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6/07 p1/CIRC/DaleDueindd-p1

NUTRITION BEHAVIORS AND BODY COMPOSITION IN COLLEGIATE
FOOTBALL PLAYERS EXPOSED TO AN OFF-SEASON TRAINING AND PILOT
NUTRITION EDUCATION PROGRAM
By

Heidi Lynn Clark

A THESIS
Submitted to
Michigan State University .
in partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE
Department of Food Science and Human Nutrition

2007

ABSTRACT
NUTRITION BEHAVIORS AND BODY COMPOSITION IN COLLEGIATE
FOOTBALL PLAYERS EXPOSED To AN OFF-SEASON TRAINING AND PILOT
NUTRITION EDUCATION PROGRAM
By

Heidi Lynn Clark

' While both nutrition and body composition play a role in athletic
performance, little research has evaluated the interplay between nutrient intake
or dietary behaviors and percent (%) body fat in athletes. The primary objective
was to evaluate macronutrient intake, dietary behaviors and % body fat changes
relative to guidelines and weight goals (gain, loss, maintain) in collegiate football
players. Methods included group and individual sports nutrition education by
Registered Dietitians. Dietary intake and body composition were measured in
January and July to evaluate the relationship between macronutrient intake, and
dietary behaviors, and % body fat. Declines in sample size from 99 (January) to
40 (July) reflected incomplete reporting and team roster changes. In January,
dietary intakes met all macronutrient recommendations (except for fiber), but not
sports nutrition recommendations. From January to July, there were significant
(p<0.05) increases in pre- and post-workout snacking. Of the three subgroups,
only the weight loss group exhibited a decrease in °/o body fat (p<0.05); overall
team % body fat also decreased. Snack frequency was positively correlated with
% body fat. While positive changes were seen in snacking behavior, overall
reported intakes fell Short of some sports nutrition recommendations, and there

was no relationship with % body fat and most dietary variables.

Copyright by
HEIDI LYNN CLARK

2007

ACKNOWLEDGEMENTS

I would like to acknowledge the support and guidance of my committee
members: my committee chair, Joe Carlson, PhD, RD for his mentorship and
facilitation at every step of the current project; Lorraine Weatherspoon, PhD, RD,
for her help in navigating the muddy waters of graduate school; Won Song, PhD,
RD, for her encouragement and support of my efforts to attend the graduate
program at MSU; and Jim Pivamik, PhD, MPH, for his insight and wisdom, and
for opening his lab group and resources to me. I would like to thank my fellow
graduate students and lab mates in Human Nutrition, Saori Obayashi, PhD, RD,
Ock Kyoung Chun, PhD, Seung-Yeon Lee, PhD, Amy Saxe, MPH, RD, Julie
Plasencia, Megumi Murashima, Sumathi Venkatesh, and Tracie Bolton for their
friendship and support. Kinesiology doctoral students Jeremy Knous and Lanay
Mudd provided invaluable support and friendship to me. Scott Sehnert RD, part
of the SNAPP team, provided access and insight into the MSU football team, and
David Solomon PhD, helped my cluttered mind sort through the statistics.

Above all, I would like to acknowledge the support of my husband, Mike,
and my children, Emily and Michael. Without their patience with my frazzled
days and long nights, I could not have maintained the focus needed to
accomplish my master’s degree in such a short time. I would never have
considered this endeavor were it not for the funding provided by the United
States Air Force, and mentoring from so many, including Lt Col Lisa Schuette,
MS, RD, Lt Col Craig Olson, MS, RD and Maj Denise Lockhart, MS, RD.

Soli Deo Gloria!

iv

TABLE OF CONTENTS

List of Tables ........................................................................... iv
I. Introduction ........................................................................... 1

Aims and Hypotheses ........................................................ 3
ll. Literature Review ..................................................................... 6

A. Diet and Performance: Recommendations and Guidelines... 6
B. Measurement of Dietary Intake: in the General Population

and in Athletes ............................................................. 7
C. Diet Characterization of the Athlete .................................. 10
D. Diet Characterization of Collegiate Football Players .............. 12
E. Diet and Performance/Body Composition: Macronutrients..... 14
F. Diet and Performance/Body Composition: Micronutrients ......21
G. Diet, Performance and Body Composition: Meal Frequency
and Eating Patterns ....................................................... 22
H. Body Composition: Methods of Assessment ...................... 27
I. Body Composition: Assessment in Athletes ....................... 33
J. Body Composition and Athletic Performance ....................... 34
K. Body Composition and Performance in Football .................. 35
L. Implications of Literature Review ...................................... 39
Ill. Methods .............................................................................. 40
Study Design .................................................................... 40
Independent Variable/Intervention ........................................ 40
Dependent Measures ......................................................... 47
Statistical Analyses ............................................................ 55
IV. Results ............................................................................... 61
Subjects .......................................................................... 61
Nutrient Intake Compared with Recommendations ..... 63
Change in Nutrient Intake ................................................... 65
Change in Weight .............................................................. 72
Change in Percent Body Fat ................................................ 73
Correlations ..................................................................... 75
V. Discussion ........................................................................... 76
Study Strengths and Limitations ........................................... 89
VI. Conclusions and Implications .................................................. 92
Appendices ............................................................................... 95
Appendix A: Educational Materials from Group Meetings .......... 95
Appendix B: Forms Used in Individual Consults ...................... 118
Appendix C: Dependent Measures ....................................... 133
Appendix D: Correlation Matrix ............................................ 139
References ............................................................................... 141

Table 1.1:
Table 2.1:
Table 2.2:
Table 4.1:
Table 4.2:

Table 4.3:

Table 4.4:
Table 4.5:
Table 4.6:
Table 4.7:
Table 4.8:

Table 4.9:

Table 4.10:
Table 4.11:

Table 4.12:
Table 4.13:
Table 4.14:
_ Table 4.15:

LIST OF TABLES

Summary of nutrient recommendations for health and
performance .............................................................. 21
Dates, topics, and attendance for group education ............ 42
Measures and location of data collection ......................... 47
Baseline team characteristics ....................................... 61
Baseline nutrient intake for MSU football players with a
completed 24-hour recall, compared to recommendations. 63
Baseline nutrient intake expressed as grams per kilogram
of body weight for MSU football players with a complete
24- hour recall. . 63
Change In reported meal and snaCk intake In WMSU fOOtball
players during the off-season ........................................ 65
Percent of MSU football players reporting pre- and post-
workout snack intake during the off-season ..................... 65
Change in reported macronutrient, fruit, vegetable and grain
intake in MSU football players during the off-season ......... 67
Change in the percent of MSU football players meeting
macronutrient recommendations during the off-season ...... 68
Change in reported fat and saturated fat intake in MSU
football players during the off-season .............................. 69
Change in the percent of MSU football players meeting
total fat and saturated fat recommendations during the off-
season ..................................................................... 69
Changes in reported kilocalorie intake at baseline and
endpoint for the entire team ......................................... 70
Changes in reported kilocalorie intake at baseline and
endpoint by weight goal group ....................................... 71
Change in weight over time by weight goal group .............. 72
Change in % body fat over time, entire team .................... 73
Change in % body fat over time, by weight goal group ....... 73
Estimated energy expenditure, football related and basal... 74

vi

I. INTRODUCTION

Nutrition plays an important role in optimizing athletic performance and
health. Specific sports nutrition guidelines range from endurance to power sports,
and are outlined in the joint position paper published by the American Dietetic
Association, Dietitians of Canada and the American College of Sports Medicine
(1 ). Nutrition factors emphasized to enhance performance include specific intake
recommendations for carbohydrate, protein, fat and hydration, as well as meal
patterns and timing of intake (1-3). Meal pattern recommendations are designed
to meet energy needs, optimize nutrient intake amounts and quality, and
enhance glycogen storage during recovery from glycogen-depleting activities. A
meal pattern with frequent meals and snacks is advocated, with emphasis on the
timing of protein and carbohydrate intake before and after training (1, 2, 4).

Research on collegiate football players reveals deficits in both nutrition
knowledge and nutrition behaviors with respect to current nutrition
recommendations (5, 6). Previous research on the Michigan State University
(MSU) football team in 2003 indicated deficits in nutrient intake when contrasted
with United States Department of Agriculture (USDA) Guidelines. Deficits
included low calcium and vitamin D intake, with carbohydrate intake comprising
only 45% of total kilocalories during the pre-season, compared to a
recommended minimum of 50% (1) . In addition, players consistently consumed
greater than the recommended 30% of kilocalories from fat, with preseason fat

intake at 37% (7).

Even though current Sports nutrition recommendations emphasize meal
frequency and timing there has been limited research on their influence on
athlete body composition. In non-athlete adults, meal frequency and meal timing
have been associated with body weight and body composition. Factors such as
breakfast consumption and increased meal frequency have been associated with
reduced risk of obesity in some (8-10) , but not all studies (11). One difficulty in
interpreting obesity research is the use of BMI or body weight as a measure of
obesity without quantifying body fatness or a % of lean body mass. Using BMI or
body weight to label an individual as over-fat is in often inappropriate, especially
in young adult athletes who may have a high BMI but have a low % body fat (12).

Body composition, specifically the percentage of fat free mass, is an
important characteristic associated with performance, with a leaner physique
associated with improved performance in a variety of Sports including rowing,
field hockey and soccer (13-15). In a study of NCAA Division 1A collegiate
football players, increases in body weight were positively correlated with bench
press and power clean performance. Increases in body fat were negatively
correlated with performance in the power clean and vertical jump. For lineman
specifically, increased % body fat was also inversely correlated with performance
in the 40 yard dash and the 20 yard shuttle (16).

Body composition, as reflected by the percentage of fat free mass, and
nutritional intake are linked to athletic performance. However, no published

research has evaluated the relationship between dietary factors such as

macronutrient intake and meal frequency, and body composition changes in
athletes.
The specific aims of this study are:
Aim 1. To identify baseline (January) macro nutrient intake and dietary
behaviors of football players and compare intake to nutrition
recommendations for health and performance.

Hypothesis 1 (H1): At baseline, the reported dietary behaviors and
macronutrient intakes of MSU football players during the off-season will be
significantly deficient when compared with the current sport nutrition
guidelines from the American College of Sports Medicine/American
Dietetic Association, and recommendations for health and disease
prevention from the Institute of Medicine (Dietary Reference Intakes).

Aim 2: To examine changes in nutrient intake and dietary behaviors
among the MSU football players over the course of an off-season nutrition
intervention (January/baseline to July/endpoint), the Spartan Nutrition and
Performance Program (SNAPP) pilot study.

H2a: There will be significant increases in playerS’ meal and snack
frequency

H2b: More individuals will meet snack timing recommendations:
pre-workout, within 45-60 minutes; and post- workout, within 60 minutes.

H2c: There will be Significant increases in carbohydrate (simple
and complex, % of total kilocalories and grams per kilogram), fiber (grams),

fruit, vegetable and grain intake (servings).

H2d: There will be result in significant decreases in total fat and
saturated fat intake (% of total kilocalorie intake).

H2e: Protein intake will be adequate (21.4 g/kg) and will not
change.

H2f: Total reported energy intake will correspond with the weight
goal group assignment, a significant increase in kilocalories in the weight
gain group, a decrease in kilocalories in the weight loss group, and no
changes in kilocalorie intake in the weight maintenance group.

Aim 3: To evaluate changes in body weight and body composition, as
measured by air displacement plethysmography (the BOD POD, Life
Measurement Instruments, Concord, CA), in MSU football players from
baseline to endpoint over the course of the off-season SNAPP pilot study.

H3a: Body weight will significantly increase for those assigned to
the weight gain group; significantly decrease for those assigned to the
weight loss group; and remain unchanged for those in the maintenance
group.

H3b: There will be a significant decrease in % body fat for the
entire team.

H30: Decreases in % body fat will differ significantly by
group: weight gain group < weight maintenance group < weight loss
group.

Aim 4: To identify relationships between macronutrient intake and dietary

behaviors, and changes in body composition.

4a: Change in % body fat will be inversely correlated to
carbohydrate (complex and simple, g/kg and % of total kilocalorie intake),
fiber (grams), protein (g/kg), fruit, vegetable and grain intakes (servings),
and to meal and snack frequency and timing.

H4b: Change in % body fat will be directly correlated to total fat

and saturated fat intake (% of total kilocalorie intake).

II. LITERATURE REVIEW

The following literature review will discuss nutrition recommendations for
sport and performance, measurement of dietary intake in the general population
and athletes, and diet characterizations of athletes, college students, and
collegiate football players. Literature relevant to macro- and micronutrient intake,
meal patterns and athletic performance will be reviewed, and various methods of
body composition assessment will be discussed, with an emphasis on air
displacement plethysmography, eg, the BOD POD. Body composition
assessment in athletes, and specifically in football players, will be discussed, as
well as the relationship between body composition and athletic performance, with
an emphasis on research on football performance and body composition.
A. Diet and Performance: Recommendations and Guidelines

According to the position paper published jointly by the American Dietetic
Association, Dietitians of Canada and the American College of Sports Medicine,
“...athletic performance, and recovery from exercise are enhanced by optimal
nutrition.”(1). The position paper gives recommendations for optimal nutrition for
athletic performance and health. Recommendation that are emphasized, to
promote athletic performance and recovery, include levels of carbohydrate,
protein, fat and fluid intake, and appropriate total energy intake. Specific
macronutrient recommendations are 6-10 grams of carbohydrate per kilogram of
body weight; 1.2-1.7 grams of protein per kilogram of body weight; 20-35% of

kilocalories from fat, and adequate fluid to maintain euhydration. Nutrient—dense

food choices are emphasized, with a goal to meet or exceed the DRl’s for all
micronutrients.

For sport and athletic performance, the goal is enhancement of
characteristics associated with success at a given task—increased endurance,
greater strength. Burke et al preface their nutrient recommendations for athletes
by saying “An important goal of the athlete’s everyday diet is to provide the
muscle substrates to fuel the training programme [sic] that will achieve optimal
adaptation for performance enhancements”(4). In a discussion of nutritional
goals and strategies for athletes, Maughan states that “Nutritional goals
associated with training Should include: maintaining energy supply to the
working muscles and other tissues; promoting tissue adaptation, growth and
repair; promoting immune function and resistance to illness and infection;
rehearsal and refinement of competition strategies”(3).

B. Measurement of Dietary Intake: in the General Population and in
Athletes

The selection of appropriate tools to measure dietary intake depends on
. the research question to be answered, the time and resources available, planned
statistical analysis, and the population to be studied (17, 18). Research with non
athletes validating the USDA 5 step multiple pass method for dietary recall
compared recalled energy intake to actual energy intake. All participants
consumed all meals at the test facility, with snacks available for take-out; all food
items were of known nutrient content, which allowed for measurement of actual

food intake. The day after each subject consumed food at the study facility, they

were called at home and asked to recall their food intake on the previous day.
Subjects were supplied with measuring guides to assist in portion estimation.
The difference between actual and recalled dietary intakes were outside the
range of :l: 2 SD for only 10% of female participants (19), while the diet recalls of
male participants were all within 2 SD, and indeed fell within a fairly narrow range
of 8-9.3% (20). Surrao et al compared four food intake methods; seven day
weighed food intake, 24-hour recall, and two versions of a food frequency
questionnaire. Subjects were 10 young and 10 older women with sedentary
lifestyles. Total energy expenditure (TEE) was measured via doubly labeled
water, and the simultaneous collection of weighed food intake. Measurements of
food intake via 24-hour recall and food frequency questionnaire were obtained
before and after the TEE measurement. When compared, the four different
methods all gave food quotient values within 1% of each other in both age
groups of women. The authors conclude that using a 24-hour recall or food
frequency questionnaire can provide comparable data to more time consuming 7
day food record methods (21 ).

The dietary patterns and nutrient intakes of athletes have been measured
in a variety of ways. Single or serial 24-hour recalls have been used, often in
conjunction with food frequency questionnaires (22, 23). Food records, or food
diaries, are also used, typically for 3 or 7 days (24-26). Questionnaires or
surveys tailored for specific research or specific populations—such as specific

age groups or athletic events—may also be developed and utilized (27, 28).

Occasionally, more complex methods such as weighing foods may also be used
(29).

There are many potential sources of error with any method of dietary
assessment. Error in recording food eaten, either by over or under reporting,
may impact the validity of the tool used. A review by Burke et al discusses the
various factors which may contribute to underreporting of intakes in athletes as
well as the general population. Body dissatisfaction; the desire by subjects to
report an “acceptable” amount of food consumed or omitting of “unacceptable”
foods; and forgetting to report snacks or second helpings were all discussed as
contributing factors to underreporting. In addition, potential for under eating in
response to keeping a food record also exists (30). A review of dietary
assessment methodologies in athletes was undertaken by Magkos and
Yannakoulia (18). They emphasized the fact that under reporting of habitual
energy intake is widespread among athletes, and the magnitude of under
reporting appears to increase as energy requirements increase.

Additionally, error in interpreting the information recorded may result in
increased variation. Research by Braakhuis et al assessed the variability found
in estimations of dietary intake, using self reported dietary intake by 52 athletes,
and coding by a total of 53 sports dietitians. Each food record was coded by 3 to
5 of the dietitians, and 13 sets of 7 day food records were coded; this resulted in
1456 athlete-days of data. The within- and between-athlete and dietitian coding
was done for each dietary nutrient, and the variances were combined to produce

a coefficient of variation (typical variation as a percent of the mean). Variability in

the mean for 7-day estimates of a nutrient was 2-3 times less than for a single
day. For 1-day records, the coder contributed less variability than the true athlete
variability, but for a 7-day record, athlete and coder variability was similar. The
authors recommended that the variability contributed by both the athlete and the
coder be considered when dietary assessment of athletes is done, either for
research or for counseling (31 ).
C. Diet Characterization of the Athlete

Despite concrete, specific, primarily evidenced-based sports-nutrition
recommendations, multiple studies have demonstrated the failure of athletes—of
many ages, sports and skill levels—to achieve these nutritional
recommendations (30). A study of adolescent soccer players (ages 14 to 16 yrs)
revealed dietary patterns that were excessive in fat and protein, and inadequate
in carbohydrate (29). A similar study by Ruiz et aI looked at a broader age range,
evaluating the dietary practices of soccer players ages 14 to 21 years (24). They
found that the youngest players had a higher calorie intake per kilogram of body
mass (which would be expected), and that all ages had lower-than-
recommended intakes of carbohydrates—47.4% of energy from carbohydrates in
14 yr olds versus 44.6% of energy from carbohydrates in adult players. The
authors noted that the nutritional intake among the soccer players studied was
not optimal, and that in fact the intake was poorer among adult players than
among adolescents. Similar findings have been seen in other sports. An
analysis of the dietary intake of elite aquatic athletes in Greece showed a familiar

pattern: excess fat and protein, inadequate carbohydrates. They also noted that

10

71% of males and 93% of females consumed inadequate amounts of at least one
antioxidant vitamin; this was related to inadequate fruit and vegetable intake (22).
Similarly, a study of Brazilian triathletes found inadequate carbohydrate and
micronutrient intakes, and noted insufficient number of meals eaten each day,
and inadequate intake from certain food groups (23). Seven-day dietary records
of Gaelic football players, soccer players and controls revealed carbohydrate
intake of 49%, 57%, 44.9% respectively. The authors concluded that the Gaelic
football players should increase their intake of total carbohydrate to meet their
performance needs (25).

In a study of both male and female collegiate athletes (multiple sports
represented), only 15% of athletes consumed adequate protein and only 26%
consumed adequate carbohydrate. Male athletes were more likely to exceed
recommended intakes of fat, saturated fat, cholesterol and sodium. Sixty-two %
of female athletes had a goal of losing more than five pounds; only 23% of male
athletes had Similar weight loss goals. A desire for weight loss was associated
with decreases in energy and macronutrient intake, but did not result in
inadequate micronutrient intakes (27). A study of NCAA division I female soccer
players analyzed body composition and dietary intake as well as performance
pre- and post- season. There were no significant changes in body mass or body
fat noted, but significantly greater intakes of total energy, carbohydrate, protein
and fat were consumed during the preseason. The authors assessed the
players’ caloric intake as adequate, but found their intake of carbohydrate and

some micronutrients to be inadequate. They concluded that less nutrient dense,

11

high fat and high protein foods displaced carbohydrate and nutrient-dense food
choices (26).

Contrasting findings were recently published by Croll et al. In a study of
nearly 5,000 middle school and high school students, youth involved in weight
related sports (ie, gymnastics, dance, ice skating, wrestling) ate breakfast more
frequently than their non-sport involved peers. Those involved in weight-related
and power team sports (ie, volleyball, basketball, football, baseball, hockey) had
higher mean intakes of protein, calcium, iron and zinc than their non-sport
involved peers. However, adolescent females had low calcium intakes,
regardless of sport participation (28).

D. Diet Characterization of Collegiate Football Players

Available research indicates that college students in general have less
than optimal eating habits. Research by Lowry et al characterized college
students based on weight management goals and practices. They found that
only 26% of college students reported eating more than five servings of fruits and
vegetables each day (32). These results were similar to those found by Racette
et al, who found that 70% of surveyed freshman consumed fewer than 5 servings
of fruits or vegetables each day (33). Driskell et al looked at upper and lower
level college students, and found few differences in the eating habits of these
students; only 57% reported eating breakfast, and 95% of undergraduates
reported eating fast food at least six to eight times per week (34).

Other research specific to collegiate football players indicates that, similar

to other athletes and to non-athlete college students, collegiate football players

12

frequently fail to meet sports and health-related nutrition recommendations. A
2001 study of the dietary practices, attitudes and physiological status of
freshman college football players revealed that the athletes reported eating out
4.8 times per week, with 55% of them reporting fast food as the most common
choice. The athletes demOnstrated nutrition knowledge deficits, citing protein as
the most important fuel for muscles and reporting a belief that vitamin and
mineral supplementation could increase energy levels (5). Research by Cole et
al assessed two separate 3-day food records for a group of college football
players (n = 28) and compared them to data from NHANES III (6). When
compared to the NHANES data, the football players were Shown to be
consuming a similar amount of total calories, but fewer calories from fat, and
more calories from protein. When assessed for dietary adequacy, the total
energy intake reported by the athletes were significantly lower than the estimated
4,000 to 5,300 calories/day needed to maintain weight for this active, young,
male population; however, the authors note that the athletes maintained weight
for the duration of the study, making it likely that participants were under
reporting food intake. The athletes’ intakes were also compared to the 1995
USDA Food Guide Pyramid. The football players reported consuming an average
of 1.6 servings of vegetables per day (compared to a recommendation of 3-5
servings/day), and 1.3 servings of fruit per day (compared to a recommendation
of 2-4 servings/day); servings of grain foods, meat and milk were within the range
recommended. The authors discussed the potential for recall error, particularly

the potential for underreporting, to impact the validity of their results. However,

13

they did conclude that changes in the dietary habits of these athletes would be
needed for them to sustain their high levels of physical activity. Another study
evaluating the dietary behaviors of collegiate football players assessed reported
intake via three, 24-hour recalls over the course of the football season. Similar to
earlier research this study indicated that on average fat intakes that were
excessive and carbohydrate intakes were below recommendations (36% and
48%, respectively). The athletes also reported eating breakfast only an average
of 4.3 (t 1.5) times/week, and eating fast-food 3.6 (:I: 1.3) times per week. The
team average was only 2.5 servings of fruit per day and 3.7 servings of
vegetables per day. Despite this relatively high number of servings (~6 servings
of fruits and vegetables), a high proportion of athletes, 50-75% of the sample,
reported consuming less than the URI for vitamins A, D, E, and folate, as well as
magnesium and fiber (7).
E. Diet and Performance/Body Composition: Macronutrients

Protein, carbohydrate and fat all play important, but differing, roles in
supporting performance and health in athletes. All macronutrients can contribute
' to meeting energy needs, but carbohydrates are the primary energy source,
followed by fat for most sports. Protein is used minimally for energy, and the use
of protein for energy can be related to inadequate energy intake or inadequate
intakes of carbohydrate, or both. Adequate protein is important for the building
and repair of muscle and other body tissues, and hormonal balance. The body
preferentially uses carbohydrate as the primary fuel during higher intensity

exercise (ie, sprinting) with less carbohydrate being used for lower intensity

14

exercise (ie, long-distance cycling or marathon running). The use of fat for fuel
continues regardless of exercise intensity, but the proportion of the total energy
used coming from fatty acid oxidation is inversely related to exercise intensity. If
adequate carbohydrate (glycogen) is not available in muscles to sustain
contractions at a given exercise intensity, exercise intensity will decrease to a
level where energy needs can be met by fat oxidation. Additionally, a higher
percentage of amino acids are used for gluconeogenesis (1, 3, 35, 36).

Actual recommendations for macronutrients vary, based on age, weight
and type of activity or sport. For carbohydrate, recommendations range from >
50% to >60% of total calories, or from 7 to 12 g/kg body weight (most common),
to as high as 19 g/kg body weight (1, 3, 4, 35, 36). Burke et al discourage the
use of macronutrients expressed as a percentage of total calories,
recommending instead that macronutrients be expressed as gram per kilogram
of body weight. Further recommendations are for carbohydrate intake
immediately after exercise (0-4hr), with 1.2 g carbohydrate/kg/hr consumed at
frequent intervals. Additionally, an intake of 7-12 9 carbohydrate/kg/day are
recommended for recovery after moderate to heavy training, with exercise
programs consisting of 4-6+ hr/day potentially requiring greater than 12 g/kg/day
(4). These recommendations can be very high for larger athletes, especially
when expressed as grams of macronutrient per kilogram of body weight.

For a 300 pound (136kg) football player, for example, 8 grams of
carbohydrate/kg body weight is equivalent to 1088 g carbohydrate/day. To

achieve such high levels of carbohydrate, especially in individuals with a high

15

body mass and/or high energy requirements, the consumption of high-sugar
foods (simple carbohydrates) including juices, sport drinks, such as candy, baked
goods, and sugary drinks may be needed, although the consumption of nutrient-
dense foods—such as starchy vegetables, dried fruit or fruit juices—should be
emphasized (4, 30).

Recommendations for protein intake also vary, and most data available
are based on physically active men. It is recommended that endurance athletes
consume 1.2 g protein/kg body weight to maintain nitrogen balance (1). Other
references encourage protein intake as high as 1.7 to 2 g/kg of body weight in
endurance or ultraendurance events such as marathons or triathalons (36).
Resistance training protein requirements are 1.6-1.7g/kg, to ensure the
maintenance and continued repair of lean tissue (1 ). Protein recommendations
for football players range from 1.4 to 1.7 g/kg body weight (36). For individuals
with a high body mass, such as offensive and defensive linemen who may weigh
300 pounds (136kg), protein requirements may range from 190 g to 231 gprotein
perday.

A review by Phillips et al discusses the importance of adequate dietary
protein intake (37). They make the somewhat obvious point that dietary intake of
adequate protein is essential for the maintenance of positive protein balance, and
state that the feeding-induced stimulation of protein synthesis is primarily
responsible for the maintenance of positive protein balance during the course of
a day, where muscle protein balance “undulates” between meals. Therefore, in

the absence of adequate dietary protein, positive muscle protein balance would

16

not be maintained. Resistance training is an anabolic stimulus for muscle protein,
resulting in muscle protein synthesis; when that training occurs in the presence of
protein, net protein balance is even more positive as fasted state gains are
greater and fasted state losses are less.

Rankin and colleagues examined the effect of milk consumption versus
the consumption of a carbohydrate-electrolyte beverage immediately post
resistance training, for the duration of a 10 week training program, in a group of
19 untrained men, 18-25 years (38). Muscle strength, body composition, serum
testosterone concentrations, cortisol and REE were measured prior to and at the
end of the training program. Additionally, four 3-day food records were
completed during the course of the study track dietary intake. Subjects were
ranked by preference for dairy foods, and then alternately assigned to the
treatment groups, to avoid a biasing effect of dairy intake. The beverages were
isoenergetic, with the carbohydrate-electrolyte supplement providing 5 kcal/kg,
1.25 g carbohydrate/kg plus electrolytes, and the milk beverage providing 5
kcal/kg, 0.92 g carbohydrate/kg, 0.21 g protein/kg and 0.06 g/kg, in addition to
the naturally occurring micronutrients in milk.

Analysis indicated that the groups were not statistically different at the
onset of training, and dietary intake did not Change during the course of the study,
as reflected in the 3-day food records. Both groups experienced statistically
significant gains in arm circumference, in testosterone levels and in strength.
However, there was no statistically significant difference between groups seen

for any parameters. The authors did comment on a trend toward significance,

17

favoring the milk group for body weight and gains in fat free mass. The authors
concluded that adaptations to training were similar between the carbohydrate-
electrolyte supplement and the milk supplement. While initially uninspiring, as
the component of the beverage appeared not to influence training adaptations,
this research is interesting in that it demonstrated a supportive role for a readily
available food item. Had the authors included a placebo arm and a protein-
based supplement (in contrast to the carbohydrate and protein containing milk,
and carbohydrate supplement), their conclusions may have differed.

303 and colleagues investigated the metabolism of soy versus milk protein
in humans (39). After consumption of a prescribed, standardized diet,
participants (n=16) consumed a soy or milk protein dose, 46kJ/kg, 15% protein;
both the soy and the milk protein had been previously nitrogen labeled, to allow
the nitrogen enrichment of urine and serum to be measured. The soy protein
produced an earlier and higher peak in the amino acid concentration of serum
pools. Two hours and eight hours after consumption, the level of dietary nitrogen
found in the urine was higher in the soy group, while the amount of nitrogen
retained was 74.7 +/- 4.1 % for the milk consumers, compared to 69.9 +/- 3.8%
for the soy consumers (p< 0.05). The incorporation of nitrogen into serum
protein was also higher in the soy group. The authors state that these results
support previous research which had denoted soy protein as a “fast” protein,
while milk protein has been denoted as a “slow” protein. The differences in rate
of appearance in urea and plasma are related to differences in digestion and

absorption rates, with soy protein have a more rapid transit than milk protein; in

18

particular, casein, is a “slow” protein. It has been concluded that milk protein,
digested more slowly than soy, supports muscle anabolism better than soy
protein, with the rapid digestion and absorption of soy protein leading to liver
protein synthesis, rather than peripheral protein synthesis (37).

Fat intake recommendations for athletes are less concrete. There is room
for variability, depending on the individual athlete, sport or event, and caloric,
protein and carbohydrate requirements. Adequate dietary fat is fundamental for
supplying essential fatty acids, and to promote the digestion and tissue uptake of
fat soluble vitamins and selected minerals. Fat is energy dense and often a
highly palatable energy source which also contributes to satiety. Energy from fat
can also play an important role in weight maintenance, especially for athletes
with high energy requirements (36). The performance nutrition consensus
published by the American Dietetic Association, the American College of Sports
Medicine and the Dietitians of Canada recommends 20-25% of total kilocalories
from fat with limited saturated fat intake and emphasis on poly and
monounsaturated fats (1 ). Sport specific recommendations also exist, with
football specific recommendations being 25-35% (36). Alternatively it has been
recommended that, once the requirements for protein and carbohydrate are met,
the balance of energy come from fat (3). Additional recommendations for fat
center around the use of intramuscular triacylglycerol (IMTG) as a fuel source
during moderate intensity exercise. One recommendation is for intakes of 2 g
fat/kg in addition to 6-10 9 carbohydrate/kg (40). However, it is possible such fat

recommendations could make it difficult to achieve adequate carbohydrate intake,

19

potentially impairing glycogen synthesis. Alternatively, poorly synthesized
attempts at achieving ‘adequate’ levels of all macronutrients, including such fat
recommendations, may lead to excess energy intake and subsequent undesired
weight gain.

Public health recommendations, with a goal of disease risk reduction and
prevention, do not vary significantly from performance recommendations, with a
goal of enhanced athletic and Sport performance. According to the 2005 Dietary
Guidelines for Americans, fat intake should be limited to 20-35% of total calories,
with only 10% coming from saturated fat; fiber-rich carbohydrate foods Should be
encouraged, and fruits, vegetables and whole grains should be emphasized as
carbohydrate sources (41 ). According to the Institute of Medicine (IOM) Dietary
Reference Intake (DRI) recommendations, 45 to 65% of calories should come
from carbohydrate, or a minimum of 130 grams of carbohydrate per day; 20 to
35% of calories should come from fat, and 10-35% from protein (42). See Table

1.1 to contrast these recommendations.

20

Table 1.1 Summary of nutrient recommendations for health and performance

 

 

Nutrient General Health Performance Football-Specific
IOM Nutrition
Recommendations Dunford et al (36)
(41,42) ACSM / ADA (1)
Carbohydrate 45-65% 50-75% or 5-11 8-10 g/kg
9/k9
Simple Sugars s 25% - --
Fiber (< 50 years) (same as DRI)
38 g/d men --
25 g/d women
Fat 20-35% suggest 20-25% 25%-35%
“remainder of
calories”
Saturated Fat <10% suggest <10% “heart healthy”
Protein 10-35% 1.2-1.7 g/kg 1.4-1.7 g/kg

 

F. Diet and Performance/Body Composition: Micronutrients

The major micronutrients studied in athletes and performances are those

_ which impact energy metabolism and macronutrient utilization. The vitamins

thiamin, riboflavin, niacin, pyridoxine, folate, cobalamin, ascorbic acid and

vitamins A and E collectively effect metabolism by influencing energy utilization,

serving as antioxidants, or catalyzing macronutrient synthesis or degradation (43).

There is some evidence, although not directly studied in athletes, that Vitamin D

is related to muscle strength and body stability (44). The minerals thought to be

most closely related to athletic performance are magnesium, iron, zinc, copper

21

and chromium (43). Additionally, calcium plays a critical role in muscle
contraction and nerve impulse regulation (45). There is also some evidence that
dietary antioxidants such as Vitamins E and C, carotenoids, and flavonoids play
a role in reducing the activity of the reactive oxygen species produced by
muscular exercise which may contribute to oxidative injury and muscle fatigue
(36, 46).

Micronutrient needs increase in athletes, as physical activity increases the
rates of many metabolic processes which are regulated by micronutrients.
Symptoms of micronutrient deficiency are broad, and sometimes difficult to
attribute to a single nutrient. They can range from muscle weakness and
decreased endurance with inadequate thiamin; while anemia can be a result of a
deficiency in one or more of the following: B 12, folic acid, cyanocobalimin and
iron. lrritability is a symptom of both niacin and magnesium deficiencies, while
appetite loss is linked to vitamin C and zinc deficiencies (43, 45).

G. Diet, Performance and Body Composition: Meal Frequency and Eating
Patterns

Many factors are known or suspected to impact weight and/or body
composition, including macronutrient intake, eating pattern and meal frequency.
Much of this research has been done in the area of obesity and weight
management in non-athletes, but a conservative application to the field of
performance nutrition is likely appropriate. Early research by Fabry and
colleagues supported a negative association between obesity and meal

frequency, with a ‘nibbling’ diet preferable to a ‘gorging’ diet (47). More recent

22

research assessed the impact of meal ingestion pattern on body weight, body
composition or energy utilization during weight loss. Keim et al evaluated ten
women who participated in a 12 week metabolic ward study, which was divided
into two periods. For the first Six weeks, four women had 70% of daily energy
intake provided by breakfast and lunch, while the remaining six women had 70%
of their intake provided by the dinner meal or evening snack. After the first six
weeks, participants crossed over into the alternate meal time. Weight loss was
greater when participants consumed more of their calories in the morning;
however, more lean mass was preserved when participants consumed a larger
evening meal. The authors concluded inclusion of larger evening meals may be
important to minimize the loss of fat-free mass (48). However, while one strength
of this study design was the use of a metabolic ward and 12 weeks of controlled
food intake, a limitation is the small participant pool of only women, and the
impact of the sequence of the two alternative meal patterns on mass and body
composition Changes.

A Swedish study compared the meal patterns of 83 obese women and 94
randomly selected reference women. A questionnaire assessed both time and
meal type for each intake episode during a typical day. The obese women
consumed more meals, and consumed a significantly greater number of meals
and a greater percentage of calories in the afternoon and evening/night. No
difference in breakfast intake was seen between the two groups. The authors
conclude that eating frequency and timing of meal are important considerations

in the treatment of obesity (11). However, it is important to note that “obesity” in

23

this study was assessed only based upon BMI criteria, not upon an objective
assessment of body composition.

In athletes, the relationship between body composition and eating patterns
has focused on muscle anabolism and the intake, timing and macronutrient
content of pre or post-exercise snacks. Both resistance exercise and protein
consumption are anabolic, encouraging muscle protein synthesis (37). Phillips et
al discussed the importance of protein intake in conjunction with resistance
exercise, stating that that when resistance training is combined with adequate
amino acid intake, net protein balance is even more positive than with exercise
alone or protein intake alone. In a fed state muscle protein gains in are greater,
and fasted state losses are minimized, when adequate amino acids are supplied
in conjunction with resistance training (37). Rasmussen et al examined the
impact of an amino acid and carbohydrate containing supplement ingested after
resistance exercise. Six subjects (three males) consumed either a treatment
drink, consisting of 35 9 sucrose with 6 g essential amino acid, one hour or three
hours after resistance training. Each subject participated twice, serving as
his/her own control, after being randomly assigned to either the amino acid drink
or the placebo at one and three hours. The consumption of the amino acid
containing beverage resulted in significant muscle anabolism as measured by
phenylalanine net balance, when contrasted with the placebo; this occurred
whether the beverage was consumed at one hour or three hours. The authors

concluded that the presence of essential amino acids and carbohydrate

24

stimulated muscle anabolism by increasing protein synthesis when ingested one
hour or three hours after resistance exercise (49).

In a randomized, double-blinded study, Tipton et al compared the anabolic
responses of 6 individuals provided an amino acid-carbohydrate supplement
either before or after a bout of resistance training. The supplement consisted of
500 ml solution with 35 g carbohydrate with 6 g protein. Amino acid
concentrations, uptake of amino acids, and blood flow to the leg were greater
when subjects were given the supplement before the workout compared to after,
leading the authors to conclude that a pre-workout snack was more effective at
supporting muscle anabolism than the post-workout snack. However, it should
be noted that the sample size was small and there was no trial with placebo both
pre and post (50). Further research by this same team examined the impact of
carbohydrate alone, amino acid alone, or carbohydrate and amino acid
administered together. Macronutrients were closed based at 35 g carbohydrate
and 6 g protein per 70kg, and the drink was consumed one hour after a
resistance training protocol. The study was double-blind, cross-over design with
each of the three treatments completed by seven subjects. A comparison of the
rate of appearance and disappearance of phenylalanine in the blood lead the
authors to conclude that the anabolic effect of carbohydrate and amino acid
administered together is approximately equivalent to the sum of their individual
effects, with carbohydrate creating an hyperinsulinemic environment conducive
to anabolism, and amino acids available as the building blocks required for

anabolism to occur (51 ).

25

Cribb and Hayes examined the effects of supplement timing on muscle
hypertrophy and strength development. Twenty three recreational male body-
builders were matched for maximal strength and randomized into one of two
groups a Single-blind study. One group, the PRE-POST group, consumed a
supplement immediately prior to and following their workout, while the MOR-EVE
(moming-evening) group consumed the identical supplement in the morning
before breakfast and in the evening of each training day. The supplement
contained, per 100 g, 40 g protein, 43 g carbohydrate, <05 9 fat, 7 g creatine
monohydrate (CrM); it was dosed at 1 g/kg body weight, such that an 80kg
participant would receive 32 g protein, 34.4 g carbohydrate, <04 9 fat, and 5.6 g
CrM in each serving (approximately 290 kilocalories). Participants were
instructed to maintain their habitual dietary intake, and submitted three diet
records over the course of the 10 week study. After 8-12 weeks on a training
program to insure all individuals had been training consistently, a 10 week
structured training program was initiated as part of the study protocol; the main
objective of the training was to increase strength and muscle size. Body
composition of subjects was assessed by DEXA at baseline and endpoint.
Muscle biopsies were taken at baseline and endpoint to determine muscle fiber
type, protein content and metabolite concentrations.

Statistical analysis via two-way ANOVA and t-test revealed significant
differences between the two supplement-timing groups (p < 0.05). Those in the
PRE-POST group demonstrated greater gains in lean body mass and greater

decreases in body fat percentage when compared to the MOR-EVE group. Both

26

groups demonstrated gains in strength, but the PRE-POST group experienced
greater gains in their one repetition maximum. In addition, the PRE-POST group
experienced greater gains in cross sectional area and contractile protein content
of the muscle fiber, and demonstrated higher concentrations of phosphocreatine,
total creatine and muscle glycogen within the muscle fiber. The authors
concluded that after 10 weeks of training, supplementation before and after each
workout produced Significantly greater improvements in strength and body
composition when compared with matched individuals who consumed the same
supplement at times separate from the workout. These findings apply to a
previously trained group of recreational, male, bodybuilders who continued to
consume their normal diet, and reinforce nutritional recommendations to
consume a protein/carbohydrate snack in the time immediately preceding and
following resistance training (52). This research would support a purported
relationship between snack timing and body composition, as snack intake
immediately before and after resistance training lead to greater gains in lean
body mass. However, it is important to note the inclusion of CrM in the
supplement administered, with no contrasting placebo (non-CrM, or non-nutritive
beverage) arm, which may limit the support of this research for recommendations
of non-specific carbohydrate-protein snack pre/post workout to optimize body
composition changes.
H. Body Composition: Methods of Assessment

A variety of techniques are used to assess body composition. These

methods can be divided into four categories: linear anthropometry (ie, skin fold

27

thickness); densitometry (ie, underwater weighing); Spectrometry (ie, the use of
bioelectric impedance); radiology (ie, dual-energy x-ray absorptiometry). All
methods of assessing body composition are susceptible to a degree of error,
either related to subject or administrator error (53). There is an inevitable level of
error with any device. Hydrodensitometry, or hydrostatic weighing, and
plethysmography, the technology used in the BOD POD, both assess body
volume—one with water displacement, the other with pressure changes (54).
Body volume can also be determined by scanning techniques, such as dual-
energy X-ray absorptiometry (DXA), or electromagnetic techniques. (54).
Hydrodensitometry, often considered the gold standard of body composition
assessment, is a tedious procedure and the reliability of measurements are
susceptible to both subject and technician error, in addition to the limitations of
the equipment (54, 55). The use of multiple body composition assessment
methods has been termed a “multiple component” or “multicomponent model”.
For example, hydrodensitometry or plethysmography may be used to assess fat
versus lean tissue, DXA to assess bone density, with a separate analysis of total
_ body water. Wagner and Heyward, in a review of technology for assessing body
composition, encourage the use of this multicomponent model as a true gold
standard (56).

The BOD POD uses a pressure-volume relationship to derive body
volume of a subject; the body volume of an individual is equal to the volume of air
in the empty chamber minus the volume of air in the chamber once the subject is

sitting inside it. This technology is referred to as air displacement

28

plethysmography. It is more accommodating for subjects and easier to
administer than typical water displacement techniques, resulting in reductions in
both subject and technician error (57).

Wagner et al cross-validated the measurement of body density (Db) by air
displacement plethysmography (the BOD POD) with hydrostatic weighing (HW),
in a study of 20 black men, aged 19-45 years. They also compared the % body
fat values obtained from the BOD POD and HW with % body fat values obtained
from dual-energy x-ray absorptiometry (DXA). The authors found that while the
correlation between the BOD POD and HW was high, and the regression data
met cross validation criteria, the BOD POD underestimated the average body
density of this sample by 0.00450 +/- 0.00718 g/cc, a small but statistically
significant amount. This resulted in an overestimation of the mean percent body
fat, regardless of which conversion formula was used; 1.73% overestimation with
the Schutte equation, and 1.92% overestimation with the Wagner equation (57).

A review by Fields et al looked at the use of air displacement
plethysmography (ie, BOD POD) in adults and children, compared to various
body composition assessment methods. Hydrostatic weighing, DXA,
measurement of total body water, measurement of total body potassium, and the
multicompartment model were compared to BOD POD in the research cited in
this review. The average study means indicated that the BOD POD and
hydrostatic weighing agreed within 1% body fat for adults and children. When
compared to dual-energy X-ray absorptiometry (DXA), BOD POD and DXA were

found to be within 1% body fat for adults and 2% for children. In the few studies

29

which compared the BOD POD to multi-compartment models, suggest a similar
average 2-3% underestimation by both BOD POD and hydrostatic weighing. The
authors attributed wide variations among study means to differences in
laboratory equipment, study design and subject characteristics; in some cases,
failure to follow the manufacturers protocol (ie, use of swim cap to cover hair)
was also considered a likely contributor to variation. The authors also evaluated
subjective ratings for various aspects of body composition assessment, including
cost, subject friendliness and user friendliness. The BOD POD had the highest
ranking. They concluded that the BOD POD is both reliable and valid as a tool to
quickly and safely assess body composition in a wide range of subjects (58).
Collins et al examined within and between laboratory precision using air
displacement plethysmography (BOD POD) (59). Using 30 healthy volunteers
(14 men), aged 19 — 40 years, BMI range 16.3-35.7, and BOD PODS in two
laboratories, the authors tested each subject twice at each laboratory.
Additionally, two other volunteers were subjected to 10 repeated test procedures
at both laboratories. The results from the test were used to asses within
laboratory precision (repeatability) and between laboratory precision
(reproducibility). The results revealed that most of the within laboratory variation
was due to within individual biological variability rather than technical imprecision.
However, the authors concluded that using a single BOD POD for longitudinal
assessments of individuals is preferable, due to potential differences between
BOD POD devices. A Similar study was performed by Ball in which duplicate

body composition tests were performed in immediate succession on 50 adults

30

using two BOD POD units located in the same laboratory. All tests were
performed by the same tester. There were small but significant differences
(1.040 :I: 0.017 for BOD POD 1; 1.039 t 0.017 for BOD POD 2) between body
density and % body fat between the two BOD POD units. Individual variations
in % body fat estimates between the two units were within acceptable ranges,
when compared with previous research, and there was no trend in individual
difference as the percent body fat varied. The author concluded that there were
no clinically Significant differences found for either body density or % body fat
estimates between the two BOD POD units, and that interdevice variability has
little impact on % body fat estimates. Additionally it was concluded that test-to-
test reliability between the units was as good as within one unit (60).

Body composition assessment methods are based upon assumptions
regarding the density of fat free mass. Research by Collins et al examined the
effect of race and musculoskeletal development on the accuracy of body
composition estimates via air displacement plethysmography (BOD POD)(61).
Estimates of body composition were made using the BOD POD, hydrostatic
weighing, DXA and a four component model used as the criterion. A total of 64
subjects, 39 black and 25 white, participated in the study; participants served
either in the control group or as part of a resistance-trained group.
Musculoskeletal development was assessed based on a mesomorphy rating
equation. The results indicated no statistically significant race by method
interaction, and no main effect for race. The BOD POD significantly

underestimated percent fat for both whites (-3.7) and blacks (-3.6) compared with

31

criterion, which indicated similar accuracy for both blacks and whites.
Additionally, the density of fat free mass for black participants was found to be
the same as that assumed by the Siri equation, and lower than the 1.113 g/mL
assumed by the race-specific Schutte equation. The authors stated that the use
of the Schutte equation, which corrects for percent fat by adding 3% fat in lean
black men, is inappropriate. It was concluded that musculoskeletal
development may have variable effects on the accuracy of the BOD POD,
because of the greater scatter seen in resistance trained individuals when
mesomorphy was plotted against the bias between percent fat via BOD POD and
criterion (61 ). These conclusions regarding muscularity mirror those of Prior et al,
who evaluated estimates of body composition from a four compartment model to
determine if muscularity or musculoskeletal development affects the density of fat
free mass. Hydrostatic weighing was used to assess body density; body water
obtained was by deuterium dilution, and bone mineral content and total body
skeletal estimates by DXA. Participants consisted of 111 collegiate athletes (67
men), and 61 non athletes (24 men). The density of fat free mass varied from
1.075 to 1.127 g/cm 3 and was strongly related to the water and protein fractions
of the fat free mass, and moderately related to the mineral fraction of the fat free
mass. The authors concluded that for the heterogeneous group of young adults
tested, the density of fat free mass and the accuracy of using the Siri equation
are not related to muscularity or musculoskeletal development. They stated that
athletes may differ from the assumed value of 1.1g/cm3 for fat free mass,

resulting in errors in the estimation of percent fat; however, these deviations

32

result from a variety of complex factors—including participation in a specific
sport—not simply differences in muscularity or musculoskeletal development (62).
I. Body Composition: Assessment in Athletes

A variety of methods have been used to evaluate the body composition of
athletes from various sports. The use of the BOD POD to assess the body
composition of collegiate wrestlers was evaluated by Utter and colleagues. They
demonstrated the effectiveness of the BOD POD in assessing body density,
percent body fat, and fat free mass when compared to hydrostatic weighing, in
collegiate athletes in both hydrated and acutely dehydrated states (63). Earlier
research specifically assessed the appropriateness of using the BOD POD to
determine % fat in collegiate football players. Body fatness was estimated from
body density; body density was obtained on 69 collegiate football players using
both the BOD POD and HW on the same day. Additionally, 20 subjects
underwent a whole body scan using DXA to assess total body mineral content
and fat. A three component model was used to calculate percent fat from these
data. The test-retest reliability for assessing percent fat was 0.99, with a
technical error of measurement of 0.45 %. The body density obtained via the
BOD POD was significantly greater than that obtained via HW(1.064 :I: 0.002 vs
1.060 t 0.002), resulting in lower percent fat estimates by the BOD POD. Similar
results were found for DXA and the three compartment model, where % body fat
estimates were significantly higher than those obtained with the BOD POD. The

authors concluded that the BOD POD is a reliable tool which requires little

33

technical expertise for assessing percent body fat; however, % body fat was
under predicted when compared to other methods (64).
J. Body Composition and Athletic Performance

The association between “appropriate” body composition and athletic
performance is well documented. However, the definitions of “appropriate” and
“performance” vary considerably between specific sports, as well as the position
played. For example, in a study comparing Gaelic football players, hurlers and
soccer players, the soccer players were found to have lower percentage of body
fat and greater aerobic capacity, while Gaelic football players and hurlers
demonstrated greater strength (15). For lightweight rowers, those athletes with
greater body mass and lower body fat had faster heat times (13). Yoshiga and
Higuchi found similar results when comparing male and female rowers,
concluding that individuals with large body size and greater aerobic capacity had
a performance advantage, and that while rowing times were typically slower for
females, that difference was minimized when members were matched by
percentage of fat free mass (65). In team sports, position also commonly impacts
what is considered an appropriate, or desirable, body composition. In a study of
basketball players by Sallet et al, centers were found to be taller and heavier and
to have greater percentage of body fat than forwards and guards, which would be
expected based on the position requirements of the players. However, it was
noted that aerobic capacity played the greatest role in predicting level of play (66).

In each of these examples, body composition appears to play a role in

performance. However, cross sectional studies are not able to establish a causal

34

relationship and parse out whether those athletes with a naturally leaner
physique are naturally better players; or whether intense training causes both the
development of a lean physique and improvements in performance. Other
factors influencing performance include aerobic capacity (15, 66), strength (15),
speed (65) or individual sport-specific factors such as hand-strength in rock-
climbing (67).
K. Body Composition and Performance in Football

Common bench marks for performance for football players include tests
of speed, strength and power. Fry and Kraemer evaluated the relationship
between performance tests (back squat, power clean, bench press, vertical jump
and 36.6 meter sprint) and player ability (starter vs nonstarter) and caliber of play
(NCAA division I- III). They found that the bench press, power clean and vertical
jump differentiated for playing ability and division of play; while the 36.6 meter
sprint differentiated playing ability, but not between NCAA divisions. While the
authors acknowledged that performance in each activity was likely impacted by
the training emphasis of each team, they concluded that the performance tests
. used in the study can successfully differentiate between athletes and may be
useful for coaches selecting starting teams. The authors also stated that athletic
trainers may use observed changes in these performance markers to evaluate
the effectiveness of strength and conditioning programs. (68).

Other research has compared the relationship between physical
characteristics and athletic performance over time. Secora et al compared

physical and performance characteristics of NCAA 1 football players in 1987 (40

35

teams) versus 2000 (37 teams). In general, they found that NCAA 1 college
football players have become bigger, stronger, faster and more powerful.
Specifically of interest to this thesis is the change over time in body composition
and weight of playing positions. Of the eight positions analyzed, six of the eight
Showed significant increases in fat free mass; four out of eight showed significant
increases in weight; three out of eight showed decreases in percent body fat,
while one position (offensive linemen) showed an increase in percent body fat.

At the same time, all positions exhibited increases in vertical jump power, and the
majority of positions demonstrated increases in bench press, squat and vertical
jump performance (69).

Selected research has evaluated Changes over time in the physical
characteristics of football players, particularly assessing body mass and body
composition changes. Noel et al performed hydrostatic weighings and Skinfold
thickness measurements on 69 NCAA division 1 football players (70). They
reviewed previously published literature in which body composition of 68 division
1 football players was reported in 1984. The goal of their research was to
ascertain whether changes in body mass observed in players over the last 10
years were accompanied by changes in body fat. They found that age, height
and BMI remained relatively constant between the two datasets; however, body
mass (weight) increased approximately 10kg in the offensive linemen, tight ends
and linebackers; other positions remained relatively constant (~3kg increase).
They noted significant increases in body fat percentage for defensive linemen,

and offensive and defensive backs. The authors conclude that the players in the

36

current study were both “bigger" and “fatter” than players in the earlier study.
They found no difference by playing year or scholarship status, but did note that
offensive and defensive lineman, and tight ends averaged greater than 25% body
fat, placing them in an unhealthy category (45).

A similar study evaluated 53 National Football League players and
analyzed their body composition via BOD POD. Body composition data was then
compared by position to earlier studies. The researchers documented significant
increases in body mass for offensive and defensive linemen in modem (2003)
NFL players compared to players in the 19703. They also noted that, while every
position in the 2003 study was categorized by BMI as overweight, obese or
severely obese, only offensive linemen were assessed as having an unhealthy
percentage of body fat (71 ).

Additional research has evaluated body composition as a predictor of
performance, with mixed results. In a study of 261 NCAA Division 1A collegiate
football players, increases in body mass (not Specifically % body fat) were.
positively correlated with increases in bench press and power clean
performance—larger players were able to lift more weight. In contrast, increases
in body fat percentage were negatively correlated with performance in the power
clean and vertical jump—players with higher body fat percentage were able to
move their body weight less effectively. For lineman Specifically, increases in
body fat were also negatively correlated with performance in the 40 yd dash and
the 20 yd shuttle—lineman with more body fat were slower ( 16). However,

contradictory research with similar subjects exists. A study with 77 NCAA

37

Division III football players evaluated the relationship of body mass, BMI, body
composition, and performance (10 and 40 yd sprints, pro shuttle run, vertical
jump, sit and reach, and bench press). Initial correlations between body fat and
performance explained 27-49% of the variance. After controlling for body mass,
partial correlations between body composition and performance were diminished,
only explaining 8-23% of the variance, as both performance and body
composition are related to body mass. They authors concluded that there is poor
correlation between body fat and performance measures in collegiate football
players (72). In another study, Davis et al evaluated 46 NCAA 1 collegiate
football players, analyzing performance variables (36.6m sprint, 18.3 m shuttle
run, vertical jump, and hang clean) and their relation to height, weight, BMI and
body composition. A stepwise multiple regression analysis revealed a prediction
model for physical performance using the Sprint and shuttle run as predictors.
Initially, body composition was significantly related to the sprint and shuttle run;
however, there was marked co-Iinearity between body composition and the
remaining variables (such as body weight), making it unsuitable for use in the
final model. The authors concluded that their findings support the contention
that stronger, lighter athletes can run and out faster than their weaker or heavier
counterparts, but they also state that traditional assumptions about body
composition and athletic performance in football may need to be reconsidered

(73).

38

L. Implications of Literature Review

The current body of literature supports the assertion that a diet adequate
in key nutrients is essential for supporting physical performance and recovery. In
addition, the literature reviewed in this chapter affirms the contention that body
composition impacts athletic performance in many sports, and that optimal body
composition and performance are both supported by certain dietary behaviors or
nutrient intakes.

The importance of nutrition for athletic performance and for overall health
is well established, with a clear role for macronutrients in athletic performance
and recovery, and a supportive role for nutrient-density and micronutrient content.
The importance of meal timing is more ambiguous, but does seem to play a role
in body weight and body composition in non-athletes and athletes; in particular,
within athletes, timing and macronutrient composition of snacks is intrinsic to
muscle anabolism and therefore lean body mass preservation and development.
As body composition, reflected as percent body fatness, is a physical
characteristic relating to physical performance, it is therefore of concern for
athletes, and the coaches, certified athletic trainers and sports-dietitians who
work with them. However, there is no published research evaluating the
relationship between macronutrient adequacy, frequency of intake or eating
patterns, and body composition within the specific subset of collegiate football

players.

39

III. METHODS
Study Design

This study was a 7-month prospective single group design using a
convenience sample of Michigan State University (MSU) football players
engaged in an off season training program which included a new sports nutrition
education protocol. The study protocol was reviewed and approved as exempt
by the MSU Institutional Review Board. The project is a collaborative effort
involving faculty, staff and students from the MSU Department of Radiology,
Division of Sport Medicine, Spartan Nutrition and Performance Program
(SNAPP) and the Departments of Human Nutrition, Kinesiology and
Intercollegiate Athletics.

Subject Inclusion/Exclusion Criteria

MSU football players 18 years of age or older participating in the off-
season football training program as of Jan 2006 were included in this study. The
players were encouraged to participate by the team athletic staff and the SNAPP
staff, though they had the option to not complete questionnaires and physical
assessments. No incentive could be provided due to the National Collegiate
Athletic Association (NCAA) regulations prohibiting such practice.
Independent Variable] Intervention

In October of 2005 the Department of Radiology, Division of Sports
Medicine initiated the development of a formalized sports nutrition program for
varsity MSU athletes. A full-time PhD, RD, and a MS, RD were hired to initiate

the Spartan Nutrition and Performance Program (SNAPP). Following

40

discussions with the head certified athletic trainer (ATC), the team physician and
selected coaches it was agreed that the MSU football team would receive sports
nutrition services. The sports nutrition intervention was implemented in
conjunction with the off-season Spartan football training program which began in
January 2006. The design for the program/ intervention was based on
methodology demonstrated to be effective in health promotion (74) and cardiac
rehabilitation (75) settings and included a combination of group meetings and

individual consults with behavioral goal setting (see Table 2.1 ).

41

Table 2.1 Dates, topics, and attendance for group education

 

Date (2006) Topic Type of Meeting Estimated
Participation
13 Jan Introduction, basic Whole Team >95%
sports nutrition,
emphasis on
carbohydrate
26 Jan Reinforced 3 groups ~95%
carbohydrate;

discussed weight
goals; provided
weight-related
handouts

2 Feb Goal setting; 3 groups ~90%
weight specific and
goal handouts;

emphasized
snacks, nutrient
densfiy

23 Feb Timing of intake; 3 groups ~50%
reinforce weight
goals

21 Mar Weight goals; 3 groups (low turn out;
supplement and meeting not held
restaurant on typical day of
information the week)

21 Jun Check progress; Whole Team (2 ~25%

reinforce nutrient time slots)
density; hydration

 

An individual, multi-faceted assessment of each player by the athletic
trainers occurs routinely as part of the off- season football training program; this

assessment was incorporated into the methods of this thesis. In January of each

42

year, athletic trainers and strength coaches make an empirical decision regarding
an appropriate weight-related goal for each player. This goal is based on the
judgment of the trainer, body composition data as well as factors such as player
size, their position and recent performance. Players were categorized as “need
to lose weight”, “need to gain weight”, and need to “maintain weight”. It was
emphasized that weight loss should be primarily fat, and weight gain should be
preferentially muscle, although in some cases simply increasing bulk is the goal.
Players were monitored and encouraged to adhere to a training regimen by the
athletic staff. During the off-season beginning January 2006, additional
education and guidance was provided by the SNAPP team.

Group Education Meetings

The first group meeting was attended by players, coaches and certified
athletic trainers (ATC). The purpose was to give an overview of SNAPP services
and summarize key concepts of the role of nutrition for football training and
performance Players were instructed to attend future group meetings designed
to help them adopt nutrition behaviors to support their training and achieve their
specific weight goals (Table 2.1 summarizes the dates and the key topic of each
meeting). The meetings were 30-45 minutes in length, and players were
assigned to one of three groups based upon their weight goal (ie, weight gain,
weight loss, maintain). Sports nutrition and general nutrition information was
provided at player meetings and was based on current recommendations from
position statements (1) and the Sports Nutrition Manual from the American

Dietetic Association (36). Education included specific information and practical

43

examples for implementing the recommendations, including the facilitation of
nutrition goal setting. See Appendix A for copies of educational materials and
handouts used in the group meetings.

Weight Loss Group- Players assigned to the weight loss group were
encouraged to formulate a specific weight related goal (i.e. number of pounds to
lose by August). They were encouraged to lose no more 2 pounds per week (a
3500 kcal deficit assumed per pound). Athletes were encouraged to achieve a
kcal deficit of 500 -1000 kilocalories per day, using a combination of caloric
restriction and increased energy expenditure, to minimize muscle loss.
Recommended strategies for decreasing calorie intake included reducing intake
of caloric beverages, reducing portions of high calorie snack foods, and reducing
the frequency of fried or other high fat food consumption. Athletes were
instructed to choose nutrient-dense, moderate fat foods, emphasizing mono- and
poly unsaturated fats. They were encouraged to follow a meal pattern with three
meals and two or more snacks per day. Timing of food intake pre and post
workout was emphasized to support performance during training and minimize
muscle catabolism during a hypocaloric period.

Weight Gain Group- Athletes assigned to the weight gain group were
encouraged to gain weight gradually, approximately two pounds per week, with a
goal of achieving target weight while minimizing the gain of excess body fat.
They were instructed to consume 500-1000 additional kilocalories each day,
above their current kilocalorie intake, and were given strategies to achieve that

goal (ie, more frequent snacking; caloric beverages). To minimize fat gain,

44

athletes were discouraged from gaining more than two pounds per week.
Athletes were instructed to choose nutrient-dense, moderate fat foods, and were
encouraged to follow a meal pattern with three meals and two or more snacks
per day. Timing of food intake pre and post workout was emphasized, to support
workout performance, recovery and muscle anabolism.

Weight Maintenance Group- Athletes assigned to the weight maintenance
group were given information on maximizing the nutrient composition of their
food choices. They were instructed to choose nutrient-dense, moderate fat foods
while maintaining energy balance and were encouraged to follow a meal pattern
with three meals and two or more snacks per day. Timing of food intake pre- and
post- workout was emphasized, to support workout performance, recovery,
muscle anabolism and improvement of overall body composition

Formal Individual Consults- Following the first individual group meeting
players were informed they could contact their ATC to request an individual
sports nutrition consult with a Registered Dietitian (RD). The protocol for the
consult included setting an overall goal (for example weight gain or loss), and
identifying specific nutrition behaviors designed to help the athlete achieve these
goals. Following the consult, the RD summarized the recommendations and
goals and electronically sent it to the player with a copy going to the head ATC
and team strength coach. The strength coaches then reinforced nutrition
recommendations to the athlete. A total of 45 players were seen individually; 9

players returned for follow up in person and 6 players chose to follow up via

45

email. Individual appointments lasted 30-40 minutes. Examples of the forms
used in this process are included in Appendix B.

Grocery Store Tours (shopping skills)—Players were given the option to
attend grocery store tours on two occasions by a SNAPP team member. The
goal of the tours was to provide the players with the information and skills to shop
cost-effectively and meet their specific nutrition goals. Times and location were
scheduled based upon team feedback, to accommodate those interested in
attending. Eleven players participated in the tours which included an aisle-by-
aisle map of the store, with food items of interest highlighted, information on meal
planning and budget shopping; suggestions for purchasing, storing and utilizing
produce items, help selecting lower fat meat and dairy foods. Questions and
discussion centered around meeting the substantial calorie needs of a student
athlete on a limited budget, with an emphasis on sources of carbohydrate and
protein.

Informal education- One to three times per week an MS, RD worked out in
the training center at the same time MSU football players performed their
workouts. This facilitated informal education opportunities. At least once each
week through the off-season, the MS, RD or PhD, RD, was approached by a
football player or group of players with nutrition related questions. These
informal discussion groups ranged in size from one to eight players for each
interaction, and included topics such as specific types of ergogenic aids or pre-

workout snack recommendations. Many players who declined to make a formal

46

individual appointment were able to interact with a sports dietitian in this informal
setting.
Dependent Measures

Outcome measures for this study were assessed at three time points over
a seven month period (January 06, May 06, and July 06). Measures included a
questionnaire on nutrition knowledge, attitudes and behaviors, 24-hour dietary
recalls, a physical activity questionnaire, height, weight and body composition
assessment. Each measure is summarized in Table 2.2 and discussed below;
see Appendix C for examples.

Table 2.2 Measures and location of data collection

 

 

January 2006 May 2006 July 2006

X X X
Nutrition
questionnaire
24-hour diet recall X X X
IPAQ physical X X X
activity
questionnaire
BOD POD X X X
Height X X X
Weight X X X
Site at which Classroom in Classroom Classroom
questionnaires and athletic adjacent to BOD adjacent to BOD
recalls were building during POD testing POD testing
completed team meeting

 

47

Nutrition Questionnaire

The nutrition questionnaire was developed as an intake questionnaire
prior to initiating the research protocol. The original purpose of the
questionnaire was to obtain athletes’ baseline knowledge and behavior data to
help tailor the educational intervention during the off season. Content of
questions was based on key components of current sports nutrition guidelines
and the food frequency questions were based on the Gladys Block Food
Frequency Questionnaire(76). The theoretical basis for selected questions
included the Theory of Reasoned Action/Planned Behavior and the theory of self
efficacy (Bandura). Example questions included ‘Which of the following is a
simple carbohydrate?” (knowledge); “How important do you feel nutrition is for
your training and performance?” (self efficacy/outcome expectancy); “Do you eat
a snack before or after a workout?” (behaviors). A copy of the questionnaire is
attached in Appendix C. The questions most relevant to the objectives and
hypotheses of this thesis include selected nutrition behavior questions (numbers
14, 15, 19 and 20).

24-hour food recall

The 24-hour recall was self-administered. Participants were instructed to
record the specific amounts and types of all food or beverages consumed during
the past 24 hours and were given examples of serving sizes via food models and
overheads (see Appendix 1). In an attempt to select the most valid recalls, they
were sorted into three categories by a PhD, RD and BS, RD (“good”, “okay",

“poor"). The grouping was based upon the quality and detail of reporting for food

48

type and serving size or amount. Those classified as “good” were the best
recalls, with good to excellent detail in food and beverage type and serving size.
Those classified as “OK" included fair to good detail in food type and serving
sizes. Those classified as “poor" contained very little detail for food or beverage
type or serving. Additionally, those that reflected a apparent gross
underreporting—eg, only three items listed, or with total amounts appearing to be
<500 kilocalories were classified as “poor”. Only those recalls sorted as “good”
or “OK” were entered and analyzed. Approximately two-thirds of the recalls were
sorted as either “good” or “OK" for each evaluation point. Recalls were entered
into Food Processor by ESHA Research (2005) using specific protocols
described below.

All data entry was conducted by two undergraduate nutrition students, with
oversight by a BS, RD. Every attempt was made to insure consistent input with
as few assumptions as possible. Uniform guidance was provided for data entry
for such common vagaries as “glass” or “bowl” listed as a serving (use 12 fl oz, 2
cups respectively). When brand specifics were not provided, the most generic
USDA value was selected from the database; a running list of generic values was
maintained, so similar assumptions would be made for all records. Whenever
possible, “ounces” was interpreted as weight or volume based upon the food item
listed, and the word “cup” was interpreted as a literal 8 oz (volumetric) cup, for
beverages.

Recall data was exported from Food Processor into Excel using ESHA

PORT SQL (ESHA Research, 2006). For confidentiality names were on an excel

49

spreadsheet were replaced with identifying numbers, and data was then exported
to SPSS for analysis. Recalls which had kcal totals less than 500 kilocalories per
day and greater than 10000 kilocalories per day were excluded from the
analyses. This range was established based upon subjective assessment of the
typical intake reported by multiple players seen individually by the PhD, RD and
MS, RD.

BODPOD, Height and Weight

Body composition and height and weight were evaluated in the MSU
Human Energy Research Laboratory, in the Department of Kinesiology.
Standing height was measured in bare feet to the nearest 0.1 cm using a wall
mounted, calibrated stadiometer (Holtain Limited). Body weight was measured
to the nearest 0.01 kilogram using a calibrated electronic scale which is part of
the BOD POD system. Body volume was measured via air displacement
plethysmography using the BOD POD version 1.69 (Life Measurement lnc.
Concord, CA). Procedures were followed as recommended by the manufacturer,
and all tests were administered by or under the direct supervision of an individual
trained and certified by a BODPOD representative to perform testing. Per
manufacturer's protocol, subjects were required to wear spandex clothing and a
Lycra cap and were requested not to eat or exercise for two hours prior to
testing. The BOD POD system was calibrated with a known-volume cylinder
prior to testing and thoracic gas volume was measured in each subject using
standard procedures. All BOD POD measures were obtained using

manufacturers instructions In the case where the difference between the first two

50

tests exceeded 150ml, 3 third test was performed. Using body weight and
volume measures, each subject’s body density was calculated, and converted

to % fat using the modified Siri equation (77); adjustment for race was made with
the Schutte equation (78).

Following the January 2006 BOD POD assessment, the strength coach
requested that eight individuals be retested because of concerns that their BOD
POD assessment may have overestimated % body fat. These athletes were re-
tested in February; at that time, 20 individuals who missed testing in January
were also tested. For those players retested, the difference between the January
and February assessments was not significant (24% difference); therefore, the
original January data was used for these individuals. Additionally, the February
data were used as the baseline for those players tested for the first time in
February.

Energy Expenditure; Exercise and Physical Activity

In order to help explain anticipated changes in body weight and body
composition, it was important to quantify energy expenditure in addition to energy
intake (quantified in the 24-hour recall).Both football and non-football energy
expenditures were quantified. Activity was quantified by METs, a unit
representing multiples of the resting metabolic rate. A person at rest has an
exercise cost of 1 MET; that person at a fast walk has an exercise cost of ~6-7
METS (45). One MET is equivalent to 1kilocalorie per kilogram of body weight
per hour. The Compendium of Physical Activities was used to classify reported

physical activities, as described below (79).

51

lntemational Physical Activity Questionnaire (IPA Q)

To capture physical activity above the level dictated by football-related
training, the lntemational Physical Activity Questionnaire (IPAQ) was selected for
use in this study. The lPAQ was developed to be either interview or self
administered, and was constructed as either a short (9 items) or long (31 items)
version. The questionnaire was designed for use by adults aged 18 to 65 years
(80). The reliability and validity of the lPAQ has been evaluated in 12 countries
(80), and it has compared favorably to other measures of physical activity (81 ).
The short (nine item), self administered version was utilized in this research and
the only modification made was an alteration to the instructions, requesting that
all physical activity recorded exclude football-related training (which was
assumed to be constant among players, as will be subsequently discussed). A
copy of the questionnaire utilized is included in Appendix 3.

After all data collection and entry was completed for lPAQ and the initial
analysis was done, it was apparent that a significant number of players had
apparently misunderstood instructions and had not limited their reporting to non-
football related activity. Mean MET-minute values for non-football activity were
higher than anticipated, with a mean of 4000-5000 MET-minutes seen for each
date (Jan, May, July) indicating a great likelihood that individuals had included
football related activities in addition to non-football activities (compare 4000 MET-
minutes reported to 3900 MET-minutes of football related activity, calculated).

Therefore, a small subset of players (n=6) were asked to retake the lPAQ,

and then discuss their answers and rationale, as a method of post-validation. Of

52

those who retook the lPAQ, the majority stated that they had not noted or heard
the instruction to include only non-football related activities. Additionally, it was
apparent that several players were either confused by the format of the lPAQ or
struggled to separate football activities from non-football activities, even during
one-on-one discussions of their average daily activities. Only one of the six
players independently completed the lPAQ accurately, with meaningful and well
rationalized answers at this post-validation, and reported similar efforts in
completing the earlier versions. With these discouraging subjective results, and
the apparently inflated lPAQ MET-minute values, the lPAQ was abandoned.

In place of the lPAQ, a decision was made to estimate average daily
energy expenditure. A systematic review of predictive equations compiled by the
Evidence Analysis Working Group of the American Dietetic Association reported
the use of the Mifflin-St Jeor equation as having the narrowest range of error,
and being the most reliable, predicting RMR within 10% of measured in more
obese and nonobese individuals, compared to other equations (82). The original
Mifflin-St Jeor equation was validated in a population which included both normal
weight and obese men and women (83). The Mifflin-St Jeor predictive equation
for males is: REE = 9.99 x weight (kg) + 6.25 x ht (cm) -- 4.92 x age + 5 (83). A
coefficient for physical activity of 1.11, “low-active”, was used and a coefficient of
1.25 for “active” (45). Both “low active” and “active” levels were calculated.

Football Related Training

Detailed, week by week information regarding off-season football related

training was obtained from the head strength and conditioning coach. Utilizing

53

this information, and the Compendium of Physical Activities (79), this information
was converted to estimate weekly energy expenditure, in METs, from football
related activities. Utilizing the body weight of each player, this was further
converted to calories expended in football related activities in a given week. The
reported training was broken into three segments of time, and assigned to the
correlating three time points where body composition data was collected.

To estimate the weekly energy expenditure via foot-ball related activities,
the following protocol was used. For example, the week of January 15-21, 2006,
the football related activity for that week was one hour of lifting for four days and
one hour of running for two to three days (mean of 2.5 days used). Referencing
the Compendium, code 02050 is “Conditioning exercise: weight training (free
weight, nautilus or universal type), power lifting or body building, vigorous effort”,
equivalent to 6.0 METs; code 12130 is “Running, 10.9 mph (5.5 min/mile)”,
equivalent to 18 METs. Based upon discussion with the strength and
conditioning staff, and observations of the actual practices, the “one hour of
running” was considered to be short bursts of extremely intense activity
interspersed with periods of rest; therefore, 20 minutes of actual running at a high
intensity was assumed. From this the following equation was used: 4 days x 60
minutes x 6.0 METs = 1440 MET-minutes; 2.5 days x 20 minutes x 18 METs =
900 MET-minutes; total of 2340 MET-minutes. In a player weighing 100 kg, the
total of 2340 would be multiplied by weight and divided by 60 (to account for
individual weight), to give calories burned per week: 2340 METs x 100 kg/60kg =

3900 calories for football related activities per week, with this practice schedule.

54

lf differences in the reported training occurred around the date of the body
composition assessment, then a weighted average was calculated. For example,
from 26 March to 15 April, the players were participating in “spring ball”, but the
following weeks, 16 April to 10 July was a “discretionary period”; there was a
substantial difference in reported activities during these times, but the more
intense period of spring ball was also shorter. Therefore, the total for the weeks
of spring ball was calculated per week, and multiplied by the total weeks; the
same was done for the discretionary period. Finally, these totals were divided by
the total number of weeks for both periods, to give a weighted average for the
caloric expenditure on average for the time of “May”.

Statistical Analyses.

All study variables were entered into SPSS for analysis, using double data
entry by a BS, RD and an undergraduate human nutrition student. Variables
came from the BOD POD (including adjusted °/o body fat, weight and height); the
nutrition questionnaire (including food frequency questions regarding fruit and
vegetable intake, grain intake, meal and snack frequency, and snack proximity
relative to daily workout); from the 24-hour recall (including total kilocalorie and
macronutrient intakes). The physical activity and energy expenditure estimates
(as previously described) were also included. Additionally, variables were
created, for example, when the total kilocalories consumed for an individual were
divided by their weight in kilograms to give an individualized kilocalorie per

kilogram value specific to reported kilocalorie intake.

55

Once all variables were in SPSS, a series of box plots were utilized to
uncover statistical outliers; each purported outlier was discussed by the research
staff and a PhD biostatistician to establish the likelihood of valid measures, or
reflected measurement error, or errors in data entry. Errors due to data entry
were replaced—such as the mistyping of a height value at 843 cm. One error in
linking name with identifying code was corrected. Outliers were examined on a
case-by—case basis, but a determination was made that none needed to be
excluded as improbable.

Over the time course of this 7-month study sample size decreased over
time. Key factors for this decline included team attrition and conflicts with
summer class and work activities. During the 7-month time course of the study,
123 individuals participated in body composition assessment at least once, 88
completed at least one nutrition questionnaire, and 52 completed at least one
dietary recall. However, only four individuals had complete data sets for all
variables at all three time points (eg, for January, May and July). Due to this
limited sample size, it was decided to only use the baseline (January) and
. endpoint (July) data for each variable, to maintain a sufficient sample size for
each analysis and to preserve statistical power.

Descriptive statistics (mean and standard deviation) were employed to
describe the sample at each of the three time points: age, weight, height, and
body composition. Additionally, baseline descriptive statistics were obtained for
each major dietary variable (ie, grams per kilogram of body weight for

carbohydrate; % fat). Unless othenrvise noted, analyses were performed with

56

paired comparisons between January and July data. Statistical significance was
set at an alpha level of p <0.05.
Analysis for Aim 1 and Related Hypothesis

For baseline (January), the mean intake, minimum, maximum and
standard deviation for total calories, percent of calorie from fat, and saturated fat,
protein and carbohydrate, as well as grams of carbohydrate, fiber, protein, total
fat and saturated fat were established using the recall data. Mean reported
exchanges and servings for fruit, vegetable and grain intake were also
established. Additionally, the grams of nutrient per kilogram of individual body
weight were calculated for carbohydrate and protein. Using the nutrition
questionnaire data, the baseline intake for mean reported meal and snack
frequency, proximity to workout (minutes), and number of fruit and vegetable
servings, and grain servings, was established.

A prion" review of literature lead to the establishment of cut off points (see
Table 1.1) to determine “meets” versus “does not meet” recommendations,
based upon current health and performance recommendations; where no
performance nutrition recommendation existed, the health recommendation was
used. For carbohydrate, the cut point for meeting recommendations was set at 2
8 g/kg and >50°/o of kilocalories from carbohydrate. For total fat, the cut point
was set at <35% of total kilocalories; saturated fat at <10%; and fiber >38 grams.
For protein, reported intake at or above 1.4 g/kg was established as the cut point
for meeting expectations. Nutrient intakes were dichotomized into one = meets

recommendations or zero = does not meet recommendations. Snack intake

57

proximal to a daily workout was originally reported as number of minutes pre or
post workout. This variable also was dichotomized to yes = consumes snack
within one hour pre (or post) workout, and no = does not consume snack pre or
post workout.

Intake at baseline is reported both as the mean of the entire team for
specific nutrients in question, with a comparison to recommendations, as well as
the percentage of individuals who meet or do not recommendations. Snack
behaviors were reported as reported frequency (ie, three times per day), minutes
both pre- and post-workout (ie, 30 minutes before and 10 minute after), and
yes/no for snack intake pre and post workout.

Analyses for Aim 2 and Related Hypotheses

Using data from January (baseline) and July (endpoint), the mean nutrient
intakes (total calories, fat calories, saturated fat; grams of carbohydrate, protein,
fat and saturated fat; average number of meals and snacks; minutes pre or post-
workout that a snack was consumed) were reviewed to establish directionality of
changes. A paired samples t-test was performed to determine if significant
differences existed between reported nutrient intakes and behaviors at the two
time points. Significant changes in fruit, vegetable and grain intake (servings)
were also evaluated with a paired samples t-test, using both reported food
frequency given in the nutrition questionnaire and dietary exchanges from the 24-
hour recall calculated by the Food Processor program.

A paired samples t-test was used to evaluate the % of total kilocalories

coming from selected macronutrients, including carbohydrate (simple and

58

iffl-— _. l:- '3‘!

complex), total fat, saturated fat, and protein. Using the dichotomized variables
described above in Aim 1, X2 analysis was performed to determine if there were
significant differences from baseline to endpoint in the number of individuals
meeting or not meeting nutrient recommendations. A X2 analysis was also used
to determine if there were significant differences from baseline to endpoint in the
number of individuals reporting pre and/or post workout snacking. Additionally,
95% confidence intervals were calculated for each variable.

To examine significant changes in total kilocalorie intake as influenced by
group assignment (eg, weight loss, gain or maintain), an analysis of variance
(ANOVA) was performed using the reported kcal intake from the January recall
and the July recall as the within-subjects factor and with group assignment set as
the between-subjects factor.

Analysis for Aim 3 and Related Hypothesis

Change in weight and change in % body fat from baseline to endpoint
(January to July) was hypothesized. A priori, weight loss was defined as a loss
>2.3 kg (5 lbs); a weight gain was defined as a gain >23 kg (5 lbs); weight
maintenance was defined as a body weight within 1: 2.3 kg (5 lbs). Because the
potential error of the body composition estimates of the BOD POD are typically 2-
3% (56-59), it was established that a change in °/o body fat should be > 3%. For
changes in body weight, paired t-tests were performed for each weight goal
group separately to look for significant changes in weight within each group. For
changes in % body fat, an ANOVA was performed to determine if significant

changes in body fat occurred from baseline to endpoint based upon weight group

59

assignment. Percent body fat January and July (as established by the BOD
POD) was used as the within subjects factor, while weight category was used as
the between subjects factor. Additionally, paired t-tests were done within each
weight goal group separately to look for significant changes in body fat within
each group.
Analysis for Aim 4 and Related Hypothesis

Using Pearson’s correlation, a correlation matrix was performed in SPSS,
utilizing % body fat and all nutrient and the 20 previously discussed dietary intake

variables.

60

lV. RESULTS
Subjects

The baseline subject characteristics are summarized in Table 4.1. Mean
age of participants was 20 years, with a range of 18-23. Of those who
participated in body composition assessment, 42 were categorized as needing to
gain weight, 12 as needing to lose weight, and 42 as needing to maintain; three
individuals who participated in baseline body composition assessment had not

been assigned a weight goal group.

Table 4.1 Baseline team characteristics

n 99

Age (years) 20.1 :1.4
Weight (kg) 106.2 1 20.0
Height (cm) 185.3 i 7.2
°/o Fat 18.8 i 8.6

 

It was hypothesized (H1) that reported dietary behaviors and
macronutrient intakes of MSU football players would not meet major nutrition
recommendations for health and performance. When macronutrients were
expressed as a percentage of total kilocalorie intake and compared to
recommendations, recommendations were met by a majority of players for %
kilocalories from carbohydrate, fat, saturated fat and protein. The majority of the
team did not meet recommendations for % kilocalories from simple sugar and

grams of fiber. When expressed in grams of nutrient per kilogram of body

61

weight, carbohydrate recommendations were met by only 5% of the team, while
only 47% of athletes achieved recommended protein intake. Tables 4.2 and 4.3
compare nutrient intake with recommendations. Note that n = 40, substantially
less than the n = 99 for baseline body composition assessment. Only 38 of those
who participated in body composition assessment completed valid 24-hour
recalls. Two individuals who did not complete the BOD POD turned in valid 24-
hour recalls. There were no statistically significant differences found for age,
weight, height, or % body fat for those who completed the 24-hour recall and

those who did not, when compared with an independent samples t-test.

62

Table 4.2 Baseline nutrient intake for MSU football players with a complete 24-
hour recall, compared to recommendations

 

Nutrient n Mean Sport- General % Meeting a
i SD Nutrition Health Sports-Nutrition
Recommendation

(N)

% Kcals from 40 53.5 % 50-75% 45-65% 65%

Carbohydrate ($12.5) (26)

% Kcals from 40 20.1 % -- < 25% 2.5%

Simple Sugar (i 9.9) (1)

% Kcals from 40 30.6 % 20-30% 25 — 35% 67.5%

Fat, Total (i 10.1) or (27)

25-35%

% Kcals from 40 9.9 % -- < 10% 50%

Saturated Fat (1 4.3) (20)

% Kcals from 40 17.7 % -- 10 — 35% 100%

Protein (i 4.8) (40)

Grams of 40 23.9 (same as 38 gIday 7.5%

Fiber (1 9.9) DRI) (men) (3)

 

" when no specific sports nutrition recommendation existed, general health
recommendations were used

Table 4.3 Baseline nutrient intake expressed as grams per kilogram of body
weight for MSU football players with a complete 24-hour recall

 

 

Nutrient n Mean Football Specific % Meeting
:1: SD Recommendation Recommendation
(N)
Carbohydrate, g/kg 38 4.2 g/kg 8 — 10 g/kg 5.3%
(t 2.1) (2)
Protein, g/kg 38 1.4 g/kg 1.4 — 1.7 gIkg 47.4%
(:1: 0.7) (18)

 

63

It was hypothesized (H2) that exposure to the SNAPP pilot nutrition
program would result in significant changes in dietary behaviors and nutrient
intakes in the MSU football team. Specifically, it was hypothesized that meal and
snack frequency, and intakes of carbohydrate (simple and complex), fiber, fruit,
vegetable, and grain would increase, while fat and saturated fat intake would
decrease, and protein intake would remain adequate, from baseline to endpoint.

It was hypothesized (H23 and H2b) that meal and snack frequency would
increase in response to the SNAPP pilot study, and snack timing would more
closely reflect guidelines (within 45-60 minutes prior to working out; within 60
minutes post workout). While the reported snack frequency did not significantly
change, the mean reported timing of pre-workout snacking did change, from
16.93 minutes to 26.82 minutes. A post hoc, X 2 analysis of reported snacking
proximally to working out revealed other changes to snack intake. At baseline,
46% of individuals reported consuming a pre-workout snack; at endpoint, that
had increased to 65%. At baseline, only 65% of individuals reported consuming
a snack after working out; at endpoint, the percent of athletes reporting a pre-
workout snack increased to 75%. There was a statistically significant difference
between baseline and endpoint for those reporting a pre-workout snack (p =
0.009) and those reporting a post-workout snack (p = 0.001 ). These changes in
snacking behavior reflect greater adherence to performance nutrition
recommendations regarding pre- and post-workout snack timing and intake. See

Tables 4.4 and 4.5.

64

Table 4.4 Change in reported meal and snack intake in MSU football players

dun'gqthe off-season a

 

n January July Mean P

($SD) ($SD) Difference

(range) (range) ($SD)
Average number 71 2.7 ($ 0.8) 2.9 ($ 0.9) -0.23 0.108
of meals (1-4) (1-6) ($ 1.17)
Average number 60 2.2 ($ 1.2) 2.2 ($1.1) -0.10 0.624
of snacks (0 — 7.5) (1 — 0) ($ 1.6)
Pre-workout 66 1 6.9 26.8 -9.9 0.005
snack, average ($ 20.7) ($23.1) (:I: 27.5)
time (minutes) (0 — 120) (0 - 90)
Post-workout 67 21 .9 28.58 -6.6 0.138
snack, average ($19.0) ($33.4) ($ 36.1)
time (minutes) (0 - 60) (O - 240)

 

a Paired t-test; differences in N reflect paired comparisons

Table 4.5 Percent of MSU football players reporting pre- and post-workout

snack intake during the off-season a

 

 

 

January July P ’
(95% Cl) (95% Cl)
Pre-Workout Snacking 36 / 77 (46%) 50 / 77 (65%) <0.009
(0.35 - 0.57) (0.54 - 0.76)
Post-Workout 50 / 77 (65%) 58/ 77 (75%) < 0.001
Snacking (0.54 - 0.76) (0.65 - 0.85)
“X2 analysis

It was hypothesized (H2c) that carbohydrate, fiber, fruit, vegetable and
grain intake would increase from baseline to endpoint in response to the SNAPP

pilot study. It was further hypothesized (H2e) that protein intake would begin,

65

and remain, adequate. See Table 4.6 for a comparison of mean reported intake
of these nutrients and food groups from January to July; no statistically significant
changes in nutrient or food group intake were seen. Table 4.7 summarizes

the % of individuals for meeting specific macronutrient recommendations at
baseline and endpoint. Note the different “n” for each variable, based upon the

number of paired, valid 24-hour recalls, or paired nutrition questionnaires.

66

Table 4.6 Change in reported carbohydrate, fruit, vegetable and grain intake in
MSU football players during the off-season a b

 

n January ($ SD) July ($ SD) Mean P
(range) (range) Difference
($ SD)

% Kcals from 22 54.9 ($ 12.0) 52.8 ($ 12.6) 2.1 (:t 18.2) 0.589
Carbohydrate (24.8 — 78.8) (28.6 — 81.9)
% Kcals from 22 21.4 ($ 11.4) 16.8 ($ 7.1) 4.6 ($ 14.3) 0.144
Simple Sugar (4.4 — 52.5) (1.3 — 51.1)
% Kcals from 22 18.0 ($ 4.7) 17.3 ($ 4.1) 0.7 ($ 5.6) 0.585
Protein (10.9 — 33.5) (8.4 -— 28.3)
Carbohydrate, 11 4.2 ($ 1.7) 4.0 ($ 1.8) 0.2 (:1: 2.7) 0.826
gIkg (1.2 — 9.3) (1.5 -— 7.7)
Protein g/kg 11 1.5 ($ 0.6) 1.4 ($ 0.6) 0.1 ($ 1.0) 0.789

(0.5 — 3.8) (0.2 — 2.9)
Fiber gIday 22 25.5 ($ 10.7) 28.0 ($ 12.1) -2.5 ($ 17.2) 0.502

(6.8 — 46.9) (5 - 108.1)
Fmit & Vegetable 71 2.0 ($ 0.9) 2.3 ($ 1.1) -0.3 ($ 1.3) 0.079
Servings/Day (1 -6) (1-6)
Bread & Grain 71 2.4 ($ 1.2) 2.8 ($ 1.4) -0.4 ($1.7) 0.094
Servings/Day (1 — 6) (1 - 6)
Exchanges, Fruit 8 4.8 ($ 3.8) 3.1 ($ 1.9) 1.7 (:l: 3.1) 0.154

' (1.2 -14.3) (0.9 —14.3)

Exchanges, 7 2.3 ($ 1.7) 2.5 ($ 1.0) -0.2 ($ 2.1) 0.803
Vegetables . (0.1 — 4.5) (0.9 - 18.9)
Exchanges, 22 13.1 ($ 8.3) 16.4( $ 8.3) -3.3 ($ 12.9) 0.247
Starch (3.4 — 43.4) (2.1 — 43.1)

 

1 Paired t-test

b Differences in N reflect paired comparisons; paired comparisons also resulted in slightly

different mean values at baseline

67

Table 4.7 Change in the percent of MSU football players meeting macronutrient
recommendations during the off-season a

 

January July P
(95% Cl) (95% Cl)
% kcals from 26 / 40 (65%) 27 I42 (64%) 0.064
Carbohydrate 2 50% (0.50 - 0.80) (0.49 — 0.79)
% kcals from Simple 28 r 40 (72%) 36 I40 (85%) 0.000
Sugar < 24.9% (0.58 — 0.86) (0.74 — 0.96)
% kcals from Protein 40 /40 (100%) 41 /42 (98%) 0.001
>10% (1.0 —1.0) (1.02 — 0.94)
Carbohydrate, g/kg 2 / 36 (5.4%) 0 / 23 (0%) 0.001b
(-0.02 — 0.13) (0 - 0)
Protein 21.4 g/kg 18 I38 (47 %) 5 / 23 (22%) 0.001 b
(0.31 — 0.63) (0.05 — 0.39)
Fiber, 2 36 g 3 / 40 (7.5%) 7 / 40 (17%) 0.001 b

(-0.01 — 0.16)

(0.05 — 0.29)

 

a X‘ analysis
b note cell size < 5

There were statistically significant increases in the number of individuals

consuming <25% of kilocalories from simple sugars, and those consuming >38 9

fiber/day. There were statistically significant decreases in the percentage of

those meeting recommendations for % of kilocalories from protein, grams of

protein per kilogram body weight, and grams of carbohydrate per kilogram of

body weight. However, calculation of the 95% confidence interval (CI) shows

that the differences may not truly be significant.

It was hypothesized (H2d) that total fat and saturated fat intake would

decrease in response to the SNAPP pilot study. Table 4.8 shows no statistically

68

significant change in the reported mean % of kilocalories coming from fat or
saturated fat; Table 4.9 shows no statistically significant change in the % of
individuals meeting recommendations regarding these nutrients.

Table 4.8 Change in reported fat and saturated fat intake in MSU football players
during the off-season a

n January ($ SD) July ($ SD) Mean P
(range) (range) Difference
($ SD)

 

% Kcals from 22 29.0 ($ 8.7) 31.3 ($ 12.3) -2.3 ($ 16.2) 0.505

Fat, Total (10.3 — 59.9) (9.7 - 58.1)
% Kcals from 22 9.9 ($ 4.0) 9.7 ($ 4.0) 0.24 ($ 6.1) 0.856
Saturated Fat (1.4 — 18.4) (1.0 — 17.6)

 

‘ Paired samples t-test; differences in N reflect paired comparisons

Table 4.9 Change in the percent of MSU football players meeting total fat and
saturated fat recommendations during the off-season a

 

January July P
(95% Cl) (95% Cl)

 

Kcals from Fat, Total < 34.9% 27 I40 (68%) 27 I 42 (64%) 0.64
(0.54 — 0.82) (0.49 — 0.79)

Kcals from Saturated Fat < 20 / 40 (50%) 22/42 (52%) 0.756
9.9% (0.35 — 0.65) (0.37 — 0.67)

 

‘ X ‘ analysis

It was hypothesized (H2f) that total kilocalorie intake from baseline to
endpoint would correspond to weight goal group assignment, with those

assigned to the weight loss group consuming fewer kilocalories, those in the

69

weight gain group consuming a greater number of kilocalories, and those in the
weight maintenance group having no statistically Significant change to kilocalorie
intake. When mean total kilocalorie intake was analyzed for the entire team with
a paired samples t-test, no statistically significant changes were seen from
baseline to endpoint (p = 0.980). An ANOVA was performed analyzing changes
in reported kilocalorie intake from baseline to endpoint within the three weight
goal groups. No statistically Significant difference was seen across time, nor was
there a statistically Significant difference seen by group (see Table 4.10 and
4.11).

Table 4.10 Changes in reported kilocalorie intake at baseline and endpoint for
the entire team a

 

 

N January (kcals) July (kcals) P
($ SD) ($ SD) -
Over Time 22 3285.01 ($ 1262) 3296.68 ($ 1348.59) 0.980
Time x Group 22 0.766
Interaction _ _
3 ANOVA

70

Table 4.11 Changes in reported kilocalorie intake at baseline and endpoint by
weight goal group a

n January July (kcals) Mean Difference P
(kcals) ($ SD) ($ SD)
($ SD)

Weight Loss 3 2606 ($1479) 3368 ($2015) -761 ($3327) 0.730a
Group

WeightGain 9 3601 ($1481) 2868 ($1213) 739($2199) 0.3473
Group

Weight 10 3203 ($ 1011) 3661 ($ 1303) -457 ($ 1805) 0.444 a
Maintenance

 

" paired samples t-test

It was hypothesized (H36) that body mass changes (or lack thereof) would
correspond with weight goal group assignment. For the weight gain group, and
the weight maintenance group, the paired samples t-test revealed no statistically
significant change in weight across time. For the weight loss group, a paired
samples t-test revealed a statistically significant weight loss from baseline to end
point. The mean weight in January was 131.8 kg; in July, the mean weight was
129.0 kg, a mean difference of 2.8 kg (6.09 lbs). This was not statistically
significant (p = 0.060), but did exceed the a priori definition of “weight loss”,

which was set at a loss of >23 kg; see Table 4.12.

71

Table 4.12 Change in weight over time by weight goal group a

n January (kg) July (kg) Mean P
($ SD) ($ SD) Difference
($ SD)

 

WeightLoss 9 131.8 ($12.1) 129.0 ($12.4) 2.8($3.9) 0.066
Group

Weight Gain 26 99.9($17.8) 100.3 ($18.7) -0.4 ($2.7) 0.437
Group

Weight 28 112.3($19.9) 111.1 ($18.2) 1.2($4.3) 0.144
Maintenance

 

7' paired samples t-test

It was hypothesized (H3b) that there would be a significant decrease in %
body fat for the entire team, and it was further hypothesized (H3c) that this
decrease in % body fat would differ by weight goal group assignment (weight
gain, lose, maintain). An ANOVA revealed a statistically significant change over
time in percent fat at baseline, to end point (p = 0.033), with the mean at baseline
being 20.6% fat, and at endpoint 19.3%. While statistically significant, and in the
direction hoped, it does not, however, meet the a priori definition of “decrease in
body fat” of >3%. The interaction between body composition change over time
by group was not Significant (p = 0.617).

Paired samples t-tests were used within each weight goal group; again,
the weight gain and maintenance groups had no Significant change in body
composition. The weight loss group did, however, demonstrate a decrease in
body fat from 28.1% in January to 25.61% in July; this is a 2.5% decrease in

percent fat, which was statistically Significant, although does not meet our a priori

72

definition of “successful body composition change" being >3%, see Table 4.13

and 4.14.

Table 4.13 Change in % body fat over time, entire team a

 

 

 

n January July P
% fat % fat
($ SD) ($ SD)
OverTime 63 20.6 8.7) 19.3 ($ 7.6) 0.033
Time x Group 63 0.617
Interaction __ _
aANOVA
Table 4.14 Change in % body fat over time, by weight goal group3
n January July Mean P
% fat % fat Difference
($ SD) ($ SD) ($ SD)
Weight Loss 9 28.1 ($ 5.5) 25.6 ($ 6.1) 2.5 ($2.6) 0.022
Group
Weight Gain 26 16.8 ($ 7.3) 16.1 ($ 6.2) 0.7 ($4.6) 0.437
Group
Weight 28 21.6 ($ 9.0) 20.2 ($ 7.8) 1.4 ($5.6) 0.187

Maintenance

 

a paired samples t-test

Secondary to hypotheses regarding nutrient intake is the kilocalories per

kilogram of energy reported by the subjects. The mean kilocalorie intake, per

kilogram of body weight, was 31.52 kcals/kg (+/- 11.48) at baseline, and 32.45

kcal/kg (+/- 16.61) at endpoint. This was not a statistically significant change, but

73

was in a positive direction. However, recommendations for football players put
kilocalorie needs closer to 40-50 kcal/kg, especially for this young population (36).

While the failure of the IPAC to truly capture non-football related physical
activity limited the ability of this study to capture variations in physical activity
level and energy expenditure for individuals, an analysis of the football related
energy expenditure estimates was still performed. A paired samples t-test
looking at the estimated energy expenditure for baseline and endpoint revealed
significant changes. The energy expenditure based on estimates of football
related activities, and estimates of total energy expenditure (football expenditure
+ estimated REE), increased significantly from January to July, see Table 4.15.
Note that the REE x 1.11 was considered a “low active”, while REE x 1.25 was
considered “active”.

Table 4.15 Estimated energy expenditure, football related and basal a

 

n January July P
(kcal expended) (kcal expended)

 

Football Specific 63 4289 5242 0.000
Activities (per week) ($ 811) (:1: 957)
Football + REE x 1.11 63 3044 ' 3191 0.000
(daily) ($ 381) ($ 390)
Football + REE x 1.25 63 3350 3593 0.016
(daily) ($ 415) ($ 809)

 

a Paired samples t-test

It was hypothesized (H4a) that decreases in % body fat would be inversely
correlated with intakes of carbohydrate, fiber, protein, fruit, vegetables and grain.

Additionally, it was hypothesized that decreases in % body fat would be directly

74

correlated to total fat and saturated fat intake. While multiple examples of
muliticollinearity were found—for example, a correlation of +0.869 between total
kilocalories reported and total kilocalories from fat—the only variable which
significantly correlated with percent body fat in July was reported daily snack
frequency (r = 0.610, p < 0.05). The correlation was positive, indicating that %
body fat increased as reported snack frequency increased. See Appendix D for

correlation matrix.

75

V. DISCUSSION

The results of this study Shed light on the eating habits and macronutrient
intake of collegiate football players during the off-season, and also demonstrate
the impact of an off-season training program and pilot nutrition education
program on body composition. This present study reveals that MSU football
players reported nutrient intake levels that were comparable to macronutrient
recommendations (except fiber), but did not meet most sports nutrition
recommendations for macronutrients (eg, grams of protein and grams of
carbohydrate per kilogram of body weight). There were selected improvements
in sports nutrition behaviors, specifically pre- and post-workout snacking. Other
changes in macronutrient intake, specifically carbohydrate and protein, were not
in the desired or expected direction relative to performance recommendations.
This may reflect changes in reporting, but may also reflect true changes in intake
which may be detrimental to performance.

Weight and % body fat decreased significantly for the entire team, but this
change appeared to be driven by losses in the weight loss group, with the weight
maintenance and weight gain groups not experiencing significant changes in
body weight or % body fat. The only significant correlation between nutrient
intake or dietary behavior and % body fat was a positive correlation between
snacking frequency and % body fat.

It was anticipated that reported dietary behaviors and macronutrient
intakes of MSU football players would not meet macronutrient recommendations

put forth by the Institute of Medicine, Dietary Reference Intakes (IOM, DRl), the

76

American College of Sports Medicine and the American Dietetic Association.
The results of this study indicate that, in January, the majority of subjects did
meet IOM, DRl macronutrient recommendations, when expressed as a % of
kilocalories. However, only 16.7% consuming at least 389 fiber/day, which
means that out of a sample of 40 only seven were meeting fiber
recommendations.

Performance nutrition guidelines recommend the expression of
carbohydrate and protein as grams of macronutrient per kilogram of body weight
(1, 3, 4, 30, 36). When expressed as grams of nutrient per kilogram of body
weight, only 5.3% of subjects met recommended carbohydrate intake; only
47.4% met protein recommendations. However, these results, based upon an
X2 analyses, had only two out of 38 individuals meeting carbohydrate
recommendations, which is a very small number upon which to base conclusions.
Few players were meeting fiber recommendations, which may indicate the
selection of carbohydrate foods of low nutrient density. A diet rich in
carbohydrate but low in micronutrients essential for carbohydrate metabolism—
such as thiamin, riboflavin and niacin—may lead to declines in endurance (43,
45, 84). AS previously noted, the small sample size influences the conclusions
which can be drawn. Contrary to what was hypothesized, at baseline the
majority of players reported intakes that met health recommendations for fat and
saturated fat.

It was hypothesized that exposure to the SNAPP pilot study would lead to

significant increases in meal and snack frequency, and also greater compliance

77

with pre- and post-workout snack timing recommendations. From baseline
(January) to endpoint (July), more individuals reported consuming both a pre-
and post workout snack. Pre-workout snack timing increased, from a mean of
17 minutes pre-workout to a mean of 27 minutes. These changes reflect greater
agreement with performance nutrition recommendations given in the SNAPP
group meetings, and provided in the SNAPP handouts. The recommendation for
pre-workout snacking is 45 to 60 minutes prior to working out, while the
recommendation for post-workout snacking is within 60 minutes. Both pre- and
post-workout snacks were emphasized heavily in the group education, individual
counseling, and in handouts; additionally, the athletic coaching staff reinforced
the importance of pre- and post-workout snacking. While it is possible that
these reported changes reflect only an increased awareness of the importance
of consuming a pre- and post-workout snack—versus actually consuming the
snack—even an increase in awareness is an important gain, and may be
attributed to the education provided.

Recent research has highlighted the importance of pre- and post- workout
nutrient consumption, to support glycogen repletion and also to support muscle
anabolism. Both carbohydrate and protein are emphasized, to supply needed
substrate and a synergistic insulin response resulting in greater anabolic gains
(2). Rasmussen and colleagues examined the impact of an amino acid-
carbohydrate supplement one hour or three hours post exercise (49). They
found that the combination of amino acids with carbohydrate was effective in

stimulating muscle protein anabolism, regardless of whether it was consumed

78

one hour or three hours post. Miller et al reported that carbohydrate and amino
acid ingested together have a beneficial effect on muscle metabolism similar to
when ingested separately, and that prior ingestion of amino acid and
carbohydrate does not diminish the metabolic response (51). Finally, Tipton et
al examined the role of an amino acid-carbohydrate supplement consumed only
pre-workout, compared with the same supplement consumed post-workout (50).
The authors reported that net muscle protein synthesis was greater if the
supplement was consumed pre- versus post-workout. They suggest that this is
related to greater amino acid delivery to the muscle. The data supporting the
role of pre- and post-workout protein and carbohydrate intake in muscle
anabolism, in conjunction with apparent changes in actual snack consumption
patterns seen in this study, seem to make snack intake an important nutrition
behavior to target for change in this population.

Research by Kerver and colleagues evaluated meal and snack patterns as
they are related to nutrient intakes in US adults (85). Utilizing the 24 hr recall
NHANES data for individuals >20 years, the authors described meal and snack
patterns, specifically, breakfast, lunch, dinner and 1 or 2 snacks. Those who
reported breakfast, lunch, dinner and 1 or 2 snacks had the greatest intake of all
micronutrients except cholesterol, vitamin B6 and sodium. Those who skipped
breakfast and had lunch, dinner and 2 snacks had the lowest intakes of all
nutrients except sodium. The authors concluded that specific meal and snack
patterns may be markers of nutrient density and dietary quality in the US

population. In light of this research, future research with athletes could

79

investigate the relationship between pre- and post-workout snacks, or snack
frequency, and nutrient density. Encouraging nutrient dense snack choices,
and snack timing pre- and post- workout, with the goal of improving muscle
anabolism may also lead to increased nutrient content of the overall diet, if the
findings of Kerver et al in the general population are mirrored in athletes.

It was anticipated that intake of carbohydrate (simple and complex, g/kg
and % of total kilocalories), fiber (grams), fruits, vegetables and grains (servings)
would increase with exposure to the SNAPP pilot program. There were no
significant changes seen in intake. Carbohydrate intake, as a percent of total
kilocalories or as grams of carbohydrate per kilogram of body weight, remained
at slightly more than 50% of calories from carbohydrate and 4 grams of
carbohydrate per kilogram of body weight. The recommended range for
carbohydrate intake is 50-75% of total calories (1 ), or at least 8 grams of
carbohydrate per kilogram (36). It would appear that, at 4 g/kg, the team mean
was very far from optimal. However, had an average player weighing 100kg
attempted to consume 800 grams of carbohydrate per day, he would have
consumed 3200 kilocalories from carbohydrate. For a 20 year old man who is
100kg and 183 cm, the Mifflin St Jeor equation with a physical activity coefficient
of 1.48 (very active) results in an estimated kilocalorie requirement of only 3019
kilocalories per day (45, 83)! Altemately, using a recommendation of 45 kcal/kg
(36), estimated kilocalorie requirements would be 4500 kilocalories per day; 800
grams of carbohydrate would provide 71% of the total kilocalorie

recommendation. This same individual requires 1.4 grams of protein per

80

kilogram of body weight, or 12% of 4500 kilocalories. It may be that 8 grams of
carbohydrate per kilogram of body weight is a recommendation that can only be
achieved by individuals with energy requirements exceeding 45 kcals/kg, without
risking an imbalance in macronutrient intake. The synthesis of macronutrient
recommendations for an individual athlete, along with weight and body
composition goals and the demands of a specific training regimen, apparently
requires careful planning. Individualized nutrition education, in addition to group
education, is an important strategy to insure performance nutrition goals are met.

While carbohydrate intake did not change significantly from baseline to
endpoint, the types of carbohydrate rich foods chosen appear to have changed.
There was not a significant increase in mean fiber intake reported, although the
mean did move in a positive direction. There was a significant increase in the
number of people meeting recommendations regarding the percentage of total
calories from simple sugars, and in the number meeting fiber recommendation
(although with noted small cell size). It appears that the consistent message
regarding nutrient dense carbohydrate choices impacted some players’ food
choices. Performance nutrition recommendations highlight the importance of
nutrient dense carbohydrate food choices to maximize the intake of essential
micronutrients (1, 4, 30, 43). Micronutrients such as the B complex vitamins are
essential for macronutrient metabolism, which is foundational for athletic
performance (45).

For all dietary intake variables where at X2 analysis was performed, a 95%

confidence interval was also calculated. These allowed for closer review of

81

results that appeared to be statistically significant, but were questionable
because of small cell size. Only % kilocalories from protein was significantly
different from January to July when reviewing the 95% Cl. Additionally, the initial
X2 analysis was not done with matched pairs; a subsequent analysis using
matched pairs (“listwise” in SPSS) did not significantly alter results as presented.
Contrary to what was hypothesized, there were no statistically significant
decreases seen in fat or saturated fat intake. It is noteworthy that baseline
values of 29% of kilocalories from fat, and 9.9 % saturated fat were lower than
reported for the general population (86), which reduced the potential for change.
This low value may also be a reflection of underreporting, or reporting bias, as
those who turned in a 24-hour recall may have been more conscientious or
interested in nutrition than those who did not turn in a recall (or turned in a recall
with less detail). Fewer people reported meeting recommendations for protein
intake at endpoint, but mean reported grams of protein per kilogram of body
weight did not change significantly (1.53 gIkg in January, 1.44 gIkg in July).
These findings are Similar to those of Gunnick (7) who followed players for the
course of an entire season and found an intake of protein at 1.3 g/kg in February
and 1.6 g/kg in August. Research by Cole et al in a group of 28 NCAA football
players showed protein intake at 1.5 g/kg and a total of 22% of calories (6).
Again, in the present study, sample size is an issue, as gram per kilogram intake
of protein was based upon a sample size of 11, which is not reflective of the
entire team, thereby limiting application to the larger population. Additionally, the

95% confidence intervals for each time point overlap, indicating that the

82

difference is not significant. Such relatively small changes—from 1.53 g/kg to
1.44 gIkg—are likely more a reflection of the potential measurement error of the
24-hour recall than true changes in macronutrient intake. If a 100 kg individual
estimated a portion of steak to be 8 oz when it was actually 10 oz, that 2 oz
underestimation (14 g protein) would result in a 0.14 g/kg under-reporting of
protein intake.

It was hypothesized that the kilocalorie intake of the MSU football team
would correspond to weight goal group assignment. No statistically significant
difference was seen between weight goal group, neither was there a significant
change in kilocalorie intake across time for the team as a whole. However,
Magkos et al noted that the magnitude of underestimation increases with the
magnitude of total kilocalorie intake in athletes (18). Additionally, increased
body Size has been associated with underreporting in the general population
(87). The standard deviation for the mean reported kilocalorie intake of each
weight goal group indicates a wide range of energy intake. It is possible that
inaccuracy in reporting obscured any differences, or changes, in kilocalorie
intake which may have existed, particularly in this group of athletes with a large
body size and high kilocalorie requirement.

One factor which may have impacted dietary intake was the presence of
training table meals. From the first day of spring semester through the
Wednesday before finals week, MSU football players have access to a team
training table four times per week. After finals week (late April) until Fall Camp

(mid August), the athletes do not participate in training table meals, nor do they

83

have access to the campus cafeteria. This difference in access to food may
have impacted meal frequency, food choices, and macronutrient intake separate
from the SNAPP pilot study. Additionally, during the spring semester,
recommendations by the SNAPP team lead to menu changes for training table
meals, which may have impacted the foods offered to the team and thereby
affected their intake. These factors were not directly accounted for in the
present study.

It was hypothesized that there would be significant changes in body mass
corresponding with weight goal group assignment. However, the only group with
a significant change in body weight was the “weight loss” group. This is not
surprising, as those assigned to the weight loss group were given specific
instructions and motivation for achieving this change. Interestingly, the weight
loss seen by this group was not statistically significant, but the mean change of
2.8 kg did meet the a pn'on' definition for successful weight loss (>2.3kg). It is

noteworthy that the weight loss group was the smallest group (n=9). A more

balanced study, with each weight goal group having a similar number of subjects,

may have revealed a statistically significant change within the weight loss group.
Changes in % body fat were not as expected; it was hypothesized that
there would be significant decreases in % body fat for the entire team, and it was
further hypothesized that this decrease in % body fat would differ by weight goal
group assignment (weight gain, loss, maintain). The decrease in % fat was

statistically significant only for the weight loss group, but did not meet the a priori

84

definition for successful body fat change (>3%), again, noting again the small
sample Size (n=9).

The change in weight seen in this study might be perceived by a given
individual as an important change, despite the lack of statistical significance.
Conversely, while the reduction in % body fat is not outside of the potential
measurement error of the BOD POD, it was statistically significant. It is important
to note that the weight loss group decreased both body weight and % fat, rather
than a decline in body weight occurring at the expense of fat free mass. While
both the weight maintenance group and the weight gain group did not have a
statistically significant change in body mass or % fat, both groups did average
small decreases in their mean level of weight and % body fat. Unfortunately,
there is no way with the current data to account for actual energy expenditure.
Estimates of foot-ball related energy expenditure relied on the practice schedule
of the entire team, and it was assumed that all individuals attended each practice
session. This may not have actually been the case, as during the off-season,
NCAA regulations forbid any activity that may be perceived as mandatory, and
no attendance was taken at practices. Additionally, as the lPAQ failed to
accurately measure reported non-football related activities and resting energy
expenditure was estimated with an activity coefficient, individual differences in
leisure time activity were not assessed. The only factor making the energy
expenditure estimate different between individuals was the use of the individual’s
body weight. Variations in football practice participation and leisure time physical

activity level not captured by the present study may have played a role in the

85

weight and body composition changes seen. Additionally, differences among
positions, and starters versus second string players, were not explored in the
present study and may have played a role in weight and body composition
changes, or lack thereof. For example, the present study posited that % body fat
would decrease from January to July, as the football season approached. It is
possible that starting players in previous season were in peak condition, with the
lowest % body fat, at the end of the season (eg, January) and it was therefore
not realistic to expect substantial decreases in % body fat during the off-season.
Research by Silvestre et al examined body composition changes which occurred
in 25 NCAA male soccer players during the season (88). They found significant
increases in body mass due to increases in muscle mass, specifically to the legs
and trunk, during the soccer season. Body composition was assessed serially by
Duthie et al in a group of 72 male rugby player from 1999 to 2003 (89). They
found no Significant change in the team mean within and between seasons, but
Significant individual variation in lean body mass within and between seasons,
with significant differences seen by position.
A variety of additional factors may have impacted the measurement of %

body fat. As noted in the methods, some players in the present study had a
baseline body composition assessment performed in February rather than in
January. While it is acknowledged that weight and/or body composition could
have changed from January to February, it is likely that these changes would
favor the null, and not bias the results. Additionally, while players were

instructed to not exercise or eat for at least two hours prior to the BOD POD

86

measurement, some players apparently ran to the appointment and may have
had an elevated body temperature. This might have resulted in lowered
estimates of % body fat.

Estimates of energy expenditure did Show a statistically significant
increase from baseline to endpoint—likely reflective of the increasing intensity
toward the end of the off-season. Taken together with the statistically Significant
decrease in body weight for the entire team this would seem to indicate that the
weight loss seen was a product of increased energy expenditure, not greater
deficits in energy intake. However, the mean energy intake was ~30 kcal/kg
both at baseline and endpoint, approximately 75% of expected for weight
maintenance. Collegiate football players engaged in intense training in addition
to school obligations, who in some cases may still be experiencing physical
growth and maturation, would be expected to have energy requirements
exceeding 40 kcal/kg (36). The kilocalories per kilogram of body weight serve as
important barometer for assessing overall intake. There was a poor correlation
between reported energy intake in the 24-hour recalls and the estimates of REE
using the Mifflin St Jeor equation. The correlation between January energy
intake and REE was -0.296, and for July was -0.80. There exist two potential
explanations: either energy intake was truly inadequate for the entire off-season,
or it was under-reported. The relatively minor decreases in weight would
indicate the latter is more likely.

It was hypothesized that % body fat would be inversely correlated with

intakes of carbohydrate, fiber, protein, fruit, vegetables and grain, and directly

87

correlated with total fat and saturated fat. The current study revealed a positive
correlation between increased body fatness and increased snack frequency, with
greater snack frequency correlated with greater % body fat. There was no
relationship seen between % body fat and other macronutrient or dietary
behavior variables. The apparent direct correlation between % body fat and
snack frequency contradicts research on the importance of snack intake in
reducing obesity risk. Early research by Fabry et al supported a negative
relationship between obesity and meal frequency, characterizing the “nibbling”
diet as less likely to contribute to the development of obesity when compared to
the “gorging” diet (47). Research by Forslund et al examined meal timing and
type and risk for obesity, reporting that meal frequency and later meal timing
were positively associated with risk for obesity (11). However, this research was
done in obese women, which limits the application in the current study population.
lf football players who reported a greater number of snacks consumed were
simply eating more calories, it would be expected that this would lead to

greater % body fat. The current data does not allow further analysis of body fat
changes in light of changes in reported pre- or post-workout snack intake, or
controlling for kilocalories provided by snacks. However, the apparent
relationship between increased snack frequency and increased body fatness,
along with the previously discussed challenges of achieving recommended
macronutrient levels, would support the importance of future nutrition education,

counseling and research in this population.

88

Study Strengths and Limitations

This study evaluated a group of athletes for whom there is little previous
data, specifically in the area of body composition, nutrient intake and dietary
behaviors. It provides insight and valuable pilot data which will serve as a
foundation for future education and research in this population. The current
study included both observational and change data. It included the use of BOD
POD administered by trained individuals to measure body composition changes,
in addition to the use of the precisely calibrated electronic scale attached to the
BOD POD, and the use of the wall mounted stadiometer. The use of both food
frequency questions and a 24-hour recall provided two complementary methods
of to assess nutrient intake and dietary behaviors, with consistent administration
of both evaluation tools. A standard protocol for 24-hour recalls data entry and
the exclusion of invalid recalls, insured quality recall data. Sample Size for
selected variables, such as body weight change and body composition change,
exceeded 60 individuals.

Results reported are of a subset of the entire team, due to missing data, or
. lack of reporting at one or more time points by some players (ie, questions about
meal frequency answered with fruit and vegetable frequency questions not
answered). In addition, there may be a significant difference between those who
chose not to participate, or who were not available a both baseline and endpoint,
and those for whom paired comparisons could be performed.

Sample size was reduced considerably for some variables, in particular for

some of the nutrient paired comparisons (n=7 for exchanges of vegetables; n=11

89

for grams of both protein and carbohydrate per kilogram of body weight); at
baseline, the weight loss group contained only 12 individuals, with nine for paired
comparisons. This was related to the number of paired, valid 24-hour recalls and
nutrition questionnaires that included the variable in question. This limits the
generalizability of these findings to the entire team. The attrition affected each
variable differently, as, for example, some players who participated in both
baseline and endpoint body composition assessment did not turn in one or both
24-hour recalls. This also limits comparison of results from one variable with the
results from another variable, when it is possible that different individuals were
represented in the sample for each variable. Additionally, the reliance on self
reported data, the use of only one 24-hour recall at each measurement point, and
variable individual follow up rate were all study limitations. The self administered
24-hour recall, without interviewer review or clarification, limits the usefulness of
associated variables, although specific instructions were provided to individuals
as they completed the recall. It is possible that those who provided more
detailed 24-hour recalls were not reflective of the entire team, but may have been
more conscientious about personal food choices or nutrient intake. Also, intakes
of numerous specific micronutrients which may play a role in performance,
recovery and health (ie, iron, Vitamin C, B vitamins) were not measured in this
current study. Further limitations include the lack of detail regarding fluid intake
and hydration, in particular alcohol intake; lack of performance data (ie, changes

in speed and strength) and biochemical data (ie, serum lipid levels). These

90

variables may play a role in future research to help explain changes which may
occur in % body fat, as well as performance.

Additionally, this study involved both observational data collection as well
as nutrition education (intervention), without the inclusion of a control group,
which limits the conclusions which can be drawn. However, it is important to
note that this was a pilot study geared to provide insight into the dietary behavior,
nutrient intake and anthropomorphic characteristics of a specific team with a goal

of tailoring future interventional strategies and materials.

91

VI. CONCLUSIONS AND IMPLICATIONS
Conclusions

Despite meeting minimum recommendations for some variables
assessed, the mean nutrient intake of MSU football players at baseline was far
from optimal. While meeting minimal lOM/DRI macronutrient recommendations,
the nutrient intake levels reported in this study did not meet performance nutrition
recommendations. In particular, the grams of protein and carbohydrate per
kilogram of body weight did not reflect current recommendations. Despite
apparent increases in those meeting fiber and Simple sugar recommendations,
the majority of players consumed inadequate fiber at both baseline and endpoint.
Players met protein recommendations at baseline, and appeared to decline at
endpoint while remaining within guidelines.

These results support the need for future nutrition intervention for this
population. In particular, the message regarding adequate carbohydrate and
protein should be strengthened to insure intake of these important
macronutrients is adequate. Additionally, nutrient dense carbohydrate food
choices should continue to be emphasized. Information about snack behaviors
should continue to emphasize the importance of pre- and post- workout snack
intake, with caution not to Simply increase total energy intake if the goal is weight

and body fat reduction.

92

Implications

Based upon the current study, future research could diverge in two
separate directions: investigations into the attitudes associated with specific
nutrition behaviors or behavior change, and investigations into the relationship
between nutrition behaviors, macronutrient intake, and measures of physical
performance as well as body composition.

1) The behavior of choosing to consume a pre- and post-workout snack
seems amenable to change within this population, as demonstrated by the
changes seen in snack behavior in the present study. Future research to
understand the attitudes, motivating factors and barriers which impact an
athlete’s decision to eat or not eat a pre- and/or post-workout snack would allow
tailoring of future interventions. Understanding the factors which influence other
dietary behaviors—such as fruit and vegetable intake, or the selection of high
fiber foods—would also be valuable.

2) Pre- and post-workout snack intake also has the potential to impact
performance measures (ie, endurance) and muscle anabolism. Future research
should focus on this behavior—and the Specific macronutrient composition of
snacks chosen—and the impact on body composition and athletic performance.

3) Micronutrient intake, and the nutrient density of food choices, may also
play a role in both performance and body composition. Future research could
explore the relationship between dietary behaviors (ie, snack intake) and nutrient

density, and athletic performance.

93

_‘ hing...

4) Longitudinal data collection of dietary behaviors and nutrient intakes in
MSU football players, as well as other Specific sports, could Shed light on
changes that occur both on and off-season, and over the course of an athlete’s
college career. The methodological lessons learned in this current study provide
valuable insight into the best construction of such a future data collection tool and

system.

94

APPENDIX A

95

SNAP

Spartan Nutrition and Performance Program
Sports & Cardiovascular Nutrition Program

WEIGHT GAIN STRA TEGIES FOR A THLETES

Energy (Calories)

Food containing carbohydrate, protein, and fat provide important nutrients and
energy (calories). Your body weight will remain the same if energy intake equals
energy expenditure. To achieve weight gain, more calories from food need to be
consumed than is expended through exercise and daily activities.

Weight gain, and particularly muscle gain, is best achieved by eating nutrient
dense calories from carbohydrate and protein with modest amounts of added fat.
This must be done in combination with a strength training program, and adequate
recovery time.

Key Tips!

* To gain one pound of weight per week, add approximately 500 cals/day (see
snack handout or below).

* If you gain the weight too quickly (more than 2 pounds/wk) you will be adding
more fat than muscle.

* Timing of intake is important—eat several meals and snacks each day. This will
ensure adequate fuel (carbohydrate and amino acids) are available for workouts
which will also help with recovery and the building of new muscle.

Carbohydrates (CHO) (4cals/g)

Types:
Simple Carbohydrates: sugar, sweets, candy, “pop”
Complex: Cereals, pasta, rice, potatoes, breads, fruits, veggies

Funcfions:
Stored as glycogen in muscle and liver. Limited storage.
Need to be supplied daily.

Best fuel for moderate & high intensity exercise. Primary fuel source in the brain.

Important for recovery & to help muscle building. Complex carbohydrates contain
many essential vitamins, and minerals

96

Amounts: 50-60% total calories ~3.4 grams per # body weight
Ex) 3.4 x 150# = 510 grams CHO
Ex) 3.4 x 250# = 850 grams CHO

Fat (9 cals/g)

Types:

Unsaturated fat (liquid at room temp) “healthy fats”: Vegetable oils i.e. canola 8
olive oil, nuts, seeds, fish oil

Saturated Fat (solid at room temp) “Unhealthy Fats”: Butter, lard, full fat dairy
products, coconut oil.

Funcfionsz
Is the primary fuel we burn at rest & during low intensity exercise.
Contain essential nutrients that aid in cell protection.

Healthy fats help regulate inflammation and the viscosity Ithickness of our blood

Amounts: 20-35% total calories (emphasize healthy fats)

Proteins (4 cals/g)

Types:
Lean red meat, poultry, fish, eggs, low fat dairy, soy, beans/legumes, nuts, seeds

Funcfions:
Muscles are made of protein.
Each protein is made of amino acids

Amino acids are the building blocks for protein synthesis Imuscle building &
repair

Note: If you are not eating enough calories or carbohydrate, protein will be used
for energy and limit your ability to make new muscle.

Amounts: 15-25% total calories
0.6-0.8 grams per # body weight
Ex) 150 pounds

0.7 grams x 150# = 105 grams
Ex) 250 pound

0.7 grams x 250# = 175 grams

Choose nutrient dense foods (carbohydrate, protein, vitamins, minerals).
0 Whole grain bread, pasta, and rice
0 Fruits and vegetables

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0 Eggs (if you have high cholesterol, consume in moderation)

o Beans (always mix with a grain, example rice & beans, beans, corn and a
tortilla)

0 Lean meats, poultry, fish

0 Dairy products (select low fat sources when possible; if lactose intolerance select
Lactaid or soy milk)

0 Nuts and seeds
0 Vegetable and fish oils
Decrease foods high in animal.(saturated) fat.
0 Fried foods (chicken, fish, potato chips, French fries)
0 High fat meats (bacon, sausage, ribs, skin on chicken, prime rib)
0 Foods loaded with cheese, sauces, and sour cream
Increase foods high in plant fat and fish oils (unsaturated fats)
0 Nuts and seeds (peanut butter, peanuts, almonds, cashews, walnuts)
o Beans
0 Vegetable oils (olive oil, canola oil, soybean oil, safflower and
sunflower oil)
0 Non-hydrogenated margarine (tub or spray margarine)
0 Fish
Choose your sugars wisely for training!
0 Instead of soda choose 100% juice, or sport drinks,
0 Instead of cakes, cookies, candy bars, and pies choose fresh fruit,
dried fruit, yogurt,
frozen yogurt, sorbet, reduced fat ice cream, whole grain muffins,
energy bars (Clifbar, Powerbar, Harvest bar, Pria bar, Luna bar)
Limit alcohol. Note: excess alcohol impairs protein synthesis/ muscle building.

98

EA TING PATTERNS FOR WEIGHT GAINERS and Sports Performance

Eat breakfast!
Breakfast is one of the most important meals for athletes. Training on an empty
stomach for an
athlete who wants to gain muscle mass is counterproductive. Instead of building
muscle you will break down muscle for energy.
0 Bowl of whole grain cereal, skim milk, banana, almonds, and a glass of
jurce.
0 PB & J sandwich, glass of milk, glass of OJ (or two pieces of fruit)
0 1 whole egg, 3 egg whites (particularly if you have high cholesterol), whole
grain toast, butter/margarine or peanut butter, jam, juice or fruit
Eat frequently!
Eating frequently (every ~3 hours) prevents athletes from energy drain and
excessive hunger.
0 Eat breakfast, lunch, dinner
0 Eat several snacks
Eat larger meals!
0 Focus on carbohydrates (pasta, rice, potatoes, cereals, breads, fruit,
vegetables)
0 Choose lean protein (lean red meat, pork, poultry, fish, low fat dairy, eggs,
beans including soy)
- Adding healthy fats will add calories, nutrients and flavor (ex. olive oil,
nuts, avocado)
- Add items to your meals (crackers, tortilla chips, bread, dips, yogurt,
milk, granola bars,
banana bread, muffin, sport bar, fresh fruit, frozen yogurt or ice cream)
Avoid feeling hungry!
A hungry athlete has low energy levels, may experience muscle wasting, and
delayed recovery.
0 Do not skip meals and snacks-«plan ahead, have snacks in your book
bag.

TIMING PATTERNS FOR WEIGHT GAINERS

Pre-training meals and snacks
- 3-5 hours prior to exercise eat a meal that is familiar to you and easily
digestible
(low fat, moderate fiber) high in carbohydrate, moderate fat and protein,
and 16-24oz.
of fluid (see Meals handout for ideas).
a 45-60 minutes before training have a small snack that is high in carb,
moderate in protein and low in fat (see 250 calorie snack handout)
Fueling during exercise
0 Drink 24 cups of Sport drink per hour (4-8 oz each 15-20 minutes) to
fuel your training,

99

replace carbohydrate and electrolyte lost, and stay hydrated.
Eating for recovery
Recovery after training, particularly weight lifting, is the best time to maximize
protein building
and weight gain.
0 Eat within 60 minutes of working out, include carbohydrate (70-100 g)
and protein
(20-30g) to stimulate greatest results (see 500 cal snacks handout).
o Follow-up 1-2 hours later with meal high in carbs and moderate protein
(Meals handout).
0 Replace fluid, electrolytes, and carbohydrate with 100% juice (orange,
apple, grape) or a sport drink. Key tip: drink 3 cups of fluid for every
pound lost during workout.

500 Calorie snack examples

Peanut Butter & Jelly Sandwich + 1 cup 1% Milk

Bagel + 2 T. Peanut Butter

Turkey and Cheese Sandwich + 2002. Sports Drink

1 cup Kashi Go Lean Crunch + 1 cup 1% Milk + 2002 Sports Drink

Be creative with your protein sources, choose those with less saturated (solid) fats. If you are
eating a plant proteins

always mix beans with a grain and nuts and seeds with a grain. For example beans and rice,
peanut butter with bread.

TIPS AND HINTS FOR YOU

100

SNAPP

Spartan Nutrition and Performance Program
Sports & Cardiovascular Nutrition Program

WEIGHT LOSS STRATEGIES FOR
A THLETES

Energy (Calories)

Food containing carbohydrate, protein, and fat provide important nutrients and
energy (calories). Your body weight will remain the same when energy intake
equals energy expenditure.

Weight loss pasicsz To minimize muscle loss, and have energy to train, you
should not lose more than 1-2 pounds per week. A one pound weight loss is
equal to a 3500 calorie deficit. To lose 1 pound per week you need a 500 calorie
deficit per day (500 cals x 7 days = 3500). You could achieve this by eating 500
fewer cals /day, or eating 250 fewer cals and expending 250 extra cals. A Simple
way to remove calories from your intake is to remove excess amounts of empty
calories in your meals and snacks. Negative side effects of losing weight too
quickly include, muscle loss, fatigue, dehydration, illness are and are common
when using inappropriate methods such as fasting, high protein diets and
laxafives.

Carbohydrates (CHO) (4cals/g)

Types:

Simple Carbohydrates: sugar, sweets, candy, “pop"
Complex: Cereals, pasta, rice, potatoes, breads, fruits, veggies

Funcflons:
Stored as glycogen in muscle and liver. Limited storage.
Need to be supplied daily.

Best fuel for moderate & high intensity exercise. Primary fuel source in the brain.
Important for recovery & to helping muscle building. Complex carbohydrates
contain many essential vitamins, and minerals

Amounts: 50-60% total calories.
~2.6 grams per # body weight

Ex) 2.6 x 150# = 390 grams CHO
Ex) 2.6 x 250# = 650 grams CHO

101

Fat (9 cals/g)

Types:

Unsaturated fat (liquid at room temp) “healthy fats”: Vegetable oils i.e. canola 8
olive oil, nuts, seeds, fish oil

Saturated Fat (solid at room temp) “Unhealthy Fats”: Butter, lard, full fat dairy
products, coconut oil.

Funcfions:

Is the primary fuel we burn at rest & during low intensity exercise.

Contains essential nutrients that aid in cell protection.

Healthy fats help regulate inflammation and the viscosity Ithickness of our blood

Amounts: 20-35% total calories (emphasize healthy fats)

Proteins (4 cals/g)

Types:
Lean red meat, poultry, fish, eggs, low fat dairy, soy, beans/legumes, nuts, seeds

Funcfions:

Muscles are made of protein.

Each protein is made of amino acids. Amino acids are the building blocks for
protein synthesis Imuscle building & repair

Noterlf you are not eating enough calories or carbohydrate, protein will be used
for energy and limit your ability to make new muscle.

Amounts: 15-25% total calories
0.6-0.8 grams per # body weight
0.8 grams x 150# = 120 grams
0.8 grams x 250# = 200 grams

KEY GUIDELINES for ACHIEVING WEIGHT LOSS

Choose Nutrient Dense foods that will provide satiety (fullness). See snack
& meal examples

0 Quality protein sources: Poultry, fish, red lean meat, beans, eggs/ egg
whites (moderation on yolks if you have high cholesterol)

0 Select Fiber rich foods: Whole grain breads, cereals, pasta, and brown
rice, legumes

0 Select Low fat dairy (milk, cottage cheese, yogurt, 8 cheese), if lactose
intolerant try Lactaid or soy.

o Consume whole pieces of fruit rather than drinking large amounts of
juice or fruit punch.

102

o Consume a variety of vegetables. Consume nuts and seeds and other
healthy fats in moderation.
Reduce the intake of foods with excess added fat.
Fried foods (chicken, potato chips, French fries)
Biscuits with gravy, croissants
Cream based soups and sauces (select those made with broth)
High fat meats (bacon, sausage, ribs, skin on chicken, prime rib)
Lower the amount of added sugar.
Instead of regular soda replace with flavored water, diluted juice, sports
drinks or diet soda in moderation.
Instead of cakes, cookies, and pies choose whole grain muffins, fruit or
yogun
Limit or avoid alcohol. Alcoholic beverages are high in calories and lead to
dehydration. Excess alcohol intake impairs muscle synthesis. Note: 1 light beer or 5 02
wine or 1.5 oz of liquor is ~100 cals

EA TING PATTERNS (see snack and meal handouts for examples)

Eat frequently throughout the day.
. Eating every few hours maintains your energy level and maximizes
recovery from training and competition.
. Avoid becoming over-hungry.
. Eat frequently to maintain satiety (fullness) and prevent overeating.
. Do not eat too fast (Once you begin eating it takes approximately 15
minutes for your brain to sense you are getting full).
. Ask yourself if you’ re hungry before you start eating.
Focus on eating and enjoy it (minimize TV while eating).
Eat breakfast everyday.
. Jump starts your engine for the day.
. Reduces chances for overeating later in day.
. Ensures a high quality workout.
. Maximizes protein building and carbohydrate storage in the muscle.
Reduce portion sizes of your high-fat and high-sugar choices
. Avoid super-sized meals and drinks.
. Share a meal with a friend or use a doggy bag.

Focus on nutrient dense choices and you can eat a larger volume of food without
excess calories (for example instead of consuming a large cheese burger, large
fries and large coke (~1800 cals) select a grilled chicken sandwich with side
salad, fruit and a baked potato and you’ll have more nutrients and ~500 fewer
calories.

FUELING PA T T ERNS

103

Pro-training meals and snacks

A 3-5 hours prior to exercise eat a meal that is familiar to you and easily
digestible (low fat, moderate fiber) high in carbohydrate, moderate fat
and protein, and 16-24oz. of fluid (see Meals handout for ideas).

0 45-60 minutes before training have a small snack containing some
protein (~10 grams) and carbohydrate (~ 40 grams) and a small
amount of fat (see 250 cal snack list)

Fueling during exercise

A Drink 2-4 cups of water per hour (4-8 02 each 15-20 minutes) to stay
hydrated. Maintaining hydration is an easy way to maintain peak
performance.

A If training or competition lasts longer than 60 minutes use sports drink
(Gatorade) instead of water.

Eating for recovery

Recovery after training, particularly weight lifting, is the best time to maximize
protein building

and weight gain.

A Eat within 60 minutes of working out, include carbohydrate (70-100 9)
and protein (20-30g) to stimulate the greatest results (see 500 cal
snacks handout).

A Follow-up 1-2 hours later with meal high in carbs and moderate protein
(Meals handout).

A Replace fluid, electrolytes, and carbohydrate with 100% juice (orange,
apple, grape) diluted with water or a Sport drink.

Key tip: drink 3 cups of fluid for every pound lost during workout.

TIPS AND HINTS FOR YOU

104

SNAPP

Spartan Nutrition and Performance Program
Sports & Cardiovascular Nutrition Program

MAINTAINING OR IMPROVING BODY
COMPOSITION

Energy (Calories)

Food containing carbohydrate, protein, and fat provide important nutrients and
energy (calories). Your body weight will remain the same if energy intake equals
energy expenditure. To maintain or improve your body composition (more
muscle and less fat) and optimize your training and performance, what you and
when you eat is important. The tips below combined with the snack and meal
handouts will help you select a food pattern that will provide you with the proper
amounts of carbohydrate, protein, fat and nutrients for training and game days.

* If Your goal is to maintain wgight or imrove your body composition- try to keep
a consistent pattern and avoid eating too few calories, or too little carbohydrate
or protein. The proper balance and the timing of intake are essential to avoid
muscle breakdown and have carbohydrates available to fuel muscles.

*If you need to gain a few pggnds of muscle add 250— 500 cals/day to promote a
gradual gain (see snack examples on back page & separate snack handoUt). If
you gain the weight too quickly (more than 2 pounds/wk) you will be adding more
fat than muscle.

* Timing of intake- eat several meals and snacks each day. This will ensure
adequate fuel (carbohydrate and amino acids for muscle) are available for
workouts which will also help with recovery and the building of new muscle (see
page 2-3 below)

Carbohydrates (CHO) (4cals/g)

Types:

Simple Carbohydrates: sugar, sweets, candy, “pop”
Complex: Cereals, pasta, rice, potatoes, breads, fruits, veggies

Funcfions:

Stored as glycogen in muscle and liver. Limited storage.
Need to be supplied daily.

105

Best fuel for moderate 81 high intensity exercise. Primary fuel source in the brain.
Important for recovery & to help muscle building. Complex carbohydrates contain
many essential vitamins, and minerals.

Amounts: 50-60% total calories ~3 grams per # body weight
Ex) 3 x 150# = 450 grams CHO
Ex) 3 x 250# = 750 grams CHO

Fat (9 cals/g)

Types:

Unsaturated fat (liquid at room temp) “healthy fats”: Vegetable oils ie. canola &
olive oil, nuts, seeds, fish oil

Saturated Fat (solid at room temp) “Unhealthy Fats”: Butter, lard, full fat dairy
products, coconut oil.

Funcfions:
Is the primary fuel we burn at rest & during low intensity exercise.
Contain essential nutrients that aid in cell protection.

Healthy fats help regulate inflammation and the viscosity Ithickness of our blood

Amounts: 20-35% total calories (emphasize healthy fats)

Proteins (4 cals/g)

Types:
Lean red meat, poultry, fish, eggs, low fat dairy, soy, beans/legumes, nuts, seeds

Funcfions:

Muscles are made of protein.

Each protein is made of amino acids. Amino acids are the building blocks for
protein synthesis Imuscle building & repair

Note: If you are not eating enough calories or carbohydrate, protein will be used
for energy and limit your ability to make new muscle.

Amounts: 15-25% total calories
0608 grams per # body weight
Ex) 150 pounds

0.7 grams x 150# = 105 grams
Ex) 250 pound

0.7 grams x 250# = 175

106

Choose nutrient dense foods (carbohydrate, protein, vitamins, minerals).
0 Whole grain bread, pasta, and rice
- Fruits and vegetables
0 Eggs (if you have high cholesterol, consume in moderation)

o Beans (always mix with a grain, example rice & beans, beans, corn and a
tortilla)

Lean meats, poultry, fish

Dairy products (select low fat sources when possible; if lactose intolerance select
Lactaid or soy milk)

0 Nuts and seeds
0 Vegetable and fish oils
Decrease foods high in animal (saturated) fat.
0 Fried foods (chicken, fish, potato chips, French fries)
0 High fat meats (bacon, sausage, ribs, skin on chicken, prime rib)
0 Foods loaded with cheese, sauces, and sour cream
Increase foods high in plant fat and fish oils (unsaturated fats)
- Nuts and seeds (peanut butter, peanuts, almonds, cashews, walnuts)
- Beans / legumes
0 Vegetable oils (olive oil, canola oil, soybean oil, safflower and
sunflower oil)
- Non-hydrogenated margarine (tub or spray margarine)
0 Fish
Choose your sugars wisely for training!
0 Instead of soda choose 100% juice, or Sport drinks,
0 Instead of cakes, cookies, candy bars, and pies choose fresh fruit,
dried fruit, yogurt,
frozen yogurt, sorbet, reduced fat ice cream, whole grain muffins,
energy bars (Clifbar, Powerbar, Harvest bar, Pria bar, Luna bar)
Limit alcohol- Note: excess alcohol impairs protein synthesis/ muscle building.

EA TING PATTERNS FOR TRAINING & PERFORMANCE

Eat breakfast!
Breakfast is one of the most important meals for athletes. Training on an empty
stomach for an
athlete who wants to gain muscle mass is counterproductive. Instead of building

muscle you will break down muscle for energy.
0 Bowl of whole grain cereal, skim milk, banana, almonds, and a glass of
juice.

PB & J sandwich, glass of milk, glass of OJ (or two pieces of fruit)
1 whole egg, 3 egg whites (particularly if you have high cholesterol), whole
grain toast, butter/margarine or peanut butter, jam, juice or fruit

107

Eat frequently!
Eating frequently (every ~3 hours) prevents athletes from energy drain and
excessive hunger.
- Eat breakfast, lunch, dinner
0 Eat several snacks
Eat Regular Meals!
0 Focus on carbohydrates (pasta, rice, potatoes, cereals, breads, fruit,
vegetables)
0 Choose lean protein (lean red meat, pork, poultry, fish, low fat dairy, eggs,
beans including soy)
- Select healthy fats to add nutrients and flavor (ex. olive oil, nuts,
avocado)
Avoid feeling hungry!
A hungry athlete has low energy levels, may experience muscle loss, and
delayed recovery.
c Do not skip meals and snacks---plan ahead, have snacks in your book
bag.

TIMING PA T T ERNS FOR PERFORMANCE

Pre-training meals and snacks
o 3-5 hours prior to exercise eat a meal that is familiar to you and easily
digestible
(low fat, moderate fiber) high in carbohydrate, moderate fat and protein,
and 162402.
of fluid (see Meals handout for ideas).
45-60 minutes before training eat a small snack that is again high in
carbs with moderate
amounts of protein and low in fat (see 250 calorie snack handout).
Fueling during exercise
0 Drink 24 cups of Sport drink per hour (4-8 02 each 15-20 minutes) to
fuel your training,
replace carbohydrate and electrolyte lost, and stay hydrated.
Eating for recovery
Recovery after training, particularly weight lifting, is the best time to maximize
protein building
and weight gain.
0 Eat within 60 minutes of working out, include carbohydrate (70-100 9)
and protein
(20-30g) to stimulate greatest results (see 500 cal snacks below & handout).
o Follow-up 1-2 hours later with meal high in carbs and moderate protein
(Meals handout).
- Replace fluid, electrolytes and carbohydrate with 100% juice (orange,
grape, etc) diluted in juice or a sport drink. Key tip: drink 3 cups
of fluid for every pound lost during workout.

108

500 Calorie snack examgles

Peanut Butter & Jelly Sandwich + 1 cup 1% Milk
Bagel + 2 T. Peanut Butter
Turkey and Cheese Sandwich + 2002. Sports Drink

1 Pita Bread + 1/4 cup Hummus or Refried Beans + 1 Banana + 2 sticks
String Cheese

1 cup Kashi Go Lean Crunch + 1 cup 1% Milk + 2002 Sports Drink
Ioz Nuts + 102 Dried Fruit + 102 Tortilla Chips + 1/2 cup Guacamole

Be creative with your protein sources, choose those with less saturated (solid) fats. If you are
eating a plant proteins always mix beans with a grain and nuts and seeds with a grain. For
example beans and rice, peanut butter with bread.

TIPS AND HINTS FOR YOU

109

MSU Football Spartan Nutrition & Performance Program (SNAPP)
Dept of Radiology, Sport and Cardiovascular Nutrition Program

Group session 1 Discussion Questions (Wt gain 8. maintenance Groups) 1l26l06

1. Matching- How many calories per gram are in the following? (answers can be used more than
once)

Protein 6) 4
Carbohydrate __ b) 7
Fat __ c) 9
Alcohol __

2. Which of the following are examples of complex carbohydrates? (select more than one)
a) bread b) corn 0) rice (1) table sugar 6) Potatoes 1‘) honey

3. Which of the following are examples of unsaturated fats? (select more than one)
a) Com oil b) Coconut oil 0) lard (1) Olive oil

4. Of the following pairs of food items circle the item that is the most “Nutrient Dense”
- 6 oz of French fries or 6 oz of baked potato - fried chicken or baked chicken

- 1 slice of white bread or 1 slice of whole grain bread -12 oz of coke or 12 oz of grape
juice

5. If a football player is not eating enough carbohydrate, what are some of the negative
consequences this athlete will experience? (select one or more)

a) low carbohydrate stores (glycogen) in liver and muscles e) early fatigue and cramping of
muscles

b) ability to do high intensity exercise will be impaired 1) increased protein will be used for
energy
c) dehydration of muscles g) all of the above

6. During football training and in season, the portion of total calories that should come from
carbohydrates is?
' a) 30-40% h) 40-50% 0) 50-60% (I) 60-70%

7. How many meals and snacks should you consume during training or in season?
meals snacks

What is the reason for this?
8. Assume you weigh yourself before and after a workout to determine how much weight you
lose during the workout. To properly re-hydrate, it is recommended you consume how many
cups of fluid per pound lost. Note a cup equals 8 ounces.

6) 1 cup b) 2 cups c) 3 cups d) 4 cups

9. If wanted to gain one pound of weight per week, approximately how many extra calories
would you need to eat per day? (assume you did not change your exercise level)

a) 250 calories b) 500 calories c) 1000 calories d) 2000 calories

110

 

 

Group session 1 Quiz Discussion Answers
1. Matching- How many calories per gram are in the following? (answers can be used more than once)

Protein _4__ a) 4
Carbohydrate _4_ b) 7
Fat _9_ c) 9
Alcohol _7_

2. Which of the following are examples of complex carbohydrates ?(select more than one)

a) bread b2 corn cl rice (1) table sugar e2 Potatoes f) honey

Notes: Table sugar and honey are simple sugars. Consume a mixture of carbohydrates but emphasize
complex carbohydrates and particularly those that are whole grain. These will provide you the most
nutrients as well as load your carbohydrate energy tanks in your muscles and your liver which will help you
perform high intensity work

3. Which of the following are examples of unsaturated fats? (select more than one)

a) Corn oil b) Coconut oil c) lard dz Olive oil
Notes: unsaturated or unsolid fats are the healthy fats that do not clog up your vessels. Unsaturated fats
also include important nutrients including vitamin E. Saturated fats (solid at room temp) tend to clog up
blood vessels and increase your risk of heart disease and strokes

4. Of the following pairs of food items circle the item that is the most “Nutrient Dense”

- 6 oz of French fries or 6 a; at baked gotato (also '/2 the calories) - fried chicken or baked chicken
§t_1/3 less cals

- 1 slice of white bread or 1 slice at whole grain bread -12 oz of coke or 12 0; at graze juice

The underlined choices all have more nutrients per amount of calories and are more nutrient Dense.

5. If a football player is not eating enough carbohydrate, what are some of the negative consequences this
athlete will experience? (select one or more)
a) low carbohydrate stores (glycogen) in liver and muscles 6) early fatigue and cramping of muscles

b) ability to do high intensity exercise will be impaired 1) increased protein will be used for energy
c) dehydration of muscles g1 all at the above

Notes: Adequate carbohydrate is ESSENTIAL for Optimizing training and performance

6. During football training and in season, the portion of total calories that should come from carbohydrates
is?

a) 30-40% b) 40-50% cl 50-60% (1) 60-70%

Notes: see menu examples for ideas. Carbohydrates not only provide important stored energy for your
muscles during exercise, they provide important nutrients that also assist in performance and help you
recover and grow stronger.

7. How many meals and snacks should you consume during training or in season?_3_ meals_2-4 snacks

What is the reason for this? To ensure a regular suggly at carbohydrates, grotein and (at, nutrients and
energy to tuel workouts, avoid muscle breakdown and gromote building or regain

8. Assume you weigh yourself before and after a workout to determine how much weight you lose during
the workout. To pr0perly re-hydrate, it is recommended you consume how many cups of fluid per pound
lost. Note a cup equals 8 ounces. 6) 1 cup b) 2 cups c) 3 cues d) 4 cups
Notes: small levels at dehydration will imgair abiligy to train and gertorm. As you become dehydrated

our heart rate will increase and our abili to cool our bod is im aired leadin to earl ati ue and
cramps.

9. If wanted to gai_n one pound of weight per week, approximately how many extra calories would you
need to eat per day? (assume you did not change your exercise level)
a) 250 calories b) 500 calories 0) 1000 calories (1) 2000 calories

111

 

 

Notes: a one gound weight gain reguires around 3500 extra calories. 500 extra calories a day x 7 days =
35 00 calories or I gound gain. To gain 2 Bounds ger week 1000 extra calories a day [or a week = 7000
calories or 2 gounds

To gain as much muscle as gossible during weight gain, weight needs to be gained gradually. A good
target is 5 00 Jun to 1000 extra calories ger day selecting nutrient dense toads with groger amount at
grotein, carbohydrate, and healthy tats. Also adhere to your training regimen and allow [or adeguate
recoveyy and rest.

 

112

MSU Football Spartan Nutrition 8. Performance Program (SNAPP)
Dept of Radiology, Sport and Cardiovascular Nutrition Program

Group session 1 Discussion Questions (Wt loss Group) 1126/06

1. Matching- How many calories per gram are in the following? (answers can be used more than
once)

Protein a) 4
Carbohydrate __ b) 7
Fat __ c) 9
Alcohol__

2. Which of the following are examples of comlex carbohydrates? (select more than one)
a) bread b) corn 0) rice (1) table sugar e) Potatoes 0 honey

3. Which of the following are examples of unsaturated fats? (select more than one)
a) Com oil b) Coconut oil 0) lard (1) Olive oil

4. Of the following pairs of food items circle the item that is the most “Nutrient Dense”
- 6 oz of French fries or 6 oz of baked potato - fried chicken or baked chicken

- 1 slice of white bread or 1 slice of whole grain bread -12 oz of coke or 12 oz of grape
juice
5. If a football player is not eating enough carbohydrate, what are some of the negative

consequences this athlete will experience? (select one or more)

a) low carbohydrate stores (glycogen) in liver and muscles 6) early fatigue and cramping of
muscles

b) ability to do high intensity exercise will be impaired 1) increased protein will be used for
energy
0) dehydration of muscles g) all of the above

6. During football training and in season, the portion of total calories that should come from
carbohydrates is?
a) 30-40% h) 40-50% 0) 50-60% (1) 60-70%

7. How many meals and snacks should you consume during training or in season?
meals snacks

What is the reason for this?

8. Assume you weigh yourself before and after a workout to determine how much weight you
lose during the workout. To properly re-hydrate, it is recommended you consume how many
cups of fluid per pound lost. Note a cup equals 8 ounces.

a) 1 cup b) 2 cups c) 3 cups d) 4 cups

9. If your goal is to lose one pound of weight per week, and you keep your exercise and physical

activity level the same, you will need to consume fewer calories per day.
a) 250 calories b) 500 calories 0) 1000 calories ' d) 2000 calories

113

S NAPP

Spartan Nutrition and Performance Program
Sports & Cardiovascular Nutrition Program

SNAPP FOOTBALL ME E TIN G 2/23/06
Fueling for Winter Conditioning;
Optimizing the Timing of Food Intake

Monday, Wednesday, & Friday
4:30- 4:45 AM - Wake up and have a small snack;

Ideally the snack should contain some Protein (~ 10-15 grams). Carbohydrates (40-50
grams) and only small amounts of fat. For example * Glass of low-fat or skim milk and
a piece of bread

* Bowl of cereal with milk. * A traditional power bar and a glass of water. * Yogurt
and a nutra-grain bar. * '/2 PB & Jelly sandwich (~ 1 T PB) and a glass of milk. See 250
snack list for other examples v

Examples you can include:

 

5:30 — 6:30 AM- Workout

7:00-7:30 - Eat Breakfast or a large snack within 30 to 60 minutes post- work out
Approximately 30 grams of Protein and 100 grams of Carbohydrates
Emphasize good sources of protein, carbs and healthy fats
See meal plans or 500 calorie snacks for examples

Mid Morning (9-10:00 ish) - Have a Snack (250-500 calories)

*PB & Jelly Sandwich and a glass of milk * Four handfuls of trail mix (peanuts, raisins,
etc)

* Powerbar and a piece of fruit * Yogurt & a piece of fruit. See snack list for other
ideas

Examples you can include:

 

Lunch (11:00- 1:00) - Should be about ‘A of your food intake for the day
Emphasize good sources of protein, carbs and healthy fats

Afternoon Workout - Make sure you have eaten something approximately 45 minutes to
2 hours before your workout. For example- if your workout is at 2:00 and your lunch
was at noon you are fine. If you lift at 3:00 and your lunch was at noon have a small
snack around 2:00 -

114

Post work out snack- have a snack within 30 -60 mins after workout (~ 500 calories)
Examples for your afternoon snack:

 

5:30 Team Meal

Evening Snack
Examples for your evening snack:

 

Key Summayy
Focus on “grazing pattern” including a good sources of Protein, Carbohydrates and

healthy fats. Focus on nutrient- dense foods. Protein- Lean sources of meats, beans,
low-fat dairy; Carbs- include whole grains, potatoes, a variety of vegetables, fruits; Fats-
emphasize the healthy unsaturated liquid fats. Hydrate Frequently!

115

S NAPP

Spartan Nutrition and Performance Program
Sports & Cardiovascular Nutrition Program

NUTRITION GOALS FOR PERFORMANCE &
HEALTH

If your goal is to gain muscle or reduce overall body weight, or to improve your ability train at a
higher intensity and recover quickly, your nutrition is important. After reviewing the below
examples, on the back side, list at least 2 specific measurable nutrition behaviors you can do to
improve your chances of reaching your performance and health goals over the next couple of
weeks. Strive to meet your goals at least 5 of 7 days per wfl.

Examgle One: John Sleepers primary goal is to gain 5-10 pounds of lean body/muscle mass.
For the last two months he has struggled to maintain his weight and is often fatigued after
workouts. He typically eats two meals and two snacks per day with no early morning snack or
lunch (sleeps until 15 min before AM workout).

Goal 1- Add 1 meal and an early morning snack that provides 750-1000 extra calories Iday.
Wake-up 45 min before workout and have a small snack. Will eat a lunch or large snack that
contains 500-750 calories.

Goal 2 - Check weight 2 times per week at the same time of day and adjust calories as needed.

Example Two: Jake Buck’s primary goal is to lose 15 pounds by the start of Spring ball.
Currently Jake eats 2-3 meals a day and one snack. Typically two of his meals/ day are at
Burger king or McDonalds. Jake also drinks at least 36 ounces or more of coke per day. Twice a
week, he usually drinks 4 beers.

Goal 1- Reduce calorie intake 500-1000 calories per day by replacing lunch at Mcdonalds or
Burger King meal with a meal at Subway (or similar) or pack a lunch that is has less fat and total
calories. For example a 1 foot long chicken sub with veges, baked chips, 16 ounces of juice and
16 ounces of water (savings ~500-700 calories compared to a burger meal with fries and a large
coke)

Goal 2 - Beverage choices- Reduce coke intake to one per day (12 ounces) and replace with
water, (will save ~ 300 cals per day). Reduce typical beer intake from 4 to 3 and drink “light" beer
(~100 cals vs 150 calories). Total savings for week 600- 700 calories

Goal 3 - In addition to regular workouts, spin on the bike for 20 minutes 3 days per week (~600
cals Iwk)

Note: The above changes would result in approximately 5500 calorie deficit or a 1.5 pound weight
loss per week. In ten weeks Jake would achieve his 15 pound weight loss goal.

Examgle Three: Frank’s body weight and body composition appears to be ideal however Jake
has elevated blood pressure and half-way through practices and games he often experiences

116

fatigue and cramps. He tends to follow a high protein low carbohydrate eating plan that is low in
fruits and vegetables.

Goal 1- select two meals per day based on examples on your SNAPP meal plan handout
Goal 2- eat a minimum of 6 fruits and vegetables per day.

Note: fruits and vegetables contain nutrients that help reduce blood pressure and cramps (ex
Potassium, magnesium). Also the SNAPP meal plans ensure you achieve carbohydrate
recommendations which will also help prevent fatigue and cramps.

Your turn- on the back, list 2 or more measurable nutrition goals you will work on over the
next several weeks. Put in folder in front of Coach Mennie's office. They will be reviewed
8. returned.

 

 

Good Luck!

The SNAPP Staff

Joe Carlson, PhD, RD; Program Director Joe.Cgrlson@;gdioloqy.msu.edu ;
Scott Sehnert, MS, RD; Coordinator Scott.sehnert@radiology.msu.edu;
Heidi Clark BS, RD; Graduate Student clarkhe4@msu.edu

117

APPENDIX B

118

SNAPP

Spartan Nutrition and Performance Program
Sports & Cardiovascular Nutrition Program

Name: Date:

 

Contact information:

 

 

 

 

 

 

Sport: Position or Event:
Date of Birth: Height: Weight:
Desired Weight: How long have you been at current weight?

 

Why are you seeing a sports dietitian?

 

What are your goals?

 

Rate your eating habits. (circle one) Good Fair Poor

Do you grocery shop? Do you cook for yourself? If yes, what foods?

 

 

 

How often do you eat out? times per week. What types of restaurants do
you visit (pizza, burgers, Chinese, etc)?

 

Do you take any vitamin/mineral or dietary supplements? If so, please list type
and amounts taken.

 

Are you currently taking any medications (over-the-counter and prescription)? If
so, list meds and dosage.

 

119

Are you allergic/intolerant to any foods? If so, please list them.

 

Describe a typical day of food intake. (time, amount, and types of food and fluids)

 

 

 

 

Do you drink Alcohol? If yes how many drinks per week (ex of 1 drink 12 oz beer,
5 oz wine, 1.5 oz of alcohol)

 

Describe your training regimen (times per week, type, intensity, and duration of
training

 

 

 

 

120

S NAPP

Spartan Nutrition and Performance Program
Sports & Cardiovascular Nutrition Program

Goal Setting for Nutrition and Performance

Primary nutrition related goal below (weight loss. ggin etc.)

 

Set two or three measurable nutrition behaviors you will do at least 5 days
per week to achieve you primary goal (also can include a exercise related goal)

1)

2)

3)

Athlete
RD

 

 

121

List your primary nutrition related goal below
Examples

* lose 10 pounds by April 15th (on average a 1 pound loss per week)

* gain 5 pounds of muscle mass by April 15th ( on average a 1/2 pound gain per
week)

List two or three measurable nutrition behaviors you will strive to do at least 5
days per week to achieve you

primary goal.

Examples for weight loss
Reduce calorie intake by 500-1000 calories per day by:

1. Replacing 1/:t of soda“pop” intake with water

2. Reduce Fast food burger meals from 2 times per day to 1 time per day and
replace with an different choice

(subway sub with 6 grams of fat or less, or a pack a homemade burrito, with a
couple of pieces of fruit,

tortilla chips; or two peanut butter and jelly sandwich’s with fruit, crackers.

3. Replace high caloric snacks (2 Donuts with 16 02 whole milk with a lower
calorie snack (bowl of cereal

with 1% fat milk)

4. In addition to regular workouts — spin on the bicycle for 20-30 minutes 3 times
per week.

Examples for Weight Gain

Increase calorie intake by 500-1000 calories per day by adding two or more 250 -
500 calorie snacks per day:
see snack list for examples

1. A power bar and 6 oz yogurt or a peanut butter and jelly sandwich and a glass
of milk

2. Check weight at the same time of the day once per week and adjust calories
intake as needed.

122

 

S NAPP

Spartan Nutrition and Performance Program
Sports & Cardiovascular Nutrition Program

Meals For Training and Competition

Serving Sizes: T: Tablespoon; t: teaspoon; cup: 8 oz; 4 oz ~ 100 g; 3 02 = palm
size

Breakfast (Each meal contains approximately 550 calories, 909. carbohydrate,
259. protein, 15g. fat)

Cereal

0 2 cup whole grain cereal (3 3 9 fiber), optional add sliced almonds or pecans
to cereal

o 1 cup berries (frozen or fresh) or sliced fruit

0 1.5 cup low-fatlskim/soy milk

- 602. 100% fruit juice

Yogurt & Grains

o 8 oz low-fat yogurt + 1/2 cup Crunchy cereal (granola, grape nuts, all bran,
kashi go lean crunch)

o 1 slice whole grain bread with 1 Tbsp natural nut butter (peanut, almond) & 1
Tbsp jam/preserves

a Piece of fruit and/or 802 juice

Bagel, Toast, or English Muffin

- Bread source (2 slices or whole muffin or small bagel) + 2 Tbsp natural nut
butter (peanut, almond)

o 1 cup of Iow-fat/skim/soy milk

. Piece of fruit or 802 juice

Complete Oatmeal

2 packet of quick oats or 2 cups cooked oatmeal

1 cup low-fatlskim/soy milk (to drink or in oatmeal)
1/2 oz. nuts (~20 peanuts, 15 almonds)

2 T. raisins

Pancakes/Waffles/French Toast

 

123

o 2 pancakes (medium size)/1 large waffle/ 2 pc French toast
0 1 Tbsp nut butter

0 1 Tbsp maple syrup, honey, or fruit jam

0 Fruit (berries, whole piece, banana slice on top)

- 1.5 cup low-fat/skim/soy milk

Eggs

0 2 eggs + 2 egg whites (scrambled, hard-boiled)

- 1 slices whole grain bread + 1 Tbsp jam/preserves

o 1.5 cup melon or berries

o 1/2 cup (2.5 02) real potato hashbrowns (w/ 1 t olive oil)
0 1 cup low-fat/skim/soy milk

Omelet

1 egg + 2 egg whites

1 cup vegetables (spinach, mushrooms, red and/or green peppers)
1 cup mixed melon (cantaloupe, watermelon, honeydew)

2 Slices whole grain bread + 2 Tbsp jam/preserves

1 cup low-fatlskim/soy milk

Grits

1 cup grits

1 cup fruit

2 slices whole wheat toast
2 boiled eggs

1 cup skim milk

Lunch (Each meal contains approximately 550 calories, 90g carbohydrate, 20g.
protein, 10g. fat)

Sandwich

_ o 2 slices of whole grain bread + 3 oz (3 slices) deli meat or tuna salad (+
lettuce, tomato, sprouts, etc). Optional mustard or light mayonnaise

0 1 cup raw veggies (baby carrots, bell peppers, cherry tomatoes) or 1 cup
salad with light dressing
1 cup or1 medium fruit
Handful of pretzels
1 cup low-fatlskim/soy milk

Wrap

o 1 large whole wheat wrap

- 1/2 medium can (~ 3 oz) tuna (mix with a small amount of spicy mustard,
mayonnaise, chopped carrots, celery, bell peppers, seasoning; or use 1 t
olive oil + vinegar) or 3 oz turkey or chicken breast

o 1 cup or1 medium fruit

124

 

o Handful of Wheat Thin crackers
o 1 medium chocolate chip cookie
o 1002 sports drink

Soup and Salad

0 2 cups broth based soup (chicken noodle, minestrone, tomato vegetable)

o 2 cups mixed salad greens with a variety of vegetables (carrots, tomato,
cucumber, beans, etc) with light dressing or vinaigrette

o 1 whole wheat roll + 1 tbutter

Handful of wheat thins

1 medium fruit

Pasta

1.5 cup pasta (white or whole wheat) with 1/2 cup tomato sauce

1 T grated parrnesan cheese

2 T low fat cottage cheese for topping

2 cups mixed salad greens (and any other raw veggies) with vinaigrette
1 piece whole grain bread (any variety)

Pasta Salad
0 1 cup pasta (white or whole wheat)
o 2 oz grilled chicken, V2 medium can tuna, or smoked salmon

o 1 cup mixed vegetables (thawed frozen peas, chopped carrots, tomatoes,
broccoli)

1 T vinaigrette dressing
1 cup or 1 medium fruit

PB & J

2 slices whole grain bread

1 T peanut butter + 1 T jam or preserves
1 cup or 1 medium fruit

8 oz low-fat yogurt

BLT

3 slices turkey bacon

2 slices whole wheat bread (toasted)
Sliced tomato

1 cup fresh vegetables

1 piece of fruit

6 oz. 100% juice

Dinner (Each meal contains approximately 550 calories, 709. carbohydrate, 359.
protein, 159. fat)

125

Stir-Fry

1 cup rice (brown, basmati, jasmine)

4 oz chicken breast, tofu, turkey breast

2 cups veggies (butternut squash, green peas, broccoli, mushrooms, onions)
1 t olive oil or spray for wok

Seasonings (soy sauce, ginger, curry)

Turkey Burger

4 oz ground white meat (turkey) or veggie burger (boca)
1 whole wheat bun, pita, bread

Grilled or sauteed mushrooms

1 slice of Swiss or other cheese

Toppings (salsa, mustard, olive spread, lettuce, tomato)
Handful of baked Lays

1 cup low-fat/skim/soy milk

Grilled Meat or Fish

4 oz lean meat (steak, poultry, fish)

Bowl of salad with 1A cup black beans and 2 Tbsp salad dressing

1 medium grilled potato (regular or sweet) with salsa & low fat plain yogurt
Fresh fruit

Mediterranean Couscous & Tofu

4 oz marinated tofu (pan fried with olive oil or grilled)
1 cup couscous
1/2 cup garbanzo beans

2 T raisins and 2 T Slivered almonds mixed in couscous
1 cup steamed veggies (broccoli, asparagus) with olive oil drizzled on top

Quesadilla

2 whole wheat tortillas

2 Tbsp red sauce or 1 tomato thinly sliced

3 slices deli turkey or lean ham

2 Tbsp low-fat mozzarella

Bowl of salad with pear and 1A cup cottage cheese

Tacos

3 oz lean protein (shrimp, chicken, ground turkey, grilled fish, tofu)

2 corn or 1 flour tortilla warmed

1 cup chopped red cabbage

1/2 sliced avocado and salsa as topping

2 Tbsp shredded low fat cheese

Italian Ice cream: 1/2 cup plain low fat yogurt+‘/2 cup reduced fat vanilla ice
cream (mix) + fresh berries

126

 

Tuna & Rice

1 medium can tuna in water

3A cup rice (basmati, brown, jasmine)

V2 cup tomato sauce mixed with tuna (add a few olives and capers)
1 Tbsp fresh grated parmesan cheese or 1 oz feta cheese

Bowl of salad with apple with 1 T vinaigrette

The Other Mac and Cheese

1 cups whole wheat pasta

1 cup steamed vegetables or 1 cup spinach pulled into pasta when done
1 T olive oil, salt, pepper, herbs

2 T sprinkled parmesan cheese (shredded)

8 oz skim or 1% milk or soy milk

Dinner Sandwich

2 slices whole grain bread toasted

1 T mustard

3 slices of deli turkey

Spinach leaves

Tomatoes

Cucumber or pickle slices

2 handfuls of tortilla chips with 1/2 avocado (pureed with fork and mixed with
balsamic vinegar, chopped onion, and chili pepper)

Chicken Penne with Butternut Squash

1.5 cups penne pasta (whole wheat or regular)

3 oz of chicken tenders

1 cup butternut squash (fresh or frozen) sauteed in 1 T olive oil with garlic,
salt, pepper, herb

Handful of spinach leaves pulled into pasta before serving

Turkey Sausage with Apples, Cranberries, and oven-roasted veggies

3 oz turkey sausage pan fried (check for lean; no fat required for cooking)

1 cup chopped potatoes (sweet and regular)

1 cup veggies

1 chopped apple

2 T dried cranberries

2 t olive oil, garlic, onions (all chopped items are mixed, put in baking dish
with lid or aluminum foil; toss with herbs, pepper and bake for 30 min. at 350
F; add salt before serving)

127

 

 

S NAPP

Spartan Nutrition and Performance Program
Sports & Cardiovascular Nutrition Program

Athletic and Healthy Snacks

 

~500 Calorie (759 CHO, 209 pro.)

Bagel + 2 T. peanut butter

Clif BarlPowerbar/Harvest Bar w/ 1602. 1% milk

Peanut butter and jelly sandwich* w/ 1c. 1% milk

2- 4” Waffles (toasted) + 3 T. peanut butter + 1 T. jam

2- 4” Waffles (toasted) + ‘A c. light syrup 8 1 c. low fat milk

1 c. Oatmeal w/ 1 c. low fat milk, 1 pear 8 1A c. trail mix

1 Fat free pudding cup, 1 bagel + 1 T. Low fat cream cheese & 1 0. low
fat milk

1 Baked Potato + ‘A c. shredded cheese + 1 c. broccoli + 2 t. butter

1 Baked potato + 1/4 c. shredded cheese w/ 1 0. low fat milk 8 V2 0. apple

sauce
1 0. Mac 8 cheese w/ 1 banana 8 1/2 c. Low fat milk
Grilled cheese w/ tomato soup, 1 0. Low fat milk 8 1 orange

Egg sandwich* WI 1 banana

16 Baby carrots + 2 T. light ranch w/ 1 pita bread + 202 can chicken + 1 T
light mayo

2 Hard boiled eggs w/ 1 slice toast + 2 T. jam + 1 orange + 1 banana +
1/20. cottage cheese

1 Low-fat fruit yogurt w/ 1 banana 8 8 02. Sports Drink

1 Fruit Smoothie” w/ 1 All Bran Bar, 1/2 c. cottage cheese

1 Low-fat fruit yogurt w/ 1 package (2 bars) Nature Valley Granola Bars
8 4 Hershey Kisses

1 Turkey sandwich* wI 2002. Sports drink

10. Kashi Go-Lean Crunch + 10. low fat milk w/ 2002. sports drink

1 Pita bread + 1A c. hummus/ refried beans w/ 1 banana 8 1 stick string
cheese

‘A Trail Mix* w/ 1c. low fat milk

Tuna Melt* on english muffin/toast w/ 1 apple

r» ii» iii r rrrr r» irrrrrr

 

128

~250 Calorie (409 CHO, 109 pro.)

1 Banana w/ 1.5 T. peanut butter

1 6. low fat yogurt + 1A c. granola

4 Fig Newtons w/ light sting cheese

1 low fat pudding 8 3 T. peanuts

1 package (2 bars) Nature Valley granola bars w/ 8 oz Skim milk
1c. low fat chocolate milk w/ 1 apple

1c. Wheaties + 10. low fat milk w/ banana

rrrrrrr

Recipes (Serving Sizes: T: Tablespoon; t: teaspoon; cup: 8 oz; 4 oz
~100g; 3 oz ~pa/m Size)

*PB&J: 2 Slices whole wheat bread, 2T. peanut butter, 1T. 100% fruit jelly
*Egg Sandwich: 2 Slices whole wheat, 1 slice cheese, 1 egg over easy

*Fruit Smoothie: 802 low-fat plain yogurt, 1 banana, 1/2 0. frozen blueberries, 1/2 0.
OJ.

*Turkey Sandwich: 2 slices whole wheat bread, 302 (2-3 slices) turkey breast,
1T. mustard, 1 Slice provolone cheese

*Trail Mix: 16. peanuts, 1c. raisins, 1/2 c. M8M’S, 1/2 c. sunflower seeds

*Tuna Melt: 302 can chunk light tuna in water, 1T. light mayo, 1T relish, 1 slice
cheddar

129

 

 

Athlete's Grocery List

PRODUCE:

Buy 5 different colors, as vibrant as possible

Fruit

0 Red - Red App/es, Strawberries, Cherries

0 Yellow/ Orange - Apricots, Can ra/oupe, Mango, Peaches, Pineapple
0 White - Bananas, Dates

0 Green - Green App/es, Green Grapes, Honeydew, Kilt/i, Avocado

0 Blue/ Purple - Blueberries, Plums, Purple Grapes
0 Other:

 

Vegetables

0 Red - Tomato, Rad/shes, Red Peppers

O Yel low/ Orange - Corn, Sweet Potato, Carrots, Yellow and Orange Peppers, Squash

0 White - Gaul/flower, Mushrooms, Onions, Parsnips, Po fafoes, Turnr'ps

0 Green - Asparagus, Dark leafy greens, Broccoli, Cabbage, Celery, Cucumbers, Peppers
0 Blue/Purple - Eggplant

O Other:

 

DELI

0 Cheese (low for or light)

0 Lunchmeaf (turkey, ham, roast beef)
0 Hummus

0 Other:

 

BAKERY

0 Bagels

0 Bread (whole grain)
0 Other:

 

ANIMAL PROTEIN

0 Chicken (skinless breast)
0 Turkey (skinless breast)
0 Beef (extra lean : “96/4 ’7
0 Pork (lean = loin)

0 Fish (nor fried)

O Other:

 

STA RC H
O Noodles (”No Vol/rs”: ramen)
0 Pasta (fry whole wheat for more fiber)

130

 

0 Rice (brown or wild rice)
0 Other:

 

CEREAL

0 Cereal/ cereal bars (.23 g fiber per serving)
0 Hot cereal (oatmeal is best)

0 Pancake mix/syrup

0 Other:

 

FROZEN FOODS

0 Frozen dessert bars

0 Frozen entrees ( Uncle Ben’s rice d pasta bowls without creamy sauces, Pagoda noodle or
rice pa ts, Healthy Choice, Smart Ones, Leon Curls/he - all need more veggies!)

0 Frozen fruits (no sugar added)

0 Frozen meal in a bag

0 Frozen pizza

0 Frozen vegetables

0 Ice cream/ Frozen yogurt

0 Waffles

0 Other:

 

 

SNACK FOODS

0 Chips (baked potato/tortilla)
<> Crackers (fry whole wheat)

0 Graham crackers

' 0 Nuts/seeds

<> Popcorn (light / 94% fat free)
0 Pretzels

0 Sports bars (<209 Pro + CHO)
0 Other:

 

' CONDIMENTS/SPICES
0 BBQ/feriyaki/soy sauce
0 Cafsup/musfard/salsa

0 Cooking oil (canola/olive)
0 Mayonnaise/Miracle Whip
0 Cooking spray

0 Salad dressing

0 Spices

CANS/ BOTTLE 5

O Applesauce

0 Fruit

0 Jam/jelly/preserves

0 Pasta sauce

 

131

0 Peanut butter

0 Refried beans (fat free)

0 Beans: black, kidney, northern
O Soup/Chili

0 Tuna

0 Vegetables

DESSERTS
0 Cookies

0 Muffins

0 Other:

 

BEVERAGES

0 Bottled water

0 100% Fruit Juice
0 Sports Drinks

PAPER GOODS
0 Aluminum foil

0 Napkins

0 Paper towels

0 Plastic wrap

0 Toilet paper

0 Other:

DAIRY/ Soy

O Butter/margarine

<> Cheese/Cottage cheese
0 Eggs/egg substitute

0 Milk/yogurt

0 Tofu (extra firm

 

132

 

APPENDIX C

133

Student #
MSU Footbgll- Spartan Nutrition and Performgnce Program (SNAPP) Survey MQY 2006

 

 

In an effort to help improve your overall football training program, the SNAPP (Spartan
Nutrition and Performance Program) staff would like you to complete this survey. This
survey will ask you questions about your nutrition knowledge and behaviors as well as
information about your physical activity outside of your football training program. In
summarizing this information, no names or student numbers will be attached to this data.
You indicate your voluntary agreement to participate by completing and returning the
survey. Thanks!

If you have any questions, please ask one of the SNAPP staff (Joe Carlson, PhD, RD (517) 355-
0120 Ext 346; Scott Sehnert MS, RD (517) 3550120 ext 346; Heidi Clark BS, RD (517)355-8474
Ext 155)

1. What is your age? years

2. What class are you in ?
a) Freshman b ) Sophomore c) Junior (1) Senior e) 5'h year or greater

3. Do you typically eat 2 or more meals a day in the dorm? Circle Yes or No
4. What is your Height _feet inches; What, is your weight? pounds

5. How important do you feel nutrition is for your training and performance?
a) little importance b) some importance 0) very important d) extremely
important

6. How important do you feel nutrition is for your health (Ex. risk for future disease, heart disease,
cancer )?

a) little importance b) some importance c) very important d) extremely
important

7. Currently what is your primary source for nutrition information?
a) Coach or athletic trainer b) parents c) friend (I) Registered Dietitian (1) Medical Doctor
e) other - please list

 

8. Which of the following is an example of a simple carbohydrate? (select more than one)
a) bread b) com 0) rice (1) table sugar e) Potatoes f)
honey

9. Which of the following is an example of a saturated fat ? (select more than one)
a) Com oil b) Coconut oil c) lard (1) Olive oil

10. What does the term glycogen refer to?
a ) Protein stores in the muscle b) carbohydrate stores in the muscle
c ) Fat stores in the muscle d ) None of the above

1 1. Assume you weigh yourself before and after a workout to determine how much weight you

lose during the workout. To properly re-hydrate, it is recommended you consume how many
cups of fluid per pound lost. Note a cup equals 8 ounces.

134

a) 1 cup b) 2 cups c) 3 cups d) 4 cups

12. Matching- How many calories per gram are in the following? (an answer can be used more
than once)

Protein _ a) 4

Carbohydrate __ b) 7

F at _ c) 9

Alcohol _ d) 12

13. Do you take any dietary supplements ?

a) Yes B) No If yes, list what type (5) you take?

14. How many times do you typically eat a day during training or in season?
meals snacks

15. Do you eat a snack before or after a workout? If Yes, fill in minutes below
minutes before working out minutes after working out

16. This past season how would you rate the dorm training table meals?
a) very poor b) poor c) fair (1) good e) very good
0 Briefly list what you liked about the training table (both foods and the setting)-

0 Briefly list what you disliked about the training table (both foods and the setting)-
17. If you are player that attends team meals at local restaurants answer the following:
0 Briefly list what you liked about the eating at the restaurants (both the foods and the
setting)-

0 Briefly list what you disliked about the eating at the restaurants (both the foods and the
setting)-

18. From the following select your two most common protein sources.
a) red meat b) chicken, turkey or fish d) nuts & seeds
(1) beans e ) dairy and milk 0 Protein Powder

19. On average, how many servings of fruits and vegetables (combined) do you eat a day? (Fruit
examples- a med sized apple or orange, '/2 cup of fruit, 6 oz of 100 % fruit juice; Vegetable examples- ‘/2
cup broccoli, carrots, peas, corn. . ..)

a) 1 or less b) 2-3 o) 4-5 d) 5-6 e) 7-8 f) 9 or more

20. On average, how many servings of breads, cereal and grains (combined) do you eat a day?
Examples 1 slice of bread, '/2 bagel, small bowl of cereal, '/2 cup rice, '/2 cup of pasta

a) 3 or less b) 4-5 c) 6-7 6) 8-9 f) 10-11 g) 12-13 h) 14 or
greater

135

 

International Physical Activity Questionnaire

We are interested in finding out about the kinds of physical activities
that you do as part of your everyday life. The questions will ask you
about the time you spent being physically active in an average week
(7 days) over the last month. Please answer each question
without including any activities involved with football—training,
practices, games, etc. Please think about the activities you do at work,
as part of your house and yard work, to get from place to place, and in
your spare time for recreation, exercise or sport (excluding football-
related activities).

Think about all the vigorous activities that you did in an average
week (7 days) over the last month. Vigorous physical activities
refer to activities that take hard physical effort and make you breath
much harder than normal. Think only about those physical activities
that you did for at least 10 minutes at a time. This does not include
football-related activities.

1. During the average week (7 days), on how many days did
you do vigorous physical activities like heavy lifting, digging,
aerobics, or fast bicycling?

days per week

No vigorous physical activities > Skip to question 3
2. How much time did you usually spend doing vigorous
activities on one of those days?
hours per day
minutes per day

Don’t know/Not sure

136

 

Think about all the moderate activities that you did in an average
week (7 days) over the last month. Moderate physical activities
refer to activities that take moderate physical effort and make you
breathe somewhat harder than normal. Think only about those
physical activities that you did for at least 10 minutes at a time. This
does not include football-related activities.

3. During the average week (7 days), on how many days did

you do moderate physical activities like carrying light loads,

bicycling at a regular pace, or doubles tennis? Do not include
walking.

days per week

No moderate physical activities > Skip to question 5

4. How much time did you usually spend doing moderate
physical activities on one of those days?

hours per day
minutes per day

Don't know/Not sure

137

Think about the time you spent walking in the average week (7
days) over the last month. This includes work and at home, walking
to travel from place to place, and any other walking you might do
solely for recreation, sport, exercise, or leisure. This does not
include football-related activities, although do include if you
walk to/from practices or other training.

5. During the average week (7 days), on how many days did
you walk for at least 10 minutes at a time?

days per week
No walking > Skip to question 7

6. How much time did you usually spend walking on one of
those days?

hours per day
minutes per day

Don’t know/Not sure

The last question is about the time you spent sitting during the
average week (7days) over the last month. Include time spent at
work, at home, while doing course work and leisure time. This may
include time spent sitting at a desk, visiting friends, reading or sitting
or lying down to watch television.

7. During the average week (7 days), how much time did you
spend sitting on a week day?

hours per day
minutes per day

Don’t know/Not sure

138

APPENDIX D

139

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141

10.

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