Published on in Vol 13 (2024)

Preprints (earlier versions) of this paper are available at, first published .
#4Corners4Health Social Media Cancer Prevention Campaign for Emerging Adults: Protocol for a Randomized Stepped-Wedge Trial

#4Corners4Health Social Media Cancer Prevention Campaign for Emerging Adults: Protocol for a Randomized Stepped-Wedge Trial

#4Corners4Health Social Media Cancer Prevention Campaign for Emerging Adults: Protocol for a Randomized Stepped-Wedge Trial


1Klein Buendel, Golden, CO, United States

2University of New Mexico Comprehensive Cancer Care Center, Albuquerque, NM, United States

3Department of Health Promotion Sciences, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, United States

4College of Nursing and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States

5Section of Nutrition, University of Colorado Denver, Aurora, CO, United States

6Department of Psychology, College of Natural Sciences, Colorado State University, Fort Collins, CO, United States

7University of Colorado Cancer Center, University of Colorado Denver, Aurora, CO, United States

8College of Nursing, University of Arizona, Tucson, AZ, United States

9Cancer Center, University of Arizona, Tucson, AZ, United States

10Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States

11Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States

12College of Medicine, University of Arizona, Tucson, AZ, United States

13Department of Medicine, University of Arizona Cancer Center, Tucson, AZ, United States

*these authors contributed equally

Corresponding Author:

David B Buller, PhD

Klein Buendel

1667 Cole Blvd

Ste 220

Golden, CO, 80401

United States

Phone: 1 303 565 4321


Background: Many emerging adults (EAs) are prone to making unhealthy choices, which increase their risk of premature cancer morbidity and mortality. In the era of social media, rigorous research on interventions to promote health behaviors for cancer risk reduction among EAs delivered over social media is limited. Cancer prevention information and recommendations may reach EAs more effectively over social media than in settings such as health care, schools, and workplaces, particularly for EAs residing in rural areas.

Objective: This pragmatic randomized trial aims to evaluate a multirisk factor intervention using a social media campaign designed with community advisers aimed at decreasing cancer risk factors among EAs. The trial will target EAs from diverse backgrounds living in rural counties in the Four Corners states of Arizona, Colorado, New Mexico, and Utah.

Methods: We will recruit a sample of EAs (n=1000) aged 18 to 26 years residing in rural counties (Rural-Urban Continuum Codes 4 to 9) in the Four Corners states from the Qualtrics’ research panel and enroll them in a randomized stepped-wedge, quasi-experimental design. The inclusion criteria include English proficiency and regular social media engagement. A social media intervention will promote guideline-related goals for increased physical activity, healthy eating, and human papillomavirus vaccination and reduced nicotine product use, alcohol intake, and solar UV radiation exposure. Campaign posts will cover digital and media literacy skills, responses to misinformation, communication with family and friends, and referral to community resources. The intervention will be delivered over 12 months in Facebook private groups and will be guided by advisory groups of community stakeholders and EAs and focus groups with EAs. The EAs will complete assessments at baseline and at 12, 26, 39, 52, and 104 weeks after randomization. Assessments will measure 6 cancer risk behaviors, theoretical mediators, and participants’ engagement with the social media campaign.

Results: The trial is in its start-up phase. It is being led by a steering committee. Team members are working in 3 subcommittees to optimize community engagement, the social media intervention, and the measures to be used. The Stakeholder Organization Advisory Board and Emerging Adult Advisory Board were formed and provided initial input on the priority of cancer risk factors to target, social media use by EAs, and community resources available. A framework for the social media campaign with topics, format, and theoretical mediators has been created, along with protocols for campaign management.

Conclusions: Social media can be used as a platform to counter misinformation and improve reliable health information to promote health behaviors that reduce cancer risks among EAs. Because of the popularity of web-based information sources among EAs, an innovative, multirisk factor intervention using a social media campaign has the potential to reduce their cancer risk behaviors.

Trial Registration: NCT05618158;

International Registered Report Identifier (IRRID): PRR1-10.2196/50392

JMIR Res Protoc 2024;13:e50392




Emerging adulthood is an important habit-forming period of life. The lives of emerging adults aged 18 to 26 years are in flux [1], as they experience lifestyle transitions and increased autonomy while taking on adult-related responsibilities (eg, financial, residential, and employment). It is an important time for health behaviors because with increased autonomy, numerous emerging adults are at risk of making unhealthy choices [2,3].

Health-compromising behaviors that increase cancer risk later in life are especially prevalent among emerging adults [4] and are linked to future cancer morbidity and mortality [5]. Many emerging adults have reduced physical activity and unhealthy eating patterns that do not meet the 2020 American Cancer Society guidelines [6] (are overweight, eat fast food [7,8], and have low self-efficacy for making healthy food choices) [9-13]. Emerging adults’ use of nicotine products [9-12,14] and multiple combustible and noncombustible products [15,16] and their alcohol use and heavy drinking (bingeing) are high [17,18]. Emerging adults are also more prone to intentional UV exposure (solar and artificial tanning) [19]; sporadic sun safety practices [20-23]; and a lack of human papillomavirus (HPV) knowledge [24,25], resulting in incomplete vaccination rates [26,27]. These modifiable cancer risk behaviors are important targets of primary prevention for emerging adults by promoting moderate to vigorous physical activity (MVPA) and dietary and sun safety skills, supporting the use of brief interventions for tobacco or alcohol use, and encouraging emerging adults to make their own health care decisions such as HPV vaccination. However, there is a lack of rigorous research on interventions for emerging adults, and information on their cancer risk behaviors is limited [28].

In the United States, rural populations have substantially higher rates of cancer [29] related to unhealthy eating [30,31], high rates of smoking and alcohol use, exposure to UV radiation and radon (riskier for smokers) [32,33], and persistent HPV infection compared with urban populations. Rural cancer disparities are exacerbated by a lack of health insurance and preventive care [34,35], low socioeconomic status [36,37], poor health literacy [36,38], fatalistic beliefs and ambiguous health information [25], and pervasive barriers to preventive health care [37,39,40].

Social media may offer a superior intervention channel for reaching and influencing emerging adults compared with health care, schools, and workplaces, including in rural areas. Emerging adults (90%) are the most engaged age group on the internet [41], and the internet is a preferred channel for health information among rural and urban emerging adults [40,42]. Rural adults use the internet (85%), as do most Hispanic (86%) and African American (85%) individuals [42]. Social media platforms are very popular with emerging adults [41,43]. Social media provide flexible, responsive, accessible, and low-cost platforms for distributing cancer information to the public from trusted voices [44]. Social media can improve information dissemination, credibility, and relevance and are often used to detect and respond to emerging issues [45] and promote engagement with personalized and impactful user-generated content [46]. Although use varies across platforms, there is growing evidence that interventions delivered using social media can improve physical activity, diet, nicotine product use, skin cancer prevention, and HPV vaccination outcomes among young adults [47-57]. By contrast, it can be challenging to implement a cancer prevention intervention in health care organizations, schools, and workplaces in low-resourced rural communities. Interventions in these channels also may not reach many emerging adults who have low preventive health care use, school enrollment, or employment. However, social media platforms also circulate inaccurate, misleading, and harmful information [58,59]. For instance, social media has spread misinformation on tobacco products [60], breast cancer prevention [61], the efficacy of cannabidiol for cancer care [62], cancer-related nutrition [63], and the mistrust of HPV vaccines [64,65], which can undermine cancer prevention efforts. Thus, interventions also need strategies to correct misinformation and provide digital and media literacy skills [66-70].

In summary, emerging adulthood is an important period for establishing cancer prevention. Many rural emerging adults experience several cancer risk factors, but successful interventions for them are lacking. With the popularity of web-based resources for emerging adults, an innovative multirisk factor intervention over social media has the potential to reach this underserved population and reduce emerging adults’ cancer risks. The goal of this trial is to modify cancer risk factors among emerging adults aged 18 to 26 years living in rural counties in the Four Corners states of Arizona, Colorado, New Mexico, and Utah using a unique, theory-based social media campaign designed with community advisers that delivers relevant, credible, and timely content on reducing multiple cancer risks to emerging adults. A multirisk factor approach [71,72] is adopted because (1) emerging adults vary in cancer risk profiles, (2) several risks cooccur and are affected by similar mechanisms [73-76], and (3) coverage of a variety of topics in the campaign will be engaging. Furthermore, the Four Corners region has a relatively high burden of poverty [77,78], significant population diversity with large Hispanic and American Indian populations [79], and low population density where distance and transportation are health care access barriers [37,80], providing a rich environment to test the efficacy of a multibehavior health promotion intervention.


Our proposed intervention will aim to aid rural emerging adults in making informed decisions to reduce cancer risks related to infrequent physical activity, unhealthy diet, alcohol intake (per 2020 American Cancer Society guidelines [6]), nicotine product use, UV exposure, and lack of HPV vaccination uptake. In addition, it will help them critically evaluate and resist misinformation and marketing that promote cancer and other health risk behaviors and support emerging adults to be media literate when using digital media. The trial will test the following hypotheses:

  • Hypothesis 1: emerging adults will increase MVPA and healthy eating patterns, reduce nicotine product and alcohol use and sunburns, and increase HPV vaccine uptake from baseline to final assessment when receiving the social media intervention.
  • Hypothesis 2: the positive impact of the social media campaign on cancer risk factors among emerging adults will be mediated by improved cancer risk knowledge and beliefs (ie, self-efficacy and response efficacy, norms, social support, and vaccine antecedents), digital and media literacy skills, misinformation identification, and family communication.

Analyses will also explore whether the impact of the campaign differs according to (1) the level of emerging adults’ engagement with it, (2) cancer risk factors, and (3) the biological sex of the participants. The prospective randomized quasi-experimental design and its large sample will provide a rigorous evaluation of the social media campaign compared with many previous studies on social media that have used less rigorous nonrandomized controlled trial designs and small samples [81-86].

We will test a social media campaign to reduce cancer risk factors among emerging adults in rural counties in the Four Corners states using a randomized stepped-wedge trial design.

Target Population and Recruitment Procedures

Emerging adults (N=1000) aged 18 to 26 years residing in rural counties in the states of Arizona, Colorado, New Mexico, and Utah will be enrolled in the study (refer to Textbox 1 for the inclusion and exclusion criteria). Many emerging adults will report ≥1 cancer risk behavior and will be residing in a variety of living arrangements, from multigenerational families to roommates to spouses or partners to alone. In the Four Corners states, 99 counties are rural (ie, Rural-Urban Continuum Codes [RUCC] 4 to 9 [87]), with >2 million residents (176,737 residents aged 19 to 25 years; annual income) [88]. Pregnant individuals will be excluded because the intervention will not provide individualized counseling and could lead to behavioral changes in diet or exercise that might be contraindicated during pregnancy.

Textbox 1. Inclusion and exclusion criteria for the sample of emerging adults.

Inclusion criteria

  • Member of the Qualtrics survey panel in year 2
  • Aged 18 to 26 years
  • Resides in a county coded as Rural-Urban Continuum Codes 4 to 9 in Arizona, Colorado, New Mexico, or Utah
  • Able to speak and read English
  • Has regular social media engagement
  • Accepts screening call from the study staff
  • Provides consent to participate

Exclusion criteria

  • Participated in community engagement activities
  • Cannot speak and read English
  • Has low or no social media engagement
  • Does not accept a screening call from the study staff
  • Does not provide consent to participate
  • Does not give permission for engagement data to be extracted from the Facebook private groups
  • If biologically female, currently pregnant

Emerging adults will be recruited from Qualtrics’ research panel, built from multiple providers that use by-invitation or double opt-in methods. Qualtrics will select adults aged 18 to 26 years residing in counties designated as RUCC 4 to 9 in the 4 selected states [87], balanced on gender, and refer emerging adults to the project’s registration website to complete a consent form. Fake, duplicate, and unqualified respondents will be screened out, and steps will be taken to ensure that they participate only once. The project staff will contact the consented emerging adults by telephone and confirm their eligibility. To avoid clustering, only 1 emerging adult per household will be enrolled. If recruitment lags, we will add Mountain West states with similar populations (ie, Idaho, Montana, Nevada, and Wyoming). We will use quotas so that the sample matches the education and the race and ethnicity of the counties. Although research panel members are required to have internet access, selection bias will be reduced as most US adults (90%), including emerging adults (100%) and rural adults (85%), have access to the internet [42]. We will confirm that the emerging adults have regular social media use (post ≥1 time per week) to allow them to engage with our campaign. We acknowledge that this criterion may impact generalizability, but social media influence those who view them regularly [89,90]. The escalating compensation schedule is designed to achieve retention for postintervention assessments.

Randomized Stepped-Wedge Trial Design

The cancer prevention social media campaign will be tested using a randomized stepped-wedge design (Figure 1).

Figure 1. Randomized stepped-wedge trial design.

Following baseline assessment (T0), emerging adults will be stratified by state, ethnicity (racial and ethnic minority individuals vs non-Hispanic White individuals), and biological sex (male vs female) and randomly assigned to 1 of 4 cohorts differing in intervention duration by the project biostatistician. All cohorts will complete postintervention assessments at weeks 13 (T1), 26 (T2), 39 (T3), 52 (T4), and 104 (T5). All data collection will be conducted using the secure web-based REDCap (Research Electronic Data Capture; Vanderbilt University) application. The primary outcomes measured at all assessment times will be MVPA, healthy eating patterns, nicotine product use, alcohol intake, sunburn, and HPV vaccination.

The social media campaign will be conducted over 12 months in 4 separate Facebook private groups. The intervention will start in each cohort at successive 13-week intervals, so cohorts will receive varying doses of campaign exposure (ie, 180 posts per 13-week interval) via stepped entry. Specifically, cohort 1 (250/1000, 25%) will start the social media campaign at week 1, receiving approximately 720 posts in 52 weeks. Cohort 2 (250/1000, 25%) will start the campaign at week 13 (approximately 540 posts), cohort 3 (250/1000, 25%) at week 26 (approximately 360 posts), and cohort 4 (250/1000, 25%) at week 39 (approximately 180 posts). Emerging adults will be told to read, react to, and comment on posts as often as they like. Exposure to cancer risk information and misinformation from other sources will be controlled by randomization.

The stepped-wedge quasi-experimental design was selected because it has several methodological advantages [91]. First, it allows for the random assignment of participants to 1 of the 4 cohorts, ensuring that potential confounding factors are evenly distributed across the groups. Each cohort is assigned to start the intervention at different times, creating a staggered, time-sequenced implementation schedule across the cohorts that creates the controlled nature of our trial. This design element allows for the within-study comparison of outcomes across different cohorts, with each cohort serving as a control for the others before they receive the intervention. Specifically, the control condition is represented by the periods when the cohorts have not yet started the intervention. For example, cohort 2 serves as a control group for cohort 1 before they commence their intervention in week 13. Second, the design also allows for comparisons between cohorts that have received the intervention for different durations. Third, by collecting data at multiple time points (T1-T5), the design facilitates the assessment of outcomes over an extended period that allows for the evaluation of trends and changes in outcomes over time within each cohort as well as comparisons between cohorts. The multiple baseline measures in cohorts 2, 3, and 4 will permit the examination of any trends or patterns before the intervention is introduced and will control threats to validity [92], as we can account for potential confounding factors, such as time-related effects or external factors, and compare cohorts at different stages of intervention exposure. This strengthens the internal validity of the study. Fourth, the design optimizes study resources by focusing on recruiting and retaining a longitudinal sample. This approach differs from securing a sample where half remain untreated, mitigating problems associated with control groups such as loss to follow-up, demoralization, disengagement, or noncomparability. It adheres to ethical standards by having all participants eventually receive the intervention but still preserves the controlled trial’s integrity.

Cancer Prevention Social Media Campaign

The social media campaign will deliver 2 posts per day (n=approximately 720 posts) over 12 months, addressing cancer risk reduction, teaching digital and media literacy, encouraging family and friend communication, responding to misinformation, and highlighting community resources. The diverse content should be engaging and should avoid fatigue, but we will assess information overload and compare varying intervention durations (ie, 13, 26, 36, and 52 weeks).

Theoretical Approach to Social Media Intervention

We will adopt a multitheoretical approach to influencing rural emerging adults’ health behaviors. Social cognitive theory (SCT) [93] and self-determination theory (SDT) [94] will provide a framework and guide message development and measures of theoretical mediators, including cancer risk perceptions, self-efficacy and response efficacy, norms, social support, relatedness, autonomy, and motivation. SCT constructs address the environment (eg, environmental risks [UV exposure] and settings [health care access]), situation (eg, social norms of risk behaviors), behavior (eg, risk-reduction knowledge and skills), expectations (eg, good health outcomes), observational learning (eg, behavioral modeling), and self-efficacy (eg, confidence to perform prevention) [95]. Three needs must be satisfied when using SDT to foster well-being [94]: (1) competence—ability to control outcomes and feel self-efficacy, (2) relatedness—innate desire to interact with others, and (3) autonomy—need to be in charge of one’s life. Effective interventions implement techniques and strategies that support these 3 basic psychological needs. In need-supportive environments and contexts, extrinsic motivation can be transformed into higher-quality, intrinsic motivation, which is linked to positive health behavior change [96].

Posts will also include engagement strategies to encourage user-generated content and incorporate testimonials. The diffusion of innovations theory (DIT) and social network principles [97] explain that social media are uniquely influential because (1) user activity increases dissemination [98] and (2) information spread among social media communities, notably by knowledgeable peers, reduces uncertainty; makes sentiments dominant [65,99]; and motivates collective action [97,100] via social comparison [97,101] and collective identity that stabilizes actions [101]. Social media’s influence is further clarified by transportation theory [102]. According to this theory, personal stories can be more influential than didactic messages and expert advice [103] partly because of the audience’s identification with the characters [104]. Users often share stories on social media.

Digital and media literacy principles, including “active inquiry and critical thinking about mediated messages” [105], will be addressed in the campaign because many emerging adults are not critical media consumers [106]. Strategies to help emerging adults navigate the media landscape, combat echo chambers of misinformation, counterargue critical information, and develop trust in public health voices are imperative [107,108]. Fact-checking and corrections are important strategies for the campaign, but counternarratives, peer correction, factual elaboration, coherence and credibility appeals, media literacy [109-113], eHealth literacy [114], and advice on safe content sharing are also essential for the posts [69,115]. Media literacy interventions have helped health-related decisions and provided skills to combat unhealthy messages across health topics [116-120], suggesting they will make emerging adults “better prepared and willing to take preventive actions recommended by health professionals” [121]. We will convey how to identify media sources, their intentions, and misinformation; conduct web searches; overcome discomfort of new behaviors; converse with others about cancer risks (skills acquisition); highlight benefits to self, family, and friends (positive outcome expectations); and reinforce that they can reduce risks (self-efficacy). We will measure digital and media literacy skills and the level of misinformation (ie, holding inaccurate beliefs) about cancer prevention as mediators.

Social Media Posts

Social media posts will contain text with images, infographics, videos, and links to websites or other social media platforms (eg, Instagram and YouTube) from government, health care, news media, and trusted sources. Preferred health content for emerging adults of any gender and various social groups (eg, race, ethnicity, income, education, and living circumstances) will be identified through community engagement methods. Messages will address SCT [93], SDT [94], DIT [97], and media literacy principles [122] described earlier. The posts will be positive, at the seventh-grade reading level, and in English. The posts will cover 5 content areas:

  • Cancer prevention: posts will address 6 cancer risk factors and various methods to improve them including behavioral skills for risk reduction, benefits of engaging in such behaviors, social support for these efforts, and strategies to minimize the social and financial costs associated with cancer risk reduction. In addition, advice from health care providers will be shared to help individuals overcome barriers to adopting healthier behaviors. Risk-reducing behaviors will be targeted by the posts include increasing MVPA and healthy eating behavior (more vegetables, fruits, and whole grains and less red and processed meats, sugar-sweetened beverages, highly processed foods, and refined grains); decreasing nicotine product use, alcohol intake, and UV exposure (sunburn); and increasing HPV vaccination. Posts will seek to improve several theoretical mediators, including self-efficacy and response efficacy and perceived risk, and to link cancer prevention to personal goals, including compatibility with values, observable benefits, and simplicity. Posts will also address cost of cancer prevention, present descriptive norms related to healthy and unhealthy behaviors, and highlight social support from family, friends, and partners. They will also promote emerging adults autonomy for their own health and decisions to adopt healthy behaviors. Posts will highlight the cancer prevention benefits of these behaviors and other benefits that may motivate emerging adults to adopt risk-reduction behaviors, including appearance (eg, avoiding skin aging), social (eg, reducing alcohol-induced partner violence), financial (eg, cost savings from quitting nicotine products), and disease prevention (eg, cardiovascular health) benefits. We will use a rotation pattern that covers the 6 cancer risk factors in at least 1 post per week and highlights 1 cancer risk factor in a series of posts each week to provide an in-depth intervention.
  • Digital and media literacy skills: media literacy posts will focus on critical thinking, skill acquisition, and misinformation correction. Posts will aim to improve emerging adults’ digital and media literacy competencies related to (1) access, (2) analysis, (3) creation, (4) reflection, and (5) action [122]. Posts will focus on assessing message credibility and quality (eg, authorship [eg, bots], purpose [eg, marketing], construction, and algorithms) and validity (eg, original source identification, images, deep fake videos, and scientific evidence). Differences in storytelling and scientific evidence will be discussed. Marketing messages will be addressed because advertisers (eg, tobacco, alcohol, and tanning industries) reach emerging adults through social media promotions [41]. Even brief exposure to these marketing messages can instill positive attitudes toward the products [123].
  • Responding to misinformation: we will use our best practices to respond to misinformation [124], that is, messages in conflict with scientific and medical information and advice. Misinformation will be identified by monitoring users’ reactions and comments, auditing the media landscape, and responding immediately [125] to forestall it from going viral. We will respond both proactively in the feed and reactively in replies to the comments. Responses will show empathic engagement and acknowledge users’ uncertainty, confusion, or motivations to prevent defensiveness and maintain trust [125]. Posts will then debunk misinformation by fact-checking, providing factual elaboration and coherence appeals with evidence-based sources [110,111], reframing information to fit existing beliefs, using resistance-to-persuasion tactics (2-sided appeals [126,127] and inoculation [128-130]), telling stories that offer personalized advice, presenting credible statistics and science [111,112,131,132], highlighting prevention actions by resistant groups [132], and depicting patient-provider interactions [132].
  • Family and friend communication: emerging adults are expected to reside in several living arrangements, often with other adults, so posts will present prompts to talk with family (eg, parents, siblings, and partners) and friends about cancer prevention (and content from the campaign posts). Posts will focus on skills for active listening, self-disclosure, support, and conflict management.
  • Referral to community resources: a website will be created that contains links to web-based tools and brief interventions to help emerging adults alter cancer risk behaviors, especially those that may be unknown to emerging adults. Examples include quit-smoking services, portals to state vaccination records, tools for managing the multidose HPV vaccination schedule, and fact sheets and guidelines from health authorities. Resources will be identified by the community advisers and maintained by the investigators. A link to this website and its relevant resources will be included regularly in posts.
Development of Cancer Prevention Posts

An agile, just-in-time process will be used to create and adapt posts to be responsive to emerging adults and to reflect current events [133]. Investigators will prepare a campaign framework for developing posts, identifying target behaviors for each cancer risk factor and key theoretical principles. Project staff will continuously audit cancer prevention information and misinformation in (1) published literature, government reports, and national surveys; (2) participant comments on posts; (3) quarterly web-based emerging adult focus groups; and (4) advisory board input. The investigators and media developers will revise posts, add emerging themes, and tailor posts to key subgroups. Initially, 3 months of campaign posts will be prepared, with additional posts developed during the campaign, creating a planned adaptive campaign. Social media posts will be written in English because rural emerging adults are the primary target group, with Hispanic and American Indian emerging adults comprising a minority of them. English continues to be the most common language on the internet, especially in the United States [134,135]. Nearly all young adults in the United States are proficient in English, even most foreign-born young adults [136].

To ensure campaign exposure, posts must regularly engage users because the Facebook algorithm presents posts from the private groups more frequently and prominently in participants’ newsfeeds when they engage more with the group’s posts. To achieve high visibility and engagement, we will post twice per day and (1) provide novel, high-interest, useful, and current-event content; (2) adjust for season; (3) link to content from other emerging adults to create descriptive norms; (4) address age differences and cultural barriers and facilitators; (5) use ethnically diverse emerging adult images that can improve health communication [137]; and (6) use formats such as stories, polls, questions and answers, videos, and visuals, and invite comments [58,98,138-141].

The campaign will be pilot-tested with rural emerging adults (N=25) meeting the inclusion and exclusion criteria displayed in Textbox 1 (13/25, 52% female individuals and ethnically diverse). It will contain a feed of 56 campaign posts (2 per day) over 4 weeks, and participants will provide feedback in focus group discussions.

Implementation of Social Media Campaign

The social media campaign will be implemented through Facebook private groups. A staff person will serve as the community manager and schedule posts twice a day on all 7 days of the week (1 message in the morning and 1 in the afternoon) to achieve reach [142,143]. In each cohort, emerging adults will receive welcome posts on purpose and ground rules (eg, respect for others) and then receive the ongoing feed delivered in their separate private group so that posts are identical across cohorts, timely, linked to current events and news stories, relevant for seasons, and engaging. We will not start each cohort at the beginning of the feed because it would require adjusting earlier posts to be current, making them dissimilar across cohorts. The community manager will monitor reactions and comments from emerging adults, answer questions, address uncertainty, and correct misinformation [58,124,133]. The community manager will promote peer influence by, for example, (1) recruiting high-frequency emerging adult users to be guest moderators for up to 3 days to schedule posts and reply to comments and (2) hosting Facebook Live events with emerging adult experts (eg, an emerging adult dietitian). If any bullying arises, the community manager will de-escalate it by (1) highlighting empathy and (2) sending direct messages to stop it. If participants leave a group, they will be contacted to see why. Project staff, except the community manager and the project coordinator, will be blinded to the cohort membership.

Facebook’s private group function possesses unique features not found in other social media platforms. These distinctive qualities offer practical methodological advantages and enhance experimental rigor. Facebook remains one of the most popular social media platforms for emerging adults, used by 70% of the population nationally (among which 70% use it daily), with the majority reporting use in nearly all demographic groups, including by age, rurality, race, ethnicity, and income [43]. Thus, nearly all emerging adults will have existing accounts and be familiar with its interface. Other social media place limits on content delivery that would restrict cancer prevention messaging, including by restricting format (eg, YouTube and Instagram mainly use video and images), length (Snapchat and TikTok deliver short videos), and permanence (Snapchat posts last for 24 hours after posting), and they all restrict the ability to link to other web content more than Facebook. Facebook’s private group function will also help ensure that posts appear in users’ feed and will limit access to posts to group members (posts cannot be shared on other social media) to avoid contamination. Finally, Facebook has superior data analytics for tracking exposure to content compared with other platforms.

Community Engagement Methods

Community-based participatory research methods will inform the study and cancer prevention campaign. Partnership processes aligned with the model by Sandoval et al [144] will provide a framework for the study: (1) knowledge of contexts that inform catchment area needs; (2) culturally informed partnership processes guiding engagement; (3) intervention and research protocols responsive to rural, low-income, and underserved conditions; and (4) participatory outcomes disseminated to partners. The research team will draw on the community networks of the Four Corners Cancer Centers Collaborative through each cancer center’s Community Outreach and Engagement program [145] to convene 2 community advisory boards for the project—an Emerging Adult Advisory Board (EAAB) and a Stakeholder Organization Advisory Board (SOAB). Up to 16 emerging adults from rural counties in the Four Corners states, diverse in gender and ethnicity, will be recruited to serve on the EAAB. The EAAB will meet quarterly with investigators in years 2 and 3 and biannually in years 1, 4, and 5. Members provide input on the social media campaign and implementation protocols (year 1) and review proposed posts (years 2 to 3), with attention to the relevance of posts for the circumstances experienced by rural emerging adults, especially the challenges experienced by low-income and marginalized emerging adults. Up to 4 stakeholder organizations that provide health promotion and cancer prevention services to rural emerging adults in each of the 4 states will be recruited for the SOAB. The SOAB will meet twice annually to review messaging, identify local resources, and plan dissemination efforts. Both advisory boards will advise on trial findings and dissemination efforts in years 4 to 5.

A series of focus groups with up to 8 rural emerging adults per group, meeting the inclusion and exclusion criteria (Textbox 1), will be conducted during the project to help develop the social media campaign. Discussions will cover cancer risk behaviors, social media use, health information seeking, misinformation, the context of health behaviors, and current issues and trends. Emerging adults will review social media posts and suggest how to engage rural emerging adults and reflect local contexts and issues. The results will be summarized and used to adapt posts to be responsive, timely, and engaging for emerging adults and targeted to key subgroups.


Primary Outcomes: Cancer Risk Behavior Outcome Measures

We will use validated self-report measures of each cancer risk behavior in the T0-T5 surveys presented in Table 1.

Table 1. Primary and secondary cancer risk behavior outcome measures.
Cancer risk behaviorMeasureMetric
Physical activity
  • Primary and secondary: Global Physical Activity Questionnaire [146]
  • Minutes per week of MVPAa (primary)
  • Meet 150 minutes per week goal (secondary)
  • Primary: Dietary Screener Questionnaire [147,148]
  • Secondary: other meal behaviors [149-152]
  • Intake per day of fruits, vegetables, whole grains or fiber, added sugars (from sugar-sweetened beverages), and red or processed meats
  • Frequency of eating meals and snacks, fast food, and skipping meals
Nicotine product use
  • Primary: 30-day prolonged tobacco or nicotine product abstinence [153]
  • Secondary: 7-day point prevalence of smoking [153]
  • Secondary: readiness to quit [154]
  • Use in the past 30 days (every day, some days, and not at all)
  • Use in the past 7 days (every day, some days, and not at all)
  • 10-point rating (1=no thought of quitting and 10=taking action to quit)
Alcohol intake
  • Primary: consumption of alcoholic drinks [155]
  • Secondary: binge drinking (male individuals: 5 drinks per sitting and female individuals: 3 drinks per sitting)
  • Number of days in the past 30 days; number of drinks per occasion
  • Number of times in the past 30 days
UV exposure
  • Primary: sunburn prevalence [156]
  • Secondary: sun protection behavior [157]
  • Number of sunburns
  • Percentage of sun exposure days using sun protection
HPVb vaccination
  • Primary: any dose of HPV vaccine [158]
  • Secondary: completion of vaccine series
  • Received 1 or more doses
  • Received 2 or 3 doses as recommended for age

aMVPA: moderate to vigorous physical activity.

bHPV: human papillomavirus.

Self-report measures are most practical for this pragmatic trial, with the large sample of emerging adults in 4 geographically large states (approximately 1,100,700 sq km), web-based recruitment, and a multirisk factor approach that makes clinical and observational measures infeasible. The recall period for nicotine abstinence, alcohol use, and sunburn prevalence measures will be 30 days, which improves reliability [156]. Physical activity and other meal behaviors will be measured for the past 7 days, and HPV vaccination will be measured from baseline.

The primary outcome measures of physical activity, diet, and nicotine abstinence will be validated in subsamples of emerging adult participants at baseline and at the 52-week posttest stage. Physical activity and diet self-report measures will be verified with (1) accelerometry (ActiGraph GT9X) [159,160] and (2) 24-hour recalls (for 3 random days; 2 weekdays and 1 weekend day) [161] in a subsample of emerging adults (139/1000, 13.9%) at baseline and repeated at 52 weeks. Nicotine abstinence self-report measures will be verified via saliva cotinine assays (Salimetrics assay) on a subsample of 15% emerging adults reporting abstinence at 52 weeks [153]. Objective measurement of alcohol use, sunburn, and HPV vaccination is infeasible within the scope of the trial.


Mediators will be assessed in all surveys. These include theoretical antecedents—cancer risk (SCT; severity and susceptibility: 6 items); self-efficacy and response efficacy (SCT) [162]; cost of cancer prevention [162]; descriptive norms (SCT; 2 items; prevalence among people you know and 5 people you know best) [163]; social support from family, friends, and partners (SCT); relatedness (SDT; 4 items) [164]; autonomy and motivation (SDT; 10-point contemplation ladder) [154,165]—family and friend communication about cancer prevention (SDT and DIT; if emerging adults shared information from feed with family and friends) [166]; and vaccine antecedents (ie, confidence, constraints, complacency, calculation, and collective responsibility [167]). Digital and media literacy will be assessed with 3 competency measures—self-perceived media literacy (4 items) [168], perceived social media literacy (6 items) [169], and eHealth literacy (8 items) [170]—along with cancer prevention misinformation (8 accurate and 8 inaccurate Likert statements recoded for belief in misinformation).

Potential Covariates

The following variables, measured at baseline, will be assessed as covariates:

  • Participant characteristics: race, Hispanic ethnicity, gender identity, biological sex, RUCC codes (4 to 9), age, education, employment, and emerging adults’ height and weight for BMI (treated as a covariate because of stability at this age) [171]
  • Household features: marital status, parenting status (children at home), household composition, food insufficiency [149], and use of government nutrition assistance programs [155]
  • Health care use: insurance status [172] and prior visit to a physician for routine preventive care
  • Cancer history: personal and family history of cancer
  • Cancer messaging: exposure to cancer information (ie, topics and sources [health care provider, social media, website, news media, and conversations]) [64] and perceived credibility of various media [133]
  • Social desirability: a socially desirable response set 5-item measure [173] to account for socially favorable response bias.
Social Media Campaign Engagement Measures

Behavioral and experiential measures of campaign engagement [174,175] will be collected, guided by the model of engagement by Perski et al [176]. Behavioral measures will be (1) staff records of posts; (2) counts of emerging adults’ views, reactions (eg, like or sad), and comments extracted in identified format using our custom-written app and coded for content and pro, anti, or neutral sentiment by 2 trained research assistants [113]; and (3) use of resources on the project website recorded by the web server. Experiential measures will be collected in each postintervention test, including time spent and frequency of reading posts, flow experience (ie, social interaction, enjoyment, and concentration) [177], cancer information overload [178], and sharing post content with others.

Statistical Analysis Plan

Hypothesis Testing

We plan to use advanced statistical methods suitable for stepped-wedge designs to compare outcomes between different cohorts and time points, which will account for both time effects and intervention effects. This includes using mixed-effects models or generalized estimating equations that can adjust for time-related trends and cohort effects. In addition, by comparing cohorts that start the intervention at different times, we will isolate the effect of our campaign from other external factors and assess whether changes in the cancer risk behaviors are more pronounced or accelerated following the introduction of our intervention compared with the periods before the intervention. In addition, our analysis will adjust for potential confounding factors that might influence cancer risk behaviors, such as age, sex, ethnicity, and other relevant sociodemographic factors. This approach ensures that the control aspects of the design are rigorously analyzed.

Specifically, the 2 hypotheses and exploratory research questions will be tested using R (R Foundation for Statistical Computing) [179] and Mplus, V8.2 (Munthén & Munthén) [180], a structural equation modeling program that allows for growth models and latent constructs (to model measurement error appropriately), repeated measures, direct and indirect effects, and moderators using interaction terms and multiple group analysis [181]. Mplus will handle missing data via full information maximum likelihood. All tests will be intent to treat. To mitigate false discovery (type I error), an α of .008 will be used (ie, the traditional α=.05÷6 [number of cancer risk factors considered]) [182]. Once the full sample is recruited, baseline data will be described and plotted, measurement models will be assessed, and transformations for normality will be examined and applied.

The effect of the treatment on each of the primary outcomes will be examined using a linear mixed-effects model for a stepped-wedge design, as outlined by Hussey and Hughes [183] and Li et al [184]. Repeated measures (6 per individual—T0, T1, T2, T3, T4, and T5 in Figure 1) will be regressed on time since the start of the study (“calendar time”) and time since the start of message exposure (“exposure time”), both of which are expressed as categorical variables via dummy codes. Any mediators found to be significantly impacted by the treatment will be subsequently examined as mediators of the treatment effect on the outcomes via formal mediation models. Multilevel mediation models will be fit as described by Preacher [185] using the structural equation modeling program. As moderation tests require a vastly larger sample than tests of main effects [186], we will examine them without conducting null hypothesis significance tests [187]. We will estimate each model, evaluate the effect magnitude as a function of the moderator, construct bootstrap CIs to illustrate uncertainty, and use false discovery rate controls [182,187]. Linear mixed-effects models for hypothesis 1 will be extended to consider effect modification because of cancer risk behaviors, campaign exposure variables, biological sex, and the time of year of data collection (to control for seasonal fluctuations in UV levels, food availability, and alcohol intake).

Finally, behavioral and experiential engagement will be tested for campaign dose response (ie, duration of the campaign) related to cancer risk behaviors. In addition, behavioral engagement with posts (ie, views, reactions, and comments) on certain topics (eg, risk behaviors, media literacy, or family communication) and formats (eg, text, video, or interactive features such as polls) will be examined to determine if they have an impact on campaign effectiveness.

Power Analysis

To determine the appropriate sample size for the stepped-wedge design, we used the Shiny Cluster Randomized Trial calculator [188], setting α at .008 and assuming, conservatively, that the within-person correlation of the repeated measures is 0.5. Power analysis focused on the main effect on outcomes at the 104-week posttest score being significantly better than preintervention scores (ie, the time-averaged intervention effect). We considered a mean standardized difference between pre- and postexposure measure of 0.2 for a continuous outcome and a difference in pre- versus postexposure prevalence of 0.40 versus 0.48 for a binary outcome. These are conservative and relatively small effects based on our past assessment of a social media campaign [166,189] and the expectation that emerging adults are unlikely to see all posts. We will achieve a power of 0.80 with 115 people per cohort for a continuous outcome and 175 per cohort for a binary outcome. We have planned for a 30% (300/1000) dropout rate, so we will enroll 1000 emerging adult participants and expect to finish the trial with approximately 175 individuals in each cohort. We will use full information maximum likelihood methods for estimation; thus, even incomplete cases will be retained.

Ethical Considerations

The WCG Institutional Review Board reviewed and approved the protocols for the research (study #20223673). Participation will be voluntary, and participants will read and sign an informed consent form approved by the institutional review board. The consent form will present the purpose of the research, the procedures, known risks and benefits, and the use and security of the data. All data collected in the study will be confidential, and participant identity will not be disclosed publicly. Emerging adults that participate in a focus group discussion will be compensated US $40. In the stepped-wedge design, participants will be paid US $30 for baseline, US $15 for 13-week, US $15 for 26-week, US $15 for 39-week, US $30 for 52-week, and US $30 for 104-week postintervention tests (US $135 in total). Those who are selected for the verification of outcome measures via accelerometry, 24-hour dietary recalls, and saliva cotinine assays will receive US $25 compensation for each measure.

Project Initiation and Administration

The study commenced in September 2022. It is supervised by a steering committee comprising the project principal investigators (D Buller and A Sussman) and coinvestigators from each site. The steering committee is meeting once a month to make design and administration decisions, plan activities, track progress, troubleshoot problems, maintain consistency in actions, implement quality controls, and communicate with the funding agency and institutional review board. In addition, the project staff at each site meet monthly with the coordinating center lead staff to ensure timely and regular communication and timeline adherence.

Project activities are being conducted by 3 subcommittees of investigators and project staff, meeting bimonthly (Figure 2). The Community Engagement Subcommittee is managing the partnerships with emerging adults and key stakeholder organizations in the rural counties of the Four Corners states. Advisory boards with members of these 2 groups have been convened and meet regularly with the research team. The Social Media Campaign Subcommittee is developing campaign content and implementation procedures. It has created a framework and procedures for creating social media posts and protocols for the community manager and responding to misinformation. The Measurement Subcommittee has identified assessment instruments, is piloting the measures with samples of emerging adults, and is developing data collection and retention procedures. A subcommittee guiding statistical analysis will be created in the future once the trial commences. The steering committee is functioning as a working group on trial recruitment issues and procedures.

Figure 2. Steering committee and subcommittees for project management.

Project Advisory Boards Meetings

The EAAB and SOAB have been formed with members recruited from each of the 4 states and met with the project investigators and staff in 2023. The EAAB members (3/8, 38% from Arizona, 2/8, 25% from Colorado, 1/8, 12% from New Mexico, and 2/8, 25% from Utah) are diverse in gender (6/8, 75% women and 2/8, 25% men), race and ethnicity (4/8, 50% White, 3/8, 38% American Indian, and 3/8, 38% Hispanic), and age (mean 22.4 years, SD 2.0; range 19-25 years). Nearly all (7/8, 88%) are students (only 1/8, 12% was employed for wages), but 62% (5/8) of the EAAB members have ≥4 years of college education. In addition, 12% (1/8) of the members live alone, 25% (2/8) of the members live with parents, 38% (3/8) of the members live with other people (not with family), and 25% (2/8) of the members live in a college dormitory. The SOAB members represent a diversity of organizations serving the health and education needs of rural counties, specifically emerging adults. Advisers self-identify as female and White (2/14, 20% were Hispanic) and have a mean age of 49.9, SD 9.1 (range 41-63) years, and all completed some college education, with most having a ≥4-year degree education. Advisory boards will meet twice a year (with the EAAB meeting quarterly during project years 2 and 3) and also communicate through Facebook groups to provide ongoing input.

The initial advisory board meeting agendas focused on introducing the project and discussing important health issues for emerging adults, social media use, misinformation on social media, and community resources for emerging adults (SOAB only). In the EAAB, participants identified nicotine product use, healthy eating, and alcohol intake as the most important health priority areas for emerging adults. The emerging adults described a passive approach to reviewing health information on social media, noting that they do not routinely initiate a search but instead encounter a high volume of posts. Furthermore, members of the EAAB were generally confident in their ability to discern the trustworthiness of the posted health information. The EAAB reviewed initially created posts on cancer risk factor reduction, providing input on text, visuals, and links to outside sources. The SOAB cited the importance of HPV vaccination, alcohol intake, and healthy eating as major health priorities for emerging adults among the 6 cancer risk factors. Most organizations represented on the SOAB use social media to reach emerging adults. The SOAB members expressed a higher degree of concern compared with emerging adults in determining the trustworthiness of posted health information. Finally, most SOAB members indicated that there are not enough community resources to address the complete list of risk factors included in our study. Subsequent EAAB and SOAB meetings will deepen the exploration of these issues.

Social Media Campaign Framework Development

A framework has been created to guide the development of social media posts. It contains information that will be used to track messages by topic, primary and secondary outcomes, theoretical mediators, communication mediators, media literacy mediators, message design features, and engagement techniques. The message text, corresponding link, and image or video used in the post will also be tracked. In addition, the date and time the post is published to the Facebook feed and the Facebook link for the message will be recorded. Investigators with content expertise in each of the 6 cancer risk behaviors have identified key precursor behaviors and effective past interventions, especially with young adults, to incorporate into the framework. In addition, specific protocols were developed for the community manager, who will administer the social media campaign, the process of responding to misinformation during the campaign, and the process for the research team to review messages before posting to the social media feed. A style guide for the post content is also in development.

Principal Findings

Emerging adulthood is an important period for the promotion and sustainment of guideline-recommended cancer prevention behaviors. Many rural emerging adults experience several cancer risk factors, but successful interventions for them are lacking. Emerging adults in the rural areas of the Four Corners states may be at a particularly high risk of adopting unhealthy cancer-preventive behaviors. With the popularity of web-based resources for emerging adults, an innovative multirisk factor intervention over social media should be able to reach this underserved population and reduce emerging adults’ cancer risk behaviors. Web-based health programs have been effective in past research [46,64,190-197]. In particular, social media has influenced cancer risk behaviors in some past studies [47-57], although studies evaluating social media with only emerging adults are uncommon, as are prospective randomized study designs [83,198-203].

Strengths and Limitations

The planned trial has several methodological strengths, including the unique multi-institutional team of investigators with expertise in each of the health behaviors of interest as well as cancer prevention and control; a diverse, rural population; an understudied age group; extensive community engagement; a multirisk factor approach; the use of social media; a rigorous study design; and high dissemination potential. The stepped-wedge design will provide experimental control, reduce error variance, avoid problems with control groups, model campaign dose, and efficiently use resources to recruit and retain the sample. We will recruit from an internet panel rather than from the community to obtain a large sample from the sparsely populated Mountain West. The social media campaign will be based on theories of health behavior change and social media influence. The stepped-wedge design and a multifaceted analysis of campaign engagement will be used to assess campaign dose effects.

Several design decisions were made to avoid or reduce potential weaknesses. Using a research panel to improve sample diversity is appropriate for a field experiment that does not aim to estimate population prevalence [204]. Research panel members may participate in multiple studies, but this does not appear to cause low-quality results [205]. Social media advertising was considered for delivering the intervention, but it is difficult to achieve advertising exposure [206] and control contamination, and any test would require a very large sample. Emerging adults with a history of cancer will be included because (1) their numbers are small [207], (2) many engage in cancer risk behaviors [208] and can benefit, and (3) excluding them would reduce generalizability. Self-report measures can contain errors; however, given the large geography and virtual environment, they are practical. We have selected validated self-report measures and will verify physical activity, diet, and nicotine abstinence measures using a subgroup validation cohort that provides more rigorously measured health behavior data. The primary evaluation will test the impact of the overall social media campaign, not the individual message strategies. To obtain insights on which strategies impact outcomes, we will examine campaign engagement measures, theoretical mediators, and the association of message topic and format with changes in cancer risk behaviors and theoretical mediators. Although most emerging adults engage with a variety of social media platforms, we have chosen to use the Facebook platform for delivering our social media campaign. This decision is based on its practicality and widespread use among emerging adults as well as its features that enhance experimental rigor.


New strategies are needed to improve public health information dissemination, correct misinformation [66-68], and promote skills to help emerging adults judge the veracity of web-based content to promote cancer risk reduction. The study will provide this innovation in several ways. A unique, theory-based social media campaign will be created that delivers relevant, credible, and timely content on reducing multiple cancer risks among emerging adults and can be translated to emerging adults in other rural regions. It promotes the reduction of cancer risk behaviors in the diverse (based on ethnicity and education) emerging adult population in the Four Corners area, which has been largely overlooked in past research. This will be one of the first studies using participatory strategies to focus an intervention on behavioral and environmental cancer risk factors and health disparities in an emerging adult population. Emerging strategies will be used to correct misinformation about cancer risk behaviors on social media, along with promoting digital and media literacy skills to emerging adults. The study should have a major impact on emerging adults’ cancer risk behavior decisions and the consumption of accurate cancer information. Finally, the findings should be applicable to other cancer communications and disease prevention efforts for rural emerging adults.


This study was supported by grants from the National Cancer Institute (R01CA268037, P30CA118100, P30CA046934, P30CA023074, and P30CA042014). The opinions are those of the authors. The funding agency had no input on the contents of this paper.

Data Availability

The data collected in this study will be made available in a publicly accessible data repository at the conclusion of the research.

Authors' Contributions

DBB, ALS, CAT, DK, DT, KLH, ELW, BJW, WGW, KN, CKB, DDG, EAB, JSG, JH, and DW conceptualized the study, designed methods, and secured extramural funding. DBB, ALS, CAT, DK, and DT are supervising project activities. AK, DDG, AKY, CFJ, KC, JB, JAT, EYBP, and AS are managing day-to-day study activities. All authors reviewed and approved the manuscript before submission.

Conflicts of Interest

DBB, BJW, WGW, KN, JB, AS, and AK receive a salary from Klein Buendel, Inc. DBB’s spouse is an owner of Klein Buendel, Inc. DK is a consultant for Merck and has received 2 Merck Investigator Studies Program research awards. All other authors declare no other conflicts of interest.

  1. Schwartz SJ. Turning point for a turning point: advancing emerging adulthood theory and research. Emerg Adulthood. Jul 08, 2016;4 (5):307-317. [CrossRef]
  2. Nelson MC, Story M, Larson NI, Neumark-Sztainer D, Lytle LA. Emerging adulthood and college-aged youth: an overlooked age for weight-related behavior change. Obesity (Silver Spring). Oct 2008;16 (10):2205-2211. [FREE Full text] [CrossRef] [Medline]
  3. White MC, Peipins LA, Watson M, Trivers KF, Holman DM, Rodriguez JL. Cancer prevention for the next generation. J Adolesc Health. May 2013;52 (5 Suppl):S1-S7. [FREE Full text] [CrossRef] [Medline]
  4. Islami F, Goding Sauer A, Miller KD, Siegel RL, Fedewa SA, Jacobs EJ, et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA Cancer J Clin. Jan 21, 2018;68 (1):31-54. [FREE Full text] [CrossRef] [Medline]
  5. Colditz GA, Wolin KY, Gehlert S. Applying what we know to accelerate cancer prevention. Sci Transl Med. Mar 28, 2012;4 (127):127rv4. [CrossRef] [Medline]
  6. Rock CL, Thomson C, Gansler T, Gapstur SM, McCullough ML, Patel AV, et al. American Cancer Society guideline for diet and physical activity for cancer prevention. CA Cancer J Clin. Jul 09, 2020;70 (4):245-271. [FREE Full text] [CrossRef] [Medline]
  7. Hertzler AA, Frary R. Dietary status and eating out practices of college students. J Am Diet Assoc. Jul 1992;92 (7):867-869. [Medline]
  8. Bowman SA, Gortmaker SL, Ebbeling CB, Pereira MA, Ludwig DS. Effects of fast-food consumption on energy intake and diet quality among children in a national household survey. Pediatrics. Jan 2004;113 (1 Pt 1):112-118. [CrossRef] [Medline]
  9. American College Health Association - National college health assessment II: reference group data report spring 2013. American College Health Association. 2013. URL:​DataReport_Spring2013.pdf [accessed 2024-01-22]
  10. Gordon-Larsen P, Adair LS, Nelson MC, Popkin BM. Five-year obesity incidence in the transition period between adolescence and adulthood: the National Longitudinal Study of Adolescent Health. Am J Clin Nutr. Sep 2004;80 (3):569-575. [FREE Full text] [CrossRef] [Medline]
  11. Mulye TP, Park MJ, Nelson CD, Adams SH, Irwin CEJ, Brindis CD. Trends in adolescent and young adult health in the United States. J Adolesc Health. Jul 2009;45 (1):8-24. [CrossRef] [Medline]
  12. Merten MJ. Weight status continuity and change from adolescence to young adulthood: examining disease and health risk conditions. Obesity (Silver Spring). Jul 2010;18 (7):1423-1428. [FREE Full text] [CrossRef] [Medline]
  13. Poobalan AS, Aucott LS, Precious E, Crombie IK, Smith WC. Weight loss interventions in young people (18 to 25 year olds): a systematic review. Obes Rev. Aug 2010;11 (8):580-592. [CrossRef] [Medline]
  14. Halperin AC, Smith SS, Heiligenstein E, Brown D, Fleming MF. Cigarette smoking and associated health risks among students at five universities. Nicotine Tob Res. Feb 2010;12 (2):96-104. [FREE Full text] [CrossRef] [Medline]
  15. Richardson A, Williams V, Rath J, Villanti AC, Vallone D. The next generation of users: prevalence and longitudinal patterns of tobacco use among US young adults. Am J Public Health. Aug 2014;104 (8):1429-1436. [CrossRef] [Medline]
  16. Levy DT, Warner KE, Cummings KM, Hammond D, Kuo C, Fong GT, et al. Examining the relationship of vaping to smoking initiation among US youth and young adults: a reality check. Tob Control. Nov 2019;28 (6):629-635. [FREE Full text] [CrossRef] [Medline]
  17. McKnight-Eily LR, Henley SJ, Green PP, Odom EC, Hungerford DW. Alcohol screening and brief intervention: a potential role in cancer prevention for young adults. Am J Prev Med. Sep 2017;53 (3S1):S55-S62. [FREE Full text] [CrossRef] [Medline]
  18. Smith DC, Dumas TM, Davis JP. Emerging adult development and substance use disorders. In: Smith DC, editor. Emerging Adults and Substance Use Disorder Treatment: Developmental Considerations and Innovative Approaches. New York, NY. Oxford University Press; 2018;16-37.
  19. Guy GPJ, Berkowitz Z, Holman DM, Hartman AM. Recent changes in the prevalence of and factors associated with frequency of indoor tanning among US adults. JAMA Dermatol. Nov 2015;151 (11):1256-1259. [FREE Full text] [CrossRef] [Medline]
  20. Buller DB, Cokkinides V, Hall HI, Hartman AM, Saraiya M, Miller E, et al. Prevalence of sunburn, sun protection, and indoor tanning behaviors among Americans: review from national surveys and case studies of 3 states. J Am Acad Dermatol. Nov 2011;65 (5 Suppl 1):S114-S123. [CrossRef] [Medline]
  21. Heckman CJ, Auerbach MV, Darlow S, Handorf EA, Raivitch S, Manne SL. Association of skin cancer risk and protective behaviors with health literacy among young adults in the USA. Int J Behav Med. Aug 2019;26 (4):372-379. [FREE Full text] [CrossRef] [Medline]
  22. Heckman CJ, Manne SL, Kloss JD, Bass SB, Collins B, Lessin SR. Beliefs and intentions for skin protection and UV exposure in young adults. Am J Health Behav. Nov 2011;35 (6):699-711. [FREE Full text] [Medline]
  23. Bowers JM, Geller AC, Schofield E, Li Y, Hay JL. Indoor tanning trends among US adults, 2007-2018. Am J Public Health. Jun 2020;110 (6):823-828. [CrossRef] [Medline]
  24. Gerend MA, Shepherd JE. Correlates of HPV knowledge in the era of HPV vaccination: a study of unvaccinated young adult women. Women Health. Jan 2011;51 (1):25-40. [FREE Full text] [CrossRef] [Medline]
  25. Mills LA, Head KJ, Vanderpool RC. HPV vaccination among young adult women: a perspective from Appalachian Kentucky. Prev Chronic Dis. 2013;10:E17. [FREE Full text] [CrossRef] [Medline]
  26. Boersma P, Black LI. Human papillomavirus vaccination among adults aged 18-26, 2013-2018. NCHS Data Brief. Jan 2020. (354):1-8. [FREE Full text] [Medline]
  27. Henry KA, Stroup AM, Warner EL, Kepka D. Geographic factors and human papillomavirus (HPV) vaccination initiation among adolescent girls in the United States. Cancer Epidemiol Biomarkers Prev. Feb 2016;25 (2):309-317. [FREE Full text] [CrossRef] [Medline]
  28. Xu L, Odum M. Cancer awareness and behavioral determinants associated with cancer prevention-a quantitative study among young adults in rural settings. J Cancer Educ. Jun 2019;34 (3):562-570. [CrossRef] [Medline]
  29. Blake KD, Moss JL, Gaysynsky A, Srinivasan S, Croyle RT. Making the case for investment in rural cancer control: an analysis of rural cancer incidence, mortality, and funding trends. Cancer Epidemiol Biomarkers Prev. Jul 2017;26 (7):992-997. [FREE Full text] [CrossRef] [Medline]
  30. Weaver KE, Geiger AM, Lu L, Case LD. Rural-urban disparities in health status among US cancer survivors. Cancer. Mar 01, 2013;119 (5):1050-1057. [FREE Full text] [CrossRef] [Medline]
  31. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, rural-urban, and racial inequalities in US cancer mortality: part I-all cancers and lung cancer and. J Cancer Epidemiol. 2011;2011:107497. [FREE Full text] [CrossRef] [Medline]
  32. Agency for Toxic Substances and Disease Registry.. Toxicological profile for radon. Centers for Disease Control and Prevention website. Centers for Disease Control and Prevention, U.S. Department of Health and Human Services; Mar 26, 2014. URL: [accessed 2024-01-25]
  33. Fioletov VE, Kimlin MG, Krotkov N, McArthur LJ, Kerr JB, Wardle DI, et al. UV index climatology over the United States and Canada from ground‐based and satellite estimates. J Geophys Res Atmos. Nov 25, 2004;109 (D22):308. [FREE Full text] [CrossRef]
  34. Colorado health access survey. Colorado Health Institute. URL: [accessed 2023-12-20]
  35. State health facts. Kaiser Family Foundation. URL: [accessed 2023-12-20]
  36. Blumling AA, Thomas TL, Stephens DP. Researching and respecting the intricacies of isolated communities. Online J Rural Nurs Health Care. 2013;13 (2). [FREE Full text] [Medline]
  37. Shell R, Tudiver F. Barriers to cancer screening by rural Appalachian primary care providers. J Rural Health. 2004;20 (4):368-373. [CrossRef] [Medline]
  38. Head KJ, Vanderpool RC, Mills LA. Health care providers' perspectives on low HPV vaccine uptake and adherence in Appalachian Kentucky. Public Health Nurs. Jul 2013;30 (4):351-360. [FREE Full text] [CrossRef] [Medline]
  39. Vanderpool RC, Dressler EV, Stradtman LR, Crosby RA. Fatalistic beliefs and completion of the HPV vaccination series among a sample of young Appalachian Kentucky women. J Rural Health. 2015;31 (2):199-205. [FREE Full text] [CrossRef] [Medline]
  40. Befort CA, Nazir N, Engelman K, Choi W. Fatalistic cancer beliefs and information sources among rural and urban adults in the USA. J Cancer Educ. Sep 2013;28 (3):521-526. [FREE Full text] [CrossRef] [Medline]
  41. McCloud RF, Kohler RE, Viswanath K. Cancer risk-promoting information: the communication environment of young adults. Am J Prev Med. Sep 2017;53 (3S1):S63-S72. [FREE Full text] [CrossRef] [Medline]
  42. Internet/broadband fact sheet. Pew Research Center. Apr 7, 2021. URL: [accessed 2023-12-20]
  43. Social media fact sheet. Pew Research Center. Apr 7, 2021. URL: [accessed 2023-12-20]
  44. Sarkar U, Le GM, Lyles CR, Ramo D, Linos E, Bibbins-Domingo K. Using social media to target cancer prevention in young adults: viewpoint. J Med Internet Res. Jun 05, 2018;20 (6):e203. [FREE Full text] [CrossRef] [Medline]
  45. Breland JY, Quintiliani LM, Schneider KL, May CN, Pagoto S. Social media as a tool to increase the impact of public health research. Am J Public Health. Dec 2017;107 (12):1890-1891. [CrossRef] [Medline]
  46. Walther JB, Pingree S, Hawkins RP, Buller DB. Attributes of interactive online health information systems. J Med Internet Res. Jul 01, 2005;7 (3):e33. [FREE Full text] [CrossRef] [Medline]
  47. Goodyear VA, Wood G, Skinner B, Thompson JL. The effect of social media interventions on physical activity and dietary behaviours in young people and adults: a systematic review. Int J Behav Nutr Phys Act. Jun 05, 2021;18 (1):72. [FREE Full text] [CrossRef] [Medline]
  48. Chau MM, Burgermaster M, Mamykina L. The use of social media in nutrition interventions for adolescents and young adults-a systematic review. Int J Med Inform. Dec 2018;120:77-91. [FREE Full text] [CrossRef] [Medline]
  49. Luo T, Li MS, Williams D, Phillippi S, Yu Q, Kantrow S, et al. Using social media for smoking cessation interventions: a systematic review. Perspect Public Health. Jan 20, 2021;141 (1):50-63. [CrossRef] [Medline]
  50. Li D, Fu L, Yang Y, An R. Social media-assisted interventions on human papillomavirus and vaccination-related knowledge, intention and behavior: a scoping review. Health Educ Res. Mar 24, 2022;37 (2):104-132. [CrossRef] [Medline]
  51. Niu Z, Bhurosy T, Heckman CJ. Digital interventions for promoting sun protection and skin self-examination behaviors: a systematic review. Prev Med Rep. Apr 2022;26:101709. [FREE Full text] [CrossRef] [Medline]
  52. Mingoia J, Hutchinson AD, Gleaves DH, Wilson C. The impact of a social media literacy intervention on positive attitudes to tanning: a pilot study. Comput Hum Behav. Jan 2019;90:188-195. [FREE Full text] [CrossRef]
  53. Pagoto SL, Waring ME, Groshon LC, Rosen AO, Schroeder MW, Goetz JM. Proof-of-concept feasibility trial of a dissonance-based sun safety intervention for young adult tanners. Ann Behav Med. Aug 02, 2022;56 (8):830-841. [FREE Full text] [CrossRef] [Medline]
  54. Piedimonte S, Leung A, Zakhari A, Giordano C, Tellier PP, Lau S. Impact of an HPV education and vaccination campaign among Canadian university students. J Obstet Gynaecol Can. Apr 2018;40 (4):440-446. [CrossRef] [Medline]
  55. Sundstrom B, Brandt HM, Gray L, Young Pierce J. It’s my time: applying the health belief model to prevent cervical cancer among college-age women. J Commun Manag. May 08, 2018;22 (2):161-178. [CrossRef]
  56. Kim M, Lee H, Kiang P, Aronowitz T, Sheldon LK, Shi L, et al. A storytelling intervention in a mobile, web-based platform: a pilot randomized controlled trial to evaluate the preliminary effectiveness to promote human papillomavirus vaccination in Korean American College Women. Health Educ Behav. Apr 20, 2020;47 (2):258-263. [FREE Full text] [CrossRef] [Medline]
  57. Gerend MA, Murdock C, Grove K. An intervention for increasing HPV vaccination on a university campus. Vaccine. Jan 22, 2020;38 (4):725-729. [CrossRef] [Medline]
  58. Steffens MS, Dunn AG, Leask J, Wiley KE. Using social media for vaccination promotion: practices and challenges. Digit Health. Nov 03, 2020;6:2055207620970785. [FREE Full text] [CrossRef] [Medline]
  59. Broniatowski DA, Jamison AM, Qi S, AlKulaib L, Chen T, Benton A, et al. Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate. Am J Public Health. Oct 2018;108 (10):1378-1384. [CrossRef] [Medline]
  60. Tan AS, Bigman CA. Misinformation about commercial tobacco products on social media—implications and research opportunities for reducing tobacco-related health disparities. Am J Public Health. Oct 2020;110 (S3):S281-S283. [CrossRef]
  61. Wilner T, Holton A. Breast cancer prevention and treatment: misinformation on Pinterest, 2018. Am J Public Health. Oct 2020;110 (S3):S300-S304. [CrossRef]
  62. Zenone M, Snyder J, Caulfield T. Crowdfunding cannabidiol (CBD) for cancer: hype and misinformation on GoFundMe. Am J Public Health. Oct 2020;110 (S3):S294-S299. [CrossRef] [Medline]
  63. Warner EL, Basen-Engquist KM, Badger TA, Crane TE, Raber-Ramsey M. The online cancer nutrition misinformation: a framework of behavior change based on exposure to cancer nutrition misinformation. Cancer. Jul 01, 2022;128 (13):2540-2548. [FREE Full text] [CrossRef] [Medline]
  64. Margolis MA, Brewer NT, Shah PD, Calo WA, Gilkey MB. Stories about HPV vaccine in social media, traditional media, and conversations. Prev Med. Jan 2019;118:251-256. [CrossRef] [Medline]
  65. Hoffman BL, Felter EM, Chu KH, Shensa A, Hermann C, Wolynn T, et al. It's not all about autism: the emerging landscape of anti-vaccination sentiment on Facebook. Vaccine. Apr 10, 2019;37 (16):2216-2223. [CrossRef] [Medline]
  66. Vaccine safety communication in the digital age: 2018 meeting report, 4-5 June 2018, Les Pensieres center for global health, Veyrier-du-Lac, France. World Health Organization. Jan 15, 2019. URL: [accessed 2024-02-02]
  67. Yang YT, Broniatowski DA, Reiss DR. Government role in regulating vaccine misinformation on social media platforms. JAMA Pediatr. Nov 01, 2019;173 (11):1011-1012. [CrossRef] [Medline]
  68. Chou WYS, Gaysynsky A, Cappella JN. Where we go from here: health misinformation on social media. Am J Public Health. Oct 2020;110 (S3):S273-S275. [CrossRef] [Medline]
  69. Chou WYS, Gaysynsky A, Vanderpool RC. The COVID-19 misinfodemic: moving beyond fact-checking. Health Educ Behav. Feb 2021;48 (1):9-13. [FREE Full text] [CrossRef] [Medline]
  70. Immunizing the public against misinformation. World Health Organization. Aug 25, 2020. URL: [accessed 2023-12-20]
  71. Spring B, King AC, Pagoto SL, Van Horn L, Fisher JD. Fostering multiple healthy lifestyle behaviors for primary prevention of cancer. Am Psychol. 2015;70 (2):75-90. [FREE Full text] [CrossRef] [Medline]
  72. Geller K, Lippke S, Nigg CR. Future directions of multiple behavior change research. J Behav Med. Feb 2017;40 (1):194-202. [CrossRef] [Medline]
  73. Cox-Martin E, Cox MG, Basen-Engquist K, Bradley C, Blalock JA. Changing multiple health behaviors in cancer survivors: smoking and exercise. Psychol Health Med. Mar 2020;25 (3):331-343. [FREE Full text] [CrossRef] [Medline]
  74. Green AC, Hayman LL, Cooley ME. Multiple health behavior change in adults with or at risk for cancer: a systematic review. Am J Health Behav. May 2015;39 (3):380-394. [CrossRef] [Medline]
  75. Prochaska JO. Multiple health behavior research represents the future of preventive medicine. Prev Med. Mar 2008;46 (3):281-285. [CrossRef] [Medline]
  76. Noar SM, Mehrotra P. Toward a new methodological paradigm for testing theories of health behavior and health behavior change. Patient Educ Couns. Mar 2011;82 (3):468-474. [CrossRef] [Medline]
  77. 2018 median household income in the United States. United States Census Bureau. Sep 26, 2019. URL: [accessed 2023-12-20]
  78. Dalaker J. The 10-20-30 provision: defining persistent poverty counties. Congressional Research Service. Apr 14, 2022. URL: [accessed 2024-02-02]
  79. LoConte NK, Gershenwald JE, Thomson CA, Crane TE, Harmon GE, Rechis R. Lifestyle modifications and policy implications for primary and secondary cancer prevention: diet, exercise, sun safety, and alcohol reduction. Am Soc Clin Oncol Educ Book. May 23, 2018;38:88-100. [FREE Full text] [CrossRef] [Medline]
  80. Lai D, Ding Q, Bodson J, Warner EL, Kepka D. Factors associated with increased HPV vaccine use in rural-frontier U.S. States. Public Health Nurs. Jul 2016;33 (4):283-294. [FREE Full text] [CrossRef] [Medline]
  81. Vereen RN, Kurtzman R, Noar SM. Are social media interventions for health behavior change efficacious among populations with health disparities?: a meta-analytic review. Health Commun. Jan 21, 2023;38 (1):133-140. [FREE Full text] [CrossRef] [Medline]
  82. Key KV, Adegboyega A, Bush H, Aleshire ME, Contreras OA, Hatcher J. #CRCFREE: using social media to reduce colorectal cancer risk in rural adults. Am J Health Behav. May 01, 2020;44 (3):353-363. [FREE Full text] [CrossRef] [Medline]
  83. Pope ZC, Barr-Anderson DJ, Lewis BA, Pereira MA, Gao Z. Use of wearable technology and social media to improve physical activity and dietary behaviors among college students: a 12-week randomized pilot study. Int J Environ Res Public Health. Sep 25, 2019;16 (19):3579. [FREE Full text] [CrossRef] [Medline]
  84. Sundstrom B, Cartmell KB, White AA, Russo N, Well H, Pierce JY, et al. HPV vaccination champions: evaluating a technology-mediated intervention for parents. Front Digit Health. 2021;3:636161. [FREE Full text] [CrossRef] [Medline]
  85. Todorovic J, Terzic-Supic Z, Djikanovic B, Nesic D, Piperac P, Stamenkovic Z. Can social media intervention improve physical activity of medical students? Public Health. Sep 2019;174:69-73. [CrossRef] [Medline]
  86. Chung AE, Skinner AC, Hasty SE, Perrin EM. Tweeting to health: a novel mHealth intervention using Fitbits and Twitter to foster healthy lifestyles. Clin Pediatr (Phila). Jan 2017;56 (1):26-32. [CrossRef] [Medline]
  87. Rural-urban continuum codes. U.S. Department of Agriculture Economic Research Service. URL: [accessed 2023-12-20]
  88. 2015-2019 American Community Survey 5-year estimates. United States Census Bureau. URL: https:/​/www.​​programs-surveys/​acs/​technical-documentation/​table-and-geography-changes/​2019/​5-year.​html [accessed 2024-01-30]
  89. Mabe AG, Forney KJ, Keel PK. Do you "like" my photo? Facebook use maintains eating disorder risk. Int J Eat Disord. Jul 2014;47 (5):516-523. [CrossRef] [Medline]
  90. Vogel EA, Ramo DE, Rubinstein ML, Delucchi KL, Darrow S, Costello C, et al. Effects of social media on adolescents' willingness and intention to use e-cigarettes: an experimental investigation. Nicotine Tob Res. Mar 19, 2021;23 (4):694-701. [FREE Full text] [CrossRef] [Medline]
  91. Hughes JP, Granston TS, Heagerty PJ. Current issues in the design and analysis of stepped wedge trials. Contemp Clin Trials. Nov 2015;45 (Pt A):55-60. [FREE Full text] [CrossRef] [Medline]
  92. Shadish WR, Cook TD, Campbell DT. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA. Houghton Mifflin Company; 2002. .
  93. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ. Prentice Hall; 1986. .
  94. Deci EL, Ryan RM. Self-determination theory. In: Van Lange PA, Kruglanski AW, Higgins ET, editors. Handbook of Theories of Social Psychology. Thousand Oaks, CA. SAGE Publications Ltd; 2012;416-437.
  95. Bandura A. Health promotion by social cognitive means. Health Educ Behav. Apr 2004;31 (2):143-164. [CrossRef] [Medline]
  96. Silva MN, Marques MM, Teixeira PJ. Testing theory in practice: the example of self-determination theory-based interventions. Eur Health Psychol. 2014;16 (5):171-180. [CrossRef]
  97. Rogers EM. Diffusion of Innovations, 5th Edition. New York, NY. Free Press; 2003. .
  98. Vos SC, Sutton J, Yu Y, Renshaw SL, Olson MK, Gibson CB, et al. Retweeting risk communication: the role of threat and efficacy. Risk Anal. Dec 2018;38 (12):2580-2598. [CrossRef] [Medline]
  99. Himelboim I, Xiao X, Lee DK, Wang MY, Borah P. A social networks approach to understanding vaccine conversations on Twitter: network clusters, sentiment, and certainty in HPV social networks. Health Commun. May 2020;35 (5):607-615. [CrossRef] [Medline]
  100. Pescosolido BA. Beyond rational choice: the social dynamics of how people seek help. Am J Sociol. Jan 1992;97 (4):1096-1138. [CrossRef]
  101. Turner RH, Killian LM. Collective Behavior, 3rd Edition. Englewood Cliffs, NJ. Prentice-Hall; 1992. .
  102. Green MC, Brock TC. The role of transportation in the persuasiveness of public narratives. J Pers Soc Psychol. Nov 2000;79 (5):701-721. [CrossRef] [Medline]
  103. Green MC. Narratives and cancer communication. J Commun. 2006;56 (suppl_1):S163-S183. [CrossRef]
  104. Slater MD, Buller DB, Waters E, Archibeque M, LeBlanc M. A test of conversational and testimonial messages versus didactic presentations of nutrition information. J Nutr Educ Behav. 2003;35 (5):255-259. [CrossRef] [Medline]
  105. Hobbs R, Jensen A. The past, present, and future of media literacy education. J Media Lit Educ. 2009;1 (1). [FREE Full text] [CrossRef]
  106. Rasi P, Vuojärvi H, Ruokamo H. Media literacy education for all ages. J Media Literacy Educ. Sep 1, 2019;11 (2):1-19. [CrossRef]
  107. Sutton J. Health communication trolls and bots versus public health agencies' trusted voices. Am J Public Health. Oct 2018;108 (10):1281-1282. [CrossRef] [Medline]
  108. Hou Z, Du F, Zhou X, Jiang H, Martin S, Larson H, et al. Cross-country comparison of public awareness, rumors, and behavioral responses to the COVID-19 epidemic: infodemiology study. J Med Internet Res. Aug 03, 2020;22 (8):e21143. [FREE Full text] [CrossRef] [Medline]
  109. Vraga EK, Bode L. I do not believe you: how providing a source corrects health misperceptions across social media platforms. Inf Commun Soc. Apr 19, 2017;21 (10):1337-1353. [CrossRef]
  110. van der Meer TG, Jin Y. Seeking formula for misinformation treatment in public health crises: the effects of corrective information type and source. Health Commun. May 2020;35 (5):560-575. [CrossRef] [Medline]
  111. Walter N, Murphy ST. How to unring the bell: a meta-analytic approach to correction of misinformation. Commun Monogr. May 15, 2018;85 (3):423-441. [CrossRef]
  112. Jacobs L, Kattumana T, Konnova A, Obasa M, Smlatic E, Vandendriessche V, et al. How storytelling can combat vaccine hesitancy: a transdisciplinary approach. Transdiscipl Insights. Dec 15, 2018;2 (1):92-103. [CrossRef]
  113. Buller DB, Walkosz BJ, Berteletti J, Pagoto SL, Bibeau J, Baker K, et al. Insights on HPV vaccination in the United States from mothers' comments on Facebook posts in a randomized trial. Hum Vaccin Immunother. Jul 11, 2019;15 (7-8):1479-1487. [FREE Full text] [CrossRef] [Medline]
  114. Tangcharoensathien V, Calleja N, Nguyen T, Purnat T, D'Agostino M, Garcia-Saiso S, et al. Framework for managing the COVID-19 infodemic: methods and results of an online, crowdsourced WHO technical consultation. J Med Internet Res. Jun 26, 2020;22 (6):e19659. [FREE Full text] [CrossRef] [Medline]
  115. Li X, Liu Q. Social media use, eHealth literacy, disease knowledge, and preventive behaviors in the COVID-19 pandemic: cross-sectional study on Chinese netizens. J Med Internet Res. Oct 09, 2020;22 (10):e19684. [FREE Full text] [CrossRef] [Medline]
  116. Primack BA, Douglas EL, Land SR, Miller E, Fine MJ. Comparison of media literacy and usual education to prevent tobacco use: a cluster-randomized trial. J Sch Health. Feb 14, 2014;84 (2):106-115. [FREE Full text] [CrossRef] [Medline]
  117. Jeong SH, Cho H, Hwang Y. Media literacy interventions: a meta-analytic review. J Commun. Jun 01, 2012;62 (3):454-472. [FREE Full text] [CrossRef] [Medline]
  118. Austin EW, Austin BW, French BF, Cohen MA. The effects of a nutrition media literacy intervention on parents' and youths' communication about food. J Health Commun. 2018;23 (2):190-199. [CrossRef] [Medline]
  119. Wade TD, Wilksch SM, Paxton SJ, Byrne SM, Austin SB. Do universal media literacy programs have an effect on weight and shape concern by influencing media internalization? Int J Eat Disord. Jul 2017;50 (7):731-738. [CrossRef] [Medline]
  120. Chang FC, Miao NF, Lee CM, Chen PH, Chiu CH, Lee SC. The association of media exposure and media literacy with adolescent alcohol and tobacco use. J Health Psychol. Apr 2016;21 (4):513-525. [CrossRef] [Medline]
  121. Austin EW, Austin BW, Willoughby JF, Amram O, Domgaard S. How media literacy and science media literacy predicted the adoption of protective behaviors amidst the COVID-19 pandemic. J Health Commun. Apr 03, 2021;26 (4):239-252. [CrossRef] [Medline]
  122. Hobbs R. Digital and media literacy: a plan of action. The Aspen Institute. 2010. URL: [accessed 2024-02-02]
  123. Buchanan L, Kelly B, Yeatman H. Exposure to digital marketing enhances young adults' interest in energy drinks: an exploratory investigation. PLoS One. Feb 2, 2017;12 (2):e0171226. [FREE Full text] [CrossRef] [Medline]
  124. Buller DB, Pagoto S, Walkosz BJ, Woodall WG, Berteletti J, Kinsey A, et al. The process of responding to COVID-19 misinformation in a social media feed. J Public Health Manag Pract. 2023;29 (4):E124-E127. [FREE Full text] [CrossRef] [Medline]
  125. Scales D, Gorman J, Jamieson KH. The Covid-19 infodemic - applying the epidemiologic model to counter misinformation. N Engl J Med. Aug 19, 2021;385 (8):678-681. [CrossRef] [Medline]
  126. Allen M. Meta‐analysis comparing the persuasiveness of one‐sided and two‐sided messages. West J Speech Commun. Dec 30, 1991;55 (4):390-404. [CrossRef]
  127. O’Keefe DJ. How to handle opposing arguments in persuasive messages: a meta-analytic review of the effects of one-sided and two-sided messages. Ann Int Commun Assoc. 1999;22 (1):209-249. ePublished on: May 18, 2016. [CrossRef]
  128. Godbold LC, Pfau M. Conferring resistance to peer pressure among adolescents: using inoculation theory to discourage alcohol use. Commun Res. Jun 30, 2016;27 (4):411-437. [CrossRef]
  129. Pfau M, Bockern SV, Kang JG. Use of inoculation to promote resistance to smoking initiation among adolescents. Commun Monogr. 1992;59 (3):213-230. ePublished on: June 2, 2009. [CrossRef]
  130. Compton J, Pfau M. Spreading inoculation: inoculation, resistance to influence, and word-of-mouth communication. Commun Theory. Feb 2009;19 (1):9-28. [CrossRef]
  131. Hopfer S. Effects of a narrative HPV vaccination intervention aimed at reaching college women: a randomized controlled trial. Prev Sci. Apr 2012;13 (2):173-182. [CrossRef] [Medline]
  132. Shelby A, Ernst K. Story and science: how providers and parents can utilize storytelling to combat anti-vaccine misinformation. Hum Vaccin Immunother. Aug 2013;9 (8):1795-1801. [FREE Full text] [CrossRef] [Medline]
  133. Buller D, Walkosz B, Henry K, Woodall WG, Pagoto S, Berteletti J, et al. Promoting social distancing and COVID-19 vaccine intentions to mothers: randomized comparison of information sources in social media messages. JMIR Infodemiology. Aug 23, 2022;2 (2):e36210. [FREE Full text] [CrossRef] [Medline]
  134. Pimienta D, Prado D, Blanco A. Twelve years of measuring linguistic diversity in the Internet: balance and perspectives. United Nations Educational, Scientific and Cultural Organization. 2009. URL: [accessed 2024-01-22]
  135. Petrosyan A. Common languages used for web content 2023, by share of websites. Statista. Feb 24, 2023. URL: [accessed 2023-12-20]
  136. Dietrich S, Hernandez E. Language use in the United States: 2019. United States Department of Commerce United States Census Bureau. Aug 2022. URL: [accessed 2024-02-02]
  137. Buller MK, Bettinghaus EP, Fluharty L, Andersen PA, Slater MD, Henry KL, et al. Improving health communication with photographic images that increase identification in three minority populations. Health Educ Res. Apr 01, 2019;34 (2):145-158. [FREE Full text] [CrossRef] [Medline]
  138. Sutton J, Gibson CB, Phillips NE, Spiro ES, League C, Johnson B, et al. A cross-hazard analysis of terse message retransmission on Twitter. Proc Natl Acad Sci U S A. Dec 01, 2015;112 (48):14793-14798. [FREE Full text] [CrossRef] [Medline]
  139. Edney S, Bogomolova S, Ryan J, Olds T, Sanders I, Maher C. Creating engaging health promotion campaigns on social media: observations and lessons from Fitbit and Garmin. J Med Internet Res. Dec 10, 2018;20 (12):e10911. [FREE Full text] [CrossRef] [Medline]
  140. Rus HM, Cameron LD. Health communication in social media: message features predicting user engagement on diabetes-related Facebook pages. Ann Behav Med. Oct 2016;50 (5):678-689. [CrossRef] [Medline]
  141. Hales SB, Davidson C, Turner-McGrievy GM. Varying social media post types differentially impacts engagement in a behavioral weight loss intervention. Transl Behav Med. Dec 2014;4 (4):355-362. [FREE Full text] [CrossRef] [Medline]
  142. Cox LK. How often should you post on Facebook? [New benchmark data]. HubSpot. Apr 20, 2015. URL: [accessed 2024-02-02]
  143. How often should you post on your social media pages? NOW Marketing Group. May 16, 2017. URL: [accessed 2023-12-20]
  144. Sandoval JA, Lucero J, Oetzel J, Avila M, Belone L, Mau M, et al. Process and outcome constructs for evaluating community-based participatory research projects: a matrix of existing measures. Health Educ Res. Aug 2012;27 (4):680-690. [FREE Full text] [CrossRef] [Medline]
  145. Bradley CJ, Anderson-Mellies A, Borrayo EA, Doherty JA, Escontrías OA, Garcia DO, et al. Ethnicity, socioeconomic status, income inequality, and colorectal cancer outcomes: evidence from the 4C2 collaboration. Cancer Causes Control. Apr 2022;33 (4):533-546. [CrossRef] [Medline]
  146. Armstrong T, Bull F. Development of the World Health Organization Global Physical Activity Questionnaire (GPAQ). J Public Health. Mar 2, 2006;14:66-70. [CrossRef]
  147. Thompson FE, Midthune D, Subar AF, Kahle LL, Schatzkin A, Kipnis V. Performance of a short tool to assess dietary intakes of fruits and vegetables, percentage energy from fat and fibre. Public Health Nutr. Dec 2004;7 (8):1097-1105. [CrossRef] [Medline]
  148. Hewawitharana SC, Thompson FE, Loria CM, Strauss W, Nagaraja J, Ritchie L, et al. Comparison of the NHANES dietary screener questionnaire to the automated self-administered 24-hour recall for children in the healthy communities study. Nutr J. Nov 27, 2018;17 (1):111. [FREE Full text] [CrossRef] [Medline]
  149. Larson NI, Neumark-Sztainer D, Story M, Burgess-Champoux T. Whole-grain intake correlates among adolescents and young adults: findings from Project EAT. J Am Diet Assoc. Feb 2010;110 (2):230-237. [CrossRef] [Medline]
  150. Neumark-Sztainer D, Wall M, Guo J, Story M, Haines J, Eisenberg M. Obesity, disordered eating, and eating disorders in a longitudinal study of adolescents: how do dieters fare 5 years later? J Am Diet Assoc. Apr 2006;106 (4):559-568. [CrossRef] [Medline]
  151. Neumark-Sztainer DR, Wall MM, Haines JI, Story MT, Sherwood NE, van den Berg PA. Shared risk and protective factors for overweight and disordered eating in adolescents. Am J Prev Med. Nov 2007;33 (5):359-369. [CrossRef] [Medline]
  152. Perrigue MM, Kantor ED, Hastert TA, Patterson R, Potter JD, Neuhouser ML, et al. Eating frequency and risk of colorectal cancer. Cancer Causes Control. Dec 2013;24 (12):2107-2115. [FREE Full text] [CrossRef] [Medline]
  153. Piper ME, Bullen C, Krishnan-Sarin S, Rigotti NA, Steinberg ML, Streck JM, et al. Defining and measuring abstinence in clinical trials of smoking cessation interventions: an updated review. Nicotine Tob Res. Jun 12, 2020;22 (7):1098-1106. [FREE Full text] [CrossRef] [Medline]
  154. Biener L, Abrams DB. The contemplation ladder: validation of a measure of readiness to consider smoking cessation. Health Psychol. 1991;10 (5):360-365. [CrossRef] [Medline]
  155. 2019 BRFSS questionnaire. Centers for Disease Control and Prevention. URL: [accessed 2023-12-20]
  156. Køster B, Søndergaard J, Nielsen JB, Olsen A, Bentzen J. Reliability and consistency of a validated sun exposure questionnaire in a population-based Danish sample. Prev Med Rep. Feb 10, 2018;10:43-48. [FREE Full text] [CrossRef] [Medline]
  157. Hillhouse J, Turrisi R, Jaccard J, Robinson J. Accuracy of self-reported sun exposure and sun protection behavior. Prev Sci. Oct 2012;13 (5):519-531. [FREE Full text] [CrossRef] [Medline]
  158. Health information national trends survey. National Institutes of Health, National Cancer Institute, Health Information National Trends Survey. URL: [accessed 2023-12-20]
  159. Hwang J, Fernandez AM, Lu AS. Application and validation of activity monitors' Epoch lengths and placement sites for physical activity assessment in exergaming. J Clin Med. Sep 11, 2018;7 (9):268. [FREE Full text] [CrossRef] [Medline]
  160. Crane TE, Skiba MB, Miller A, Garcia DO, Thomson CA. Development and evaluation of an accelerometer-based protocol for measuring physical activity levels in cancer survivors: development and usability study. JMIR Mhealth Uhealth. Sep 24, 2020;8 (9):e18491. [FREE Full text] [CrossRef] [Medline]
  161. Harnack L, Stevens M, Van Heel N, Schakel S, Dwyer JT, Himes J. A computer-based approach for assessing dietary supplement use in conjunction with dietary recalls. J Food Compost Anal. Feb 2008;21 (Suppliment 1):S78-S82. [FREE Full text] [CrossRef] [Medline]
  162. Sherer M, Maddux JE, Mercandante B, Prentice-Dunn S, Jacobs B, Rogers RW. The self-efficacy scale: construction and validation. Psychol Rep. 1982;51 (2):663-671. [CrossRef]
  163. Sheeran P, Orbell S. Augmenting the theory of planned behavior: roles for anticipated regret and descriptive norms. J Appl Soc Psychol. Oct 1999;29 (10):2107-2142. [CrossRef]
  164. Vlachopoulos SP, Michailidou S. Development and initial validation of a measure of autonomy, competence, and relatedness in exercise: the basic psychological needs in exercise scale. Meas Phys Educ Exerc Sci. 2006;10 (3):179-201. [CrossRef]
  165. McGee R, Williams S, Kypri K. College students' readiness to reduce binge drinking: criterion validity of a brief measure. Drug Alcohol Depend. Jun 01, 2010;109 (1-3):236-238. [CrossRef] [Medline]
  166. Buller DB, Pagoto S, Baker K, Walkosz BJ, Hillhouse J, Henry KL, et al. Results of a social media campaign to prevent indoor tanning by teens: a randomized controlled trial. Prev Med Rep. Apr 18, 2021;22:101382. [FREE Full text] [CrossRef] [Medline]
  167. Betsch C, Schmid P, Heinemeier D, Korn L, Holtmann C, Böhm R. Beyond confidence: development of a measure assessing the 5C psychological antecedents of vaccination. PLoS One. Dec 07, 2018;13 (12):e0208601. [FREE Full text] [CrossRef] [Medline]
  168. Tully M, Vraga EK, Bode L. Designing and testing news literacy messages for social media. Mass Commun Soc. 2020;23 (1):22-46. [CrossRef]
  169. Tandoc E, Yee AZ, Ong J, Lee JC, Duan X, Zheng H, et al. Developing a perceived social media literacy scale: evidence from Singapore. Int J Commun. Jun 2021;15:2484-2505.
  170. Norman CD, Skinner HA. eHEALS: the eHealth literacy scale. J Med Internet Res. Nov 14, 2006;8 (4):e27. [FREE Full text] [CrossRef] [Medline]
  171. Ross KM, Eastman A, Wing RR. Accuracy of self-report versus objective smart-scale weights during a 12-week weight management intervention. Obesity (Silver Spring). Mar 2019;27 (3):385-390. [FREE Full text] [CrossRef] [Medline]
  172. 2018 NSCH guide to topics and questions. Data Resource Center for Child & Adolescent Health. 2018. URL: https:/​/www.​​learn-about-the-nsch/​topics_questions/​2018-nsch-guide-to-topics-and-questions [accessed 2023-12-20]
  173. Hays RD, Hayashi T, Stewart AL. A five-item measure of socially desirable response set. Educ Psychol Meas. 1989;49 (3):629-636. [CrossRef]
  174. Michie S, Yardley L, West R, Patrick K, Greaves F. Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop. J Med Internet Res. Jun 29, 2017;19 (6):e232. [FREE Full text] [CrossRef] [Medline]
  175. Short CE, DeSmet A, Woods C, Williams SL, Maher C, Middelweerd A, et al. Measuring engagement in eHealth and mHealth behavior change interventions: viewpoint of methodologies. J Med Internet Res. Nov 16, 2018;20 (11):e292. [FREE Full text] [CrossRef] [Medline]
  176. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. Jun 2017;7 (2):254-267. [FREE Full text] [CrossRef] [Medline]
  177. Kaur P, Dhir A, Rajala R. Assessing flow experience in social networking site based brand communities. Comput Hum Behav. Nov 2016;64:217-225. [FREE Full text] [CrossRef]
  178. Jensen JD, Pokharel M, Carcioppolo N, Upshaw S, John KK, Katz RA. Cancer information overload: discriminant validity and relationship to sun safe behaviors. Patient Educ Couns. Feb 2020;103 (2):309-314. [FREE Full text] [CrossRef] [Medline]
  179. R Core Team. R: a language and environment for statistical computing. The Comprehensive R Archive Network. URL: [accessed 2024-02-02]
  180. Muthen LK, Muthen BO. Mplus User’s Guide. Eighth Edition. Los Angeles, CA. Muthen & Muthen; 2017. .
  181. Ryu E, Cheong J. Comparing indirect effects in different groups in single-group and multi-group structural equation models. Front Psychol. May 11, 2017;8:747. [FREE Full text] [CrossRef] [Medline]
  182. Glickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol. Aug 2014;67 (8):850-857. [CrossRef] [Medline]
  183. Hussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials. Feb 2007;28 (2):182-191. [CrossRef] [Medline]
  184. Li F, Hughes JP, Hemming K, Taljaard M, Melnick ER, Heagerty PJ. Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: an overview. Stat Methods Med Res. Feb 2021;30 (2):612-639. [FREE Full text] [CrossRef] [Medline]
  185. Preacher KJ, Zyphur MJ, Zhang Z. A general multilevel SEM framework for assessing multilevel mediation. Psychol Methods. Sep 2010;15 (3):209-233. [CrossRef] [Medline]
  186. Gelman A, Hill J, Vehtari A. Regression and Other Stories. Cambridge, UK. Cambridge University Press; Jul 23, 2020. .
  187. Cumming G. The new statistics: why and how. Psychol Sci. Jan 2014;25 (1):7-29. [CrossRef] [Medline]
  188. Hemming K, Kasza J, Hooper R, Forbes A, Jaljaard M. A tutorial on sample size calculation for multiple-period cluster randomized parallel, cross-over and stepped-wedge trials using the Shiny CRT Calculator. Int J Epidemiol. Jun 01, 2020;49 (3):979-995. [CrossRef] [Medline]
  189. Buller DB, Pagoto S, Henry KL, Baker K, Walkosz BJ, Hillhouse J, et al. Persisting effects of a social media campaign to prevent indoor tanning: a randomized trial. Cancer Epidemiol Biomarkers Prev. Apr 01, 2022;31 (4):885-892. [FREE Full text] [CrossRef] [Medline]
  190. Villarruel AM, Loveland-Cherry CJ, Ronis DL. Testing the efficacy of a computer-based parent-adolescent sexual communication intervention for Latino parents. Fam Relat. Dec 01, 2010;59 (5):533-543. [FREE Full text] [CrossRef] [Medline]
  191. Deitz DK, Cook RF, Billings DW, Hendrickson A. A web-based mental health program: reaching parents at work. J Pediatr Psychol. Jun 2009;34 (5):488-494. [FREE Full text] [CrossRef] [Medline]
  192. Donovan E, Wood M, Frayjo K, Black RA, Surette DA. A randomized, controlled trial to test the efficacy of an online, parent-based intervention for reducing the risks associated with college-student alcohol use. Addict Behav. Jan 2012;37 (1):25-35. [FREE Full text] [CrossRef] [Medline]
  193. Williamson DA, Walden HM, White MA, York-Crowe E, Newton RL, Alfonso A, et al. Two-year internet-based randomized controlled trial for weight loss in African-American girls. Obesity (Silver Spring). Jul 2006;14 (7):1231-1243. [FREE Full text] [CrossRef] [Medline]
  194. Cavallo DN, Tate DF, Ries AV, Brown JD, DeVellis RF, Ammerman AS. A social media-based physical activity intervention: a randomized controlled trial. Am J Prev Med. Nov 2012;43 (5):527-532. [FREE Full text] [CrossRef] [Medline]
  195. Napolitano MA, Hayes S, Bennett GG, Ives AK, Foster GD. Using Facebook and text messaging to deliver a weight loss program to college students. Obesity (Silver Spring). Jan 2013;21 (1):25-31. [FREE Full text] [CrossRef] [Medline]
  196. Valle CG, Tate DF, Mayer DK, Allicock M, Cai J. A randomized trial of a Facebook-based physical activity intervention for young adult cancer survivors. J Cancer Surviv. Sep 2013;7 (3):355-368. [FREE Full text] [CrossRef] [Medline]
  197. Young SD, Cumberland WG, Lee SJ, Jaganath D, Szekeres G, Coates T. Social networking technologies as an emerging tool for HIV prevention: a cluster randomized trial. Ann Intern Med. Sep 03, 2013;159 (5):318-324. [FREE Full text] [CrossRef] [Medline]
  198. Morrison L, Chen C, Torres JS, Wehner M, Junn A, Linos E. Facebook advertising for cancer prevention: a pilot study. Br J Dermatol. Oct 2019;181 (4):858-859. [FREE Full text] [CrossRef] [Medline]
  199. Brandt HM, Sundstrom B, Monroe CM, Turner-McGrievy G, Larsen C, Stansbury M, et al. Evaluating a technology-mediated HPV vaccination awareness intervention: a controlled, quasi-experimental, mixed methods study. Vaccines (Basel). Dec 10, 2020;8 (4):749. [FREE Full text] [CrossRef] [Medline]
  200. Sinicrope PS, Koller KR, Prochaska JJ, Hughes CA, Bock MJ, Decker PA, et al. Social media intervention to promote smoking treatment utilization and cessation among Alaska native people who smoke: protocol for the connecting Alaska native people to quit smoking (CAN quit) pilot study. JMIR Res Protoc. Nov 22, 2019;8 (11):e15155. [FREE Full text] [CrossRef] [Medline]
  201. Jane M, Hagger M, Foster J, Ho S, Kane R, Pal S. Effects of a weight management program delivered by social media on weight and metabolic syndrome risk factors in overweight and obese adults: a randomised controlled trial. PLoS One. Jun 02, 2017;12 (6):e0178326. [FREE Full text] [CrossRef] [Medline]
  202. Meacham MC, Ramo DE, Prochaska JJ, Maier LJ, Delucchi KL, Kaur M, et al. A Facebook intervention to address cigarette smoking and heavy episodic drinking: a pilot randomized controlled trial. J Subst Abuse Treat. Mar 2021;122:108211. [FREE Full text] [CrossRef] [Medline]
  203. Ramo DE, Kaur M, Corpuz ES, Satre DD, Delucchi K, Brown SA, et al. Using Facebook to address smoking and heavy drinking in young adults: protocol for a randomized, controlled trial. Contemp Clin Trials. May 2018;68:52-60. [FREE Full text] [CrossRef] [Medline]
  204. Schonlau M, Couper MP. Options for conducting web surveys. Statist Sci. May 2017;32 (2):279-292. [CrossRef]
  205. Matthijsse SM, de Leeuw ED, Hox JJ. Internet panels, professional respondents, and data quality. Methodology. Oct 2015;11 (3):81-88. [CrossRef]
  206. Hornik R. Measuring campaign message exposure and public communication environment exposure: some implications of the distinction in the context of social media. Commun Methods Meas. Apr 20, 2016;10 (2-3):167-169. [FREE Full text] [CrossRef] [Medline]
  207. Cancer stat facts: cancer among adolescents and young adults (AYAs) (Ages 15-39). National Institutes of Health, National Cancer Institute, Surveillance, Epidemiology, and End Results Program. URL: [accessed 2023-12-20]
  208. Choi DK, Helenowski I, Hijiya N. Secondary malignancies in pediatric cancer survivors: perspectives and review of the literature. Int J Cancer. Oct 15, 2014;135 (8):1764-1773. [FREE Full text] [CrossRef] [Medline]

DIT: diffusion of innovations theory
EAAB: Emerging Adult Advisory Board
HPV: human papillomavirus
MVPA: moderate to vigorous physical activity
REDCap: Research Electronic Data Capture
RUCC: Rural-Urban Continuum Codes
SCT: social cognitive theory
SDT: self-determination theory
SOAB: Stakeholder Organization Advisory Board

Edited by A Mavragani; submitted 29.06.23; peer-reviewed by M Skiba, J Makin, J Gorman; comments to author 06.11.23; revised version received 28.12.23; accepted 02.01.24; published 22.02.24.


©David B Buller, Andrew L Sussman, Cynthia A Thomson, Deanna Kepka, Douglas Taren, Kimberly L Henry, Echo L Warner, Barbara J Walkosz, W Gill Woodall, Kayla Nuss, Cindy K Blair, Dolores D Guest, Evelinn A Borrayo, Judith S Gordon, Jennifer Hatcher, David W Wetter, Alishia Kinsey, Christopher F Jones, Angela K Yung, Kaila Christini, Julia Berteletti, John A Torres, Emilia Yessenya Barraza Perez, Annelise Small. Originally published in JMIR Research Protocols (, 22.02.2024.

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