Better Ask Than Tell: Responses to mHealth Interrogative Reminders and Associations With Colorectal Cancer Screening Subsequent Uptake in a Prospective Cohort Intervention
Bibliographic record
Abstract
BACKGROUND: Text message (short message service, SMS) interrogative reminders were adopted in population screening for the early detection of colorectal cancer (CRC). OBJECTIVE: This study aims to examine responses to text message (SMS) reminders and associate responses with senders' characteristics, message type (interrogative/declarative), and subsequent screening uptake. METHODS: We conducted a prospective cohort intervention. Text message (SMS) reminders to undergo CRC screening, randomized into interrogative and declarative phrasing, were sent to nonadherent 40,000 women and men (age 50-74 years) at CRC average risk. We analyzed recipient responses by message phrasing, recipient characteristics, and for content, the latter predicting subsequent CRC screening per program database. RESULTS: While interrogative text message (SMS) reminders elicited 7.67% (1475/19,227) responses, declarative ones elicited 0.76% (146/19,262) responses. Text message (SMS) responses were content analyzed and grouped into attitudes toward CRC screening (1237/1512, 81.8% positive) and intention to screen (1004/1512, 62.6%). Text message (SMS) respondents screened significantly more than nonrespondents after 6 months (415/1621, 25.6% vs 3322/36,868, 9.0%; χ12=487.5, P<.001); 1 year (340/1621, 21.0% vs 4711/36,868; χ12=91.5, P<.001); and 2 years (225/1621, 13.9% vs 3924/36,868; χ12=16.9, P<.001) following the reminders. In a multivariable logistic regression among text message (SMS) respondents, screening after 6 months was significantly predicted by older age, past sporadic screening, attitudes, and intentions. CONCLUSIONS: Interrogative text message (SMS) reminders reached previously uninvolved sectors in the CRC target population-men, sporadic-screenees, and the "never-tested" before. This novel application resulted in a population-level, incrementally enhanced screening. Asking patients about their future health behavior may be relevant for enhancing other health behaviors in preventive medicine and clinical settings.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".