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

Santiago Hors-Fraile, Math J.J.M. Candel, Francine Schneider, Shwetambara Malwade, Francisco J. Nunez-Benjumea, Shabbir Syed-Abdul, Luis Fernandez-Luque, Hein De Vries

研究成果: 雜誌貢獻文章同行評審

2 引文 斯高帕斯(Scopus)


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.

期刊Electronics (Switzerland)
出版狀態已發佈 - 4月 1 2022

ASJC Scopus subject areas

  • 控制與系統工程
  • 訊號處理
  • 硬體和架構
  • 電腦網路與通信
  • 電氣與電子工程


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