Abstract
BackgroundThe Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT) is a machine learning recommender system with a database of messages to motivate smoking cessation. PERSPeCT uses the collective intelligence of users (ie, preferences and feedback) and demographic and smoking profiles to select motivating messages. PERSPeCT may be more beneficial for tailoring content to minority groups influenced by complex, personally relevant factors.ObjectiveThe objective of this study was to describe and evaluate the use of PERSPeCT in African American people who smoke compared with white people who smoke.MethodsUsing a quasi-experimental design, we compared African American people who smoke with a historical cohort of white people who smoke, who both received up to 30 emailed tailored messages over 65 days. People who smoke rated the daily message in terms of perceived influence on quitting smoking for 30 days. Our primary analysis compared daily message ratings between the two groups using a t test. We used a logistic model to compare 30-day cessation between the two groups and adjusted for covariates.ResultsThe study included 119 people who smoke (African Americans, 55/119; whites, 64/119). At baseline, African American people who smoke were significantly more likely to report allowing smoking in the home (P=.002); all other characteristics were not significantly different between groups. Daily mean ratings were higher for African American than white people who smoke on 26 of the 30 days (P<.001). Odds of quitting as measured by 30-day cessation were significantly higher for African Americans (odds ratio 2.3, 95% CI 1.04-5.53; P=.03) and did not change after adjusting for allowing smoking at home.ConclusionsOur study highlighted the potential of using a recommender system to personalize for African American people who smoke.Trial RegistrationClinicalTrials.gov NCT02200432; https://clinicaltrials.gov/ct2/show/NCT02200432International Registered Report Identifier (IRRID)RR2-10.2196/jmir.6465
Highlights
Computer-tailored health communication (CTHC) increases the personal relevance of health messaging by matching the messages to an individual or group’s characteristics [1] and can be effective in motivating behavior change [2,3,4,5,6,7,8]
The objective of this study was to describe and evaluate the use of PERSPeCT in African American people who smoke compared with white people who smoke
African American people who smoke were significantly more likely to allow smoking in the home compared with whites (P=.002); all other characteristics were balanced between the two groups
Summary
Computer-tailored health communication (CTHC) increases the personal relevance of health messaging by matching the messages to an individual or group’s characteristics [1] and can be effective in motivating behavior change [2,3,4,5,6,7,8]. Instead of using rule-based approaches, companies like Amazon use a special class of machine learning systems (recommender systems) to select messages. These systems combine the collective intelligence of their users (ie, the observed and inferred preferences of users as they interact with the system) and their user profiles [10,11,12]. As reported in our prior randomized trial [13], the PERSPeCT system outperformed the standard rule-based system in terms of daily message ratings and 30-day cessation. The Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT) is a machine learning recommender system with a database of messages to motivate smoking cessation. Trial Registration: ClinicalTrials.gov NCT02200432; https://clinicaltrials.gov/ct2/show/NCT02200432 International Registered Report Identifier (IRRID): RR2-10.2196/jmir.6465
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