Intelligent Insights to Provide Better Understanding of Health
Transforming passive data storage into an active health management tool
Role: Lead designer | Responsibilities: UX, UI | Duration: Jun - Sept 2025
Challenge: The biometric results screen on our web portal and mobile app displayed raw biometric data without context, forcing users to interpret trends themselves
Users of our remote monitoring tools (blood pressure cuff and weight scales) receive biometric data that they can view in our web portal or mobile app — blood pressure, pulse, weight and BMI. However, they were lacking the context to understand what those numbers actually meant for their health.
At the time, both our web portal and mobile app treated the results screen as simple data repositories. Users could select a biometric category and view a basic line graph alongside a chronological list of measurements for their chosen time period.
While this provided a centralized place to store health data from their remote monitoring devices, it placed the entire burden of interpretation on the user leading to disengagement.
Opportunity: Surfacing intelligent trend analysis would reduce cognitive load and transform passive data storage into an active tool for health management
By surfacing trend analysis when users come to the results screen, we could help them quickly understand their current health trajectory without manually comparing past readings.
This would reduce cognitive load, increase engagement on the platforms, and empower users to make more informed health decisions.
The MVP Solution:
An insight summary that appears at the top of the results screen, providing users with immediate context for their biometric data.
This project is iterative and continues to evolve. This case study focuses on iteration 1, which launched in October 2025. My goal for the MVP was to answer two critical user questions: What does this data mean for my health? And Am I doing better or worse than before?
Key information includes:
Comparison values: Previous vs current averages with the exact amount of change
Trend direction: Visual indicators showing whether measurements increased or decreased
Health classification: Clear labels showing result categories like ‘Elevated’ or ‘High’ and whether that classification changed over time
Plain language insight: A brief summary explaining the trend
Overall, this eliminated the need for users to manually calculate trends or interpret raw numbers, giving them actionable insights at a glance.
To validate my design decisions before launch, I conducted a brief user survey with 289 respondents, focusing on comprehension and usefulness.
User research
Survey questions:
What was your initial reaction to this new results summary feature?
How easy was it to understand the health information shown?
What would make this information more helpful for managing your health?
Which additional health insights would be most valuable to you?
Key findings:
Positive reception: 52% of participants reported being “very interested” in the new feature, validating the need for contextualized health data
Excellent comprehension: 71% found the information “very easy” to understand, confirming that my approach of combining numerical comparisons, plain-language summaries, and health classifications was effective
Enhancement opportunities: Users identified areas where additional context would increase value such as diet/exercise recommendations, expected normal ranges, and actionable solutions to address health issues
Future feature priorities: Comparison to healthy ranges based on age, time of day patterns in blood pressure readings, and progress tracking toward personal health goals
Impact:
The strong validation results gave the team confidence to move forward with the proposed design approach. User feedback also informed our iteration 2 roadmap.
Reflection and Next Steps
At the time of writing this case study, this feature launched 1 month ago. We’re still in the early stages of understanding its impact so I don’t yet have quantitative data to share on how users are engaging with the feature.
What we are presently tracking:
Feature adoption rate (% of users viewing insights)
Time spent on updated results screen experience vs the previous experience
Correlation between insight views and continued platform engagement