A Digital Lifestyle App for Hypertension During Pregnancy: Mixed Methods Intervention Development Study Using the Person-Based Approach
Bibliographic record
Abstract
Background: Chronic hypertension affects 1%-5% of pregnancies, increasing women's risks of adverse pregnancy outcomes and life-long cardiovascular disease risk. Therefore, care management during pregnancy includes close monitoring of blood pressure and medication. Healthy dietary and physical activity behaviors have proven beneficial effects on blood pressure outside and during pregnancy. However, little is known about the best way to support women with chronic hypertension during pregnancy to adopt such behaviors, which could improve pregnancy outcomes, as well as future cardiovascular health. Objective: This study aims to develop and optimize a digital lifestyle intervention-the DAPHNY (Diet and Activity for Pregnancy Hypertension) app-with those who have experienced chronic hypertension during pregnancy. Methods: Guided by the person-based approach to intervention development, a review of literature and continuous expert input, including from patient and public representatives, informed the planning stage. This was followed by focus groups with maternity health professionals (n=23) and think-aloud interviews with women who had experience of chronic hypertension during pregnancy (n=11). A content analysis, underpinned by theoretical modeling using the capability opportunity motivation-behavior model, informed 3 logic models to visualize modifications for meaningful engagement with an intervention and sustained behavior change. The intervention was modified iteratively, leading to a first version of the digital intervention that was tested by women (n=10) to further optimize acceptability and engagement. App use data and user engagement patterns were captured. Results: An evidence-based, theoretically informed lifestyle app, named DAPHNY, was developed. Key features included in logic models and implemented into a first version of the app comprised supportive messaging to acknowledge challenges of hypertensive pregnancy, goal setting and progress reports for feedback on behaviors, information about health consequences to shape knowledge, credible source endorsement, and a reward or recognition system to acknowledge effort had been made. Engagement with the DAPHNY app during user testing demonstrated variability across users, with a mean of 13 (SD 6.84) sessions per participant. Session duration was variable, with a median of 36 seconds (range: 5 seconds to 5 minutes, 20 seconds). Action-based pages, including recording blood pressure (40 sessions) and step count (39 sessions), were accessed more frequently than informational pages, which required a deeper level of app engagement. Conclusions: Development of the DAPHNY app, underpinned by an established behavioral framework for developing digital interventions, provided new data insights about how to support women with chronic hypertension to engage in healthy behaviors, a currently overlooked aspect of blood pressure management. Future iterations should focus on increasing engagement and supporting implementation through streamlined content and integration with existing health systems and self-monitoring data. Rigorous, larger-scale studies including comprehensive process evaluation would determine potential clinical effectiveness, implementation strategies, and impact for women and health care professionals.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".