Whole Communities–Whole Health (WCWH) is a research institute at UT Austin that studies how biological, environmental, and social factors influence the well-being of children and families in Eastern Travis County.
What is LENA
LENA is a technology system that uses a small wearable device and software to measure children’s language environments and interactions. The system processes the collected data into measures such as child vocalizations and conversational turns.
Within the WCWH study, these findings are shared with participating caregivers through individualized reports to help them understand their child’s language experiences.
What Problem were We Solving
The original LENA reports contained technical language, dense explanations, and complex data visualizations.
For caregivers with different literacy levels and data familiarity, important findings could feel overwhelming rather than useful. Users needed help understanding what each metric represented, how to interpret developmental percentiles, and which information mattered most.
Problem Statement
"How might we make child development data more accessible, understandable, and actionable for caregivers?"
I began by auditing the original report and working with the research team to understand its content, constraints, and communication challenges.
The initial review revealed that:
key findings competed with supporting details
technical terms lacked clear explanations
visuals required considerable interpretation
the report was not optimized for mobile viewing
Deliverables
After
01
Redesigned the report for small screens with a clearer reading flow and section structure.
Added a clickable table of contents so caregivers can quickly jump to relevant sections.
02
Clearer Data Interpretation
Surfaced key metrics in a scannable overview before users explore detailed results.
Added plain-language explanations, percentile guidance, and research context to help caregivers understand what the results mean.
03
Supportive Visual Communication
04
Actionable Next Steps
Presented talking tips in a more approachable format to encourage everyday language interactions.
Curated community resources with practical details such as audience, cost, format, and contact information.
05
Because child-development results can feel personal or concerning, I also designed emotional supportive content to help caregivers relieve unnecessary anxiety.
Added contextual language explaining why a single recording may not fully represent a child’s typical day.
Clarified data privacy and gave caregivers a direct way to contact the research team with questions.
Validation & Impact
User Testing
I developed moderator scripts and scenario-based tasks to evaluate how caregivers navigated and interpreted the redesigned report.
During each session, participants were asked to think aloud as they explored the report, interpreted graphs and percentiles, explained results in their own words, and described moments of confusion or confidence
This project taught me that accessible information design is not simply about reducing text. It requires balancing research accuracy with the needs, confidence, and lived experiences of the people receiving the information.
I also learned that comprehension and confidence are closely connected. A user may understand a graph but still feel uncertain about interpreting its significance. This shifted my focus from designing only for task completion to designing for reassurance, trust, and meaningful action.
1
Keep working on qualitative coding and thematic analysis in MAXQDA.
2
Conduct the remaining round of usability testing.
3
Refine language, visualizations, and recommendations.
4
Prepare the report for implementation in the app Hornsense.