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. Author manuscript; available in PMC: 2026 Feb 28.
Published in final edited form as: Health Commun. 2025 Feb 28;40(12):2547–2555. doi: 10.1080/10410236.2025.2466115

Do Perceived Message Effectiveness Ratings Change in Response to Repeated Message Exposures?

Youjin Jang 1, Noel T Brewer 1,2, Nisha Gottfredson O’Shea 3, Marissa G Hall 1,2,4, Seth M Noar 1,5
PMCID: PMC12353103  NIHMSID: NIHMS2056703  PMID: 40019180

Abstract

Perceived message effectiveness (PME) is used in message pre-testing and as an indicator of campaign receptivity. Studies have yet to examine whether PME changes in response to repeated exposures to messages and whether the pattern of change differs for effects perceptions (i.e., perceived impact of messages on intended outcomes) versus message perceptions (i.e., judgments of a message’s ability to foster message processing). To address these gaps, we conducted a 3-week randomized clinical trial (RCT) with parallel assignment among 1,514 US adolescents aged 13 to 17 years who were susceptible to vaping or used e-cigarettes in the past 30 days. Repeated exposures to vaping prevention video ads over time increased both effects perceptions (p<.001) and message perceptions (p<.05), with a larger mean increase for effects perceptions than message perception (mean difference of .32 vs .05). Our findings suggest that effects perception measures are more likely to change in response to repeated message exposures over time. Understanding distinct patterns in PME following repeated exposure could help researchers better interpret PME data in both formative and process evaluations, particularly for health campaigns aimed at behavior change.

Keywords: Perceived message effectiveness (PME), repeated message exposure, vaping, adolescents

Health communication campaigns involve several types of evaluation: before (formative evaluation), during (process evaluation), and after (outcome evaluation) (Rice & Atkin, 2013). Formative and process evaluations ensure that campaigns are grounded in a solid understanding of the target audience and convey campaign messages effectively, helping lead to a successful campaign. A widely used tool in formative and process evaluations is ratings of perceived message effectiveness (PME), or the potential for persuasive messages to have an impact (Dillard et al., 2007). While campaign evaluators use PME to select the most promising messages during campaign development (e.g., Zhao et al., 2016), they also employ it to assess audience receptivity to messages during a campaign’s execution (e.g., Duke et al., 2015). In the absence of behavioral data—as indicators of actual message impact—PME can serve as a measure of audience receptivity after campaign implementation (Baig et al., 2021; Dutra et al., 2023; Ma et al., 2023; Noar et al., 2020).

Maximizing the benefits of PME in campaigns requires considering factors that might influence PME ratings, including characteristics of both messages and people. One factor that has garnered little empirical attention is the possible impact of repeated exposures to messages on PME ratings. When PME ratings are used for pre-testing, participants are typically exposed to a message only once. However, when PME serves as an indicator of message receptivity for an already implemented campaign, target audience members may have seen or heard the messages many times. It is unclear whether campaign evaluators should expect PME scores to differ between these two applications of PME – pre-testing messages versus assessing campaign receptivity – due to the limited understanding of how repeated messages exposure may (or may not) affect PME ratings. Therefore, it is essential to understand the effect of repeated message exposures on PME ratings, as real-world campaigns often involve multiple message exposures. Without this understanding, campaign evaluators cannot accurately interpret PME data or make informed decisions about potential message efficacy.

To better understand the effect of repeated exposure to messages on PME, the current project aimed to explore 1) how repeated exposure to the FDA Real Cost vaping prevention ads and control videos influence adolescents’ PME ratings, and 2) whether the impact of repeated exposure differed among different types of PME.

Perceived Message Effectiveness

PME has two dimensions: message and effects perceptions (Baig et al., 2019). Message perceptions are judgments of a message’s capability to foster processing towards persuasion, such as a message being perceived as attention-grabbing, meaningful, or informative. These perceptions are grounded in persuasion theories such as the Elaboration Likelihood Model (Cacioppo & Petty, 1984). Conversely, effects perceptions are judgments about how effectively a message can modify behavioral antecedents, based on its perceived ability to change beliefs about the consequences of the behavior or the behavior itself. Effects perceptions are rooted in theories of behavior, such as the Reasoned Action Approach (Fishbein & Ajzen, 2010).

Despite the conceptual distinction between message and effects perceptions, their frequent interchangeable use and combined measurement in PME research has sparked criticism over the adequacy and utility of PME measurement practices (Noar, Bell, et al., 2018). Some researchers have gone further in questioning PME’s diagnostic utility altogether (O’Keefe, 2018, 2020). However, the sole meta-analysis that suggested PME has limited diagnostic utility for actual effectiveness (O’Keefe, 2018) included a very heterogenous mix of measures (Noar, Barker, et al., 2018). To advance our understanding of benefits and limitations of PME measures, studies need careful conceptualization and measurement of the PME construct. The focus of message perceptions on facilitating initial processing – and effects perceptions on behavioral antecedents – suggests they should not be considered interchangeable. Message perceptions include evaluations of credibility and informativeness, while effects perceptions assess perceived impact on motivational states, which could include emotional responses such as concern or worry.

While prior analyses of The Real Cost campaign have predominantly used message perceptions measures of PME (Duke et al., 2015; Zhao et al., 2016, 2019), research suggests that effects perceptions may be a better proxy for the potential behavioral impact of messages (Baig et al., 2021; Brennan et al., 2014; Rohde et al., 2021). Moreover, a recent randomized clinical trial (RCT) of ads from The Real Cost found that effects perceptions fully mediated the effect of message exposure on susceptibility to vaping, whereas message perceptions only partially mediated this relationship, exhibiting a small effect (Ma et al., 2023). Given these findings, it is plausible to conclude that message and effects perceptions function differently in their relationships with target audience attitudes and behaviors, leading to the possibility that the impact of repeated message exposures on these two constructs might also differ.

Effects of Repeated Message Exposure

Achieving high levels of exposure is one of the most important determinants of the success or failure of a campaign (Hornik, 2002). First, increasing the frequency of exposure to a message enhances the likelihood that individuals will learn and internalize the arguments within the messages, as each additional exposure provides another opportunity for the message to be processed. Secondly, repeated exposure can lead to priming effects, making individuals more receptive to consider the advocated behavioral changes. This receptiveness increases as the messages become more familiar (i.e., mere exposure effect, Zajonc, 2001), reducing resistance and increasing openness to the content. Third, frequent exposure to a message can foster social interactions around the topic, enhancing the message’s dissemination beyond initial recipients through word-of-mouth, and thus facilitating a broader reach and potential impact (e.g., Hall et al., 2015; Morgan et al., 2018). However, some studies have shown that overexposure may lead to message fatigue, resulting in reactance and disengagement (S. Kim & So, 2018), such that an increase in message frequency led to a decreased ability to recall the message (Baseman et al., 2013). Another study, however, found that repeated exposure to messages resulted in reactance but not message fatigue (Skurka & Keating, 2024).

Additionally, the effect of repeated exposures can vary based on factors such as time interval between exposures, frequency of exposure (e.g., Baseman et al., 2013), and receivers’ previous attitudes and beliefs (e.g., Dijkstra & Bos, 2015). A meta-analysis on advertising repetition found that both time between exposures and frequency of exposure shape message effects (Schmidt & Eisend, 2015). This study found that when there was more time between repeated exposures, ads had a stronger impact on brand attitude. In contrast, when exposures occurred in rapid succession with little to no time in between, ads had a stronger impact on recall. Additionally, the relationship between exposure frequency and brand attitude followed an inverted U-shaped curve, peaking at around ten exposures before declining, whereas the effect on recall increased linearly. While previous studies have primarily examined how repeated message exposure affects outcomes such as message recall, less attention has been given to effects on PME and the factors that may moderate the impact of exposure on PME.

While repeated message exposures can positively or negatively affect persuasive outcomes, there is a gap in the literature regarding its effect on PME. A recent systematic review indeed indicated that the relationship between message repetition and perceived message quality (including PME) is inconsistent across studies (Keating & Totzkay, 2024). For example, repetition was found to be either positively or negatively linked to perceived message credibility in some studies (Ernst et al., 2017; Koch & Zerback, 2013), while in another case, no significant relationship between message repetition and PME (i.e., message perception) was observed (Stephens & Rains, 2011). However, it is important to note that these findings are all related to message perceptions (i.e., perceived characteristics of messages themselves) and are based on cross-sectional studies, making the longitudinal relationship between message repetition and PME, particularly when considering effects perceptions, more inconclusive. Given that PME is used not only in pre-testing – where a single exposure is likely – but also as an indicator of campaign receptivity – where multiple exposures are likely – understanding how repeated message exposure affects PME and identifying any moderating factors is important for the appropriate interpretation of PME data in campaign research. Therefore, we pose three research questions (RQs):

RQ1. How does repeated exposure to vaping prevention advertisements affect perceived message effectiveness (message and effects perceptions)?

RQ2. Does prior exposure to the advertisements before the study (none vs. any exposure) interact with the effect of repeated exposures on perceived message effectiveness (message and effects perceptions)?

RQ3. Does vaping status (vaped in past 30 days vs. not) interact with the effect of repeated exposures on perceived message effectiveness (message and effects perceptions)?

Methods

Participants

We recruited US adolescents from online panels managed by Qualtrics for a trial evaluating vaping prevention ads. Additional details about the procedures and ads from this trial are provided in the main trial paper, which examines the impact of the ads on vaping susceptibility (Noar et al., 2022). Other published works from this trial include a mediation analysis of the impact of The Real Cost ads on vaping susceptibility (Kieu et al., 2025) and an analysis of how PME ratings at Visit 1 predict the ads’ impact on vaping susceptibility over time (Ma et al., 2024).

To be in the trial, participants had to be ages 13 to 17 years, read and speak English, complete an online survey in English, and be susceptible to e-cigarette use (as indicated by a score of 2 or greater on any of five e-cigarette susceptibility items on a 4-point response scale that ranged from 1 = definitely not to 4 = definitely yes). For example, two of the questions were, “Do you think you might use an e-cigarette or vape soon?” and “If one of your best friends were to offer you an e-cigarette or vape, would you use it?” The target sample size of 1,500 was selected to allow for a potential dropout rate of up to 33% during the trial. With an expected intraclass correlation coefficient of .70, the trial was powered to detect an effect size of d = .25 or larger between the combined intervention arms and the control arm.

Of the 1,708 adolescents who met the inclusion criteria, 151 declined to participate, and 43 were eligible but could not enroll as the trial quota had been reached, resulting in a final sample size of 1,514 (See Table 1 for the detailed information about the participants). Retention at visit 3 was 89%. Beyond participants lost to follow-up, only one participant did not provide responses to the outcome measure at visit 2, and the same was true for visit 3.

Table 1.

Participant Characteristics (n = 1,514)

Overall

n (%)		 Real Cost Arms
(n=1010)
n (%)		 Control Arm
(n=504)
n (%)
Age, years (mean, standard deviation) 15.2 (1.2) 15.2 (1.2) 15.3 (1.2)
Gender
 Male 1140 (75) 747 (74) 393 (78)
 Female 358 (24) 248 (25) 110 (22)
 Other responses 15 (1) 15 (2) 1 (<1)
Race
 White 1081 (71) 712 (71) 369 (73)
 Black or African American 371 (25) 253 (25) 118 (23)
 Asian 14 (1) 8 (1) 6 (1)
 Native Hawaiian or other Pacific Islander 4 (<1) 4 (<1) 0 (0)
 Multiracial 33 (2) 25 (3) 8 (2)
 Other* 10 (<1) 7 (<1) 3 (<1)
 Missing 1 (<1) 1 (<1) 0 (0)
Hispanic 176 (12) 115 (11) 61 (12)
Sexual orientation
 Heterosexual 1432 (95) 947 (94) 485 (96)
 Lesbian, gay, bisexual, pansexual, or queer 61 (4) 48 (5) 13 (3)
 Prefer not to say/missing 20 (1) 15 (2) 6 (1)
Adolescent’s education
 Dropped out of school or <High school 238 (15) 160 (16) 78 (15)
 Some high school 1067 (71) 715 (71) 352 (70)
 High school or GED 123 (8) 79 (8) 44 (9)
 Some college 85 (6) 55 (5) 30 (6)
 Missing 1 (<1) 1 (<1) 0 (0)
Mother’s education
 <Bachelor’s degree 262 (17) 187 (19) 75 (15)
 Bachelor’s degree 533 (35) 352 (35) 181 (36)
 Master’s degree 578 (38) 378 (37) 200 (40)
 Doctorate degree 123 (8) 82 (8) 41 (8)
 Missing 18 (1) 11 (1) 7 (1)
Tobacco use (past 30 days)
 Used e-cigarette 922 (61) 612 (61) 310 (62)
 Used cigarette 825 (55) 539 (53) 286 (57)
 Used other tobacco product 871 (58) 572 (57) 299 (59)
Lived with somebody who…
 Smoked cigarettes 539 (36) 340 (34) 199 (40)
 Used e-cigarettes 443 (29) 279 (28) 164 (33)
 Used chewing tobacco, snuff, or dip 218 (14) 145 (14) 73 (15)
 Smoked cigars, cigarillos, or little cigars 203 (13) 126 (13) 77 (15)
 Used another form of tobacco 119 (8) 82 (8) 37 (7)
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Note. GED = general educational diploma.

*

Selected “other” option for race

Procedures

Trial recruitment occurred from September to November 2021. Informed parental consent and adolescent assent were obtained online before the commencement of the surveys. The Institutional Review Board reviewed and approved study procedures. The trial was registered prior to data collection at clinicaltrials.gov (NCT04836455).

Participants had four weekly online visits, referred to as Visits 1–4, accessing the surveys from their own personal electronic devices. After enrollment, we randomly assigned participants to one of three trial arms using Qualtrics’ randomization feature with a 1:1:1 allocation ratio: 1) FDA Real Cost vaping prevention trial arm featuring health harms-themed video ads; 2) FDA Real Cost vaping prevention trial arm featuring addiction-themed video ads; or 3) control arm featuring neutral videos about vaping, created by investigators.

Trial Design and Protocol

Participants viewed three ads specific to their assigned trial arm during each of the first three weekly sessions, with ad presentation order randomized in each session. The stimuli were selected from the Real Cost campaign’s e-cigarette prevention ads. The research team initially identified several ads aligned with health harms and addiction themes, ultimately selecting the three in each category believed to have the highest potential impact on youth. The health harms-themed ads focused on physical and health consequences of vaping, such as lung damage, while the addiction-themed ads highlighted nicotine’s addictive nature and its effects on adolescent development. Control videos, created by the research team, were neutral in content, providing factual information on vaping devices and e-cigarette ingredients without any persuasive messaging or health risk warnings (see Supplementary Table 1 for ads). Analyses from the main trial paper found no differences between the health harms and addiction-themed ads on key outcomes; therefore, these arms were combined for analysis.

Survey and Data Collection

During the Visit 1, the survey assessed vaping and smoking behaviors. Next, participants viewed advertisements for their assigned trial arm. The survey then assessed effects and message perceptions for each ad as well as susceptibility to vaping and smoking, beliefs about vaping and smoking, and demographic information. In subsequent weeks of the trial (Visits 2 and 3), participants answered survey questions concerning their susceptibility to vaping and smoking, their beliefs about these behaviors, and their vaping and smoking behaviors. After completing the survey in Visits 2 and 3, participants viewed the same set of three ads from their respective trial arm and answered PME items. They then completed a final survey at Visit 4. Participants received incentives worth up to $35 for completing the trial surveys. We piloted the trial procedures with 51 adolescents before the main trial.

Measures

Predictors

Previous Ad Exposure.

After watching the ads at Visit 1, participants reported how often they had seen each ad before participating in the trial. The response scale ranged from not at all (coded as 1) to11 or more times (coded as 5). Limited variability in the distribution (M = 1.64, SD = .93) led us to dichotomize the variable to distinguish between those with no prior exposure to the ads (coded as 0) and those with any prior exposure to the ads (coded as 1).

Vaping Status.

During the Visit 1 survey, participants reported the number of days they used an e-cigarette or vape in the 30 days preceding Visit 1. Responses ranged from 0 to 30 days (M = 9.16, SD = 7.54). Following convention (e.g., Nicksic & Barnes, 2019; Sun et al., 2022), we dichotomized vaping status to distinguish between those who had not used an e-cigarette in the past 30 days (coded as 0) and those with any usage (coded as 1). This approach enabled us to focus on the binary distinction of current users versus susceptible non-users.

Outcomes

Effects Perceptions.

The surveys assessed effects perceptions using the UNC PME Scale for Youth, which has three items (Noar et al. 2023). The items read, “How much does this ad…” “make you worry about what vaping will do to you?”; “make you think vaping is a bad idea?”; and “discourage you from vaping?” The 5-point response scale ranged from not at all (coded as 1) to a great deal (5). We took the mean of the three items to create an effects perceptions scale, which had high reliability (α= .91 to .92 for Visits 1–3). Higher scores indicated stronger effects perceptions.

Message Perceptions.

The surveys assessed message perceptions using 6 items developed by Davis et al. (2013) and used by the FDA (Duke et al., 2015). The items assess whether the ad “grabs my attention,” “is meaningful,” “is informative,” “is convincing,” “is worth remembering,” and “is powerful.” The 5-point scale ranged from strongly disagree (coded as 1) to strongly agree (5). Higher scores indicated stronger message perceptions. We took the mean of the 6 items to create a message perceptions scale, which had high reliability (α = .89 to .91, for Visits 1–3).

Statistical Analyses

The main trial outcomes have been published elsewhere (Noar et al., 2022), as have the ability of PME ratings at Visit 1 to predict the impact of ads on susceptibility to vaping over time (Ma et al., 2024). Here we focus on the impact of repeated exposure on PME. Analyses combined the two Real Cost trial arms (health harms and addiction-themed ads) and compared the effect of repeated exposure to Real Cost ads vs. control ads on PME. We collapsed the Real Cost arms together to simplify analyses and because prior work has shown the Real Cost health harms and addiction-themed ads to have similar effects (Noar et al., 2022).

To examine the impact of repeated message exposure on PME (RQ1), we conducted a 2 (Trial arms: Real Cost vs. control arm) X 3 (Exposure sessions: 1 vs. 2 vs. 3 sessions) mixed analysis of variance (ANOVA) using both effects perception and message perception as outcomes. To examine whether prior ad exposure interacted with repeated exposures’ impact on PME (RQ2), we repeated the 2 × 3 × 2 ANOVA, adding prior ad exposure (none versus any before the study) as another between-subjects factor (any prior exposure to ads vs. no exposure). To examine whether vaping status interacted with repeated exposures’ impact on PME (RQ3), we repeated the 2 × 3 × 2 ANOVA, adding participants’ vaping status (vaped in the past 30 days vs. not) as another between-subjects factor. We only report the interaction effects of interest in the results section, as proposed in our research questions.

Combining the two Real Cost arms resulted in unequal sample sizes (Real Cost vs. Control), thereby increasing the likelihood of violating the assumption of homogeneity of variance-covariance matrices (Tabachinick & Fidell, 2013). Consequently, we used Pillai’s criterion to ensure the robustness when exploring research questions. Analyses used two-tailed tests and a critical alpha of .05. We used the Benjamini-Hochberg correction to control the false discovery rate for interaction effects (Benjamini & Hochberg, 1995). Analyses used SPSS version 29.

To assess sensitivity of our results and potential bias related to missing values, we compared the results from the mixed ANOVAs with raw data to those from generalized linear mixed modeling (GLMM) with imputed data. For the GLMM analysis, we first imputed the data using multiple imputation. We then employed GLMM with random slopes to account for individual differences and to model the effect of trial arms and exposure sessions (model 1). Additionally, we introduced the effect of prior ad exposure (model 2) and participants’ vaping status (model 3) as potential moderators. The results of the GLMM analyses with imputed data were consistent with those obtained from the original mixed ANOVAs with raw data, suggesting the missing data did not introduce bias. Therefore, we report the ANOVA results in the manuscript for simplicity and clarity.

Results

Effects of Repeated Message Exposure

Across the three exposure sessions, more ad exposure sessions led to higher effects perceptions (F (2, 1309) = 86.65, p < .001; Figure 1). Effects perceptions increased from Visit 1 to Visit 3 by a mean difference of .32 (SD = .95). The Real Cost ads also led to higher effects perceptions compared to the control ads (M = 4.04, SE = .03 vs. M = 3.17, SE = .04; F (1, 1310) = 260.58, p < .001; d = .89). In addition, ad exposure sessions interacted with trial arm (interaction F (2, 1309) = 9.48. p < .001); the control video arm exhibited a mean increase in effects perceptions from Visit 1 to Visit 3 of .42, while the Real Cost arms increased by only .26 (Figure 1).

Figure 1:

Figure 1:

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Impact of Repeated Message Exposure on Effects Perceptions (top panel) and Message Perceptions (bottom panel)

Across the three exposure sessions, more ad exposure sessions led to higher message perceptions (F (2, 1309) = 4.26, p < .05; Figure 1). However, the increase from Visit 1 to Visit 3 was minimal, at a mean difference of .05 (SD = .58). The Real Cost ads also elicited higher message perceptions than control ads (M = 4.32, SE = .02 vs. M = 4.10, SE = .03; F (1, 1310) = 35.20, p < .001, d = .32). Ad exposure sessions did not interact with trial (F (2, 1309) = 2.70, p =.07).

Effects of Moderators

Previous ad exposure did not interact with exposure sessions for either outcome (both p > .06). The three-way interaction effect of trial arm, exposure sessions, and previous exposure was also not statistically significant for either outcome (both p > .25).

Vaping status interacted with exposure sessions, F (2, 1307) = 4.81, p < .05. After first exposure, adolescents who had not vaped in the past 30 days scored higher on effects perceptions than those who had vaped, although the difference was not statistically significant (Figure 2). However, after three exposure sessions, effects perceptions of both arms became similar, indicating that the increase in effects perceptions among those who had vaped in the past 30 days was greater than those who had not vaped. The three-way interaction between trial arm, repeated exposure, and vaping status was not statistically significant, F (2, 1307) = 0.23, p = .79. Vaping status did not interact with exposure sessions, F (2, 1307) = 0.18, p = .84. The three-way interaction between trial arm, exposure sessions, and vaping status was also not statistically significant, F (2, 1307) = 2.05, p = .15. More detailed findings on PME over time, moderated by these factors, appear in Supplementary Table 2.

Figure 2:

Figure 2:

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Interaction of Repeated Message Exposure and Vaping Status on Effects Perceptions (top panel) and Message Perceptions (bottom panel)

Discussion

PME has been as a key tool for message pre-testing and understanding receptivity to health communication campaigns. Considering that PME measures are employed in both formative and process evaluations, which respectively involve single and multiple exposures, it is vital to understand how repeated exposures may influence PME. Despite the importance of PME, the effect of repeated message exposure on PME ratings has seldom been studied. This project investigated how repeated exposure to FDA’s Real Cost vaping prevention ads and control videos affected adolescents’ PME ratings, exploring differences in the impact on effects and message perceptions by using data from an RCT with a large sample of US adolescents.

Our central finding is that more exposure sessions led to an increase in effects perceptions. This indicates that multiple ad exposures strengthened recipients’ perceptions of the messages’ potential to change their behaviors. A prior study (M. Kim & Cappella, 2019) exhibited a pattern of findings consistent with our data, showing effects perceptions to generally increase with greater message exposures. However, their participants were exposed to different messages in a single exposure session, one at a time, which confounded message content with exposure. In contrast, the current study explored the impact of multiple exposures to the same message, providing a stronger test of the impact of repeated exposure on effects perceptions over time.

We also found that more message exposure sessions led to increased message perceptions, but this effect was very small. Thus, while effects perceptions increased with repeated message exposures, recipients’ evaluations of the messages’ characteristics—such as meaningfulness, informativeness, or convincingness—remained relatively constant. This consistency in message perceptions suggests that such perceptions may be more stable across repeated exposures compared to effects perceptions, which appear more responsive to change after repeated exposures to messages. Although message processing appeared similar across sessions (as gauged by message perceptions), the persuasive impact of the messages grew over time (as gauged by effects perceptions). This is consistent with the effects of the RCT on actual effectiveness outcomes, which found that the Real Cost ads had an impact on several outcomes, including risk beliefs about, attitudes toward, and susceptibility to vaping (Noar et al., 2022).

We also found that effect sizes of trial arms were much larger for effects perceptions than message perceptions. This is likely because effects perceptions are focused on the persuasiveness of the messages in discouraging vaping behavior, whereas message perceptions are evaluative of the message in general, not specific to discouraging vaping (e.g., this message is informative). This is an important point, especially that the (likely ineffective) control videos had message perceptions scores above a mean of four (out of five) at all three timepoints. While effects perceptions direct participants to rate the persuasiveness of the messages in discouraging vaping behavior, message perceptions are rated irrespective of that goal. Given that, effects perceptions data may be more useful when the goal is behavior change (Noar et al., in press).

This study also examined possible moderators that might influence the relationship between repeated exposure to messages and PME, including previous ad exposure before the trial and participants’ vaping status. Previous exposure to ads before the study did not interact with the effect of repeated message exposure to influence effects nor message perceptions. We also did not find any three-way interaction effects. However, participants’ vaping status did interact with the effect of repeated exposure to influence effects perception but did not interact for message perception. Non-vapers reported higher effects perceptions compared to vapers initially, but after three exposures, effects perceptions of non-vapers and vapers were similar. While this finding suggests that health messaging may have a differential impact based on prior vaping behavior, the moderation effect of vaping status was modest. This underscores the need for caution when interpretating these findings, yet it may still indicate the value of repeated exposure to amplify message impact among current vapers.

Implications for Campaigns

Our findings offer important insights into the differences between effects and message perceptions, building upon a growing body of research indicating that these two forms of PME function quite differently (Baig et al., 2021; Brennan et al., 2014; Ma et al., 2023; Rohde et al., 2021). Compared to message perceptions, effects perceptions were more susceptible to increases in response to repeated exposures and showed larger differences between Real Cost and control videos, and these findings have two-fold implications for campaigns. First, when using PME ratings as a measure of receptivity for campaigns that have been implemented in the field, effects perceptions scores are likely to be higher than pre-test PME scores, whereas this is unlikely to be the case for message perceptions. For example, when examining the receptivity of an anti-tobacco campaign using effects perception, campaign evaluators should expect – if the campaign is resonating – effects perception scores to be higher than scores during ad pre-testing. In contrast, when examining receptivity using message perceptions, campaign evaluators should not expect their message perceptions scores to be higher than the those during pre-testing, even if the campaign is resonating. Thus, when assessing receptivity of campaigns in the field, the interpretation of effects and message perceptions data differs. Future studies should examine the extent to which these findings hold in the context of other types of messages, health topics, and populations.

Second, when using PME in message pre-testing, researchers should recognize that there is no clear cut-off that indicates “message effectiveness,” and that comparing campaign ads to one another or to neutral control videos may be helpful in selecting more promising ads. Both types of PME demonstrated that Real Cost ads were more promising than the control ads, which is consistent with the effects of these ads on susceptibility to vaping (Noar et al., 2022) and risk beliefs (MacMonegle et al., 2024; MacMonegle et al., 2022). However, effects perceptions showed much larger differences between Real Cost and control videos than message perceptions, echoing other studies that have had similar results (Noar et al., 2020). Overall, these findings suggest that effects perceptions are more sensitive to detecting differences among messages than are message perceptions.

Strengths and limitations

Strengths of this study include the randomized design, use of high-quality advertisements from a national campaign, and multiple exposures to ads over time. This study also had limitations. Although it is one of the few studies that have examined the effect of repeated exposure on PME using an RCT, the number of exposures to messages was limited, which might not adequately capture the saturation point or the possible declining effectiveness of message impact over time. More extensive studies involving a wider range of exposure doses could further our understanding of the dose-response relationship between message exposure and PME (e.g., Skurka & Keating, 2024). It is also important to note that the control ads consisted of narrated text-only messages, whereas Real Cost ads incorporated dynamic visual elements, and structural and visual features in videos are likely to influence message processing and effectiveness. Additionally, this study’s design may not account for the longer-term effects (i.e., beyond three weeks) of repeated message exposure over time. Longitudinal studies, extending beyond a month, could provide insights into how effects and message perceptions evolve over time. Also, this study examined how PME changed over time in response to vaping prevention ads among adolescents, and the generalizability of findings to other types of messages and populations remains to be established. Finally, the reliance on participants’ self-reporting of measures such as previous ad exposure before the trial may be subject to recall bias, with under or over-reporting of ad recognition.

Conclusion

This study examined how PME changed over time after repeated exposure to FDA’s Real Cost vaping prevention ads. The results suggest that repeated exposure enhanced effects perceptions over time in this context, with minimal change in message perceptions over time. These findings contribute to a deeper understanding of how adolescents process and react to health messages in response to repeated exposures over time, offering valuable insights for optimizing the use and interpretation of PME in campaigns.

Supplementary Material

Supplementary Tables

Role of funding sources:

This project was supported by grant number R01CA246600 from the National Cancer Institute and FDA Center for Tobacco Products (CTP). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the Food and Drug Administration.

Footnotes

Disclosures:

Seth Noar has served as a paid expert witness in litigation against tobacco and e-cigarette companies.

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