Swap Up: Four Years of Behavior Change Through Adolescent Nutrition Education in Oklahoma

Abstract

Oklahoma Tobacco Settlement Endowment Trust’s youth nutrition education campaign, Swap Up, is designed to provide Oklahoma adolescents (13–18 years old) with accessible and actionable information to make healthy daily nutrition choices. Four years of online survey data (n = 856) and a subset of longitudinal responses (n = 295) were analyzed to assess the campaign’s reach, resonance, and related nutrition-supporting beliefs and behaviors. A majority of participants reported aided campaign awareness (75%) and at least one form of digital engagement with campaign content (70%). A series of linear mixed-effects models with the full sample and longitudinal subsample demonstrated that digital engagement consistently predicted positive receptivity, nutritional beliefs, and nutritional behaviors and intentions. Aided campaign awareness, without digital engagement, was not a consistent predictor of campaign outcomes. A social and digital engagement-focused creative and media strategy was key to the success of Swap Up, highlighting the critical role of digital strategies in promoting nutrition-supporting, healthier beliefs and behaviors among an adolescent audience.

Keywords

obesity prevention nutrition nutrition education public health public health campaigns digital and social media public health campaigns behavior change

In 2021, Oklahoma Tobacco Settlement Endowment Trust (TSET) launched a statewide education campaign called “Swap Up” to prevent and reduce obesity among adolescents (ages 13–18; Wagner et al., 2022). Swap Up was developed in response to Oklahoma’s higher-than-average obesity rates among adults and children (Centers for Disease Control and Prevention, 2019; Data Resource Center for Child & Adolescent Health, 2021). Presenting additional challenges, at the time of campaign development, Oklahoma had a poverty rate consistently higher than the national average (Phillips, 2020) and a ranking as the fourth-highest number of fast-food restaurants in the United States (Datafiniti, n.d.; Tyler, 2018; Zaheer, 2023). Oklahoma has both rural and urban counties, with varying access to grocery stores carrying fresh fruits and vegetables (Blanchard & Matthews, 2008). Nutrient-rich foods and drinks can be less available and less affordable than fast food at chain restaurants and convenience stores, especially for young people. In early campaign research, teens agreed with and understood the benefits of consuming nutrient-rich foods and drinks but felt they lacked control (Fernandez et al., 2020). The realities of these systemic and structural barriers necessitated a unique educational approach that empowered Oklahoma adolescents.

In the years since campaign launch, factors associated with economic insecurity and obesity in Oklahoma have worsened. From 2021 to 2023, the food insecurity rate was 15.4%, up from 14.6% in 2018–2020 (Coleman-Jensen et al., 2021; Rabbitt et al., 2024), making Oklahoma the fifth hungriest state in the United States (Economic Research Service, 2025). In 2023, the poverty rate was 15.9%, compared to 12.5% nationally (Oklahoma Policy Institute, 2024). In addition, 38.7% of Oklahoma adults were considered to be experiencing obesity, compared to approximately 20% nationally (Centers for Disease Control and Prevention, 2025), and 33.8% of children ages 10–17 were considered overweight or experiencing obesity compared to 31.1% nationally (2022–2023; United Health Foundation, 2022). Healthy living continues to be an uphill battle for Oklahoma adolescents. While an education campaign cannot solve these structural challenges, it can acknowledge them while offering sound nutrition advice to support daily, positive nutrition-related habits.

Swap Up Campaign Approach

Swap Up equips Oklahoma adolescents with nutritional tools to make healthy choices by promoting the consumption of fresh fruits, vegetables, and water. The goal is to reinforce healthy patterns that will be carried into adulthood and positively impact statewide adult obesity trends. Messages acknowledge and offer solutions to everyday challenges, through attainable and appealing “swaps” for fast food or sugary drinks. The campaign utilizes Rescue Agency’s SAVI^TM (Specific, Acceptable, Viable, Impactful) messaging framework, a strategic approach to message development (Beckerley & Jordan, 2020). See Table 1 for a description of the SAVI messaging framework components and examples. See Wagner et al. (2022) for a detailed overview of SAVI and Swap Up. SAVI was developed based on key principles of Social Cognitive Theory and Social Norm Theory. Social Cognitive Theory assumes that humans make health decisions based on personal (thoughts and feelings), behavioral (health knowledge/skill), and environmental (external) factors, and that self-efficacy and perceived benefits are motivators in the process (Adhikari et al., 2018; Bagherniya et al., 2018). Social Norm Theory posits that health behavior is learned, and similar others’ behaviors normalize and justify an individual’s own unhealthy behaviors (McAlaney et al., 2011; Wang et al., 2014). Taken together, these theories suggest that clear, logical information is needed to change behavior and that it must be combined with tailored appeals that model familiar people and situations (Beckerley & Jordan, 2020). Given the realities of nutrition access in Oklahoma, swap messages are also designed to be within an adolescent’s current budget and available options. In other words, the cost of a swap and the effort to obtain it is equivalent to obtaining a meal or drink from a fast-food restaurant or convenience store.

Table 1.

Applying SAVI Messaging Framework to Swap Up.

SAVI component	Description	Swap Up application
Specific	Must include a specific example of what the audience can do to change their behavior.	Swap sugary drinks for water when preparing for a big game to boost your energy and focus.
Acceptable	Must be acceptable within the cultural, familial, and social contexts of the audience’s lives.	When visiting a drive-through, choose something veggie-packed like salad instead of something greasy like nachos (instead of “Don’t eat at fast-food restaurants or convenience stores”).
Viable	Must be realistic within the constraints of our audience’s available time, budget, and skills.	Don’t expect a teen to be able to revamp their family meals or overhaul their diet; instead focus on a reasonable individual action they can control, like what they buy outside of the home and what’s within their budget.
Impactful	If adopted, the message would cause a meaningful impact on the audience’s nutrition.	Swap sugary vending machine snacks like candy for mixed nuts to avoid experiencing a crash (The impact on the teen’s mood/well-being is clear, and the impact on their nutrition is valuable because vending machines are a regular occurrence).

Swap Up seeks to provide nutrition education to adolescents across Oklahoma, regardless of their current weight, due to increased risk of adulthood obesity. A fundamental strategy to ensure the campaign will reach and resonate with all adolescents is separate tailored content and media for urban and rural audiences. This was supported by early qualitative research where teens viewed draft concepts and described message and source characteristics that did and did not resonate (Fernandez et al., 2020). Rural teens were most receptive to messages delivered by teens who looked and sounded like they were from rural areas, and urban teens preferred settings that were typical places to eat and drink, such as a fast-food restaurant or a school. All content and media were subsequently focused on one of three audiences: Rural, Urban, or Statewide (both Urban and Rural). Video ad concepts were developed utilizing the same main idea, such as “Mood Food.” Two 30-second ads were developed: one tailored for a Rural audience (“Mood Food”) and one for an Urban audience (“Sad Foods”). See Supplemental Document 1: Table S1 for screenshots and descriptions of video advertisements.

Social and Digital Media Engagement in Public Health

Awareness and engagement are commonly cited measures of digital campaign delivery and performance. Awareness is an indicator of campaign delivery, often measured through self-reported campaign awareness or impressions, a digital metric indicating ad exposure among the intended audience. Engagement refers to interaction with campaign content, such as video viewing, liking, clicking on a post to view more, commenting, or sharing (Kite et al., 2023; Reuter et al., 2021). Engagement has emerged as the preferred metric of campaign success, over and above awareness, because interaction suggests the user is interested in the content and is likely to have read or heard the intended message (Kite et al., 2023). While there are a number of studies linking social and digital engagement to public health campaign outcomes, the extent to which engagement can drive behavior change is not yet understood (Ghahramani et al., 2022). This is partly because evaluators must rely on self-reported engagement to understand the relationship between message delivery and desired campaign outcomes (Clar et al., 2014; Romberg et al., 2020).

For Swap Up, digital engagement is core to campaign delivery. The campaign placement takes an ecological approach to campaign media that includes multi-channel, mass-marketing strategies to promote the modeling of healthy behaviors and audience engagement with nutrition messages (Evans et al., 2010). Social and digital media are common sources of news and health information for adolescents in the United States (Common Sense Media & SurveyMonkey, 2019; Rideout et al., 2021). By placing engaging content on various social media platforms where teens regularly consume content and seek information (i.e., Instagram, Snapchat, YouTube, and streaming audio [Spotify and Pandora]), Swap Up makes nutrition education a normal and accessible part of teens’ daily lives. (Note: While a large portion of the audience used and spent time on TikTok from 2021 to 2024, the platform was not permitted for use via Oklahoma Governor executive order.) Ads reflect a look and tone that is similar to content adolescents like, presented in ad types that facilitate interaction, such as GIFs (2–3 second videos), clickable ads to view longer-form 30-second videos or nutrition quizzes on the campaign website (SwapUpOK.com), and poll voting on social media. Over four years, Swap Up digital media achieved over 202 million impressions and 16 million engagements.

To support adolescent nutrition education and Swap Up visibility among adults, Rescue Agency and TSET worked with an Oklahoma media agency, VI Marketing and Branding, to deliver advertisements on broadcast television, over-the-top (OTT) streaming services, cinema, school posters, outdoor billboards, and radio stations in programs consumed by parents and other influential adults in adolescents’ lives.

Early evaluation results were promising. In Year 1, five months after launch, adolescents who reported digital engagement with Swap Up also reported the highest levels of main message recognition, perceived campaign relevance, and changes in nutritional behaviors compared to those reporting only aided awareness or no awareness of the campaign. Nearly half of the sample (44%) had reported digital engagement with Swap Up (Wagner et al., 2022). See Supplemental Document 1: Table S2 for example digital assets in Years 2 through 4.

Current Research

We evaluated self-reported outcomes among Oklahoma adolescents from 2021 to 2024. Each year, TSET tracked aided brand and ad awareness, digital engagement, perceived relevance of campaign messages, nutritional beliefs, past 30-day nutritional behaviors, and nutrition-related behavioral intentions. We hypothesized that digital engagement would increase over four years of the campaign and that those who engaged online with Swap Up would report increased levels of positive nutritional outcomes compared to those who did not engage.

Materials and Methods

Design and Procedures

Oklahoma adolescents (n = 687) participated in an annual cross-sectional online evaluation survey from 2021 through 2024. Participants were recruited through paid advertisements on social media platforms (i.e., Facebook, Instagram; n = 345), market research panels (n = 153), and by recontacting consenting participants from previous years (n = 358), resulting in 856 total observations. Recontacted participants include Baseline respondents; Baseline responses were not included in the current analysis.

Potential participants were provided with a screener link. Eligible participants were current Oklahoma residents, 13–18 years old. Participants who were 18 years old at initial survey completion were invited to participate in subsequent follow-up surveys until age 20. Eligible and consenting individuals were invited to complete the evaluation survey.

All eligible participants provided electronic assent or consent. Participants received links to the appropriate form and a parent form via email. The form provided parents/guardians with the opportunity to opt their minor child out of participation in the study. Participants received an incentive ($10–$15 for social media, panel points equivalent to $10, and $20 for returning).

Measures

Demographics

Participants reported age (calculated from birthdate), gender (male, female, or other identity [write-in]), and race/ethnicity.

County

Participants self-reported zip code and were categorized as living in an urban or rural county based on population density of the county (Brandberg Ltd., 2025). See Figure 1 for county designations.

Figure 1.

Map of County Designations.

Zip Code-Level Economic Status

To measure the likelihood of experiencing economic insecurity, each participant was assigned a value from 1 to 100, corresponding to the percentage of residents within their zip code who lived below 100% of the federal poverty level within the last 12 months (Simplemaps.com, n.d.; U.S. Census Bureau, 2023).

Obesity Risk

Adapted from the Oklahoma Youth Risk Behavioral Survey (YRBS; Centers for Disease Control and Prevention, 2019), participants described their weight (5 = very overweight, 4 = slightly overweight, 3 = about the right weight, 2 = slightly underweight, 1 = very underweight). Responses were dichotomized as Overweight (4–5) or Not Overweight (1–3).

Campaign-Related Beliefs

Participants indicated agreement (1 = strongly disagree to 5 = strongly agree) with four nutrition-related beliefs that align with campaign messages: “Eating fruits and vegetables is important to me,” “Eating fruits and vegetables will make me feel good,” “Drinking water increases my energy,” and “Avoiding sugary drinks can help reduce sugar spikes of highs and lows (prevent energy crashes).” A composite variable was averaged (Cronbach’s α = 0.76).

Past 30-Day Nutrition-Related Behaviors

Participants responded to four questions asking if they “have considered or been trying to” engage in healthy behaviors in the past 30 days (from YRBS; Centers for Disease Control and Prevention, 2019). If a participant answered yes, they were provided with a multi-select list of corresponding items.

Increasing Water

Participants who reported considering or trying to increase the “amount of water” they drank in the past 30 days answered 11 items (see Supplemental Document 3: Table S3). A summed score ranged 0–11.

Decreasing Sugary Drinks

Participants who reported considering or trying to decrease the “amount of soda/pop or other sugary drinks (e.g., Coke, milkshake, Frappuccino, slushies)” they consumed over the past 30 days answered 14 items (see Supplemental Document 3: Table S4). A summed score ranged 0–14.

Increasing Fruits and Veggies

Participants who reported considering or trying to increase the “amount of fruits/vegetables” they ate over the past 30 days answered 12 items (see Supplemental Document 3: Table S5). A summed score ranged 0–12.

Decreasing Greasy, Fried, and Sugary Foods

Although tangentially related to campaign goals, we sought to document reports of decreasing unhealthy food consumption. Participants who reported considering or trying to decrease the “amount of greasy, fried, or sugary foods” they ate over the past 30 days answered 14 items (see Supplemental Document 3: Table S6). A summed score ranged 0–14.

Next 7-Day Nutritional Intentions

Prior to campaign awareness items or ad exposure, participants reported the likelihood of engaging in six food- and four drink-related behaviors in the next seven days (1 = I definitely will not to 5 = I definitely will). Food- and drink-related items were averaged separately.

Next 7-Day Drink Intentions

Items included “Drink more water,” “Avoid drinking soda or pop,” “Replace soda or pop with plain or sparkling water,” and “Replace a sugary drink (milkshake, slushie, or Frappuccinos) with plain or sparkling water” (Cronbach’s α = 0.79).

Next 7-Day Food Intentions

Items included “Eat more fruits,” “Eat more vegetables,” “Eat fruit in place of a dessert or sweet,” “Eat vegetables as a snack,” “Skip adding sauce such as ranch dressing or queso,” and “Eat a baked option in place of a fried option” (Cronbach’s α = 0.84).

Swap Up Aided Campaign Awareness

Participants were presented with nutrition-related brand logos and asked if they had heard of each (“Yes,” “No,” “Don’t know”). Next, they viewed Swap Up campaign advertisements and reported previous exposure (“Yes,” “No,” “Not sure”; see Supplemental Document 1: Table S1). Participants whose reported zip code was designated “rural” saw rural-tailored ads and vice versa for “urban”; in 2021, all participants viewed “Fuel for Football.” Participants who recognized the Swap Up logo or video ad(s) were considered Swap Up Aware.

Swap Up Perceived Relevance

Participants rated agreement (1 = strongly disagree to 5 = strongly agree) with three statements based on SAVI framework components: “This ad gave me a specific example of something I could do to be healthier,” “The recommendation in the ad is something I find acceptable to try at least once (e.g., acceptable among my friends, acceptable in my culture),” and “The recommendation in the ad is something I could realistically do over and over again as a new routine (e.g., It is fast, cheap, easy).” Due to small sample sizes, a single composite variable was created by averaging across all ads.

Swap Up Digital Engagement

Digital engagement was assessed using the multi-select item, “Have you ever seen, interacted with, or shared any Swap Up social media posts, videos, or advertisements?” (see Supplemental Document 3: Table S8). Those who selected any response were considered Swap Up Engaged.

Swap Up Awareness/Engagement

A three-level analytic variable was created combining campaign awareness (Y/N) and digital engagement (Y/N). Participants were categorized as Aware/Engaged (n = 406), Aware/Not Engaged (n = 165), or Not Aware (n = 285).

Statistical Analyses

Sample characteristics were summarized using descriptive statistics for the full sample (n = 856). A cross-sectional subsample (n = 687) included only data for the first year of participation among multi-year respondents. Differences in proportions and means for demographics and urban-rural county designation, awareness, and engagement were assessed using chi-square tests of independence and t-tests within the cross-sectional sample. Utilizing the full sample (n = 856), campaign awareness and digital engagement were summarized by year.

Hypotheses related to campaign outcomes were tested utilizing linear mixed-effects models (REML), which is a robust regression analysis that accounts for repeated measures from the same respondent. Within mixed-effects models, estimates and errors for fixed effects (between-subjects variable) and random effects (within-subjects variable) are accounted for. Analyses were conducted with the full sample (n = 856) and a longitudinal subsample including data for all multi-year respondents (n = 126 respondents, n = 295 cases).

For each model using the full sample, the three-level Swap Up Awareness/Engagement variable was set as the fixed effect, with Not Aware as a comparison group. For the longitudinal subsample, subgroup sizes for the three-level Swap Up Awareness/Engagement variable were determined to be insufficient and unevenly distributed across the outcome variables. Therefore, the binary Swap Up Aided Campaign Awareness and binary Swap Up Digital Engagement variables were utilized as fixed effects in separate models. These models were run to test stability and consistency of potential associations with campaign exposure measures.

For all linear mixed models, covariates included obesity risk, county, zip code-level economic status, age, race/ethnicity, gender, and survey year. A variable indicating which cases came from the same participant (Person_ID) was utilized as the random effect. The default covariance structure was used for all models (Variance Components), and follow-up tests were run with a Bonferroni adjustment to account for multiple comparisons. Analyses were conducted using IBM SPSS Statistics Version 29.0.2.0(20).

Results

Sample Characteristics

The mean age of the sample was 16.8 (SD = 1.5), with a majority being female (62%) and White, non-Hispanic (58%). The full sample included 73% urban (n = 622) and 27% rural (n = 234) participants, with 45% self-reporting as overweight (see Table 2). Race and ethnicity composition of the sample was reflective of the state of Oklahoma (e.g., 61.6% White alone, 12.4% Black/African American alone, 18.7% Hispanic, 6.2% Asian/Pacific Islander, 1.1% American Indian; [U.S. Census Bureau, 2021]).

Table 2.

Sample Characteristics, Full Sample (n = 856).

Variable	n	%
Survey Wave
Year 1 (6/16/21–7/27/21)	200	23.4
Year 2 (5/5/22–5/25/22)	232	27.1
Year 3 (1/31/23–3/3/23)	221	25.8
Year 4 (2/13/24–3/19/24)	203	23.7
Age in Years (M = 16.8 [SD = 1.5])
13	14	1.6
14	52	6.1
15	95	11.1
16	149	17.4
17	191	22.3
18	282	32.9
19*	52	6.1
20*	21	2.5
Race/Ethnicity
White, non-Hispanic	500	58.4
Black, non-Hispanic	70	8.2
Hispanic/Latino	119	13.9
Asian/Pacific Islander, non-Hispanic	26	3.1
American Indian/Native American, non-Hispanic	52	6.1
Other or 2 or more races, non-Hispanic	89	10.4
Gender Identity
Female	528	61.7
Male	321	37.5
Other identity ^	7	0.8
County
Urban	622	72.7
Rural	234	27.3
Zip Code Level Economic Status
Average of full sample living below poverty level, based on participant zip code	822	15.3
Self-Reported Obesity Risk
Overweight	388	45.3
Not overweight	468	54.7

Respondents who were 18 years old were invited to participate in subsequent waves until 20 years old.

^Response option added during Year 4.

Campaign Delivery, Engagement, and Perceived Relevance

At Year 4, 75% of the full sample reported aided awareness, and 70% reported engagement with the Swap Up campaign. Awareness varied from 58% to 75% across years. Digital engagement increased from 44% in Year 1% to 70% in Year 4. In Year 4, participants reported an average of two digital engagements (see Table 3).

Table 3.

Swap Up Aided Campaign Awareness and Digital Engagement by Year, Full Sample (n = 856).

Year	Aided awareness% (n)	At least 1 digital engagement% (n)	Average # of digital engagementsM (SD)
Year 1 (2021)	72% (143)	44% (87)	0.7 (0.9)
Year 2 (2022)	63% (147)	49% (114)	0.8 (1.1)
Year 3 (2023)	58% (129)	52% (114)	1.0 (1.3)
Year 4 (2024)	75% (152)	70% (141)	1.9 (1.5)

Within the cross-sectional subsample (n = 687), the most commonly reported digital engagements were “Seen a GIF/short video from Swap Up online” (29%) and “Opened or seen a story on social media from Swap Up” (23%). Urban participants (38%) and those reporting Awareness (43%) were significantly more engaged overall compared to rural participants (12%) and those Unaware (7%), respectively (see Supplemental Document 3: Table S7).

Swap Up perceived relevance by ad ranged 3.8–4.3. Participants reporting aided awareness (4.1, SD = 0.69) and digital engagement (4.1, SD = 0.68) had significantly higher perceived relevance ratings than those unaware (3.9, SD = 0.78) or unengaged (3.9, SD = 0.76), respectively. Perceived relevance did not differ by county or self-reported obesity risk (see Supplemental Document 3: Table S8).

Beliefs, Behaviors, and Intentions

Campaign-Related Beliefs

The grand mean for campaign-related beliefs was 4.09 (SD = 0.67; n = 687). Participants who reported aided awareness (4.12, SD = 0.65) and digital engagement (4.18, SD = 0.65) had significantly higher perceived relevance ratings than those unaware (3.98, SD = 0.69) or unengaged (4.00, SD = 0.67), respectively (ps < .001). Campaign-related beliefs did not differ by county or self-reported obesity risk.

Past 30-Day Behaviors

The most endorsed past 30-day behaviors were: “Set a goal to drink more water” (60%), “Purchased, or asked others to purchase, more fruits/vegetables to have at home” (59%), “While I still had these foods [greasy, fried, and sugary], I tried to eat a bit less of them by taking a smaller serving or not finishing all of the food” (52%), “Tried to find different ways to relax or manage stress/boredom [rather than eating greasy, fried, and sugary foods]” (45%), and “Set a limit on how many sodas/sugary drinks I should drink each day, or limited the times/days I drink them” (44%). Past 30-day behaviors did not differ by county or self-reported obesity risk overall, though some individual items did significantly differ. For county, a greater proportion of urban participants endorsed five of the behaviors compared to rural participants. For self-reported obesity risk, 15 individual items differed; a greater proportion of self-reported overweight participants endorsed 14 of the 15 behaviors compared to not self-reported overweight (see Tables S5–S8 in Supplemental Document 3).

Nutritional Intentions

Grand means for next 7-day drink and food intentions were 3.61 (SD = 0.88) and 3.42 (SD = 0.81), respectively (ns = 687). For both variables, means significantly differed by self-reported obesity risk. Those self-reporting being overweight rated next 7-day drink (3.71, SD = 0.85) and food (3.53, SD = 0.82) intentions higher than those self-reporting being not overweight (drink: 3.54, SD = 0.91; food: 3.34, SD = 0.80; ps < .05). In addition, next 7-day nutritional intentions significantly differed between those reporting awareness (drink: 3.66, SD = 0.85; food: 3.54, SD = 0.83) and those not aware of the campaign (drink: 3.53, SD = 0.94; food: 3.22, SD = 0.73; ps < .05), as well as those reporting engagement (drink: 3.78, SD = 0.81; food: 3.61, SD = 0.82) and those not engaged (drink: 3.45, SD = 0.92; food: 3.24, SD = 0.76; ps < .001). Next 7-day intentions did not differ by county.

Awareness and Engagement Predicting Campaign Outcomes

All full sample mixed-effects models yielded statistically significant effects of Awareness/Engagement on the outcome. The models accounted for 45%–70% of the total variance across campaign outcomes, suggesting a moderate to strong explanatory value. Awareness/Engagement alone explained 3%–10% of the total variance in outcomes. For models utilizing the longitudinal subsample, all mixed-effects models yielded statistically significant effects of Aided Awareness and Digital Engagement on the outcome. The models accounted for 42%–75% of the total variance across campaign outcomes, suggesting a moderate to strong explanatory value. Aided Awareness alone explained 1%–2% of the total variance in outcomes, and Digital Engagement alone explained 1%–6%.

For the full sample models, we observed a significant positive effect of Swap Up Awareness/Engagement on each outcome. Utilizing Not Aware as a comparison group, there was a significant main effect of combined Awareness and Engagement, but not Awareness alone. Furthermore, participants who reported combined Awareness and Engagement had significantly higher ratings compared to those Aware (but not engaged) and those Not Aware. Ratings for Aware and Not Aware did not differ from each other. For longitudinal models, we observed a similar pattern. There was a significant positive effect of Digital Engagement; those who reported being Engaged had significantly higher ratings compared to those Not Engaged; the pattern for those reporting aided awareness was inconsistent. For five out of the seven outcomes, Aware participants did not differ in their ratings compared to those Not Aware.

The random intercept term was statistically significant for all models, and explanatory value of the models increased with inclusion, suggesting that the term should remain in the models (see Table 4 for model coefficients and indicators of model fit and Supplemental Document 2 for bar graphs representing marginal means from each model).

Table 4.

Linear Mixed Model Statistics.

Model	Fixed effect				Model fit			Random effect
Model	Coeff.	SE	p-value	95% CI	AICc/BIC	Marginal R²	Conditional R²	Coeff. (SE)	p-value
FULL SAMPLE (CROSS-SECTIONAL AND LONGITUDINAL PARTICIPANTS)
Swap Up perceived relevance (Fig. S1)	3.94	.04	<.001	3.86-4.02	1,697.51/1,706.89	.02	.70	.36 (.03)	<.001
Term: Aware & Engaged (ref: Not Aware)	.23	.05	<.001	.12-.33
Term: Aware Only (ref: Not Aware)	.025	.06	ns	-.10-.15
Swap Up campaign-related beliefs (Fig. S2)	4.00	.04	<.001	3.93-4.08	1,615.99/1,625.37	.03	.56	.24 (.03)	<.001
Term: Aware & Engaged (ref: Not Aware)	.23	.05	<.001	.13-.33
Term: Aware Only (ref: Not Aware)	.02	.06	ns	-.10-.14
Past 30-day nutrition-related behaviors (four models, Fig. S3)
Increasing water	2.42	.13	<.001	2.16-2.67	2,787.55/2,796.56	.07	.62	2.31 (.28)	<.001
Term: Aware & Engaged (ref: Not Aware)	1.31	.17	<.001	.81-1.46
Term: Aware Only (ref: Not Aware)	.17	.20	ns	-.21-.56
Decreasing sugary drinks	2.71	.20	<.001	2.31-3.10	2,454.47/2,462.97	.07	.50	2.93 (.65)	<.001
Term: Aware & Engaged (ref: Not Aware)	1.35	.25	<.001	.85-1.8
Term: Aware Only (ref: Not Aware)	-.06	.32	ns	-.68-.56
Increasing fruits and veggies	3.42	.18	<.001	3.07-3.77	2,732.82/2,741.58	.05	.46	2.49 (.54)	<.001
Term: Aware & Engaged (ref: Not Aware)	.97	.23	<.001	.56-1.36
Term: Aware Only (ref: Not Aware)	-.38	.28	ns	-.92-.17
Decreasing greasy, fried, and sugary foods	3.42	.18	<.001	3.07-3.78	2,732.82/2,741.58	.05	.46	2.49 (.54)	<.001
Term: Aware & Engaged (ref: Not Aware)	.97	.23	<.001	.53-1.42
Term: Aware Only (ref: Not Aware)	-.38	.28	ns	-.92-.17
Nutritional intentions (two models, Fig. S4)
Next 7-day drink intentions	3.55	.05	<.001	3.45-3.65	2,074.05/2,083.43	.02	.61	.48 (.05)	<.001
Term: Aware & Engaged (ref: Not Aware)	.24	.07	<.001	.10-.37
Term: Aware Only (ref: Not Aware)	-.09	.08	ns	-.25-.07
Next 7-day food intentions	3.25	.05	<.001	3.16-3.34	1,890.64/1,900.03	.03	.70	.46 (.04)	<.001
Term: Aware & Engaged (ref: Not Aware)	.33	.06	<.001	.21-.45
Term: Aware Only (ref: Not Aware)	.12	.07	ns	-.02-.26
LONGITUDINAL SUBSAMPLE
Swap Up perceived relevance (two models, Fig. S1)
Aided Awareness	4.17	.08	<.001	4.03-4.32	449.44/456.66	.02	.59	.21 (.04)	<.001
Term: Aware (ref: Not Aware)	.19	.08	<.05	.05-.34
Digital Engagement	4.19	.06	<.001	4.07-4.31	446.02/453.24	.03	.56	.12 (.04)	<.001
Term: Engaged (ref: Not Engaged)	.022	.06	<.01	.09-.35
Swap Up campaign-related beliefs (two models, Fig. S2)
Aided Awareness	4.21	.08	<.001	4.05-4.37	497.62/504.84	.01	.48	.19 (.04)	<.001
Term: Aware (ref: Not Aware)	.16	.08	ns	-.002-.33
Digital Engagement	4.12	.06	<.001	4.00-4.25	479.94/487.16	.07	.48	.16 (.03)	<.001
Term: Engaged (ref: Not Engaged)	.34	.07	<.001	.20-.48
Past 30-day nutrition-related behaviors (Fig. S3)
Increasing water (two models)
Aided Awareness	2.59	.27	<.001	2.06-3.11	912.68/919.52	.01	.65	2.66 (.47)	<.001
Term: Aware (ref: Not Aware)	.49	.26	ns	-.02-1.0
Digital Engagement	2.56	.22	<.001	2.13-2.99	908.32/915.11	.03	.64	2.32 (.28)	<.001
Term: Engaged (ref: Not Engaged)	.68	.23	<.01	.22-1.14
Decreasing sugary drinks (two models)
Aided Awareness	2.62	.42	<.001	1.79-3.46	800.47/806.73	.02	.47	2.69 (.74)	<.001
Term: Aware (ref: Not Aware)	.84	.45	ns	-.05-.1.73
Digital Engagement	2.49	.31	<.001	1.88-3.11	792.72/798.98	.06	.47	2.42 (.70)	<.01
Term: Engaged (ref: Not Engaged)	1.26	.36	<.01	.55-1.97
Increasing fruits and veggies (two models)
Aided Awareness	3.21	.31	<.001	2.60-3.82	882.25/888.91	.01	.43	1.66 (.42)	<.001
Term: Aware (ref: Not Aware)	.34	.33	ns	-.32-.99
Digital Engagement	2.97	.23	<.001	2.52-3.44	874.51/881.16	.04	.44	1.59 (.39)	<.001
Term: Engaged (ref: Not Engaged)	.82	.27	<.01	.29-1.35
Decreasing greasy, fried, and sugary foods (two models)
Aided Awareness	3.35	.36	<.001	2.65-4.06	946.18/952.85	.02	.43	2.16 (.55)	<.001
Term: Aware (ref: Not Aware)	.70	.38	ns	-.05-1.45
Digital Engagement	3.29	.28	<.001	2.75-3.83	941.89/947.85	.04	.42	2.32 (.28)	<.001
Term: Engaged (ref: Not Engaged)	.97	.32	<.01	.34-1.61
Nutritional intentions (Fig. S4)
Next 7-day drink intentions (two models)
Aided Awareness	3.81	.11	<.001	3.60-4.03	660.70/667.92	.01	.62	.49 (.08)	<.001
Term: Aware (ref: Not Aware)	.15	.11	ns	-.06-.37
Digital Engagement	3.78	.09	<.001	3.60-3.96	656.92/664.14	.02	.60	.49 (.08)	<.001
Term: Engaged (ref: Not Engaged)	.24	.09	<.05	.05-.42
Next 7-day food intentions (two models)
Aided Awareness	3.48	.11	<.001	3.28-3.69	619.19/626.41	.01	.75	.62 (.10)	<.001
Term: Aware (ref: Not Aware)	.25	.10	<.05	.06-.44
Digital Engagement	3.55	.09	<.001	3.37-3.73	620.23/627.45	.01	.75	.61 (10)	<.001
Term: Engaged (ref: Not Engaged)	.21	.08	<.05	.05-.38

Discussion

We found that Swap Up reached (75%) and engaged (70%) the intended audience, successfully delivering nutrition education messages to Oklahoma adolescents. Online engagement with Swap Up content increased during campaign implementation, whereas aided awareness was more variable over the same period. In mixed-effects models, the combined Awareness/Engagement variable was a significant predictor of every outcome, and those who were both Aware and Engaged reported the highest levels of agreement/behaviors. Awareness alone was not a significant predictor of campaign outcomes, and outcomes among those aware of the campaign did not differ compared to those not aware. This pattern of results was consistent in longitudinal models, further demonstrating positive associations of Swap Up campaign engagement on outcomes. Overall, this study corroborates Year 1 evaluation results (Wagner et al., 2022) and provides evidence of Swap Up’s effectiveness in Oklahoma.

Within the cross-sectional subsample, Swap Up ads were rated as relevant, especially among those Aware and Engaged. Campaign-related beliefs were also highly endorsed, with Aware and Engaged participants reporting significantly higher ratings than Not Aware/Not Engaged. Neither beliefs nor campaign relevance scores differed by county or self-reported obesity. Taken together, these findings suggest that Swap Up continues to resonate with all Oklahoma adolescents.

Interestingly, we found that participants who self-reported being overweight were more likely to endorse or agree with certain behavioral items, including information seeking, nutrition tracking, and making efforts to consume less, compared to those not overweight. This suggests that participants who self-identify as overweight may be proactively seeking support. They may also be inclined to over-report nutritional behaviors and intentions to protect their self-esteem or self-image (Puhl & Brownell, 2003).

Implications for Public Health Practice

A majority of participants engaged with campaign digital content (70%), demonstrating that the campaign employed effective digital engagement strategies. Positive associations between campaign engagement, perceived relevance, and nutrition-related outcomes suggest that prioritization of engagement is key in a digital nutrition education campaign for adolescents. This study contributes to a growing body of evidence positioning engagement as a driver of behavior change in digital health campaigns. As highlighted by Kite et al. (2023), an assumption has been that engagement with campaign content is key to initiating behavior change outcomes targeted by campaign messaging. However, few studies explore this hypothesized relationship and provide evidence directly linking campaign engagement to health outcomes. The current study demonstrates a measurable association utilizing both cross-sectional and longitudinal samples, addressing a current gap in the literature.

This study provides support for SAVI’s approach, driven by both Social Cognitive Theory and Social Norm Theory. By prioritizing platforms and ads that promote engagement, alongside content tailored to appeal to Oklahoma teens and their media consumption habits, lifestyle, humor, and visual interests, Swap Up provides information, tips, and motivating arguments that influence teens’ nutritional knowledge, attitudes, and behavior. The campaign also portrays messengers and situations that provide positive normative examples of nutritional choices, showing Oklahoma adolescents how easy and acceptable it could be to implement. Findings suggest the campaign approach was successful in promoting positive nutrition-supporting beliefs, attitudes, and behaviors among the intended audience.

Study Limitations

As with most studies, the current study has limitations. Economic status was measured through a zip code-level measure, rather than at the individual level. While this is an imprecise measure of economic insecurity, it helps account for potential structural barriers and nutrition access by location that is relevant to the current study. Further, although data were collected from a subset of participants over multiple years, the relatively small longitudinal sample size limited model complexity. In addition, while the sampling plan was designed to replicate the actual population of Oklahoma adolescents (e.g., 66% urban and 34% rural [Health Resources and Services Administration, Maternal and Child Health Bureau, 2024], vs. 73% urban and 27% rural in the current study), the overrepresentation of youth from urban areas in the cross-sectional sample limits the generalizability of linear mixed model findings to adolescents residing in rural areas.

Effective and efficient paid digital media management involves leveraging platform algorithms and conducting real-time optimization to reach the right audience with the right message at the right moment. The dynamic nature of paid digital and social media therefore undermines experimental design, making it difficult to withhold campaign media from a subset of an intended audience (i.e., assignment to control group). Instead of leveraging experimental design, the current study utilized quasi-experimental methods, comparing self-reported awareness/engagement to not aware/engaged participants. While inclusion of covariates helps to minimize error due to non-random assignment, potential recall bias and the convenience sampling approach can further limit generalizability of the current study findings.

This study relied on self-reported data, which may introduce recall or social desirability biases that impact the accuracy of measures. For campaign-related activity, where possible, self-reported data is compared to media metrics to assess veracity. Specifically regarding the self-reported measure of digital engagement, four years of campaign analytics for Swap Up corroborate a high level of digital activity, with over 202 million impressions and 16 million engagements across platforms, ads, and posts, including over 2,500 comments, saves, and shares. These metrics provide an important contextual indicator of the overall reach and interaction with the campaign, suggesting that youth were indeed extensively engaging with its digital components, thereby lending some support to the patterns observed in self-reported data.

Finally, across models, we observed a relatively low explanatory value for campaign awareness/engagement. This could be due to multiple factors, including unmeasured variables, convenience sampling, or biases introduced through non-random assignment. More longitudinal studies isolating effects of digital engagement on campaign effectiveness are needed.

Conclusions

The current study provides continued evidence of Swap Up’s reach, engagement, and impact on healthier nutrition-supporting beliefs and behaviors among Oklahoma adolescents. Findings demonstrate that over four years of implementation, Swap Up successfully engaged its intended audience and achieved high perceived relevance and agreement with nutritional beliefs across urban and rural audiences. This highlights the campaign’s ability to connect with adolescents statewide. Importantly, Engagement, and not Awareness alone, was a significant factor in driving nutritional behaviors and future intentions, which reinforces the importance of resonant content on engaging platforms. These findings underscore the critical role of digital engagement in nutrition education and behavior change. Future research should explore ways to deepen engagement and consider longitudinal study design to further assess long-term changes in nutritional behaviors among adolescents.

Supplemental Material

sj-docx-1-heb-10.1177_10901981251377064 – Supplemental material for Swap Up: Four Years of Behavior Change Through Adolescent Nutrition Education in Oklahoma

Supplemental material, sj-docx-1-heb-10.1177_10901981251377064 for Swap Up: Four Years of Behavior Change Through Adolescent Nutrition Education in Oklahoma by Dana E. Wagner, Taylor Goldman, Julia Geer, Sophia Villarreal, Kelli A. Brodersen, Sjonna Whitsitt-Paulson, Dylan M. Jasna and Kelsey E. Nation in Health Education & Behavior

Supplemental Material

sj-docx-2-heb-10.1177_10901981251377064 – Supplemental material for Swap Up: Four Years of Behavior Change Through Adolescent Nutrition Education in Oklahoma

Supplemental material, sj-docx-2-heb-10.1177_10901981251377064 for Swap Up: Four Years of Behavior Change Through Adolescent Nutrition Education in Oklahoma by Dana E. Wagner, Taylor Goldman, Julia Geer, Sophia Villarreal, Kelli A. Brodersen, Sjonna Whitsitt-Paulson, Dylan M. Jasna and Kelsey E. Nation in Health Education & Behavior

Supplemental Material

sj-docx-3-heb-10.1177_10901981251377064 – Supplemental material for Swap Up: Four Years of Behavior Change Through Adolescent Nutrition Education in Oklahoma

Supplemental material, sj-docx-3-heb-10.1177_10901981251377064 for Swap Up: Four Years of Behavior Change Through Adolescent Nutrition Education in Oklahoma by Dana E. Wagner, Taylor Goldman, Julia Geer, Sophia Villarreal, Kelli A. Brodersen, Sjonna Whitsitt-Paulson, Dylan M. Jasna and Kelsey E. Nation in Health Education & Behavior

Footnotes

Acknowledgements

We would like to acknowledge Kathleen Thomas for her unwavering support and steady leadership overseeing Swap Up. Your dedication to public health for Oklahomans is admirable. Additionally, Melissa Vilas, Jessica Gresham, Pamela Buchwald, Michelle Bellon, Lisa Ott, Parker Goldman, Adam Hakes, and Sarah Khoylow for their pivotal contributions to Swap Up implementation and evaluation over the last four years. We also thank Carolyn Stalgaitis and Dawnyéa Jackson for their scientific consultation and review of the draft manuscript.

ORCID iD

Dana E. Wagner

Author Contributions

Conceptualization, Dana Wagner; Data curation, Julia Geer and Sophia Villarreal; Formal analysis, Dana Wagner, Taylor Goldman, Julia Geer and Sophia Villarreal; Funding acquisition, Kelli Brodersen, Sjonna Whitsitt-Paulson, Dylan Jasna and Kelsey Nation; Methodology, Dana Wagner, Taylor Goldman and Julia Geer; Project administration, Kelli Brodersen, Sjonna Whitsitt-Paulson, Dylan Jasna and Kelsey Nation; Visualization, Julia Geer and Sophia Villarreal; Writing—original draft, Dana Wagner and Taylor Goldman; Writing—review & editing, Dana Wagner, Kelli Brodersen, Sjonna Whitsitt-Paulson, Dylan Jasna and Kelsey Nation.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for this study and manuscript was provided by the Oklahoma Tobacco Settlement Endowment Trust (TSET) under grant HC23-03. TSET is a voter-approved constitutional trust created in 2000 to invest 75% of Oklahoma’s annual Master Settlement Agreement payment in improving health for future generations. Only earnings from these investments are used to fund TSET programs and research, including this study and manuscript. Each evidence-based initiative supported by the trust aims to combat Oklahoma’s two leading causes of preventable death—cancer and cardiovascular disease—by promoting healthy eating, physical activity, and tobacco-free lifestyles.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: At the time of initial submission, D.E.W., T.G., J.G., and S.V. were employees of Rescue Agency, which received funding from Oklahoma Tobacco Settlement Endowment Trust to implement the campaign and research described in this manuscript. There are no other conflicts to disclose.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Supplemental Material

Supplemental material for this article is available online at .

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by Advarra Institutional Review Board (#PRO10824).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

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