Bioelectrical Impedance Analysis and Vasoconstriction Taylor Guffey Lauren Morgan Harry Han Shelby Hassberger Daniel Kim Elizabeth Morris Rachel Patel Radu Reit Taylor introduces

Problem Statement & Purpose Develop a hypothesis in which we test a factor, other than misuse and malfunction, that affects the analysis of body fat percentage. The purpose of this study is to evaluate whether the temperature of the room can affect the body fat percentage reading for an individual. Lauren -There is no reliably accurate device to measure body fat % -Figure out what factors are hindering this accuracy

Hypothesis Groundwork Buono study finds ambient air temperature affects BF% reading 20°C difference1 Why air temperature? Deghan study concludes vasodilation decreases BF% reading2 Increase in skin temperature Therefore, vasoconstriction increases BF% reading Decrease in ambient air temperature to decrease skin temperature

Hypothesis Null Hypothesis Alternative Hypothesis There will be no difference in the readings of body fat percentage from 24oC to 4oC as measured by the bioelectrical impedance analysis. Alternative Hypothesis There will be a statistically significant increase in body fat percentage readings from 24oC to 4oC as measured by the bioelectrical impedance analysis. Lauren -If p < .05 then the null hypothesis is rejected.

Pilot Study Follows trend suggested by Deghan Mean Hot Room 19.3 Cold Room 21.6 Standard Deviation 4.274 3.511 T-score 1.96

Sample Size Sample size derived from pilot study 23 estimated for statistical significance 24 used in experiment Error with input of subject A1 data Taylor -Sample size derived from pilot study -24 used because in the first trial we recognized an error when inputting data into the BIA -BMED1300 students only due to IRB restrictions

Materials/Methodology 2 groups, 12 subjects each 24°C hot room (PBL room in Whitaker) BIA readings=Control Space heater to maintain hot room temperature 4°C cold room (in IBB) Thermometer used to monitor room temperature Read and sign consent form Given Instruction sheet Assigned alphanumeric ID on name tag Given Data card with corresponding ID Offered a jacket for experiment (both rooms)

Materials/Methodology Clock to record time of subject’s entry Height taken with meter stick Weight taken with spring scale Survey given to determine ineligible subjects Omron HBF-360 Fat Analyzer to measure body fat % of subject http://image3.examiner.com/images/blog/wysiwyg/image/omron_HBF-306.jpg

Methodology Two groups Time limited to 3 hours 38 minutes per trial A1-12 and B1-12 Time limited to 3 hours 38 minutes per trial 2 trials Subjects enter hot room 30 seconds apart Last 2 subjects enter 1 minutes apart

Simulation Ten Minutes Later Ten Minutes Later Waiting Area Height Survey & Time Table Weight Heater BIA Reading Station Chair Door Waiting Area Ten Minutes Later Ten Minutes Later Waiting Area Lauren -Subject enters, time is recorded -height station, height written on data sheet -weight station, weight written on data sheet -sent to time and survey table -fill out gender and age on data sheet -survey given, subject sits down -waits 10 minutes -goes to BIA reading station -BIA taken twice for accuracy -subject is escorted to cold room -time recorded as subject enters -subject waits 10 minutes -BIA taken twice again -Subject is allowed to leave experiment Time & BIA Reading Station Door Warm Room 24oC Cold Room 4oC

Data Subjects Height Weight (lbs) Gender Age Hot BIA Cold BIA A1 5’10” 6' 2" 184 Male 19 13.5 17.35 A2 5' 8.5" 148 18 15.9 17.65 A3 5' 2" 137 Female 26.35 27.55 A4 5' 11" 179 16.65 16.7 A5 5' 5" 141 29.7 30.5 A6 5' 8" 165 20 18.25 19.05 A7 5' 9.75" 166 17.25 A8 5' 4.75" 139 25.1 A9 5' 8.25" 135 17.7 18.7 A10 155 A11 6' 0" 164 13.2 15.6 A12 5' 11.5" 182 19.45 20.6 B1 6' 2" 11.95 13.65 B2 5' 5.25" 29.15 29.3 B3 5' 10" 151 7.5 9.85 B4 6' 4" 211 13.95 14.6 B5 5' 10.75" 153 14.7 16.95 B6 6' 1" 186 18.05 18.9 B7 163 13.7 14.4 B8 170 11.3 13.1 B9 175 24.5 25.6 B10 5' 11.75" 138 9.65 11.05 B11 6' 1.5" 171 10.35 11.75 B12 5' 10.5" 195 18.6 19.95 Taylor -This is the information inputted into the BIA device and recorded. -The Hot BIA and Cold BIA numbers are the averages of the two readings taken in each room. -Subject A1 was identified as an input error on our part, therefore was thrown out

T-Score Calculation XD = Mean of the differences Lauren -as you can see here, we used this specific t-score formula where we plugged in the mean of differences, standard deviation of differences, and the sample size to compute our 9.608 t-score mentioned in the previous slide. -mu not = 0 as if there was no difference between hot and cold XD = Mean of the differences SD = Standard Deviation of the differences n = Sample size μ0=Population mean

Statistical Analysis Student’s Matched-Paired One-Tailed T-test Statistics t-score 9.608 p-value 1.2423E-09 Standard Deviation 0.617402438 Mean of Difference 1.237 Taylor -Dependent Student’s one-tailed t-test… matched pairs because of one sample pool tested twice -t-score of 9.608 gives the p-value below… t-score calculation will be shown on next slide p-value is smaller than .05, therefore our null hypothesis can be rejected. Power is 1 therefore type II errors are 0% (ask Parry if this is acceptable to say) ****A TYPE II (beta) ERROR is when the null hypothesis is not rejected which in fact it should be Null Hypothesis is rejected p < .05 Critical T-value=1.717 Required Mean of Difference ≥ .221

Analysis Critical T-score = 1.717 Mean of Difference = 0.221 Taylor Critical T-score = 1.717 Mean of Difference = 0.221 Our T-score = 9.608 Mean of Difference = 1.236

Outliers Q1 Q2 Q3 Lauren White – minimum to maximum Red- outlier range Median is about 1.25 Interquartile range is .75 Symmetric for the most part No plots beyond whiskers, so no outliers

Discussion Null hypothesis is rejected Statistically significant increase in BIA readings Average increase in BF% reading by 1.237% Data supports the alternative hypothesis Taylor

Improvements Smaller sample size Multiple devices per room Small p-value Multiple devices per room (same device used on subject throughout) Lauren

References 1Buono, M. J., Burke, S., Endemann, S., Graham, H., Gressard, C., Griswold, L., et al. (2004). The effect of ambient air temperature on whole-body bioelectrical impedance. Physiological Measurement, 25(1), 119-123. 2Dehghan, M., & Merchant, A. (2008). Is bioelectrical impedance accurate for use in large epidemiological studies? Nutrition Journal, 7(1), 26.

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