People Who Appear Disengaged With Your Health Campaign Are Not Alike

Measuring low health information engagement as an outcome does not tell us why people are disengaged

A health or wellness campaign may tell us who opened an email, visited a website, scheduled a screening, or enrolled in a program.

Those measures are useful.

But people who do not engage can reach the same information-avoidance behavior through very different beliefs and perceptions.

That distinction matters if we want to determine what to do next.

Perceived versus objective health risks

This matters because health communications can implicitly treat perceived concern as though it accurately identifies who most needs information.

It may not.

What people believe about their health risk does not always match their actual health risk.

Research comparing perceived and objective cardiometabolic risk shows mismatches in both directions (DiPietro et al., 2022). Some people underestimate risk, which may reduce the perceived relevance of preventive information. Others overestimate risk and may experience concern disproportionate to their measured risk.

Health risk and treatment efficacy perceptions can shape information behavior

Whether people engage with health information can often depend on two related beliefs: whether they think a threat applies to them and whether they believe an effective response is available.

Populations that disengage from health information are often at higher health risk, have lower health intervention efficacy beliefs, engage in riskier health behaviors, and have worse health outcomes (Kim et al., 2021; Koh, 2023; Orom et al., 2024; Shen et al., 2025; Simon et al., 2024). Information avoidance is associated with lower screening, poorer risk knowledge, low self-efficacy, mistrust, information overload, and characteristics associated with underserved populations; all of which can increase the possibility of missed prevention or delayed care (Emanuel et al., 2015; Orom & Allard, 2025).

Research on health risk perception suggests that beliefs about healthcare efficacy may matter more (Rimal & Real, 2003; Turner et al., 2006).

Someone who perceives substantial risk and believes effective action is possible is different from someone who doubts that anything useful can be done (Skagerlund et al., 2020).

From the outside, these people can all appear “unengaged.”

They may be disengaging for very different reasons, forming subgroups.

Tailoring messages to psychographic groups

This suggests an important role for psychographics, the study and classification of people using psychological characteristics such as beliefs, attitudes, motivations, values, interests, and perceptions. Psychographic information can help explain why people facing similar circumstances interpret the same health opportunity differently.

A psychographic tailored message intended to increase risk awareness might help someone whose beliefs led them to underestimate the threat. It may do little for someone who already recognizes the threat but doubts that the recommended action will help. The same message could create additional distress for someone already more concerned than their measured risk warrants (Cho & Salmon, 2007; Rains et al., 2019).

Creating more messages for small subgroups can add more work to already busy health and wellness teams. AI could help. An experiment with AI-generated psychographic segment-specific messages increased diabetes preventive intentions, suggesting broad persuasive benefits across psychologically distinct groups (Yin & Ma, 2026).

This is not an argument for replacing demographic, clinical, or contextual information. It argues for adding a psychographic dimension. (Cevasco & Alghamdi, 2025). This can help us tailor messages to improve engagement without using private clinical health information.

The practical question therefore changes from:

“Who didn't engage?”

to:

“Are there meaningfully different psychographic groups among the people who didn't engage, and what might explain their different responses?”

That is a useful starting point for better tailoring health communication.

References

Cevasco, K. E., & Alghamdi, A. A. (2025). Vaccination risk perception attitude framework market segmentation with psychographic factors. Journal of Social Marketing. https://doi.org/10.1108/JSOCM-02-2024-0052

Cho, H., & Salmon, C. T. (2007). Unintended Effects of Health Communication Campaigns. Journal of Communication, 57(2), 293–317. https://doi.org/10.1111/j.1460-2466.2007.00344.x

DiPietro, L., Rimal, R., Tjaden, A. H., Bailey, C. P., & Napolitano, M. A. (2022). Is the Risk Perception Attitude Framework Associated with the Accuracy of Self-Reported vs Actual Cardiometabolic Risk and Physical Activity in Young Adults with Overweight/Obesity? American Journal of Lifestyle Medicine, 155982762211422. https://doi.org/10.1177/15598276221142294

Emanuel, A. S., Kiviniemi, M. T., Howell, J. L., Hay, J. L., Waters, E. A., Orom, H., & Shepperd, J. A. (2015). Avoiding cancer risk information. Social Science & Medicine, 147, 113–120. https://doi.org/10.1016/j.socscimed.2015.10.058

Kim, J.-N., Kim, K.-Y., & Kim, S.-M. (2021). Integrating Risk Perception Attitude Framework and Subjective Norms for Predicting Smokers’ Health Information Seeking. Business Communication Research and Practice, 4(1), 41–50. https://doi.org/10.22682/bcrp.2021.4.1.41

Koh, H. (2023). Extending the purview of risk perception attitude (RPA) framework to understand health insurance-related information seeking as a long-term self-protective behavior. Journal of American College Health, 71(2), 496–506. https://doi.org/10.1080/07448481.2021.1895807

Orom, H., & Allard, N. C. (2025). Information avoidance is associated with lower willingness to be screened for dementia. Journal of Alzheimer’s Disease : JAD, 108(1), 421–427. https://doi.org/10.1177/13872877251376720

Orom, H., Ramer, N. E., Allard, N. C., McQueen, A., Waters, E. A., Kiviniemi, M. T., & Hay, J. L. (2024). Colorectal cancer information avoidance is associated with screening adherence. Journal of Behavioral Medicine, 47(3), 504–514. https://doi.org/10.1007/s10865-024-00482-6

Rains, S. A., Hingle, M. D., Surdeanu, M., Bell, D., & Kobourov, S. (2019). A Test of The Risk Perception Attitude Framework as a Message Tailoring Strategy to Promote Diabetes Screening. Health Communication, 34(6), 672–679. https://doi.org/10.1080/10410236.2018.1431024

Rimal, R. N., & Real, K. (2003). Perceived Risk and Efficacy Beliefs as Motivators of Change.: Use of the Risk Perception Attitude (RPA) Framework to Understand Health Behaviors. Human Communication Research, 29(3), 370–399. https://doi.org/10.1111/j.1468-2958.2003.tb00844.x

Shen, Z., Wei, S.-Y., Adeeb, H., & Naeem, S. B. (2025). The negative consequences of avoiding health information on health risk behaviors: An SOR perspective. Information Development, 02666669241308211. https://doi.org/10.1177/02666669241308211

Simon, K. A., Driver, R., Rathus, T., Cole, A., Kalinowski, J., Watson, R. J., & Eaton, L. A. (2024). HIV Information Avoidance, HIV Stigma, and Medical Mistrust among Black Sexual Minority Men in the Southern United States: Associations with HIV Testing. AIDS and Behavior, 28(1), 12–18. https://doi.org/10.1007/s10461-023-04218-6

Skagerlund, K., Forsblad, M., Slovic, P., & Västfjäll, D. (2020). The Affect Heuristic and Risk Perception – Stability Across Elicitation Methods and Individual Cognitive Abilities. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.00970

Turner, M. M., Rimal, R. N., Morrison, D., & Kim, H. (2006). The Role of Anxiety in Seeking and Retaining Risk Information: Testing the Risk Perception Attitude Framework in Two Studies. Human Communication Research, 32(2), 130–156. https://doi.org/10.1111/j.1468-2958.2006.00006.x

Yin, Q., & Ma, X. (2026). Bridging Audience Segmentation and Message Intervention: AI-Generated HBM-Based Messages for Diabetes Prevention. Journal of Health Communication, 1–15. https://doi.org/10.1080/10810730.2026.2706638