BME100 f2017:Group4 W0800 L3

BME 100 Fall 2017	Home People Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3 Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6 Course Logistics For Instructors Photos Wiki Editing Help
OUR TEAM Name: Monica De La Cruz Name: Ally Coen Name: Brock Connolly Name: Jose Loya Name: Christian Cuciniello LAB 3 WRITE-UP Descriptive Stats and Graph Heart Rate Data Temperature Data Inferential Stats Temperature Readings Data A paired two-tailed T-Test was performed on the Temperature data. 'P-Value: 1.47792E-31 Since this p-valve is extremely small, there is significant evidence to support that the oral temperature readings and the Spree temperature readings are significantly different. This means that the Spree temperature readings were not very good. Heart Rate Readings Data A paired two-tailed T-Test was performed on the Heart Rate data. 'P-Value: 0.427116193 Since this p-value is larger than 0.05, the data shows that there is not a significant difference between the Pulse Ox heart rate readings and the Spree heart rate readings. This means that the two data sets are alike. Since the data sets are very similar, the data shows that the Spree heart rate readings are good. Design Flaws and Recommendations There is a significant difference between the temperature readings of an oral thermometer and the temperature readings of the Spree headpiece. The Spree headpiece does not provide accurate readings and they deviate a large amount from the gold standard temperature readings. The reasons for this difference could be that the temperature reader is not close enough to the head/forehead that is being measured. The temperature cannot be measured if the thermometer is not directly on the part being measured. There is a difference between the heart rate readings of a Pulse Ox reader and the heart rate readings of the Spree headpiece. The difference between the two is not as big as the temperature readings. The pulse rate readings from the Spree headpiece were more accurate when being compared to the gold standard readings. A way to better improve these readings is to place the pulse reader closer to the head, almost pressed against, to ensure that the most accurate results are measured. Experimental Design of Own Device The experimental design for our device would have to consist of different constraints. 1. Sample would consist of at least 300 diabetic patients from around the United States. Our sample will be 50% male and 50% female. Our sample with consist of a variety of ages, body weights, and race. 2. All patients will get their glucose levels measures by both the SImpeSweat glucometer(our device) and a blood sample glucose test. 3. For the the SimpleSweat glucometer test, the SimpleSweat glucometer will be turned on and placed on patient's upper portion of the arm to begin inducing heat and generating glucose readings. 4. Immediately after the SimpleSweat glucometer test, the patient will then receive a blood glucose test to get the known, accurate measurement of glucose in the blood. 5. A paired two-tailed T-test will be performed on the two sets of data to determine how accurate the glucose readings of the SimpleSweat glucometer were. We decided to test our device using this method to ensure that the we were getting the best results possible. The blood glucose test would serve as a gold standard for our testing. A blood test would be the closest way to find the accurate amount of glucose in the blood. The SimpleSweat glucometer takes a bit of time to warm up the skin enough to produce sweat, but as soon as the measurements were recorded, the patient would then use a blood test to compare results. This will help us better improve our device, and how well it actually works. A sample size of 300 patients is a reasonable amount of people because if we were to have less participants, our statistical analysis would not be very accurate when basing it on less people.