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Smoker-to-smoker (S2S) peer marketing and messaging to disseminate tobacco interventions
Investigator (PI): Sadasivam, Rajani
Performing Organization (PO): (Current): University of Massachusetts, Medical School, Department of Population and Quantitative Health Sciences, Division of Health Informatics and Implementation Science / (508) 856-8999
Supporting Agency (SA): Patient-Centered Outcomes Research Institute (PCORI)
Initial Year: 2017
Final Year: 2021
Record Source/Award ID: PCORI/CDR-1603-34645
Funding: Total Award Amount: $2,099,965
Award Type: Contract
Award Information: PCORI: More information and project results (when completed)
Abstract: Background and significance: Smoking is the number one preventable cause of death. We are focused on disseminating an effective, evidence-based digital intervention for smoking cessation--Decide2Quit--via methods that are widely used outside of health care but not tested for health interventions. Peer marketing is used effectively to recruit new customers (i.e., companies use tools to encourage current customers to recruit their friends to likewise become customers). Companies like Amazon use machine learning algorithms (recommender systems) to personalize content. In our prior studies, we have demonstrated the efficacy of smoker-to-smoker (S2S) functions (peer recruitment and recommender computer-tailored health communication [CTHC]) to disseminate Decide2Quit and improve its effectiveness. We will test these functions in a comparative effectiveness study. Study aims: Aim 1 is, using a 2X2 factorial design with 1,200 smokers, test the impact of the S2S enhanced functions to increase dissemination (recruitment and repeated use) and effectiveness of Decide2Quit. Aim 2 is to follow participants in the 2X2 factorial trial for six months, testing S2S. Study description: Participants will be smokers who are over 19 years of age, can read or speak English, and have internet access at home. Comparators: Standard functions: We will use online recruitment using search engine advertisements. This method is the "state of the art" approach to recruit smokers, with demonstrated efficacy. We will use standard CTHC that uses expert written if/then/else rules tailor messages to smokers based on readiness to quit. S2S functions: S2S peer recruitment: This toolset includes a Facebook app, an online training video, and a recruitment tracker. As is common in peer recruitment approaches, to initiate the waves, we will recruit the first wave (wave 0 or "direct recruits") of smokers through online advertisements, similar to the comparison. These direct recruits and subsequent waves (wave 1, wave 2, etc.) will be given the peer recruitment toolset for further recruitment. Recommender CTHC: In recommender systems, machine learning algorithms use the data (patient profiles, and implicit and explicit feedback ratings) to select the variables and generate the rules that specify which message should be selected. Analytic methods: Our hypotheses will test both dissemination and effectiveness. We will test as dissemination outcomes both recruitment time (time to recruit each participant) and repeated use of the system. We will test six-month cessation using seven-day point prevalence, biochemically verified using saliva NicAlert, and risk reduction (reduction in number of cigarettes smoked from date of registration), a patient/investigator recommended outcome.
MeSH Terms:
  • Adult
  • Aged
  • Algorithms
  • Comparative Effectiveness Research
  • Humans
  • Internet
  • Machine Learning
  • Middle Aged
  • Outcome Assessment, Health Care
  • Patient Education as Topic /*methods
  • * Peer Group
  • Precision Medicine
  • Risk
  • Smoking /*adverse effects
  • Smoking Cessation /*methods
  • Social Media
  • * Social Support
  • Software
  • Tobacco Use Disorder /*therapy
  • Video Recording
  • Young Adult
Country: United States
State: Massachusetts
Zip Code: 01605
UI: 20181453
CTgovId: NCT03224520
Project Status: Ongoing