A study published in JAMA Network Open found that viral peer recruitment may be an effective tool for improving the effectiveness of smoking cessation interventions.
Investigators from the University of Massachusetts Chan Medical School recruited participants who registered at the digital tobacco intervention site between 2017 and 2019 for this study. Participants were randomly assigned into 4 groups: machine learning (ML) messaging plus viral toolkit (group A; n=205), ML messaging without viral toolkit (group B; n=207), standard messaging plus viral toolkit (group C; n=190), and standard messaging without viral toolkit (group D; n=199). The primary outcome was at 6-months achieving a 7-day smoking abstinence.
The intervention comprised 2 email messages every week for 4 weeks followed by 1 weekly email for 6 months. The messages were selected from a bank of expert-written messages and the standard messaging group received messages selected based on readiness to quit at baseline whereas the ML messages were selected based on feedback from 900 current and former smokers. The viral toolkit component was a social media plug-in that allowed participants to send private recruitment messages to friends and family and every week the participants were emailed and encouraged to invite their peers to join the study.
The study groups included 69.8%-78.2% women, 26.5%-34.0% were aged 19-34 years, 78.6%-80.7% were White, and 64.0%-68.2% smoked 6-20 cigarettes per day.
The highest smoking cessation rate at 6 months was reported by group C (46.3%), followed by group A (43.4%), group D (34.2%), and group B (27.5%).
At 6 months, the smoking cessation rate was higher for group A compared with group B (adjusted odds ratio [aOR], 1.91; 95% CI, 1.20-3.03; P =.005) but was not higher than groups C (aOR, 0.82; 95% CI, 0.52-1.28; P =.39) or D (aOR, 1.24; 95% CI, 0.79-1.95; P =.41).
Stratified by individual interventions, the recipients of the viral toolkit had a higher cessation rate than those who did not (aOR, 1.48; 95% CI, 1.11-1.98; P =.01) whereas there was no significant effect of ML or standard messaging interventions (aOR, 0.81; 95% CI, 0.61-1.08; P =.16).
In the sensitivity analysis, the viral toolkit was only favored over no toolkit among the subset of participants who were allocated to the intervention (aOR, 2.56; 95% CI, 1.66-3.96; P <.001) and not among all randomly assigned participants (aOR, 1.26; 95% CI, 0.86-1.86; P =.23).
This study may have been limited by its retention rate (52.7%). However, this value was similar to many published trials about smoking cessation.
The study authors concluded, “Our results suggest that viral recruitment may be beneficial not only for spreading the intervention, but also for motivating smokers to quit smoking. This may open new opportunities to design digital interventions for smoking cessation as team efforts and build collaborative tools for people who smoke to not only refer, but also engage with one another throughout the intervention. Our results further suggest that through digital methods, we may have the potential to reach a larger proportion of individuals with effective methods, resulting in a greater impact.”
Faro JM, Chen J, Flahive J, et al. Effect of a machine learning recommender system and viral peer marketing intervention on smoking cessation: a randomized clinical trial. JAMA Netw Open. 2023;6(1):e2250665. doi:10.1001/jamanetworkopen.2022.50665
This article originally appeared on Psychiatry Advisor