Previous Article in Journal
Swearing in Sport and Exercise: Development and Validation of a New Questionnaire
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Psychology International
Volume 7
Issue 2
10.3390/psycholint7020038
psycholint-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

What Drives Academic Performance: Lifestyle, Mental Health, and Biological Traits Among Medical Students in a Southeast Asian Context

by
Ngoc Bao Dang
1,
Phuc Thai Tran
2,
Hoa Thi Tran
3,
Quang Ngoc Phan
3 and
Nam Hoang Tran
4,*
1
College of Health Sciences, VinUniversity, Hanoi 131000, Vietnam
2
Faculty of Nursing, Thai Binh University of Medicine and Pharmacy, Thai Binh 410000, Vietnam
3
The Center Service for Technology Science of Medi-Phar., Thai Binh University of Medicine and Pharmacy, Thai Binh 410000, Vietnam
4
Research Center for Higher Education, Tokushima University, Tokushima 770-8502, Japan
*
Author to whom correspondence should be addressed.
Psychol. Int. 2025, 7(2), 38; https://doi.org/10.3390/psycholint7020038
Submission received: 23 April 2025 / Revised: 8 May 2025 / Accepted: 13 May 2025 / Published: 14 May 2025
(This article belongs to the Section Neuropsychology, Clinical Psychology, and Mental Health)
Browse Figure
Versions Notes

Abstract

:
Academic success in medical education is shaped by a complex interaction of biological predispositions, lifestyle choices, and mental health status. Understanding these factors is essential for student-centered educational reform and well-being support systems. This study investigates the association between biological traits, lifestyle behaviors, psychological stress, and academic outcomes among Vietnamese medical students. A cross-sectional survey of 1227 students from a Vietnamese medical university was conducted, with valid GPA data from 1038 participants. Data on biological (age, sex, BMI, blood group), lifestyle (diet, sleep, exercise, screen time, self-study), stress-related (DASS-21 scores, life domain-specific stressors), and social–academic factors (major, year, roommates) were collected. Linear regression models were applied to explore relationships with GPA. Lifestyle factors (R2 = 0.032, p = 0.001) such as eating dinner (p = 0.001), self-study hours (p = 0.005), and having breakfast (p = 0.046) were positively associated with GPA. Biological variables had a smaller impact (R2 = 0.013), with age showing a modest positive association (p = 0.001). Mental health scores (DASS-21) explained 1.2% of GPA variance (p = 0.007), with depression positively and stress negatively influencing performance. Academic year was a consistent predictor across models (p = 0.001), and multivariate regression combining all categories (R2 = 0.048, p < 0.001) confirmed these relationships. In conclusion, regular mealtime patterns, particularly having breakfast and dinner, and consistent self-study routines are stronger predictors of GPA than biological or general stress markers. Educational institutions should promote healthy daily routines and academic mentoring.

Graphical Abstract

1. Introduction

Academic performance remains a core metric for evaluating student success in medical training programs, where learners face exceptionally high cognitive and emotional demands. Grade Point Average (GPA), a cumulative measure of academic achievement, is not only tied to academic progression but also influences scholarship opportunities, clinical placements, and postgraduate career prospects. A cohort study conducted among medical students in Korea has found that graduate GPA strongly predicted internship clinical performance, alongside preadmission GPA (Kim et al., 2016). Another large cross-sectional study among Kuwaiti medical graduates has found that higher graduation GPA was positively associated with pursuing clinical fellowships, earning higher monthly income, achieving greater career progress, and reporting higher satisfaction with both life and career (El Abd et al., 2024). Given these wide-ranging impacts, understanding the multifactorial influences on their academic outcomes has therefore become an important focus within medical education.
A wide body of literature has identified various determinants of academic performance, ranging from cognitive abilities and motivational traits to psychosocial engagement and contextual factors. Within this framework, lifestyle behaviors, such as regular sleep, nutritious meals, and physical activity, have been shown to play a significant role in cognitive function and academic achievement (Gilbert & Weaver, 2010; Lemma et al., 2014). In parallel with these health behaviors, effective, adequate self-study practices and time management strategies are essential components for navigating the high-stakes environment of medical education (Artino, 2012).
Mental health is another well-documented link to academic success. Elevated levels of stress, anxiety, and depression can impair concentration, reduce motivation, and ultimately undermine academic outcomes (Dyrbye et al., 2006; Fernandes et al., 2023). The Depression, Anxiety, and Stress scale (DASS) has been widely used to quantify these psychological dimensions in university settings and has shown strong associations with GPA and course satisfaction (Lovibond & Lovibond, 1995).
Biological characteristics, while often less emphasized, may also impact academic performance through mechanisms such as developmental maturity, circadian rhythm preferences, and health conditions (Goldin et al., 2020). Age, sex, and BMI, for instance, have yielded inconsistent findings in relation to GPA, with some studies suggesting minimal effects and others pointing to nuanced moderating roles (Alswat et al., 2017; Brignac et al., 2011).
Despite the wealth of existing research, most studies examine these variables in isolation or focus primarily on Western student populations. Recent regional studies have begun to address the interplay between various factors among university students including stress (Matsuura et al., 2024b), lifestyle (Matsuura et al., 2024a), living status (Matsuura et al., 2022), university entrance adaptation (Matsuura et al., 2023b), international student adaptation (Matsuura et al., 2023a), further contextualizing the complexity in academic settings. These works also intersect with research on student well-being and academic resilience during the COVID-19 pandemic (Phan et al., 2024; H. Tran et al., 2022).
Few studies have applied a multidimensional approach that concurrently evaluates biological, lifestyle, mental, and academic–social factors. Even fewer have explored these associations in Southeast Asian contexts such as Vietnam, where cultural, educational, and social dynamics may differ significantly from high-income countries. For instance, a longitudinal study explored factors influencing academic stress among secondary school students in Vietnam including variables such as family dynamics, lifestyle behaviors, and academic pressures (T. V. Tran et al., 2024). Another research study of university students in Vietnam identified factors influencing happiness, including perceived financial burden, year in university, academic motivation, and self-reported depression and anxiety (Tien Nam et al., 2024). These studies often focus on specific aspects rather than adopting a holistic approach. The need for integrating biological, lifestyle, mental, and academic–social factors is evident to fully understand and support student well-being and academic success in Vietnam.
The present study aims to fill this gap by conducting a comprehensive analysis of the determinants of GPA among Vietnamese medical students. By integrating lifestyle habits, mental health indicators, biological traits, and academic–social context into a single analytic model, this study contributes to a more holistic understanding of what drives academic success in medical education, which is practically valuable for universities seeking to support student well-being and performance.

2. Materials and Methods

2.1. Study Design and Participants

This cross-sectional study was conducted at a major medical university in northern Vietnam in the 2023–2024 academic year. The target population included all undergraduate students enrolled in general medicine, pharmacy, nursing, traditional medicine, and medical technology programs. Participants were recruited through university-wide announcements, and participation was voluntary. A total of 1227 students completed the online self-administered questionnaire. Among them, 1038 students provided complete and valid responses for the primary outcome variable, GPA, and were included in the final analysis.

2.2. Data Collection Instrument

A structured questionnaire was designed by the research team. The instrument included five sections:
  • Demographics and Biological Characteristics: age, sex, height, weight, body mass index (BMI), and blood group (ABO and Rh).
  • Lifestyle Behaviors: eating habits (frequency of breakfast, lunch, and dinner) and the consumption of alcohol, tobacco, and coffee; physical exercise frequency; average hours of sleep per night; bedtime; daily use of electronic devices (in hours); and self-reported average study time outside of class (in hours).
  • Academic and Social Context: academic year, academic major, type of residence (dormitory, rental, family home), number of roommates, and hometown location.
  • Mental Health Status: measured using the DASS-21 scale, a shortened version of the DASS, which has been validated in prior studies (Norton, 2007). Participants rated each item on a 4-point Likert scale (0 = never to 3 = always).
  • Stress Sources: participants rated domain-specific stress levels (academic, financial, health-related, family, social/romantic, food/lifestyle-related) on a scale from 0 (not at all) to 4 (very strong).
Academic performance was measured using the self-reported cumulative GPA, based on a 10-point grading scale. Students were asked to provide their most recent official GPA, and values were cross-checked for plausibility during data cleaning.

2.3. Statistical Analysis

Data were analyzed using IBM SPSS Statistics Version 28 for Windows (IBM Corp., Armonk, NY, USA). Descriptive statistics were computed for all variables. Continuous variables were reported as means and standard deviations (SDs), while categorical variables were presented as frequencies (N) and percentages (%). Multiple linear regressions were conducted to evaluate the relationship between independent variables (biological, lifestyle, mental health, and academic–social factors) and GPA. Separate models were first estimated for each category of predictors. A final multivariate regression model was constructed to identify the strongest predictors while controlling multicollinearity and interaction effects. The threshold for statistical significance was set at p < 0.05. Assumptions of normality, linearity, and homoscedasticity were checked before model interpretation.

3. Results

Table 1 presents the demographic and biological characteristics of the study sample (N = 1227). The majority of students were female (72.0%) and enrolled in either medicine (52.2%) or pharmacy (30.9%) programs. Most participants were from provinces outside Thai Binh (71.4%) and lived in rented accommodations (60.3%). The mean age was 20.2 years (SD = 1.79), with an average BMI of 20.35 (SD = 2.93), indicating a predominantly normal-weight population. Regarding blood type, group O was the most common (56.6%), and Rh positivity was slightly more prevalent (53.6%). GPA data were available for 1038 students, with a mean GPA of 6.57 (SD = 2.16).
Table 2 outlines the students’ lifestyle characteristics, focusing on dietary habits, substance use, physical activity, sleep behaviors, and academic engagement. Notably, 28.4% of students reported regularly skipping breakfast, while only 1.5% and 1.4% reported skipping lunch or dinner, respectively. Alcohol consumption was generally low, with nearly two-thirds abstaining entirely. Tobacco use was rare, with 97.3% reporting no usage. A majority of students consumed coffee occasionally (48.0%), and 12.0% engaged in daily exercise. Students averaged nearly 7 h of sleep per night, went to bed around 11:36 p.m., spent approximately 5.9 h per day using electronics, and reported about 3.1 h per day of self-directed study.
The mental health and perceived stress profiles of participants are summarized in Table 3. On average, students reported moderate levels of general stress (mean = 2.40, SD = 0.97), with academic stress scoring highest among domain-specific stressors (mean = 2.78, SD = 1.04), followed by financial stress (mean = 2.60, SD = 1.23). Lower stress levels were observed in domains related to love life (mean = 1.66, SD = 1.00), friendships, and food/lifestyle. DASS-21 scores indicated moderate to high levels of depression (mean = 10.12), anxiety (mean = 10.30), and stress (mean = 9.31), with standard deviations suggesting a wide range of experiences across the student body.
To better understand the predictors of academic performance, multiple linear regression models were performed across four domains: biological characteristics, lifestyle behaviors, mental health status, and academic–social context. Separate models for each category were analyzed, followed by an integrated model combining significant predictors. Table 4 presents a summary of the most influential variables associated with GPA (N = 1038), highlighting both the direction and strength of their statistical associations. This comparative overview enables a clearer understanding of which factors consistently predict academic outcomes among medical students. The final combined model yielded an R2 of 0.048 (p < 0.001), with breakfast consumption, self-study hours, depression, stress, academic year, and major emerging as the most significant predictors of GPA.

4. Discussion

4.1. Main Findings

This study investigated a comprehensive set of predictors of academic performance in a large cohort of Vietnamese medical students. The findings demonstrated that lifestyle behaviors, particularly consistent self-study, and regular meal consumption were the strongest positive predictors of GPA. Mental health variables, specifically higher depression and lower stress scores also significantly predicted GPA. In contrast, biological traits and most demographic characteristics had minimal influence.

4.2. Lifestyle Factors and Academic Performance

The results revealed a strong relationship between regular meal patterns and academic success. Students who consistently consumed dinner and breakfast had significantly higher GPA scores, while those who skipped lunch reported higher GPA. These findings are broadly consistent with prior systematic reviews among university students, such as (Burrows et al., 2017; Edefonti et al., 2014), which suggest positive links between meal regularity and academic achievement. Mechanistically, regular meals stabilize blood glucose and support sustained concentration (Hoyland et al., 2009; Micha et al., 2011). Further supporting the importance of breakfast, a randomized controlled trial found that adolescents who consumed a low-glycemic index breakfast and combined this with morning exercise demonstrated significantly greater improvements in mathematical performance and cognitive reaction times (Kawabata et al., 2021). Further reviews highlight the cognitive advantages of breakfasts with lower glycemic responses, particularly for memory, attention, and reasoning tasks (Edefonti et al., 2014). Beyond nutrition, consistent mealtimes act as zeitgebers, synchronizing circadian rhythms and supporting cognitive and hormonal balance (BaHammam & Pirzada, 2023; Ruddick-Collins et al., 2020). Early eating improves insulin sensitivity and emotional regulation, whereas irregular meal timing may lead to circadian misalignment and metabolic disturbances, even when calorie intake is unchanged (BaHammam & Pirzada, 2023). These patterns may reflect the underlying traits of self-control. Individuals with higher self-regulation tend to maintain structured routines and exhibit better academic and psychological outcomes (Tangney et al., 2004). Our findings on self-study time further support this, emphasizing time management as a key academic success factor.
Interestingly, variables such as sleep duration, bedtime, exercise, and screen time were not significantly related to GPA. This contrasts with earlier studies (Curcio et al., 2006) that found links between sleep quality and academic performance, as well as studies supporting the benefits of physical activity. The lack of association in our study may reflect compensatory strategies among students, such as increased caffeine use, reliance on peer support, or prioritization of academic tasks over health behaviors. Qualitative or mixed approaches in future research could help uncover these compensatory mechanisms, particularly in high-pressure academic environments where performance is prioritized. Additionally, cultural or institutional norms may influence how students trade off sleep and physical activity for study time, factors that may not be captured through quantitative data alone. These nuances underscore the complexity of academic behavior and suggest the need for a broader interpretive lens when examining lifestyle–health–academic interactions.
Academic buoyancy—the ability to rebound from daily setbacks—may help buffer the effects of suboptimal health behaviors (Martin, 2013). Likewise, social support mitigates stress but is most effective among students with lower negative affect (Çivitci, 2015).

4.3. Mental Health and Stress Correlates

Prior studies found that diagnosed depression was associated with a substantial negative impact on academic performance (Eisenberg et al., 2009; Hysenbegasi et al., 2005). Contrary to these findings, our study revealed a positive association between depression scores and GPA among Vietnamese medical students, while stress remained negatively associated. This divergence may reflect cultural norms, perfectionism, and high academic motivation among medical students, consistent with prior observations of high-achieving populations (Hewitt et al., 2002; Malik et al., 2023).
Students in competitive environments may internalize distress without immediate impact on performance due to perfectionistic drive and fear of failure (Stoeber & Otto, 2006). In several high-pressure academic settings in Asia, including Japan, Korea, and China, similar patterns have been noted, where academic overachievement may coexist with elevated mental health symptoms (Chang et al., 2021; Henning et al., 2011). This reflects a cultural ethos in which enduring hardship and internalizing emotional strain are often viewed as necessary components of academic success, leading students to prioritize performance over well-being.
From a neurobiological perspective, traits like rumination and hypervigilance linked to mild depression may enhance cognitive vigilance via heightened prefrontal activity (Korgaonkar et al., 2014), while diminished dopamine reward pathways may drive continued effort out of obligation. This temporary cognitive overcompensation aligns with the concept of cognitive reserve, where structured habits buffer against internal stressors (Barnett et al., 2006; Scarmeas & Stern, 2003). However, this compensation is not limitless. Chronic stress can lead to neurotoxicity and impairments in attention, memory, and emotion regulation (Keller et al., 2019). While manageable stress can briefly enhance performance (Yerkes-Dodson Law) (Pelz, 2024), prolonged HPA axis activation impairs key brain structures, degrading executive function and decision-making (Dyrbye et al., 2006; Pelz, 2024).
Finally, students with stronger executive functions and emotional reappraisal strategies report lower stress levels and better coping, especially at graduate levels (Awomokun, 2022). Interventions enhancing these skills—such as mindfulness, cognitive reappraisal, or neurofeedback—may help mitigate stress-related academic decline and promote sustained resilience.

4.4. Biological and Demographic Predictors

Among biological variables, only age demonstrated a modest yet statistically significant association with GPA, suggesting possible maturational or coping advantages among older students. Other factors such as sex, BMI, blood group, and Rh factor were not significant. These findings support prior literature showing limited and inconsistent effects of biological factors on academic performance (Goldin et al., 2020).
Demographically, students in higher academic years tended to perform better, which may reflect increased familiarity with the learning environment, better coping strategies, and greater academic skill accumulation over time. Medical major students showed higher GPA in the multivariate model, possibly due to selection mechanisms (Wouters et al., 2016), motivation (Wu et al., 2020), or curricular alignment with GPA scales.

4.5. Comparative and Contextual Considerations

The integrated model accounted for nearly 5% of GPA variance, a modest but meaningful proportion in educational research. While other latent variables such as intelligence, socioeconomic status, or quality of instruction were not measured, the inclusion of psychosocial and behavioral dimensions strengthens this model. Research indicates that factors such as depression, anxiety, and stress are prevalent among medical students and can significantly impact academic outcomes (Karim et al., 2022). The cultural context of Vietnam, including societal emphasis on academic achievement and resilience through hardship, may partially explain unique patterns such as the positive depression–GPA relationship. It was reported that many Asian medical students are “pushed” into medical careers by extrinsic factors such as parental expectations and tend to focus intensely on academic achievement, sometimes at the expense of their well-being (Henning et al., 2011).
However, a more nuanced understanding of these cultural dynamics is necessary. In Vietnam, academic success is often closely tied to notions of family honor, social mobility, and filial obligation (Różycka-Tran et al., 2020; Xu et al., 2024). These values can create strong motivational pressures but may also lead to the internalization of stress, as failure is often equated with personal inadequacy or familial disappointment. Furthermore, norms around emotional restraint may discourage open expression of psychological distress, leading to the underreporting of stress and anxiety. These cultural traits may partially explain the paradoxical finding that higher depression scores are associated with higher academic achievement in this context.
Few studies have applied such a broad analytic framework to medical students in low- and middle-income countries (LMICs). This study contributes to the literature from Southeast Asia, offering insight into context-specific determinants of academic performance and supporting the need for localized interventions in educational and mental health policy.

4.6. Implications for Policy and Practice

Universities and medical schools should encourage structured study routines and promote healthy lifestyle practices. Given the associations with meal regularity, efforts to improve student nutrition through affordable campus dining options may have academic benefits (Florence et al., 2008). Additionally, academic advising should integrate time management training and stress management support (Ahmady et al., 2021). To enhance practical applications, institutions should consider how to balance the promotion of healthy routines, such as consistent meal schedules, with addressing the underlying stressors that may contribute to psychological distress. This includes designing interventions that simultaneously support physical habits and offer mental health support. For instance, time management programs could be complemented by stress-reduction workshops and access to peer support networks.
Mental health services should be tailored to account for both visible and masked distress, recognizing that high-performing students may still be psychologically vulnerable (Bashir et al., 2020). Incorporating psychological education, early screening using DASS-21 or similar tools (Henry & Crawford, 2005), and building referral pathways to counseling may improve student well-being and prevent burnout.
Furthermore, targeted interventions may be especially beneficial for subgroups identified as vulnerable in this study, such as students who skip meals regularly or report high levels of stress. These groups could benefit from integrated health-promotion programs that combine nutritional support, counseling, and academic mentoring to address both behavioral and emotional contributors to academic performance.

4.7. Strengths and Limitations

The study’s strengths include its large sample size, multi-domain analysis, and application of standardized scale. However, the cross-sectional design limits causal inference. Self-reported GPA and mental health data may introduce social desirability bias and response bias, though checks for plausibility and internal consistency were performed. In addition, since first-year students typically did not yet have a GPA, they were more likely to be excluded from regression analyses, which may have slightly affected the representativeness of academic year comparisons. Future studies could employ longitudinal designs and include qualitative components to better understand the mechanisms behind observed relationships. Furthermore, low explained variance suggests that unmeasured factors (such as motivation, teaching quality) play significant roles in GPA determination. Including socioeconomic status (such as family income and parental education) and preadmission academic performance as control variables in future work could also clarify whether observed relationships are confounded by external factors like financial stability or prior academic ability.

5. Conclusions

This study underscores the importance of adopting a multifactorial approach in examining academic performance among medical students. While traditional predictors such as demographic and biological factors had limited explanatory power, behavioral and psychosocial variables—especially structured self-study habits and consistent meal routines—emerged as the most meaningful predictors of GPA. Mental health factors, particularly stress and depression, also played notable roles, reflecting the dual pressures and adaptive mechanisms faced by students in rigorous academic settings.
These findings provide actionable insights for academic institutions, suggesting that support strategies should extend beyond curriculum content to include health promotion, psychological services, and personalized academic coaching. Given the unique sociocultural backdrop of Vietnamese education, this research contributes context-sensitive evidence to the global medical education literature and opens avenues for future research on interventions targeting student wellness and performance.
Future investigations should consider longitudinal designs and experimental interventions to better understand causal mechanisms and evaluate the impact of targeted lifestyle and mental health programs. By fostering a holistic academic environment, universities can better prepare future healthcare professionals for both academic and personal success.

Author Contributions

Conceptualization, N.B.D. and N.H.T.; methodology, N.B.D., Q.N.P., and N.H.T.; software, N.B.D. and N.H.T.; validation, all authors; formal analysis, N.H.T.; investigation, P.T.T., H.T.T., and Q.N.P.; resources, all authors; data curation, N.B.D. and N.H.T.; writing—original draft preparation, N.B.D. and N.H.T.; writing—review and editing, all authors; visualization, N.H.T. and N.B.D.; supervision, N.H.T.; project administration, N.B.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the IRB of Thai Binh University of Medicine and Pharmacy (#926 on 7 September 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study when they checked the agree button on the online survey form.

Data Availability Statement

The data presented in this study are available from the corresponding author via reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ABOBlood Group System (A, B, AB, O)
BMIBody Mass Index
DASS-21Depression Anxiety Stress Scales—21-Item Version
GPAGrade Point Average
HPAHypothalamic–Pituitary–Adrenal Axis
RhRhesus Factor
SDStandard Deviation
SPSSStatistical Package for the Social Sciences

References

  1. Ahmady, S., Khajeali, N., Kalantarion, M., Sharifi, F., & Yaseri, M. (2021). Relation between stress, time management, and academic achievement in preclinical medical education: A systematic review and meta-analysis. Journal of Education and Health Promotion, 10, 32. [Google Scholar] [CrossRef] [PubMed]
  2. Alswat, K. A., Al-Shehri, A. D., Aljuaid, T. A., Alzaidi, B. A., & Alasmari, H. D. (2017). The association between body mass index and academic performance. Saudi Medical Journal, 38(2), 186–191. [Google Scholar] [CrossRef]
  3. Artino, A. R. (2012). Academic self-efficacy: From educational theory to instructional practice. Perspectives on Medical Education, 1(2), 76–85. [Google Scholar] [CrossRef] [PubMed]
  4. Awomokun, T. R. (2022). Relationship between stress, executive function and emotional regulation of students. In Minot State University ProQuest dissertations & theses (Issue 1). Minot State University. [Google Scholar]
  5. BaHammam, A. S., & Pirzada, A. (2023). Timing matters: The Interplay between early mealtime, circadian rhythms, gene expression, circadian hormones, and metabolism—A narrative review. Clocks and Sleep, 5(3), 507–535. [Google Scholar] [CrossRef] [PubMed]
  6. Barnett, J. H., Salmond, C. H., Jones, P. B., & Sahakian, B. J. (2006). Cognitive reserve in neuropsychiatry. Psychological Medicine, 36(8), 1053–1064. [Google Scholar] [CrossRef]
  7. Bashir, M. B. A., Mohamed, S. O. A., Nkfusai, C. N., Bede, F., Oladimeji, O., Tsoka-Gwegweni, J. M., & Cumber, S. N. (2020). Assessment of minor psychiatric morbidity, stressors, and barriers of seeking help among medical students at the University of Khartoum, Khartoum, Sudan. The Pan African Medical Journal, 35, 87. [Google Scholar] [CrossRef]
  8. Brignac, A., Bellar, D., Judge, L., Smith, J., Mazerat, N., & Trosclair, D. (2011). The relationship of BMI to grade point average, age and multiple fitness tests. Journal of Strength and Conditioning Research, 25, 121. [Google Scholar] [CrossRef]
  9. Burrows, T. L., Whatnall, M. C., Patterson, A. J., & Hutchesson, M. J. (2017). Associations between dietary intake and academic achievement in college students: A systematic review. Healthcare, 5(4), 60. [Google Scholar] [CrossRef]
  10. Chang, J.-J., Ji, Y., Li, Y.-H., Pan, H.-F., & Su, P.-Y. (2021). Prevalence of anxiety symptom and depressive symptom among college students during COVID-19 pandemic: A meta-analysis. Journal of Affective Disorders, 292, 242–254. [Google Scholar] [CrossRef]
  11. Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic performance. Sleep Medicine Reviews, 10(5), 323–337. [Google Scholar] [CrossRef] [PubMed]
  12. Çivitci, A. (2015). The moderating role of positive and negative affect on the relationship between perceived social support and stress in college students. Kuram ve Uygulamada Egitim Bilimleri, 15(3), 565–573. [Google Scholar] [CrossRef]
  13. Dyrbye, L. N., Thomas, M. R., & Shanafelt, T. D. (2006). Systematic review of depression, anxiety, and other indicators of psychological distress among U.S. and Canadian medical students. Academic Medicine: Journal of the Association of American Medical Colleges, 81(4), 354–373. [Google Scholar] [CrossRef] [PubMed]
  14. Edefonti, V., Rosato, V., Parpinel, M., Nebbia, G., Fiorica, L., Fossali, E., Ferraroni, M., Decarli, A., & Agostoni, C. (2014). The effect of breakfast composition and energy contribution on cognitive and academic performance: A systematic review. The American Journal of Clinical Nutrition, 100(2), 626–656. [Google Scholar] [CrossRef] [PubMed]
  15. Eisenberg, D., Golberstein, E., & Hunt, J. B. (2009). Mental health and academic success in college. The BE Journal of Economic Analysis & Policy, 9(1), 1–40. [Google Scholar]
  16. El Abd, R., Alshatti, R., Sultan, S., AlOtaibi, N., & Al-Sabah, S. (2024). Medical school performance as measured by GPA: What can it predict? Research Square. [Google Scholar] [CrossRef]
  17. Fernandes, M. d. S. V., Mendonça, C. R., da Silva, T. M. V., Noll, P. R. e. S., de Abreu, L. C., & Noll, M. (2023). Relationship between depression and quality of life among students: A systematic review and meta-analysis. Scientific Reports, 13(1), 6715. [Google Scholar] [CrossRef]
  18. Florence, M. D., Asbridge, M., & Veugelers, P. J. (2008). Diet quality and academic performance. The Journal of School Health, 78(4), 209–241. [Google Scholar] [CrossRef]
  19. Gilbert, S. P., & Weaver, C. C. (2010). Sleep quality and academic performance in university students: A wake-up call for college psychologists. Journal of College Student Psychotherapy, 24(4), 295–306. [Google Scholar] [CrossRef]
  20. Goldin, A. P., Sigman, M., Braier, G., Golombek, D. A., & Leone, M. J. (2020). Interplay of chronotype and school timing predicts school performance. Nature Human Behaviour, 4(4), 387–396. [Google Scholar] [CrossRef]
  21. Henning, M. A., Hawken, S. J., Krägeloh, C., Zhao, Y., & Doherty, I. (2011). Asian medical students: Quality of life and motivation to learn. Asia Pacific Education Review, 12(3), 437–445. [Google Scholar] [CrossRef]
  22. Henry, J. D., & Crawford, J. R. (2005). The short-form version of the Depression Anxiety Stress Scales (DASS-21): Construct validity and normative data in a large non-clinical sample. The British Journal of Clinical Psychology, 44(Pt 2), 227–239. [Google Scholar] [CrossRef] [PubMed]
  23. Hewitt, P. L., Caelian, C. F., Flett, G. L., Sherry, S. B., Collins, L., & Flynn, C. A. (2002). Perfectionism in children: Associations with depression, anxiety, and anger. Personality and Individual Differences, 32(6), 1049–1061. [Google Scholar] [CrossRef]
  24. Hoyland, A., Dye, L., & Lawton, C. L. (2009). A systematic review of the effect of breakfast on the cognitive performance of children and adolescents. Nutrition Research Reviews, 22(2), 220–243. [Google Scholar] [CrossRef]
  25. Hysenbegasi, A., Hass, S. L., & Rowland, C. R. (2005). The impact of depression on the academic productivity of university students. The Journal of Mental Health Policy and Economics, 8(3), 145–151. [Google Scholar]
  26. Karim, M. R., Ahmed, H. U., & Akhter, S. (2022). Behavioral and psychosocial predictors of depression in Bangladeshi medical students: A cross-sectional study [version 1; peer review: 2 approved]. F1000Research, 11, 745. [Google Scholar] [CrossRef]
  27. Kawabata, M., Lee, K., Choo, H.-C., & Burns, S. F. (2021). Breakfast and exercise improve academic and cognitive performance in adolescents. Nutrients, 13(4), 1278. [Google Scholar] [CrossRef]
  28. Keller, A. S., Leikauf, J. E., Holt-Gosselin, B., Staveland, B. R., & Williams, L. M. (2019). Paying attention to attention in depression. Translational Psychiatry, 9(1), 279. [Google Scholar] [CrossRef]
  29. Kim, T., Chang, J.-Y., Myung, S. J., Chang, Y., Park, K. D., Park, W. B., & Shin, C. S. (2016). Predictors of undergraduate and postgraduate clinical performance: A longitudinal cohort study. Journal of Surgical Education, 73(4), 715–720. [Google Scholar] [CrossRef]
  30. Korgaonkar, M. S., Williams, L. M., Song, Y. J., Usherwood, T., & Grieve, S. M. (2014). Diffusion tensor imaging predictors of treatment outcomes in major depressive disorder. The British Journal of Psychiatry: The Journal of Mental Science, 205(4), 321–328. [Google Scholar] [CrossRef]
  31. Lemma, S., Berhane, Y., Worku, A., Gelaye, B., & Williams, M. A. (2014). Good quality sleep is associated with better academic performance among university students in Ethiopia. Sleep & Breathing = Schlaf & Atmung, 18(2), 257–263. [Google Scholar] [CrossRef]
  32. Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the beck depression and anxiety inventories. Behaviour Research and Therapy, 33(3), 335–343. [Google Scholar] [CrossRef] [PubMed]
  33. Malik, S., Ghayas, S., & Khalid, S. (2023). Relationship between academic perfectionism and depression: Role of self concealment among students. Pakistan Journal of Education, 39(2), 35–52. [Google Scholar] [CrossRef]
  34. Martin, A. J. (2013). Academic buoyancy and academic resilience: Exploring ‘everyday’ and ‘classic’ resilience in the face of academic adversity. School Psychology International, 34(5), 488–500. [Google Scholar] [CrossRef]
  35. Matsuura, Y., Abe, Y., Motoki, Y., Tran, N. H., & Yasui, T. (2023a). Menstrual abnormalities in female international students in Japan: Changes during pre-arrival, difficult, and current periods. European Journal of Investigation in Health, Psychology and Education, 13(7), 1362–1377. [Google Scholar] [CrossRef]
  36. Matsuura, Y., Tran, H., Nguyen, B. T., Phan, Q. N., Nguyen, K. T., & Yasui, T. (2024a). Menstruation-related symptoms and associated factors among Female University Students in Vietnam. Youth, 4(1), 344–356. [Google Scholar] [CrossRef]
  37. Matsuura, Y., Tran, H., & Yasui, T. (2023b). The changes in menstrual and menstrual-related symptoms among Japanese female University Students: A prospective cohort study from three months to nine months after admission. Healthcare, 11(18), 2557. [Google Scholar] [CrossRef]
  38. Matsuura, Y., Tran, N. H., & Yasui, T. (2022). Differences in menstruation-related symptoms of university students depending on their living status in Japan. Healthcare, 10(1), 131. [Google Scholar] [CrossRef]
  39. Matsuura, Y., Tran, N. H., & Yasui, T. (2024b). Association between menstruation-related symptoms and the type of stress in Japanese female university students: A prospective cohort study from admission to the second year. Women, 4(3), 254–264. [Google Scholar] [CrossRef]
  40. Micha, R., Rogers, P. J., & Nelson, M. (2011). Glycaemic index and glycaemic load of breakfast predict cognitive function and mood in school children: A randomised controlled trial. British Journal of Nutrition, 106(10), 1552–1561. [Google Scholar] [CrossRef]
  41. Norton, P. J. (2007). Depression Anxiety and Stress Scales (DASS-21): Psychometric analysis across four racial groups. Anxiety, Stress, and Coping, 20(3), 253–265. [Google Scholar] [CrossRef]
  42. Pelz, B. (2024). Neuropsychological and psychiatric determinants of peak performance in high-stress professions. International Journal of Psychiatry Research, 7(6), 1–10. [Google Scholar] [CrossRef]
  43. Phan, N. Q., Dang, B. N., Nguyen, T. K., Pham, T. D., & Tran, H. (2024). Navigating well-being and Academic Success: Insights from university dormitory life. ACAH2024 Conference Proceedings, 1, 807–817. [Google Scholar]
  44. Różycka-Tran, J., Jurek, P., Truong, T. K. H., & Olech, M. (2020). The implications of filial piety in study engagement and study satisfaction: A polish-vietnamese comparison. Frontiers in Psychology, 11, 525034. [Google Scholar] [CrossRef]
  45. Ruddick-Collins, L. C., Morgan, P. J., & Johnstone, A. M. (2020). Mealtime: A circadian disruptor and determinant of energy balance? Journal of Neuroendocrinology, 32(7), e12886. [Google Scholar] [CrossRef]
  46. Scarmeas, N., & Stern, Y. (2003). Cognitive reserve and lifestyle. Journal of Clinical and Experimental Neuropsychology, 25(5), 625–633. [Google Scholar] [CrossRef]
  47. Stoeber, J., & Otto, K. (2006). Positive conceptions of perfectionism: Approaches, evidence, challenges. Personality and Social Psychology Review, 10(4), 295–319. [Google Scholar] [CrossRef]
  48. Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72(2), 271–324. [Google Scholar] [CrossRef]
  49. Tien Nam, P., Thanh Tung, P., Phuong Linh, B., Hanh Dung, N., & Van Minh, H. (2024). Happiness among university students and associated factors: A cross-sectional study in Vietnam. Journal of Public Health Research, 13(3), 22799036241272400. [Google Scholar] [CrossRef]
  50. Tran, H., Nguyen, N. T., Nguyen, B. T., & Phan, Q. N. (2022). Students’ perceived well-being and online preference: Evidence from two universities in Vietnam during COVID-19. International Journal of Environmental Research and Public Health, 19(19), 12129. [Google Scholar] [CrossRef]
  51. Tran, T. V., Nguyen, H. T. L., Tran, X. M. T., Tashiro, Y., Seino, K., Van Vo, T., & Nakamura, K. (2024). Academic stress among students in Vietnam: A three-year longitudinal study on the impact of family, lifestyle, and academic factors. Journal of Rural Medicine: JRM, 19(4), 279–290. [Google Scholar] [CrossRef]
  52. Wouters, A., Croiset, G., Galindo-Garre, F., & Kusurkar, R. A. (2016). Motivation of medical students: Selection by motivation or motivation by selection. BMC Medical Education, 16, 37. [Google Scholar] [CrossRef] [PubMed]
  53. Wu, H., Li, S., Zheng, J., & Guo, J. (2020). Medical students’ motivation and academic performance: The mediating roles of self-efficacy and learning engagement. Medical Education Online, 25(1), 1742964. [Google Scholar] [CrossRef] [PubMed]
  54. Xu, T., Zuo, F., & Zheng, K. (2024). Parental educational expectations, academic pressure, and adolescent mental health: An empirical study based on CEPS survey data. International Journal of Mental Health Promotion, 26(2), 93–103. [Google Scholar] [CrossRef]
Table 1. Demographic and biological characteristics of participants (N = 1227).
Table 1. Demographic and biological characteristics of participants (N = 1227).
VariableCategoryN%MeanSD
MajorMedicine64052.2
Pharmacy37930.9
Nursing16013.0
Traditional Medicine433.5
Medical Technology50.4
Academic YearYear 125020.4
Year 229123.7
Year 322718.5
Year 425420.7
Year 514611.9
Year 6594.8
SexFemale88472.0
Male34328.0
Age-20.201.793
Age (years)- 20.21.79
Height (cm)- 160.627.83
Weight (kg)- 52.759.71
BMI (kg/m2)- 20.352.93
Blood Group (ABO)Group O69556.6
Group A18615.2
Group B28223.0
Group AB645.2
Rh FactorRh (−)56946.4
Rh (+)65853.6
HometownThai Binh City846.8
Thai Binh Province24119.6
Other Provinces87671.4
Abroad262.1
ResidenceStudent Dormitory34027.7
Rental74060.3
My Home14712.0
Roommates (count)- 2.151.42
Table 2. Lifestyle characteristics of participants (N = 1227).
Table 2. Lifestyle characteristics of participants (N = 1227).
VariableCategoryN%MeanSD
Eating habitsNo breakfast34828.4%
No lunch181.5%
No dinner171.4%
Alcohol (0–3)Not consumed78964.3%
Sometimes42734.8%
Weekly70.6%
Daily40.3%
Tobacco (0–3)Not used119497.3%
Sometimes201.6%
Weekly40.3%
Daily90.7%
Coffee (0–3)Not consumed51842.2%
Sometimes58948.0%
Weekly756.1%
Daily453.7%
Exercise (0–3)Not done1048.5%
Sometimes78263.7%
Weekly19415.8%
Daily14712.0%
Hours sleeping- 6.841.41
Time to bed 1- 23.601.09
Hours using electronics- 5.863.22
Self-study hours- 3.091.84
1 Time to bed is coded on a 24 h scale ranging from 21 (9:00 p.m.) to 27 (3:00 a.m. of the following day) to capture the late-night sleep patterns frequently observed in university students.
Table 3. Perceived stress and mental health indicators of participants (N = 1227).
Table 3. Perceived stress and mental health indicators of participants (N = 1227).
VariableMeanSD
Stress—General (0–4 scale)2.400.97
Stress—Academic2.781.04
Stress—Health2.110.98
Stress—Friends1.990.99
Stress—Love1.661.00
Stress—Family1.931.05
Stress—Money2.601.23
Stress—Lifestyle/Food2.031.00
Depression (DASS-21, 0–3 scale)10.128.03
Anxiety (DASS-21, 0–3 scale)10.308.26
Stress (DASS-21, 0–3 scale)9.317.93
Table 4. Summary of significant predictors of GPA by regression models (N = 1038).
Table 4. Summary of significant predictors of GPA by regression models (N = 1038).
CategoryPredictorB (Unstd.)β (Std.)p-ValueDirectionR2 (Model)
LifestyleBreakfast0.1030.0670.046Positive0.032
Lunch−0.542−0.1950.001Negative
Dinner0.5450.1870.001Positive
Self-Study Hours0.1030.0900.005Positive
BiologicalAge0.1330.1100.001Positive0.013
Mental HealthDepression0.1050.4000.001Positive0.012
Stress−0.070−0.2660.044Negative
Academic–SocialAcademic Year0.1540.1020.003Positive0.039
Major (Medicine)0.3960.0910.008Positive
Combined ModelAll the Above--<0.01Mixed0.048
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Dang, N.B.; Tran, P.T.; Tran, H.T.; Phan, Q.N.; Tran, N.H. What Drives Academic Performance: Lifestyle, Mental Health, and Biological Traits Among Medical Students in a Southeast Asian Context. Psychol. Int. 2025, 7, 38. https://doi.org/10.3390/psycholint7020038

AMA Style

Dang NB, Tran PT, Tran HT, Phan QN, Tran NH. What Drives Academic Performance: Lifestyle, Mental Health, and Biological Traits Among Medical Students in a Southeast Asian Context. Psychology International. 2025; 7(2):38. https://doi.org/10.3390/psycholint7020038

Chicago/Turabian Style

Dang, Ngoc Bao, Phuc Thai Tran, Hoa Thi Tran, Quang Ngoc Phan, and Nam Hoang Tran. 2025. "What Drives Academic Performance: Lifestyle, Mental Health, and Biological Traits Among Medical Students in a Southeast Asian Context" Psychology International 7, no. 2: 38. https://doi.org/10.3390/psycholint7020038

APA Style

Dang, N. B., Tran, P. T., Tran, H. T., Phan, Q. N., & Tran, N. H. (2025). What Drives Academic Performance: Lifestyle, Mental Health, and Biological Traits Among Medical Students in a Southeast Asian Context. Psychology International, 7(2), 38. https://doi.org/10.3390/psycholint7020038

Article Metrics

Back to TopTop