Correlating Blood Glucose, Physical Activities, Sleep Sessions with Google Fit and Charts
Project Overview
The correlation between blood glucose levels, physical activities, and sleep sessions offers a valuable perspective for individuals with diabetes. By visualizing this relationship in a graph, it becomes easier to identify patterns, optimize activity levels, and understand the impact of sleep on blood glucose control. This comprehensive overview enables individuals to make informed decisions, adjust their lifestyle, and enhance their diabetes management strategies for improved health outcomes.
My role as the android developer involved setting up authentication and synchronization with Google Fit to streamline data to the API. The main challenge was implementing a lazy loading chart capable of handling heavy data to ensure a seamless user experience.
Problem
Managing blood glucose levels is crucial for individuals with diabetes, but understanding the impact of physical activities and sleep patterns on blood glucose control poses a significant challenge. Without a comprehensive solution to correlate these variables, individuals struggle to make informed decisions, optimize their lifestyle choices, and mitigate the risk of hypoglycemia. This knowledge gap creates a need for a system that can effectively correlate and visualize the relationship between blood glucose levels, physical activities, sleep sessions, and the variation of blood glucose level.
Solution
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To address the problem of understanding the correlation between blood glucose levels, physical activities, sleep sessions, and the risk of hypoglycemia, a solution was developed to provide individuals with valuable insights for effective diabetes management and hypoglycemia prevention.
I utilized WorkManager to schedule automatic synchronization of Google Fit data, seamlessly integrating it with our API. To visualize the correlation of this data with the user's blood glucose levels, I implemented interactive charts and comprehensive report pages, offering detailed insights.
Enhanced Automatic Syncing with WorkManager: To implement streamline data synchronization with Google Fit, I employed WorkManager, a powerful background task management library. This solution ensured automatic and efficient syncing of data between the user's device and Google Fit, reducing manual intervention and improving the overall data syncing process.
Comprehensive Charts for Health Metrics:The application included interactive and graphical charts for tracking heart rate, blood glucose levels, meals, physical activities, and sleep segments. These charts provided users with visual representations of their health data, enabling them to monitor trends, identify patterns, and make informed decisions regarding their overall well-being.
Lazy Loading Mechanism Using Nodes:To handle heavy data and ensure a seamless user experience, I implemented a lazy loading mechanism using nodes for the charts related, blood glucose, insulin, meals and physical activities. This approach allowed the application to load data progressively as the user interacted with the chart, preventing any performance degradation and ensuring smooth data presentation.