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Dietary Habits and Anthropometric
Measurements in College Students:
A Cross-Cultural Comparison
Denise Albina
Elizabeth Chlopek
Melinda Hamilton-Smith
Rachel Hudes
Kim McDonough
Sehba Nasir
1
Objectives of the Study
• To determine effects of acculturation and ethnicity
on the following:
1. Dietary intake and snack consumption
2. Anthropometric measurements and
biochemical markers
3. Exercise patterns
2
Participant Recruitment
• Campus wide e-mail
• Table tents around the cafeteria, computer labs
and library
• Flyers in bathroom stalls
• Speaking to classes
• Sports team and clubs/organization
• Recruitment through poster board display, and
passing out flyers
• Incentive for completion: free assessment
testing comparative to hospital tests cost
3
Study Subjects
• Inclusion:
• Currently enrolled at Benedictine University
• Age 18-28
• All ethnicities
• Exclusion:
• Not currently enrolled at Benedictine University
• Refusal to sign the consent form
• Pregnant
• Participant did not fast for 10-12hrs
• Reasons for Withdrawal:
• Failure to make or keep appointments
4
Total Study Subjects
Subjects were required to complete the following:
Survey 1:
- Completion of 22 question survey pertaining
to student status, demographics, health status
and exercise habits
Survey 2:
- Completion of 19 question food intake
questionnaire focusing on food preferences, meal
patterns, cultural habits and changes in food habits
Health Assessment:
- Collection of anthropometric measurements, lipid
values, blood pressure and InBody assessment
5
Data Collection Tools:
6
Data Collection Adherence for Accuracy
• Students were assigned measurement stations
• Height and waist circumference were read on two
separate occasions for accuracy
• Blood Pressure was taken after student was resting in
a chair for >5minutes where 10 separate readings were
taken(5 per arm).
7
Data Collection Instrument:
Block Fat Screener
• A means of providing a brief and inexpensive method of determining
fat intake.
• The block fat screener contains 17 food items that are typically
high in fat.
• Each food item was given a score 1-5 based on how often the
participant consumed these food items.
• The fat score was calculated as the sum of the numerical values
of all 17 food items.
• The fat score was utilized in mathematical equations to calculate
total fat(grams), percent total fat, saturated fat (grams), and
cholesterol intake (milligrams).
• 150 participants completed the Block Fat Screener.
8
Data Collection Instrument:
Fruit-Vegetable-Fiber Screener
• Inexpensive method of determining fruit, vegetable
and fiber intake.
• Screener includes 10 items:
o the first seven determine fruit servings using USDA
Food Pyramid definitions of servings.
o The last 3 items determine average grams of fiber
consumed on a given day.
• Predictive equations provide estimates for fruit and
vegetable servings, vitamin C(mg), Magnesium(mg),
potassium(mg) and dietary fiber(g)
• Data collection was done online as part of a second
survey.
9
Data Collection Instrument: Godin
Leisure-Time Questionnaire (GLTQ)
• An inexpensive method to measure the frequency and
intensity of exercise, and frequency of leisure time
physical activity (LTPA) of a 7 day period. This
questionnaire is comprised of two questions.
• GLTQ was Included in online survey 1
• Results (Self reported)
– Frequency
of weekly LTPA (often, sometimes, or rarely/never).
– Reported frequency of each intensity of exercise was used to
calculate total
•
METs (metabolic equivalents) for each; strenuous,
moderate, and mild METs.
• Total Godin Score
– Calculated for each subject.
– Used to represent the sum of total weekly METs.
10
Snack Habits
Sehba Nasir
11
What the Literature Says:
• Many studies have shown a dietary pattern of snack consumption
among the college population (1,2,3).
• A recent study in 2009, had findings that university students from
Scotland (a more western country) consumed more chocolate, bars and
crisps (p<0.05) when compared to university students from Greece (a
country considered to be transitioning from a traditional diet) (3).
• A study in 2010, found that U.S. born Mexicans reported eating
significantly more non-Mexican fast food, snacks, desserts candy and
sugars compared to Foreign born Mexicans (p<0.001) (4).
• Another study in 2010, found high-acculturated Korean Americans ate
more western foods and significantly more raw vegetable salad (p<.027)
compared to low-acculturated Korean American college students (5).
12
Ethnicities
13
Generations
14
Student Status Demographic
15
Variables
• Snack Preferences
• How snacks are
obtained
• Ethnicities
• Generation
• Student Status –
part time or full time
How Snacks are Obtained
Snack Foods
In the survey, a snack was defined as “something to
temporarily tide a person’s hunger and provide a brief
supply of energy to the body.”
Snack Preferences:
1)Milk/Yogurt, 2) Pop or Soda, 3) Fruits or vegetables, 4) Cookies or
Cakes, 5) Ice Cream, 6) Potato Chips or Other Chips, 7) Personally
Prepared foods (Traditional), 8) Juice, 9) Snack Bars, 10) Breads or
Cereals, 11) Nuts 12) Candy, 13) Pizza/Hamburger and 14)
Sandwich.
Frequencies of Variables
The top three snack food preferences that had the greatest frequency of
“yes” responses were Fruits and Vegetables, Milk/Yogurt, and Snack
Bars.
Ho1: Snack Foods among Ethnicities N= 137
• Ho1a: There is no relationship in selection of Fruits and
Vegetables as a snack food and ethnicities. Rejected.
• Ho1b: There is no relationship in selection of Traditional
Foods (personally prepared) as a snack food and ethnicities.
Rejected.
χ2
Snack Food
df
p
Eta
Fruits or
Vegetables
12.15
3
<0.01*
0.27
Traditional
food
14.14
3
0.003*
0.27
Ho1: Snack Foods among Ethnicities N= 137
• Ho1c: There is no relationship in selection of Candy as a snack
food and ethnicities. Rejected.
• Ho1d: There is no relationship in selection of Pizza/Hamburger
as a snack food and ethnicities. Rejected.
χ2
Snack Food
df
p
Eta
Candy
7.96
3
<0.05*
0.23
Pizza/Hamb
urger
8.94
3
<0.05*
0.21
Ho1: Snack Foods among Ethnicities N= 137
• Ho1e: There is no relationship in selection of Sandwich as a
snack food and ethnicities. Rejected.
χ2
Snack Food
Sandwich
9.15
df
3
p
0.027*
Eta
0.15
Snack Foods among Ethnicities
Significant Findings:
•
Among those responding “yes” to the consumption of Fruits and Vegetables
as snacks, ethnic groups with the highest intake were White (83.3% of
respondents, n=60) and White (Middle Eastern) (85.7% of respondents,
n=12).
•
Among those responding “yes” to the consumption of Traditional Foods
(personally prepared) as snacks, ethnic groups with the highest intake were
Asian Indian and Chinese (36.8% of respondents, n=14).
•
Among those responding “yes” to the consumption of Candy as snacks, ethnic
groups with the highest intake were Asian Indian and Chinese (42.1% of
respondents, n=16).
•
Among those responding “yes” to the consumption of Pizza/Hamburger as
snacks, ethnic groups with the lowest intake were White (6.9% of
respondents, n=5) and Hispanic or Latino(7.7% of respondents, n=1).
•
Among those responding “yes” to the consumption of Sandwich as snacks,
ethnic group with the lowest intake is White (19.4% of respondents, n=14).
Ho2: Snack Foods among Generations N= 138
• Ho2a: There is no relationship in selection of Fruits or Vegetables
as a snack food and generations. Rejected.
• Ho2b: There is no relationship in selection of Personally Prepared
(Traditional Foods) as a snack food and generations. Rejected.
χ2
Snack Food
df
p
Eta
Fruits or
Vegetables
10
4
0.04*
0.27
Traditional
Food
15.35
4
0.004*
0.26
Ho2: Snack Foods among Generations N= 138
• Ho2c: There is no relationship in selection of Sandwich as a
snack food and generations. Rejected.
χ2
Snack Food
Sandwich
15.15
df
3
p
0.004*
Eta
0.27
Snack Foods among Generations
Significant findings:
•
Among those responding “yes” to the consumption of Fruits and
Vegetables as snacks, generations with the highest intake were 5th
generation (87% of respondents, n=40) and 4th (84.6% of respondents,
n=11). Among those responding “yes,” the intake increased with
each generation from 1st to 5th generation.
•
Among those responding “yes” to the consumption of Traditional
Foods (personally prepared) as snacks, generations with the highest
intake were 1st (22.6% of respondents, n=7), 2nd (35.9% of
respondents, n=14), and 3rd (22.2% of respondents, n=2). Among
those responding “yes,” the intake declined in the 4th and 5th
generation.
•
Among those responding “yes” to the consumption of Sandwich as
snacks, generations with the highest intake were 2nd (46.2% of
respondents, n=18), 1st (38.7% of respondents, n=12), and 3rd (33.3%
of respondents, n = 3). Among those that responded “yes,” the
generations after the 3rd decline in their intake of Sandwiches as a
snack.
Ho3: There is no relationship between how
snacks are obtained and student’s ethnicity
Rejected. A significant interaction was found
(χ2(4)= 13.71, p=0.008, eta = 0.251).
Significant Findings:
Among those that responded as “varies” to obtaining snacks,
the ethnicity with the highest response, is White (Middle
Eastern) (78.6% of respondents, n=11). The White (Middle
Eastern) is also the ethnicity that had 0 responses to “prepared
at home” to obtaining snacks (0% of respondents, n=0) and the
ethnicity with the lowest response to “prepared at home” is
Asian Indian and Chinese (2.6% of respondents, n=1)
Ho4: There is no relationship between how
snacks are obtained and student status.
Accepted.
No significant relationship was found (χ2(2)= 1.52,
p=0.462, eta = 0.11). How snacks are obtained and
student status appears to be independent.
Snack Consumption Conclusions
• From all 14 snack foods that were compared to
ethnicities, 5 snack foods (fruits or veg., traditional
foods, candy, pizza/hamburger and sandwiches) were
found to have a significant relationship to ethnicities.
• Fruits or vegetables snack foods have a significant
relationship to generations. The intake increased with
each generation from 1st to 5th generation.
• Traditional (personally prepared) snack foods and
sandwiches as snacks have a significant relationship to
generations. The intake of both the snack foods
declined in the 4th and 5th generations.
Snack Consumption Conclusions
• How snacks are obtained (prepared at home,
purchased, or varies) and a student’s ethnicity has
a significant relationship N=137.
• How snacks are obtained has no significant
relationship to student status (part time or full time)
N=127.
Exercise
Rachel Hudes
31
Benefits of Physical Activity
• Physical activity (PA) can help prevent a variety of
chronic illnesses, that have become prevalent in
westernized society.
• PA has been shown to have many positive effects on
a variety of metabolic health factors.
o
Decreasing triglyceride total cholesterol, and LDL levels.
 Protective against CVD and metabolic syndrome.
o
Increasing HDL levels.
 Protective against CVD and metabolic syndrome.
o
Decreasing BMI and PBF.
 Protective against obesity, diabetes, HTN, CVD, metabolic
syndrome.
 Aids in weight loss/weight management.
32
La Forge R, 2002
Physical Activity Guidelines for Adults
At least 150 minutes of moderate intensity physical
activity or 75 minutes of strenuous intensity aerobic
physical activity per week for significant health benefits.
Strenuous Exercise
• Jogging/running
• Swimming laps
• Singles tennis
Department of Health and Human Services, 2009
Moderate Exercise
• Walking fast
• Water aerobics
• Doubles tennis.
33
Previous Research
• Physical Activity and College Students:
o
As freshman, 29% of students did not exercise regularly;
by senior year the amount of physically inactive did not
change.
o
More than 50% of undergraduates were sedentary or
exercised on an irregular basis.
o
29% of college students living on campus and 28% living
off campus reported being sedentary or lightly active.
Racette SB, 2008
Wallace LS, 2000
Brevard PB, 1996
34
Previous Research
Physical Activity and Acculturation:
• Foreign born Mexicans participated in fewer weekly bouts of
low intensity physical activity compared to U.S. born
Mexicans.
o
Inactivity and low intensity physical activity increased with
generation of residence in the United States among Mexicans and
Cubans.
• Studies on Arabic women in America suggest that physical
inactivity is more common than American women.
Gordon-Larsen P, 2003
Qahoush R, 2010
35
Ho5a: There is no difference in intensity of
exercise across ethnicities.
• One way ANOVA compared the intensity of
exercise across ethnicities.
• Ho5a Rejected.
o
o
o
o
Strenuous METs: (F(4,223) = 3.21, p=.01)
Moderate METs: (F(4,224) = 2.57 p=.04)
Mild METs: (F(5,216) = 1.47, p=.202)
Total Godin Score: (F(5,223) = 2.81, p=.001)
36
Distribution of Intensity of Exercise within Ethnicity
35
31.2
30
25
22.4
19.8
20
15
14.8
12.9
11.7
10
8.5
13.3
11.5
9.6
11
8.3
7.5
6
7.4
5
0
Asian Indian and Chinese
Black or African
American
Strenuous METs (n= 228)
Hispanic or Latino
Moderate METs (n= 229)
White
(European or N.
Africa origins)
Mild METs (n= 228)
White (Middle East)
Ethnicity Distribution for Strenuous and Moderate METs
Strenuous METs
19.8%
14.8%
Moderate METs
16%
16.1%
11.8%
18.3%
25.2%
22.4%
31.2%
24.6%
Asian Indian and
Chinese
Strenuous: (n= 68
Moderate: (n= 68)
Black or African
American
Strenuous: (n= 14)
Moderate: (n= 14)
Hispanic or Latino
Strenuous: (n= 17)
Moderate: (n= 17)
White (European or
N. Africa origins)
Strenuous: (n= 105)
Moderate: (n= 107)
White (Middle East)
Strenuous: (n= 25)
Moderate: (n= 24)
Data compared to Strenuous and Moderate METs was compared across all
ethnicities as one statistical group
Ethnicity Distribution for Mild METs and Total Godin Score
Mild METs
17%
Total Godin Score
17.2%
18%
15.9%
14%
13.3%
24.1%
25.5%
26.5%
29.1%
Asian Indian and
Chinese
Mild: (n= 66)
Godin: (n= 68)
Black or African
American
Mild: (n= 13)
Godin: (n= 14)
Hispanic or Latino
Mild: (n= 17)
Godin: (n= 17)
White (European or
N. Africa origins)
Mild: n= 108)
Godin: (n= 108)
White (Middle East)
Mild: (n= 24)
Godin: (n= 26)
Data compared to Mild METs and Total Godin Score was compared across all
ethnicities as one statistical group
Ho5b: There is no difference in intensity of
exercise across genders.
Independent-samples t test compared the intensity of
exercise across genders.
Ho5b Accepted.
•
•
•
•
Strenuous METs: (t(229) = 2.51, p=.52)
Moderate METs: (t(230) = .64, p=.96)
Mild METs: (t(229) = -1.5, p=.7)
Total Godin Score: (t(234)= 1.28, p=.57)
40
Distribution of Intensity of Exercise within Genders
30
25.4
25
20
17.7
15
11.8
10.7
9.9
10
7.7
5
0
Male
Strenuous METs (n= 228)
Female
Moderate METs (n= 229)
Mild METs (n= 228)
Gender Distribution for Strenuous and Moderate METs
Male
Strenuous:(n= 62)
Moderate:(n= 62)
Strenuous METs
41%
59%
Moderate METs
47.6%
Female
Strenuous: (n= 169)
Moderate (n= 170)
52.4%
Data compared to Strenuous and Moderate METs was compared across
genders as one statistical group
Gender Distribution for Mild METs and Total Godin Score
Male
Mild: (n= 63)
Godin: (n= 64)
Mild METs
56.5%
43.5%
Total Godin Score
46.5%
Female
Mild: (n= 168)
Godin: (n= 172)
53.5%
Data compared to Mild METs and Total Godin Score was compared across
genders as one statistical group
Ho6a: There is no difference in intensity of
exercise between TC risk categories.
•
•
•
•
Strenuous METs: (t(95) = 1.5, p=.64)
Moderate METs: (t(97) = -.001, p=.34)
Mild METs: (t(99) = .002, p=.06)
Total Godin Score: (t(99) = .9, p=.001)
Ho6a Accepted Independent-samples t test
Total Cholesterol
Low Risk:
n, Mean + SD
High Risk:
n, Mean + SD
Strenuous METs
84, 20.7 + 21.7
13, 11.1 + 19.8
Moderate METs
86, 11.9 + 10.7
13, 11.9 + 16.5
Mild METs
88, 9.2 + 11.9
13, 9.2 + 8.8
Total Godin Score
88, 40.5 + 31.5
13, 32.2 + 28.3
44
Ho6b: There is no difference in intensity of exercise
between HDL cholesterol risk categories.
•
•
•
•
Strenuous METs: (t(92) = -.74, p=.09)
Moderate METs: (t(94) = 1.17, p=.26)
Mild METs: (t(96) = .06, p=.12)
Total Godin Score: (t(96) = -.03, p=.68)
Ho6b Accepted Independent-samples t test
HDL Cholesterol
Low Risk:
n, Mean + SD
High Risk:
n, Mean + SD
Strenuous METs
66, 20.0 + 23.7
28, 16.4 + 16.3
Moderate METs
67, 11 + 10.9
29, 14 + 12.8
Mild METs
69, 9.5 + 8.2
29, 9.6 + 17.3
Total Godin Score
69, 39.3 + 31.2
29, 39.1 + 32.3
45
Ho6c: There is no difference in the intensity of
exercise between LDL cholesterol risk categories.
•
•
•
•
Strenuous METs: (t(71) = .74, p=.16)
Moderate METs: (t(73) = .12, p=.96)
Mild METs: (t(73) = 1.62, p=.49)
Total Godin Score: (t(73) = 1.02, p=.02)
Ho6c Accepted Independent-samples t test
LDL Cholesterol
Low Risk:
n, Mean + SD
High Risk:
n, Mean + SD
Strenuous METs
41, 20.0 + 26.0
32, 16.0 + 17.2
Moderate METs
43, 12.7 + 11.6
32, 12.3 + 13.2
Mild METs
43, 12.1 + 14.5
32, 7.4 + 8.6
Total Godin Score
43, 43.6 + 38.6
32, 35.8 + 22.9
46
Ho6d: There is no difference in the intensity
of exercise between TG risk categories.
•
•
•
•
Strenuous METs: (t(75) = .90, p=.47)
Moderate METs: (t(77) = .92, p=.23)
Mild METs: (t(77) = .33, p=.85)
Total Godin Score: (t(77) = 1.03, p=.97)
Ho6d Accepted Independent-samples t test
Triglycerides
Low Risk:
n, Mean + SD
High Risk:
n, Mean + SD
Strenuous METs
69, 18.8 + 22.7
8, 11.3 + 18.5
Moderate METs
71, 12.3 + 12.5
8, 8.1 + 8.8
Mild METs
71, 9.8 + 12.7
8, 8.3 + 9.3
Total Godin Score
71, 40.2 + 33.2
8, 27.6 + 28.1
47
Ho7: There is no difference in intensity of
exercise between BMI categories.
•
•
•
•
Strenuous METs: (t(88) = 1.02, p=.61)
Moderate METs: (t(90) = -1.95, p=.96)
Mild METs: (t(92) = -1.21, p=.05)
Total Godin Score: (t(92) = -.68, p=.61)
Ho7 Rejected Independent-samples t test
BMI
Low Risk:
n, Mean + SD
High Risk:
n, Mean + SD
Strenuous METs
62, 22.5 + 21.4
28, 17.4 + 23.5
Moderate METs
64, 11.3 + 11.4
28, 16.4 + 11.4
Mild METs
66, 8.5 + 8.1
28, 11.7 + 17.5
Total Godin Score
66, 40.6 + 30.5
28, 45.5 + 34.8
48
Ho8: There is no difference between intensity
of exercise and PBF risk categories.
•
•
•
•
Strenuous METs: (t(84) = 1.51, p=.43)
Moderate METs: (t(86) = -2.0, p=.15)
Mild METs: (t(87) = -.05, p=.32)
Total Godin Score: (t(87) = .21, p=.85)
Ho8 Accepted Independent-samples t test
PBF
Low Risk:
n, Mean + SD
High Risk:
n, Mean + SD
Strenuous METS
33, 23.7 + 25.3
53, 16.5 + 19.2
Moderate METS
35, 9.6 + 9.1
53, 14.5 + 12.6
Mild METS
35, 9.6 + 8.1
54, 9.7 + 13.8
Total Godin Score
35, 41.5 + 33.5
54, 40.1 + 29.1
49
Ho9a: There is no relationship between
frequency of LTPA and ethnicity.
•
Ho5a: Rejected, p= .001
Frequency of LTPA
Ethnicity
Total
Often
Sometimes
Rarely/Never
Asian Indian and
Chinese
17 (22.4%)
23 (22.5%)
31 (52.5%)
Black or African
American
3 (3.9%)
7 (6.9%)
4 (6.8%)
14 (5.9%)
Hispanic or Latino
6 (7.9%)
8 (7.8%)
4 (6.8%)
18 (7.6%)
White (European or N.
Africa origins)
41 (53.9%)
55 (53.9%)
12 (20.3%)
12 (45.6%)
White (Middle East)
9 (11.8%)
9 (8.8%)
8 (13.6%)
26 (11.0%)
71 (30.0%)
(x2 (8) = 26.0, p= .001), Eta= .28
Ho9b: There is no relationship between
frequency of LTPA and gender.
• Chi-square test of independence to determine a relationship between frequency
of LTPA and gender.
– Gender: (x2 (2) = 2.22, p= .33)
– No significance was found between frequency of LTPA and gender.
Often
Male (n= 25)
Female (n= 51)
LTPA Distribution within Gender
Male
Female
21.8%
39.1%
39.1%
26.1%
Sometimes
Male (n= 25)
Female (n= 79)
29%
44.9%
Data compared to frequency of LTPA was compared across
genders as one statistical group
Rarely/Never
Male (n= 14)
Female (n= 46)
Ho10: There is no difference between frequency of
LTPA and metabolic risk categories.
One-way ANOVA
LTPA
Frequency
Total
Cholesterol
n, mean, + SD
HDL Cholesterol
n, mean, + SD
LDL Cholesterol
n, mean, + SD
Triglycerides
n, mean, + SD
Body Mass
Index
n, mean, + SD
Percent Body Fat
n, mean, + SD
Often
35, 157.6 + 28.5
34, 50.0 + 9.9
25, 95.3 + 21.1
26, 93.4 + 38.3
35, 23.9 + 3.0
35, 25.4 + 9.1
Sometimes
44, 171.3 + 28.9
43, 51.0 + 14.1
33, 106.3 + 24.8
42, 94.1 + 39.6
46, 23.2 + 4.7
46, 26.2 + 9.3
Rarely/Never
24, 172.0 + 34.3
23, 55.8 + 19.9
18, 102.0 + 33.7
20, 117.5 + 89.3
24, 22.2 + 3.8
24, 24.8 + 7.7
Total
103,166.0 + 30.2
100, 51.8 + 14.5
76, 101.6 + 26.3
80, 99.7 + 56.1
105, 23.2 + 4.0
105, 25.6 + 8.8
Ho10: There is no difference between frequency
of LTPA and metabolic risk categories.
• Ho10a: There is no difference between frequency of LTPA and TC.
Accepted. (F(2,100) = 2.46, p=.09)
• Ho10b: There is no difference between frequency of LTPA and HDL cholesterol.
Accepted. (F(2,97) = 1.23, p=.30)
• Ho10c: There is no difference between frequency of LTPA and LDL cholesterol.
Accepted. (F(2,73) = 1.25, p=.29)
• Ho10d: There is no difference between frequency of LTPA and TG.
Accepted. (F(2,77) = 1.35, p=.27)
• Ho11: There is no difference between frequency of LTPA and BMI.
Accepted. (F(2,102) = 1.24, p=.29)
• Ho12: There is no difference between intensity of exercise and PBF.
Accepted. (F(2,102) = .19, p=.83)
53
Hypothesis Summary
• Statistically significant findings
o
Ho5a: There is no difference in intensity of exercise across
ethnicities.
 Rejected for Total Godin Score (p=.01), Moderate METs (p=.04), and
Strenuous METs (p=.001).
o
Ho6a: There is no difference in the intensity of exercise between
TC risk categories
 Rejected for Total Godin Score (p=.001).
o
Ho6c: There is no difference in the intensity of exercise between
LDL cholesterol risk categories.
 Rejected for Total Godin Score (p=.02).
o
Ho6e: There is no difference in intensity of exercise between BMI
categories.
 Rejected for Mild METs (p=.05).
o
Ho7a: There is no relationship between frequency of LTPA and
ethnicity.
 Rejected (p=.001).
54
Trends
• Low risk TC and PBF categories had higher mean strenuous METs
and Total Godin Score.
• Low risk HDL cholesterol category had higher mean strenuous
METs.
• Low risk LDL and TG categories had higher mean strenuous,
moderate, and mild METs.
• Overweight BMI category had higher mean mild and moderate METs
and Total Godin Score.
• The more LTPA reported the higher mean BMI and TGs.
• The more LTPA reported the lower mean HDL.
55
Conclusions
• BMI is higher with more LTPA, Moderate METs, Mild METs and
higher Total Godin Score.
o Muscle mass increases weight; may be contributing to
higher BMI in individuals.
o May not be the best tool to use alone to determine disease
risks.
• Higher Total Godin Score in low risk TC, LDL and HDL
cholesterol, TG, and PBF categories.
o Supports research on the benefits of exercise on these
metabolic risk factors.
• Significant difference between LTPA frequency and ethnicity.
o Displays a need to emphasize the importance of exercise to
college students of various ethnic backgrounds.
56
Anthropometrics
Elizabeth Chlopek
57
What the Literature Says:
• Of the methods used to measure body fat and its distributions,
anthropometric measurements play an important role in clinical practice.
• The risk for overweight and obesity has shown to be independently
associated with excess abdominal fat. (1)
• Body mass index (BMI) is most widely used to measure total adiposity and
to categorize obesity, while waist circumference (WC) is an alternate
marker for abdominal adiposity. (1)
o
Some research shows BMI as a indicator for man of overweight/obese
status. With women, waist to hip ratio and waist circumference was a better
indicator of overweight/obesity status. (2)
• Some research has shown WC as a better indicator of abdominal obesity
(1)
• More research is needed to determine what anthropometric measures
perform the best in assessing obesity related health risks and what cut-off
points should be used in clinical settings.
o
Also, related ethnic differences need to be better understood. (1)
1 Xu, F., Wang Y., Lu, L., Liang, Y., Wang, Z., Hong, X., & Li, J.
2. Kamadjeu, R., Edwards, R., Atanga, J., Kiawi, E., Unwin, N., & Mbanya, J
Ho13: There is no difference between
ethnicity and anthropometric measures.
Ho13a: There is no difference between ethnicity and BMI.
• F(4,102) = .271, p = .896
Ho13b: There is no difference between ethnicity and umbilicus waist
circumference.
• F(4,102) = .317, p = .866
Ho13c: There is no difference between ethnicity and iliac waist
circumference.
• F(4,102) = 1.417, p = .234
Ho13d: There is no difference between ethnicity and percent body fat
(PBF).
• F(4,102) = .417, p = .796
Descriptives: Anthropometric Measures
Ethnicity & Anthropometrics
Ethnicity & Anthropometrics
Ethnicity & Anthropometrics
Ho14: There is no association between iliac crest
waist circumference and umbilicus waist
circumference.
Ho14: Rejected, p < .001
Umbilicus WC
(cm)
Umbilicus WC (cm)
Iliac Crest (cm)
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
(r(107) = .892, p < .01)
Iliac Crest WC (cm)
1
107
.892
P < .001
107
.892
1
107
107
Ho15a: There is no relationship between umbilicus
waist circumference risk categories and BMI
categories.
Ho15(a): Rejected, p < .001
Waist Circum Risk Um * BMI Category 2 Crosstabulation
BMI Category
2
Overweight/Obes
Waist Circum At Risk
(>102cm or
Risk Um
>88cm)
Normal
(<101.9cm or
<87.9cm)
Total
Normal
0
e
Total
8
8
.0%
79
7.5%
20
7.5%
99
73.8%
79
18.7%
28
92.5%
107
73.8%
26.2%
100.0%
(x²(1) = 24.395 p < .001)
65
Ho15b: There is no relationship between iliac
crest waist circumference risk categories and
BMI categories.
Ho15(b): Rejected, p < .001
Waist Circum Risk Iliac * BMI Category 2 Crosstabulation
BMI Category 2
Normal
Waist Circum At risk (>102cm or
Risk Iliac
>88cm)
Normal (<101.9cm or
<87.9cm)
Total
Overweight/Ob
Total
ese
5
13
18
4.7%
74
12.1%
15
16.8%
89
69.2%
79
14.0%
28
83.2%
107
73.8%
26.2%
100.0%
(x²(1) = 23.757, p = < .001)
66
Ho16a: There is no relationship between
umbilicus waist circumference risk categories
and PBF categories.
Ho16(a): Rejected p < .05
Waist Circum Risk Um * PBF Category 2 Crosstabulation
PBF Category 2
Normal Fat/Obese Total
Waist Circum
Risk Um
At Risk (>102cm
or >88cm)
Normal (<101.9cm
or <87.9cm)
Total
1
7
8
.9%
61
6.5%
38
7.5%
99
57.0%
62
35.5%
45
92.5%
107
57.9%
42.1% 100.0%
(x²(1) = 7.327, p < .05)
67
Ho16b: There is no relationship between iliac
waist circumference risk categories and PBF
categories.
Ho16(b): Rejected, p < .001
Waist Circum Risk Iliac * PBF Category 2 Crosstabulation
PBF Category 2
Normal Fat/Obese
Waist Circum
Risk Iliac
At risk (>102cm or
>88cm)
Normal (<101.9cm
or <87.9cm)
Total
(x²(1) = 15.13, p < .001)
Total
3
15
18
2.8%
59
14.0%
30
16.8%
89
55.1%
62
28.0%
45
83.2%
107
57.9%
42.1%
100.0%
68
Ho17: There is no relationship between BMI
categories and PBF categories.
Ho17: Rejected, p < .001
BMI Category 2 * PBF Category 2 Crosstabulation
PBF Category 2
Normal
Fat/Obese
BMI
Normal
Category 2
Overweight/Obese
Total
Total
58
21
79
54.2%
4
19.6%
24
73.8%
28
3.7%
62
22.4%
45
26.2%
107
42.1%
100.0%
57.9%
(x²(1) = 29.663, p < .001)
69
Gender & Anthropometric Measures
Ho18: There is no difference between Gender and BMI.
-Accepted
Ho19: There is no difference between Gender and Percent
Body Fat. - Rejected
Ho20: There is no difference between Gender and umbilicus
crest waist circumference. - Accepted
Ho21: There is no difference between Gender and iliac waist
circumference. - Rejected
Ho18: There is no relationship between
Gender and BMI.
BMI * Gender Crosstabulation
Ho18: Accepted, p > .05
Gender
Male
Female Total
BMI
Underweight
(<18.4)
1
.9%
Normal
(18.5-24.9)
21
19.6%
Overweight
(25-29.9)
4
3.7%
Obese I (3034.9)
5
4.7%
Total
(x²(1) = 4.368, p = .224)
31
29.0%
9
10
8.4% 9.3%
48
69
44.9% 64.5%
14
18
13.1% 16.8%
5
10
4.7% 9.3%
76
107
71.0% 100.0
% 71
Ho19: There is no difference between Gender
and Percent Body Fat.
Rejected, p < .05
(F(1,105) = 26.8, p = < .05).
Ho20: There is no relationship between Gender
and umbilicus crest waist circumference.
Ho20: Accepted
(x²(1) = 1.14, p > .05)
Ho21: There is no relationship between
Gender and iliac waist circumference.
Ho21: Rejected
(x²(1) = 5.766, p < .05)
Summary of Hypotheses
Ethnicity and Anthropometrics:
• Ho13a: There is no difference between ethnicity and BMI. -Accepted
• Ho13b: There is no difference between ethnicity and umbilicus waist
circumference. -Accepted
• Ho13c: There is no difference between ethnicity and iliac waist
circumference. -Accepted
• Ho13d: There is no difference between ethnicity and percent body fat
(PBF). –Accepted
Ethnicity and Waist Circumference Measures:
• Ho14: There is no association between iliac crest waist circumference
and umbilicus waist circumference. -Rejected
Waist Circumference Risk and BMI:
Ho15: There is no relationship between waist circumference risk categories
and BMI categories:
• Ho15(a): There is no relationship between umbilicus waist
circumference risk categories and BMI categories. – Rejected
• Ho15(b): There is no relationship between iliac crest waist
circumference risk categories and BMI categories. – Rejected
Summary of Hypotheses cont.
Waist Circumference Risk and Percent Body Fat (PBF):
Ho16: There is no relationship between waist circumference risk categories
PBF categories:
• Ho16(a): There is no relationship between umbilicus waist circumference
risk categories and PBF categories. - Rejected
• Ho16(b): There is no relationship between iliac waist circumference risk
categories and PBF categories. – Rejected
Body Mass Index (BMI) and Percent Body Fat (PBF):
• Ho17: There is no relationship between BMI categories and PBF
categories. - Rejected
Gender and Anthropometrics:
• Ho18: There is no difference between
• Ho19: There is no difference between
Rejected
• Ho20: There is no difference between
circumference. - Accepted
• Ho21: There is no difference between
circumference. - Rejected
Gender and BMI. -Accepted
Gender and Percent Body Fat. Gender and umbilicus crest waist
Gender and iliac waist
Anthropometric Conclusions
• Trends show differences among ethnicities
o However does not show any significance
• Waist circumference measurements are highly
correlated with BMI
• Waist circumference measurements are highly
correlated with PBF
o Iliac crest WC more highly correlated
• Iliac crest WC and PBF are significantly different
between genders
Lipids
Kim McDonough
Lipid Levels Among College Students
• Acculturation among college students is
limited
• Acculturation to new lifestyle and
independent living can cause altered lipid
values
o Students that move away from home tend
to change their dietary habits
o Unhealthy diets
Keown TL, 2009
Pollard TM, 1995
Altered Lipid Levels
• Various changes lead to risk of altered lipid
levels among college students
Changes in dietary habits related to acculturation
and changes in living arrangements
o Decreased physical activity
o Increase in alcohol consumption
o Stress
o
• Increase risk for changes in lipid values
o
o
Risk of Metabolic syndrome
Risk of increased weight
Keown TL, 2009
Pollard TM, 1995
Brunt AR, 2008
Kemmyda L, 2008
Risk Factors: Lipid Levels
Lipid Level
Risk Factor Value
Total Cholesterol
> 200 mg/dl
HDL Cholesterol
Women < 50 mg/dl
Men < 40 mg/dl
LDL Cholesterol
> 100 mg/dl
Triglycerides
> 150 mg/dl
Blood Glucose
> 100 g/dl
Blood Pressure
> 130/85 mm/hg
Ho22: There is no difference among ethnicities and
cholesterol values:
Ethnicity
N
Mean
Std. Deviation
Asian Indian and Chinese
23
162.8696
30.92999
Black or African American
4
171.5
32.51154
Hispanic or Latino
12
163.3333
22.05503
White (European or N. Africa
origins)
58
172.5345
34.4405
White (Middle East)
Total
8
105
150.125
167.619
24.10357
31.90954
(F(4,100) = 1.148, p=.339)
M= 167.62 (sd=31.91)
The null hypothesis was accepted.
Ho23: There is no relationship between cholesterol
values
Cholesterol and LDL: (r(76)= .841, p=.000) Null hypothesis was rejected.
Cholesterol and HDL: (r(100)=.304, p=<.01) Null Hypothesis was rejected.
Ho24: There is no relationship between total
cholesterol and triglycerides
(r(80) = .393, p=.000).
The null hypothesis was rejected.
Ho25: There is no relationship between total
cholesterol values and blood pressure
Right arm BP and left arm BP: (r(104)=.346, p=.000)
The null hypothesis was rejected
Ho26: There is no difference between mean
cholesterol values among gender
Total Cholesterol Means Among Gender
Total Cholesterol
Total HDL Chole
Total LDL Chole
Gender
Male
N
30
Mean
165.1667
Female
75
168.6000
33.55149
3.87419
Male
29
43.1724
9.68087
1.79769
Female
73
55.4247
14.55346
1.70335
Male
22
111.1818
26.19639
5.58509
Female
56
98.7679
27.11926
3.62396
(t(100)=-4.175, p<.001)
Std. Deviation Std. Error Mean
27.75519
5.06738
The null hypothesis was rejected.
Ho27: There is no difference in blood
pressure between gender
Blood Pressure Means Among Gender
Gender
BP3 RArm systolic Male
N
31
Mean
111.9032
Std. Deviation
13.52862
Std. Error
Mean
2.42981
Female
76
102.4605
10.57726
1.21329
31
69.0645
10.57965
1.90016
Female
76
66.4605
9.37577
1.07547
Male
31
111.7097
10.78021
1.93618
Female
76
102.0132
10.05650
1.15356
31
70.3548
8.33286
1.49663
76
65.9605
9.17742
1.05272
BP3 RArm diastolic Male
BP3 LArm systolic
BP3 LArm diastolic Male
Female
R arm systolic: (t(105) =3.854, p<.001).
L arm systolic: (t(105) =4.431, p<.001).
R arm diastolic: (t(105) =1.255, p=.212).
L arm diastolic: (t(105) =2.305, p=.023).
Null hypothesis was rejected.
Null hypothesis was rejected.
Null hypothesis was accepted.
Null hypothesis was rejected.
Conclusion
• No significant difference between lipid
values and ethnicities
• Correlations noted among lipid values
– Total cholesterol and LDL cholesterol
– Total cholesterol and HDL cholesterol
– Cholesterol and blood pressure
– Right and left arm blood pressure
Conclusion
• Significant relationships noted between lipid
values and gender
– Males more likely to have low HDL cholesterol
compared to females
– Males more likely to have high LDL cholesterol
compared to females
– Males more likely to have high left arm blood
pressure compared to females
– Males more likely to have higher right arm
systolic blood pressure compared to females
– Males more likely to have higher left arm systolic
and diastolic blood pressure compared to females
Fat Intake
Denise Albina
What the Literature Says….
• College students tend to eat less fiber and more fat
than is recommended.
• Over half of female college students tend to
underestimate fat content in foods and dietary fat
intake.
• Minority ethnic groups tend to have a higher dietary fat
intake.
• Improper fat intake (diets high in saturated fat) places
individuals at increased risk for cardiovascular disease
as lipid serum levels are increased.
Ho28: Gender and Fat Intake
Ho28a: There is no difference in fat intake between males
and females. (rejected p=0.01)
Ho28b: There is no difference in saturated fat intake between
males and females. (rejected p=0.01)
Ho29: Ethnicity and Fat Intake
• Ho29a: There is no difference in fat intake between ethnicities.
(Accepted p=0.43)
• Ho29b: There is no difference in saturated fat intake between
ethnicities. (Accepted p=0.68)
Ho30: Body Mass Index and Fat Intake
• Ho30a: There is no difference between fat intake and body mass
index. (Accepted p=0.72)
• Ho30b: There is no difference between saturated fat intake and body
mass index. (Accepted p=0.46)
Fat Intake and Body Fat Percent
• Ho31a: There is no difference between fat intake and body fat percent.
(Accepted p=0.14)
• Ho31b: There is no difference between saturated fat intake and body
fat percent. (Accepted p=0.31)
Fat Intake and Total Cholesterol
• Ho32a: There is no difference between total fat intake and total
cholesterol. (Accepted p=0.53)
• Ho32b: There is no difference between saturated fat intake and total
cholesterol. (Accepted p=0.95)
Fat Intake and Triglycerides
• Ho33a: There is no difference between total fat intake and
triglycerides. (Accepted p=0.68)
• Ho33b: There is no difference between saturated fat intake and
triglycerides. (Accepted p=0.79)
Fat Intake and HDLs
• Ho34a: There is no difference between total fat intake and HDLs.
(Accepted p=0.87)
• Ho34b: There is no difference between saturated fat intake and HDLs.
(Rejected p=0.04)
Ho35: Fat Intake and LDLs
• Ho35a: There is no difference between total fat intake and LDLs.
(Accepted p=0.46)
• Ho35b: There is no difference between saturated fat intake and LDLs.
(Accepted p=0.78)
Summary of Hypotheses
Ho28: Gender and Fat Intake
Ho28a: There is no difference in fat intake between males and females ; Rejected
p=0.01
Ho28b: There is no difference in saturated fat intake between males and females ;
Rejected p=0.01
Ho29: Ethnicity and Fat intake
Ho29a: There is no difference in fat intake between ethnicities ; Accepted p=0.43
Ho29b: There is no difference in saturated fat intake between ethnicities ; Accepted
p=0.68
Ho30: Body Mass Index and Fat Intake
Ho30a: There is no difference between fat intake and body mass index ; Accepted
p=0.72
Ho30b: There is no difference between saturated fat intake and body mass index ;
Accepted p=0.46
Ho31: Fat Intake and Body Fat Percent
Ho31a: There is no difference between fat intake and body fat percent ; Accepted
p=0.14
Ho31b: There is no difference between saturated fat intake and body fat percent. ;
Accepted p=0.31
Summary of Hypothesis
Ho32: Fat Intake and Total Cholesterol
Ho32a: There is no difference between total fat intake and total cholesterol ;
Accepted p=0.53
Ho32b: There is no difference between saturated fat intake and total cholesterol. ;
Accepted p=0.95
Ho33: Fat Intake and Triglycerides
Ho33a: There is no difference between total fat intake and triglycerides ; Accepted
p=0.68
Ho33b: There is no difference between saturated fat intake and triglycerides ;
Accepted p=0.79
Ho34: Fat Intake and HDLs
Ho34a: There is no difference between total fat intake and HDLs ; Accepted p=0.87
Ho34b: There is no difference between saturated fat intake and HDLs ; Rejected
p=0.04
Ho35: Fat Intake and LDLs
Ho35a: There is no difference between total fat intake and LDLs ; Accepted p=0.46
Ho35b: There is no difference between saturated fat intake and LDLs ; Accepted
p=0.78
Conclusions
• There are significant differences in total fat and saturated fat
intake between males and females. Males are shown to
consume on average more saturated fat than women and less
total fat than woman.
• There is a significant difference between saturated fat intake
and total HDL levels.
• There are no significant differences across ethnicity, BMI
categories, and percent body fat categories when compared
total fat intake and saturated fat intake.
• There are no significant differences with total cholesterol,
triglycerides, LDLs when compared to total fat intake and
saturated fat intake.
• All 150 participants who completed the block fat screener were
shown to have a fat intake above the recommended daily
allowance of <30% fat.
Fruit Vegetable & Fiber
Melinda Hamilton-Smith
What the Literature Says:
• Different forms of antioxidants present in fruits and
vegetables as well as the fiber content and the rich
amount of vitamins, minerals, and trace elements
provide health benefits.
• The benefits of antioxidants have been shown to
include; reducing LDL oxidation, altering cholesterol
metabolism, and lowering of blood pressure
• Fruit, vegetable, fiber and fat intake are most closely
associated with morbidity and mortality
What the Literature Says:
•
Outpatient controlled study: increased their overall fruit vegetable and fiber
consumption over a 10 day period. Total cholesterol decreased by 16%,
LDL decreased by 22%, VLDL decreased by 35%, fasting insulin
decreased 68%. fasting glucose decreased 5% but was not statistically
significant.
•
Cross-sectional study of 422 males self reported intake found associations
between intake of fiber-rich foods, and alcohol to be large determinants of
cardiovascular disease risk factors including increases in serum lipid
levels, waist circumference, and blood pressure.
•
Eighty-eight healthy subjects randomly assigned to a control group with
high fiber plant foods, fruits, berries, vegetables, whole grains, rapeseed
oil, nuts, fish and low-fat milk products, while avoiding salt and added
sugars or saturated fats. Significant lowering of plasma levels of
cholesterol, LDL-C, HDL-C, apoA1, ApoB, ApoB/ApoA1 ratio, and
LDL/HDL ratio.
Ethnicity and fruit, vegetable, & fiber intake
Ho36a: There is no difference between ethnicity and fruit and vegetable
intake
Accepted: P = 0.386
Ho36b: There is no difference between ethnicity and dietary fiber intake
Accepted: P = 0.821
N
Servings
Fruits & Vegetables
Dietary Fiber
Intake
Mean +/- SD
Mean +/- SD
Asian Indian and Chinese
38
5.34 +/- 1.85
19.87+/- 5.49
Black or African American
5
5.96 +/-2.44
21.58+/- 7.64
Hispanic or Latino
13
5.84 +/- 2.13
20.66+/- 6.36
White (European or origins)
73
5.97 +/-1.72
20.098+/- 4.98
White ()
14
6.32 +/-1.77
21.63+/- 5.62
Total
143
5.83 +/-1.83
20.29+/- 5.36
Fruits, vegetables, fiber, & gender
Ho37a: There is no difference between fruit and vegetable intake and
gender.
o Accepted: P = 0.423
Ho37b: There Is no difference between gender and dietary fiber intake
o Rejected: P = .000
N
Servings of fruits and
vegetables
Mean +/- SD
Dietary fiber intake
(grams)
Mean +/- SD
Male
38
6.03 +/- 1.72
23.34 +/- 4.93
Female
106
5.75+/- 1.86
19.22 +/- 5.08
Total
144
5.82 +/- 1.82
20.30 +/- 5.34
Fruits, vegetables, fiber, & gender
Servings of Fruits and
Vegetables
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Dietary Fiber Intake (grams)
25
20
15
Males
Females
10
5
0
Males
Females
p=0.254
F(1,142) = 0.65, p > 0.05
Males
Females
p=0.000
F(1,142) = 18.68, p < 0.05
Fruits, vegetables, and gender
Ho37c: There is no relationship between gender and meeting the
recommendations for fruit and vegetable intake
Accepted: P = 0.096
Does not meet
recomendations
Male
Count
Grand
Mean
10
28
38
% within Gender
26.3%
73.7%
100.0%
% within
FruitVegRecommendation
s
20.8%
29.8%
26.8%
7.0%
19.7%
26.8%
38
66
104
% within Gender
36.5%
63.5%
100.0%
% within
FruitVegRecommendation
s
79.2%
70.2%
73.2%
% of Total
Female
Meets
recomendation
s
Count
Gender Dependent
.096
FruitVegRecommendations
Dependent
.096
Eta
(x²(1) = 0.25, p>.05).
Fruits, vegetables, and gender
Males
Females
26.3%
36.5%
65.5%
73.7%
n=38
n=104
Does not meet recommendations
p=0.254
(x2(1) = 0.25, p > 0.05
Meets recommendations
Fiber and gender
Ho37d: There is no relationship between gender and meeting the
recommendations for fiber intake.
Rejected: P = .008
Gender
Male
Female
Does not meet
recommendatio
ns
Meets
recommendatio
ns
25
9
34
% within Gender
73.5%
26.5%
100.0%
% within
FiberRecommendations
21.0%
52.9%
25.0%
% of Total
18.4%
6.6%
25.0%
94
8
102
% within Gender
92.2%
7.8%
100.0%
% within
FiberRecommendations
79.0%
47.1%
75.0%
Count
Count
(x²(1) = 8.09, p<.05).
Fiber and gender
Males
Females
7.8%
26.5%
73.5%
92.2%
n=34
n=102
Does not meet recommendations
p=0.004
(x2(1) = 8.09, p = <0.05
Meets recommendations
Fruits, vegetables and blood Lipids
• Ho38a: There is no difference between fruit and vegetable intake and normal or
high total cholesterol
o Accepted: p = 0.717
• Ho38d: There is no difference between fruit and vegetable intake and normal or
high risk triglyceride levels
o Accepted: p = 0.362
N
Servings Fruits and vegetables
Mean +/- SD
Normal Total Cholesterol
91
5.81 +/- 1.86
High (=>200) Total Cholesterol
14
5.62 +/- 1.35
Total
105
5.78 +/- 1.80
Normal Triglyceride level
73
5.69 +/-1.84
High Risk (>150) Triglyceride level
9
5.12 +/-1.42
Total
82
5.63+/-1.80
Blood Lipids
Fruits, vegetables and blood Lipids
• Ho38b: There is no difference between fruit and vegetable intake and normal or
low HDL cholesterol
•
Accepted: p = 0.362
• Ho38c: There is no difference between fruit and vegetable intake and normal or
high LDL cholesterol
•
Accepted: p = 0.693
N
Servings Fruits and vegetables
Mean +/- SD
Low HDL Cholesterol (<50 or <40)
29
5.52 +/- 1.74
Normal HDL Cholesterol
73
5.89 +/- 1.86
Total
102
5.79 +/- 1.82
Normal LDL Cholesterol
45
5.63 +/- 1.75
High (>100) LDL Cholesterol
33
5.78 +/- 1.54
Total
78
5.69 +/- 1.66
Blood Lipids
Fruits, vegetables and blood Lipids
• Ho38e: There is no association between fruit and vegetable intake and
blood lipid levels
•
Accepted: p >0.05
ServingsFruitVeg
Total Cholesterol
Total HDL Cateogry
Total LDL Chole
Pearson Correlation
.105
Sig. (2-tailed)
.286
N
105
Pearson Correlation
.091
Sig. (2-tailed)
.362
N
102
Pearson Correlation
.047
Sig. (2-tailed)
.682
N
Total Triglycerides
78
Pearson Correlation
-.110
Sig. (2-tailed)
.326
N
82
Fiber and blood Lipids
• Ho39a: There is no difference between dietary fiber intake and normal or high
total cholesterol
Accepted: p = 0.338
• Ho39d: There is no difference between dietary fiber intake and normal or high
risk triglyceride levels
o Accepted: p = 0.146
N
Dietary Fiber Intake
Mean +/- SD
Normal Total Cholesterol
91
20.54 +/- 5.42
High (=>200) Total Cholesterol
14
19.08 +/- 4.28
Grand Mean
105
20.34 +/- 5.29
Low HDL Cholesterol (<50 or <40)
29
19.87 +/- 5.22
Normal Triglyceride level
73
20.14 +/-5.22
High Risk (>150) Triglyceride level
9
17.52 +/-3.11
Grand Mean
82
19.85 +/-5.08
Fiber and blood Lipids
• Ho39b: There is no difference between dietary fiber intake and normal
or high HDL cholesterol
o Accepted: p = 0.585
• Ho39c: There is no difference between dietary fiber intake and normal
or high LDL cholesterol
o Accepted: p = 0.161
N
Dietary Fiber
Mean +/- SD
Normal HDL Cholesterol
73
20.51 +/- 5.43
Grand Mean
102
20.33 +/- 5.35
Normal LDL Cholesterol
45
19.33 +/- 4.62
High (>100) LDL Cholesterol
33
20.83 +/- 4.60
Grand Mean
78
19.96 +/- 4.64
Fiber and blood Lipids
• Ho39e: There is no association between dietary fiber intake and blood
lipid levels
o Accepted: p > .05
DietaryFiberGrams
Total Cholesterol
Total HDL Cateogry
Total LDL Chole
Pearson Correlation
.052
Sig. (2-tailed)
.601
N
105
Pearson Correlation
.055
Sig. (2-tailed)
.585
N
102
Pearson Correlation
.055
Sig. (2-tailed)
.631
N
Total Triglycerides
78
Pearson Correlation
-.090
Sig. (2-tailed)
.422
N
82
Fruits, vegetables, fiber and BMI
• Ho40a: There is no difference between fruit and vegetable intake and
BMI
o Accepted: p = 0.287
• Ho40b: There is no difference between dietary fiber intake and BMI
o Accepted: p = 0.188
BMI
Underweight (<18.4)
Normal (18.5-24.9)
Overweight (25-29.9)
Obese I (30-34.9)
Total
N
Servings Fruits
and vegetables
Mean +/- SD
Dietary Fiber Intake
Mean +/- SD
10
69
18
10
107
4.87 +/- 1.38
5.99 +/- 1.93
5.58 +/- 1.45
5.98 +/- 1.78
5.81 +/- 1.81
17.15 +/- 4.08
20.82 +/- 5.72
20.00 +/- 3.85
21.50 +/- 4.31
20.40 +/- 5.25
Hypothesis Summary
• Fruits vegetables ,fiber, and ethnicity
o Ho36a: There is no difference between ethnicity and fruit and
vegetable intake
 Accepted: P = 0.386
o Ho36b: There is no difference between ethnicity and dietary fiber
intake
 Accepted: P = 0.821
• Fruits, vegetables, fiber, and gender
o Ho37a: There is no difference between fruit and vegetable intake
and gender.
 Accepted: P = 0.423
o Ho37b: There Is no difference between gender and dietary fiber
intake
 Rejected: P = .000
o Ho37c: There is no relationship between gender and meeting the
recommendations for fruit and vegetable intake
 Accepted: P = 0.096
o Ho37d: There is no relationship between gender and meeting the
recommendations for fiber intake
 Rejected: P = .008
Hypothesis Summary
• Fruits, vegetables and blood Lipids
o Ho38a: There is no difference between fruit and vegetable intake
and normal or high total cholesterol
 Accepted: p = 0.717
o Ho38b: There is no difference between fruit and vegetable intake
and normal or low HDL cholesterol
 Accepted: p = 0.362
o Ho38c: There is no difference between fruit and vegetable intake
and normal or high LDL cholesterol
 Accepted: p = 0.693
o Ho38d: There is no difference between fruit and vegetable intake
and normal or high risk triglyceride levels
 Accepted: p = 0.372
o Ho38e: There is no association between fruit and vegetable intake
and blood lipid levels
o Accepted: p > 0.05
Hypothesis Summary
• Fiber and blood Lipids
o Ho39a: There is no difference between dietary fiber intake and
normal or high total cholesterol
 Accepted: p = 0.338
o Ho39b: There is no difference between dietary fiber intake and
normal or high HDL cholesterol
 Accepted: p = 0.585
o Ho39c: There is no difference between dietary fiber intake and
normal or high LDL cholesterol
 Accepted: p = 0.161
o Ho39d: There is no difference between dietary fiber intake and
normal or high risk triglyceride levels
 Accepted: p = 0.146
o Ho39e: There is no association between dietary fiber intake and
blood lipid levels
 Accepted: p > .05
Hypothesis Summary
• Fruits, vegetables, fiber and BMI
o Ho40a: There is no difference between fruit and vegetable intake
and BMI
 Accepted: p = 0.287
o Ho40b: There is no difference between dietary fiber intake and BMI
 Accepted: p = 0.188
Conclusion:
• Fruit, vegetable, and fiber intake was not found to be significantly
affected by ethnic background.
• A statistically significant difference was found in fiber intake. Males
were found to consume more fiber on a daily bases averaging
23.34g of fiber/day where females average intake was only
19.22g/day. Further, 52.9% of males were found to meet the dietary
recommendations for fiber and only 47.1% of females met these
same recommendations.
• The amount of fiber, fruits and vegetables consumed by the
students did not show any significant difference in their blood lipid
levels (TC, LDL, HDL, and TG) or BMI category.
Strengths
•
•
•
•
•
Use of health instruments and tools.
Properly trained research team.
Varied recruitment techniques.
Included ethnicities with little data available.
Research problem addressed with little data
available.
• Continuation of research conducted the previous
year.
Weaknesses
•
•
•
•
•
•
Ethnicities varied in size
Small total sample size
Timing of study
Length of recruitment time
Volunteer bias
Clarity of
questionnaires/directions
Future Research
• Larger university may help to increase sample size.
• Collecting data from the same amount of subjects from
each ethnicity.
• Comparing data between specific years in college.
(Freshman vs. Seniors)
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