Short Message Service Text Message Support for Weight Loss in Patients With Prediabetes: Pragmatic Trial
Bibliographic record
Abstract
BACKGROUND: To reach all 84.1 million US adults estimated to have prediabetes warrants need for low-cost and less burdensome alternatives to the National Diabetes Prevention Program (NDPP). In a previous randomized controlled trial, we demonstrated the efficacy of a 12-month short message service text message support program called SMS4PreDM amongst individuals with prediabetes. OBJECTIVE: The study aimed to evaluate the implementation and effectiveness of SMS4PreDM in a pragmatic study following dissemination in a safety net health care system. METHODS: English- and Spanish-speaking patients at risk for diabetes (eg, glycated hemoglobin 5.7-6.4) were referred by their providers and offered either NDPP classes, SMS4PreDM, or both. This analysis focuses on weight change among 285 SMS4PreDM-only participants who began the year-long intervention between October 2015 and April 2017 with accompanying pre- and postweights, as compared with 1233 usual-care control patients at risk for diabetes, who were identified from electronic health records during this time but not referred. Weight outcomes included time-related mean weight change and frequency of either ≥3% weight loss or gain. Mixed linear models adjusted for age, gender, race, ethnicity, preferred language, and baseline weight. A secondary analysis was stratified by language. We also assessed implementation factors, including retention and cost. RESULTS: SMS4PreDM participants had high retention (259 of 285 patients or 91.0% completion at 12-months, ) and a time-related mean weight loss of 1.3 pounds (SE 0.74), compared with the control group's slight mean weight gain of 0.25 pounds (SE 0.59; P=.004). Spanish-speaking SMS4PreDM participants (n=130) had a time-related mean weight loss of 1.11 pounds (SE 1.22) compared with weight gain of 0.96 pounds (SE 1.14) in Spanish-speaking controls (n=382, P<.001). English-speaking intervention participants (n=155) had a comparable time-related mean weight change (-0.89 pounds; SE 0.93) as English-speaking controls (n=828; 0.31 pounds gained; SE 0.62, P=.14). Overall, frequency of achieving ≥3% weight loss was comparable between groups (54 of 285 or 19.0% of SMS4PreDM participants [95% CI 14.8-23.9] vs 266 of 1233 or 21.6% of controls [95% CI 19.3-24.0]; P=.33). Nonetheless, more controls had ≥3% weight gain compared with intervention participants (337 of 1233 or 27.3% of controls [95% CI 24.9-29.9] vs 57 of 285 or 20.0% of SMS4PreDM participants [95% CI 16.8-25.1]; P=.01). SMS4PreDM delivery costs were US $100.92 per participant. CONCLUSIONS: Although SMS4PreDM was relatively low cost to deliver and demonstrated high retention, weight loss outcomes may not be sufficient to serve as a population health strategy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".