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Identifying Message "Recipes" for High Perceived Effectiveness: Cognitive, Social, and Emotional Profiles of Vaping Public Education Messages Among Young Adults Who Vape

Liu, J.; Peng, Q.; Malik, M.; Wang, Y.; Norton, E.; Ye, T.; Markey, C.; Worsdale, A.; Sweet, L. H.

2026-10-06 public and global health
10.64898/2026.10.04.26364669 medRxiv
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Introduction: Vaping public education campaigns are a central prevention strategy for young adults (YAs), but message effectiveness varies widely and formative testing typically evaluates one message attribute at a time. This study examined how four perceptual dimensions, including cognitive engagement, social relevance, emotional arousal, and emotional valence, relate to perceived message effectiveness (PME), and whether multiple combinations of these dimensions correspond to similarly high PME. Methods: In a cross-sectional online message-testing study conducted from March to April 2025, 540 U.S. YAs who vape each evaluated 15 messages randomly sampled from a corpus of 229 text-image vaping public education messages, yielding 8,100 message evaluations. Cross-classified multilevel models estimated associations between each perceptual dimension and PME. An Explainable Boosting Machine probed nonlinear associations, and k-means clustering of high-predicted-PME perceptual configurations identified distinct profiles. Analyses were conducted in 2026. Results: Cognitive engagement (b = 0.20, 95% CI [0.18, 0.22]) and social relevance (b = 0.15, 95% CI [0.13, 0.17]) showed the strongest associations with PME, followed by emotional arousal (b = 0.06, 95% CI [0.04, 0.08]); emotional valence showed no linear association (b = 0.00, 95% CI [-0.02, 0.02]). The Explainable Boosting Machine revealed a U-shaped valence pattern, with higher predicted PME at both the negative and positive ends of the scale. Clustering yielded five distinct perceptual profiles with similarly high predicted PME (4.10-4.25). Conclusions: Multiple perceptual configurations correspond to similarly high PME, supporting a configurational approach to formative message testing rather than a single optimal message formula.

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