research final paper

Running Head: EATING HABITS OF YOUNG ADULTS 1

A Comparison of Active and Non Active Young Adults Healthy Lifestyle

Krisha Caronongan

University of Windsor

2EATING HABITS OF YOUNG ADULTS

Abstract

Studies that are conducted on eating patterns and physical activity are mostly focused on

adolescents and older adults. However, the investigation that takes place in this study will assess

the eating habits of active and non active young adults to determine if there is a statistical

significance within the two types of participants. The participants who volunteered are

University of Windsor Students from both the Human Kinetics Faculty and the non Kinesiology

Faculties within the campus. The instruments used are food diaries and BMI. The participants

will then record what they have consumed for a period of three days. Then the volunteers will be

categorized and analyzed through the Chi-Square analysis. After the analysis, the data showed

no statistical difference between the two types of participants. Therefore, there is no significant

difference between active and non active young adults in eating habits and physical activity.

Word count: 145


Today young adults have two choices; whether to be healthy or not. There is a general

assumption that being active means that an individual is healthier, however the same could be

said for an individual who is not active meaning they are unhealthy. Many people, even from

studies that have been made, think being active is the key to being healthy however there are

many aspects to being healthy. There are also many disruptions that get in the way of living an

all-around healthy lifestyle; transitioning into college or university as a young adult as this a

common age accompanied by dramatic and inappropriate weight gain, students being exposed to

the partying scene, having a busier schedule due to more school work, studying, and work, and in

general not a worthy food selection on school campus therefore taking away the healthy option

(Racette et al., 2005, p. 245). Of course one can work around any obstacle they may face, but as

a young adult it is difficult to become accustomed to a new schedule. It is more likely that a

young adult will demonstrate unhealthy behaviours because these alternatives are more

accessible than any healthy behaviour.

The transition between living at home and going away to university can be a factor in the

changes of eating habits and physical activity that can occur when a young adult enters

university. Independence can mean that the individual can have an increase in responsibility,

hurried lifestyle which can affect their eating, and exercising habits (Mohindra et al., 2009, p.

15). Transitioning from high school to university is a very large adjustment for young adults

making it difficult to get used to a new schedule as well as lifestyle. Students who are not used to

studying as much in high school are now being forced to spend more time doing so, resulting in

less time for physical activity. It is also important to recognize that health is not always a primary

driving force in many dietary choices and that personal, social, cultural, and environmental

expectation and experiences can often have a greater influence on food choices (Bisogni et al.,


2012, p. 412). Another factor of the transition is the social pressures, such as tagging along to

parties where there is alcohol present. Also, no longer having family around to dictate the

individual’s eating habits can cause a new university student to exhibit poor eating habits

(Mohindra et al., 2009, p. 15). For example, many students in high school became accustomed to

coming home to home cooked meals but due to the increased work load in university, students

now have to find time to make their own meals, often unhealthier choices. Fast foods and frozen

dinners become more convenient as it is easier to make between classes, or studying periods.

Another factor the reviewed literature describes is the influence parents have on a young

adult if they live at home (Young, 2001, p. 483). The eating habits of parents can influence what

and when the individual consumes and at what volume, yet the research pertaining to the

influence of parents for eating habits is limited. There is also limited evidence on specific eating

behaviours and providing nutritional advice (Young, 2001, p. 483). Therefore, parental

influence on alimentation can need to be further examined and clearly explained.

There is a lack of research about the eating habits (Wills, 2005, p. 444). Physical activity

and sedentary behaviour patterns resulting in lack of knowledge as to whether or not negative or

positive exercise behaviours of young adults truly affects the healthiness of a person (Nelson et

al., 2006, p. 444). Young adults are the missing puzzle piece to studies referring to physical

activity and an individual’s healthy or unhealthy behaviours due to the fact that research is done

more so on young children or older adults. At a young age negative behaviours are easier to alter

as a child can stop an action before it becomes a bad habit as well as an older adult can receive

treatment and care to help them along the way to make healthier and life lasting decisions; but

what can a young adult do to obtain a healthy life from now on?


There are no true studies which help a young adult face these obstacles and there most

definitely is not as much guidance for young adults transitioning into a new part of their lives

however these problems can easily be fixed by proper research as well as more research. Proper

research would consist of the correct calculations taking into account every factor, keeping track

of the same people and all the people being surveyed, and making sure each participant feels

comfortable enough to give the correct information in order to receive the most accurate results a

study. Another problem within studies is the amount of assumptions made by researchers. In a

study done by Susan B. Racette et al., five stages of readiness were studied during the first and

second year for college students; the five stages were pre-contemplation which is lack of

intention to exercise or eating healthy, contemplation which is the thinking of exercising or

eating healthy, preparation which is planning to exercise or eat healthy, action which is current

exercising or healthy eating, and lastly maintenance which is sustaining any exercise behaviour

or eating behaviour. In this study it was stated that the first three stages signified lack of exercise

therefore the last two stages representing current exercise participation; however there was an

assumption made of all college students despite this statement. It was assumed that most students

in their first two years of college do not have any time for exercise, because they are in the pre-

contemplation stage, but a large amount of students were in the action and maintenance stages

for exercise. Instead, the reviewed literature proves first and second year students have already

been maintaining exercise; this means that the individual has been exercising for over 6 months

prior to their school year (Racette et al., 2009, p. 249). Evidently it is easier for these students to

keep up with constant exercise.

This brings us to the next assumption within this study which is that the second year

students start to think about improving their eating habits (Racette et al., 2009, p. 248). So why


do researchers think that the key to being healthy is being active without maintaining a healthy

diet? This may be due to the assumption that second year students are better at managing their

time which can increase their participation in physical activity more than students who are in

their first year of university. Therefore, students entering college may be exercising, however

they are most definitely not eating healthy which should go hand in hand.

The purpose of this study is to examine if there are true differences between active young

adults eating healthier than non-active young adults. The main rationale of doing this case study

is to fill the gap of missing research on young adults and create an interest to further evaluate

young adults and their eating habits. It may be true that there is a difference between active

young adults being more educated on a healthy lifestyle than non-active young adults however

this does not mean one group is healthier than the other group. Every individual is different so

there is no true way to tell who is healthier than whom, yet it is easy to see who works harder

toward a healthier lifestyle. For example, there are plenty of differences between sport students

and non-sport students, displaying that sport students have a significantly lower intake of fat but

a higher percentage of energy from protein compared to non-sport students (Lake et al., 2009, p.

449). This shows that there is a difference between active and non-active young adults because

active young adults are educated on living a healthy lifestyle and consume foods that have a

lower fat content.

From the information collected, a hypothesis was formed which states that there is a

significant difference between active young adults eating healthier than non-active young adults.

This is assumed because active young adults partake in healthier routines and know the

importance of active living. The same conclusion can be made with non-active young adults

because in most cases, they are not taught the benefits of living a healthy lifestyle and do not


participate in physical activity. Young adults who are more sedentary and spend long periods of

time sitting rather that not being physically active, tend to consume foods with higher fat content

unlike eating fruits and vegetables (Lake et al., 2009, p.450). These studies prove that a more

lifestyle sedentary results in a more unhealthy life because of the lack of exercise young adults

take part in. This is evidently true because young adults transitioning into college have a hard

time with time management and spend most of their time watching television and playing video

games all while eating junk foods high in fat, rather than spending their time studying,

exercising, and eating all the right foods with low fat content. This concludes that non-active

young adults who are more sedentary are not healthier than active young adults because they

exhibit more sedentary behaviours and as well as consume more foods that are considered

unhealthy. It also provides young adults many ways to fix their unhealthy behaviours by default.

Young adults can make negative behaviours develop into positive behaviours by becoming less

sedentary, becoming more physically active in between their studies, and eating foods low in fat

content while being active to get positive results from their healthy behaviours.

When all the information is gathered together there is a main goal to this case study that

will be achieved. This goal is to justify the idea of active young adults being healthier than non-

active young adults, and this is what this study will provide with depth and truthfulness. In

conclusion, it will provide that a young adult who is active will have healthier eating habits than

a young adult who is not active.

Method

Participants

The participants required for the research are young adults who are post secondary students


attending the University of Windsor. The participants also need to be both active young adults

and non active emerging adults. The participant number needed did not have to relate to the

population within the university that the study is taken place. Also, there are no specific

requirements that are asked from the participants who contributed. At the beginning of the study

there were 132 participants but one contributor dropped out and did not want to be a part of the

research any longer. After all of the participants have agreed to the study, the total amount of

participants is 131 (n=131). The participants used are University of Windsor students that range

from kinesiology students to non-kinesiology students (non-kin=56, kin=75). The age ranges of

the participants are from 18-22 years old. The average age of the participants in the kinesiology

program is 19.8133 (SD=1.95692). The average age of participants that are not in the

kinesiology program is 19.9643 (SD=1.76804). Therefore, the average age of the participants as

a whole is 19.8779 (SD=1.87298). The average age for the male participants is 19.8889

(SD=1.97559). The average age for the female participants is 19.8644 (SD= 1.75634). Thus,

making the total average age of the participants to be 19.8779 (SD=1.87298).

The participants were then asked how physically active each participant is on a daily

basis which ranges from minimal physical activity, 1-2 hours of physical activity and +2 hours of

physical activity. They are then separated into three sections low active, moderate active and

high active. The participants are also measured in weight and height and categorized as either

underweight, normal weight or over weight based on their weight. The participants are not pure

volunteers due to the fact that they are being compensated for being a part of the study. The

participants that contributed are given a full credit course for participating. Lastly the

participants are assured that the information they provide are used solely for the purpose of the

study.


Instruments and Apparatus

The instruments used are Body Mass Index (BMI) and food diaries. The research was

conducted within the University of Windsor campus. Food diaries can be used to assess the

dietary intake of an individual and the number of servings an individual eats each day. The

numbers of servings that the individual consumes is then compared to and use to determine if the

individual has met the recommended servings for the individual's age group.

The food diaries in this case study are used to measure the fruit and vegetable, meat and

alternative, milk and alternative, other foods and grain intake of each participant. (table:) The

food diaries are given to the participants to record the food for a series of three days. The food

diary is used to determine whether or not the participants are consuming the recommended

amount nutrient intake according to the Canada’s Food Guide. Then after the food diaries have

been collected, each participant is then places in a category of either under consumed, met

recommended or over consumed for each of the food groups. (table:) The goal of having each

participant record their dietary intake is to determine how healthy the participants are in relation

to their intake of each food group.

The Body Mass Index (BMI) is a calculation that it used to determine if an individual is

under weight, normal weight or overweight which is based on the individual's calculated number.

BMI numbers can range from. The BMI's purpose in this case is to categorize the participants

depending on the calculated BMI. As shown in the appendix, the formula for the BMI is weight

in kilograms divided by height in meters squared. (BMI=weight(kg)/height(m)²) (table...) The

weight and height is used in the calculation process of the BMI and it is done so at the beginning

of the case study. Once it was calculated, each participants is then separated based on their BMI

calculations of either being underweight, normal weight or over weight. However, some athletes


can be measured as overweight because the BMI does not take into consideration the difference

between fat mass and muscle mass thus, making an athlete seem over weight when instead the

individual may be in the normal or underweight category. The BMI and food diaries are the key

instruments that are used to throughout the research that was completed.

Procedures

The procedures that took place within the research are food diaries completed by the participants

and the calculation of BMI. The food diaries are given to the participants to record their dietary

intake of each meal and food group. The participants will record what they consumed for

breakfast, lunch, dinner and any snacks that the participants had during the day. Each participant

will record how much of each food groups the participant has eaten or drank throughout the day

for each meal that the individual eats. This is recorded by the participants putting a check mark

on each column per food group to indicate how many servings the participant has eaten at that

day. (table...) The participants will continue this pattern for a series of three days. Once the three

days is over the participants will return the food diaries to be analyzed. The participants will

then be put into one of the three sections which are under consumed, met recommended and over

consumed for each food groups and based on whether the participant is male or female.

BMI is then used to determine which category the participant will be assigned to depending on

the BMI number of each participant. (table..) Before BMI can be calculated, each participant

must provide their height and weight. Once the two measurements are given and the calculation

of BMI is completed, the participants will the categorized as either a kinesiology student or a

non-kinesiology student and as well as the BMI males and females within each category. (table..)

The BMI of each participant will be assigned to either underweight, normal weight or

overweight. The data collected will also analyze if there is a significant difference between the


BMI of male and female kinesiology students and the BMI of male and female non-kinesiology

students.

Design

The design of the research is a survey research. Each participant are then categorized in

either under consumed, met recommended and over consumed to compare the participants eating

habits to their physical activity in separate 3x3 contingency tables. The physical activity on a

daily basis is recorded and categorized into non-active, moderately active and high active based

whether they report engaging in +2, 1-2 or <1 exercise per day. The data will then be cross

tabulated between the physical activity and consumption of food groups in comparison to the

recommended food groups. The food groups and recommended servings come from Canada's

Food Guide. Once, the data is cross tabulated, it will then be analyzed to see whether the data is

either not significant or if it is significant.

Results

The purpose of the case study is to identify if there is a relationship between University of

Windsor students that are both physically active and eat healthier and students who only exhibit

physical activity without healthy eating. Thus, determining the statistical significance of activity

level and eating habits of university students is completed. A three by three contingency table of

individual food groups and activity levels was done. A Chi-Square analysis of the data was

completed to validate the statistical significance between the two variables. However, unlike the

proposed hypotheses, the results of the study show no statistical significance between regarding

the two variables tested.

The data collected will then be put into three by three contingency tables by each food

group and will be cross tabulated with activity level using Chi-Square analysis which will


determine the statistical significance of the two relationships. The three by three contingency

table cross-tabbing milk by activity level Chi-Square analysis confirm no statistical significant

relationship between the two variables. x² (4, N = 131) = 4.209, p = 378 (V = .127) The three by

three contingency table cross-tabbing fruits and vegetables by activity level Chi-Square analysis

show no statistical significant relationship between the two variables.: x² (4, N = 131) = 2.314, p

= .678 (V = .094). The three by three contingency table cross-tabbing grains by activity level

Chi-Square analysis interpreted no statistical significant relationship between those mentioned

above: x² (4, N = 131) = 3.401, p = .493 (V = .114). The three by three contingency table cross-

tabbing meat and activity level Chi-Square analysis resulted to no statistically significant

relationship between the two: x² (4, N = 131) = 4.329, p = .363 (V = .129). The three by three

contingency table cross-tabbing other and activity level Chi-Square analysis concluded in no

statistical significant difference in the two variables mentioned: x² (4, N = 131) = 8.030, p = .090

(V = .175).

Table 14

Chi-Square Analysis Table

Food Groups Significance df N x² VMilk .378 4 131 4.209 .127Fruits and Vegetables

.678 4 131 2.314 .094

Grains .493 4 131 3.401 .114Meat .363 4 131 4.329 .129Other .090 4 131 8.030 .175Notes: df = degrees of freedom, N = number of participants, x² = Chi-Square value, V = Cramer’s V

Discussion

Exhibiting regular physical activity does not mean that the individual part takes in having

healthy eating habits nor does it mean that non active individuals eat non healthy foods in a


regular basis. The goal of this study is to determine whether there is statistical significance

between active young adults and non active young adults and their eating habits. The proposed

hypothesis was that there was a statistical significance between the two types of participants.

However, based on the results found within this study, there is no statistical significance that

shows active young adults do have healthier eating habits than non active young adults. The

analysis of the collected information shows no correlation between eating habits of the

participants and their physical activity.

The expected findings that were proposed prior to the study conclude that there are

apparent differences in eating habits between young adults who are active and those who are not.

In the studies that were examined, the results had a positive correlation between eating habits and

physical activity. However, the unexpected conclusion is that there is a negative correlation

between eating habits and physical activity in young adults. As a result, there is no cohesion

between the reviewed literature and the actual findings of the case study. Although, the

limitations and delimitations within the reviewed literature and the case study are similar to the

research conducted.

The investigation that occurred can be taken in a future direction of test re test to justify

the findings that take place during the examination of active and non active young adult eating

patterns. The test re test can assess the eating habits of the participants by controlling the aliment

consumption of the participants over a certain period of time. Another method to test re test the

study is to have the participants take part in an exercise program and record the nutrient

consumption and serving size during the period of the exercise plan. After that period, have the

participants discontinue the exercise plan and compare the food intake and serving size of the


participants. These are some methods other researchers can use to do a test re test of the

investigation.

The study conducted on the Weight Changes and Dietary Patterns by Racette et al.,

discussed the limitation of “BMI classify college students as overweight is not always

appropriate, because BMI does not differentiate between fat and fat-free masses” (Racette et al.,

2009, p. 250). This limitation is also found within the study conducted because BMI was one of

the measurements used to categorize the participants. BMI can be a limitation because the

participants who are highly active can be placed in the obese category for having a higher fat free

mass which weighs more than regular fat.

An additional limitation found within the reviewed literature is that there could be bias on

the participants who took part of the study based on their low or high body weights (Racette et

al., 2009, p. 250). Some people who chose to not volunteer for the study could be self conscious

and worry that they will be judged based on their weight. Also, the participants can also choose

to not continue the participate in the study if they have gained weight, exhibit poor eating habits

and did not exercise in a regular basis (Racette et al., 2009, p. 250). This can be due to the their

busy schedules and fluctuations of weight and eating habits.

Another limitation that the reviewed literature has comes from Sedentary Behaviours

among Australian Adolescents discussed “the use of self report questionnaires is always a

limitation” (Hardy et al., 2006, p. 540). The use of self reporting questionnaires is a limitation

that is found within the research concluded because the participants can forget or be dishonest

when they are recording the alimentation. Self- reposting questionnaires can also cause the


participant to limit the study by eating healthy on the days that they are asked to keep a record of

their food intake.

When a young adult enters university they have added responsibility, a hurried lifestyle

and no dietary limitations from family can cause the individual to skip meals and resort to quick

meals that are not healthy (Mohindra et al., 2009, p. 15). This is a limitation that is described by

many of the reviewed literature because university students live in a fast pace lifestyle causes

poor consumption of nutritional foods and skipping meals. Also, the routines of university

students tend to be erratic and irregular which can also cause poor exercise and eating habits.

A limitation can be found within the study concluded could arise with the participants and

the given responsibility that was given to them for this study. The participants within the

research study can eat healthy in the three days that they are asked to record their alimentation

intake. This can be a limiting factor because the participants can chose to be dishonest and leave

out information that can comprise the data collected by over estimating the conclusions made.

Since the participant number is not a sample size of the university, the ethnic distribution of the

participants may not represent the population and it is also not recorded in during the data

collection. This can be a limiting factor because the conclusions made within the study may not

apply to different ethnicities within the university population.

Another limitation that the participants have throughout the study is the differences of

food intake between the active young adults and the non active young adults. The active young

adults feel they can eat any type of food due to their exercise routines. These young adults feel

they can over consume without any consequence to their health and body. Unlike non active

young adults who eat healthier because they are not exercising in a regular basis. In fact, the


reverse conclusion occurred because non active participants know that they need to consume

foods with less fat content than those who go to the gym in a regular basis. This is a limiting

factor because it causes the data collected to become insignificant and unable to draw

conclusions after the Chi-Square analysis.

The process of targeting the participants is also a limiting factor to the study. The process

of participant targeting is a limitation of the study because it narrowed the participants that can

be a part of the study. If there are more variation and different groups were targeted besides

second year university kinesiology students and non kinesiology student there could be more

variation and improve the findings that was concluded. Therefore, the process in targeting the

participants is a limiting factor.

The food diaries that the participants recorded their intake of nourishment can also be a

limiting factor. The food diaries are a not valid and not reliable source of measurement for

evaluating the eating habits of the subjects. The food diaries can also contain missing

information that the participants did not include all of the foods consumed by the participants.

Also, the participants may have also over or under estimated the portion size of the alimentation

that was recorded. Food diaries are also inconsistent in what it is intended to measure. As a

result, the use of food diaries can also be a limiting factor that occurred within the study.

The use of BMI can also be a limiting factor to the study that took place. As discussed in

the reviewed literature, BMI does not take into account the difference between fat and fat free

mass within individuals. An example of this can be found in athletes who are lean and have

more muscle mass can be placed in the overweight and obese category which can misrepresent

the number of individuals who are in the obese and overweight categories. BMI is also


unreliable and invalid measurement at the level of the study that occurred because BMI is only

helpful to individuals who are not regularly physically active.

There are apparent limitations within the study but there are also delimiting factors within

the research that was concluded. One of the delimiting factors that occurred the participants are

second year university students. If different groups were examined, there could be more

variation and better finding that can be concluded. Also, different groups could add to the ethnic

distribution of the participants that were included in the case study. If the participants range of

years attending university, there could be more conclusions made that can be applied to the

general population.

The use of food diaries in the case study is also a delimiting factor within the

investigation. A food diary as a measurement of the participant eating habits are a delimitation

because the diaries only recorded three days of the which is not a sufficient way of measuring the

participants eating habits. Since the food diaries only measured three days of nourishment

intake, it is not enough time to assess whether there is a statistical significance in eating habits

between active and non active young adults. This is delimitation because in order to assess

eating patterns of an individual, it needs to be more than three days to determine if one under

consumes, met the recommended or over consumes certain food groups. As a result, food diaries

can be both a limitation and delimitation to the study that was conducted.

It is thought that having a healthy lifestyle can aid students becoming more productive

citizens and exhibit healthier eating patterns than those who do not life a healthy lifestyle (Janse

van Rensburg, et al., 2013, p. 3). There is also an assumption throughout the reviewed literature

that there is a significant difference between active and non active young adults. However, the


investigation to prove the significance instead shows no statistical significance between active

and non active young adults eating habits and physical activity. The perceived thought that there

is a difference between the two active young adults is clarified within this examination and in

hopes to inspire further research within the field of emerging adults and their lifestyle.


Appendix

Table 1: Body Mass Index of Underweight participants

Cross Tabulation of Activity Level and Sex

CountSex TotalFemale

Activity.level 1-2 hours physical activity daily 1 1

Total 1 1

Table 2: Body Mass Index of Normal Weight participants


CountSex TotalMale Female

Activity.level

Minimal physical activity 8 17 251-2 hours physical activity daily 10 39 49

+2 hours physical activity daily 19 0 19

Total 37 56 93

Table 3: Body Mass Index of Overweight participants



CountSex TotalMale Female

Activity.level

Minimal physical activity 8 0 81-2 hours physical activity daily 7 2 9

+2 hours physical activity daily 14 0 14

Total 29 2 31Table 4: Body Mass Index of Obese participants


CountSex TotalMale

Activity.level

Minimal physical activity 2 21-2 hours physical activity daily 1 1

+2 hours physical activity daily 3 3

Total 6 6

Table 5

Cross Tabulation of Major and Activity Level


Activity Level Total

Minimal physical

activity

1-2 hours

physical activity

daily

+2 hours

physical activity

daily

Major

Kinesiology

Count 16 23 36 75

Expected Count 20.0 34.4 20.6 75.0

% within Major 21.3% 30.7% 48.0% 100.0%

% within Activity Level 45.7% 38.3% 100.0% 57.3%

% of Total 12.2% 17.6% 27.5% 57.3%

Non-Kinesiology

Count 19 37 0 56

Expected Count 15.0 25.6 15.4 56.0

% within Major 33.9% 66.1% 0.0% 100.0%


% of Total 14.5% 28.2% 0.0% 42.7%

Total

Count 35 60 36 131

Expected Count 35.0 60.0 36.0 131.0

% within Major 26.7% 45.8% 27.5% 100.0%


% of Total 26.7% 45.8% 27.5% 100.0%

Symmetric Measures

Value Approx. Sig.

Nominal by NominalPhi .535 .000

Cramer's V .535 .000

N of Valid Cases 131

Table 6

Cross Tabulation of Sex and Activity Level



Minimal physical

activity

1-2 hours

physical activity

daily

+2 hours

physical activity

daily

Sex

Male

Count 18 18 36 72

Expected Count 19.2 33.0 19.8 72.0

% within Sex 25.0% 25.0% 50.0% 100.0%


% of Total 13.7% 13.7% 27.5% 55.0%

Female

Count 17 42 0 59

Expected Count 15.8 27.0 16.2 59.0

% within Sex 28.8% 71.2% 0.0% 100.0%


% of Total 13.0% 32.1% 0.0% 45.0%

Total

Count 35 60 36 131

Expected Count 35.0 60.0 36.0 131.0

% within Sex 26.7% 45.8% 27.5% 100.0%


% of Total 26.7% 45.8% 27.5% 100.0%

Symmetric Measures

Value Approx. Sig.




Table 7

Cross Tabulation of Sex and Major


Major Total

Kinesiology Non-Kinesiology

Sex

Male

Count 54 18 72

Expected Count 41.2 30.8 72.0

% within Sex 75.0% 25.0% 100.0%

% within Major 72.0% 32.1% 55.0%

% of Total 41.2% 13.7% 55.0%

Female

Count 21 38 59


% within Sex 35.6% 64.4% 100.0%

% within Major 28.0% 67.9% 45.0%

% of Total 16.0% 29.0% 45.0%

Total

Count 75 56 131


% within Sex 57.3% 42.7% 100.0%

% within Major 100.0% 100.0% 100.0%

% of Total 57.3% 42.7% 100.0%

Symmetric Measures

Value Approx. Sig.




Table 8

Mean and Standard Deviation of the Participant’s Age in Years and Sex


in yearsSex Mean N Std.

DeviationMale 19.8889 72 1.97559Female 19.8644 59 1.75634Total 19.8779 131 1.87298

Table 9

Mean and Standard Deviation of the Participant’s Age in Years and Major

in yearsMajor Mean N Std.

DeviationKinesiology 19.8133 75 1.95692Non-Kinesiology

19.9643 56 1.76804

Total 19.8779 131 1.87298

Table 10


Body Mass Index Calculation Sheet with Example

MASS: _______ lbs * 0.454 = _______ kg

e.g. 180 lbs * 0.454 = 81.72kg (do not round this number yet)

HEIGHT: _______ inches * 0.0254 = __________ m

e.g. 6 feet 2 inches:

6 feet is 72 inches (12 inches in each foot) + 2 inches = 74 inches tall

74 inches * 0.0254 = 1.8796m (do not round this number yet)

BMI: _______mass / (_______height)2 = _______

e.g. 81.72kg/1.8796m²

= 81.72kg/ 3.5328m

= 23.13 (round to one decimal place) FINAL ANSWER: 23.1 BMI

Instructions:

1. Complete the math conversion on the first page of the questionnaire

2. Round to one decimal place before entering into SPSS

BMI = weight(kg)/height(m)2


Health Risk Classification According to Body Mass Index (BMI)

Classification BMI Category (kg/m2)

Risk of developing health problems

Underweight < 18.5 Increased

Normal Weight 18.5 - 24.9 Least

Overweight 25.0 - 29.9 Increased

Obese class I 30.0 - 34.9 High

Obese class II 35.0 - 39.9 Very high

Obese class III >= 40.0 Extremely high

Note: For persons 65 years and older the 'normal' range may begin slightly above BMI 18.5 and extend into the 'overweight' range.

Source: Health Canada. Canadian Guidelines for Body Weight Classification in Adults. Ottawa: Minister of Public Works and Government Services Canada; 2003

Table 12

Chi-Square Analysis of the Food Groups

Food Groups Significance df N x² VMilk .378 130 131 4.209 .127Fruits and Vegetables

.678 130 131 2.314 .094

Grains .493 130 131 3.401 .114Meat .363 130 131 4.329 .129Other .090 130 131 8.030 .175Notes: df = degrees of freedom, N = number of participants, x² = Chi-Square value, V = Cramer’s V

Table 13

Cross Tabulation of Milk Consumption and Activity Level



Minimal

physical

activity

1-2 hours

physical

activity daily

+2 hours

physical

activity daily

Milk

Under-consumed

Count 3 6 4 13

Expected Count 3.5 6.0 3.6 13.0

% within milk new 23.1% 46.2% 30.8% 100.0%

% within Activity

Level8.6% 10.0% 11.1% 9.9%

% of Total 2.3% 4.6% 3.1% 9.9%

Met Recommended

Count 13 30 11 54

Expected Count 14.4 24.7 14.8 54.0

% within milk 24.1% 55.6% 20.4% 100.0%

% within Activity

Level37.1% 50.0% 30.6% 41.2%

% of Total 9.9% 22.9% 8.4% 41.2%

Over-consumed

Count 19 24 21 64

Expected Count 17.1 29.3 17.6 64.0

% within milk 29.7% 37.5% 32.8% 100.0%

% within Activity

Level54.3% 40.0% 58.3% 48.9%

% of Total 14.5% 18.3% 16.0% 48.9%

Total

Count 35 60 36 131

Expected Count 35.0 60.0 36.0 131.0

% within milk 26.7% 45.8% 27.5% 100.0%

% within Activity

Level100.0% 100.0% 100.0% 100.0%

% of Total 26.7% 45.8% 27.5% 100.0%

Chi-Square Tests

Value df Asymp. Sig. (2-

sided)

Pearson Chi-Square 4.209a 4 .378


Likelihood Ratio 4.259 4 .372

Linear-by-Linear Association .012 1 .913


a. 2 cells (22.2%) have expected count less than 5. The minimum expected

count is 3.47.

Symmetric Measures

Value Approx. Sig.




a. Not assuming the null hypothesis.

b. Using the asymptotic standard error assuming the null hypothesis.

Table 14

Cross Tabulation of Fruit and Vegetables and Activity Level

Activity.level Total


Minimal

physical

activity

1-2 hours

physical

activity daily

+2 hours

physical

activity daily

fruit_veg_ne

w

Under-consumed

Count 31 56 33 120

Expected Count 32.1 55.0 33.0 120.0

% within

fruit_veg_new25.8% 46.7% 27.5% 100.0%

% within

Activity.level88.6% 93.3% 91.7% 91.6%

% of Total 23.7% 42.7% 25.2% 91.6%

Met

Recommended

Count 3 3 1 7


% within

fruit_veg_new42.9% 42.9% 14.3% 100.0%

% within

Activity.level8.6% 5.0% 2.8% 5.3%

% of Total 2.3% 2.3% 0.8% 5.3%

Over-consumed

Count 1 1 2 4


% within

fruit_veg_new25.0% 25.0% 50.0% 100.0%

% within

Activity.level2.9% 1.7% 5.6% 3.1%

% of Total 0.8% 0.8% 1.5% 3.1%

Total

Count 35 60 36 131

Expected Count 35.0 60.0 36.0 131.0

% within

fruit_veg_new26.7% 45.8% 27.5% 100.0%

% within

Activity.level100.0% 100.0% 100.0% 100.0%

% of Total 26.7% 45.8% 27.5% 100.0%


Chi-Square Tests


sided)






count is 1.07.

Symmetric Measures

Value Approx. Sig.







Table 15

Cross Tabulation of Grains and Activity Level

Crosstab


Minimal

physical

activity

1-2 hours

physical

activity daily

+2 hours

physical

activity daily

grains_new

Under-consumed

Count 33 50 31 114

Expected Count 30.5 52.2 31.3 114.0

% within grains 28.9% 43.9% 27.2% 100.0%

% within Activity

Level94.3% 83.3% 86.1% 87.0%

% of Total 25.2% 38.2% 23.7% 87.0%

Met Recommended

Count 1 8 3 12


% within grains_new 8.3% 66.7% 25.0% 100.0%

% within

Activity.level2.9% 13.3% 8.3% 9.2%

% of Total 0.8% 6.1% 2.3% 9.2%

Over-consumed

Count 1 2 2 5



% within

Activity.level2.9% 3.3% 5.6% 3.8%

% of Total 0.8% 1.5% 1.5% 3.8%

Total

Count 35 60 36 131

Expected Count 35.0 60.0 36.0 131.0


% within

Activity.level100.0% 100.0% 100.0% 100.0%

% of Total 26.7% 45.8% 27.5% 100.0%

Chi-Square Tests



sided)






count is 1.34.

Symmetric Measures

Value Approx. Sig.






Table 16


Cross Tabulation of Meat and Alternatives and Activity Level


Minimal

physical

activity

1-2 hours

physical

activity daily

+2 hours

physical

activity daily

meat_alt_ne

w

Under-consumed

Count 7 12 8 27


% within

meat_alt_new25.9% 44.4% 29.6% 100.0%

% within Activity.level 20.0% 20.0% 22.2% 20.6%

% of Total 5.3% 9.2% 6.1% 20.6%

Met

Recommended

Count 14 25 8 47

Expected Count 12.6 21.5 12.9 47.0

% within

meat_alt_new29.8% 53.2% 17.0% 100.0%


% of Total 10.7% 19.1% 6.1% 35.9%

Over-consumed

Count 14 23 20 57

Expected Count 15.2 26.1 15.7 57.0

% within

meat_alt_new24.6% 40.4% 35.1% 100.0%


% of Total 10.7% 17.6% 15.3% 43.5%

Total

Count 35 60 36 131

Expected Count 35.0 60.0 36.0 131.0

% within

meat_alt_new26.7% 45.8% 27.5% 100.0%


% of Total 26.7% 45.8% 27.5% 100.0%


Chi-Square Tests


sided)






count is 7.21.

Symmetric Measures

Value Approx. Sig.






Table 17


Cross Tabulation of Other and Activity Level


Minimal

physical

activity

1-2 hours

physical

activity daily

+2 hours

physical

activity daily

other_new

Under-consumed

Count 3 3 8 14


% within other_new 21.4% 21.4% 57.1% 100.0%

% within

Activity.level8.6% 5.0% 22.2% 10.7%

% of Total 2.3% 2.3% 6.1% 10.7%

Met

Recommended

Count 8 16 10 34


% within other_new 23.5% 47.1% 29.4% 100.0%

% within

Activity.level22.9% 26.7% 27.8% 26.0%

% of Total 6.1% 12.2% 7.6% 26.0%

Over-consumed

Count 24 41 18 83

Expected Count 22.2 38.0 22.8 83.0

% within other_new 28.9% 49.4% 21.7% 100.0%

% within

Activity.level68.6% 68.3% 50.0% 63.4%

% of Total 18.3% 31.3% 13.7% 63.4%

Total

Count 35 60 36 131

Expected Count 35.0 60.0 36.0 131.0

% within other_new 26.7% 45.8% 27.5% 100.0%

% within

Activity.level100.0% 100.0% 100.0% 100.0%

% of Total 26.7% 45.8% 27.5% 100.0%

Chi-Square Tests



sided)



Linear-by-Linear Association 4.011 1 .045



count is 3.74.

Symmetric Measures

Value Approx. Sig.







References

Adamson, A. J., Lake, A. A., Townshend, T., Alvanides, S., & Stamp, E. (2009, October). Diet,

physical activity, sedentary behaviour and perceptions of the environment in young

adults. Journal of Human Nutrition & Dietetics, 22(5), 444-454.

Hardy, L. L., Dobbins, T., Booth, M. L., Denney-Wilson, E., & Okely, A. D. (2006). Sedentary

Behaviours among Australian Adolescents. Australian and New Zealand Journal of Public

Health, 30(6), 534-540. Retrieved from Scopus.

Janse van Rensburg, C. & Surujlal, J., 2013, ‘Gender differences related to the health and

lifestyle patterns of university students’, Health SA Gesondheid 18(1), 1-8.

http://dx.doi.org/10.4102/hsag.v18i1.735

Macdiarmid, J I, Loe, J, Kyle, J, et al. (2013). “It was an education in portion size”. Experience

of eating a healthy diet and barriers to long term dietary change. Appetite, 71, 411-419.

Mohindra, N. A., Nicklas, T. A., O'Neil, C. E., Yang, S. T., & Berenson, G. S. (2009, August).

Eating patterns and overweight status in young adults: the Bogalusa Heart Study. International

Journal or Food Sciences and Nutrition, 60(3), 14-25. doi:10.1080/09637480802322095

Racette, S. B., Deusinger, S. S., Strube, M. J., Highstein, G. R., & Deusinger, R. H. (2005, May).

Weight changes, exercise, and dietary patterns during freshman and sophomore years of

college. Journal of American College og Health, 53(6), 245-251. Retrieved from SPORTDiscus.

Young, E. M., & Fors, S. W. (2001, December). Factors Related to the Eating Habits of Students in

Grades 9-12. Journal of School Health, 71(10), 483-488.

http://dx.doi.org/10.4102/hsag.v18i1.735

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