Short answer
When designing fertility apps for pregnancy prevention, ensure the core functionality is scientifically sound and clearly communicated, rather than relying heavily on aesthetic appeal or secondary features.
- Field
- Innovation & Markets
- Source
- mHealth (2018)
- Method
- Exploratory pilot study using a web-based survey.
- Sample
- 1,000 participants
- Evidence
- Strong effect
Users of fertility apps for pregnancy prevention prioritize science-based information and accurate fertile day identification, indicating a need for design that emphasizes scientific credibility and core functionality. This innovation & markets research insight is drawn from a 2018 study published in mHealth. Using Exploratory pilot study using a web-based survey. with 1,000 participants, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing fertility apps for pregnancy prevention, ensure the core functionality is scientifically sound and clearly communicated, rather than relying heavily on aesthetic appeal or secondary features.
Fertility App Design: Prioritizing Science-Based Features for Pregnancy Prevention
Users of fertility apps for pregnancy prevention prioritize science-based information and accurate fertile day identification, indicating a need for design that emphasizes scientific credibility and core functionality.
mHealth · 2018
Key Findings
- 01A significant portion of women use or intend to use fertility apps for pregnancy prevention.
- 02Users across different knowledge levels highly value science-based information and accurate identification of fertile days.
- 03A majority of users have some level of knowledge about fertility and reproduction.
Application
Design takeaway
When designing fertility apps for pregnancy prevention, ensure the core functionality is scientifically sound and clearly communicated, rather than relying heavily on aesthetic appeal or secondary features.
How to apply
When developing or refining a fertility app, conduct user research to validate the scientific basis of features and ensure clear communication of how fertile days are calculated. Test the accuracy of the fertile day prediction algorithm rigorously.
Project actions
- 01When researching a health app, look for evidence of its scientific backing.
- 02Consider how you will present scientific information to users in a clear and understandable way.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Explores an under-researched area of digital health.
- +Combines quantitative and qualitative data for a richer understanding.
Limitations
Self-reported data can be unreliable. The sample may not be representative of all users.
Reliability & validity
Reliability could be improved with a larger, more diverse sample and standardized questionnaires. Validity is supported by the qualitative data complementing survey findings, but self-report introduces potential bias.
Think critically
How might the perceived accuracy of fertility apps influence user behavior and potentially lead to unintended pregnancies, and what design interventions could mitigate this risk?
Design Principles
"User trust in health-related applications is built upon transparency and demonstrable scientific accuracy."
Understanding user priorities in health-related applications is crucial for market success. Designing fertility apps that align with user expectations for scientific accuracy and reliable core functions can lead to greater adoption and potentially better health outcomes.
What This Means for Your Design
People want fertility apps to be based on real science and accurately tell them when they are most fertile if they want to avoid pregnancy.
How to use in your project
- 1.Use this study to justify the importance of scientific accuracy and evidence-based design in your own health-related design project.
Add to My Project
Quick Cite
Paragraph starter
Research indicates that users of fertility apps for pregnancy prevention prioritize science-based features and accurate fertile day identification. This suggests that design efforts should focus on ensuring the scientific validity of the app's core functions and clearly communicating this to users to build trust and ensure effective use.
Source
mHealth
User profile and preferences in fertility apps for preventing pregnancy: an exploratory pilot study
journal · 2018
View sourceQuestions About This Research
- What does the research say about fertility app design: prioritizing science-based features for pregnancy prevention?
- When designing fertility apps for pregnancy prevention, ensure the core functionality is scientifically sound and clearly communicated, rather than relying heavily on aesthetic appeal or secondary features. Evidence: mHealth (2018).
- Why does "Fertility App Design: Prioritizing Science-Based Features for Pregnancy Prevention" matter for design?
- Understanding user priorities in health-related applications is crucial for market success. Designing fertility apps that align with user expectations for scientific accuracy and reliable core functions can lead to greater adoption and potentially better health outcomes.
- How can designers apply this research?
- When designing fertility apps for pregnancy prevention, ensure the core functionality is scientifically sound and clearly communicated, rather than relying heavily on aesthetic appeal or secondary features.
- What were the main findings?
- A significant portion of women use or intend to use fertility apps for pregnancy prevention.. Users across different knowledge levels highly value science-based information and accurate identification of fertile days.. A majority of users have some level of knowledge about fertility and reproduction.
- What research method was used?
- Exploratory pilot study using a web-based survey. with 1,000 participants.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2018 journal from mHealth.
- What should I do differently in my next project?
- When developing or refining a fertility app, conduct user research to validate the scientific basis of features and ensure clear communication of how fertile days are calculated. Test the accuracy of the fertile day prediction algorithm rigorously.
- What are the limitations?
- The study was a pilot and relied on self-reported data, potentially introducing bias. The recruitment method via Facebook may not represent all potential users.