Descriptive study
Gender
The percentage of males in this study was higher than the Percentage of females, as the percentage of male participants reached 60% compared to the percentage of 40% of female participants.
Table 1: Presenting the results of the descriptive study on the gender of the participants.
Gender
|
Frequencies
|
Percentage
|
Male
|
129
|
60%
|
Female
|
86
|
40%
|
Total
|
215
|
100%
|
Age
The age of the participants in the study ranged into two classes, the first class was 19-24 years old, and the other class was 25-30 years old. The vast majority of the sample was 19-24 years old, at a rate of 80.9%.
Table 2: Presenting the results of the descriptive study on the Age of the participants.
Age
|
Frequencies
|
Percentage
|
19-24 years old
|
174
|
80.9%
|
25-30 years old
|
41
|
19.1%
|
Total
|
215
|
100.0%
|
BMI
The body mass index percentages of the sample members were calculated, where the highest percentage were individuals with a healthy and normal weight which was 58.1%, and the percentage of individuals with an overweight was 28.4%.
Table 3: Presenting the results of the descriptive study on the BMI of the participants.
BMI
|
Frequencies
|
Percentage
|
< 18.5
(below normal)
|
7
|
3.3%
|
18.5 - 24.9
(normal and healthy weight)
|
125
|
58.1%
|
25 - 29.9 (indicating overweight)
|
61
|
28.4%
|
>30
(indicates obesity)
|
22
|
10.2%
|
Total
|
215
|
100.0%
|
Antihypertensive medications
The vast majority of the sample did not take antihypertensive drugs, with the rate of 99.9%, while only one of the members of the sample had antihypertensive drugs.
Table 4: Presenting the results of the descriptive study on the taking of antihypertensive medications by the participants.
Antihypertensive medications
|
Frequencies
|
Percentage
|
Yes
|
1
|
0.5%
|
No
|
214
|
99.5%
|
Total
|
215
|
100%
|
Steroids
The majority of the sample did not take steroids, at 94.9%, while only 11 individuals from the sample took steroids as a prescribed medication.
Table 5: Presenting the results of the descriptive study on the taking of steroids by the participants.
Steroids
|
Frequencies
|
Percentage
|
Yes
|
11
|
5.1%
|
No
|
204
|
94.9%
|
Total
|
215
|
100%
|
Smoking
32.1% of the sample members were smokers (including hookahs and electronic cigarettes), compared to 67.9% of the sample who were not smokers.
Table 6: Presenting the results of the descriptive study on Smoking by the participants.
Smoking
|
Frequencies
|
Percentage
|
Yes
|
69
|
32.1%
|
No
|
146
|
67.9%
|
Total
|
215
|
100%
|
Familial history
26% of the sample had a familial history of diabetes (having a mother, father, brother, son... with diabetes), while 74% of the sample had no familial history.
Table 7: Presenting the results of the descriptive study on the Familial history of the participants.
Familial History
|
Frequencies
|
Percentage
|
Yes
|
56
|
26.0%
|
No
|
159
|
74.0%
|
Total
|
215
|
100%
|
Doing Physical activities
When the sample members were asked about doing physical exercises or simple exercises, 78.6% did walking and housework, compared to 18.6% who did exercises in sports clubs and hard activities, and 2.8% did not do any physical activity.
Chart 8: Graphical representation illustrating the descriptive study on the Physical activity of the participant.
Physical activities
|
Frequencies
|
Percentage
|
No-physic activity
|
6
|
2.8%
|
Simple activity
|
169
|
78.6%
|
Hard activity
|
40
|
18.6%
|
Total
|
215
|
100%
|
Diet for carbohydrates & starches
The consumption rates of starches and carbohydrates in the diet ranged from low in carbohydrates and starches (14%), moderate in carbohydrates and starches (65.5% of the individuals), and high in starches and carbohydrates (20.5%).
Chart 9: Graphical representation illustrating the descriptive study on the diet for carbohydrates and starches of the participant.
Carbohydrates & starches
|
Frequencies
|
Percentage
|
Low
|
30
|
14%
|
Moderate
|
141
|
65.6%
|
High
|
44
|
20.5%
|
Total
|
215
|
100%
|
Diet for protein
Protein consumption in the diet was between low protein (4.7% of the sample), medium protein (80.5% of the sample), and high protein (14.9% of the sample).
Chart 10: Graphical representation illustrating the descriptive study on the diet for proteins of the participant.
Protein
|
Frequencies
|
Percentage
|
Low
|
10
|
4.7%
|
Moderate
|
173
|
80.5%
|
High
|
32
|
14.9%
|
Total
|
215
|
100%
|
Analytical study
Correlation between diet and risk factors (BMI)
To study the correlation between diet and body mass index, we studied the axis (the number of meals eaten per month related to several foods); Then, a one-way analysis of variance test was conducted, and by discussing the results presented in the table, we note that the probability value is equal to 0.00, meaning that there is an important and significant correlation between the routine diet and the body mass index.
Table 1: presenting the results of studying the correlation between the general routine diet (number of servings of some foods) and body mass index BMI.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
55.45
|
0.000
|
Within groups
|
140.5
|
To study the correlation between a high-carbohydrates diet and BMI, we conducted a one-way analysis of variance test. Discussing the results in the table, we note that the probability value is 0.00, meaning that there is a significant correlation between the percentage of carbohydrates in the diet and body mass index.
Table2: presenting the results of a study of the relationship between the percentage of carbohydrates in the diet and body mass index.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
12.88
|
0.000
|
Within groups
|
183.11
|
For the correlation between the proportion of proteins in the diet and the body mass index, we conducted a one-way analysis of variance test, and by discussing the results presented in the table, we note that the probability value is equal to 0.854, meaning that there is therefore an important and significant correlation between the proportion of proteins in the diet and the body mass index.
Table 3: presenting the results of the study on the correlation between the proportion of proteins in the diet and body mass index.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
0.173
|
0.854
|
Within groups
|
195.8
|
Correlation between diet and risk factors (Familial history)
To study the correlation between diet and the presence of a familial history, we conducted a one-way analysis of variance test, and by discussing the results presented in the table below, we note that the probability value is equal to 0.000, meaning that there is therefore an important and significant correlation between the general diet and the presence of a familial history.
Table 4: presenting the results of the study of the correlation between the diet and the risk factor (presence of a familial history).
ANOVA
|
Contrast
|
Probability
|
Between groups
|
16.46
|
0.000
|
Within groups
|
51.59
|
To examine the correlation between the proportion of carbohydrates in the diet and the presence of a familial history, we conducted a one-way analysis of variance test; Discussing the results presented in the table 5, we notice that the probability value is 0.000, meaning that there is an important and significant correlation between proportion of carbohydrates in the diet and the presence of a family history. That is, the effect of the risk factor (the presence of a familial history) is affected by the proportion of carbohydrates in the diet.
Table 5: presenting the results of the study of the correlation between the proportions of carbohydrates in the diet and the risk factor (presence of a familial history).
ANOVA
|
Contrast
|
Probability
|
Between groups
|
3.28
|
0.000
|
Within groups
|
64.77
|
To find the correlation between the proportion of proteins consumed in the diet and the presence of a familial history, a one-way analysis of variance test was performed; Discussing the results presented in the table 6, we notice that the probability value is 0.01, meaning that there is an important and significant relationship between the percentage of proteins in the diet and the presence of a familial history. That is, the effect of the presence of a familial history is related to the percentage of proteins in diet.
Table 6: presenting the results of the study of the correlation between the proportion of proteins in the diet and the risk factor (presence of a familial history).
ANOVA
|
Contrast
|
Probability
|
Between groups
|
1.69
|
0.01
|
Within groups
|
66.36
|
Correlation between diet and risk factors (Gender)
For the correlation between the general diet and the effect of the gender of the sample member, we conducted a one-way analysis of variance test, and by discussing the results presented in the table 7, we note that the probability value is equal to 0.000, meaning that there is therefore an important and significant relation between the number of meals and the gender of the participant.
Table 7: presenting the results of the study of the correlation between diet and the gender of the participant.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
29.84
|
0.000
|
Within groups
|
58.74
|
To study the correlation between the proportion of carbohydrates in the diet and the effect of the participant’s gender, we conducted a one-way analysis of variance test. By discussing the results in the table, we note that the probability value is 0.305, meaning that there is therefore no significant correlation between the proportion of carbohydrates in the diet and the gender of the participant.
Table 8: presenting the results of the study of the correlation between the proportion of carbohydrates in the diet and the gender of the participant.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
0.58
|
0.305
|
Within groups
|
88.0
|
To study the correlation between the proportion of proteins in the diet and the effect of the gender of the sample member, we conducted a one-way analysis of variance test, and by discussing the results presented in the table 9 we note that the probability value is equal to 0.07, meaning that there is therefore no significant and significant relationship between the proportion of proteins in the diet and the gender of the participant.
Table 9: presenting the results of the study of the correlation between the proportions of proteins in the system and the gender of the participant.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
1.24
|
0.07
|
Within groups
|
87.342
|
Correlation between diet and risk factors (Age)
To study the correlation between the general routine diet and the age of the participant, we conducted a one-way analysis of variance test, and by discussing the results presented in the table, we note that the probability value is equal to 0.000, meaning that there is therefore an important and significant correlation between the monthly routine diet and the age of the participant.
Table10: presenting the results of the study of the correlation between the patient’s general diet and the age of the participant.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
20.653
|
0.000
|
Within groups
|
32.643
|
To study the correlation between the percentage of carbohydrates in the diet and the effect of the participant’s age, we conducted a one-way analysis of variance test. By discussing the results in the table, we note that the probability value is 0.036, meaning that there is therefore an important and significant correlation between the percentage of carbohydrates in the diet and the age of the participant.
Table 11: presenting the results of the study of the correlation between the diet, the proportions of carbohydrates in it, and the age of the participant.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
0.97
|
0.036
|
Within groups
|
52.31
|
To study the correlation between the proportion of proteins in the diet and the effect of the participant’s age, we conducted a one-way analysis of variance test. By discussing the results presented in the table, we note that the probability value is equal to 0.015, meaning that there is therefore an important and significant correlation between the proportion of proteins in the diet and the age of the participant.
Table 12: presenting the results of the study of the correlation between the diet, the proportions of proteins in it, and the age of the participant.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
1.24
|
0.015
|
Within groups
|
52.05
|
Risk levels among participants
We calculated the risk rate for each patient individually, and all risk factors were considered according to the reference equation:
After calculating the level of preparedness for each patient, we classified it into three categories:
- First category: the alert level (risk factor) is less than 1%.
- Second category: The level of alertness (risk factor) ranges between 1% to 10%.
- Third category: The alert rate (risk factor) is higher than 10%.
Table 13: presenting the results of the preparedness rates among sample members.
1st category (< 1%)
|
87
|
40.5%
|
2nd category (1-10%)
|
105
|
48.8%
|
3rd category (>10%)
|
23
|
10.7%
|
Total
|
215
|
100%
|
Correlation between diet and level of risk
To find the correlation between the general diet followed by the sample member and the level of risk, we conducted a one-way analysis of variance test, and based on the probability value equal to 0.000, we notice the presence of a significant effect and a fundamentally significant correlation between the general diet and the level of alertness among the participants.
Table 14: presenting the results of the study of the correlation between the general diet per month and the level of preparedness among participants.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
25.06
|
0.000
|
Within groups
|
65.88
|
To study the correlation between the proportion of carbohydrates in the diet and the level of alertness, we conducted a one-way analysis of variance test, and based on the probability value equal to 0.184, we note that there is no significant effect or significant correlation between the percentage of carbohydrates in the diet and the level of alertness.
Table 15: presenting the results of the study of the correlation between the proportion of carbohydrates in the diet and the level of preparedness among participants.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
1.441
|
0.184
|
Within groups
|
89.50
|
For the correlation between the proportion of proteins in the diet and the level of alertness, we conducted a one-way analysis of variance test, and based on the probability value equal to 0.691, we note that there is no significant effect or correlation between the proportion of proteins in the diet and the level of alertness.
Table 16: presenting the results of a study of the correlation between the proportion of proteins in the diet and the level of preparedness among participants.
ANOVA
|
Contrast
|
Probability
|
Between groups
|
0.317
|
0.691
|
Within groups
|
90.632
|
Correlation between dietary pattern and risk levels
After the diet axis was studied and the results showed its effect on alertness rates, each dietary pattern within the axis was studied separately, according to a one-way analysis of variance test, and the results were presented in the table. There was a significant effect of the number of times one consumes red meat, fish, eggs, vegetables, fruits, legumes, fast food, dairy, and cheese on the risk of diabetes. Concerning some drinks, there was a significant effect of drinks containing caffeine, soft drinks, and milk, and the number of times alcohol was consumed.
This reflects the importance of adhering to a balanced diet due to its impact on diabetes risk levels, and eating moderate and balanced meals of the aforementioned influential foods.
Table 17: presenting the results of the study of the effect of the type of foods in the diet and the level of preparedness among participants.
Dietary pattern
|
Probability
|
Average number of weekly meals of red meat
|
0.041
|
Average number of weekly meals of poultry meat
|
0.702
|
Average number of weekly servings of fish
|
0.003
|
Average number of times eggs are eaten
|
0.03
|
Average number of times vegetables excluding potatoes
|
0.03
|
Average number of times eating rice, potatoes and pastries
|
0.876
|
Average number of times you eat fruits
|
0.01
|
Average number of times you eat legumes
|
0.01
|
Average number of times bread is eaten
|
0.42
|
Average number of times sweets are eaten
|
0.342
|
Average number of times fast food are eaten
|
0.03
|
Average number of times dairy , cheese eaten
|
0.008
|
Average intake of caffeinated beverages
|
0.012
|
Average number of times milk is consumed
|
0.07
|
Average number of times drink soft drinks
|
0.011
|