examined whether countermarketing messages’ beneficial effects on intentions are larger among young adults compared with older adults.
METHODS
On August 7, 2023, we recruited a national convenience sample of US adults through the survey company CloudResearch Connect. CloudResearch recruits adults to its panel using online advertisements and word of mouth. CloudResearch uses e-mail and web dashboard invitations to invite panelists to participate in specific research studies.
Participants were eligible for this study if they lived in the United States and were aged 18 years or older. To maximize statistical power to detect moderation by age group, we used quotas to ensure that approximately half of participants were young adults (aged 18–29 years) and half were older adults (aged ≥ 30 years).
Approach
The study was guided by a conceptual model of how countermarketing and health education messages affect behavior (Appendix Figure A, available as a supplement to the online version of this article at https://ajph.org). The study adopted a 3-arm, between-subjects, randomized controlled design. We randomized participants to 1 of 3 trial arms with a 1:1:1 simple allocation ratio: (1) countermarketing messages discouraging sugary drink consumption, (2) health education messages discouraging sugary drink consumption, or (3) control messages (neutral messages about safe driving). The survey software automatically randomized participants. Figure 1 depicts the CONSORT diagram.
FIGURE 1—
CONSORT Flow Diagram
aOne third of median completion time (median = 10 minutes, 16 seconds).
Message and Image Design
We developed the text and images for the study based on existing media campaigns and best practices. The countermarketing messages followed principles of effective countermarketing campaigns, including describing industry manipulation of consumers, appealing to emotions, describing health consequences, and criticizing the industry for using targeted marketing.6 The health education messages focused on describing the sugar content in sugary drinks and the health consequences of sugary drink consumption (e.g., weight gain, diabetes), using text and images adapted from previous campaigns.3 The control messages were matched to the countermarketing and health education messages on length, but discussed a neutral topic unrelated to sugary drinks (safe driving), similar to a previous study.11 The control messages were adapted from messages created by the National Highway Traffic Safety Administration. All messages were presented as Instagram posts to increase realism. Messages were matched for approximate length across the 3 trial arms (27–49 words each, including words in the main image plus the caption). We did not conduct formal pretesting of the messages. Sample messages and images are shown in Figure 2, and all messages and images are shown in Appendix Figure B.
FIGURE 2—
Example Messages and Images Used in the Trial for (a) Countermarketing Messages Discouraging Sugary Drink Consumption, (b) Health Education Messages Discouraging Sugary Drink Consumption, and (c) Control Messages (Neutral Messages About Safe Driving)
Note. All messages and images are shown in Appendix Figure B (available as a supplement to the online version of this article at https://ajph.org).
Procedures
Participants provided electronic informed consent and completed an online survey programmed in Qualtrics. Participants first answered unrelated survey questions about environmental sustainability labels for a separate study, then completed the randomized trial for the present study. For the present study, participants viewed the 3 messages from their randomly assigned arm 1 at a time (displayed in order, see Appendix Figure B). The survey displayed each message for 10 seconds before participants could advance to the next message. After viewing their messages, participants answered survey questions as described in the next section.
Measures
The primary outcome was intentions to consume sugary drinks. We focused on intentions because a meta-analysis of experiments demonstrated that changing intentions leads to changes in behavior.17 We assessed intentions to consume sugary drinks with 2 items18 (e.g., “In the next week, I plan to drink sugary drinks like sodas, sports drinks, or fruit drinks”).
Guided by our conceptual model, the survey also assessed 4 sets of secondary outcomes. First, we assessed perceived weight stigma of the messages using 3 items19 (e.g., “These messages increase blame towards people for being overweight”). Second, we assessed 3 message reactions: perceived message effectiveness for discouraging sugary drink consumption, negative feelings about consuming sugary drinks, and anticipated social interactions, each assessed with 1 item.18,20 Third, we assessed negative attitudes toward sugary drink companies using 1 item.21 Fourth, we assessed message reactance (i.e., opposition to messages because of feelings that one’s autonomy is being threatened) using 2 items.22 We excluded anger toward the message from our measure of message reactance because including it substantially reduced internal consistency: Cronbach α including anger = 0.49; Spearman– Brown reliability coefficient excluding anger = 0.69. Response options for all items ranged from low (coded as 1) to high (coded as 5). We selected these outcomes because they may be indicative of messages’ potential to elicit long-term behavior change.17,23,24 We show all survey items in Appendix Table A.
Finally, the survey assessed participant characteristics including potential moderators such as age, gender, trait reactance (i.e., a predisposition to perceiving situations as threatening one’s freedom25), body mass index (BMI, defined as weight in kilograms divided by the square of height in meters [kg/m2]), and perceived weight status.26
Analysis
We preregistered the analysis plan before data collection (https://aspredicted.org/HY8_KVV). We used ordinary least squares regression, regressing each outcome on indicator variables for trial arm (excluding the control as the referent). We used the models to estimate average differential effects (i.e., differences in predicted means between arms) for the countermarketing and health education arms versus the control arm and to test whether the effects of the countermarketing and health education arms differed from one another. We also converted the average differential effects to Cohen ds (i.e., standardized effects) to interpret whether differences were small (d = 0.20), medium (d = 0.50), or large (d = 0.80).27
We conducted 3 sets of planned exploratory moderation analyses (all preregistered except where noted). First, we examined whether the effects of trial arm on intentions to consume sugary drinks were moderated by age, gender, race, ethnicity, educational attainment, income, or trait reactance (the race, ethnicity, education, and income tests were not preregistered). Second, we examined whether the effects of trial arm on perceived weight stigma were moderated by gender, BMI, or perceived weight status. Third, we examined whether the effects of trial arm on message reactance were moderated by age, gender, or trait reactance. Moderation analyses used the same model as the main analyses with additional terms for the moderator and interactions between the moderator and the trial arms. For all moderation analyses, we tested for moderation by examining the joint significance of the coefficients on all interaction terms and report the effect of the countermarketing and health education messages at each level of the categorical moderators and at the mean plus or minus 1 standard deviation of the continuous moderators.
Previous studies of countermarketing messages have found effects on sugary drink intentions or selection of approximately Cohen d = 0.2 to 0.4.11,13 To be conservative, we estimated sample size needs to detect a somewhat smaller standardized effect size of d = 0.15. Analyses indicated that a sample size of 2097 participants (699 per arm) would provide 80% power to detect a standardized difference between arms of Cohen d = 0.15 or larger, assuming α = 0.05. To account for potential missing data, we aimed to recruit approximately 2150 participants. Per the preregistration, analyses excluded participants who did not complete the survey, yielding an analytic sample of 2169 participants (Figure 1).
All analyses used 2-tailed statistical tests with α = 0.05. Analyses were conducted in Stata version 18 (StataCorp LLC, College Station, TX) and were replicated by a second analyst.
RESULTS
Approximately half (49%) of participants were young adults (aged 18–29 years), consistent with recruitment goals (Table 1). Approximately 71% identified as White, 12% as Black or African American, 9% as Asian or Pacific Islander, 6% as other or multiracial, and 1% as American Indian or Alaskan Native. Approximately one third (34%) had educational attainment of some college or less, and 36% had a household income less than $50 000 per year. Compared with the US population, the sample had a higher proportion of young adults (consistent with recruitment goals), people identifying as White or Asian or Pacific Islander, people with a college degree, and people with a household income less than $75 000, and a lower proportion of people identifying as Latino and with a household income of $75 000 or more (Appendix Table B).
TABLE 1—
Participant Characteristics in an Online Randomized Controlled Trial of Countermarketing, Health Education, and Control Messages: United States, 2023
| Characteristic | Countermarketing Messages (n = 724), No. (%) | Health Education Messages (n = 722), No. (%) | Control Messages (n = 723), No. (%) |
| Age, y | |||
| 18–29 | 360 (50) | 362 (50) | 350 (48) |
| 30–44 | 236 (33) | 219 (30) | 236 (33) |
| 45–59 | 89 (12) | 93 (13) | 98 (14) |
| ≥ 60 | 39 (5) | 48 (7) | 39 (5) |
| Gender | |||
| Woman | 341 (47) | 339 (47) | 323 (45) |
| Man | 361 (50) | 369 (51) | 388 (54) |
| Nonbinary or another gender | 22 (3) | 14 (2) | 12 (2) |
| Race | |||
| American Indian/Alaska Native | 7 (1) | 14 (2) | 8 (1) |
| Asian or Pacific Islander | 70 (10) | 64 (9) | 70 (10) |
| Black or African American | 100 (14) | 89 (12) | 77 (11) |
| White | 486 (67) | 510 (71) | 535 (74) |
| Other or multiracial | 61 (8) | 45 (6) | 33 (5) |
| Latino(a) or Hispanic | 71 (10) | 87 (12) | 78 (11) |
| Education | |||
| High-school diploma or less | 104 (14) | 89 (12) | 96 (13) |
| Some college | 147 (20) | 141 (20) | 154 (21) |
| College graduate or associates degree | 371 (51) | 391 (54) | 353 (49) |
| Graduate degree | 102 (14) | 101 (14) | 120 (17) |
| Household income, annual, $ | |||
| 0‒24 999 | 102 (14) | 96 (13) | 95 (13) |
| 25 000‒49 999 | 154 (21) | 179 (25) | 163 (23) |
| 50 000‒74 999 | 149 (21) | 159 (22) | 147 (20) |
| ≥ 75 000 | 318 (44) | 288 (40) | 318 (44) |
| Household size | |||
| 1–2 | 337 (47) | 355 (49) | 334 (46) |
| 3–4 | 299 (41) | 295 (41) | 308 (43) |
| ≥ 5 | 88 (12) | 72 (10) | 81 (11) |
| No. of children | |||
| 0 | 477 (66) | 497 (69) | 485 (67) |
| 1–2 | 212 (29) | 198 (27) | 206 (28) |
| ≥ 3 | 35 (5) | 27 (4) | 32 (4) |
| Political party identification | |||
| Democrat | 424 (59) | 389 (54) | 411 (57) |
| Republican | 170 (23) | 180 (25) | 168 (23) |
| Independent or other | 130 (18) | 153 (21) | 144 (20) |
Note. The sample size was n = 2169 US adults. Missing data ranged from 0.0% to 0.05%. Percentages may not sum to 100% because of rounding.
Intentions to Consume Sugary Drinks
Both the countermarketing messages (difference vs control = −0.27; 95% confidence interval [CI] = −0.40, ‒0.13; P < .001; Cohen d = −0.20) and health education messages (difference vs control = −0.45; 95% CI = −0.58, ‒0.31; P < .001; d = −0.35) led to lower intentions to consume sugary drinks than the control messages (Table 2). The effect of the countermarketing messages on intentions to consume sugary drinks was weaker than the effect of the health education messages (difference, countermarketing vs health education = 0.18; 95% CI = 0.05, 0.31; P = .009; d = 0.14).
TABLE 2—
Effects of Health Education Messages and Countermarketing Messages on Intentions, Perceived Weight Stigma, Message Reactions, Negative Attitudes Toward Sugary Drink Companies, and Message Reactance in US Adults, 2023
| Outcome | Countermarketing Messages, Mean (SD) | Health Education Messages, Mean (SD) | Control Messages, Mean (SD) | Countermarketing vs Control | Health Education vs Control | Countermarketing vs Health Education | |||
| ADE (95% CI) | d | ADE (95% CI) | d | ADE (95% CI) | d | ||||
| Intentions to consume sugary drinks | 2.53 (1.32) | 2.35 (1.25) | 2.80 (1.33) | −0.27 (−0.40, −0.13) | −0.20 | −0.45 (−0.58, −0.31) | −0.35 | 0.18 (0.05, 0.31) | 0.14 |
| Perceived weight stigma | 1.93 (1.05) | 2.36 (1.13) | 1.19 (0.59) | 0.74 (0.64, 0.84) | 0.87 | 1.16 (1.07, 1.26) | 1.29 | −0.43 (−0.53, −0.33) | −0.39 |
| Message reactions | |||||||||
| Perceived message effectiveness for discouraging sugary drinks | 3.59 (1.15) | 3.94 (1.08) | 1.30 (0.79) | 2.29 (2.18, 2.39) | 2.31 | 2.63 (2.53, 2.74) | 2.79 | −0.35 (−0.45, −0.24) | −0.31 |
| Anticipated social interactions about messages | 2.42 (1.22) | 2.54 (1.25) | 1.81 (1.12) | 0.61 (0.48, 0.73) | 0.52 | 0.73 (0.61, 0.86) | 0.62 | −0.13 (−0.25, −0.002) | −0.10 |
| Negative feelings about consuming sugary drinks | 4.10 (0.90) | 4.33 (0.90) | 3.07 (0.54) | 1.03 (0.95, 1.11) | 1.39 | 1.26 (1.18, 1.35) | 1.71 | −0.23 (−0.32, −0.15) | −0.26 |
| Negative attitudes about sugary drink companies | 3.44 (0.91) | 3.34 (0.89) | 3.09 (0.86) | 0.35 (0.26, 0.44) | 0.40 | 0.25 (0.16, 0.35) | 0.29 | 0.10 (0.005, 0.19) | 0.11 |
| Message reactance | 2.54 (1.02) | 2.35 (1.04) | 1.91 (0.92) | 0.63 (0.53, 0.74) | 0.65 | 0.44 (0.33, 0.54) | 0.44 | 0.20 (0.09, 0.30) | 0.19 |
Note. ADE = average differential effect; CI = confidence interval; d = Cohen d. The sample size was n = 2169 US adults. There were no missing data on any outcome.
We did not observe evidence that the effect of trial arm on intentions to consume sugary drinks was moderated by gender, race, ethnicity, educational attainment, income, or trait reactance (all Ps for interactions > 0.44; Appendix Table C). Similarly, the interaction terms for age did not reach statistical significance (P for interaction = 0.08), though the descriptive pattern suggested potential differences by age. Among older adults (≥ 30 years), the countermarketing messages appeared to have a weaker effect on intentions to consume sugary drinks than the health education messages (differences vs control = −0.31; 95% CI = −0.50, ‒0.12; d = −0.23 and −0.60; 95% CI = −0.79, ‒0.41; d = −0.45, respectively; Appendix Table C). By contrast, among young adults (aged 18‒29 years), the countermarketing messages had similar effects on intentions to consume sugary drinks as the health education messages (differences vs control = −0.23; 95% CI = −0.42, ‒0.04; d = −0.18 and −0.30; 95% CI = −0.49, ‒0.12; d = −0.25, respectively).
Perceived Weight Stigma
Both the countermarketing messages and the health education messages led to higher perceived weight stigma than the control messages (differences vs control = 0.74; 95% CI = 0.64, 0.84; d = 0.87 and 1.16; 95% CI = 1.07, 1.26; d = 1.29, respectively; both Ps < .001). The countermarketing messages led to lower perceived weight stigma than the health education messages (difference = −0.43; 95% CI = −0.53, ‒0.33; P < .001; d = −0.39).
Effects of the countermarketing and health education messages on perceived weight stigma appeared to be moderated by gender (P for interaction < .001), BMI (P for interaction = .002), and perceived weight status (P for interaction = .07; Appendix Table D). Specifically, the detrimental effects of both the countermarketing and health education messages on perceived weight stigma appeared to be stronger for participants who identified as women compared with men, who reported higher compared with lower BMI, and who perceived themselves to be overweight or (for the health education messages only) underweight compared with those who perceived themselves to be “about the right weight.”
Message Reactions
Both the countermarketing messages and the health education messages led to stronger message reactions than the control messages, including higher perceived message effectiveness for discouraging sugary drink consumption, more anticipated social interactions about the messages, and more negative feelings about consuming sugary drinks than the control messages (all Ps < .001, Table 2). For all 3 message reactions, the countermarketing messages led to weaker message reactions than the health education messages (all Ps < .05).
Negative Attitudes Toward Sugary Drink Companies
Both the countermarketing messages and the health education messages led to more negative attitudes toward sugary drink companies than the control messages (Ps < .001; Table 2). The countermarketing messages led to more negative attitudes toward sugary drink companies than the health education messages (P = .04).
Message Reactance
Both the countermarketing messages and the health education messages led to higher message reactance than the control messages, with a larger effect for the countermarketing messages (all Ps < .001). The effects of the health education and countermarketing messages on message reactance did not appear to be moderated by age, gender, or trait reactance (Ps > .11; Appendix Table E).
DISCUSSION
In this online randomized trial with a large national sample of US adults, both countermarketing and health education messages led to lower intentions to consume sugary drinks. Both countermarketing and health education messages, however, elicited more perceived weight stigma than control messages. The countermarketing messages were not as effective as the health education messages in reducing intentions to consume sugary drinks in the overall sample, but they elicited substantially less perceived weight stigma. These results suggest that countermarketing messages could serve as a useful and potentially less-stigmatizing alternative to health education messages for reducing sugary beverage consumption, but they will need to be further refined to maximize their effectiveness and minimize their unintended effects on weight stigma.
Both the countermarketing messages and the health education messages reduced participants’ intentions to consume sugary drinks. Both types of messages also increased perceived discouragement from consuming sugary drinks, negative feelings about consuming sugary drinks, and anticipating talking with others about the messages. Because these outcomes can be predictive of behavior change,17,18,20,23 our results add to the growing body of evidence that exposure to sugary drink messages—whether countermarketing or health education messages—could improve diet-related outcomes.2,3 Exploratory moderation analyses revealed evidence of potentially differential effects on intentions by age. For older adults, the countermarketing messages appeared to have weaker effects on decreasing intentions to consume sugary drinks than the health education messages. For young adults, however, the countermarketing messages performed similarly to the health education messages. Countermarketing messages may therefore be a promising avenue for improving young adults’ dietary behavior, an important finding given that young adults consume more sugary drinks than older adults1 and that their dietary behaviors track into later adulthood.28 However, given the exploratory nature of our moderation analyses, replication of this finding is warranted.
Both the health education and the countermarketing messages led to higher perceived weight stigma than the control messages, especially for people who identified as women and those with higher BMI. These findings are potentially concerning given that weight stigma is widespread and harmful to mental and physical health.15,16 Importantly, the countermarketing messages led to less perceived weight stigma than the health education messages, perhaps because the health education messages focused on the health consequences of sugary drinks. This focus may have implied that individuals are primarily responsible for their body weight,29 an attribution of responsibility that tends to increase weight stigma.15 By contrast, the countermarketing messages focused on deceptive, misleading, and harmful industry marketing practices and may therefore have implied that industry is at least partially responsible for body weight.6 Although more refinement is needed, our results suggest that countermarketing messages may be a strategy for improving diet-related behaviors while reducing harmful unintended effects on weight stigma.
The countermarketing messages elicited stronger negative attitudes toward sugary drink companies than both the control messages and the health education messages, perhaps because they emphasized deceptive industry marketing practices. This finding is important because increasing negative attitudes toward companies could be a mechanism through which countermarketing messages reduce consumption of unhealthy foods and beverages,30 though it will be challenging to fully counter the persuasive effects of the beverage industry’s marketing practices. Still, eliciting negative attitudes toward sugary drink companies could have the additional benefit of boosting the public’s support for wider policy changes to reduce sugary drink consumption.6 Campaign developers may therefore wish to focus countermarketing efforts toward communities considering sugary drink reduction policies so that campaigns can simultaneously support both individual behavior change and policy adoption.
The countermarketing messages elicited more message reactance than the health education messages, which could reduce their ability to change behavior given that reactance is theorized to undermine the beneficial effects of health messages.31 However, research has found that the benefits of evocative messages for spurring behavior change can outweigh their effects on reactance.24
Limitations
This study had several limitations. First, we recruited a convenience sample that differed somewhat from the US population, perhaps in part because we oversampled young adults. However, gender, race, ethnicity, education, and income did not moderate the effect of the messages on intentions. Moreover, previous studies indicate that randomized experiments conducted with convenience samples yield similar results as those conducted with nationally representative samples,32,33 including when analyzing specific subgroups.33 Second, participants had only 1 brief exposure to the messages in the context of an online survey. Repeated or longer exposure to the messages could yield larger effects,34 while exposure in naturalistic settings with more competition for audience attention could yield smaller effects.
Third, we measured self-reported outcomes (e.g., intentions), so effects on behavioral outcomes (e.g., purchases or consumption) remain unknown. A meta-analysis of experiments found that medium-to-large changes in intentions (d = 0.64) lead to small-to-medium changes in behavior (d = 0.41).17 In our study, a single exposure to the messages led to small-to-medium changes in intentions, suggesting that these messages could lead to small reductions in sugary drink consumption. Small reductions in sugary drink consumption can have meaningful public health benefits,35 but additional strategies beyond media campaigns will be needed to address diet-related chronic diseases. Moreover, the success of media campaigns may depend on the resources available to develop and disseminate them; social media may be a promising avenue for dissemination given its relatively low cost per person reached.
Fourth, we did not pretest the messages used in the trial; future studies could refine messages with qualitative or quantitative pretesting to maximize their beneficial effects and minimize unintended consequences. Fifth, it is possible that other features of messages, such as valence or tone, could influence message effectiveness, but we did not test this directly. Sixth, trial messages focused only on discouraging sugary drink consumption, but media campaigns may be even more effective if they also encourage water consumption.11
Key strengths of the study include the randomized design, the realistic messages and images that mirrored recent real-world campaigns, and measurement of both intended and unintended consequences.
Conclusions
This randomized trial with a large sample of US adults suggests that countermarketing messages hold promise for reducing sugary drink consumption—especially among young adults—while eliciting less perceived weight stigma than health education messages. Public health departments and nonprofits interested in using media campaigns to reduce sugary drink consumption should consider adopting countermarketing messages in addition to health education messages, though countermarketing messages might need to be refined further to minimize weight stigma and maximize effects on behavior.
ACKNOWLEDGMENTS
This project was funded in part by a grant from the Department of Population Medicine, Harvard Pilgrim Health Care Institute to J. Petimar and A. H. Grummon.
We thank Sara Cathey for graphic design support.
CONFLICTS OF INTEREST
The authors have no conflicts of interest to disclose.
HUMAN PARTICIPANT PROTECTION
The Stanford University (69580) and Harvard Pilgrim Health Care (882) institutional review boards approved the study. We preregistered the study design and statistical analysis plan before data collection on ClinicalTrials.gov (NCT05953194) and AsPredicted.org (https://aspredicted.org/HY8_KVV).
REFERENCES
- 1. Centers for Disease Control and Prevention . Get the facts: sugar-sweetened beverages and consumption. April 11 , 2022. . Available at: https://www.cdc.gov/nutrition/data-statistics/sugar-sweetened-beverages-intake.html . Accessed October 6, 2023. [Google Scholar]
- 2.Wakefield MA, Loken B, Hornik RC. Use of mass media campaigns to change health behaviour. Lancet. 2010;376(9748):1261–1271. 10.1016/S0140-6736(10)60809-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Kraak VI, Consavage Stanley K, Harrigan PB, Zhou M. How have media campaigns been used to promote and discourage healthy and unhealthy beverages in the United States? A systematic scoping review to inform future research to reduce sugary beverage health risks. Obes Rev. 2022;23(5):e13425. 10.1111/obr.13425 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Farley TA, Halper HS, Carlin AM, Emmerson KM, Foster KN, Fertig AR. Mass media campaign to reduce consumption of sugar-sweetened beverages in a rural area of the United States. Am J Public Health. 2017;107(6):989–995. 10.2105/AJPH.2017.303750 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Morley BC, Niven PH, Dixon HG, Swanson MG, McAleese AB, Wakefield MA. Controlled cohort evaluation of the LiveLighter mass media campaign’s impact on adults’ reported consumption of sugar-sweetened beverages. BMJ Open. 2018; 8(4):e019574. 10.1136/bmjopen-2017-019574 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Palmedo PC, Dorfman L, Garza S, Murphy E, Freudenberg N. Countermarketing alcohol and unhealthy food: an effective strategy for preventing noncommunicable diseases? Lessons from tobacco. Annu Rev Public Health. 2017;38(1): 119–144. 10.1146/annurev-publhealth-031816-044303 [DOI] [PubMed] [Google Scholar]
- 7.Harris J, Fleming-Milici F. Food marketing to adolescents and young adults: skeptical but still under the influence. In: Folkvord F, ed. The Psychology of Food Marketing and Overeating. London, UK: Routledge; 2019:25–43. 10.4324/9780429274404-3 [DOI] [Google Scholar]
- 8.Choi YY, Andreyeva T, Fleming-Milici F, Harris JLUS. Households’ children’s drink purchases: 2006–2017 trends and associations with marketing. Am J Prev Med. 2022;62(1):9–17. 10.1016/j.amepre.2021.06.013 [DOI] [PubMed] [Google Scholar]
- 9.Hawaii State Department of Health. Sweet lies. Living Healthy Hawaii. 2023. Available at: https://livinghealthy.hawaii.gov/sweetlies. Accessed March 13, 2024.
- 10.LaRose JG, Leahey TM, Hill JO, Wing RR. Differences in motivations and weight loss behaviors in young adults and older adults in the National Weight Control Registry. Obesity (Silver Spring). 2013;21(3):449–453. 10.1002/oby.20053 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Krieger J, Kwon T, Ruiz R, Walkinshaw LP, Yan J, Roberto CA. Countermarketing about fruit drinks, alone or with water promotion: a 2019 randomized controlled trial in Latinx parents. Am J Public Health. 2021;111(11):1997–2007. 10.2105/AJPH.2021.306488 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Harris JL, Phaneuf L, Fleming-Milici F. Effects of sugary drink countermarketing videos on caregivers’ attitudes and intentions to serve fruit drinks and toddler milks to young children. Am J Public Health. 2022;112(suppl 8):S807–S816. 10.2105/AJPH.2022.307024 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Bryan CJ, Yeager DS, Hinojosa CP. A values-alignment intervention protects adolescents from the effects of food marketing. Nat Hum Behav. 2019;3(6):596–603. 10.1038/s41562-019-0586-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Hayward LE, Vartanian L. Potential unintended consequences of graphic warning labels on sugary drinks: do they promote obesity stigma? Obes Sci Pract. 2019;5(4):333–341. 10.1002/osp4.353 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Puhl RM, Himmelstein MS, Pearl RL. Weight stigma as a psychosocial contributor to obesity. Am Psychol. 2020;75(2):274–289. 10.1037/amp0000538 [DOI] [PubMed] [Google Scholar]
- 16.Puhl R, Suh Y. Health consequences of weight stigma: implications for obesity prevention and treatment. Curr Obes Rep. 2015;4(2): 182–190. 10.1007/s13679-015-0153-z [DOI] [PubMed] [Google Scholar]
- 17.McDermott MS, Oliver M, Iverson D, Sharma R. Effective techniques for changing physical activity and healthy eating intentions and behaviour: a systematic review and meta-analysis. Br J Health Psychol. 2016;21(4):827–841. 10.1111/bjhp.12199 [DOI] [PubMed] [Google Scholar]
- 18.Grummon AH, Brewer NT. Health warnings and beverage purchase behavior: mediators of impact. Ann Behav Med. 2020;54(9):691–702. 10.1093/abm/kaaa011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Puhl R, Luedicke J, Lee Peterson J. Public reactions to obesity-related health campaigns: a randomized controlled trial. Am J Prev Med. 2013;45(1):36–48. 10.1016/j.amepre.2013.02.010 [DOI] [PubMed] [Google Scholar]
- 20.Donnelly GE, Zatz LY, Svirsky D, John LK. The effect of graphic warnings on sugary-drink purchasing. Psychol Sci. 2018;29(8):1321–1333. 10.1177/0956797618766361 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Nan X, Heo K. Consumer responses to corporate social responsibility (CSR) initiatives: examining the role of brand-cause fit in cause-related marketing. J Advert. 2007;36(2):63–74. 10.2753/JOA0091-3367360204 [DOI] [Google Scholar]
- 22.Hall MG, Sheeran P, Noar SM, Ribisl KM, Boynton MH, Brewer NT. A brief measure of reactance to health warnings. J Behav Med. 2017;40(3): 520–529. 10.1007/s10865-016-9821-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Noar SM, Barker J, Bell T, Yzer M. Does perceived message effectiveness predict the actual effectiveness of tobacco education messages? A systematic review and meta-analysis. Health Commun. 2020;35(2):148–157. 10.1080/10410236.2018.1547675 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Hall MG, Sheeran P, Noar SM, et al. Negative affect, message reactance and perceived risk: how do pictorial cigarette pack warnings change quit intentions? Tob Control. 2018;27(e2):e136–e142. 10.1136/tobaccocontrol-2017-053972 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hong SM, Faedda S. Refinement of the Hong psychological reactance scale. Educ Psychol Meas. 1996;56(1):173–182. 10.1177/0013164496056001014 [DOI] [Google Scholar]
- 26.Harris KM, Udry JR. National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994‒2018 [Public Use]. Chapel Hill, NC: Carolina Population Center, University of North Carolina-Chapel Hill, Inter-university Consortium for Political and Social Research; 2022. 10.3886/ICPSR21600.v25 [DOI] [Google Scholar]
- 27.Cohen J. Statistical Power Analysis for the Behavioral Sciences. New York, NY: Academic Press; 2013. 10.4324/9780203771587 [DOI] [Google Scholar]
- 28.Dunn JE, Liu K, Greenland P, Hilner JE, Jacobs DR. Seven-year tracking of dietary factors in young adults: the CARDIA study. Am J Prev Med. 2000;18(1): 38–45. 10.1016/S0749-3797(99)00114-2 [DOI] [PubMed] [Google Scholar]
- 29.Holm S. Obesity interventions and ethics. Obes Rev. 2007;8(s1):207–210. 10.1111/j.1467-789X.2007.00343.x [DOI] [PubMed] [Google Scholar]
- 30.Ajzen I. Consumer attitudes and behavior. In: Haugtvedt C, Herr P, Cardes F, eds. Handbook of Consumer Psychology. New York, NY: Lawrence Erlbaum Associates; 2008:525–548. [Google Scholar]
- 31.Brehm SS, Brehm JW. Psychological Reactance: A Theory of Freedom and Control. New York, NY: Academic Press; 2013. [Google Scholar]
- 32.Jeong M, Zhang D, Morgan J, et al. Similarities and differences in tobacco control research findings from convenience and probability samples. Ann Behav Med. 2019;53(5):476–485. 10.1093/abm/kay059 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Coppock A, Leeper TJ, Mullinix KJ. Generalizability of heterogeneous treatment effect estimates across samples. Proc Natl Acad Sci USA. 2018; 115(49):12441–12446. 10.1073/pnas.1808083115 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Schmidt S, Eisend M. Advertising repetition: a meta-analysis on effective frequency in advertising. J Advert. 2015;44(4):415–428. 10.1080/00913367.2015.1018460 [DOI] [Google Scholar]
- 35.Grummon AH, Smith NR, Golden SD, Frerichs L, Taillie LS, Brewer NT. Health warnings on sugar-sweetened beverages: simulation of impacts on diet and obesity among US adults. Am J Prev Med. 2019;57(6):765–774. 10.1016/j.amepre.2019.06.022 [DOI] [PMC free article] [PubMed] [Google Scholar]