ArticleCancer prevention research (Philadelphia, Pa.)2020
Participatory Design of a Personalized Genetic Risk Tool to Promote Behavioral Health.
Article in Cancer prevention research (Philadelphia, Pa.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Examining Challenges to Co-Design Digital Health Interventions With End Users: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Feasibility of precision smoking treatment in a low-income community setting: results of a pilot randomized controlled trial in The Southern Community Cohort Study.Addiction science & clinical practice · 2024Trial
- Proof of Concept of a Personalized Genetic Risk Tool to Promote Smoking Cessation: High Acceptability and Reduced Cigarette Smoking.Cancer prevention research (Philadelphia, Pa.) · 2021Trial
- Translational Lifestyle Medicine Approaches to Cardiovascular-Kidney-Metabolic Syndrome.Healthcare (Basel, Switzerland) · 2025Review
- Genomic medicine to reduce tobacco and related disorders: Translation to precision prevention and treatment.Addiction neuroscience · 2023Article
- In-vivo design feedback and perceived utility of a genetically-informed smoking risk tool among current smokers in the community.BMC medical genomics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Despite major advancements in genomic medicine, research to optimize the design and communication of genetically informed interventions in behavioral health has lagged. The goal of this study was to engage potential end users in participatory codesign of a personalized genetically informed risk tool to intervene on high-risk health behaviors. We used structured interviews to examine end-user attitudes and interest in personalized genetics, qualitative interviews to guide iterative design of a genetically informed tool, and questionnaires to assess acceptability and potential utility of the tool. Participants expressed strong demand for using personal genetics to inform smoking and alcohol-related disease risk and guide treatment (78%-95% agreed). Via iterative design feedback, we cocreated a genetically informed risk profile featuring (i) explanation of genetic and phenotypic markers used to construct a risk algorithm, (ii) personalized risks and benefits of healthy behavior change, and (iii) recommended actions with referral to freely available resources. Participants demonstrated sufficient understanding and cited motivating behavior change as the most useful purpose of the tool. In three phases, we confirmed strong desire for personalized genetics on high-risk health behaviors; codesigned a genetically informed profile with potential end users; and found high acceptability, comprehensibility, and perceived usefulness of the profile. As scientific discovery of genomic medicine advances in behavioral health, we must develop the tools to communicate these discoveries to consumers who stand to benefit. The potential of genomic medicine to engage populations and personalize behavioral health treatment depends, in part, on preparatory studies to design for the future implementation of genetically informed interventions.
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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.