Integrating SMS Text Messages Into a Preventive Intervention for Postpartum Depression Delivered via In-Home Visitation Programs: Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: The Mothers and Babies (MB) Course is recognized by the US Preventive Services Task Force as an evidence-based preventive intervention for postpartum depression (PPD) that should be recommended to pregnant women at risk for PPD. OBJECTIVE: This report examines the feasibility and acceptability of enhancing the MB 1-on-1 intervention by adding 36 SMS text messages that target 3 areas: reinforcement of skills, between-session homework reminders, and responding to self-monitoring texts (ie, MB Plus Text Messaging [MB-TXT]). METHODS: In partnership with 9 home visiting programs, 28 ethnically and racially diverse pregnant women (mean 25.6, SD 9.0 weeks) received MB-TXT. Feasibility was defined by home visitors' adherence to logging into the HealthySMS platform to enter session data and trigger SMS text messages within 7 days of the in-person session. The acceptability of MB-TXT was measured by participants' usefulness and understanding ratings of the SMS text messages and responses to the self-monitoring SMS text messages. RESULTS: On average, home visitors followed the study protocol and entered session-specific data between 5.50 and 61.17 days following the MB 1-on-1 sessions. A high proportion of participants responded to self-monitoring texts (25/28, 89%) and rated the text message content as very useful and understandable. CONCLUSIONS: This report contributes to a growing body of research focusing on digital adaptations of the MB course. SMS is a low-cost, accessible digital tool that can be integrated into existing interventions. With appropriate resources to support staff, it can be implemented in community-based organizations and health care systems that serve women at risk for PPD. TRIAL REGISTRATION: ClinicalTrials.gov NCT03420755; https://clinicaltrials.gov/ct2/show/NCT03420755.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".