How do cancer risk perception, benefit perception of quitting, and cancer worry influence quitting intention among current smokers: A study using the 2013 HINTS

ABSTRACT

We proposed a conceptual model to explain current smokers’ quitting intentions based on theories from cognitive perspectives and theories that recognize the impact of affect. The model was tested by participants of the 2013 Health Information National Trends Survey (HINTS 4) who identified themselves as current smokers (N = 481). Structural equation modeling (SEM) analyses were conducted. Findings suggest that intention to quit smoking is directly predicted by benefit perception of quitting, but not by cancer risk perception or cancer worry; cancer worry has a positive influence on both cancer risk perception and benefit perception of quitting; the indirect path from cancer worry to quitting intention through benefit perception of quitting is significant. Theoretical and practical implications of findings were discussed.

KEYWORDS:

• Benefit perception
• risk perception
• quitting intention
• smoking
• worry

Notes

1. The sample was recruited through a two-step process. First, a stratified sample was selected from a database of residential addresses used by Marketing Systems Group (MSG). Then, one adult was selected within each sample household using the Next Birthday Method (NCI, 2013). The sample tracked closely the U.S. population on age, race, Hispanic ethnicity, geographical region, employment status, and other demographic elements.

2. Another two items measuring cancer risk perception are: (1) “I feel like I could easily get cancer in my lifetime.” with five responses as follows: 1 = I feel very strongly that this will NOT happen; 2 = I feel somewhat strongly that this will NOT happen; 3 = I feel I am just as likely to get cancer as I am to not get cancer; 4 = I feel somewhat strongly that this WILL happen; 5 = I feel very strongly that this WILL happen. (2) “Compared to other people your age, how likely are you to get cancer in your lifetime?” with responses ranging from 1 = Much less likely to 5 = Much more likely.

3. An examination of the bivariate correlation matrix did not reveal any problems related to multicollinearity, because the maximum correlation coefficient between any two predicting variables was 0.56, which is below the 0.7 collinearity threshold (Dormann et al., 2013). Also, preliminary examinations revealed that all assumptions of structural equation modeling (linearity, multivariate normality, homoscedasticity) were met.

4. Several fit indices were applied to examine the goodness of fit of the hypothesized model. For an SEM model with a continuous or a categorical outcome, values greater than 0.90 for comparative fit index (CFI) and Tucker-Lewis index (TLI) indicate a good fit of the model to the data (McDonald & Ho, 2002); values smaller than 0.05 for root mean square error of approximation (RMSEA) suggest a close fit, while values between 0.05 and 0.08 are considered a reasonable fit (McDonald & Ho, 2002). For an SEM model with a categorical outcome, Schreiber et al. (2006) suggested that weighted root mean square residual (WRMR) smaller than 0.90 showed a good fit.

5. Although the model tested in this study was proposed based on existing theoretical frameworks and empirical studies, some other models can serve as alternative explanations for the outcome variable (quitting intention). We also tested two such models, including that worry, risk perception, and benefit perception each independently predict quitting intention, and that risk and benefit perceptions lead to worry and then quitting intention. Results of fit statistics indicated that these two alternative models did not provide a better fit to the data than the original proposed model.