ReviewJournal of medical Internet research2016
Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century.
Review in Journal of medical Internet research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Health Recommender Systems: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Effect of a Machine Learning Recommender System and Viral Peer Marketing Intervention on Smoking Cessation: A Randomized Clinical Trial.JAMA network open · 2023Trial
- Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment.Journal of medical Internet research · 2016Trial
- Testing a Machine Learning-Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial.JMIR research protocols · 2025Article
- A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.Aging clinical and experimental research · 2023Review
- Article
- Human Decision-making in an Artificial Intelligence-Driven Future in Health: Protocol for Comparative Analysis and Simulation.JMIR research protocols · 2022Article
- An Overview of Innovative Approaches to Support Timely and Agile Health Communication Research and Practice.International journal of environmental research and public health · 2022Review
- Evaluating the use of a recommender system for selecting optimal messages for smoking cessation: patterns and effects of user-system engagement.BMC public health · 2021Article
- Natural Language Processing-Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study.JMIR research protocols · 2021Article
- A future of digital leadership that is behavioural by design.Future healthcare journal · 2020Article
- 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 · 2020Article
- Dissemination and Effectiveness of the Peer Marketing and Messaging of a Web-Assisted Tobacco Intervention: Protocol for a Hybrid Effectiveness Trial.JMIR research protocols · 2019Article
- Mining User-Generated Content in an Online Smoking Cessation Community to Identify Smoking Status: A Machine Learning Approach.Decision support systems · 2019Article
- Transforming health policy through machine learning.PLoS medicine · 2018Article
- 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 : official journal of the Multinational Association of Supportive Care in Cancer · 2017Article
- HealthRecSys: A semantic content-based recommender system to complement health videos.BMC medical informatics and decision making · 2017Article
- Prioritizing the mHealth Design Space: A Mixed-Methods Analysis of Smokers' Perspectives.JMIR mHealth and uHealth · 2016Article
- How recommender systems could support and enhance computer-tailored digital health programs: A scoping review.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundWhat is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC.
objectiveThe objective of our study was to uncover barriers and challenges to using recommender systems in health promotion.
methodsWe conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results.
resultsWe describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems.
conclusionsWe promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.