A Method to Deliver Automated and Tailored Intervention Content: 24-month Clinical Trial
Bibliographic record
Abstract
BACKGROUND: The use of digital technologies and software allows for new opportunities to communicate and engage with research participants over time. When software is coupled with automation, we can engage with research participants in a reliable and affordable manner. Research Electronic Data Capture (REDCap), a browser-based software, has the capability to send automated text messages. This feature can be used to automate delivery of tailored intervention content to research participants in interventions, offering the potential to reduce costs and improve accessibility and scalability. OBJECTIVE: This study aimed to describe the development and use of 2 REDCap databases to deliver automated intervention content and communication to index participants and their partners (dyads) in a 2-arm, 24-month weight management trial, Partner2Lose. METHODS: Partner2Lose randomized individuals with overweight or obesity and cohabitating with a partner to a weight management intervention alone or with their partner. Two databases were developed to correspond to 2 study phases: one for weight loss initiation and one for weight loss maintenance and reminders. The weight loss initiation database was programmed to send participants (in both arms) and their partners (partner-assisted arm) tailored text messages during months 1-6 of the intervention to reinforce class content and support goal achievement. The weight maintenance and reminder database was programmed to send maintenance-related text messages to each participant (both arms) and their partners (partner-assisted arm) during months 7-18. It was also programmed to send text messages to all participants and partners over the course of the 24-month trial to remind them of group classes, dietary recall and physical activity tracking for assessments, and measurement visits. All text messages were delivered via Twilio and were unidirectional. RESULTS: Five cohorts, comprising 231 couples, were consented and randomized in the Partner2Lose trial. The databases will send 53,518 automated, tailored text messages during the trial, significantly reducing the need for staff to send and manage intervention content over 24 months. The cost of text messaging will be approximately US $450. Thus far, there is a 0.004% known error rate in text message delivery. CONCLUSIONS: Our trial automated the delivery of tailored intervention content and communication using REDCap. The approach described provides a framework that can be used in future behavioral health interventions to create an accessible, reliable, and affordable method for intervention delivery and engagement that requires minimal trial-specific resources and personnel time. TRIAL REGISTRATION: ClinicalTrials.gov NCT03801174; https://clinicaltrials.gov/ct2/show/NCT03801174?term=NCT03801174.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".