Merlin Mosaic
Summary
As a Type 1 Diabetes patient I want to understand my "full health and activity picture" since it will help me manage my day to day disease with the help of an integrated data picture combining existing data streams (Continuous Glucose Monitor, Diet, Activity, Sleep, Weather, Mood...)
Description
I want to understand my "full health and activity picture" since it will help me manage my day to day disease better. I already have my glucose data coming from my CGM . But I want to understand what else is happening and is there a correlation to what I eat, what activities I do , the weather, my mood and so on with my glucose level. The idea is to capture all these disparate data points automatically / passively together in a dairy so I can view a full picture in my dairy. Points of information: readings from my CGM; what I ate (scan the food labels perhaps for auto entry and sugar calculation?); my activity / steps ; time of day; weather.
Thematic Area
4 -Measuring Causal Factors
Patient Champion
Team Composition would benefit from
ML, human factors, DB, UX / ; Type 1 diabetes expert, web / mobile app, Data science.
References/Resources
https://developers.glooko.com/docs/statistics/cgmhttps://developer.dexcom.com/overviewhttps://stackoverflow.com/questions/3117645/how-to-get-food-product-data-from-barcodehttps://clipdrop.co/https://scholarcommons.scu.edu/cgi/viewcontent.cgi?article=1041&context=cseng_seniorhttps://www.barcodelookup.com/https://siftfoodlabels.com/https://openweathermap.org/Strava API: https://developers.strava.com/https://developer.garmin.com/
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4. Measurement of causal factors
Today you may feel fine, but tomorrow you may feel it is the worst day of your life. What changed? Is it something you did or is it something that happened to you? These are questions that patients ask themselves all the time. What external and internal factors influence my well-being? These factors may not always be obvious, such as, for example, the weather. Patients would like to be able to measure these influences automatically and match them to their subjective well-being. This could provide them with deeper insight into their situation while also providing an unbiased (e.g. without flaws of memory) overview of the course of their disease, which could support them in meetings with HCPs.
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