UMMS Affiliation

Department of Population and Quantitative Health Sciences; School of Medicine; Meyers Primary Care Institute; Graduate School of Biomedical Sciences; UMass Worcester Prevention Research Center

Publication Date

2020-04-27

Document Type

Article

Disciplines

Artificial Intelligence and Robotics | Health Communication | Health Services Administration | Race and Ethnicity | Substance Abuse and Addiction | Telemedicine

Abstract

BACKGROUND: 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. 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.

OBJECTIVE: 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.

METHODS: Using 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.

RESULTS: The 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.

CONCLUSIONS: Our study highlighted the potential of using a recommender system to personalize for African American people who smoke.

TRIAL REGISTRATION: ClinicalTrials.gov NCT02200432; https://clinicaltrials.gov/ct2/show/NCT02200432.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/jmir.6465. Kathleen M Mazor, Thomas K Houston, Rajani S Sadasivam. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 27.04.2020.

Keywords

computer-tailored health communication, health disparities, machine learning, smoking cessation

Rights and Permissions

Copyright © Jamie M Faro, Catherine S Nagawa, Jeroan A Allison, Stephenie C Lemon, Kathleen M Mazor, Thomas K Houston, Rajani S Sadasivam. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 27.04.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

DOI of Published Version

10.2196/18064

Source

Faro JM, Nagawa CS, Allison JA, Lemon SC, Mazor KM, Houston TK, Sadasivam RS. Comparison of a Collective Intelligence Tailored Messaging System on Smoking Cessation Between African American and White People Who Smoke: Quasi-Experimental Design. JMIR Mhealth Uhealth. 2020 Apr 27;8(4):e18064. doi: 10.2196/18064. PMID: 32338619. Link to article on publisher's site

Journal/Book/Conference Title

JMIR mHealth and uHealth

Related Resources

Link to Article in PubMed

PubMed ID

32338619

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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