How Science Is Revolutionizing Maternity Decisions Through Mixed Methods Research
Imagine sitting in a doctor's office, holding a positive pregnancy test, and being handed a list of complex prenatal screening options with unfamiliar terms like "cell-free DNA testing," "nuchal translucency screening," and "amniocentesis." This overwhelming scenario is a reality for millions of expectant parents who must make crucial decisions about genetic screening with limited understanding and significant time constraints 5 . The number of available prenatal tests has increased dramatically in recent years, turning what should be informed choices into stressful dilemmas.
Expectant parents face complex decisions with limited understanding and time pressure, leading to stress and uncertainty.
A mobile app that explains tests in clear language, helps clarify personal values, and prepares for provider conversations.
Now, envision a different experience: using a thoughtfully designed mobile app on your own time that explains these tests in clear language, helps you clarify your personal values, and prepares you to have more meaningful conversations with your healthcare provider. This isn't a futuristic fantasy—it's the exact solution that researchers in Canada are developing through an innovative scientific approach 1 . This revolutionary app aims to transform prenatal care by empowering women to make informed decisions consistent with their values and preferences before even stepping into their doctor's office.
Shared decision-making (SDM) represents a significant shift from traditional medical approaches where doctors made decisions for patients. In the SDM model, healthcare providers and patients work together to make medical choices that align with both clinical evidence and the patient's personal values, preferences, and circumstances 3 . This collaborative approach is particularly valuable in situations where multiple reasonable options exist, each with different benefits, risks, and implications—exactly the case with prenatal genetic testing.
Prenatal screening decisions are uniquely complex because they involve:
Without proper support, pregnant women often experience decisional conflict—a state of uncertainty about which course of action to take. Research shows that decision support tools can significantly reduce this conflict by helping women feel more informed and aware of their options 3 .
The prenatal screening app is being developed using mixed methods research—an innovative approach that strategically integrates both quantitative and qualitative research methods to draw on the strengths of each . Think of it as gathering both the "what" (quantitative) and the "why" (qualitative) to form a complete picture:
Collects numerical data (through surveys, metrics, etc.) to answer questions like "how many?" or "how much?"
Gathers non-numerical information (through interviews, observations, etc.) to understand experiences, motivations, and perspectives 4
Mixed methods research is particularly valuable in healthcare because it:
Provides different viewpoints on the same issue, validating findings through "triangulation"
Builds a complete picture that numbers or stories alone cannot provide
Creates better measures that reflect real-world experiences
As one research guide explains, "Mixed methods can help you gain a more complete picture than a standalone quantitative or qualitative study, as it integrates benefits of both methods" 4 .
The research protocol for the analytical prenatal screening app follows a three-phase, exploratory sequential mixed methods design 1 2 . This means the study begins with qualitative data collection and analysis, then uses those findings to inform a subsequent quantitative phase. The entire process is designed to ensure the final app truly meets users' needs rather than reflecting developers' assumptions.
| Phase | Primary Focus | Methods | Participants |
|---|---|---|---|
| Phase 1 | Assessing needs and preferences | Semi-structured interviews and questionnaires | 90 pregnant women and their partners |
| Phase 2 | Decision model development | Analytic Hierarchy Process (AHP) | Multidisciplinary team and potential users |
| Phase 3 | App development and testing | Iterative prototyping and usability testing | 15 pregnant women and their partners |
The first phase involves in-depth engagement with 90 pregnant women and their partners (if available) recruited from three different clinical settings in Quebec City and Montreal 1 . This diverse recruitment strategy ensures the app will address various perspectives and healthcare experiences. Through semi-structured interviews, researchers explore:
Women's attitudes toward using mobile apps for health decisions
Current use of health-related applications
Specific expectations for a prenatal testing decision app
The simultaneous use of self-administered questionnaires collects sociodemographic information and measures intentions to use such an app and the perceived importance of various decision criteria. The integration of these qualitative and quantitative approaches from the very beginning exemplifies the power of mixed methods to capture both breadth and depth of understanding.
The second phase employs a sophisticated mathematical approach called the Analytic Hierarchy Process (AHP) to develop the decision model that will power the app 1 . This method allows users to assign relative importance to different criteria when deciding between options. For example, a user might indicate that understanding the accuracy of a test is twice as important to them as considering its cost.
Patients
Family Physicians
Decision Science Experts
Engineers
SDM Specialists
Genetics Experts
Bioethicists
The model is validated with both potential users and a multidisciplinary team that includes patients, family physicians, decision science experts, engineers, and specialists in shared decision-making, genetics, and bioethics 1 . This collaborative validation process helps ensure the tool is both scientifically sound and practically useful.
The final phase involves developing a functional prototype based on findings from the first two phases, then testing it through three iterative cycles with 15 pregnant women and their partners 1 . This iterative approach allows researchers to identify and address usability issues early, creating a more polished and intuitive final product. Participants complete questionnaires about their experience, with results analyzed using descriptive statistics to guide improvements.
Initial prototype testing to identify major usability issues and functional problems.
Refined prototype testing to improve user interface and experience based on initial feedback.
Final prototype validation to ensure the app meets user needs and is ready for deployment.
The research protocol employs what mixed methods experts would classify as an exploratory sequential design 8 , which begins with qualitative data collection and analysis, then builds toward quantitative testing. Here's how it works in practice:
While the specific study referenced is ongoing, systematic reviews of similar interventions demonstrate the potential impact of such tools. Research shows that digital decision support tools for pregnant women generally:
About prenatal testing options
By helping women feel more informed
| Outcome Measure | Effect of Decision Support Tools | Significance |
|---|---|---|
| Knowledge | Higher knowledge scores compared to standard care | Women better understand risks, benefits, and limitations of tests |
| Decisional Conflict | Decreased feelings of uncertainty and informed choice | Women feel more clear about their values and decisions |
| Screening Uptake | Mixed effects, varying by tool and context | Decisions become more aligned with personal values rather than default options |
The power of mixed methods research shines through in these findings—while quantitative data shows that knowledge scores improve, qualitative research helps explain why: women report that the tools help them understand complex information at their own pace and clarify what matters most to them personally.
The current landscape of pregnancy-related mobile apps is crowded, but most have significant limitations. A systematic review of 64 mobile apps containing prenatal genetic testing information found concerning gaps in quality 5 :
| Quality Dimension | Current State in Most Apps | Future Needs |
|---|---|---|
| Information Coverage | Limited number of tests mentioned; incomplete information | Comprehensive coverage of all available tests |
| Evidence Base | 98% of apps show no evidence of testing with users | Evidence-based development with user testing |
| Source Reliability | 75% provide no references; only 9% cite authoritative sources | Credible, well-referenced information from authoritative organizations |
| Readability | High reading level requirements | Accessible language and visual information |
| Customization | Limited personalization features | Tailored content based on user values and needs |
The app being developed through this research protocol aims to address these limitations by incorporating evidence-based information, testing extensively with users, citing authoritative sources, and allowing customization based on personal values and preferences.
Creating an effective decision support app requires more than just programming skills. It draws on a diverse toolkit of research methods, theoretical frameworks, and technical components:
| Toolkit Component | Function | Application in Prenatal Screening App |
|---|---|---|
| Mixed Methods Research | Integrates qualitative and quantitative approaches | Combines interviews (why) with surveys (what) for complete understanding |
| Analytic Hierarchy Process (AHP) | Multiple criteria decision-making method | Helps users weigh importance of different factors in testing decisions |
| Unified Theory of Acceptance and Use of Technology (UTAUT) | Predicts technology adoption | Ensures app aligns with user expectations and capabilities 2 |
| Shared Decision-Making (SDM) Framework | Guides collaborative patient-provider decisions | Structures information to prepare women for conversations with healthcare providers |
| Iterative Usability Testing | Identifies and resolves interface problems | Refines app design through multiple testing cycles with real users |
The development of this analytical mobile app represents more than just technological innovation—it signals a broader shift toward person-centered care in maternity services. By giving women evidence-based information in an accessible format and helping them clarify their values, such tools have the potential to transform the prenatal experience from one of anxiety and confusion to one of empowerment and confidence.
The ultimate goal is not to replace healthcare providers with technology, but to create a collaborative ecosystem where digital tools prepare patients for more productive conversations with their clinical team. As one researcher noted, these tools "will contribute to high-quality shared decision making between pregnant women and their health care team" 1 .
The promise of this research extends beyond prenatal care—it offers a model for how we might approach many complex medical decisions where personal values and scientific evidence must be carefully balanced. In an era of increasingly personalized medicine, such tools may become essential bridges between technical medical information and the unique individuals who must apply it to their lives.