Factors Affecting the Cognitive Skills of University Students in Malaysia: Technology Use, Sleep Patterns, and Eating Habits

Factors Affecting the Cognitive Skills of University Students in Malaysia: Technology Use, Sleep Patterns, and Eating Habits

Nur Faezah Jamal, Nurhasniza Idham Abu Hasan

Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.905000408

Received: 14 May 2025; Accepted: 20 May 2025; Published: 19 June 2025

ABSTRACT

University students are increasingly exposed to digital technology, often leading to irregular sleep patterns and unhealthy eating habits that can lead to cognitive function problems. Understanding lifestyle factors that affect cognitive function is important for academic achievement and the well-being of students. This study was conducted to investigate the factors that influence the cognitive skills of university students with a specific focus on technology use, sleep patterns, and eating habits. This study used a convenience sampling method and was conducted on 54 university students in Malaysia. The main findings revealed that sleep patterns showed a significant impact on cognitive skills, while technology use and eating habits only had a minimal impact. In addition, gender did not significantly influence sleep quality, where there was no significant difference between male and female students. Therefore, these findings can offer valuable insights to certain people, such as university policymakers, educators, and professionals who want to foster an environment conducive to cognitive development and optimal academic performance.

Keywords: Cognitive skills, Eating habits, Sleep patterns, Technology use, University students

INTRODUCTION

Cognitive skills can be defined as mental processes that include memory, attention, critical thinking, and problem-solving. According to [1], cognitive theory emphasizes the processing of information that occurs in students’ minds during the teaching and learning process. Among university students, these skills play an important role in completing coursework and engaging in analytical reasoning for academic success. However, cognitive performance cannot be determined by intellectual abilities alone; it can also be shaped by various lifestyle and behavioural factors in this increasingly technologically driven world.

In today’s digital era, university students are increasingly dependent on technology for academic, social, and entertainment purposes. Although digital technology provides many benefits, excessive use, especially for non-academic purposes, has raised concerns about its effects on cognitive health. According to a study conducted by [2], excessive use of smartphones has been linked to decreased cognitive and academic performance among university students. Similarly, [3] conducted a systematic review of 67 empirical studies and found a consistent association between mobile technology use and decreased cognitive performance, especially in tasks that require sustained attention. Prolonged exposure to and use of technology can affect cognitive development depending on the type of technology and how it is used [4].

In addition, irregular sleep patterns have also become common among university students because they are often driven by academic pressure, screen exposure, and social activities. Many studies have been conducted linking insufficient sleep to impaired cognitive performance. Among them, studies conducted by [5] and [6] claimed that less time for sleep and irregular sleep patterns harmed memory consolidation, leading to decreased academic performance among university students. According to [7], a study was conducted to assess sleep quality and its relationship with cognitive function using the Pittsburgh Sleep Quality Index (PSQI). Through this study, it was found that more than half of the students were categorized as sleep-deprived, and this has led to poor concentration during lectures, a low attention span, and a decrease in logical reasoning skills to solve problems. There is also a study conducted in Malaysia that found that university students who have poor sleep quality show lower GPAs compared to those maintaining a sleep schedule [8]. Through these studies, it was found that good sleep quality and patterns play an important role in improving students’ cognitive function.

However, apart from the use of technology and sleep patterns, eating habits are also often overlooked among students, where nutrition plays an important role in students’ mental health. Intake of less nutritious food and irregular meal schedules can affect a student’s cognitive performance and health. A study conducted by [9] and a study in Malaysia by [10] revealed that students who do not eat breakfast show slower thinking and weaker problem-solving abilities compared to those who eat a balanced diet. Meanwhile, [11] showed a direct correlation between diet quality and academic performance among Canadian students, reinforcing the importance of a balanced and nutrient-rich diet.

Although studies have been conducted to separately examine the effects of technology, sleep patterns, and eating habits on cognitive function, comprehensive analyses that integrate these variables are still lacking among university students. Addressing this gap is important, as cognitive impairment can lead to decreased academic performance and can affect long-term health. Therefore, this study was conducted to investigate the relationship between technology use, sleep patterns, and eating habits, and their collective impact on the cognitive skills of university students. Understanding all these factors comprehensively about the effects of these modern lifestyle behaviours on cognitive skills can provide awareness among students to improve academic performance. Institutions and educators can use this insight to create supportive environments that prioritize physical and mental health. By encouraging healthy habits, offering wellness programs, and integrating mental health supports, institutions can help students better manage stress and improve focus, leading to improved overall well-being.

MATERIALS AND METHODS

Study design

A cross-sectional study using a convenience sampling method was conducted among students of UiTM Perak Branch, Tapah Campus, Malaysia. A total sample of 54 university students participated in the study, where the questionnaire was distributed through WhatsApp and Telegram Messenger. All the participation was voluntary, and all data were kept confidential. These platforms were chosen due to their widespread usage among university students, making them effective channels for reaching a large and diverse group of potential respondents promptly and efficiently.

Research instrument

The questionnaire was designed to capture key information related to the study. It consisted of 5 sections, including demographic profiles, electronic device usage, eating habits, sleep patterns measured using the Pittsburgh Sleep Quality Index (PSQI) [12], and cognitive skills. The demographic section included 5 items, the electronic device usage section contained 5 items [13], the eating habits section had 6 items [14], the PSQI section had 7 items, and the cognitive skills section included 14 items [15]. The demographic section aimed to gather background information about the respondents, while the other sections were designed to explore factors that may influence cognitive skills. Questions were evaluated using a 5-point Likert scale of agreement (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree).

Statistical analysis

The data was compiled and analyzed using the Statistical Package for Social Science (SPSS) version 26.0 [16]. The primary objective of the analysis was to gain insights into the demographic characteristics of the students. Descriptive statistics were employed to achieve this. Descriptive statistics are essential in summarizing and presenting the main features of a dataset in a concise and comprehensible manner. This approach allows for an overview of the data through measures such as frequencies, percentages, means, and standard deviations, which help identify patterns, trends, and distributions within the dataset. In our case, the descriptive statistics analysis was specifically used to evaluate and describe the demographic attributes-such as gender, age, faculty, current semester, and Cumulative Grade Point Average (CGPA) of the students. By applying this method, we were able to present the data in an easily interpretable way, providing a clear snapshot of the student population at the institution.

This study utilizes various inferential analysis techniques to examine and draw conclusions about a broader population based on a sample of data. Inferential analysis allows for generalizations to be made, moving beyond the immediate sample to make inferences about the entire population. One primary method used in this study is regression analysis, which explores the relationship between gender and sleep patterns among the participants. Additionally, an Independent Sample T-test was conducted to compare sleep duration between male and female students, assessing if there were significant differences between the two groups. To further explore the impact of multiple factors on a single outcome, multiple regression analysis was applied. This method is used to examine the combined effects of technology usage, sleep patterns, and eating habits on cognitive skills in university students. By using these inferential techniques, the study aims to provide valuable insights into the factors that influence students’ cognitive abilities, making inferences that extend beyond the immediate sample to the larger population of university students.

The internal consistency reliability of the questionnaire was assessed using Cronbach’s alpha. Cronbach’s Alpha is a statistic commonly used to assess the reliability, or internal consistency, of a set of items or a scale. A higher Cronbach’s Alpha coefficient suggests that the items on the scale are more consistent in measuring the same underlying construct.

Table 1. The Cronbach’s Alpha

Based on the data presented in Table 1, the Cronbach’s alpha reliability test for the items in each construct shows values exceeding 0.5. Therefore, the items used to measure students’ behaviour towards cognitive skills are deemed reliable and acceptable.

RESULTS AND DISCUSSIONS

Socio-demographic information

Table 2 shows the summary of the socio-demographic profiles of the respondents. A total of 54 respondents participated in the study, comprising 22 males (40.7%) and 32 females (59.3%). The majority of respondents (88.9%) were 18 to 20 years old. Of all the respondents, 29 (53.7%) were from the Faculty of Computer and Mathematical Sciences (FSKM), 9 (16.7%) were from the Faculty of Applied Sciences (FSG), and 16 (29.6%) were from the Faculty of Accountancy (FP). Regarding the CGPA, most students (72.2%) have a CGPA of 3.00 and above, indicating strong academic performance. Nearly a quarter have a CGPA between 3.50 and 4.00, indicating a significant number of high achievers.

Table 2. Socio-demographic profiles of the respondents

Student’s sleep quality

Table 3. Independent Sample T-test of Gender Influence on Students’ Sleep Quality

An Independent Samples T-test was conducted to compare the mean sleep quality between male (Mean = 20.09, SD = 4.439) and female students (Mean = 29.84, SD = 5.310) presented in Table 3. The assumption of equal variances was tested using Levene’s Test, which indicated no significant difference in sleep quality between the two groups (F = 1.438, p = 0.236). The T-test results, assuming unequal variances, showed no significant difference in sleep quality scores (PSQI) between male and female students, with t-value = 0.179, p = 0.858, and a 90% confidence interval of [-2.06, 2.56]. Therefore, there is insufficient evidence to conclude that mean sleep quality scores differ significantly between male and female students. The results indicate that gender does not impact sleep quality.

These findings are consistent with similar research that shows gender was not a significant predictor of sleep quality in Chinese medical students [17]. Meanwhile, a study conducted by [18] claimed that among university students in Korea, gender also had no significant effect on sleep quality despite gender differences in psychological factors. Some studies report that women subjectively rate their sleep patterns as worse than men (subjective assessment); however, when sleep is measured using objective tools (using the Pittsburgh Sleep Quality Index (PSQI)), there are often no significant differences in sleep quality by gender [19]. Thus, gender alone is not a determining factor in sleep quality when controlling for other variables (e.g., stress, lifestyle).

The Effect of Technology Use, Sleep Patterns, and Eating Habits on Cognitive Skills among University Students.

Table 4. Regression Analysis of Cognitive Skills

^a: Dependent variable: Total Score Cognitive Skills

*Significant p-value < 0.1

Table 4 presents the results of a regression analysis examining the effect of technology usage, eating habits, and sleep patterns (independent variables) on cognitive skills (dependent variable). The unstandardized coefficient for sleep patterns (B = 0.615, t-value = 2.456, p-value = 0.018) indicates a significant positive relationship. This result emphasized the importance of sleep patterns in enhancing cognitive performance. However, the coefficients for technology usage (B = -0.251, t-value = -0.425, p-value = 0.673) and eating habits (B = -0.353, t-value = -0.683, p-value = 0.498) are negative. This suggests that these variables do not have a substantial effect on cognitive skills. The constant term (B = 24.245, t-value = 2.162, p-value = 0.035) represents the predicted baseline level of cognitive skills when all predictors are held at zero. This analysis shows that only sleep patterns have a significant determinant on cognitive skills, whereas technology usage and eating habits appear to have minimal influence.

This finding is similar to a study by [20] claiming that university students with consistent sleep showed significantly higher GPAs than those with irregular sleep patterns. A meta-analysis of 33 studies revealed a negligible effect of digital media use on academic performance, suggesting that technology use has minimal impact on cognitive outcomes [21]. Additionally, a controlled laboratory study involving 120 participants found no significant difference in cognitive test performance between breakfast intakes when controlling for baseline nutritional status [22]. Meanwhile, there is a contradiction in the results of a study conducted by [23], which stated that no significant correlation was observed between sleep quality and academic performance among Saudi medical students due to the behaviour of students.

CONCLUSION

This study explored the effects of technology use, sleep patterns, and eating habits on the cognitive skills of university students. The results revealed that sleep patterns showed a significant effect on cognitive skills, while technology use and eating habits showed only a minimal influence. Gender also did not significantly affect sleep quality, with no significant difference between male and female students in mean sleep quality scores.

These results are in line with existing research that emphasizes the role of good sleep quality in memory consolidation, attention, and problem-solving. The lack of significant effects of technology use and eating habits suggests that moderate screen time and diverse eating habits may not significantly affect cognitive function in a healthy student population. Therefore, these findings serve as a basis for future studies that contribute to strategies to improve student well-being in Malaysia. Students should adopt healthier daily routines that can lead to long-term improvements in both students’ cognitive skills and overall quality of life.

ACKNOWLEDGEMENT

The authors extend their gratitude to Universiti Teknologi MARA (UiTM), Perak Branch, and all survey participants for their invaluable support and feedback. All contributions are gladly accepted and highly appreciated.

REFERENCES

1. Ormrod, J. E., Anderman, E. M., & Anderman, L. H. (2023). Educational psychology: Developing learners. Pearson. One Lake Street, Upper Saddle River, New Jersey 07458.
2. Yıldırım, R., & Yılmaz, M. (2025). The Impact of Smartphone Use on University Students’ Education. TOJET, 24(2).
3. Wilmer, H. H., Sherman, L. E., & Chein, J. M. (2017). Smartphones and cognition: A review of research exploring the links between mobile technology habits and cognitive functioning. Frontiers in Psychology, 8, 605.
4. Loes, C. N., & Saichaie, K. (2016). Cognitive effects of technology over four years of college. Journal for the Study of Postsecondary and Tertiary Education, 1, 181-196.
5. Hershner, S. D., & Chervin, R. D. (2014). Causes and consequences of sleepiness among college students. Nature and science of sleep, 73-84.
6. Walker, M. (2017). Why we sleep: Unlocking the power of sleep and dreams. Simon and Schuster.
7. Cheng, S. H., Shih, C. C., Lee, I. H., Hou, Y. W., Chen, K. C., Chen, K. T., Yang, Y. K & Yang, Y. C. (2012). A study on the sleep quality of incoming university students. Psychiatry research, 197(3), 270-274.
8. Hashim, H., Xuan, N. J., Zhi, N. Y., & Sen, N. J. (2023). Poor Sleep Quality Linked to Lower Academic Performance in Dental Students: A Cross-Sectional Study. Malaysian Journal of Medicine & Health Sciences, 19(5).
9. Benton, D., & Parker, P. Y. (1998). Breakfast, blood glucose, and cognition. The American journal of clinical nutrition, 67(4), 772S-778S.
10. Cheng, S. H., & Rebecca Yew, L. Q. (2024). Breakfast Skipping: Influencing Factors and Its Impact on Cognitive Function and Academic Performance among Malaysian University Students. Perceptual and Motor Skills, 00315125251329999.
11. Florence, M. D., Asbridge, M., & Veugelers, P. J. (2008). Diet quality and academic performance. Journal of school health, 78(4), 209-215.
12. Buysse, D. J., Reynolds III, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry research, 28(2), 193-213.
13. Screen Time and University Students’ Sleep Patterns Survey – Survey Ideas | Start Question. (2023, February 17). Start question – Create Online Surveys and Forms. https://www.startquestion.com/survey-ideas/screen-time-and-university-students-sleep-patterns-survey.
14. Haris, H., Sabri, H., Oneh, L., Buraera, F., Yusoff, N. D. M., & Ibrahim, M. (2024). The Impacts of Lifestyle Study and Eating Behaviour Quality on Academic Performance among University Students. Journal of Hospitality, Tourism and Wellness Studies (JHTW), 1(1), 103-114.
15. Broadbent, D. E., Cooper, P. F., FitzGerald, P., & Parkes, K. R. (1982). The cognitive failures questionnaire (CFQ) and its correlates. British journal of clinical psychology, 21(1), 1-16.
16. IBM Corp. Released. (2019). IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp.
17. Chen, J., Zhang, Y., & Zhou, X. (2020). Effects of gender, medical school class year, and majors on sleep quality in Chinese medical students: a systematic review and meta-analysis. Sleep and Breathing, 24, 259-266.
18. Chang, A. K., & Choi, J. (2016). Predictors of sleep quality among young adults in Korea: gender differences. Issues in mental health nursing, 37(12), 918-928.
19. Krishnan, V., & Collop, N. A. (2006). Gender differences in sleep disorders. Current opinion in pulmonary medicine, 12(6), 383-389.
20. Okano, K., Kaczmarzyk, J. R., Dave, N., Gabrieli, J. D., & Grossman, J. C. (2019). Sleep quality, duration, and consistency are associated with better academic performance in college students. NPJ science of learning, 4(1), 16.
21. Adelantado-Renau, M., Moliner-Urdiales, D., Cavero-Redondo, I., Beltran-Valls, M. R., Martínez-Vizcaíno, V., & Álvarez-Bueno, C. (2019). Association between screen media use and academic performance among children and adolescents: a systematic review and meta-analysis. JAMA pediatrics, 173(11), 1058-1067.
22. Gibson, E. L., & Green, M. W. (2002). Nutritional influences on cognitive function: mechanisms of susceptibility. Nutrition research reviews, 15(1), 169-206.
23. Almalki, A., Shehata, M., Siddiqui, K., Albulushi, H., Alshehri, N., Aldumri, A., Alghamdi, S., Khasati, A., Wali, S. O., & Ayoub, O. (2025). Sleep Quality Among a Sample of Medical Students and the Association with Academic Performance: An Updated Data. Journal of Epidemiology and Global Health, 15(1), 8.