Health inequities are persistent. One challenge of equitably administering public health services lies in identifying population segments who are at elevated risk, and understanding how to reach them effectively with resources that meet their needs. Audience segmentation, the process of identifying homogeneous subgroups within heterogeneous populations, is necessary in public health communications to increase equity by allowing campaign planners to create content that is tailored to a higher-risk audience's needs and delivered via targeted media channels to reach them effectively (1). Though public health has slowly incorporated segmentation into campaign development and delivery, the field too often segments based on demographics such as gender, race, or ethnicity alone, which is insufficient and can lead to stereotyping, exclusion, and the reinforcement of disparities (2–4).

Recently, public health and social marketers have embraced the use of psychographics such as mindsets, values, and worldviews to segment audiences. Promising results indicate that using psychographics alone or in combination with demographics leads to more clearly-defined audience segments than demographics alone (2–7). In particular, psychographic segmentation identifies audiences with unique behavioral needs who can be reached via specific media channels (1–3, 6, 7). Additionally, psychographic segmentation can inform campaign development in a way that demographic segmentation alone cannot, as the worldviews, values, and mindsets that define psychographic segments can be incorporated into message framing and content to increase persuasion (8).

Psychographic audience segmentation is effective because it creates groups based on shared characteristics that directly influence behavior, rather than demographic characteristics that may only proxy direct behavioral influences (5, 8). Though a range of approaches can be used in psychographic segmentation, many cutting-edge public health communication campaigns have segmented teens and young adults using peer crowds. Peer crowds are macro-level, reputational subcultures with shared values, interests, media preferences, visual cues, and behavioral norms (9, 10). Broader than the peer groups with which a young person interacts on a daily basis, peer crowds represent shared culture and identity across communities (9, 11). Peer crowds are ideal for health communication audience segmentation because campaign content can address the specific behavioral norms of a targeted peer crowd, using the crowd's shared values and mindsets to increase message relevance and tailoring visual cues to increase appeal and information processing. Peer crowds also tap into a key factor in teen and young adult behavior—social influence (12)—by identifying groups of youth with shared norms who can be reached together and whose behavior influences each other. Finally, peer crowd campaigns can be delivered efficiently by focusing resources on media channels where crowd members naturally gather, increasing campaign exposure among the target crowd without increasing campaign waste (13, 14).

Peer crowds fulfill a symbolic role within adolescent culture, each representing a particular set of values and lifestyles that a youth can embrace or reject as they explore their place within the social environment (15–17). Crowds form through both selection (individuals with shared values, interests, and experiences seeking out each other) and socialization (crowd members reinforcing shared culture, mindsets, and behaviors as a means of maintaining identity and belonging) (18, 19). Peer crowds influence behavior by providing a prototype of normative crowd behavior, which a young person may emulate to signal to others their identity as a member of that crowd (20–22).

One way of understanding the role of values and mindset in adolescent peer crowd culture is through the mindsponge model (23, 24). Though initially developed to explain processes of acculturation and cross-cultural business (23), the mindsponge model has been applied to describe other psychosocial phenomenon (24) and can be a useful way to think about how a person's values and mindsets evolve during adolescence. As peers overtake the family as a teen's primary source of socialization and culture (25–27), the values and mindsets that the youth learned from their family may be challenged by new values held by their peers. The relevance, compatibility, and costs/benefits of these new values will influence if and how a teen incorporates these values into their comfort zone and eventually their mindset, as well as which previously-held values they may drop (23, 24). The values that pass a teen's filters will become part of their core beliefs, affecting behavior and which values they may accept in the future (24). This dynamic process may explain how youth develop peer crowd identifications via adoption of a crowd's cultural values, how a teen's peer crowd identification may evolve over time, and why peer crowds tend to fade in relevance in adulthood as other cultural contexts with their own value sets such as the workplace and family become a person's primary sources of socialization.

Peer crowds have been the subject of cross-disciplinary research for decades (Table 1). Research in the U.S. and in other, primarily Western, countries consistently identifies similar teen peer crowds with particular values, mindsets, interests, and behaviors across settings and methods. The Hip Hop and Alternative peer crowds reliably demonstrate elevated risk across a range of health topics including tobacco use, substance use, and mental health. Other crowds such as Country and Popular tend to have moderate risk levels, while Mainstream is typically at low risk across health topics.

Peer crowds provide a purposeful means of audience segmentation that maximizes equity by allowing communication campaigns to create tailored content delivered via targeted channels to higher-risk crowds (13). Experimental studies indicate that peer crowd-matched educational materials can influence behavioral attitudes and beliefs (55–57). In real-world applications (see Supplementary Figure 1 for examples), youth and young adult peer crowd-targeted campaigns have been associated with high levels of awareness, positive receptivity, and desired behavior change (14, 58–63). Peer crowd segmentation for public health communication represents a promising means of increasing equity by focusing resources on crowds with disproportionate risk. However, this approach requires both an effective and practical means of distinguishing members of a given crowd, and an understanding of the values, interests, visual cues, and preferred media channels of the crowd to inform message content, framing, style, and placement.

Despite convergence across studies, no “gold standard” exists for measuring peer crowd identity (64). Existing methods each have benefits and drawbacks for audience segmentation purposes. Ethnographic methods, wherein researchers observe and interview teens in a single community to understand the prevalent crowds, provide detailed portraits of peer crowds but are not sufficiently generalizable or scalable to be used in campaign audience segmentation (50, 65–68). Other studies use a peer-nominated social-type rating approach, whereby students at a given school place their classmates into peer crowds (16, 20, 43). This approach also lacks scalability and generalizability as it requires students to know each other sufficiently well to assign crowd affiliation, and agreement between raters is often low (43, 64). Furthermore, peer ratings focus on others' perceptions of an individual's crowd rather than the individual's self-perception, which is of greater relevance to public health communication segmentation as it more directly influences behavior (31) and avoids stereotyping.

Other approaches directly measure an individual's identity by asking youth to select their peer crowd affiliation from a list of names. Iterations of this approach include forcing selection of a single crowd, allowing selection of multiple crowds, or indicating degree of affiliation with multiple crowds, and may or may not include detailed crowd descriptions (28–32, 34, 42–45, 47, 52, 54–57, 69–71). For example, the Peer Crowd Questionnaire and College Peer Crowd Questionnaire ask participants to confirm if each crowd from an established list exists in their area (e.g., Jocks, Brains, Burnouts, Populars, Non-Conformists, or None/Average), and with which of the crowds they identify (18, 33, 35, 38, 72–75). Though more scalable for audience segmentation, using crowd names suffers from desirability and other biases related to the names and descriptions used. Colloquial crowd names often differ across communities, and individuals may avoid selecting crowd names they perceive as undesirable or having negative connotations even if they personally identify with the group that the name was intended to represent (15, 31, 43, 64). Additionally, forcing youth to select a single crowd fails to account for the multifaceted nature of identity, as many individuals identify with more than one crowd (31, 32, 34, 37, 45). Finally, name-based self-reports provide none of the insight needed to create a targeted campaign such as a crowd's values, worldviews, mindsets, or visual cues, leaving campaign planners without the insights necessary to tailor content.

To address shortcomings of direct measures, indirect measures ask youth to report characteristics associated with peer crowds and assign affiliation using these data. Several studies have collected data on activities (e.g., studying, athletics, partying) or adjective pairs (e.g., quiet/loud, popular/unpopular, athletic/not athletic) and applied cluster analysis techniques to assign youth to peer crowd clusters (76–80). Though a cluster analysis approach creates homogeneous segments, it is unclear if the clusters reflect interactional crowds with shared behavioral norms, making application to health communication campaigns limited. Other indirect approaches tap into the visual cues of peer crowd identity (11, 53). One example, the I-Base Survey^®, includes a grid of photos of unknown youth with each photo representing a single peer crowd based on qualitative research (36, 81). Respondents select photos that would and would not fit with their group of friends, resulting in a score indicating strength of identification with each crowd (11, 13, 37, 40, 41, 58–63, 82). While formal validation studies of the I-Base Survey have not been published, the instrument has been used extensively across U.S. communities and over time with consistent results, indicating the I-Base Survey has a level of applied validity and utility in audience segmentation (11, 13, 36, 37, 40, 41, 82–85). This approach overcomes issues associated with asking individuals directly and forcing identification with a single crowd, and provides insight into the visual cues that should be applied to a communication campaign to attract the attention of the targeted crowd. However, photos must be updated regularly to stay abreast of fashion trends and must be adjusted to reflect the racial and ethnic demographics of the community.

While several approaches exist to measure peer crowd identification, to date there is no formally validated, scalable, and replicable peer crowd segmentation tool. With this in mind, we developed and validated a mindset-based teen peer crowd measurement tool that addresses many of the shortcomings of existing instruments, and more directly measures the psychographics that make peer crowds unique segments that can be effectively targeted in health communications. We focused on values and mindsets as these characteristics influence behavior, are predictive of health risk, and can effectively segment audiences (23, 24, 86–89). To develop the instrument, we conducted formative research leading to the large-scale validation survey described in detail here. The result is a survey that effectively segments teens into distinct psychographic subgroups with unique health risk profiles, while also providing insight into the values and mindsets that should be incorporated into a health communication campaign targeting any given crowd. As the study described herein was conducted in Virginia, we refer to the instrument as Virginia's Mindset Lens Survey (V-MLS). However, as peer crowds transcend geographic boundaries, we believe V-MLS findings are likely generalizable across the U.S. and anticipate future work to establish the survey's applicability outside the state. Additionally, future cross-cultural adaptation work could ensure applicability of the instrument in other countries, where cultural differences require care to ensure local crowds and values are reflected accurately. The V-MLS represents an evolution in peer crowd measurement methods that can complement existing strategies and tools to identify, target, and tailor efforts to higher-risk segments to address health disparities.

Sample descriptives are presented in Table 2. The racial/ethnic distribution of the sample mirrored census data for Virginia, and just over half identified as female. The proportion of the sample scoring highest for each peer crowd was largely similar between the I-Base Survey and V-MLS. Health behavior responses mirrored those reported in the 2019 VYS (104).

We assessed convergent and discriminant validity using a multi-trait multi-method matrix (Table 3). Reliability, shown in the matrix diagonal, was consistently higher for the V-MLS than the I-Base Survey, with McDonald's ω values for the new survey (0.56–0.77) falling in or near the acceptable range for scales with fewer than 10 items (0.60 or higher) (105). Correlations between I-Base Survey and V-MLS scores for a given peer crowd were statistically significant and positive for all five crowds (convergent validity), ranging from 0.17 (Hip Hop) to 0.55 (Country). With one exception, all other correlations in the matrix (discriminant validity) were negative, not statistically significant, or positive but weaker than the corresponding convergent validity correlations. The correlation coefficient between I-Base Survey Hip Hop score and V-MLS Popular score was 0.18 (p < 0.001), which was weaker than the coefficient between the two Popular scores (0.26, p < 0.001) but marginally stronger than the coefficient between the two Hip Hop scores (0.17, p < 0.001). Overall, the V-MLS demonstrated convergent and discriminant validity.

To examine predictive ability, we conducted a series of regressions using I-Base Survey and V-MLS peer crowd scores to separately predict behavior, ACEs, resilience, and personal traits while controlling for demographics (Table 4). Results for the two surveys largely aligned. Higher Hip Hop scores on both surveys were associated with significantly increased odds of current LCC, vape, and marijuana use, and higher ACE scores. For Alternative, higher scores on both surveys were associated with significantly increased odds of current marijuana use and feeling sad or hopeless; reduced odds of current smokeless tobacco use; higher ACE and sensation-seeking scores; and lower resilience scores. Higher Popular scores on both surveys were associated with increased odds of current alcohol use, higher SPI scores, and lower ACE scores. For Country, higher scores on both surveys were associated with significantly increased odds of current cigarette and smokeless tobacco use, reduced odds of feeling sad or hopeless, and lower sensation-seeking scores. Higher Mainstream scores on both surveys were associated with significantly reduced odds for all behaviors except feeling sad, and with lower ACE, SPI, and sensation-seeking scores. Similar patterns across surveys demonstrated that the V-MLS has sufficient capability to predict key characteristics associated with peer crowds that should inform targeted health communication campaign development.

The current manuscript describes validation and predictive ability testing for the new Mindset Lens Survey in Virginia, a mindset-based teen peer crowd segmentation survey that groups teens into crowds with distinct behavioral and psychographic characteristics. The V-MLS represents an evolution in peer crowd measurement for health communications segmentation as it more directly segments youth based on the values and mindsets that may influence peer crowd behavior. By using psychographics to segment teens into groups with real-world social influence, the V-MLS provides health marketers with the audience insights necessary to create targeted, tailored health campaigns that can more equitably address disparities.

The V-MLS demonstrated reliability and convergent and discriminant validity, elements that to our knowledge have not been reported for existing peer crowd measurement tools. Importantly, peer crowd risk profiles from the V-MLS align with existing literature, further reinforcing the validity of the new tool. Specifically, associations previously reported between the Alternative peer crowd and marijuana use, mental health struggles, and ACEs; the Country peer crowd and smokeless tobacco; the Hip Hop peer crowd and tobacco use, substance use, and ACEs; the Mainstream peer crowd and reduced risk across behaviors; and the Popular peer crowd and vaping, alcohol, and reduced ACEs were also observed when using the V-MLS (11, 28, 34, 35, 40, 41, 45, 47).

As with any new instrument, while the V-MLS demonstrates strong reliability and validity, it does not exactly match results derived from existing methods. In the regressions assessing predictive ability, there were several instances in which one of the survey tools produced a statistically significant result while the other did not. In most of these cases, however, the non-significant result trended in the same direction as the significant result, indicating the disparity may be due to power limitations. Generally, results of regression analyses indicate that the V-MLS is effective at identifying crowds with distinct behaviors, experiences, and traits, information that can inform health communication campaign development. In fact, results may indicate that we created a survey that more directly taps into the core psychographic characteristics that differentiate peer crowds, compared to indirect methods. Upcoming research efforts applying the instrument to representative teen surveys in Virginia and other states will allow us to further explore this issue and to demonstrate its generalizability to U.S. states beyond Virginia.

Based on the strength of our findings, we believe the V-MLS represents progress in peer crowd measurement methods and will be a useful tool for health communication campaign segmentation. The instrument is brief, acceptable to respondents, and can easily be appended to ongoing data collections such as representative surveys to inform audience segmentation. It allows youth to pick a primary and secondary response for each question, adding depth to our understanding by providing insight into the intensity of agreement with the values and the peer crowds they represent. Additionally, the V-MLS brings researchers one step closer to directly measuring the core characteristics that differentiate peer crowds—values, mindsets, and worldviews—making it a more efficient means of gathering insightful peer crowd data. The V-MLS provides information critical to effective health communication by revealing the values and mindsets that characterize each peer crowd and that should be incorporated into message framing for a peer crowd-targeted campaign (9, 14, 83). Per the mindsponge model, the values that pass through a teen's filters and comfort zone to reach their core beliefs will influence their behavior and reinforce acceptance of similar values and mindsets in the future (23, 24). By framing health messages based on the values and interests shared by teens within higher-risk audience segments, campaigns can increase the effectiveness of their messages through greater tuning in from the right audience, stronger message receptivity once processed, and ultimately a greater impact on behavior (9). These improvements will increase a campaign's ability to persuade those at higher risk, making the campaign's outcomes more equitable.

While critical to successful message development, information provided by the V-MLS does not address other important elements of campaign development such as the look, style, and feel of the campaign or the media channels where content should be placed (9, 14). As such, we believe that peer crowd visual cue data such as that provided by the I-Base Survey or qualitative data collection remains important to inform the style of campaign ads and actors, while data on media channel preferences should also be collected to ensure tailored content efficiently reaches the target crowd (83). Together, these data can provide the detailed portrait of teen peer crowds necessary to inform targeted health communication campaigns. Additionally, health communication campaigns should still rely on information processing frameworks such as 3D creativity management theory or prototype-willingness model, and campaign design frameworks such as Social Branding or social marketing benchmark criteria to translate audience insights into effective campaigns (14, 63, 106–108).

This manuscript describes the validity and predictive ability of Virginia's Mindset Lens Survey, a new mindset-based teen peer crowd segmentation survey that complements existing methods to provide audience insights necessary to create peer crowd-targeted health communication campaigns. The V-MLS addresses many of the shortcomings of previous peer crowd measurement tools when applied to audience segmentation, and demonstrates reliability and validity. The survey is a brief and practical instrument that can be appended to surveillance surveys and, when combined with relevant information processing and campaign design frameworks, can inform the design and tailoring of health campaigns for higher-risk teens alongside complementary instruments providing visual cue and media use data. Future research seeks to replicate and extend findings in representative samples in Virginia and other U.S. states, explore cross-cultural adaptation opportunities, and apply the survey to real-world health communication campaign development.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by Advarra IRB. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

All authors conceived of and designed the research study. CS and MD implemented the research. CS analyzed the data and wrote the first draft. All authors contributed to article revisions and approved the submitted version.

This research was funded by the Virginia Foundation for Healthy Youth.

Authors CS, JJ, and MD are employed by Rescue Agency, a research and campaign contractor for the Virginia Foundation for Healthy Youth, the study sponsor and employer of authors DS and HB. Rescue Agency holds the proprietorship and trademark for the I-Base Survey and Mindset Lens Survey used in this manuscript. As employees of the funding agency, the Virginia Foundation for Healthy Youth, DS and HB were involved in study design and manuscript writing.

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