Applying Collective Intelligence in Health Recommender Systems for Smoking Cessation: A Comparison Trial

Background: Health recommender systems (HRSs) are intelligent systems that can be used to tailor digital health interventions. We compared two HRSs to assess their impact providing smoking cessation support messages. Methods: Smokers who downloaded a mobile app to support smoking abstinence were ran...

Full description

Saved in:
Bibliographic Details
Published inElectronics (Basel) Vol. 11; no. 8; p. 1219
Main Authors Hors-Fraile, Santiago, Candel, Math J. J. M., Schneider, Francine, Malwade, Shwetambara, Nunez-Benjumea, Francisco J., Syed-Abdul, Shabbir, Fernandez-Luque, Luis, de Vries, Hein
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2022
Subjects
Algorithms
Applications programs
Behavior
Collaboration
Computers
Customization
Design
Feedback
Hybrid systems
Intelligence (information)
Intervention
Knowledge
Medical screening
Messages
Mobile computing
Questionnaires
Recommender systems
Smoking
Smoking cessation
User needs
User profiles
Online AccessGet full text

Cover

More Information
Summary:Background: Health recommender systems (HRSs) are intelligent systems that can be used to tailor digital health interventions. We compared two HRSs to assess their impact providing smoking cessation support messages. Methods: Smokers who downloaded a mobile app to support smoking abstinence were randomly assigned to two interventions. They received personalized, ratable motivational messages on the app. The first intervention had a knowledge-based HRS (n = 181): it selected random messages from a subset matching the users’ demographics and smoking habits. The second intervention had a hybrid HRS using collective intelligence (n = 190): it selected messages applying the knowledge-based filter first, and then chose the ones with higher ratings provided by other similar users in the system. Both interventions were compared on: (a) message appreciation, (b) engagement with the system, and (c) one’s own self-reported smoking cessation status, as indicated by the last seven-day point prevalence report in different time intervals during a period of six months. Results: Both interventions had similar message appreciation, number of rated messages, and abstinence results. The knowledge-based HRS achieved a significantly higher number of active days, number of abstinence reports, and better abstinence results. The hybrid algorithm led to more quitting attempts in participants who completed their user profiles.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11081219