Linda D Cameron1, Brian Williams. 1. University of California, Merced, 5200 North Lake Road, Merced, CA, 95343, USA, lcameron@ucmerced.edu.
Abstract
BACKGROUND: Many countries are implementing graphic warnings for cigarettes. Which graphic features influence their effectiveness remains unclear. PURPOSE: To identify features of graphic warnings predicting their perceived effectiveness in discouraging smoking. METHOD: Guided by the Common-Sense Model of responses to health threats, we content-analyzed 42 graphic warnings for attributes of illness risk representations and media features (e.g., photographs, metaphors). Using data from 15,536 survey participants, we conducted stratified logistic regressions testing which attributes predict participant selections of warnings as effective. RESULTS: Images of diseased body parts predicted greater perceived effectiveness; OR = 6.53-12.45 across smoking status (smoker, ex-smoker, young non-smoker) groups. Features increasing perceived effectiveness included images of dead or sick persons, children, and medical technology; focus on cancer; and photographs. Attributes decreasing perceived effectiveness included infertility/impotence, addictiveness, cigarette chemicals, cosmetic appearance, quitting self-efficacy, and metaphors. CONCLUSIONS: These findings on representational and media attributes predicting perceived effectiveness can inform strategies for generating graphic warnings.
BACKGROUND: Many countries are implementing graphic warnings for cigarettes. Which graphic features influence their effectiveness remains unclear. PURPOSE: To identify features of graphic warnings predicting their perceived effectiveness in discouraging smoking. METHOD: Guided by the Common-Sense Model of responses to health threats, we content-analyzed 42 graphic warnings for attributes of illness risk representations and media features (e.g., photographs, metaphors). Using data from 15,536 survey participants, we conducted stratified logistic regressions testing which attributes predict participant selections of warnings as effective. RESULTS: Images of diseased body parts predicted greater perceived effectiveness; OR = 6.53-12.45 across smoking status (smoker, ex-smoker, young non-smoker) groups. Features increasing perceived effectiveness included images of dead or sick persons, children, and medical technology; focus on cancer; and photographs. Attributes decreasing perceived effectiveness included infertility/impotence, addictiveness, cigarette chemicals, cosmetic appearance, quitting self-efficacy, and metaphors. CONCLUSIONS: These findings on representational and media attributes predicting perceived effectiveness can inform strategies for generating graphic warnings.
Authors: Daniel J Coletti; Mary Brunette; Majnu John; John M Kane; Anil K Malhotra; Delbert G Robinson Journal: Schizophr Bull Date: 2015-08-27 Impact factor: 9.306
Authors: Amy McQueen; Erika A Waters; Kimberly A Kaphingst; Charlene A Caburnay; Vetta L Sanders Thompson; Sonia Boyum; Matthew W Kreuter Journal: J Health Commun Date: 2016-07-13
Authors: Jennifer Cornacchione Ross; Jessica L King; Allison J Lazard; Seth M Noar; Beth A Reboussin; Desmond Jenson; Erin L Sutfin Journal: Nicotine Tob Res Date: 2021-01-22 Impact factor: 4.244
Authors: Jennifer Murray; Brian Williams; Gaylor Hoskins; Silje Skar; John McGhee; Shaun Treweek; Falko F Sniehotta; Aziz Sheikh; Gordon Brown; Suzanne Hagen; Linda Cameron; Claire Jones; Dylan Gauld Journal: Pilot Feasibility Stud Date: 2016-08-15