When my colleague forwarded me an email about a summer computer science research experience at Rice for teachers I thought, “this MUST be a gimmick”. NO WAY they’re going to have a bunch of secondary teachers actually try something difficult. As I read more about the SWITCH program and its partnership with PATHS-UP I couldn’t contain the hopeful smile as I crossed my fingers and hit the submit button on my application. PATHS-UP vision is to make real changes in the health care outcomes of underserved populations by developing cost-effective technology solutions that can be delivered at the point-of-care(POC). Two things I care deeply about, revolutionary tech AND combating the crippling effects of poverty on the health of African-American and Latino populations in the US. Against the backdrop of COVID-19, the disproportionate effect of this virus on African American and Latino populations highlights the importance of that vision. We must be vigilant in sharing information in order to close the gap.
We started the week with some background readings and a presentation from Rice graduate student Souptik Barua. His research applied machine learning algorithms to the data obtained from Continuous Glucose Monitors (CGM) devices to identify important biomarkers in study participants, mostly Mexican-American, who are at risk of, or already have Type 2 diabetes. The CGM data for these study participants was monitored for 2 weeks and then the participants were provided a steady supply of fresh vegetables, at no cost to them, to incorporate into their diet for 3 months. After that interval, another 2 weeks of CGM data was obtained in order to be able to examine the health impact of the vegetables and to see if anything else of interest would reveal itself through the data.
Turns out, vegetables are good for you and so is data! While the study is only a small sample size one thing was made obvious. Timing is everything! Living with Type 2 diabetes means monitoring your blood sugar levels and precisely when blood sugar levels are measured can have a significant impact on a diagnosis. Data showed that study participants with Type 2 diabetes showed a drastic rise in blood sugar levels that peaked around 1 hour after meal time compared to their pre diabetic and normal counterparts. Data taken 2 hours after meal time showed a normalized trend across all groups.
This outcome highlights a very important point for me: When it comes to making a healthcare diagnosis or deciding on a treatment doctors, researchers and scientists need as much data as they can get. The human body is a complex network of systems and seemingly countless data points. Technology, like the CGMs and machine learning, is the nexus that can lead to cost effective solutions for medicine’s most challenging problems and societies most vulnerable communities.
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