Published on in Vol 18, No 11 (2016): November

Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment

Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment

Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment

Journals

  1. Kruse G, Park E, Shahid N, Abroms L, Haberer J, Rigotti N. Combining Real-Time Ratings With Qualitative Interviews to Develop a Smoking Cessation Text Messaging Program for Primary Care Patients. JMIR mHealth and uHealth 2019;7(3):e11498 View
  2. Hors-Fraile S, de Vries H, Malwade S, Luna-Perejon F, Amaya C, Civit A, Schneider F, Bamidis P, Syed-Abdul S, Li Y. Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model. IEEE Access 2019;7:176525 View
  3. Wang X, Zhao K, Cha S, Amato M, Cohn A, Pearson J, Papandonatos G, Graham A. Mining user-generated content in an online smoking cessation community to identify smoking status: A machine learning approach. Decision Support Systems 2019;116:26 View
  4. Faro J, Orvek E, Blok A, Nagawa C, McDonald A, Seward G, Houston T, Kamberi A, Allison J, Person S, Smith B, Brady K, Grosowsky T, Jacobsen L, Paine J, Welch Jr J, Sadasivam R. Dissemination and Effectiveness of the Peer Marketing and Messaging of a Web-Assisted Tobacco Intervention: Protocol for a Hybrid Effectiveness Trial. JMIR Research Protocols 2019;8(7):e14814 View
  5. Cheung K, Durusu D, Sui X, de Vries H. How recommender systems could support and enhance computer-tailored digital health programs: A scoping review. DIGITAL HEALTH 2019;5:205520761882472 View
  6. Herbst E, McCaslin S, Hassanbeigi Daryani S, Laird K, Hopkins L, Pennington D, Kuhn E. A Qualitative Examination of Stay Quit Coach, A Mobile Application for Veteran Smokers With Posttraumatic Stress Disorder. Nicotine & Tobacco Research 2020;22(4):560 View
  7. Chung J. Peer Influence of Online Comments in Newspapers: Applying Social Norms and the Social Identification Model of Deindividuation Effects (SIDE). Social Science Computer Review 2019;37(4):551 View
  8. Triantafyllidis A, Tsanas A. Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature. Journal of Medical Internet Research 2019;21(4):e12286 View
  9. Cheung K, Wijnen B, de Vries H. A Review of the Theoretical Basis, Effects, and Cost Effectiveness of Online Smoking Cessation Interventions in the Netherlands: A Mixed-Methods Approach. Journal of Medical Internet Research 2017;19(6):e230 View
  10. Faro J, Nagawa C, Allison J, Lemon S, Mazor K, Houston T, Sadasivam R. Comparison of a Collective Intelligence Tailored Messaging System on Smoking Cessation Between African American and White People Who Smoke: Quasi-Experimental Design. JMIR mHealth and uHealth 2020;8(4):e18064 View
  11. Hors-Fraile S, Malwade S, Spachos D, Fernandez-Luque L, Su C, Jeng W, Syed-Abdul S, Bamidis P, Li Y. A recommender system to quit smoking with mobile motivational messages: study protocol for a randomized controlled trial. Trials 2018;19(1) View
  12. Leung R. Increasing the Impact of JMIR Journals in the Attention Economy. Journal of Medical Internet Research 2019;21(10):e16172 View
  13. Calero Valdez A, Ziefle M. The users’ perspective on the privacy-utility trade-offs in health recommender systems. International Journal of Human-Computer Studies 2019;121:108 View
  14. Hurley N, Spatz E, Krumholz H, Jafari R, Mortazavi B. A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders. ACM Transactions on Computing for Healthcare 2021;2(1):1 View
  15. Faro J, Nagawa C, Orvek E, Smith B, Blok A, Houston T, Kamberi A, Allison J, Person S, Sadasivam R. Comparing recruitment strategies for a digital smoking cessation intervention: Technology-assisted peer recruitment, social media, ResearchMatch, and smokefree.gov. Contemporary Clinical Trials 2021;103:106314 View
  16. Wolff J, Pauling J, Keck A, Baumbach J. Success Factors of Artificial Intelligence Implementation in Healthcare. Frontiers in Digital Health 2021;3 View
  17. De Croon R, Van Houdt L, Htun N, Štiglic G, Vanden Abeele V, Verbert K. Health Recommender Systems: Systematic Review. Journal of Medical Internet Research 2021;23(6):e18035 View