honors thesis - final draft
TRANSCRIPT
EFFECTS OF DIETARY PROTEIN AND AEROBIC EXERCISE ON FUNCTIONAL CONNECTIVITY IN BRAIN REWARD CENTERS: A RESTING-‐STATE fMRI STUDY
By
Lexie Buchs
A Thesis Submitted in Partial Fulfillment Of the Requirements for an Undergraduate Degree with Honors
(Dietetics)
The College of Health and Human Sciences
Purdue University
May 2015
West Lafayette, Indiana
Approved by:
Reader: Richard Mattes, Ph.D.
Reader: Tara Henagan, Ph.D.
___________________________________________________________________
Honors Research Mentor: Wayne Campbell, PhD
2
ABSTRACT
The Salience Network (SN) interprets internal and external stimuli for emotion,
homeostatic regulation, and reward. The Default Mode Network (DMN) reflects resting
state brain activity. Previous data have demonstrated a disruption of these networks in
obesity. The purpose of this study was to examine the effects of dietary protein and aerobic
exercise on resting state activity in the SN and DMN using functional Magnetic Resonance
Imaging (fMRI) in 8 women ages 18-‐45 years old with a BMI of 30 to 40 kg/m2. On testing
days, breakfast and lunch were identical while dinner meals varied in protein (Normal
Protein: 15% vs. High Protein: 30% of energy as protein). Total energy intake on testing
days was prescribed at approximately 80% of the participants’ estimated daily energy
requirements to stimulate one day of moderate energy restriction. Participants completed
a pre-‐dinner scan five hours after lunch. After the pre dinner scan, subjects either rested or
exercised for 30 minutes at 60% of their estimated VO2max. Dinner was consumed
immediately after exercise or rest. The postprandial fMRI scan was completed one hour
after dinner. The independent component analysis did not reveal a SN but did reveal a
DMN. However, DMN activity was not influenced by meal consumption, acute aerobic
exercise, or the amount of protein at dinner. Resting state brain activity may not be
influenced by acute interventions and therefore long term inventions may be necessary for
normalizing resting-‐state neural activity in obese women.
3
ACKNOWLEDGEMENTS
I would like to thank Dr. Campbell for his guidance and support throughout this
project and for giving me many opportunities to learn about research. Thank you to Drew
Sayer for patiently mentoring me and for all of his help with this project. Without your
guidance and direction, I would not have been able to complete the honors degree. Thank
you to Greg Tamer for completing the data analysis and for providing his expertise
throughout the study. I would also like to thank the study participants for their dedication
and compliance to this study. This study was funded by the Indiana CTSI.
4
TABLE OF CONTENTS
Abstract …………………………………………………………………………………………………………..…………….2
Acknowledgements ………………………………………………………………………………………..………………3
List of Tables and Figures …………………………………………………………………………….………………...5
Introduction ………………..…………………………………………………………………………….………………......6
Subjects and Methods …………………………………………………………………………..…………………..........9
Results ……………………………………………………………………………..............................................................12
Discussion ……………………………………………………………………………......................................................13
References ……………………………………………………………………………......................................................17
Appendix …………………………………………………………………………….........................................................26
5
LIST OF TABLES AND FIGURES
Table 1: Subject Characteristics.…………………………………………………………………………………….19
Table 2: Dinner (High Protein or Normal Protein) …………………………………………………………20
Figure 1: Study Design …………………………………………………………………………………….……………22
Figure 2: Default Mode Network …………………………………………………………………...………………23
Figure 3. Pre-‐Meal Default Mode Network Activity………………………………………...……………….24
Figure 4. 1-‐Hour Post-‐Meal Default Mode Network Activity……………………………………..……..25
6
INTRODUCTION
Increased activity in a brain region results in a locally increased blood response in
that area and also an increased ratio of oxygenated to deoxygenated blood. Functional
Magnetic Resonance Imaging (fMRI) scan detects the difference of magnetization in
oxygen-‐rich versus oxygen-‐poor blood [1]. The resultant blood flow response is detected
by the fMRI scan as an increase in the blood-‐oxygen-‐level-‐dependent (BOLD) contrast, and
this is used as a marker of brain activity.
The human brain is organized into networks and the intrinsic activity of these
networks can be measured in the resting state using fMRI. These networks are important,
because it is becoming increasingly evident that they are organizational features of the
brain [2]. The Salience Network (SN) and the Default Mode Network (DMN) are two
networks that have been shown to be associated with feeding behavior. The DMN consists
of the posterior cingulate cortex, cuneus/precuneus, medial prefrontal cortex, medial
temporal lobe, and inferior parietal cortices. The SN consists of the anterior cingulate
cortex and insula. The DMN reflects baseline brain function in the resting state. The SN
reflects feeding behavior and reward and involves assessing internal and external stimuli.
Previous studies have found activation of the DMN and SN to be increased in overweight
and obese individuals in comparison to lean individuals [2]. Results from previous studies
have led to the idea that abnormal or increased activation in these networks may
contribute to overeating, and there is also a correlation between obesity and activation of
these networks [3]. Understanding these networks in overweight and obese individuals
and how acute and long-‐term changes in network activity are associated with food intake
7
behavior would be helpful when strategizing how to normalize network activity to reduce
overeating.
Moderate increases in dietary protein [4] and exercise [13] are common strategies
for weight control and therefore may represent potential interventions for normalizing
resting activity in obese individuals. For example, a 6-‐month exercise intervention
decreased resting state activity in the DMN although the intervention did not change
resting state activity in the SN [2]. However, the effects of acute exercise on the resting
state activity of the SN and DMN, or whether dietary protein modulates resting state
activity of these networks, have not been investigated. The purpose of this study is to
investigate the acute effects of aerobic exercise and dietary protein on the resting state
activity in the SN and DMN.
The broad aim of the study is to determine the acute effects of dietary protein intake
and aerobic exercise on resting state activity in the SN and DMN of obese women. Our
decision to include only obese women was guided by previous research demonstrating
greater neural responses to visual food cues in obese compared to healthy-‐weight
individuals [5-‐10] and also in women compared to men [11]. We hypothesize that network
resting state activity will be decreased 1-‐hour after consuming dinner compared to the pre
dinner assessment. We further hypothesized that a high protein dinner will elicit a greater
reduction in resting state activity compared to a normal protein dinner. Acute aerobic
exercise will result in a relatively greater resting state activity compared to rest.
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The rank order of resting state SN and DMN activity under all conditions and time points is
hypothesized to be:
NPEx > NPR > HPEx > HPR
NPR: Normal Protein/Rest
HPR: High Protein/Rest
NPEx: Normal Protein/Exercise
HPEx: High Protein/Exercise
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SUBJECTS AND METHODS
Subjects
Potential participants were recruited from public advertisements (flyers). Study
inclusion was based on the following criteria: 1) Women ages 18-‐45 years; 2) body mass
index between (BMI) 30-‐40 km/m2; 3) non-‐smoking; 4) not diabetic; 5) not pregnant or
lactating; 6) weight stable (± 3kg) for 3 months; 7) not severely claustrophobic; 8) and
willing to eat study food. Due to the use of the MRI scanner, participants with implanted
pacemakers and/or automated defibrillators or any ferromagnetic metal implanted in their
body were excluded from the study.
There were 41 total contacts, of which 11 women were screened for inclusion
criteria. Of these, 9 women were approved and began the study. Eight women completed all
study procedures.
The Purdue Biomedical Institutional Review Board approved all study procedures.
All subjects provided written informed consent regarding purpose, procedures, and
potential risks of the study. Each subject received monetary compensation for
participation.
Baseline Assessments
BMI (kg/m2) was determined by measuring the participants weight and height.
These measurements were completed at the Clinical Research Center at Purdue University.
The YMCA cycle sub-‐maximal exercise test was used to estimate each participant’s
maximal oxygen consumption.15
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Experimental Design and Procedures
The study consisted of five testing days for each participant. On the first testing day
the sub-‐maximal exercise test was completed. The remaining four testing days were
completed in random order and each testing day was separated by at least seven days. The
following four experimental conditions were evaluated: normal dietary protein with rest
(NPR), high dietary protein with rest (HPR), normal dietary protein with exercise (NPEx),
and high dietary protein with exercise (HPEx). On testing days, breakfast and lunch were
consumed in the metabolic research kitchen and dinner consumed at the Purdue MRI
Facility. Breakfast, lunch, and dinner provided approximately 20%, 30%, and 30% of the
participants estimated energy requirement, respectively. Total meals provided to the
participants included approximately 80% of the estimated daily energy requirement to
simulate one day of moderate energy restriction. Breakfast and lunch were identical on all
testing days but dinner meals varied in macronutrient distribution. The macronutrient
distribution of breakfast and lunch were 15% protein, 60% carbohydrate, and 25% fat. The
normal protein (NP) dinners were 15% protein, 60% carbohydrate, and 25% fat, while the
high protein (HP) dinner provided 30% of energy as protein, 45% carbohydrate, and 25%
fat., (Table 2) . Subjects were blinded to the protein level of the dinner meals. Dietary fat
intake was held constant and carbohydrate intakes adjusted to offset differences in protein
intake for the HP and NP dinners. On two of the four testing days participants pedaled on a
cycle ergometer for 30 minutes at 60% of their VO2max. On the other two testing days
participants rested for 30 minutes in a waiting room at the MRI facility. Participants
arrived at the Purdue MRI Facility on each of the four testing days at 5 pm. The study
design is found in Figure 1.
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Appetite Questionnaire: On testing days, participants rated their appetite (hunger and
fullness) every hour from 8am until 5pm as well as immediately before and after 1)
consumption of meals, 2) the exercise/sedentary activity, and 3) fMRI scans. Appetite was
rated using a 100-‐mm quasilogarithmic visual analog scale, with descriptors ranging from
“barely detectable” to “strongest sensation imaginable of any kind” [12].
Brain Scan using fMRI: Participants lay in a supine position and closed their eyes with no
external interaction but were instructed to stay awake. Participants were scanned in a 3
Tesla MRI scanner (GE Signa HDx). The entire head was scanned, and the areas of interest
were the SN and DMN.
Statistical Analysis: Independent Component Analysis (ICA) was utilized to identify
resting state networks (SN and DMN). This analysis was completed using the AFNI
software (available from: http://afni.nimh.nih.gov/). Repeated measure ANOVA (Mixed
Procedure) was used to examine main effects of exercise (exercise vs. rest), protein (high
vs. normal), time (before vs. 60 minutes after dinner), and all interactions on resting state
networks. These analyses were completed using SAS (Version 9.2). All data are presented
as mean ± SEM. Statistical significance was assigned when P < 0.05 and Tukey-‐Kramer
adjustment was used for post-‐hoc analyses as needed.
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RESULTS
Subject Characteristics
According to our inclusion criteria, the 8 women who completed the study
procedures were 29 ± 3 years old and had an average BMI of 35 ± 1.1 kg/m2 (Table 1).
Salience Network
After analyzing the resting state scans, the ICA did not reveal a SN.
Default Mode Network
The DMN was revealed and is shown in Figure 2. There was no change in DMN
activity among interventions indicating that the high protein dinner versus normal protein
dinner, aerobic exercise versus rest did not have independent or interactive effects on
network activities (Figure 3 and Figure 4). The ANOVA model demonstrated trend
(unadjusted p=0.0454, adjusted p=0.1134) for an increase in DMN activity 1-‐hour after
eating when subjects rested before dinner. However, this was not statistically confirmed
after correcting for multiple comparisons.
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DISCUSSION
The present study evaluated the effects of protein consumption and aerobic exercise
on the acute activity of two resting-‐state reward networks, the SN and DMN. Acute changes
in these two networks have never been studied. We hypothesized there would be a general
meal-‐induced reduction in SN and DMN activity 60 minutes after dinner. We further
hypothesized that a high protein dinner would result in a greater reduction in resting state
activity compared to a normal protein dinner. Acute aerobic exercise would result in a
relatively greater resting state activity compared to rest.
These hypotheses were based on previous research showing dietary protein [4] and
aerobic exercise [5] influencing subjective appetite sensations. Previous research has
shown that acute higher protein diets increase satiety in comparison to lower protein diets
and this results in a decreased energy intake [4]. A long-‐term high protein diet has been
shown to result in weight loss [16]. The relationship between dietary induced
thermogenesis and satiety [4], specifically because the thermic effect of protein is greater
then fat and carbohydrate, may be the reasoning behind dietary protein’s satiating effects.
Previous research has also demonstrated that aerobic exercise influences subjective
appetite and energy balance, though the results are sometimes conflicting [13]. Further, it
has been suggested that exercise effects on appetite may differ in men versus women;
specifically exercise has a tendency to increase hunger in women relative to men [13].
Sensations of appetite may be influenced by activity in DMN and SN-‐related brain
structures [2,14]. Also, exercise training has previously been shown to decrease DMN
activity [2]. This did not occur in this study, but instead there were no significant changes
in DMN activity after meal consumption and among interventions. These results suggest
14
that acute interventions may not influence resting state brain activity and therefore long-‐
term inventions may be necessary for normalizing resting-‐state neural activity in obese
women. Another possibility is that greater intensity, duration, and caloric expenditure of
exercise may be necessary to elicit acute changes in brain activity.
Looking at Figures 3 and 4, it seems that primarily the high protein with rest
condition drove the trend for an increase of DMN activity on resting days. These results are
contrary to our hypothesis of a greater reduction of DMN activity with a high protein meal.
However, the increase in DMN activity was not statistically confirmed after correcting for
multiple comparisons. The independent component analysis did not reveal a SN, and
therefore intervention effects on SN activity could not be evaluated.
A previous study assessed the effects of a 6-‐month exercise training intervention on
the DMN and SN in overweight and obese males and females. DMN activity was decreased
following the 6-‐month exercise-‐training program relative to baseline. However, greater fat
mass loss was associated with greater reductions in DMN activity [2]. This correlation
between fat loss and DMN activity cannot be used to infer causality. It is possible that
exercise training and improvements in fitness reduced DMN activity. Conversely, exercise
training may decrease fat mass, which may also decrease DMN activity. Our results show
that acute aerobic exercise, which did not influence overall fitness level or fat mass, did not
influence DMN activity. These results suggest that modulation of resting state brain activity
may be driven by adaptations to chronic exercise training rather than acute exercise.
The resting state SN and DMN are important because they process homeostatic
information. The DMN is specifically associated with self-‐monitoring behavior [3] and is
more active during interoceptive processing, which is related to processing of internal
15
stimuli. The SN is associated with the reward system and shows greater activation when an
individual is anticipating food consumption [3]. We expected to observe a SN because
previous studies have revealed this network using the same standard techniques [2, 3, 11].
However our analysis did not reveal this network.
Strengths and Limitations
The strengths of this study include extensive dietary controls and supervised
exercise sessions to ensure adherence to our diet and exercise interventions. All subjects
were blinded to the protein content of the meals, so any cognitive biases were avoided.
Our small homogenous group of subjects, obese young women, is a limitation. A
larger subject group may provide greater statistical power to detect a SN and changes in
DMN activity among interventions. Including a more heterogeneous group of men and
various age groups would increase the generalizability of these findings. Inclusion of a
normal weight group would enable comparisons of resting state brain activity in normal
weight versus obese women. Also this would allow an investigation of whether weight
status influences acute effects of exercise and meal consumption on resting state brain
activity. In this study, scanning was completed in the evenings, beginning at 5pm; whereas
most existing research completed resting state scanning in the morning. This may have
influenced our results, however further research is needed to confirm time of day effects.
Further Research
Since this pilot study was the first to test and analyze the effect of protein
consumption and aerobic exercise on acute activity in these reward networks, further
research should be done to confirm that there is no change in activity from these
interventions. Further research should especially be done with a larger subject group,
16
along with both men and women of varying BMI’s. Previous research showed decreased
reward network activity in a 6-‐month exercise training intervention [2], therefore further
research should be done to determine at what time point exercise training begins to
decrease network activity.
Conclusion
In conclusion, neither high protein meals nor aerobic exercise had acute effects on
DMN activity in obese women ages 18-‐45 years old. Conclusions cannot be made regarding
the effects of dietary protein or exercise on SN activity. Acute dietary protein and aerobic
exercise may not be modulators of resting-‐state neural activity in obese women and
therefore may not be effective strategies for decreasing resting-‐state neural activity in
obese women.
17
REFERENCES
1. Huettel, S.A., A.W. Song, and G. McCarthy, Functional magnetic resonance imaging. 2nd ed. 2008, Sunderland, Mass.: Sinauer Associates. xvi, 542 p.
2. McFadden, K.L., et al., Effects of exercise on resting-‐state default mode and salience network activity in overweight/obese adults. Neuroreport, 2013. 24(15): p. 866-‐71.
3. Garcia-‐Garcia, I., et al., Alterations of the salience network in obesity: A resting-‐state fMRI study. Hum Brain Mapp, 2012.
4. Halton, T.L. and F.B. Hu, The effects of high protein diets on thermogenesis, satiety and weight loss: a critical review. J Am Coll Nutr, 2004. 23(5): p. 373-‐85.
5. Martin, L.E., et al., Neural mechanisms associated with food motivation in obese and healthy weight adults. Obesity (Silver Spring), 2010. 18(2): p. 254-‐60.
6. Karhunen, L.J., et al., Regional cerebral blood flow during food exposure in obese and normal-‐ weight women. Brain, 1997. 120 ( Pt 9): p. 1675-‐84.
7. Rothemund, Y., et al., Differential activation of the dorsal striatum by high-‐calorie visual food stimuli in obese individuals. Neuroimage, 2007. 37(2): p. 410-‐21.
8. Stice, E., et al., Relation of reward from food intake and anticipated food intake to obesity: a functional magnetic resonance imaging study. J Abnorm Psychol, 2008. 117(4): p. 924-‐35.
9. Horstmann, A., et al., Obesity-‐Related Differences between Women and Men in Brain Structure and Goal-‐Directed Behavior. Front Hum Neurosci, 2011. 5: p. 58.
10. Goldstone, A.P., et al., Fasting biases brain reward systems towards high-‐calorie foods. Eur J Neurosci, 2009. 30(8): p. 1625-‐35.
11. Cornier, M.A., et al., Sex-‐based differences in the behavioral and neuronal responses to food. Physiol Behav, 2010. 99(4): p. 538-‐43.
12. Stubbs, R.J., et al., The use of visual analogue scales to assess motivation to eat in human subjects: a review of their reliability and validity with an evaluation of new hand-‐held computerized systems for temporal tracking of appetite ratings. Br J Nutr, 2000. 84(4): p. 405-‐15.
13. Stensel, D., Exercise, appetite and appetite-‐regulating hormones: implications for food intake and weight control. Ann Nutr Metab, 2010. 57 Suppl 2: p. 36-‐42.
14. Tregellas, J.R., et al., Altered default network activity in obesity. Obesity (Silver Spring), 2011. 19(12): p. 2316-‐21.
15. Thompson, W.R., N.F. Gordon, and L.S. Pescatello, eds. ACSM's Guidelines for Exercise Testing and Prescription. 8th Edition ed. 2010, Lippincott Williams & Wilkins: Philadelphia, PA.
16. Wycherley, T.P., L.J. Moran, P.M. Clifton, M. Noakes, and G.D. Brinkworth, Effects of
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energy-‐restricted high-‐protein, low-‐fat compared with standard-‐protein, low-‐fat diets: a meta-‐analysis of randomized controlled trials. Am J Clin Nutr, 2012. 96(6): p. 1281-‐ 98.
19
TABLES AND FIGURES
Table 1. Subject Characteristics
20
Table 2. Dinner (High Protein or Normal Protein) Macronutrient Composition1 Property High Protein Meal Normal Protein Meal Total Energy (kcals) 811.9 811.9 Protein (g, % Energy) 60.9, 30% 30.4, 15% Carbohydrate (g, % Energy)
91.3, 45% 121.8, 60%
Fat (g, % Energy) 22.6 25% 22.6, 25% 1All values are mean ± SEM.
21
Figure1: Study Design. Schematic of testing day procedures.
Figure 2: Default Mode Network. AFNI was used to create statistical parametric maps to
depict resting state Default Mode Network activity with all sessions combined (n=64 total
sessions).
Figure 3: Pre-‐Meal Default Mode Network Activity. All values are mean ± SEM. Repeated
measures ANOVA (MIXED Procedures, SAS, version 9.2) was used to test for differences in
Default Mode Network Activity on the 4 testing days. Default Mode Activity was not
different on these testing days.
Abbreviations: NPR, Normal Protein/Rest; HPR, High Protein/Rest; NPEx, Normal
Protein/Exercise; HPEx, High Protein/Exercise
Figure 4: Post-‐Meal Default Mode Network Activity. All values are mean ± SEM. Repeated
measures ANOVA (MIXED Procedures, SAS, version 9.2) was used to test for differences in
Default Mode Network Activity on the 4 testing days. Default Mode Activity was not
different on these testing days.
Abbreviations: NPR, Normal Protein/Rest; HPR, High Protein/Rest; NPEx, Normal
Protein/Exercise; HPEx, High Protein/Exercise
22
Figure 1. Study Design
23
Figure 2. Default Mode Network
24
Figure 3. Pre-‐Meal Default Mode Network Activity
0
1
2
3
4
5
6 D
MN
Act
ivity
(z-s
core
)
NPR HPR NPEx HPEx
Pre-‐Meal Default Mode Network Activity
25
Figure 4. 1-‐Hour Post-‐Meal Default Mode Network Activity
0
1
2
3
4
5
6 DMN Activity (z-‐score)
NPR HPR NPEx HPEx
1-‐Hour Post-‐Meal Default Mode Network Activity
26
APPENDICES
27
APPENDIX 1
Recruitment Flyer
28
Women Ages 18 to 45
Needed for a Research Study
Prof. Wayne Campbell
Department of Nutrition Science, Purdue University
We are looking for overweight women who would like to volunteer for a research study evaluating whether exercise performed before dinner
affects brain activity in response to viewing pictures of food. Participants will be compensated $200 for completing this study.
INTERESTED VOLUNTEERS SHOULD BE:
ü Female ü Age: 18 to 45 ü Overweight ü Not Smoking ü Not Pregnant
Measurements taken during the study will include brain activity using
functional magnetic resonance imaging, questionnaires about appetite, and a blood draw.
FOR MORE INFORMATION, contact
Drew @ (765) 494-8313 or Email: [email protected] Department of Nutrition Science, Purdue University; West Lafayette,
IN 47907
Drew
765-‐494-‐8313
John
ap
olza
n@pu
rdue
.edu
76
5-49
6-64
80
Drew
765-‐494-‐8313
Drew
765-‐494-‐8313
John
ap
olza
n@pu
rdue
.edu
76
5-49
6-64
80
Drew
765-‐494-‐8313
John
ap
olza
n@pu
rdue
.edu
76
5-49
6-64
80
Drew
765-‐494-‐8313
John
ap
olza
n@pu
rdue
.edu
76
5-49
6-64
80
Drew
765-‐494-‐8313
Drew
765-‐494-‐8313
John
ap
olza
n@pu
rdue
.edu
76
5-49
6-64
80
Drew
765-‐494-‐8313
John
ap
olza
n@pu
rdue
.edu
76
5-49
6-64
80
Drew
765-‐494-‐8313
John
ap
olza
n@pu
rdue
.edu
76
5-49
6-64
80
29
APPENDIX 2
Consent Form
31
APPENDIX 3
Appetite Questionnaire
32
APPETITE LOG Study code:___________
Please place one mark on each scale that best reflects your answer to each of the following questions at this time.
1. How strong is your feeling of hunger? 1. ____
Not at all Extremely
2. How strong is your feeling of fullness? 2. ____
Not at all Extremely
3. How strong is your desire to eat? 3. ____
Not at all Extremely
4. How strong is your “urge to eat”? 4. ____
Not at all Extremely
5. How strong is your preoccupation with thoughts of food? 5. ____
Not at all Extremely
6. How strong is your feeling of thirst? 6. ____
Not at all Extremely
33
7. How strong is your desire to eat something salty? 7. ____
Not at all Extremely
8. How strong is your desire to eat something fatty? 8. ____
Not at all Extremely
9. How strong is your desire to eat something sweet? 9. ____
Not at all Extremely
10. The shakiness of your hand is… 10. ____
Not at all Extremely
11. How strong is your grip? 11. ____
Not at all Extremely
12. How itchy is your scalp? 12. ____
Not at all Extremely
34
APPENDIX 4
YMCA Submaximal Protocol
35
YMCA Submaximal Protocol
1st Stage 150 kgm (0.5 kp)
HR: <80 HR: _____
750 kgm (2.5 kp)
HR: _____
900 kgm (3.0 kp)
HR: _____
1050 kgm (3.5 kp)
HR: _____
HR: 80-‐89 HR: _____
600 kgm (2.0 kp)
HR: _____
750 kgm (2.5 kp)
HR: _____
900 kgm (3.0 kp)
HR: _____
HR: 90-‐100 HR: _____
450 kgm (1.5 kp)
HR: _____
600 kgm (2.0 kp)
HR: _____
750 kgm (2.5 kp)
HR: _____
HR: >100 HR: _____
300 kgm (1.0 kp)
HR: _____
450 kgm (1.5 kp)
HR: _____
600 kgm (2.0 kp)
HR: _____
36
APPENDIX 5
Resume
37
Lexie Buchs Education Purdue University – West Lafayette, IN 10/2011 – Present Bachelor of Science degree with Honors, Major: Dietetics, GPA: 3.40
Study Abroad - Dublin Institute of Technology – Dublin, Ireland 1/2014 – 5/2014
Adult and Child First Aid/CPR/AED Certified Blood Born Pathogen Certified
Work Experience Wiley Dining Court – Purdue University 10/2014 – Present Food Preparation, Food Service, Meal Preparation, Cleaning and Sanitation Campbell Nutrition Science Lab – Purdue University 5/2014 – Present MRI Secondary Operator, Data Entry into Microsoft Excel, Miscellaneous Lab Work Metabolic Kitchen – Purdue University 5/2014 - 8/2014 Data Entry, Food Preparation for scientific research studies Buffalo Wild Wings – Auburn, IN 5/2012 - 8/2012
Waitress, Cashier, Greeter, Cleaning and Sanitation Auburn Community Pool – Auburn, IN 5/10 - 8/10 & 5/11 - 8/11 Lifeguard, Swim Lesson Instructor Brown House Restaurant – Auburn, IN 3/2010 - 7/2010 Cook, Cashier, Food Service, Cleaning and Sanitation
Volunteer Experience Data Collection for a Pantry Study in a Purdue University Nutrition Lab Fall 2014 Completed 24-hour recalls with study participants and entered data into Microsoft Excel Lafayette Soup Kitchen Fall 2014 Serve and Prepare Food Mentor at the Ireland Pre-Departure Meeting 11/20/2014 Assisting students in finding housing, and preparing students to leave for Ireland Mentor at the Study Abroad Fair 9/10/2014 Advocating to interested students about the perks of studying abroad and answering questions Ecuador Medical Mission Trip 12/15/2012 - 12/23/2012 Assisted doctors and nurses in hospitals and visited children in orphanages Delta Zeta Painted Turtle 5K – Benefitting the Starkey Hearing Foundation 4/27/2013 Organized and participated in the race event Delta Zeta Turtle Tug - Benefitting the Painted Turtle Camp 10/2011 & 10/2012 Organized the competition, Team Leader Delta Zeta ‘Bowlarama’ - Benefitting the Starkey Hearing Foundation 4/2011 Organized the tournament, facilitated the event
Organizations & Societies Accomplishments & Awards Academy of Nutrition & Dietetics Honors Society.org Purdue University Nutrition Society Phi Sigma Theta Honors Society Purdue University Caduceus Club National Society of Collegiate Scholars Saint Michael’s Church Parishioner Intel International Science Fair 1st Place Air force Award 2011, Competitor 2010 & 2011
4546 Cr 16 Waterloo, IN 46793 � 260 908 1652 � [email protected]