Use of Internet Viral Marketing to Promote Smoke-Free Lifestyles among Chinese Adolescents

<div><p>Purpose</p><p>Youth smoking is a global public health concern. Health educators are increasingly using Internet-based technologies, but the effectiveness of Internet viral marketing in promoting health remains uncertain. This prospective pilot study assessed the efficacy of an online game-based viral marketing campaign in promoting a smoke-free attitude among Chinese adolescents.</p><p>Methods</p><p>One hundred and twenty-one Hong Kong Chinese adolescents aged 10 to 24 were invited to participate in an online multiple-choice quiz game competition designed to deliver tobacco-related health information. Participants were encouraged to refer others to join. A zero-inflated negative binomial model was used to explore the factors contributing to the referral process. Latent transition analysis utilising a pre- and post-game survey was used to detect attitudinal changes toward smoking.</p><p>Results</p><p>The number of participants increased almost eightfold from 121 to 928 (34.6% current or ex-smokers) during the 22-day campaign. Participants exhibited significant attitudinal change, with 73% holding negative attitudes toward smoking after the campaign compared to 57% before it. The transition probabilities from positive to negative and neutral to negative attitudes were 0.52 and 0.48, respectively. It was also found that attempting every 20 quiz questions was associated with lower perceived smoking decision in future (OR  = 0.95, p-value <0.01).</p><p>Conclusions</p><p>Our online game-based viral marketing programme was effective in reaching a large number of smoking and non-smoking participants and changing their attitudes toward smoking. It constitutes a promising practical and cost-effective model for engaging young smokers and promulgating smoking-related health information among Chinese adolescents.</p></div

Summary of LTA.

#<p>Item response probability is the probability to choose an item conditional on the persons' latent class membership. For example, in the question ‘number of friends of boys as affected by smoking’, a participant in Class 1 would have a 0.98 probability to select ‘Less’, 0.02 to select ‘Indifferent’ and 0.01 to select ‘More’. These probabilities help interpreting the latent classes and are conceptually similar to factor loadings in factor analysis. For detailed technical specifications, please refer to Lanza <i>et al</i> and Rindskopf <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099082#pone.0099082-Lanza1" target="_blank">[41]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099082#pone.0099082-Rindskopf1" target="_blank">[42]</a>.</p

Factors associated with the number of referrals.

<p>*p-value <0.05; **p-value <0.01. Results estimated by zero-inflated negative binomial model using all 928 users who completed the registration process.</p

Referral process of the campaign.

<p>Left panel: number of non-smokers versus current or ex-smokers at each referral level. Right panel: Age distribution of all users in referral pathways originating from level-1 users in 10 to 14, 15 to 19, 20 to 24 age groups.</p