Evidence mapPaperPMID 40199288Full record

ArticleYearbook of medical informatics2024

Precision Prevention through Social Media: Report of Four Cases.

Elia Gabarron, Guillermo Lopez-Campos, Shauna Davies, Taridzo Chomutare, Iris Thiele Isip Tan, Carolyn Petersen

Abstract read
In one paragraph

Article in Yearbook of medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from 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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Elia GabarronDepartment of Education, ICT and Learning, Østfold University College, Halden, Norway.
Guillermo Lopez-CamposQueen's University Belfast, Belfast, United Kingdom.
Shauna DaviesFaculty of Nursing, University of Regina, Canada.
Taridzo ChomutareNorwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway.
Iris Thiele Isip TanMedical Informatics Unit, College of Medicine, University of the Philippines Manila, Philippines.
Carolyn PetersenDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrecision prevention involves using biological, behavioral, socioeconomic, and epidemiological data to improve health for a particular individual or group. With almost 63% of the global population using social media, these platforms show promise to deliver tailored messaging and personalized interventions to individuals.

objectivesTo describe the personalization elements and behavior components used in a sample of precision prevention interventions delivered through social media.

methodsTo identify examples of cases, a search was done on clinicaltrials.gov, searching for 'other terms: prevention' + 'Intervention/Treatment: social media intervention' + 'study results: With results. The selected cases were described, personalization elements reported, and their adopted intervention components were coded according to the Behavior Change Wheel (BCW) framework.

resultsA total of four cases employing personalization in their interventions were identified. Three of these cases targeted women's health. The intervention period varied from two to eight months, with participant commitment ranging from active involvement on five out of seven days to monthly participation. The BCW interventions of persuasion and incentivization, were most frequently utilized, while education and coercion were used sparingly in the selected cases. Notably, none of the four cases reported the use of training, restrictions, or modeling.

conclusionsSocial media has the potential to serve as a tool for digital phenotyping and contribute to the advancement of precision prevention. Challenges include the social media platform set-up and ensuring all ethical considerations are met.

Indexed as

Precision MedicineSocial MediaAdultFemaleHumans

Identifiers

PMID40199288
PMCPMC12020558

What Socratic holds

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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 Socratic graph.