Social Media and mHealth Technology for Cancer Screening: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cancer is a leading cause of death, and although screening can reduce cancer morbidity and mortality, participation in screening remains suboptimal. OBJECTIVE: This systematic review and meta-analysis aims to evaluate the effectiveness of social media and mobile health (mHealth) interventions for cancer screening. METHODS: We searched for randomized controlled trials and quasi-experimental studies of social media and mHealth interventions promoting cancer screening (breast, cervical, colorectal, lung, and prostate cancers) in adults in MEDLINE, Embase, PsycINFO, Scopus, CINAHL, Cochrane Central Register of Controlled Trials, and Communication & Mass Media Complete from January 1, 2000, to July 17, 2020. Two independent reviewers screened the titles, abstracts, and full-text articles and completed the risk of bias assessments. We pooled odds ratios for screening participation using the Mantel-Haenszel method in a random-effects model. RESULTS: We screened 18,008 records identifying 39 studies (35 mHealth and 4 social media). The types of interventions included peer support (n=1), education or awareness (n=6), reminders (n=13), or mixed (n=19). The overall pooled odds ratio was 1.49 (95% CI 1.31-1.70), with similar effect sizes across cancer types. CONCLUSIONS: Screening programs should consider mHealth interventions because of their promising role in promoting cancer screening participation. Given the limited number of studies identified, further research is needed for social media interventions. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42019139615; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=139615. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2019-035411.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".