Promoting Health Behavior Change in the Preconception Period: Combined Approach to Intervention Planning
Bibliographic record
Abstract
BACKGROUND: Half of women begin pregnancy above the healthy weight range, increasing the risk of complications and adversely affecting the lifelong health of their babies. Maternal obesity remains the strongest risk factor for offspring obesity across childhood, adolescence, and adulthood. Previous research suggests that women should be encouraged to be within a healthy weight range before conception to improve health outcomes. OBJECTIVE: We outlined the intervention planning and design process to develop an evidence-informed eHealth intervention to promote weight management. The intervention, based on psychological theories and behavior change techniques, has been developed for women affected by overweight or obesity who intend to become pregnant. The Begin Better web application is part of an integrated program being evaluated in a clinical trial to assess if weight management before pregnancy can influence clinical outcomes for mothers and babies. METHODS: Our intervention development process was guided by intervention mapping and person-based methods. This study documents steps 2 to 4 of a 6-step iterative intervention mapping approach informed by the Information-Motivation-Behavioral Skills model and the findings of a previous interview study. We defined behavior change objectives for each of the Information-Motivation-Behavioral Skills behavioral determinants as well as theory-based behavior change techniques and practical strategies. We also used persuasive system design principles to assist in translating these strategies into a digital environment. RESULTS: The resultant intervention comprises nutritional and physical activity content along with psychological strategies, which are notably absent from mainstream weight management programs. Strategies to increase motivation, garner social support, and promote self-care are integral to maintaining engagement with the intervention, which aims to improve lifestyle behaviors and enhance well-being. Important elements include tracking mechanisms for percentage progress toward goals to enable feedback on behaviors and outcomes; in-application messages of praise on entry of goals or habits; and strategies to prompt habit formation and action planning via small, easily achievable steps toward positive change. CONCLUSIONS: Design decisions and processes for idea generation about intervention content, format, and delivery are often not reported. In this study, we respond to this gap in the literature and outline a process that is potentially transferable to the development of other interventions.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".