Potential of Using Twitter to Recruit Cancer Survivors and Their Willingness to Participate in Nutrition Research and Web-Based Interventions: A Cross-Sectional Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".