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The Influence of Health Awareness, Exercise Frequency, and Dietary
Habits on : Application of
Ordinary Least Square Estimation
Mohd Azry Abdul Malik*., Nursyamimi Adnan., Hairani Iqma Hasri., Aisyah Humairah Rasli., Sarah
Sofea Rohani Abdul Majid., Norafefah Mohamad Sobri., Nor Azima Ismail., Nor Fatihah Abd Razak.,
Omar Kairan., Nur Syaliza Hanim Che Yusof
College of Computing, Informatics, and Mathematics, Universiti Teknologi MARA (UiTM), 18500
Machang, Kelantan, Malaysia
*
Corresponding Author
ABSTRACT
Recently, there has been a growing emphasis on promoting physical well-being through improved health
awareness, regular physical activity, and healthier dietary practices. Recognizing the critical association between
physical health and academic success, universities have increasingly implemented wellness initiatives to support
students' overall well-being. This study aims to examine the influence of exercise frequency, health awareness,
and dietary habits on students' physical well-being. A total of 76 participants from a prominent higher education
institution in Kelantan were surveyed using self-administered questionnaires. Data were analyzed using multiple
linear regression and independent t-tests. Findings reveal that exercise frequency and dietary habits significantly
affect students’ physical well-being, whereas health awareness showed no significant influence. The study offers
important insights for higher education administrators and policymakers in developing strategies to foster
positive physical well-being among university students.
Keywords: Health Awareness, Exercise Frequency, Dietary Habits, Students, Physical Well Being
INTRODUCTION
Physical well-being is a critical determinant of university students’ health, academic success, and overall quality
of life. Prior research has identified physical fitness and Body Mass Index (BMI) as significant predictors of
students’ academic and personal performance (Jiang et. al., 2021). Recent advancements, such as the use of
wearable technologies, have further enhanced students’ ability to manage body weight and increase awareness
of physical activity, contributing positively to overall well-being (Wang et. al., 2021). However, students
continue to face difficulties in balancing academic responsibilities with sufficient rest, regular exercise,
appropriate nutrition, and psychosocial needs (Lee & Loke, 2025). The growing prevalence of lifestyle-related
diseases in the university-aged population underscores the importance of promoting regular physical activity as
a means of fostering long-term health and resilience (Kljajević et. al., 2021; Saghafi-Asl et. al., 2020)
Health awareness is also a key factor influencing students' lifestyle behaviors and health outcomes. Elevated
levels of health awareness are associated with improved health practices, including consistent exercise, balanced
dietary habits, and proactive self-care, which contribute to better physical and mental health (Zhang et. al., 2021).
Moreover, self-awareness plays a vital role in motivating students to take ownership of their well-being through
a preventive approach to health management (Wagani et. al., 2021). Nonetheless, students often experience
transitional health challenges, such as poor sleep quality and increased psychosocial stressors (Adam & Moore,
2007; Bulbotz et. al., 2001: Tsai & Li, 2004). The demands of a “24-hour society” (Gosta, 2021) and sedentary
behaviors particularly in technology, intensive fields, further highlight the need for integrated health education
and physical activity promotion (Li, 2024). Exercise frequency is widely recognized as essential for both
physical and mental health, aiding in stress resilience and long-term fitness (Gumasing et. al., 2022; Xi, 2024).
Despite technological advances that support exercise tracking, participation rates remain low, particularly among
female students (Lee & Loke, 2005; Zhou et. al., 2021). Dietary patterns also significantly influence student
health, with poor nutrition linked to mental health concerns and increased obesity risk due to excessive
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
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consumption of fast food and sugary beverages (Solomou et. al., 2024; Syed et. al., 2020). These trends suggest
the need for structured interventions addressing both diet and exercise.
Despite ongoing initiatives, further research is necessary to understand how health awareness, exercise
frequency, and dietary habits collectively affect students’ physical well-being in diverse academic contexts. This
study investigates these relationships among students in the Diploma in Mathematical Sciences program at UiTM
Kelantan, Machang Branch. The outcomes aim to inform targeted strategies for improving student lifestyles,
academic performance, and overall well-being within the university setting.
METHODOLOGY
Study Design, Sample and Instrumentation
This study uses a non-experimental, correlational research design, which observes and analyzes the relationship
between variables without any manipulation. It aims to examine how health awareness, exercise frequency, and
dietary habits affect physical well-being among university students. Data were collected through a self-
administered questionnaire shared via Google Forms. The target population includes around 360 students from
the Department of Mathematical Sciences at Universiti Teknologi MARA (UiTM), Kelantan Branch, Machang
Campus. Stratified random sampling was used to ensure a balanced representation across different student
groups. Using Raosoft software, the minimum sample size was set at 76, and a 25-item questionnaire was
developed to gather the required information.
Similarly, another study applied a cross-sectional, quantitative approach to explore how social support, academic
pressure, and financial stress affect mental health among students. Data were gathered through a self-
administered questionnaire, with 76 students from the same campus selected using simple random sampling.
The questionnaire had two sections: Part A focused on demographic details, and Part B included questions on
psychological health, social support, academic pressure, and financial stress, with responses rated on a five-point
Likert scale from Strongly Agree to Strongly Disagree. A summary of the questionnaire items and sources is
provided in Table 1.
Table 1: Instrumentation
Variable
Source
Scale
Demographic
Multiple-choice questions
Physical well-being
Exercise frequency
Dietary Habits
(Wang et. al., 2020)
Likert scale from 1
(strongly disagree) to 7
(strongly agree)
Health awareness
(Gould 1988)
Study Framework
The study framework is illustrated in Figure 1, with physical well-being as the dependent variable, while physical
well-being, exercise frequency, dietary habits are the independent variables.
Figure 1: Conceptual Framework
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Method of Analyis
Before conducting the analysis, data screening was performed to address missing values, remove duplicate
responses, and identify outliers. The quantitative data were standardized to ensure compatibility for statistical
analysis. Both descriptive and inferential statistical methods were used in the data analysis.
Descriptive statistics were employed to outline the demographic profiles of the respondents. Multiple Linear
Regression (MLR) was used to to assess the impact of exercise frequency, health awareness, and dietary habits
on physical well-being. MLR helps to understand how changes in the independent variables are associated with
changes in the dependent variable. The general formula for Multiple Linear Regression is:
𝑌 = 𝛽
0
+ 𝛽
1
𝑋
1
+ 𝛽
2
𝑋
2
+ ⋯ + 𝛽
𝑘
𝑋
𝑘
+∈ (1)
Where, Y is the dependent variable, 𝛽
0
is the y-intercept (constant term), 𝛽
1
, 𝛽
1
,…, 𝛽
𝑘
are the coefficients of the
independent variables of 𝑋
1
, 𝑋
2
, … , 𝑋
𝑘
. ∈ is the error term or residual, representing the difference between the
observed and predicted values of Y. MLR estimates the coefficients (β values) that minimize the sum of the
squared differences between the observed and predicted values of the dependent variable.
The Ordinary Least Squares (OLS) method is commonly used to estimate the parameters in Multiple Linear
Regression (MLR). The OLS method uses matrix algebra to simplify the estimation process. Represent the model
in matrix form:
𝑌 = 𝑋𝛽 + 𝜖 (2)
Where, Y is n × 1 vector of dependent variable values. X is an n × (k+1) matrix of independent variables
(including a column of ones for the intercept). β is a (k+1) ×1 vector of parameters and ϵ is an n×1 vector of
errors. The OLS estimate of the parameter vector β is obtained using the formula:
𝛽
=
(
𝑋
𝑇
𝑋
)
−1
𝑋
𝑇
𝑌 (3)
Where, X
T
is the transpose matrix of X and (X
T
X)
-1
is the inverse of the matrix X
T
X.
FINDINGS
Demographics of respondent
Table 2 presents the demographic characteristics of the respondents. The majority were female (51.2%), while
males accounted for 48.8%. In terms of age distribution, most respondents were 19 years old (37.5%), followed
by those aged 18 (32.5%) and 20 (30%). Regarding academic level, the highest proportion of students were in
Part 3 (37.5%), followed by Part 1 (32.5%) and Part 5 (30%). A significant majority (96.3%) reported a CGPA
between 3.0 and 4.0, while only 3.8% had a CGPA between 2.0 and 3.0. Finally, with respect to residency status,
67.5% of students were residents, and 32.5% were non-residents.
Table 2: Demographic Profile of respondents
Characteristic
Category
Percentage (%)
Frequency
Gender
Female
51.2
39
Male
48.8
41
Age
18 years
32.5
26
19 years
37.5
30
20 years
30.0
24
Academic Level
Part 1
32.5
26
Part 3
37.5
30
Part 5
30.0
24
CGPA
3.0 – 4.0
96.3
3
2.0 – 3.0
3.8
77
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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
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Residency Status
Resident
67.5
26
Non-resident
32.5
54
Model Adequacy Checking
Model adequacy checks include the assumption of linearity between independent and dependent variables,
normality of residuals, homoscedasticity, and multicollinearity [18-20].
Linearity
Table 3 presents the Pearson correlation analysis results for the relationship between Physical Well-Being and
three variables. Health Awareness exhibits a strong positive linear relationship with Physical Well-Being, with
a Pearson correlation coefficient of 0.643. Likewise, Exercise Frequency shows a similarly strong positive linear
relationship, with a Pearson correlation coefficient of 0.730. Dietary Habit also demonstrates a strong positive
linear relationship with Physical Well-Being, with a Pearson correlation coefficient of 0.691. All correlations
are statistically significant, as indicated by a p-value of 0.000, suggesting a robust association among all
variables.
Table 3: Pearson Correlation Analysis
Dependent variable
Independent variable
Pearson correlation
p-value
Physical Well-being
Health awareness
0.643
<0.05
Exersice frequency
0.730
<0.05
Dietary habits
0.691
<0.05
Homoscedasticity
Figure 2 presents a scatter plot of residuals versus predicted values, with the data points appearing to be randomly
distributed. This randomness indicates consistent variance in the residuals, confirming that the assumption of
homoscedasticity is satisfied.
Figure 2: The Scatter Plot of Residual by Predicted Value
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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Normality
Figure 3 shows that the data points closely follow a straight line, indicating that the residuals are approximately
normally distributed. This observation supports a key assumption of Multiple Linear Regression, thereby
strengthening the validity of the model.
Figure 3: Normal Probability of Residual
Multicollinearity
Table 4 presents the Tolerance and Variance Inflation Factor (VIF) values for each variable. The Tolerance
values for Health Awareness, Exercise Frequency, and Dietary Habits are 0.399, 0.440, and 0.420, respectively,
all of which exceed the threshold of 0.1. Similarly, the VIF values for these variables are 2.507, 2.272, and 2.383,
respectively, all of which are well below the threshold of 10. These results suggest that multicollinearity is not
a concern among the variables.
Table 4: Multicollinearity Test
Variables
Collinearity Statistics
Findings
TOL
VIF
Health awareness
0.399
2.507
Exersice frequency
0.440
2.272
No Multicollinearity
Dietary habits
0.420
2.383
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Significant of model
Table 5 shows that the regression model is highly significant (F = 39.124, p = 0.000), with an R² value of 0.607.
This indicates that 60.7% of the variation in the dependent variable (physical well-being) is explained by the
predictors (health awareness, exercise frequency, and dietary habits).
Table 5: Analysis of Variance for MLR test
Model
ANOVA
F
Sig
R Square
1
Regression
39.124
<0.05
0.607
Significant of independent variable
Table 6 presents the results of the regression analysis, where the significance of each independent variable is
assessed using a t-test. Variables with p-values less than 0.05 are considered significant, indicating a meaningful
contribution to the dependent variable. Exercise frequency (β = 0.382, p < 0.05) and dietary habits (β = 0.281, p
< 0.05) have a significant impact on students' physical well-being, while health awareness (β = 0.2106, p =
0.334) does not show a significant effect.
Table 6: Final Result
Variable
Beta
t-statistics
95% confidence level
Significant
Lower Bound
Upper Bound
Constant
1.607
3.805
0.766
2.449
<0.05
Health awareness
0.106
0.971
-0.112
0.325
0.334
Exersice frequency
0.382
4.054
0.194
0.569
<0.05
Dietary habits
0.281
2.803
0.081
0.481
0.006
Summary of The Findings
The results of the entire study are summarized in Table 7.
Table 7: Summary of The Findings
Relationships
Findings
There is a significant influence of health awareness on students’ physical well-being
Not Supported
There is a significant influence of exersice frequency on students’ physical well-being
Supported
There is a significant influence of dietary habits on students’ physical well-being
Supported
CONCLUSION
In conclusion, the findings of this study underscore the significant impact of exercise frequency and dietary
habits on the physical well-being of university students. While health awareness is often considered a cornerstone
of well-being promotion, the results indicate that awareness alone does not significantly influence physical health
outcomes. This suggests that tangible lifestyle behaviors, such as engaging in regular physical activity and
maintaining a nutritious diet, play a more direct role in enhancing students’ physical well-being than simply
being informed about health matters.
These insights are particularly valuable for higher education institutions aiming to support student wellness in a
more practical and results-oriented manner. By focusing resources on programs that encourage active
participation in fitness and healthy eating, universities can create more effective wellness initiatives. The study's
implications call for a shift from awareness-based campaigns to behavior-focused interventions that promote
sustainable health practices, ultimately contributing to better academic performance and overall student success.
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ACKNOWLEDGEMENTS
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit
sectors.
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