A Complex mHealth Coaching Intervention to Prevent Overweight, Obesity, and Diabetes in High-Risk Women in Antenatal Care: Protocol for a Hybrid Type 2 Effectiveness-Implementation Study
Bibliographic record
Abstract
BACKGROUND: Women with overweight and obesity are at higher risk of developing complications in pregnancy such as gestational diabetes and longer-term chronic conditions. Research concerning health behavior change interventions during pregnancy and postpartum shows promising effects, but implementation into routine services is sparsely investigated. Most interventions focus on the antenatal or postpartum life stages, failing to meet the needs of women. IMPACT DIABETES Bump2Baby is a multicenter project across 4 high-income countries developed to test the implementation of an antenatal and postpartum evidence-based mobile health (mHealth) coaching intervention called Bump2Baby and Me (B2B&Me) designed to sit alongside usual care in the perinatal period. OBJECTIVE: We aim to explore the feasibility and implementation of the B2B&Me intervention and investigate the effectiveness of this intervention in women at risk of gestational diabetes. METHODS: IMPACT DIABETES Bump2Baby is a hybrid type 2 effectiveness-implementation study, which integrates an evidence-based mHealth coaching app that includes personalized health behavior change coaching provided by health care professionals alongside antenatal care from the first antenatal visit to 12 months postpartum. The mHealth app offers the possibility of synchronous calls, asynchronous contact (including coach-participant text and video messaging exchanges tailored to the participant's needs), and ongoing access to an extensive library of bespoke intervention materials. Participants will interact asynchronously with their health coach throughout the intervention via the app. This randomized controlled trial across 4 clinical sites within Ireland, the United Kingdom, Spain, and Australia will recruit 800 women in early pregnancy to evaluate the effectiveness on postpartum weight. The Exploration, Preparation, Implementation, and Sustainment implementation framework is the theoretical underpinning of the study. The implementation evaluation will be assessed at the individual, hospital staff, and broader community levels using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. Data sources for the RE-AIM evaluation will include app and platform analytics, screening and training records, participant medical records, key informant interviews, participant and partner exit interviews, cost data, study questionnaires, staff surveys, and blood sample analyses. RESULTS: The study was approved and registered with the Australian New Zealand Clinical Trials Registry on November 19, 2020. Recruitment commenced on February 9, 2021, and data collection is ongoing. Publication of the results is expected in 2024. CONCLUSIONS: This is the first hybrid effectiveness-implementation study of an 18-month mHealth coaching intervention in at-risk women that we are aware of. As research aims to move toward real-world implementable solutions, it is critical that hybrid studies are conducted. The data from this large multicenter study will be useful in planning the potential implementation and scale-up of evidence-based perinatal health behavior change interventions. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12620001240932; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380020&isReview=true. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51431.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.008 |
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".