---
title: Managing Diabetes with Six Sigma and Statistics, Part II
description: Managing Diabetes with Six Sigma and Statistics, Part II
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# Managing Diabetes with Six Sigma and Statistics, Part II
Minitab Blog Editor | 11/16/2012
Topics: [Healthcare](https://blog.minitab.com/en/blog/tag/healthcare), [Six Sigma](https://blog.minitab.com/en/blog/tag/six-sigma)
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In my last [post](https://blog.minitab.com/blog/real-world-quality-improvement/managing-diabetes-with-six-sigma-and-statistics-part-i?hsLang=en), I discussed how quality professional Bill Howell chose to manage his diabetes diagnosis by treating it as a Six Sigma project. Since a key metric in controlling his disease was keeping his blood glucose levels below 125 mg/dL, he tested his blood using a meter three times per day and then charted his data to see how his levels changed over time. He ensured his measurement systems were producing reliable data, and assessed the potential causes for his increased blood glucose levels. He then chose to focus on the causes he could control and analyze himself.
## **Tracking Daily Calories**
One of these causes was his unhealthy diet. To minimize his symptoms, Howell’s doctor recommended following a daily 1,800-calorie diet, which included 50 grams of fats and 200 carbohydrates. Using bar charts with reference lines showing daily limits, he tracked each day’s total calories, fats, and carbohydrates. The charts helped him keep his diet in check, and showed him where making diet changes might help him meet other project goals, such as keeping his cholesterol down.
## **Analyzing the Data**
After recording and graphing several months of daily blood glucose levels and diet information, Howell analyzed his data to identify sources of variation. To determine if his three daily blood tests produced the same average level, he ran an ANOVA (**Stat** > **ANOVA** > **One-Way**) in [Minitab](http://www.minitab.com/products/minitab/).
The results revealed that the evening blood sample average, taken before dinner, was statistically lower than the morning and night average readings. The analysis also suggested that the evening reading was more uniform, because it had a lower standard deviation than the other times of day.
Howell also wanted to identify how process inputs (calories, fats, carbohydrates, and pills consumed) affected his process output (blood glucose levels). A four-panel scatterplot (**Graph** > **Scatterplot**) revealed a clear relationship between the number of blood glucose-lowering pills consumed and blood glucose levels. The plot shows that it took about 30 pills for Howell to reach target levels of around 100 mg/dL.
## **Relying on Control Charts**
To identify gaps between current performance and goal performance, Howell used [Control Charts](https://blog.minitab.com/blog/understanding-statistics/what-control-chart-should-i-use?hsLang=en) (**Stat** > **Control Charts**) to graph his diet, pill intake, and glucose levels in relation to predetermined upper and lower bounds. If his data fell outside of the bounds, Howell knew that his process changed, and he could adjust accordingly.
The Individuals Control Chart (**Stat** > **Control Charts** > **Variables Charts for Individuals** > **Individuals**) below shows the total calories Howell consumed in a two-month span. The chart reveals a stable process and shows that he met his caloric intake requirements the majority of the time, with the exception of one data point falling above the upper control limit (UCL). On this day, Howell consumed more calories than his target caloric intake of 1,800 calories, so he ate fewer calories the following day.
Howell also used Xbar-R Charts (**Stat** > **Control Charts** > **Variables Charts for Subgrops** > **Xbar-R**) to evaluate the spread between the three daily blood glucose test results (lower chart) and the average of test results for each day (upper chart). Both charts show medication level (either 1 or 2 pills per day) in relation to time.
## **Under Control?**
Howell’s approach to managing his disease was very thorough, but how well did he meet his key performance objectives? By tracking his dietary intake and following his doctor’s prescribed diet, exercise, medication, and blood testing plan, he brought his daily blood glucose level down to the 125 mg/dL target level. Just two months after starting the project, his long-term process level average in December was several points below the target, at 116.3 mg/dL.
To make sure his blood test results fell into the predefined specification limits (70 mg/dL – 150 mg/dL), Howell used [Minitab’s Process Capability Analysis](http://support.minitab.com/en-us/minitab/17/topic-library/quality-tools/capability-analyses/basics/uses-of-capability-analysis/) (**Stat** > **Quality Tools** > **Capability Analysis**). He found that 97.85% of his test results met the criteria for success.
By continuing to follow his process, Howell eventually weaned himself completely from the medication he initially took to lower blood glucose levels. By August 2010, he was able to maintain stable blood glucose levels without any pills. He attributes this change to controlling his diet and recording and charting everything he ate.
Howell says he is healthier than he’s been in years! He’s dropped nearly 45 pounds and has seen an almost complete reduction in all of his symptoms, including dry mouth, blurry vision and inability to sleep.
## **Have you ever used Six Sigma methods to manage a health problem?**
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