ClimateCare

Inspiration

Climate change is making extreme heat, cold, and humidity fluctuations more frequent—and older adults are among the most affected. During my work in community elder care and long-term care research, I repeatedly encountered the same questions from older adults: “Is today safe for me?”, “How should I dress?”, and “What precautions should I take?”

Most weather apps give only numbers, not guidance. I wanted to build something simple, accessible, evidence-informed, and genuinely useful for older adults in their daily lives. That is how ClimateCare—ThermoGuard for Seniors began.

What it does

ClimateCare transforms three basic weather inputs—temperature, humidity, and wind—into:

• a seven-level thermal risk classification
  
• personalized health and safety recommendations
  
• risk-triggered YouTube educational prompts (heatstroke / hypothermia prevention)
  
• indoor and outdoor dressing guidance tailored for older adults
  

The tool is fully low-code but intentionally designed with an AI-ready data structure, enabling future machine-learning risk prediction and an AI wardrobe module that can recommend outfits based on user-uploaded clothing images.

How we built it

We used a low-code platform (Glide) to rapidly build an MVP with:

• simplified thermal formulas:
  $$HI = T + 0.1\times RH$$
  $$WC = T - 0.2\times Wind$$
  
• a 7-level thermal risk model combining heat and cold indicators
  
• a structured database for recommendations and dressing logic
  
• lightweight video prompts embedded directly into the interface
  

The system is organized around structured environmental features, which makes it AI-ready for future expansion. I also created a horizontal workflow and an AI roadmap flow to guide interface logic and version planning.

Challenges we ran into

• Designing recommendations that were simple enough for older adults yet still meaningful
  
• Creating dressing logic that works for both heat and cold risk levels
  
• Maintaining accessibility while keeping the architecture AI-expandable
  
• Building a useful MVP under tight time constraints
  
• Ensuring clarity with limited space in a mobile-friendly UI
  

Accomplishments that we’re proud of

• Building a fully functional climate-health MVP in just a few days
  
• Clear, science-informed thermal indicators
  
• A complete end-to-end flow from input → risk classification → guidance
  
• Incorporating health education, not just weather data
  
• Designing an AI upgrade roadmap, including smart outfit generation
  
• Staying focused on a vulnerable population that truly benefits from accessible tools
  

What we learned

• Low-code tools can deliver high-impact health solutions when the logic is well designed
  
• Weather data needs translation into actionable guidance to be meaningful
  
• Older adults benefit most from simple, structured, predictable advice
  
• Designing for accessibility improves the overall system clarity
  
• Building an AI-ready foundation early makes future upgrades far easier
  

What’s next for ClimateCare — ThermoGuard for Seniors

We plan to evolve the MVP into an AI-augmented tool with:

• ML-based personalized thermal risk prediction
  
• generative AI adaptive safety recommendations
  
• an AI wardrobe module that analyzes user-uploaded clothing to generate safe, weather-appropriate outfits
  
• integration with official weather alerts for automated safety notifications
  
• multilingual support for broader accessibility
  
• expanded datasets for more accurate local risk profiling
  

Ultimately, our goal is to build a scalable, equitable climate-health assistant that helps older adults stay safe every day.

Built With

• lowcode