Abstract P386: Design of a Tailored Text-message Intervention for Weight Loss
Bibliographic record
Abstract
Introduction: Tailoring information is a commonly used health communication technique to individualize behavior change programs. However, few studies have applied individualized tailoring to a text-message intervention. Using tailoring with text-messages may be a powerful behavior change tool as this technology is inexpensive, asynchronous, and is ubiquitous across socio-economic segments. We developed a tailored, “push/pull,” text-message intervention for overweight/obese English and Spanish-speaking participants that targets weight management behaviors. The development of the theory-driven logic rules for individualized text-messages is described. Methods: A robust web-based participant intake system was developed to gather and store the data needed to individualize the messages by personalization, feedback, and content matching. Feedback and content matching was based on both a baseline weight management behavior survey and continual text message-based assessments related to individual behaviors and perceived barriers. The system is designed so that participants who show rapid and sustained progress can advance through the content, while those having difficulties can receive additional tips and suggestions. Participants will be scheduled to receive 3-4 interactive messages per day from our library of over 6,000 text messages. We categorized the messages into five groups: 1) Subject matter: Content related to nutrition and physical activity behaviors; 2) Behavioral core strategies based on Self-Regulation Theory (e.g. self-monitoring, goal setting); 3) Message type (e.g. reminder, feedback); 4) Personalization (e.g. favorite restaurants, friends’ names); and 5) Location related messages. We also developed a “like/ don’t like” feature so that the system can continuously learn user preferences for message types and adjust the type of future messages sent accordingly. Conclusion: A large-scale randomized controlled trial is underway to evaluate the efficacy of this text-message intervention. While this intervention is aimed at weight loss, our tailoring technique will likely apply to other health behaviors as well.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".