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Record W2937434126 · doi:10.2196/cancer.7850

Potential of Using Twitter to Recruit Cancer Survivors and Their Willingness to Participate in Nutrition Research and Web-Based Interventions: A Cross-Sectional Study

2019· article· en· W2937434126 on OpenAlexvenueno aff
Laura Keaver, Aisling McGough, Mengxi Du, Winnie Chang, Virginia R. Chomitz, Jennifer D. Allen, Deanna J. Attai, Lisa Gualtieri, Fang Fang Zhang

Bibliographic record

VenueJMIR Cancer · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
FundersPatient-Centered Outcomes Research Institute
KeywordsHealth Information National Trends SurveyPsychological interventionSocial mediaMedicinePromotion (chess)National Health and Nutrition Examination SurveyFamily medicineCross-sectional studyHealth promotionIntervention (counseling)Environmental healthPublic healthHealth informationHealth careWorld Wide WebNursingPopulationPolitical sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Social media is rapidly changing how cancer survivors search for and share health information and can potentially serve as a cost-effective channel to reach cancer survivors and invite them to participate in nutrition intervention programs. OBJECTIVE: This study aimed to assess the feasibility of using Twitter to recruit cancer survivors for a web-based survey and assess their willingness to complete web-based nutrition surveys, donate biospecimens, and to be contacted about web-based nutrition programs. METHODS: We contacted 301 Twitter accounts of cancer organizations, advocates, and survivors to request assistance promoting a web-based survey among cancer survivors. The survey asked respondents whether they would be willing to complete web-based nutrition or lifestyle surveys, donate biospecimens, and be contacted about web-based nutrition programs. Survey promotion rate was assessed by the percentage of Twitter accounts that tweeted the survey link at least once. Survey response was assessed by the number of survey respondents who answered at least 85% (26/30). We compared the characteristics of cancer survivors who responded to this survey with those who participated in the National Health and Nutrition Examination Survey (NHANES) 1999-2010 and evaluated factors associated with willingness to complete web-based surveys, donate biospecimens, and be contacted to participate in web-based nutrition programs among those who responded to the social media survey. RESULTS: Over 10 weeks, 113 Twitter account owners and 165 of their followers promoted the survey, and 444 cancer survivors provided complete responses. Two-thirds of respondents indicated that they would be willing to complete web-based nutrition or lifestyle surveys (297/444, 67.0%) and to be contacted to participate in web-based nutrition interventions (294/444, 66.2%). The percentage of respondents willing to donate biospecimens were 59.3% (263/444) for oral swab, 52.1% (231/444) for urine sample, 37.9% (168/444) for blood sample, and 35.6% (158/444) for stool sample. Compared with a nationally representative sample of 1550 cancer survivors in NHANES, those who responded to the social media survey were younger (53.1 years vs 60.8 years; P<.001), more likely to be female (93.9% [417/444] vs 58.7% [909/1550]; P<.001), non-Hispanic whites (85.4% [379/444] vs 64.0% [992/1550]; P<.001), to have completed college or graduate school (30.1 [133/444] vs 19.9% [308/444]; P<.001), and to be within 5 years of their initial diagnosis (55.2% [244/444] vs 34.1% [528/1550]; P<.001). Survivors younger than 45 years, female, and non-Hispanic whites were more willing to complete web-based nutrition surveys than older (65+ years), male, and racial or ethnic minority survivors. Non-Hispanic whites and breast cancer survivors were more willing to donate biospecimens than those with other race, ethnicity or cancer types. CONCLUSIONS: Twitter could be a feasible approach to recruit cancer survivors into nutrition research and web-based interventions with potentially high yields. Specific efforts are needed to recruit survivors who are older, male, racial and ethnic minorities, and from socioeconomically disadvantaged groups when Twitter is used as a recruitment method.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.440
GPT teacher head0.582
Teacher spread0.142 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2019
Admission routes1
Has abstractyes

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