Participant engagement with a text message delivered intervention for weight loss and maintenance of weight loss in the postpartum period
Bibliographic record
Abstract
Abstract Introduction: Postpartum weight management is difficult for many mothers due to the demands of parenthood. Women have highlighted a need for support but experience barriers to engaging with lifestyle interventions hence more adaptable approaches are required. This work examined participants’ engagement with a 12-month, theory-based, automated, text message (SMS) delivered, intervention supporting postpartum weight management. Methods: SMS content was informed by: 1) the ‘Health Action Process Approach’ (HAPA)1; 2) behaviour change techniques associated with effectiveness in weight management interventions2; 3) women's accounts of postpartum weight-related experiences; and 4) personal and public involvement. A two-arm pilot RCT recruited women within two years postpartum, with a BMI ≥ 25 kg/m2, through community sources and social media. Women were randomised via a secure remote system to receive the intervention or an active control delivering child development messages. Participants received 353 messages during the 12 month intervention. Two-way messages were used to assess engagement: 50 messages prompted women to respond with their weight; 36 interactive messages requested participants’ to respond ‘Yes/No’ to a question which then triggered a feedback message. Participant engagement with two-way messages was calculated as a percentage of replies sent by women and was categorised as ‘high’ or ‘low’ according to the median number of replies sent. Weight was measured at 0, 3, 6, 9 and 12 months. Results: 51 of 100 women recruited were randomised to receive the intervention. In months 0–6, (47%) and (95%) of participants responded to the weight messages and the ‘Yes/No’ messages respectively. In months 7–12, the responses were (77%) and (86%) respectively. Participants who were high engagers with weight messages had greater mean weight loss compared with low engagers at all time points: at 12 months high engagers (n = 18) lost -2.66 kg and had a reduction in waist circumference at 12 months of -8.9 cm, compared to changes in low engagers (n = 18) of -0.84 kg and -3.6 cm. Likewise, high engagers with ‘Yes/No’ messages had greater mean weight loss compared with low engagers at all time points: at 12 months high engagers (n = 16) lost -2.87 kg and had a reduction in waist circumference of -9.4 cm, compared to changes in low engagers (n = 20) of -0.86 kg and -3.6 cm. Discussion: The use of two-way text messages was a useful way to encourage engagement with this SMS-delivered intervention. Higher engagement resulted in more weight loss compared to low engagement.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".