The SmokefreeTXT (SFTXT) Study: Web and Mobile Data Collection to Evaluate Smoking Cessation for Young Adults
Bibliographic record
Abstract
BACKGROUND: Text messaging (short message service, SMS) has been shown to be effective in delivering interventions for various diseases and health conditions, including smoking cessation. While there are many published studies regarding smoking cessation text messaging interventions, most do not provide details about the study's operational methods. As a result, there is a gap in our understanding of how best to design studies of smoking cessation text messaging programs. OBJECTIVE: The purpose of this paper is to detail the operational methods used to conduct a randomized trial comparing three different versions of the National Cancer Institute's SmokefreeText (SFTXT) program, designed for smokers 18 to 29 years of age. We detail our methods for recruiting participants from the Internet, reducing fraud, conducting online data collection, and retaining panel study participants. METHODS: Participants were recruited through website advertisements and market research online panels. Screening questions established eligibility for the study (eg, 18 to 29 years of age, current smoker). Antifraud measures screened out participants who could not meet the study requirements. After completing a baseline survey, participants were randomized to one of three study arms, which varied by type and timing of text message delivery. The study offered US $20 gift cards as incentives to complete each of four follow-up surveys. Automated email reminders were sent at designated intervals to increase response rates. Researchers also provided telephone reminders to those who had not completed the survey after multiple email reminders. We calculated participation rates across study arms and compared the final sample characteristics to the Current Population Survey to examine generalizability. RESULTS: Recruitment methods drove 153,936 unique visitors to the SFTXT Study landing page and 27,360 began the screener. Based on the screening questions, 15,462 out of 27,360 responders (56.51%) were eligible to participate. Of the 15,462 who were eligible, 9486 passed the antifraud measures that were implemented; however, 3882 failed to verify their email addresses or cell phone numbers, leaving 5604 who were invited to complete the baseline survey. Of the 5604 who were invited, 4432 completed the baseline survey, but only 4027 were retained for analysis because 405 did not receive the intervention. CONCLUSIONS: Although antifraud measures helped to catch participants who failed study requirements and could have biased the data collected, it is possible that the email and cell phone verification check excluded some potentially eligible participants from the study. Future research should explore ways to implement verification methods without risking the loss of so many potential participants. CLINICALTRIAL: Clinical Trials.gov NCT01885052; https://clinicaltrials.gov/ct2/show/NCT01885052; (Archived by WebCite at http://www.webcitation.org/6iWzcmFdw).
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".