KnowBody
A research-based health tracking application developed by a University of Toronto Computer Science student and a Med School student. We invested 12 hours searching and finding the correct researches, then hand-picked 18 different studies through hours of hard work. We do not use LLMs or other AI tools to suggest anything to users—we believe they are more valuable than that. Our algorithms are built only and only from those 18 hand-picked researches.
Features:
Research-Backed Daily Tracking: Monitor vital health metrics through a curated dashboard where every "Daily Health Tracking" goal is tied to a specific clinical study.
The Life Expectancy Engine: Using longitudinal data from our research pool, our algorithm calculates health projections based on current lifestyle habits grounded in evidence, not AI vibes.
Medical Report Generation: Translate your raw data into a structured format that you can actually take to a doctor’s appointment to facilitate better clinical conversations.
Evidence-Based Streaks: Motivation matters. Our streak system isn't just a gamified number; it rewards consistency in habits that our research shows have the highest impact on long-term wellness.
Future: In the future, we can leverage tools like Semantic Scholar or PubMed APIs to fetch new studies. However, instead of letting an AI summarize them blindly, we will implement a Peer-Review Verification Layer where only studies with high "Impact Factors" and reproducible results are ingested into our algorithms.
We translated complex medical jargon into a "Pulse" dashboard that any user can understand. We built a fully functional health tracker for ConUHacksX that prioritizes human expertise and scientific fact over trending tech.
Our Key Research Sources: National Academies of Sciences (Hydration) USDA Dietary Guidelines (Alcohol) CDC/Surgeon General (Smoking) WHO Physical Activity Guidelines (Exercise) American Heart Association (Heart Health) Journal of Medical Internet Research (Health Tracking)


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