Effectiveness and cost-effectiveness of a 12-month automated text message intervention for weight management in postpartum women with overweight or obesity: protocol for the Supporting MumS (SMS) multisite, parallel-group, randomised controlled trial
Bibliographic record
Abstract
Introduction The reproductive years can increase women’s weight-related risk. Evidence for effective postpartum weight management interventions is lacking and engaging women during this life stage is challenging. Following a promising pilot evaluation of the Supporting MumS intervention, we assess if theory-based and bidirectional text messages to support diet and physical activity behaviour change for weight loss and weight loss maintenance, are effective and cost-effective for weight change in postpartum women with overweight or obesity, compared with an active control arm receiving text messages on child health and development. Methods and analysis Two-arm, parallel-group, assessor-blind randomised controlled trial with cost-effectiveness and process evaluations. Women (n=888) with body mass index (BMI) ≥25 kg/m 2 and within 24 months of giving birth were recruited via community and National Health Service pathways through five UK sites targeting areas of ethnic and socioeconomic diversity. Women were 1:1 randomised to the intervention or active control groups, each receiving automated text messages for 12 months. Data are collected at 0, 6, 12 and 24 months. The primary outcome is weight change at 12 months from baseline, compared between groups. Secondary outcomes include weight change (24 months) and waist circumference (cm), proportional weight gain (>5 kg), BMI (kg/m 2 ), dietary intake, physical activity, infant feeding and mental health (6, 12 and 24 months, respectively). Economic evaluation examines health service usage and personal expenditure, health-related quality of life and capability well-being to assess cost-effectiveness over the trial and modelled lifetime. Cost–utility analysis examines cost per quality-adjusted life-years gained over 24 months. Mixed-method process evaluation explores participants’ experiences and contextual factors impacting outcomes and implementation. Stakeholder interviews examine scale-up and implementation. Ethics and dissemination Ethical approval was obtained before data collection (West of Scotland Research Ethics Service Research Ethics Committee (REC) 4 22/WS/0003). Results will be published via a range of outputs and audiences. Trial registration number ISRCTN16299220 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 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".