This paper is in the following e-collection/theme issue:

Web-based and Mobile Health Interventions (3297) Smoking Cessation (556)

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

Authors of this article:

Rajani Shankar Sadasivam1 Author Orcid Image ;   Erin M Borglund1 Author Orcid Image ;   Roy Adams2 Author Orcid Image ;   Benjamin M Marlin2 Author Orcid Image ;   Thomas K Houston1, 3 Author Orcid Image
Article Authors Cited by (41) Tweetations (5) Metrics

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, Malwade S, Luna-Perejon F, Amaya C, Civit A, Schneider F, Bamidis P, Syed-Abdul S, Li Y, de Vries H. 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 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
  18. Rodriguez D, Lawrence K, Luu S, Yu J, Feldthouse D, Gonzalez J, Mann D. Development of a computer-aided text message platform for user engagement with a digital Diabetes Prevention Program: a case study. Journal of the American Medical Informatics Association 2021;29(1):155 View
  19. Zhou Q, Chen Z, Cao Y, Peng S. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. npj Digital Medicine 2021;4(1) View
  20. Siontis G, Sweda R, Noseworthy P, Friedman P, Siontis K, Patel C. Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials. BMJ Health & Care Informatics 2021;28(1):e100466 View
  21. Bickel W, Tomlinson D, Craft W, Ma M, Dwyer C, Yeh Y, Tegge A, Freitas-Lemos R, Athamneh L. Predictors of smoking cessation outcomes identified by machine learning: A systematic review. Addiction Neuroscience 2023;6:100068 View
  22. Naegelin M, Weibel R, Kerr J, Schinazi V, La Marca R, von Wangenheim F, Hoelscher C, Ferrario A. An interpretable machine learning approach to multimodal stress detection in a simulated office environment. Journal of Biomedical Informatics 2023;139:104299 View
  23. Lisowska A, Wilk S, Peleg M. SATO (IDEAS expAnded wiTh BCIO): Workflow for designers of patient-centered mobile health behaviour change intervention applications. Journal of Biomedical Informatics 2023;138:104276 View
  24. Hors-Fraile S, Candel M, Schneider F, Malwade S, Nunez-Benjumea F, Syed-Abdul S, Fernandez-Luque L, de Vries H. Applying Collective Intelligence in Health Recommender Systems for Smoking Cessation: A Comparison Trial. Electronics 2022;11(8):1219 View
  25. Faro J, Chen J, Flahive J, Nagawa C, Orvek E, Houston T, Allison J, Person S, Smith B, Blok A, Sadasivam R. Effect of a Machine Learning Recommender System and Viral Peer Marketing Intervention on Smoking Cessation. JAMA Network Open 2023;6(1):e2250665 View
  26. Chen J, Houston T, Faro J, Nagawa C, Orvek E, Blok A, Allison J, Person S, Smith B, Sadasivam R. Evaluating the use of a recommender system for selecting optimal messages for smoking cessation: patterns and effects of user-system engagement. BMC Public Health 2021;21(1) View
  27. Honka A, Nieminen H, Simila H, Kaartinen J, Gils M. A Comprehensive User Modeling Framework and a Recommender System for Personalizing Well-Being Related Behavior Change Interventions: Development and Evaluation. IEEE Access 2022;10:116766 View
  28. Lee D, Sadasivam R, Stevens E. Developing Mood-Based Computer-Tailored Health Communication for Smoking Cessation: Feasibility Randomized Controlled Trial. JMIR Formative Research 2023;7:e48958 View
  29. Trinkley K, An R, Maw A, Glasgow R, Brownson R. Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions. Implementation Science 2024;19(1) View
  30. Killian J, Jain M, Jia Y, Amar J, Huang E, Tambe M. New Approach to Equitable Intervention Planning to Improve Engagement and Outcomes in a Digital Health Program: Simulation Study. JMIR Diabetes 2024;9:e52688 View
  31. Sadasivam R, Nagawa C, Wijesundara J, Flahive J, Nguyen H, Larkin C, Faro J, Balakrishnan K, Ha D, Nguyen C, Vuong A, Phan P, Pham Q, Allison J, Houston T. Peer Texting to Promote Quitline Use and Smoking Cessation Among Rural Participants in Vietnam: Randomized Clinical Trial. International Journal of Public Health 2024;69 View
  32. Bucher A, Blazek E, Symons C. How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review. Mayo Clinic Proceedings: Digital Health 2024;2(3):375 View
  33. Cai Y, Yu F, Kumar M, Gladney R, Mostafa J. Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review. International Journal of Environmental Research and Public Health 2022;19(22):15115 View
  34. Gaysynsky A, Heley K, Chou W. An Overview of Innovative Approaches to Support Timely and Agile Health Communication Research and Practice. International Journal of Environmental Research and Public Health 2022;19(22):15073 View
  35. Ananthakrishnan A, Milne-Ives M, Shankar R, Meinert E. Evaluation of health recommender systems: a scoping review protocol. BMJ Open 2024;14(10):e083359 View
  36. Stevens E, Lee D, Stevens H, Sadasivam R. The role of mood in shaping reactions to smoking cessation messages among adults who smoke: a multimodal investigation. BMC Public Health 2024;24(1) View
  37. Ananthakrishnan A, Milne-Ives M, Cong C, Shankar R, Lakey B, Alexander J, Tapuria A, Marchal A, Joy E, Meinert E. The evaluation of health recommender systems: A scoping review. International Journal of Medical Informatics 2024:105697 View
  38. Weingott S, Parkinson J. The application of artificial intelligence in health communication development: A scoping review. Health Marketing Quarterly 2024:1 View

Books/Policy Documents

  1. Harrer M, Terhorst Y, Baumeister H, Ebert D. Digitale Gesundheitsinterventionen. View
  2. Hao L, Goetze S, Hawley M. HCI International 2023 – Late Breaking Papers. View