Crowdsourced Peer- Versus Expert-Written Smoking-Cessation Messages

Abstract

Background Tailored, web-assisted interventions can reach many smokers. Content from other smokers (peers) through crowdsourcing could enhance relevance. Purpose To evaluate whether peers can generate tailored messages encouraging other smokers to use a web-assisted tobacco intervention (Decide2Quit.org). Methods Phase 1: In 2009, smokers wrote messages in response to scenarios for peer advice. These smoker-to-smoker (S2S) messages were coded to identify themes. Phase 2: resulting S2S messages, and comparison expert messages, were then emailed to newly registered smokers. In 2012, subsequent Decide2Quit.org visits following S2S or expert-written e-mails were compared. Results Phase 1: a total of 39 smokers produced 2886 messages (message themes: attitudes and expectations, improvements in quality of life, seeking help, and behavioral strategies). For not-ready-to-quit scenarios, S2S messages focused more on expectations around a quit attempt and how quitting would change an individual’s quality of life. In contrast, for ready-to-quit scenarios, S2S messages focused on behavioral strategies for quitting. Phase 2: In multivariable analysis, S2S messages were more likely to generate a return visit (OR=2.03, 95% CI=1.74, 2.35), compared to expert messages. A significant effect modification of this association was found, by time-from-registration and message codes (both interaction terms p<0.01). In stratified analyses, S2S codes that were related more to “social” and “real-life” aspects of smoking were driving the main association of S2S and increased return visits. Conclusions S2S peer messages that increased longitudinal engagement in a web-assisted tobacco intervention were successfully collected and delivered. S2S messages expanded beyond the biomedical model to enhance relevance of messages.

Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment

“…The messaging database included 261 messages that were developed in our previous RCT and included both expert-written messages and peer-written messages [17]. Messages written by experts (study designers, behaviorists, physicians, nurses) were developed through an iterative expert group review process.…”

“…The current guidelines provided evidence-based content on successful cessation strategies. The SCT, which incorporates vicarious learning and verbal persuasion, informed the content of the expert messages [17]. Messages reflected theoretical determinants of quitting, such as positive outcome expectations and self-efficacy enhancing small goals [19].…”

“…These messages were then reviewed for use in our system. More details of our methodology to generate peer-written messages have been previously published [17]. Peer-written messages included the more “social” and “real-life” aspects of smoking cessation and represented the day-to-day issues associated with smoking cessation and the social and interpersonal influences on quitting.…”

Social predictors of cessation treatment use among smokers with serious mental illness

“…Social influences play an important role in quitting smoking among individuals without mental illness (Cohen & Lichtenstein, 1990;Gulliver, Hughes, Solomon, & Dey, 1995;Yang et al, 2013), and some smoking cessation interventions have leveraged social support in the cessation process with promising results (Coley et al, 2013;Hennrikus et al, 2010;Stewart et al, 2010). To better understand social influences on smoking cessation and how they may be incorporated into treatment models, prior research in the general population has distinguished between explicit and implicit social influences on smoking cessation (van den Putte, Yzer, & Brunsting, 2005).…”

Using New Technologies to Improve the Prevention and Management of Chronic Conditions in Populations

“…Individuals can now interact with, shape, and even disseminate their own intervention messages through their social networks by using features, such as comments, likes, shares, and tweets. However, it is critically important that programs delivered via such platforms are designed and delivered with an understanding of the user experience and how individuals will respond to, shape, and share such content and programs (25,29). For example, a recent study revealed that positive and derisive viewer comments shaped other users' evaluations of the credibility of antimarijuana health messages delivered via YouTube (127).…”