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

2. 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

3. 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

4. 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.

5. 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

6. 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

7. 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)

8. 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

9. 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

10. 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

11. 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

12. 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 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

13. Statistical Analysis Student’s Matched-Paired One-Tailed T-test
Statistics
t-score
9.608
p-value
1.2423E-09
Standard Deviation
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 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

14. 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

15. 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

16. 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

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

18. 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),
2Dehghan, M., & Merchant, A. (2008). Is bioelectrical impedance accurate for use in large epidemiological studies? Nutrition Journal, 7(1), 26.

19. Questions?