Gendered Dimensions of Menstrual Health: Lifestyle, Biology, and Coping Strategies Among Female Medical Students

Abstract

This study aims to explore the associations between menstrual health, lifestyle behaviors, biological traits, and coping strategies among female students at a Vietnamese medical university. A cross-sectional survey was conducted among 884 female students across five academic majors. Data on demographics, menstrual patterns, biological characteristics, lifestyle behaviors, and coping mechanisms were collected. Statistical analyses included descriptive statistics, correlation, and logistic regression to identify significant predictors of self-reported menstrual changes post university admission. Of the 884 participants, 49.8% reported menstrual changes after entering university. Among the lifestyle-related factors, increased daily electronic use (mean = 5.83 h) and later bedtimes (mean = 23:58) were associated with menstrual change (p < 0.01). Older age and higher academic year emerged as significant predictors of menstrual changes (p < 0.001). Additionally, students with blood groups A and B exhibited a higher risk compared to those with group O (p < 0.05), and Rh-positive status was also significantly associated with menstrual changes (p = 0.05). In terms of knowledge and coping practices, students who had premenstrual syndrome awareness since school were significantly less likely to report menstrual changes (p = 0.003). Although use of pain relief, particularly painkillers, correlated with higher reported pain severity, it was not directly linked to menstrual change. On the other hand, clinic consultations were positively associated with menstrual changes (p = 0.003), while students who relied on their mothers as counselors exhibited a protective association (p = 0.001). Menstrual health in university-aged women is influenced by a complex interplay of lifestyle behaviors, biological traits, and menstrual knowledge. Early education and structured coping support may serve as protective factors. The findings call for targeted menstrual health programs in university settings.

1. Introduction

Menstrual health is a vital indicator of reproductive and overall well-being in women, particularly during adolescence and young adulthood, as it reflects hormonal balance, nutritional status, and overall physiological function [1]. Disruptions in menstrual patterns can signal underlying health concerns such as polycystic ovary syndrome (PCOS), thyroid dysfunction, or stress-related hypothalamic amenorrhea. Among university students, especially those in demanding programs like medicine, the transition to adult independence often brings substantial shifts in daily routines, including irregular sleep cycles, skipped meals, reduced physical activity, and extended screen time—all of which are known to influence menstrual cyclicity [2,3]. Additionally, rapid lifestyle adjustments coincide with ongoing physiological maturation, making young women particularly susceptible to cycle irregularities during these formative academic years [4,5,6].

Female medical university students represent a unique population due to the demanding academic environment and often rigid schedules. These students are prone to irregular routines, sleep deprivation, and limited physical activity, all of which may impact menstrual health. Recent evidence has underscored the multifactorial nature of menstruation-related symptoms and their sensitivity to contextual stressors, lifestyle habits, and living environments. In a study of international students in China, researchers found that menstrual disorders were common and closely associated with lifestyle changes and acculturation challenges faced by female students [7]. A cohort study showed associations between different types of stress and menstrual symptoms among Japanese university students [8]. Complementary findings were reported among Vietnamese students, where lifestyle and behavioral factors were significantly linked with menstruation-related discomfort [9]. Longitudinal studies have also documented changes in menstrual patterns over academic years [5], while variations based on living status and international student status have been explored in Japan [10,11]. These studies build a compelling case for localized investigations into how contextual, cultural, and biological factors interact to shape menstrual health, particularly among young women navigating transitional life stages. Moreover, in culturally conservative societies like Vietnam, menstrual health remains a sensitive topic. Students are often reluctant to seek help, compounded by the limited availability of formal school-based counseling and support systems [12].

Despite a growing body of international literature on menstrual health, few studies in Vietnam have specifically examined the non-psychological predictors of menstrual changes among university-aged women, particularly those in high-demand academic programs like medicine. The existing research often focuses on stress-related factors, leaving a gap in our understanding of how biological traits and modifiable health behaviors independently contribute to menstrual irregularities in this population.

To address this gap, the present study investigates the prevalence and predictors of menstrual changes among female medical students in Vietnam, with a focus on biological characteristics (e.g., age, blood group), health behaviors (e.g., sleep patterns, screen time, breakfast habits), menstrual knowledge, and symptom management strategies (e.g., use of pain relief, counseling sources). The study intentionally excludes psychological stress variables to isolate the influence of non-psychological correlates on menstrual health.

This investigation is guided by the following research questions:

• What biological and academic factors (e.g., age, academic year, blood group) are associated with self-reported menstrual changes among female medical students?
• How do lifestyle behaviors—particularly electronic device use and bedtime—predict the likelihood of experiencing menstrual changes during university life?
• To what extent do menstrual knowledge and coping strategies, such as PMS awareness and the use of informal support (e.g., mothers), influence menstrual health outcomes?

By focusing on these questions, the study contributes localized, data-driven evidence to the menstrual health literature and supports the design of non-psychological, behavior-focused interventions for reproductive well-being in academic settings.

2. Materials and Methods

2.1. Study Design and Setting

A cross-sectional survey study was conducted in December 2023 at a major medical university in Vietnam. The target population included female students enrolled across five academic majors: Medicine, Pharmacy, Nursing, Traditional Medicine, and Medical Technology. The university where the study was conducted has over 6000 undergraduate students, with approximately 50% being female.

2.2. Participants and Sampling

The inclusion criteria specified that participants must be female undergraduate students enrolled in one of the five targeted medical-related majors at the university and willing to provide informed consent. The exclusion criteria included students who had not reached menarche or submitted incomplete survey responses.

The sample size was calculated using Cochran’s formula for estimating proportions, assuming a 95% confidence level, 5% margin of error, and an anticipated prevalence of 50% to ensure maximum validity [13]. The formula was further adjusted using Cochran’s finite population correction for the university’s estimated 3000 female students, yielding a required minimum sample size of approximately 341. The final sample of 884 participants exceeded this requirement, enhancing the statistical power and subgroup analysis potential.

2.3. Instrument and Variables

The participants completed a structured questionnaire developed by the authors, based on a review of relevant literature and previously validated instruments related to menstrual health, lifestyle behaviors, and psychosocial variables [5,6,8,9,10,11,14]. The variables included the following:

Demographic and biological characteristics: age, academic year, major, height, weight, body mass index (BMI), blood group (ABO, Rh), hometown, residence type, number of roommates.

Menstrual health: menarche age, cycle regularity, cycle length, bleeding duration and amount, pain severity, premenstrual symptoms.

Lifestyle factors: sleep hours, bedtime, electronic usage, meal patterns (breakfast/lunch/dinner), alcohol, tobacco, caffeine use, exercise.

Knowledge and coping: PMS knowledge source, coping practices (pain relief methods), counselor type (mother, friend, clinic, none).

2.4. Data Collection Procedure

The survey was administered online using Google Forms. The participants were invited to participate via a URL link and QR code. Before beginning the questionnaire, the participants were shown an introduction page outlining the study purpose and procedures. Informed consent was obtained by requiring participants to check a box indicating their voluntary agreement to participate. All responses were collected anonymously.

2.5. Data Analysis

The data were analyzed using the IBM Statistical Package for the Social Sciences (SPSS) version 28.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were computed to summarize demographic, behavioral, and menstrual health characteristics. To identify predictors of menstrual change (changed vs. unchanged), binary logistic regression was performed. The selection of variables was based on theoretical relevance and prior literature. The Nagelkerke R Square statistic was used to evaluate the model’s explanatory power. In addition, associations between key behavioral and knowledge-based variables and menstrual change were assessed using appropriate bivariate tests. The Chi-square test was applied to analyze associations between menstrual change and categorical variables, such as PMS knowledge (yes/no), counseling source (mother, friend, none), and clinic consultation (yes/no). The Mann–Whitney U test was used for ordinal or non-normally distributed continuous variables, such as menstrual pain severity, when comparing groups.

3. Results

3.1. Characteristics of Participants

Table 1. Demographic and biological characteristics of the participants (n = 884).

Variable	n	%	Mean	SD
Major
Medicine	435	49.2%
Pharmacy	269	30.4%
Nursing	146	16.5%
Traditional medicine	30	3.4%
Medical technology	4	0.5%
Grade
Year 1	182	20.6%
Year 2	217	24.5%
Year 3	159	18.0%
Year 4	191	21.6%
Year 5	92	10.4%
Year 6	43	4.9%
Age			20.13	1.594
Height			157.07	6.057
Weight			48.98	6.766
BMI			19.95	4.835
ABO blood type
Group O	495	56.0%
Group A	141	16.0%
Group B	206	23.3%
Group AB	42	4.8%
Rh blood type
Rh (−)	413	46.7%
Rh (+)	471	53.3%
Hometown
Same city	49	5.5%
Same province	190	21.5%
Other provinces	629	71.2%
Abroad	16	1.8%
Residence type
Student dormitory	242	27.4%
Rental apartment	545	61.7%
My home	97	11.0%
Number of roommates			2.21	1.447

SD, standard deviation; BMI, body mass index.

shows a detailed breakdown of the demographic and biological characteristics of the participants. These include academic major, grade level, age, height, weight, BMI, blood group, and residential background. Understanding these attributes offers essential context regarding the sample’s educational, physiological, and geographic diversity, which may influence variations in menstrual health outcomes.

3.2. Menstrual Characteristics

Table 2. Menstrual characteristics of the participants (n = 884).

Variable	n	%	Mean	SD
Menstruation change
Unchanged	444	50.2%
Changed	440	49.8%
Menarche (10–23)			13.34	1.383
Regularity
Irregular	432	48.9%
Regular	452	51.1%
Cycle Length
<21 days	53	6.0%
21–35 days	653	73.9%
>35 days	178	20.1%
Bleeding days
<3 days	37	4.2%
3–7 days	808	91.4%
>7 days	39	4.4%
Bleeding Amount
Low (<4 pads)	320	36.2%
Moderate (5~7 pads)	522	59.0%
Heavy (>8 pads)	19	2.1%
Missing	23	2.6%
Menstrual Pain Severity (0–10)			4.11	2.643
PMS Fullness (1–5)			1.75	0.908
PMS Backpain (1–5)			2.81	1.035
PMS Headache (1–5)			1.78	0.936
PMS Nausea (1–5)			1.42	0.748
PMS Breast Tenderness (1–5)			2.17	1.095
PMS Constipation (1–5)			1.56	0.856
PMS Irritability (1–5)			2.89	1.129
PMS Fever (1–5)			1.21	0.564
PMS Diarrhea (1–5)			1.55	0.917

SD, standard deviation; PMS, premenstrual syndrome.

details menstrual characteristics, such as regularity, cycle length, bleeding duration, and volume. It also includes information on pain severity and a breakdown of common premenstrual symptoms (PMS), offering a comprehensive overview of the menstrual experiences among respondents. These insights are critical for identifying symptom patterns and understanding the physiological burden associated with menstruation among female university students.

3.3. Coping Characteristics

Table 3. Coping characteristics of the participants (n = 884).

Variable	n	%
Pain relief use
Any method	292	33.0%
Painkillers	161	18.2%
Hot packs	127	14.4%
Hot drinks	43	4.9%
Counselor
None	189	21.4%
Mother	536	60.6%
Friends	338	38.2%
Clinic	142	16.1%
PMS knowledge
Don’t know	304	34.4%
Since university	138	15.6%
Since high school	442	50.0%

PMS, premenstrual syndrome.

summarizes coping characteristics including sources of support, pain relief methods, and PMS awareness. Specifically, it details the proportion of students who report seeking help from family members (especially mothers), friends, or healthcare services such as clinics or pharmacies. It also categorizes the various coping strategies employed to alleviate PMS symptoms and menstrual pain, such as the use of over-the-counter analgesics, hot packs, warm beverages, and hormonal treatments. This table provides insight into both informal and formal mechanisms adopted by students to manage menstrual discomfort, as well as their level of awareness about premenstrual syndrome prior to university.

3.4. Lifestyle Characteristics

Table 4. Lifestyle characteristics (n = 884).

Variable	n	%	Mean	SD
Eating habits
Skipping breakfast	255	28.8%
Skipping lunch	9	1.0%
Skipping dinner	9	1.0%
Alcohol (0–3)
Not use	639	72.3%
Sometimes	244	27.6%
Weekly	1	0.1%
Tobacco (0–3)
Not use	878	99.3%
Sometimes	5	0.6%
Weekly	1	0.1%
Coffee (0–3)
Not use	401	45.4%
Sometimes	410	46.4%
Weekly	47	5.3%
Daily	26	2.9%
Exercise (0–3)
Never	88	10.0%
1–2 times/week	634	71.7%
3–4 times/week	104	11.8%
Daily	58	6.6%
Hours sleeping			6.88	1.389
Bedtime (21–27)			23.58	0.966
Hours using electronics			5.83	3.052
Hours of self-study			3.08	1.762

SD, standard deviation.

describes lifestyle-related factors, including dietary habits, substance use, sleep, and exercise. It presents the frequency of behaviors such as skipping breakfast, caffeine intake, alcohol and tobacco consumption, hours of electronic device use, and engagement in physical activity. These variables offer a snapshot of students’ daily routines and health-related practices, which are highly relevant in assessing modifiable risk factors for menstrual disruption.

3.5. Key Predictors of Menstrual Change

To identify key predictors of menstrual change among female medical university students, a binary logistic regression analysis was conducted. The model incorporated a combination of demographic, biological, lifestyle, and behavioral variables, including age, academic year, BMI, blood group, sleep-related variables, electronic device usage, breakfast habits, and PMS-related knowledge and health-seeking behavior.

Table 5. Key predictors of menstrual change (n = 884).

Variable	B	S.E.	Wald	df	Sig.	Exp(B)	95% C.I. for Exp(B)
Age	0.108	0.027	15.758	1	0.000 **	1.114	1.057–1.175
Academic year	0.221	0.056	15.562	1	0.000 **	1.248	1.118–1.392
BMI	0.019	0.021	0.809	1	0.368	1.019	0.978–1.062
Major (Pharmacy vs. Medicine)	−0.217	0.072	9.005	1	0.003 **	0.805	0.692–0.937
Blood group A	0.128	0.051	6.300	1	0.012 *	1.137	1.029–1.256
Blood group B	0.117	0.043	7.420	1	0.006 **	1.124	1.034–1.222
Rh factor (+)	0.075	0.038	3.840	1	0.050 *	1.078	1.000–1.162
Sleep hours	−0.024	0.034	0.495	1	0.478	0.976	0.914–1.043
Bedtime	0.062	0.026	5.672	1	0.017 *	1.064	1.011–1.120
Electronics use (h/day)	0.091	0.021	18.756	1	0.000 **	1.096	1.051–1.144
Skipping breakfast	0.108	0.058	3.502	1	0.061	1.114	0.994–1.248
PMS knowledge (since school)	−0.165	0.055	8.993	1	0.003 **	0.848	0.759–0.946
Clinic consultation (yes)	0.168	0.056	9.000	1	0.003 **	1.183	1.059–1.321

B, unstandardized regression coefficient; S.E., standard error; Wald, Wald Chi-square test statistic for assessing the significance of individual predictors; df, degrees of freedom; Sig., significance level (p-value); Exp(B), exponentiated coefficient (odds ratio); CI, confidence interval; BMI, body mass index; PMS, premenstrual syndrome. * p < 0.05, ** p < 0.01. Nagelkerke R² = 0.216. This indicates that approximately 21.6% of the variance in menstrual change is explained by the variables included in the model.

summarizes the regression output presenting the coefficients, odds ratios (Exp(B)), and statistical significance levels for each variable. Of the 884 participants, 49.8% reported menstrual changes after entering university.

Age and academic year were both statistically significant predictors (p < 0.001). Specifically, for each one-year increase in age, the odds of reporting a menstrual change increased by approximately 11.4% (OR = 1.114, 95% CI: 1.057–1.175). Similarly, higher academic year was associated with 24.8% higher odds of reporting menstrual change (OR = 1.248, 95% CI: 1.118–1.392), indicating that more senior students were more likely to experience disruptions in their menstrual patterns. Students in the Pharmacy major had significantly lower odds of reporting menstrual change compared to those in Medicine (OR = 0.805, p = 0.003), implying a protective association for this subgroup.

With respect to blood groups, both A and B types were significantly associated with increased risk of menstrual change compared to group O. Participants with blood group A had 13.7% higher odds (OR = 1.137, p = 0.012), while those with blood group B had 12.4% higher odds (OR = 1.124, p = 0.006). Additionally, being Rh-positive showed statistical significance (p = 0.05), with a modest increase in odds (OR = 1.078). Although BMI was included in the model, it was not a significant predictor (p = 0.368).

Regarding lifestyle behaviors, bedtime and electronic device use were both statistically significant. Later bedtimes were associated with a 6.4% increase in the odds of menstrual change per hour (OR = 1.064, p = 0.017). Similarly, each additional hour of daily electronic device use increased the odds by 9.6% (OR = 1.096, p < 0.001), suggesting a strong relationship between screen exposure and menstrual disruption. In contrast, total sleep hours was not a significant predictor (p = 0.478). Skipping breakfast approached statistical significance (p = 0.061), with a trend toward increased odds of menstrual change (OR = 1.114).

Among coping-related variables, students who had PMS knowledge since school were significantly less likely to report menstrual change (OR = 0.848, p = 0.003), indicating a protective effect of early menstrual education. On the other hand, those who had consulted a clinic were more likely to report menstrual change (OR = 1.183, p = 0.003), possibly reflecting higher symptom severity or health-seeking behavior in those experiencing irregularities. These findings underscore the multifactorial nature of menstrual health among university students, with biological traits, academic exposure, lifestyle behaviors, and menstrual awareness all playing significant roles.

3.6. Behavioral and Knowledge-Based Associations

To further contextualize the findings of the logistic regression model,

Table 6. Summary of behavioral and knowledge-based associations (n = 884).

Variable	Associated with Menstrual Change	Statistical Test	p-Value
PMS knowledge (since school)	Lower likelihood	Chi-square test	0.003 **
Pain relief use (any method)	Not directly associated	Chi-square test	>0.05
Painkiller use	Associated with pain severity	Mann–Whitney U test	<0.01 **
Clinic consultation	Higher likelihood	Chi-square test	0.003 **
No counselor	No significant difference	Chi-square test	>0.70
Counselor = mother	Slight protective effect	Chi-square test	0.001 **

Menstrual change was treated as a binary outcome (changed vs. unchanged). Categorical variables were analyzed using the Chi-square test. Ordinal variables, such as pain severity (0–10 scale), were analyzed using the Mann–Whitney U test due to non-normal distribution. PMS, premenstrual syndrome. ** p < 0.01 considered statistically significant.

presents a summary of selected behavioral and knowledge-based factors and their statistical association with reported menstrual change. Students who reported having learned about PMS since school were significantly less likely to report menstrual changes compared to those who had no prior knowledge or only learned during university (p = 0.003), suggesting a protective effect of early menstrual education. The use of any pain relief methods was not significantly associated with menstrual change, while the use of painkillers specifically was significantly associated with higher reported pain severity (p < 0.01), as determined by a Mann–Whitney U test. This suggests that students with more intense symptoms may be more likely to use medication, though not necessarily more likely to report menstrual changes. Students who had sought clinic consultations were significantly more likely to report menstrual changes (p = 0.003), possibly reflecting a greater symptom burden or health awareness. In contrast, students who reported relying on their mother as a primary counselor were less likely to experience menstrual change (p = 0.001), indicating that informal support systems may play a protective or stabilizing role. No significant associations were found for students who reported no counselor, indicating the absence of formal or informal guidance does not necessarily predict menstrual disruption. These findings emphasize the importance of menstrual literacy and supportive relationships in mitigating menstrual health disruptions during university life.

4. Discussion

This study aimed to investigate the prevalence and predictors of menstrual change among female medical students in Vietnam, with a focus on non-psychological correlates such as biological traits, lifestyle behaviors, and coping strategies. While previous studies have frequently centered on stress and mental health factors, particularly in Western or urban contexts, our research fills an important literature gap by isolating behavioral and physiological predictors within a culturally specific Southeast Asian university population. The findings contribute to a growing body of menstrual health literature by offering localized evidence and highlighting the influence of screen time, bedtime, early menstrual education, and familial support on menstrual health—factors that have not been sufficiently explored in Vietnam. These insights provide a foundation for future interventions targeting health education, behavior change, and informal support networks within academic settings. This study identifies modifiable lifestyle and behavioral predictors of menstrual health changes in young women, independent of stress or mental health factors. Nearly half (49.8%) of the participants reported changes in their menstrual patterns after entering university, which is comparable to other studies [5,7,15,16], indicating that menstrual health is influenced by a complex interaction between age, academic exposure, sleep, blood group, and early symptom awareness, as well as help-seeking behaviors.

4.1. Biological and Academic Predictors of Menstrual Change

Older students and those in higher academic years were significantly more likely to report menstrual changes, possibly due to cumulative exposure to academic workload and lifestyle disruptions over time. These results align with a previous study, which reported that 48.3% of female university students in Ho Chi Minh City experienced menstrual irregularities [4], comparable to our finding of 48.9%. Their regression analysis identified those in nursing majors were 2.5 times more likely to report irregular cycles compared to students in medical technology programs. Intriguingly, although academic stress showed a significant univariate relationship with menstrual irregularity in their study, it did not remain significant in multivariate analysis, echoing our own choice to exclude psychological stress in modeling but highlighting its potential as a confounder or indirect influencer.

The significant relationship between blood group (A, B vs. O) and menstrual change aligns with limited evidence suggesting ABO-linked differences in vascular and hormonal function yet should be interpreted cautiously. While not fully understood, this biological influence warrants further investigation. While this association has not been widely explored in the context of menstrual health, existing hematologic literature provides a biological rationale for such findings. ABO blood group has been shown to exert significant influence over von Willebrand factor (VWF) biology, a key component in primary hemostasis. Individuals with blood group O have approximately 25–30% lower plasma VWF levels compared to non-O groups, which may confer protection against vascular and thrombotic disorders by reducing platelet adhesion and clot formation potential. In contrast, groups A and B are associated with elevated VWF levels, enhanced platelet binding, and reduced clearance rates, all of which may contribute to a procoagulant state and altered vascular reactivity, potentially impacting uterine blood flow and menstrual regulation [17]. The finding that Rh-positive status was marginally associated with menstrual changes (p = 0.050) is novel and may represent a statistical artifact. These biological factors should be explored in future research using physiological biomarkers and a larger, more diverse sample.

4.2. Lifestyle Behaviors and Circadian Disruption

Sleep duration was not a significant predictor in the adjusted model, despite being a widely presumed factor in menstrual disruption. Notably, electronic device usage and sleep timing, rather than sleep duration, emerged as strong predictors. This suggests circadian misalignment as a more critical factor than overall sleep quantity. Circadian disruption has been shown to interfere with the hypothalamic–pituitary–ovarian (HPO) axis, the key hormonal pathway governing female reproductive function [18]. A human in silico genomic study found overlapping genetic pathways between premature ovarian insufficiency (POI) and insomnia, particularly involving neural circadian control [19]. This supports the growing understanding that menstrual health is tightly regulated by circadian physiology, and that maintaining regular sleep-wake and light exposure patterns may play a protective role.

Although the association between skipping breakfast and menstrual changes in this study approached but did not reach statistical significance (p = 0.061), there is strong biological plausibility supporting the link between irregular or delayed meal timing and reproductive health disturbances. Eating at inconsistent or biologically inappropriate times (such as skipping breakfast or eating late at night) can desynchronize central and peripheral body clocks, alter cortisol and melatonin rhythms, disrupt clock gene expression, impair glucose tolerance, reduce insulin sensitivity, and lead to metabolic shifts that could impact hormone balance and reproductive function, particularly in women of reproductive age [20]. Such findings support the idea of promoting regular daily eating routines among university students.

4.3. Role of Menstrual Knowledge and Coping Strategies

Students who were educated about PMS before university were significantly less likely to report menstrual changes, suggesting that symptom literacy may facilitate earlier recognition and better coping. A study demonstrated that a menstrual self-care education program delivered to adolescents with intellectual disabilities and their mothers significantly improved both knowledge and menstrual hygiene skills, while also reducing anxiety around menstrual symptoms [21]. On the other hand, health-seeking behaviors, particularly clinic consultations, also raise the possibility of reverse causality—that is, among those with menstrual changes, students may more commonly seek care in response, indicating a higher symptom burden or awareness. This may reflect a higher symptom burden or greater health awareness of changes, rather than these consultations causing the changes.

Reliance on mothers for support, in other words, mothers as students’ primary counselors, correlated with a slight protective effect, while students with no counselor showed no significant difference. This might be due to emotional support or learned self-care behaviors from familial role models, like how studies support the value of shared educational models, where mother–daughter, intergenerational communication is strengthened to nurture an emotionally safe environment, and care dependency is reduced through increased adolescent autonomy and confidence [21].

Interestingly, while the use of pain relief methods such as analgesics was not significantly associated with menstrual changes, it was correlated with greater perceived pain severity, suggesting that these behaviors are responsive strategies rather than preventive ones. Similarly, a study reported that female college students who experienced greater levels of premenstrual, menstrual, and intermenstrual discomfort demonstrated significantly higher use of coping methods, whether aggressive (active symptom management) or passive (withdrawal, rest) [22]. The study found positive correlations between discomfort severity and coping frequency, emphasizing that coping intensity scales with symptom burden. Again, these findings reinforce the notion that pain management behaviors may not prevent menstrual disruption but rather serve as indicators of symptom severity, urging the need for tailored menstrual education that differentiates between proactive, preventive strategies and reactive symptom relief.

4.4. Implications for Menstrual Health Education and Campus Support

The findings from this study carry important implications for both institutional policy and educational programming. Menstrual health, often overlooked in student wellness strategies, must be prioritized as a key component of campus health services. Universities should implement structured menstrual education initiatives beginning in students’ first year. This can include informational sessions, menstrual tracking guidance, and myth-busting workshops tailored to Vietnam’s cultural contexts. Educational content should address not only the physiology of menstruation but also sleep hygiene, screen time management, and healthy lifestyle practices. Moreover, clinical support for menstrual issues should be normalized and destigmatized.

This aligns with evidence from a study which showed that ethnic minority high school students in mountainous areas of Vietnam had low baseline reproductive health knowledge, particularly about menstruation, ovulation, and puberty signs [23]. After attending a series of extracurricular seminars designed around small-group discussions and interactive materials, students’ knowledge improved dramatically across all domains. It also supports our study’s observation that school-based education, especially when offered early and in an engaging format, correlates with reduced menstrual irregularity and improved self-care confidence among young women. Yet, even in high-income settings, menstrual education remains fragmented, inconsistent, and stigmatized [24]. It echoes concerns in Vietnam, where formal menstrual education is sparse, and many students learn from family or friends, often reinforcing misinformation, which collectively stress the urgency for systemic curricular reform, ensuring that menstrual education is not only biomedical but also culturally affirming, developmentally appropriate, and empowering for adolescents.

By integrating menstrual health support into routine health checks or student wellness consultations, institutions can ensure early intervention for students experiencing significant changes or pain. Given that students commonly consult mothers or friends for menstrual concerns, peer educators or trained student mentors may bridge gaps in professional support access. Empowering peer-based models could also address cultural reluctance to engage with formal systems.

Stress, including academic, economic, and peer-related stress, is significantly correlated with menstrual symptom severity, as measured by the Menstrual Distress Questionnaire (MDQ) [9]. This reinforces our decision to intentionally exclude psychological stress from the current study and yet indirectly supports our finding that students in higher academic years experienced more menstrual changes, possibly due to chronic stress accumulation. Still, it must be admitted that the exclusion of psychological stress measures, while intentional, may omit an important confounder in the relationship between lifestyle and menstrual health.

Considering our contemporary digital proliferation, menstrual tracking tools and early detection programs could be developed and offered to students, allowing for timely intervention if menstrual patterns become disrupted. A student co-designed, mobile-based educational module on sex and menstrual health was evaluated in a randomized controlled trial and found an increase in menstrual knowledge among the target group, with students appreciating the ability to review content at their own pace [25]. Despite the absence of major differences in post-intervention attitudes between groups, participants favored video-based formats, suggesting that interactive methods may better sustain attention and normalize discussion of menstruation. All things considered, these measures not only promote better reproductive health outcomes but also support academic retention and reduce absenteeism related to menstrual discomfort or dysfunction.

4.5. Limitations and Future Directions

This study has several limitations that warrant consideration. First, due to the cross-sectional design, causal relationships cannot be inferred. Second, although our aim was to focus on behavioral and biological factors, we did not include psychological stress—a well-established disruptor of menstrual regularity—as a variable. This omission may have limited our ability to fully account for confounding or interactive effects, and we recommend that future models incorporate validated stress measures to improve analytical precision. Third, although academic year was treated as a variable in the regression analysis, we did not stratify the results by academic year, which could have helped to clarify the cumulative effects of academic progression. We recognize this as a promising direction for future research. Fourth, the study relied on self-reported data, which may be subject to recall or reporting bias. Fifth, physiological biomarkers (e.g., hormone levels) were not collected, limiting biological validation of certain associations such as blood group effects. Lastly, although cultural context was acknowledged as important, it was not directly measured and should be explored in future qualitative or mixed-methods studies. Despite these limitations, the study offers novel and context-specific insights into modifiable factors affecting menstrual health in a Vietnamese university setting. Future studies should consider longitudinal tracking, incorporation of psychological and physiological assessments, and sampling across multiple institutions to strengthen external validity and comprehensiveness.

5. Conclusions

Menstrual health changes in university students are influenced by age, academic exposure, lifestyle patterns, and self-reported knowledge and coping practices related to menstrual health. Simple but impactful behaviors like managing screen time, improving bedtime routines, and seeking reliable menstrual education can improve students’ menstrual well-being. Educational institutions should consider targeted health programs and create open environments for menstrual health dialogue.