Text Messaging as Adjunct to Community-Based Weight Management Program
Bibliographic record
Abstract
Increasing obesity rates are still a public health priority. The primary aim of this study was to evaluate the effect of tailored text messages on body weight change in overweight and obese adults in a community-based weight management program. A secondary aim was to detect behavioral changes in the same population. The study design was quasi-experimental with pretest and posttest analysis, conducted over 12 weeks. A total of 28 participants were included in the analysis. Body weight, eating behaviors, exercise and nutrition self-efficacy, attitude toward mobile technology, social support, and physical activity were assessed at baseline and at 12 weeks. Text messages were sent biweekly to the intervention but not to the control group. At 12 weeks, the intervention group had lost significant weight as compared with the control group. There was a trend toward an improvement in eating behaviors, exercise, and nutrition self-efficacy in the intervention group, with no significant difference between groups. A total of 79% of participants stated that text messages helped in adopting healthy behaviors. Tailored text messages appear to enhance weight loss in a weight management program at a community setting. Large-scale and long-term intervention studies are needed to confirm these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".