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

Web-based and Mobile Health Interventions (3297) Research Priorities Setting (22) Tools, Programs and Algorithms (266) Theoretical Frameworks and Concepts (252) Methods and Instruments in Medical Informatics (126) Behavior Change (712)

Published on in Vol 18, No 3 (2016): March

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century

Authors of this article:

Rajani Shankar Sadasivam1 Author Orcid Image ;   Sarah L Cutrona1, 2, 3 Author Orcid Image ;   Rebecca L Kinney1 Author Orcid Image ;   Benjamin M Marlin4 Author Orcid Image ;   Kathleen M Mazor2 Author Orcid Image ;   Stephenie C Lemon5 Author Orcid Image ;   Thomas K Houston1, 6 Author Orcid Image
Article Authors Cited by (37) Tweetations (11) Metrics

Journals

  1. Hartzler A, BlueSpruce J, Catz S, McClure J. Prioritizing the mHealth Design Space: A Mixed-Methods Analysis of Smokers’ Perspectives. JMIR mHealth and uHealth 2016;4(3):e95 View
  2. SABIBULLAH MOHAMED H. UNDERSTANDING HINDSIGHT, INSIGHT AND FORSIGHT DATA TO LARGE-SCALE DISTRIBUTED DATA INTELLIGENCE (ALGORITHMS) MACHINE: A SCALE-OUT REVIEW. i-manager’s Journal on Pattern Recognition 2017;4(3):32 View
  3. Weng S, Yang M, Hsiao P. A factor-identifying study of the user-perceived value of collective intelligence based on online social networks. Internet Research 2018;28(3):696 View
  4. 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
  5. Sadasivam R, Borglund E, Adams R, Marlin B, Houston T. Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment. Journal of Medical Internet Research 2016;18(11):e285 View
  6. Sanchez Bocanegra C, Sevillano Ramos J, Rizo C, Civit A, Fernandez-Luque L. HealthRecSys: A semantic content-based recommender system to complement health videos. BMC Medical Informatics and Decision Making 2017;17(1) View
  7. Hors-Fraile S, Rivera-Romero O, Schneider F, Fernandez-Luque L, Luna-Perejon F, Civit-Balcells A, de Vries H. Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review. International Journal of Medical Informatics 2018;114:143 View
  8. 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
  9. Castillo V, Martínez-García A, Soriano-Equigua L, Maciel-Mendoza F, Álvarez-Flores J, Juárez-Ramírez R. An interaction framework for supporting the adoption of EHRS by physicians. Universal Access in the Information Society 2019;18(2):399 View
  10. Ashrafian H, Darzi A. Transforming health policy through machine learning. PLOS Medicine 2018;15(11):e1002692 View
  11. 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
  12. Short C, James E, Rebar A, Duncan M, Courneya K, Plotnikoff R, Crutzen R, Bidargaddi N, Vandelanotte C. Designing more engaging computer-tailored physical activity behaviour change interventions for breast cancer survivors: lessons from the iMove More for Life study. Supportive Care in Cancer 2017;25(11):3569 View
  13. Kreps G. Online Information and Communication Systems to Enhance Health Outcomes Through Communication Convergence. Human Communication Research 2017;43(4):518 View
  14. 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
  15. 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
  16. Patel R. A future of digital leadership that is behavioural by design. Future Healthcare Journal 2020;7(3):194 View
  17. Leung Y, Wouterloot E, Adikari A, Hirst G, de Silva D, Wong J, Bender J, Gancarz M, Gratzer D, Alahakoon D, Esplen M. Natural Language Processing–Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study. JMIR Research Protocols 2021;10(1):e21453 View
  18. 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
  19. 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
  20. Doreswamy N, Horstmanshof L. Human Decision-making in an Artificial Intelligence–Driven Future in Health: Protocol for Comparative Analysis and Simulation. JMIR Research Protocols 2022;11(12):e42353 View
  21. 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
  22. 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
  23. 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
  24. Leung Y, Ng S, Duan L, Lam C, Chan K, Gancarz M, Rennie H, Trachtenberg L, Chan K, Adikari A, Fang L, Gratzer D, Hirst G, Wong J, Esplen M. Therapist Feedback and Implications on Adoption of an Artificial Intelligence–Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study. JMIR Cancer 2023;9:e40113 View
  25. Woodman R, Mangoni A. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future. Aging Clinical and Experimental Research 2023;35(11):2363 View
  26. 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

Books/Policy Documents

  1. Rana S, Dey M, Prieto J, Dudley S. Recommender System with Machine Learning and Artificial Intelligence. View
  2. Kreps G, Wright K, Burke-Garcia A. Environmental Health Literacy. View
  3. Paimre M. Information Literacy in Everyday Life. View
  4. Cheung K, Hors-Fraile S, de Vries H. Digital Health. View
  5. Meriem H, Abdelwahed E, Qassimi S. Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). View
  6. Hao L, Goetze S, Hawley M. HCI International 2023 – Late Breaking Papers. View
  7. Samih A, Hamane Z, Ghadi A, Fennan A. International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023). View