Information Seeking From Media and Family/Friends Increases the Likelihood of Engaging in Healthy Lifestyle Behaviors

Abstract

The amount of cancer-related information available to the general population continues to grow; yet, its effects are unclear. This study extends previous cross-sectional research establishing that cancer information seeking across a variety of sources is extensive and positively associated with engaging in health-related behaviors. The authors studied how active information seeking about cancer prevention influenced three healthy lifestyle behaviors using a 2-round nationally representative sample of adults ages 40–70 years (n = 1,795), using propensity scoring to control for potential confounders including baseline behavior. The adjusted odds of dieting at follow-up were 1.51 (95% CI: 1.05, 2.19) times higher for those who reported baseline seeking from media and interpersonal sources relative to nonseekers. Baseline seekers ate 0.59 (95% CI: 0.28, 0.91) more fruits and vegetable servings per day and exercised 0.36 (95% CI: 0.12, 0.60) more days per week at 1-year follow-up compared with nonseekers. The effects of seeking from media and friends/family on eating fruits and vegetables and exercising were independent of seeking from physicians. The authors offer several explanations for why information seeking predicts healthy lifestyle behaviors: information obtained motivates these behaviors; information sought teaches specific techniques; and the act of information seeking may reinforce a psychological commitment to dieting, eating fruits and vegetables, and exercising.

Acknowledgments

The authors acknowledge the funding support of the National Cancer Institute's Center of Excellence in Cancer Communication located at the Annenberg School for Communication, University of Pennsylvania (P50-CA095856-05).

Notes

¹The statistics reported in this section were run on the full samples that correspond to those that were used in the treatment analyses for each behavior. That is, data were multiply imputed and then filtered as appropriate for the behavior. Diet: N = 1282, imputed 5 times = 6410; Fruits and vegetables: N = 1780, imputed 5 times = 8900; Exercise: N = 1795, imputed 5 times = 8975.

^a Centers for Disease Control and Prevention (2005).

^b Horrigan & Smith (2007).

Note. Model 1: Lagged association, uncontrolled; Model 2: Lagged association, adjusting for baseline behavior; Model 3: Lagged association, adjusting for baseline behavior and propensity quintiles; Model 4: Lagged association, adjusting for baseline behavior, propensity quintiles, and seeking from doctors and medical professionals. F&V = fruits and vegetables.

²The minimum and maximum values from the last imputed dataset were selected for removal; those respondents were removed from all imputed datasets to maintain consistency across datasets. This procedure was repeated for all three analyses (diet, exercise, fruits and vegetables).

³We found that several variables were not fully balanced with the propensity score. That is, the distribution of respondents for each potential confounder was not equal across propensity quintiles. We added these variables, along with the propensity, to the treatment effects model to make sure that our claims were not sensitive to misspecification of the propensity model. Results were consistent with those described, indicating the odds of dieting at follow-up were 1.52 (95% CI: 1.04, 2.20) higher for seekers from media and nonclinical interpersonal sources compared with nonseekers.

⁴We conducted an additional nested model that added the interaction of seeking and propensity quintiles. A likelihood ratio test indicated that the interaction terms did not significantly contribute to a change in likelihood, F (4, 936.7) = .80, ns, and these terms were thus removed..

⁵We conducted an additional nested model that added the interaction of seeking and propensity quintiles. A likelihood ratio test indicated that the interaction terms did not significantly contribute to a change in likelihood, F (4, 1000) = .17, n.s., and these terms were removed.

⁶We found that two variables were not fully balanced with the propensity score. We added these variables, along with the propensity quintiles, to the treatment effects model. Results were consistent with those described, indicating that seekers from media and nonclinical interpersonal sources ate on average 0.46 more servings of fruits and vegetables than nonseekers (b = .46, p < .001).

⁷Related variables: perceived behavioral control, injunctive norm, descriptive norm, attitude (good/bad), attitude (pleasant/unpleasant), beliefs that eating fruits and vegetables/exercising/maintain healthy weight: reduces colon cancer, heart disease, and breast/prostate cancer risks.

⁸We did not include intention as a potential confounder originally because its causal role is ambiguous. It could be a confounder or it could be a mediator between seeking and later behavior. If it is a confounder it is appropriate to adjust for it; if it is a mediator adjusting for it will cause the seeking and behavior relationship to be underestimated. The original results can be viewed as a more liberal version of the analysis; the version controlling for intention a conservative version. Even with the conservative version, we continued to see effects on two of the three outcomes, and nonsignificant but consistent effects on the third.

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