Introduction

Premenstrual Syndrome (PMS) is a common health issue characterized by the periodic recurrence of physical, psychological, and behavioral symptoms during the luteal phase of the menstrual cycle1. It has a wide range of symptoms, like changes in appetite, weight fluctuations, abdominal discomfort, headaches, breast tenderness, nausea, mood swings, anxiety, irritability, fatigue, and restlessness2. Furthermore, studies have highlighted a link between PMS and various mental health disorders, particularly depressive and anxiety disorders, indicating shared underlying etiological factors3,4. The prevalence of PMS among women of reproductive age globally is reported to be 47.8%5. In Turkey, a meta-analysis identified that 52.2% of women experienced PMS6. According to the diagnostic criteria established by the American College of Obstetricians and Gynecologists7, the presence of at least one emotional or somatic symptom that significantly affects social, work, or school life is required for a diagnosis of PMS. PMS is not classified as a mental disorder in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR). In contrast, Premenstrual Dysphoric Disorder (PMDD) represents a more severe form of PMS and is classified as a mental disorder in the DSM-5-TR. According to the diagnostic criteria for PMDD, a woman should experience at least 5 out of 11 symptoms in the cognitive-emotional, behavioural and physical domains that appear in the last week of the luteal phase and disappear with or just before the onset of menstruation. These symptoms should disappear completely after the onset of menstruation and should not be explained by an exacerbation of another mental disorder8. A systematic review of 44 studies (50.659 participants) reported a pooled PMDD prevalence of 3.2% for confirmed diagnosis and 7.7% for provisional diagnosis, with community-based confirmed samples showing 1.6%9. The underlying pathophysiology of PMS and PMDD is not completely understood, but it is thought to involve disruptions in the biological rhythms that regulate our physiological processes.

Alterations in biological rhythms have been associated with PMDD10; these rhythms involve regular fluctuations in biological processes aimed at optimizing physiological functioning11. The most extensively studied aspect of circadian rhythms is the sleep-wake cycle, which is regulated by the light-dark cycle and has a significant influence on various circadian processes. Sleep problems are widely reported among women, with symptoms often centred in the premenstrual period10. In recent years, more and more attention has been paid to the role of sleep-related factors in menstrual symptoms12. Sleep disturbances have been identified as important variables affecting cognitive and psychomotor functions during PMS. Moreover, circadian rhythm disturbances have been associated with PMS because circadian rhythms regulate physiological and biological functions throughout the day. Individual differences in sleep preferences and wakefulness levels lead to the emergence of chronotypes that divide people into morning, evening and intermediate types13. Morning chronotype individuals, also known as early types or morning larks, prefer to be active in the morning, sleep early, and wake up early. Evening chronotype individuals, also called late types or night owls, prefer to be active in the evening, sleep late, and wake up late. Intermediate chronotypes, or neutral types, have no strong preference for morning or evening activity. As a natural consequence of these sleep and activity preferences, morning types reach their peak physical and mental performance early in the day after waking. In contrast, evening types experience their best mental and physical performance before going to sleep14.

However, modern society mostly operates in accordance with the natural rhythms of morning chronotypes. This situation causes a mismatch known as “social jetlag,” particularly for evening and intermediate types. Social jetlag is essentially a mismatch between an individual’s preferred circadian phase and their social time15 and manifests as an inconsistency between an individual’s chronotype and their daily sleep schedule16. This persistent mismatch, consistent social jetlag, can have adverse effects on physical and mental health and potentially lead to chronic health issues17. Studies by Qu et al.18 have indicated that individuals with an evening chronotype and social jetlag are at a higher risk of experiencing depressive symptoms compared to those with a morning chronotype or intermediate chronotype. Furthermore Weiss et al.19, suggested that social jetlag is a significant risk factor for depression. Previous research has shown that significant social jetlag is linked to poorer sleep quality20, an increased likelihood of depression21, and more severe premenstrual symptoms12.

Shift workers, such as nurses, frequently experience a misalignment between their internal biological clocks and socially imposed schedules, resulting in more pronounced disruptions to circadian rhythms compared to non-shift workers22. This disruption is particularly evident among nurses with an evening chronotype, who are more likely to report poor sleep quality, often associated with emotional disturbances23. Even minor misalignments between circadian and social clocks have been shown to impact menstrual symptoms12. In this context, it has been emphasized that designing work schedules for shift-working nurses that consider chronotype and social jet lag is crucial for achieving sustainable nursing performance20. However, these findings are not limited to actively working nurses; prospective nurses, who are the health professionals of the future, are also at risk of facing similar health and performance risks when they switch to shift work in the following years. Therefore, the evaluation of variables such as chronotype, social jet lag and sleep patterns in pre-nurses may contribute to the prediction of possible risks before the transition to the profession and to both protect individual health and support a sustainable professional life.

In the literature, more and more studies are being conducted to understand the relationship between chronotype and health outcomes. University students in particular are more prone to sleep disorders due to environmental factors that disrupt sleep-wake cycles24. Given that women are almost twice as likely as men to develop depressive or anxiety disorders25 and menstrual symptoms can contribute to more serious psychological outcomes such as depression and anxiety26, this group may represent a particularly vulnerable group. Circadian rhythm variations throughout the menstrual cycle are particularly relevant for women sensitive to hormonal fluctuations10. Female nursing students, who represent the majority of the profession, often face early morning classes, clinical training, and are likely to continue rotating shifts after graduation27. While PMS has been linked to mental health issues such as anxiety and depression28, its effects on circadian rhythm disruptions are less well understood. This underscores the importance of considering PMS in the nursing profession.

Considering the potential interaction between PMS, circadian rhythms, and mental health, we predict that nursing students with PMS, will exhibit significantly higher levels of mental health problems and circadian rhythm disorders compared to those without PMS. To formally evaluate these predictions, this study was designed to investigate the impact of PMS on mental health and its relationship with chronotype and social jetlag.

Research questions

  • Are there significant differences between students with and without PMS in terms of mental health problems?

  • Do students with PMS differ significantly in their chronotype and social jetlag compared to students without PMS?

Method

Study design

This study used a cross-sectional descriptive and comparative design to assess the impact of PMS on mental health and its association with chronotype and social jetlag among nursing students. Data collection tools were the Personal Information Form, including sleep patterns, medical history, the Premenstrual Syndrome Scale (PMSS), the Morningness Eveningness Questionnaire (MEQ), the Beck Depression Inventory (BDI), and the Beck Anxiety Inventory (BAI), and the Determination of Social Jetlag Status. Social jetlag scores were also calculated based on participants’ weekday and weekend sleep hours. Participants were categorized into PMS and without PMS groups based on PMSS scores exceeding 50% of the maximum possible score (111 points and over)29. Reference research reported depression risk rates of 16% in individuals without PMS and 84% in those with PMS30. Assuming different prevalence rates in our study (20% without PMS and 50% with PMS), a power analysis indicated that including at least 78 participants (at least 39 per group) would yield 80% power at a 95% confidence level. The study comprised a total of 98 female students, with 50 having PMS and 48 without PMS.

Participants

The population of this study comprised female nursing students enrolled at the Recep Tayyip Erdoğan University Faculty of Health Sciences during the spring semester of 2023. No specific sampling method was employed; instead, all students who voluntarily responded to the online survey and met the inclusion criteria (age range and self-reported PMS symptoms) were included in the study. Ninety-eight nursing students aged 18 and above who consented to participate were included in the sample. Students who met the inclusion criteria and accepted the invitation to participate in the study gave their consent through a Google Form and then answered the questions. Exclusion criteria were the history of gynecological diseases (polycystic ovary syndrome, pelvic trauma, etc.), oral contraceptive use, and psychiatric illness.

The study included only female participants, with no significant differences observed between the PMS and non-PMS groups in terms of age or body mass index (BMI) (Table 1). Although lifestyle factors such as physical activity, nutrition, and sleep patterns were not systematically evaluated, the homogeneity of the sample in terms of demographic characteristics likely minimized potential confounding effects.

Participants completed the assessments without restriction to a specific menstrual cycle phase. They reported symptoms they typically experience during the premenstrual period, as the study aimed to evaluate general symptom patterns rather than phase-specific variations.

Data collection forms

The data for the study were collected through the “Premenstrual Syndrome Scale,” “Sociodemographic Questionnaire,” “The Morningness Eveningness Questionnaire,” “Determination of Social Jetlag Status,” “Beck Depression Inventory,” and “Beck Anxiety Inventory”.

Sociodemographic questionnaire

The form includes three questions about the participants’ socio-demographic characteristics, such as age, height, and weight. In addition, clinical characteristics such as disease, medication use, and sleep hours are also questioned.

The premenstrual syndrome scale (PMSS)

The PMSS consists of 44 items, which are rated on a five-point Likert scale. The PMSS was developed by Gencdogan in 2006, who also conducted its validity and reliability studies for the Turkish adaptation29. It has nine sub-dimensions that measure different aspects of premenstrual syndrome, including depressive feelings, anxiety, fatigue, irritability, depressive thoughts, pain, changes in appetite, changes in sleeping habits, and swelling29. The PMSS has five response options, scored as follows: 1 - never, 2 - rarely, 3 - sometimes, 4 - frequently, and 5 - always. The total score to be obtained from the scale ranges from 44 to 220. Those who score 111 or higher on the PMSS may have premenstrual syndromes. PMS is considered present if more than 50% of the sub-dimensions have high values29. Cronbach’s alpha (α) is 0.75 for the original scale and 0.98 for this study.

The morningness eveningness questionnaire (MEQ)

Originally developed by31 the questionnaire underwent a reliability and validity study for its Turkish adaptation by32. This Likerttype scale comprises 19 questions, with varying scoring criteria: 1–4 points for questions 3–9 and 13–16, 1–5 points for questions 1, 2, 10, 17, and 18, 0–6 points for questions 11 and 19, and 0–5 points for question 12. Based on the total score, individuals are classified into three circadian types: “morning type” scoring between 59 and 86 points, “intermediate type” scoring between 42 and 58 points, and “evening type” scoring between 16 and 41 points. Evening-type individuals tend to sleep and get up late, exhibiting better productivity in the evening, while morning-type individuals prefer early bedtimes and early mornings, being most active in the morning31,32. Cronbach’s alpha (α) was found to be 0.81 in the original scale and 0.77 in our study.

Determination of social jetlag

Social jetlag reflects the disparity between sleep patterns on workdays and holidays, indicating circadian misalignment22. This measure, derived from the difference between median sleep hours on weekdays and weekends, determines the inconsistency between biological rhythms and social schedules. Unlike transient jetlag resulting from travel, social jetlag persists over time16. Following common practice, social jetlag was categorized as < 1 h (low), 1–2 h (moderate), and > 2 h (high)17,21,33.

The Beck depression inventory (BDI)

This scale used to determine depression status was first developed by Beck in 196134. It was translated into Turkish by Hisli, and its validity and reliability were demonstrated to be suitable for use in Turkey35. The BDI is a 21-item self-assessment scale that measures depressive symptoms and characteristic approaches. Each item scores between 0 and 3, and the total score is obtained by summing these values. The total score varies between 0 and 63. In the validity and reliability study of the scale for Turkish, the cut-off score was accepted as 1735. In this study, the data were divided into two groups: depressed and non-depressed, according to the scale using the cut-off score of 17, and the data were evaluated accordingly. Cronbach’s alpha (α) was calculated as 0.80 for the original scale and 0.91 in our study.

The Beck anxiety inventory (BAI)

BAI is a self-assessment scale developed by36 to determine the frequency of anxiety symptoms experienced by individuals. It is a Likert-type scale consisting of 21 items and scored between 0 and 3. Its validity and reliability in Turkey were performed by37. Symptoms are rated on a 4-point scale ranging from “none” (0) to “severe.” Participants rate the extent to which each symptom has bothered them in the past month on a scale from 0 (not at all) to 3 (severe: I can hardly bear it). Total scores range from 0 to 63, with scores of 16 indicating moderate to severe anxiety. 13 items assess physiological symptoms, 5 items describe impaired cognitive functioning, and 3 items assess both somatic items and symptoms of impaired cognitive functioning. An increase in the total score indicates the severity of anxiety37. Cronbach’s alpha (α) was found to be 0.93 for the original scale and 0.92 for this study.

Data analysis

All statistical analyses were performed using SPSS 25.0 (IBM SPSS Statistics 25) software (Armonk, NY: IBM Corp.). Continuous variables were defined by mean ± standard deviation, median (IQR: 25th and 75th percentiles), minimum and maximum values, and categorical variables by number and percent. Shapiro-Wilk and Kolmogorov Smirnov tests were used for the determination of normal distribution. If parametric test conditions were satisfied, the independent samples t-test was used for group comparisons, and if not, the Mann-Whitney Utest was used to compare groups. The chi-square test was used for categorical variables. Correlation between continuous variables was assessed using Pearson correlation analysis. A binary logistic regression analysis was employed to determine the risk factors for the presence of PMS. Linear regression analysis was used to examine the effect of the presence of PMS on the scale scores examined in the study. Statistical significance was determined as p ≤ 0.05.

Ethical considerations

Institutional permission and approval from the Recep Tayyip Erdoğan University Non-Interventional Clinical Research Ethics Committee (dated March 16, 2023, numbered 2023/63) were obtained for the research conducted within the relevant department of the university. Written informed consent was obtained from the students included in the sample. All study procedures adhered to the guidelines outlined in the Declaration of Helsinki.

Results

Table 1 presents the basic demographic characteristics of the participants. The participants had a mean age of 21.13 ± 1.46, a mean height of 162.76 ± 5.35, a mean weight of 57.64 ± 8.63, and a mean body mass index (BMI) of 21.75 ± 3.04. No statistical differences were observed between participants with and without PMS concerning age, height, weight, and BMI values (Table 1). The mean PMS total score was 135.45 ± 42.64. No secondary outcomes or additional variables, such as medication use or comorbid mental health conditions, were assessed in this study. The analysis focused solely on the primary variables of interest, and no unexpected findings related to other factors were observed.

Table 1 Sociodemographic characteristics of participants based on their PMS status.
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The Premenstrual Syndrome Scale demonstrated excellent internal consistency. The total scale showed a Cronbach’s α of 0.983 (95% CI: 0.978–0.987). Subscale reliabilities ranged from 0.839 to 0.967: depressive feelings (α = 0.967), anxiety (α = 0.929), fatigue (α = 0.930), irritability (α = 0.950), depressive thoughts (α = 0.957), pain (α = 0.839), changes in appetite (α = 0.903), changes in sleeping habits (α = 0.892), and swelling (α = 0.934).

Nursing students with PMS had significantly higher depression (BDI: 21.2 ± 9.87 in students with PMS and 11.21 ± 8.85 in those without PMS, p < 0.001) and anxiety (BAI: 22.62 ± 12.04 in students with PMS and 12.44 ± 9.40 in those without PMS, p < 0.001) (Table 2). The results indicate that students with higher PMSS scores were more likely to report symptoms of depression and anxiety (Figs. 1 and 2). No statistically significant difference was found in PMS status according to chronotypes. Similarly, in terms of PMS status based on social jetlag classes, no statistically significant difference was observed (p > 0.05) (Table 2). Figures 3 and 4 illustrate the distribution of social jetlag and MEQ total scores among with PMS and without PMS groups.

Table 2 Social jetlag, depression, anxiety, and MEQ values of the participants based on their PMS status.
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Fig. 1
figure 1

Comparison of BDI total scores between PMS (+) and PMS (–) Groups.

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Fig. 2
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Comparison of BAI total scores between PMS (+) and PMS (–) Groups.

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Fig. 3
figure 3

Comparison of MEQ total scores between PMS (+) and PMS (–) Groups.

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Fig. 4
figure 4

Comparison of social jetlag total scores between PMS (+) and PMS (–) Groups.

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According to the results of the chi-square analysis, there was no statistically significant difference between the PMS group and without PMS group in terms of either chronotype distribution (χ²=1.812, p > 0.05) or social jetlag distribution (χ²=0.142, p > 0.05). In both groups, the vast majority of participants were found to have an intermediate chronotype and 1–2 h of social jetlag (Table 3).

Table 3 Distribution of social jetlag and chronotype classes of participants based on their PMS status.
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Correlation analyses showed a significant positive relationship between PMSS total scores and Beck Anxiety Inventory scores in both PMS and without PMS group. Specifically, in the without PMS group (r = 0.379, p = 0.008), and in the PMS group (r = 0.350, p = 0.013). PMSS total scores and social jetlag showed a non-significant positive relationship in the without PMS group (r = 0.268, p = 0.065), whereas a significant positive correlation was observed in the PMS group (r = 0.351, p = 0.013) (Table 4).

Table 4 Correlation of PMS symptom severity with social Jetlag, depression, anxiety, and MEQ score according to PMS Status.
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Regarding the factors with a statistically significant effect on the presence of PMS, it was noted that depression and anxiety scores significantly increased the risk of PMS (p = 0.0001, O.R = 1.126; p = 0.0001, O.R = 1.094, respectively). Age, BMI, social jetlag score, and MEQ scores did not exhibit a statistically significant effect on the presence of PMS (p > 0.05) (Table 5).

Table 5 Factors affecting the presence of PMS.
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The impact of PMS presence on BMI, social jetlag score, BDI score, BAI score, and MEQ scores was examined, and it was observed that the presence of PMS had a statistically significant effect on depression and anxiety scores (p < 0.001), Std. Beta = 0.474; (p < 0.001), Std. Beta = 0.429, respectively). The other factors examined were not statistically significantly affected by the presence of PMS (p > 0.05) (Table 6).

Table 6 Effects of PMS on social jetlag, depression, anxiety, and MEQ values.
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Discussion

Premenstrual disorders are characterized by psychological and somatic symptoms that occur during the luteal phase, negatively affect daily activities, and typically subside with the onset of menstruation38. Circadian rhythm disruptions, often caused by demanding academic schedules and irregular sleep patterns, are thought to exacerbate menstrual symptoms among nursing students. Therefore, this study investigated the impact of premenstrual syndrome on mental health and its association with chronotype and social jetlag in this population.

The mean PMSS total score was 135.45 ± 42.64 (Table 2). In relevant studies conducted with nursing students in the literature, the PMSS score was found to be 140.68 ± 34.69 by Akın and Erbil39,143.89 ± 25.76 by Çetin et al.40. Notably, the scale cut-off point is typically above ≥ 111, with similar values in these studies.

Anxiety and depression scores were significantly higher in individuals with PMS compared to those without PMS (p < 0.01) (Table 2). Our finding of higher depression in students with PMS is consistent with the results of41 who found a similar relationship among medical students. Özeren et al. (2013)42 discovered a significant relationship between PMS, PMDD, and depression among hospital employees. PMS and PMDD are recognized for their association with anxiety, depressed mood, and other psychiatric comorbidities, which can greatly impact individuals’ quality of life and result in medical costs due to factors such as absenteeism and reduced productivity43. The current study findings underscore the significantly higher levels of depression and anxiety in individuals with PMS, highlighting the importance of understanding these issues and developing effective treatment methods to enhance quality of life and reduce medical costs. Therefore, monitoring university students for the progression of premenstrual symptoms to PMDD is crucial.

It is stated that sleep quality according to the circadian rhythm affects menstrual symptoms and irregularities, and evening chronotype and poor sleep quality are associated with more severe symptoms44. In this study, no statistically significant difference was found when analyzing PMS status according to chronotypes (Table 3). In contrast,12 reported that individuals with an evening chronotype experienced more severe PMS. Arslan and Deniz45 found in their study that PMS symptoms were more intense in women with evening chronotype. Uekata et al.20 found that menstrual pain increased and PMS symptoms were more severe in individuals with evening chronotype. Meram et al.46 reported a moderate association between chronotype and menstrual symptom severity, with morning chronotype linked to milder symptoms. While our study did not find significant results for chronotype and PMS, previous studies suggest that evening chronotypes are more prone to circadian misalignment, which could indirectly worsen PMS symptoms through sleep disruption. The lack of significant associations in this study may be due to the characteristics of the sample, as participants were mainly nursing students who had not yet experienced irregular shift work schedules that can cause circadian disruption. Additionally, their young age and potential flexibility in sleep-wake patterns may have protected them from the negative health effects often seen in populations with chronic social jetlag. However, the observed trends highlight that chronotype plays an important role in this group, as these students are likely to encounter shift work in their future careers, which potentially increases the risk of menstrual discomfort and related health issues. Larger and more diverse longitudinal studies are needed to better understand the link between chronotype and menstrual symptoms among nursing students.

Menstrual symptoms have been linked to social jetlag, which occurs when there is a mismatch between an individual’s circadian rhythm and their actual sleep schedule12,47. However, in this study, no significant differences were found between students with and without PMS in total Social Jetlag scores (Table 2) or in the distribution of social jetlag and chronotype classes (χ², p > 0.05, Table 3). This seems to contrast with previous research, where disruptions in biological rhythms due to evening chronotype and social jetlag are consistently associated with increased menstrual symptoms10,12,48,49 and poorer mental health50,51. Despite the lack of significant differences between groups, further analysis revealed a more complex relationship. Interestingly, even though social jetlag scores were similar, higher social jetlag within the PMS group was positively linked to PMS severity. Additionally, PMSS total scores correlated positively with anxiety levels in both groups (p < 0.05) (Table 4). This pattern suggests that social jetlag may not be a primary risk factor for developing PMS but rather an exacerbating factor that worsens symptoms in those already affected. The study by Komada et al.12 supports this view, indicating that social jetlag is an important factor associated with increased severity of menstrual symptoms. This relationship may be explained by the complex interaction between the biological clock (circadian rhythm) and reproductive hormones10. The additive effect of circadian desynchronization caused by social jetlag on the hormonal fluctuations present in PMS may further disrupt neural systems involved in mood regulation and stress response (e.g., the HPA axis), thereby intensifying symptoms52. Moreover, the connection to mental health is reinforced by21 who found that social jetlag was significantly linked to a higher likelihood of experiencing depressive symptoms. Therefore, these findings suggest that comprehensive interventions addressing both hormonal symptoms and sleep-wake stability, including the reduction of social jetlag, could be especially helpful for individuals with severe PMS symptoms.

In the present study, age, BMI, social jetlag score, and MEQ scores did not exhibit a statistically significant effect on the presence of PMS (p > 0.05). However, logistic regression analysis indicated that depression and anxiety scores significantly increased the risk of PMS (Table 5). Yücel et al.30 and Padhy et al.53 observed a significant correlation between PMS and depression, highlighting that individuals diagnosed with major depression often experience more severe PMS or PMDD symptoms. Lee at al.54 identified a significant association between depression and PMS among female high school students, suggesting that effective depression interventions could potentially alleviate PMS symptoms55. In the present study, regression analysis also highlighted a statistically significant impact of PMS on depression and anxiety scores (Table 6). The results indicate that students with higher PMSS scores were more likely to report symptoms of depression and anxiety, supporting findings by Açıkgöz et al.3. In a study by Balaha et al.4 involving medical students, the prevalence of anxiety and depression was notably higher within the PMS group. Collectively, these findings underscore the bidirectional relationship between PMS and depression, wherein depression may exacerbate PMS symptoms, and PMS symptoms may, in turn, elevate depression risk. Therefore, healthcare professionals should adopt a dual approach that addresses both PMS and depression to support individuals effectively.

This study underscores the relationship between PMS and mental health issues like depression and anxiety among university nursing students. Consistent with the current literature, it suggests that PMS may increase the risk of depression, while depression could exacerbate PMS symptoms. Moreover, the study reveals that students experiencing PMS exhibited significantly higher depression and anxiety scores, underscoring the significant impact of PMS on mental well-being and the necessity for effective interventions in this domain. However, despite non-significant findings regarding factors such as chronotype and social jetlag, the literature indicates that disturbances in circadian rhythm and sleep quality might influence PMS symptoms12,45,47.

Strengths and limitations

The study was conducted exclusively on female nursing students at a state university in Türkiye, so the findings cannot be generalized to the wider population. One limitation of this study is the reliance on self-report questionnaires, which may be subject to bias. Additionally, PMS presence was assessed solely through a retrospective scale score without prospective instruments to report two symptomatic daily-rating scores, as it is required for the diagnosis of PMDD in the DSM-5-TR8. Another limitation of this study is that the diagnosis of PMS was not clearly differentiated from the premenstrual exacerbation of underlying depressive or anxiety disorders, as highlighted in the DSM-5-TR8. Nonetheless, the study’s single-centered approach involving a heterogeneous sample of university students adds value to its findings in a local context. One limitation of the present study is that medical and neurological illnesses were not excluded from the sample. However, none of the participants reported having severe or uncontrolled conditions that could influence the study variables, so the potential impact on the findings is likely minimal.

Conclusion

The findings indicate that nursing students with PMS experience higher levels of depression and anxiety, suggesting that PMS may be an important factor influencing their mental well-being.

This study highlights the need for targeted mental health interventions for nursing students with PMS, as depression and anxiety significantly contribute to PMS severity. Furthermore, the observation that both social jetlag and anxiety levels rise with greater PMS severity indicates that circadian rhythm disruptions could play a role in managing symptoms.

While the findings highlight the association between PMS and mental health, the study’s limitations should be considered. Future research could use longitudinal designs, include more diverse populations, and explore additional psychological and biological factors related to PMS.