Assessing the Feasibility of a Web-Based Weight Loss Intervention for Low-Income Women of Reproductive Age: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Low-income women of reproductive age are at increased risk for obesity and resulting increases in the risk of maternal/fetal complications and mortality and morbidity. Very few weight-loss interventions, however, have been targeted to this high-risk group. Based on the high prevalence of social media use among young and low-income individuals and previous successes using group formats for weight-loss interventions, the use of social media as a platform for weight-loss intervention delivery may benefit low-income women of reproductive age. OBJECTIVE: Examine the feasibility of delivering group-based weight-loss interventions to low-income women of reproductive age using face-to-face meetings and Web-based modalities including social media. METHODS: Participants attended a family planning clinic in eastern North Carolina and received a 5-month, group- and Web-based, face-to-face weight-loss intervention. Measures were assessed at baseline and 20 weeks. RESULTS: Forty participants enrolled, including 29 (73%) African American women. The mean body mass index of enrollees was 39 kg/m(2). Among the 12 women who completed follow-up, mean weight change was -1.3 kg. Participation in the intervention was modest and retention at 5 months was 30%. Returnees suggested sending reminders to improve participation and adding activities to increase familiarity among participants. CONCLUSIONS: Engagement with the intervention was limited and attrition was high. Additional formative work on the barriers and facilitators to participation may improve the intervention's feasibility with low-income women of reproductive age.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".