Factors Associated with Premenstrual Symptoms: A Study among Graduate-Level Students of the Institute of Science and Technology, Tribhuvan University

Factors Associated with Premenstrual Symptoms: A Study among Graduate-Level Students of the Institute of Science and Technology, Tribhuvan University | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Factors Associated with Premenstrual Symptoms: A Study among Graduate-Level Students of the Institute of Science and Technology, Tribhuvan University Srisa Rijal, Gauri Shrestha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5115166/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jul, 2025 Read the published version in BMC Women's Health → Version 1 posted 14 You are reading this latest preprint version Abstract Background Premenstrual Symptoms refer to the psychological, physical, and behavioral symptoms that recur during the luteal phase of menstruation, impacting the day-to-day activities of females. However, there is limited information on the prevalence and factors associated with Premenstrual Symptoms among graduate-level female students at the Institute of Science and Technology, Tribhuvan University. Methods This study employed institutional-based cross-sectional research methods. Data were gathered from all female participants of Institute of Science and Technology, Tribhuvan University and only 285 female participants fulfill the inclusion criteria. The data was collected using a self-administered questionnaire for demographic, socio-economic, behavioral and menstrual-health related factors and to measure the Premenstrual Syndrome a standard tool Premenstrual Symptoms Screening Tool (PSST) was used. Factors associated with PMS were analyzed using Multiple Binary Logistic Regression. Result Among the 285 respondents, 40% reported experiencing Premenstrual Symptoms. The most common physical symptoms were joint or muscle pain (56.49%), breast tenderness (45.26%), abdominal pain (44.91%), acne flare-ups (40.35%), and fatigue (35.09%). The factors such as age at menarche [OR = 0.689, 95% CI = (0.544, 0.872)], respondents with dysmenorrhea [OR = 2.032, 95% CI = (1.073, 3.847)], respondents who consume menstrual delaying medicine [OR = 11.015, 95% CI = (4.075, 29.774)] and respondents having mild stress [OR = 2.713, 95% CI = (1.174, 6.271)], moderate stress [OR = 3.934, 95% CI = (1.488, 10.399)], severe stress [OR = 19.267, 95% CI = (5.343, 69.474)] were found to be significant at a 5% level of significance with the Premenstrual Symptoms. Conclusion The factors associated with Premenstrual Symptoms are Stress, Menstrual delaying medicine, dysmenorrhea, and age at menarche of respondents. The result suggests a potential relationship between menstrual delaying medicine and PMS symptoms. Multiple Binary Logistic Regression Physical symptoms of PMS Premenstrual Symptoms Proportion of PMS Figures Figure 1 Background Premenstrual Symptoms manifest right before and during the menstrual cycle, encompassing irritability, mood fluctuations, feelings of depression, tension, anxiety, fatigue, cramping, backache, weight gain, breast tenderness and swelling. Nearly 200 symptoms have been associated with the term premenstrual syndrome. In simple words, PMS is the collaboration of different physical, psychological and behavioral symptoms and conditions that develop before the menstrual cycle ( 1 ). Females experience a variety of symptoms during this time, some of which are: melancholy, anxiety, irritability, bloating in the stomach, breast discomfort, and fatigue ( 2 ). The origins of PMS are uncertain, but it is hypothesized that hormonal fluctuations, particularly changes in estrogen and progesterone levels may be contributing factors. Women with deficiencies in specific nutrients like calcium, magnesium, vitamins B, and vitamins E, those with high sugar levels, regular consumption of caffeinated beverages and alcohol intake as well as those experiencing psychological conditions such as persistent stress and major depression are believed to be at a higher risk of developing PMS ( 3 ). It is among the most prevalent issues affecting women in fertile age. The physical, mental, or behavioral symptoms reappear during the secretory phase of the menstrual cycle and impact a woman's everyday interactions with others, her employment and her connections with her family ( 4 ) ( 2 ). In the United States, between 70 and 90 percent of teenage girls experience PMS and between 17.2 and 67.5 percent of females aged 15 to 25 in Turkey reported having the condition ( 5 ). The collective analysis of 17 studies reveals that the combined incidence of PMS was 47.8%. Iran exhibited the highest prevalence at 98%, while France reported the lowest at 12%. The prevalence of PMS showed an upward trend from 1996 to 2011. The prevalence of PMS may vary due to the use of different assessment tools and sample sizes in studies ( 6 ). The common symptoms of PMS are mood swings, headaches, irritability, tiredness and anger, restlessness, snoring, headaches, joint/muscle pain, bloating, mastalgia, and weight gain ( 7 ) ( 8 ) ( 9 ). Several studies indicate a notable correlation between PMS and various factors such as age, marital status, BMI, age at menarche, family income, menstrual flow, bleeding duration, the intensity of dysmenorrhea, negative Rh blood type, caffeine consumption, and depression ( 10 ) ( 11 ) ( 12 ). The occurrence of mild PMS among medical and dental students at Nepal Medical College and Teaching Hospital in Kathmandu was found to be 53.5%, and the prevalence of moderate to severe PMS was determined to be 46.5% ( 2 ). Presently, PMS has become a prevalent issue among Nepalese students, with approximately 53.8% of female respondents out of 364 utilizing complementary alternative treatments such as hot water bags, physical exercise, herbal remedies, dietary adjustments, vitamins and massages instead of conventional medicine when experiencing PMS symptoms. Only 7.3% sought medical consultation for their condition. Most of the study based on the prevalence of PMS but there is a gap in the factors associated with the prevalence of PMS, especially in the case of IOST, T.U ( 13 ). Hence this study mainly focused not only on the prevalence but also on the factors associated with it. This study aims to identify the factors associated with PMS in the IOST, Tribhuvan University. Methods Study design The study was conducted on all the females who enrolled in the IOST, T.U. of academic year 2077 and 2079. The exclusion criteria were currently pregnant, consuming medication related to heart disease, diabetes, depression, anxiety, and other psychometric disorders and currently suffering from Polycystic Ovarian Disease (PCOD) which may cause irregular periods. The irregular periods may be the reason for different causes such as thyroid, PCOS, incidence of chronic diseases, anxiety, depression and other illnesses. Therefore only those females whose menstruation cycle lies in between 21 to 35 days and who menstruate regular for two consecutive months before the data collection duration ( 14 ) were taken as a participants of this study. The inclusion criteria of this study were females with regular menstruation for at least two consecutive months before data collection duration and females of age between 15–49 years. Out of 429 total female population of IOST, TU, only 285 met the inclusion criteria therefore further details are collected from those who met the inclusion criteria. Data Collection A cross-sectional research approach was adopted which is entirely based on primary data directly collected from individuals using a self-administered questionnaire. The data collection was started from 13 February 2024 to 29 February 2024 after obtaining ethical clearance from the Institutional Review Committee (IRC) and the registration number was IRCIOST-24-0002. This study includes a questionnaire on demographic, socioeconomic, menstrual-health-related and behavioral variables. Questionnaire While preparing questionnaire, demographic variables, socio-economic variables, menstrual-health related variables and behavioral variables were prepared with the help of different literature, to measure PMS standard tool Premenstrual Symptoms Screening Tool (PSST) ( 15 ) was used. PMS The status of Premenstrual Symptoms (PMS) was measured using a standard tool called the Premenstrual Symptoms Screening Tool (PSST) ( 15 ). The PSST is a scale for measuring Premenstrual Symptoms (PMS) that includes a list of premenstrual symptoms and impairment. The PMDD was assessed using the criteria given in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). PSST consists of 19 questions listed in two domains which are measured on a 4-point Likert scale including not at all, mild, moderate and severe. In the first domain symptoms of the premenstrual phase were listed and asked females “Do you experience some or any of the following premenstrual symptoms that start before your period and stop within a few days after bleeding?” The symptoms are anger, anxiety/tension, tearfulness, moods of depression, decreased interest in work activities, home activities, social activities, a distraction from work, fatigue/lack of energy, overeating/food craving, insomnia, hypersomnia, feeling overwhelmed or out of control and other physical symptoms such as breast tenderness, headaches, joint/muscles pain, bloating, weight gain. In the second domain, the symptoms of the first domain interfere with domains or not which is also termed as impairments. All the symptoms were measured according to the perceptions of the respondent but anxiety and depression status (normal, mild, moderate and severe) were measured using another standard tool termed Depression, Anxiety and Stress Scale (DASS-21), DASS-21 DASS-21 is a standard tool that contains 21 questions measured in a 4-points Likert scale starting from 0 to 3 for measuring anxiety, depression, and stress levels among respondents ( 16 ). It contains 21 questions on depression, anxiety and stress each with 7 questions. Total scores were obtained for each and multiplied by 2 as the DASS-21 is the short form of DASS-42 to measure the status of depression, anxiety and stress to measure its status whether it is normal, mild, moderate or severe. Internal Consistency Test The internal consistency of PSST and DASS-21 were 0.968 and 0.775 respectively during the pilot survey where only 35 participants were used. After completing the final data collection, the internal consistency of PSST and DASS-21 within 285 respondents were found to be 0.961 and 0.848 respectively. This suggests the variability and consistency of PSST and DASS-21 tools within this population. Covariates A questionnaire includes demographic variables (age, weight), socioeconomic variables (employment status, monthly family income, monthly personal expenditure, education of mother and occupation of mother), menstrual-health-related variables (age at menarche, menstrual flow, menstrual cycle in days, use of pads per day, bleeds last in days, consumption of menstrual delaying medicine and dysmenorrhea) and behavioral variables (Alcohol consumption, Smoking habit, tea/coffee Consumption, Sleeping (Hours), Physical Exercise, Meditation, Stress) Data processing and Analysis This study involved both descriptive and inferential statistical analysis. The dependent variable in the study had a dichotomous nature, with the absence of PMS and the presence of PMS. For the categorical independent variable, the Chi-square test was applied to the variable that has a minimum expected cell frequency of 5 or greater than 5 but Fisher’s exact test was applied instead of Yate’s correction of Chi-square when the minimum expected cell frequency is less than 5. For the continuous independent variables, independent t-test is not applied due to the non-normality of the data therefore a non-parametric test Mann-Whitney U test was applied. In multivariate analysis, the nature of data is ordinal in nature but while using ordinal logistic regression, the data doesn’t follow proportional odds (parallel line) assumption which is an important assumption of ordinal logistic regression model therefore the idea to use ordinal logistic regression was dropped. After violating the assumption of ordinal logistic regression model multinomial regression was not applied due to the less sample size existing in the category of dependent variable. To overcome these problems, ordinal variable was converted into binary and hence multiple binary logistic regression was applied. Hosmer and Lemeshow test was applied for the goodness of fit of the model. The level of significance in this study was 5%. Data was analyzed using SPSS version 20 and R programming version 4.3.1. Results Descriptive statistics Out of 285 females, the PMS was reported in 114 (40%) in the IOST, T.U. Figure 1 illustrates the physical symptoms experienced by respondents during their premenstrual phase. Among the 285 participants, 56.49% reported joint or muscle pain, 45.26% felt breast tenderness, 44.91% reported abdominal pain, 40.35% experienced acne flare-ups, 35.09% felt fatigued, 23.16% had headaches, 17.54% dealt with constipation/diarrhea, 11.93% experienced heaviness and 12.63% reported various other symptoms such as mood swings, back pain, vomiting, and being short-tempered. The descriptive statistics in Table 1 show that 82.8% of respondents were single, and 90.9% of the participants were unemployed. The education qualification of the majority of the respondent’s mothers was primary (31.6%) whereas only 10.5% were illiterate. Most of the participant’s mothers were housewives. 29.7% of respondent’s family’s monthly income was above 50000 and 34.6% of the respondents had personal monthly expenditures between 5000–10000. 68.8% of respondents had normal menstrual flow whereas only 1.4% had a heavy flow of menstrual bleeding. 64.4% of respondents experienced pain during menstruation i.e. dysmenorrhea, 95.5% did not use any kind of contraceptive methods and 87% of respondents did not consume menstrual delaying medicine. While moving to tea/coffee consumption 26% of the respondents did not consume tea or coffee and 34.7% consumed it daily. About 96.1% of the respondents did not consume alcohol and 96.5% of respondents did not smoke. The majority of the respondents 59.6% exercised sometimes and 49.1% meditated sometimes. Table 1 Descriptive Statistics of the Categorical Variables Variables Total Female Status of PMS Absence Presence Total Females n (%) 285 171(60) 114(40) Demographic Variables Marital Status, n (%) Single 236(82.8) 148(86.5) 88(77.2) Married 47(16.5) 22(12.9) 25(21.9) Divorced 2(0.7) 1(0.6) 1(0.9) Socio-economic variables Employment Status, n (%) Unemployed 259(90.9) 154(90.1) 105(92.1) Employed 26(9.1) 17(9.9) 9(7.9) Education status of Mother, n (%) Illiterate 30(10.5) 23(13.5) 7(6.1) Primary 90(31.6) 55(32.2) 35(30.7) Secondary 83(29.1) 47(27.5 36(31.6) Above Secondary 82(28.8) 46(26.9) 36(31.6) Occupation of Mother, n (%) House Wife 213(74.7) 131(76.6) 82(71.9) Business 32(11.2) 16(9.4) 16( 14 ) Teacher 23(8.1) 13(7.6) 10(8.8) Government Employee 11(3.9) 8(4.7) 3(2.6) Other 6(2.1) 3(1.8) 3(2.6) Family Income (Monthly) ( ᶬ ) , n (%) ≤ 20000 24(8.5) 19(11.3) 5(4.4) 20000–30000 48( 17 ) 24(14.3) 24(21.1) 30000–40000 66(23.4) 36(21.4) 30(26.3) 40000–50000 60(21.3) 43(25.6) 17(14.9) ≥ 50000 84(29.8) 46(27.4) 38(33.3) Personal Expenditure (Monthly) ( ᶬ ) , n (%) ≤ 5000 49 (17.3) 29(17.1) 20(17.5) 5000–10000 98(34.6) 59(34.9) 39(34.2) 10000–15000 78(27.5) 49(28.9) 29(25.4) 15000–20000 23(8.1) 12(7.1) 11(9.6) ≥ 20000 35(12.3) 20(11.8) 15(13.2) Menstrual Health-Related Variables Menstrual Flow, n (%) Low 85(29.8) 61(35.7) 24(21.1) Normal 196(68.8) 109(63.7) 87(76.3) Heavy 4(1.4) 1(0.6) 3(2.6) Dysmenorrhea, n (%) No 101 (35.4) 77(45) 24(21.1) Yes 184(64.6) 94(55) 90(78.9) Contraceptive Use, n (%) No 272 (95.4) 164(95.9) 108(94.7) Yes 13(4.6) 7(4.1) 6(5.3) Menstrual Delaying Medicine No 248(87) 165(96.5) 83(72.8) Yes 37( 13 ) 6(3.5) 31(27.2) Behavioral Variables Tea/coffee Consumption, n (%) Never 74(26) 49(28.7) 25(21.9) Sometimes 75(26.3) 41( 24 ) 34(29.8) Often 37( 13 ) 25(14.6) 12(10.5) Daily 99(34.7) 56(32.7) 43(37.7) Alcohol Consumption, n (%) No 274(96.1) 168(98.2) 106(93) Yes 11(3.9) 3(1.8) 8( 7 ) No 275(96.5) 165(96.5) 110(96.5) Yes 10(3.5) 6(3.5) 4(3.5) Exercise/Workout, n (%) Never 42(14.7) 26(15.2) 16( 14 ) Sometimes 170(59.6) 103(60.2) 67(58.8) Often 55(19.3) 31(18.1) 24(21.1) Always 18(6.3) 11(6.4) 7(6.1) Meditation, n (%) Never 98(34.4) 58(33.9) 40(35.1) Sometimes 140(49.1) 82(48) 58(50.9) Often 36(12.6) 23(13.5) 13(11.4) Always 11(3.9) 8(4.7) 3(2.6) Stress, n (%) Normal 197(69.1) 144(84.2) 53(46.5) Mild 31(10.9) 15(8.8) 16( 14 ) Moderate 25(8.8) 9(5.3) 16( 14 ) Severe 32(11.2) 3(1.8) 29(25.4) Missing value ( ᶬ ) The mean age and weight of the respondents were found to be 24.97 years and 52.74 kg with respective standard errors of mean 0.088 years and 0.434 kg. The mean of working hours was 3.23 hours with a standard error of 0.21 hours. The average age at menarche was 12.97 years with the standard error 0.077 years. The mean menstrual cycle was 29.49 days with a standard error of 0.18 days. The average number of pads used by the respondents per day was 3.29 pads with a standard error of 0.07. The average number of days of bleeds last during menstruation was 4.91 days with a standard error of 0.077 days. The mean sleeping hours was 7.47 hours in a day with a standard error of 0.066 hours which is shown in Table 2 . Table 2 Descriptive Statistics of the Continuous Variables Variables Total Female Status of PMS Absence Presence Age (years) Mean ± SE 24.97 ± 0.088 24.94 ± 0.119 25.02 ± 0.128 Weight (Kgs.) Mean ± SE 52.74 ± 0.434 52.61 ± 0.575 52.94 ± 0.662 Working Hours Mean ± SE 3.23 ± 0.21 3.17 ± 0.246 3.33 ± 0.408 Age at menarche(years) Mean ± SE 12.97 ± 0.077 13.20 ± 0.100 12.61 ± 0.114 menstrual cycle (days) Mean ± SE 29.49 ± 0.128 29.59 ± 0.173 29.32 ± 0.189 Numbers of pads use per day Mean ± SE 3.29 ± 0.07 3.24 ± 0.093 3.36 ± 0.107 Bleeds lasts (days) Mean ± SE 4.91 ± 0.077 4.78 ± 0.096 5.10 ± 0.126 sleeping hours Mean ± SE 7.47 ± 0.066 7.43 ± 0.096 7.52 ± 0.087 Bivariate Analysis In the bivariate analysis, the age at menarche, family income, menstrual flow, dysmenorrhea, menstrual delaying medicine, alcohol consumption, and stress were found to be significant at a 5% level of significance whereas other variables such as marital status, employment status, occupation of mother, education qualification of mother, personal expenditure, contraceptive use, tea/coffee consumption, smoking habit, exercise/workout, meditation, age of the respondents, the weight of the respondents, working hours, menstrual cycle, number of pads used per day, bleeds last and sleeping hours in a day were found to be insignificant at the same 5% level of significant, which is shown in Table 3 . Table 3 List of Significant Variables Variables p-value Age at menarche(Years) 0.0001 *** Alcohol Consumption 0.030 ** Dysmenorrhea 0.001 ** Family Income (Monthly) 0.029 * Consumption of Menstrual Delaying Medicine (within 1 year) 0.001 * Menstrual Flow 0.009 ** Stress 0.0001 ** Chi-square Test( * ), Fisher exact Test( ** ), Mann-Whitney U test ( *** ) In the multiple binary logistic regression only four variables age at menarche [OR = 0.689, 95% CI = (0.544, 0.872)], respondent with dysmenorrhea [OR = 2.032, 95% CI = (1.073, 3.847)], respondents who consume menstrual delaying medicine [OR = 11.015, 95% CI = (4.075, 29.774)], and respondents having mild stress [OR = 2.713, 95% CI = (1.174, 6.271)], moderate stress [OR = 3.934, 95% CI = (1.488, 10.399)], severe stress [OR = 19.267, 95% CI = (5.343, 69.474)] were found to be significant at a 5% level of significance. It states that when one year increases in the age at menarche the chances of occurrence of PMS is decreased by 0.371 years. It was found that the likelihood of experiencing PMS was 2.032 times higher in respondents who experienced dysmenorrhea compared to those who did not. This suggests that dysmenorrhea is moderately associated with an increased risk of developing PMS. Similarly, the chances of occurrence of PMS was found to be 11.015 times greater in those respondents who consumed menstrual delaying medicine than those who did not consume it. The chances of occurrence of PMS was found to be 2.713 times greater in respondents who had mild stress, 3.934 times greater in those who had moderate stress, and 19.267 times greater in those who had severe stress than those with normal stress which is shown in Table 4 . Table 4 Multivariate Analysis of PMS Variables Categories Β S.E. Wald p-value Odds Ratio 95% C.I. for Odds Ratio Lower Upper Age at menarche - -0.371 0.119 9.597 0.001 0.689 0.545 0.872 Dysmenorrhea No® - - - - - - - Yes 0.709 0.325 4.739 0.029 2.032 1.073 3.847 Menstrual delaying medicine No® - - - - - - - Yes 2.399 0.507 22.364 0.001 11.015 4.075 29.774 Stress Normal® - - - - - - - Mild 0.998 0.427 5.456 0.019 2.713 1.174 6.271 Moderate 1.369 0.495 7.629 0.005 3.934 1.488 10.399 Severe 2.958 0.654 20.439 0.001 19.267 5.343 69.474 Intercept 3.080 1.555 3.920 0.047 21.779 ® denotes the reference group Discussion This study aimed to investigate the factors associated with premenstrual syndrome (PMS) among female students at the Institute of Science and Technology, Tribhuvan University (IOST, T.U.). The results showed that the prevalence of PMS was 40%, which is similar to a study conducted on working women in various sectors (such as healthcare workers, advocates, bankers, teachers, and engineers) in Kochi city, South India ( 17 ) However, a study at KIST Medical College in Lalitpur reported a higher prevalence of PMS, possibly due to differences in the methods used to measure PMS ( 18 ). The study found that participants who reported dysmenorrhea had a higher likelihood of experiencing PMS compared to those who did not report dysmenorrhea. This finding is consistent with a study conducted at Lumbini Medical College and Teaching Hospital in Palpa ( 19 ) and another study among secondary-level students in Arba Minch town, Ethiopia, which also linked dysmenorrhea with PMS ( 20 ). In our cultural context, women often avoid participating in festivals or rituals during menstruation due to traditional beliefs. This has led to the misconception that menstruation should be "delayed" when it coincides with a festival or event. As a result, some women use menstrual-delaying medication, which can lead to symptoms like weight gain, mood changes, and headaches during the luteal phase. These symptoms are similar to those experienced during PMS, and are thought to be side effects of hormonal contraceptives, which also delay menstruation and contribute to PMS ( 21 ). However, in this study, the use of contraceptives did not show a significant association with PMS, possibly due to limited data on contraceptive use. The study also highlighted that the use of medications like norethisterone and dydrogesterone to delay menstruation could worsen PMS symptoms due to hormonal changes. These medications affect progesterone levels, which are crucial in managing mood swings and physical discomfort associated with PMS. Norethisterone, in particular, has been linked to emotional distress and irritability due to hormonal fluctuations, and altering the menstrual cycle with these medications can disrupt the balance of estrogen and progesterone, intensifying PMS symptoms ( 23 ). The study found a strong association between stress (mild, moderate, and severe) and the likelihood of PMS, which is also consistent with a study conducted at the Faculty of Public Health, Airlangga University in Indonesia ( 24 ). The higher association between stress and PMS may be due to confounding factors, such as academic stress, workload, financial problems, relationship issues, health concerns, and other internal factors of stress. However, detailed information on these confounding factors was not explored in this study. Conclusion The results of the study show that the most common physical symptoms of PMS among the female students of IOST, T.U. are joint or muscle pain (56.49%), breast tenderness (45.26%), abdominal pain (44.91%), acne flare-ups (40.35%), and fatigue (35.09%). The overall prevalence of PMS was 40% among the 285 female respondents. The study identified several factors associated with PMS, including stress, the use of menstrual-delaying medication, dysmenorrhea, and the age at which menstruation began. The findings suggest that higher stress levels increase the likelihood of experiencing PMS. Therefore, it is recommended that women manage their stress through medication or by finding ways to divert their minds from stressful situations.This study did not address dietary factors or the specific treatment approaches for PMS. For future research, it is suggested that the consumption patterns of menstrual-delaying medication be explored in greater detail, with the support of expert clinicians. Limitations The study was conducted without a dedicated clinician to assess the clinical aspects, and detailed information about the use of menstrual delay medicine was not gathered. Additionally, dietary factors like food intake and nutrition were not considered in the study. The data was entirely based on the respondents' perceptions, which means there is a possibility of recall bias affecting the results. Abbreviations ACOG American College of Obstetricians and Gynecologists BMI Body Mass Index C.I Confidence Interval DASS Depression, Anxiety and Stress Scale D.F. Degree of Freedom DSM-V Diagnostic and Statistical Manual of Mental Disorder Edition fifth IOST Institute of Science and Technology OR Odds Ratio PCOD Polycystic Ovarian Disease PCOS Polycystic ovary syndrome PMDD Premenstrual Dysphoric Disorder PMS Premenstrual Syndrome (Symptoms) PSST Premenstrual Symptom Screening Tool T.U. Tribhuvan University Declarations Acknowledgment I want to express gratitude to all the faculties of the Central Departments of Statistics (CDS), Tribhuvan University, for their helpful feedback and useful advice during this article, all the respondents who provide data and the Kathmandu Center for Research and Education (KCRE) for providing a grant for this study. Author's Contribution Srisa Rijal prepared this article under the supervision of Prof. Dr. Gauri Shrestha, who guided the research, data analysis, and manuscript preparation. Funding Information The Kathmandu Center for Research and Education (KCRE) provides funding for this study. Data availability The actual data used in this study are provided by the correspondence author in need. Ethical approval and consent In accordance with the declaration of the Institutional Review Committee of the Institute of Science and Technology, this study was approved as it maintained ethical principles set by the Nepal Health Research Council. The approved registration number is IRCIOST-24-0002. After obtaining ethical approval from the Institutional Review Committee, a written consent form and questionnaire were provided to each participant. Each participant signed a consent form before filling out the questionnaire. The information of participants was kept confidential. Consent for publication Not applicable Competing interest No competing financial interests exist. References Woods NF. Women’s health: The menstrual cycle. Premenstrual symptoms: Another look. Public Health Rep. 1987;102(4 SUPPL. JULY/AUG.):106–12. Manandhar SA, Pramanik T, Amatya M, Silvanus V. 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Bhandari S, Dwa Y, Maharjan M, Maskey S, Thakur M, Sharma S. Premenstrual Syndrome among Medical Students of a Medical college: A Descriptive Cross-sectional Study. J Nepal Med Assoc. 2023 Apr 1;61(260):347–50. Aryal S, Thapa B, Pant SB. Premenstrual Syndrome and Premenstrual Dysphoric Disorder in Medical and Nursing Students of a Tertiary Care Teaching Hospital in Nepal. Nepal J Obstet Gynaecol. 2018 Jan 15;12(1):12–6. Ali M, Dejene Y, Gultie T, Gebreselassie R, Hailu M, Kebede A. Premenstrual Syndrome and Associated Factors among Students in Secondary Schools in Arba Minch Town , Southern Ethiopia : A Cross - Sectional Study , 2021. 2023;(4):23–35. Martin D, Sale C, Cooper SB ESKI. atric Exercise Science. The article appears here in its accepted, peer-re-viewed form, as it was provided by the submitting author. It has not been copyedited, proofread, or formatted by the publisher. Jsep [Internet]. 2017;28:588–95. Available from: https://research.rug.nl/files/30892751/Den_Hartigh_et_al._Short_and_long_term_PM_JSEP_accepted_version_Pure.pdf Andani RW. Relationship Between Degree of Stress and Physical Activity of Female Students With Premenstrual Syndrome. J Berk Epidemiol. 2020;8(2):125. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5115166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427517392,"identity":"d1feef22-ad57-4ec0-a3fc-46ba0c90833b","order_by":0,"name":"Srisa Rijal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3PsWrCQBzH8TsOzBJwjaTknqBwQZAOJY/R+R8OzKLQ0UmuBDLlDTL0FSqFTh1O/kMX8QXsYBGcOsStBYfeCcWlSR0F7zvcEO6TH0eIy3WWUWWOgBGPKtwTHdlven0SYWy+9kH3DwROWmMdKQxJ7R9IG+EV5vX3643Xzf1B0Pt6zx7v8MOsJNG1+puIZVr0ym3AAjREwHY8Ww2FIbI/0A3EpwXxtXmLJQA4nlVgiU5fGggvab7bG8It0YBZXGV1KyELqkK7IrAjYwUIPBy1r4gFLcIrQ2Jk8w0ZYvwUju41iOa38NLb7D71VEZvDwrJLXJeZc91PUmiJvKbPO4ebor267bkuKv+v+1yuVyX1Q/fGGNWHPDmHQAAAABJRU5ErkJggg==","orcid":"","institution":"Tribhuvan University","correspondingAuthor":true,"prefix":"","firstName":"Srisa","middleName":"","lastName":"Rijal","suffix":""},{"id":427517395,"identity":"077e6a65-10df-4b8e-be41-3aa2d4a68a5b","order_by":1,"name":"Gauri Shrestha","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Gauri","middleName":"","lastName":"Shrestha","suffix":""}],"badges":[],"createdAt":"2024-09-19 08:17:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5115166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5115166/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-025-03925-7","type":"published","date":"2025-07-28T16:21:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79331938,"identity":"d79d4a0e-b456-4e8b-96eb-bd0a0f950d3e","added_by":"auto","created_at":"2025-03-27 06:50:35","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":222005,"visible":true,"origin":"","legend":"\u003cp\u003ePhysical Symptoms of PMS\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5115166/v1/68bfcce8d907add2b3fc9f59.jpeg"},{"id":88268171,"identity":"774cc969-e772-4743-975c-ae7ff34fe6dc","added_by":"auto","created_at":"2025-08-04 16:49:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1109266,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5115166/v1/6b2f7f44-3fa7-4e9f-a36f-786dff109068.pdf"},{"id":79331939,"identity":"c83139c1-0191-4349-badc-7a26cd7b12cc","added_by":"auto","created_at":"2025-03-27 06:50:35","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29706,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementry1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5115166/v1/840b92659543d6deb01d5c44.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Associated with Premenstrual Symptoms: A Study among Graduate-Level Students of the Institute of Science and Technology, Tribhuvan University","fulltext":[{"header":"Background","content":"\u003cp\u003ePremenstrual Symptoms manifest right before and during the menstrual cycle, encompassing irritability, mood fluctuations, feelings of depression, tension, anxiety, fatigue, cramping, backache, weight gain, breast tenderness and swelling. Nearly 200 symptoms have been associated with the term premenstrual syndrome. In simple words, PMS is the collaboration of different physical, psychological and behavioral symptoms and conditions that develop before the menstrual cycle (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Females experience a variety of symptoms during this time, some of which are: melancholy, anxiety, irritability, bloating in the stomach, breast discomfort, and fatigue (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The origins of PMS are uncertain, but it is hypothesized that hormonal fluctuations, particularly changes in estrogen and progesterone levels may be contributing factors. Women with deficiencies in specific nutrients like calcium, magnesium, vitamins B, and vitamins E, those with high sugar levels, regular consumption of caffeinated beverages and alcohol intake as well as those experiencing psychological conditions such as persistent stress and major depression are believed to be at a higher risk of developing PMS (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is among the most prevalent issues affecting women in fertile age. The physical, mental, or behavioral symptoms reappear during the secretory phase of the menstrual cycle and impact a woman's everyday interactions with others, her employment and her connections with her family (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In the United States, between 70 and 90 percent of teenage girls experience PMS and between 17.2 and 67.5 percent of females aged 15 to 25 in Turkey reported having the condition (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The collective analysis of 17 studies reveals that the combined incidence of PMS was 47.8%. Iran exhibited the highest prevalence at 98%, while France reported the lowest at 12%. The prevalence of PMS showed an upward trend from 1996 to 2011. The prevalence of PMS may vary due to the use of different assessment tools and sample sizes in studies (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The common symptoms of PMS are mood swings, headaches, irritability, tiredness and anger, restlessness, snoring, headaches, joint/muscle pain, bloating, mastalgia, and weight gain (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Several studies indicate a notable correlation between PMS and various factors such as age, marital status, BMI, age at menarche, family income, menstrual flow, bleeding duration, the intensity of dysmenorrhea, negative Rh blood type, caffeine consumption, and depression (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe occurrence of mild PMS among medical and dental students at Nepal Medical College and Teaching Hospital in Kathmandu was found to be 53.5%, and the prevalence of moderate to severe PMS was determined to be 46.5% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Presently, PMS has become a prevalent issue among Nepalese students, with approximately 53.8% of female respondents out of 364 utilizing complementary alternative treatments such as hot water bags, physical exercise, herbal remedies, dietary adjustments, vitamins and massages instead of conventional medicine when experiencing PMS symptoms. Only 7.3% sought medical consultation for their condition. Most of the study based on the prevalence of PMS but there is a gap in the factors associated with the prevalence of PMS, especially in the case of IOST, T.U (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Hence this study mainly focused not only on the prevalence but also on the factors associated with it. This study aims to identify the factors associated with PMS in the IOST, Tribhuvan University.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe study was conducted on all the females who enrolled in the IOST, T.U. of academic year 2077 and 2079. The exclusion criteria were currently pregnant, consuming medication related to heart disease, diabetes, depression, anxiety, and other psychometric disorders and currently suffering from Polycystic Ovarian Disease (PCOD) which may cause irregular periods. The irregular periods may be the reason for different causes such as thyroid, PCOS, incidence of chronic diseases, anxiety, depression and other illnesses. Therefore only those females whose menstruation cycle lies in between 21 to 35 days and who menstruate regular for two consecutive months before the data collection duration (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) were taken as a participants of this study. The inclusion criteria of this study were females with regular menstruation for at least two consecutive months before data collection duration and females of age between 15\u0026ndash;49 years. Out of 429 total female population of IOST, TU, only 285 met the inclusion criteria therefore further details are collected from those who met the inclusion criteria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eA cross-sectional research approach was adopted which is entirely based on primary data directly collected from individuals using a self-administered questionnaire. The data collection was started from 13 February 2024 to 29 February 2024 after obtaining ethical clearance from the Institutional Review Committee (IRC) and the registration number was IRCIOST-24-0002. This study includes a questionnaire on demographic, socioeconomic, menstrual-health-related and behavioral variables.\u003c/p\u003e\n\u003ch3\u003eQuestionnaire\u003c/h3\u003e\n\u003cp\u003eWhile preparing questionnaire, demographic variables, socio-economic variables, menstrual-health related variables and behavioral variables were prepared with the help of different literature, to measure PMS standard tool Premenstrual Symptoms Screening Tool (PSST) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) was used.\u003c/p\u003e\n\u003ch3\u003ePMS\u003c/h3\u003e\n\u003cp\u003eThe status of Premenstrual Symptoms (PMS) was measured using a standard tool called the Premenstrual Symptoms Screening Tool (PSST) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The PSST is a scale for measuring Premenstrual Symptoms (PMS) that includes a list of premenstrual symptoms and impairment. The PMDD was assessed using the criteria given in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). PSST consists of 19 questions listed in two domains which are measured on a 4-point Likert scale including not at all, mild, moderate and severe. In the first domain symptoms of the premenstrual phase were listed and asked females \u0026ldquo;Do you experience some or any of the following premenstrual symptoms that start before your period and stop within a few days after bleeding?\u0026rdquo; The symptoms are anger, anxiety/tension, tearfulness, moods of depression, decreased interest in work activities, home activities, social activities, a distraction from work, fatigue/lack of energy, overeating/food craving, insomnia, hypersomnia, feeling overwhelmed or out of control and other physical symptoms such as breast tenderness, headaches, joint/muscles pain, bloating, weight gain. In the second domain, the symptoms of the first domain interfere with domains or not which is also termed as impairments.\u003c/p\u003e \u003cp\u003eAll the symptoms were measured according to the perceptions of the respondent but anxiety and depression status (normal, mild, moderate and severe) were measured using another standard tool termed Depression, Anxiety and Stress Scale (DASS-21),\u003c/p\u003e\n\u003ch3\u003eDASS-21\u003c/h3\u003e\n\u003cp\u003eDASS-21 is a standard tool that contains 21 questions measured in a 4-points Likert scale starting from 0 to 3 for measuring anxiety, depression, and stress levels among respondents (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It contains 21 questions on depression, anxiety and stress each with 7 questions. Total scores were obtained for each and multiplied by 2 as the DASS-21 is the short form of DASS-42 to measure the status of depression, anxiety and stress to measure its status whether it is normal, mild, moderate or severe.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInternal Consistency Test\u003c/h2\u003e \u003cp\u003eThe internal consistency of PSST and DASS-21 were 0.968 and 0.775 respectively during the pilot survey where only 35 participants were used. After completing the final data collection, the internal consistency of PSST and DASS-21 within 285 respondents were found to be 0.961 and 0.848 respectively. This suggests the variability and consistency of PSST and DASS-21 tools within this population.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eA questionnaire includes demographic variables (age, weight), socioeconomic variables (employment status, monthly family income, monthly personal expenditure, education of mother and occupation of mother), menstrual-health-related variables (age at menarche, menstrual flow, menstrual cycle in days, use of pads per day, bleeds last in days, consumption of menstrual delaying medicine and dysmenorrhea) and behavioral variables (Alcohol consumption, Smoking habit, tea/coffee Consumption, Sleeping (Hours), Physical Exercise, Meditation, Stress)\u003c/p\u003e\n\u003ch3\u003eData processing and Analysis\u003c/h3\u003e\n\u003cp\u003eThis study involved both descriptive and inferential statistical analysis. The dependent variable in the study had a dichotomous nature, with the absence of PMS and the presence of PMS. For the categorical independent variable, the Chi-square test was applied to the variable that has a minimum expected cell frequency of 5 or greater than 5 but Fisher\u0026rsquo;s exact test was applied instead of Yate\u0026rsquo;s correction of Chi-square when the minimum expected cell frequency is less than 5. For the continuous independent variables, independent t-test is not applied due to the non-normality of the data therefore a non-parametric test Mann-Whitney U test was applied. In multivariate analysis, the nature of data is ordinal in nature but while using ordinal logistic regression, the data doesn\u0026rsquo;t follow proportional odds (parallel line) assumption which is an important assumption of ordinal logistic regression model therefore the idea to use ordinal logistic regression was dropped. After violating the assumption of ordinal logistic regression model multinomial regression was not applied due to the less sample size existing in the category of dependent variable. To overcome these problems, ordinal variable was converted into binary and hence multiple binary logistic regression was applied. Hosmer and Lemeshow test was applied for the goodness of fit of the model. The level of significance in this study was 5%. Data was analyzed using SPSS version 20 and R programming version 4.3.1.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eOut of 285 females, the PMS was reported in 114 (40%) in the IOST, T.U. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the physical symptoms experienced by respondents during their premenstrual phase. Among the 285 participants, 56.49% reported joint or muscle pain, 45.26% felt breast tenderness, 44.91% reported abdominal pain, 40.35% experienced acne flare-ups, 35.09% felt fatigued, 23.16% had headaches, 17.54% dealt with constipation/diarrhea, 11.93% experienced heaviness and 12.63% reported various other symptoms such as mood swings, back pain, vomiting, and being short-tempered.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe descriptive statistics in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show that 82.8% of respondents were single, and 90.9% of the participants were unemployed. The education qualification of the majority of the respondent\u0026rsquo;s mothers was primary (31.6%) whereas only 10.5% were illiterate. Most of the participant\u0026rsquo;s mothers were housewives. 29.7% of respondent\u0026rsquo;s family\u0026rsquo;s monthly income was above 50000 and 34.6% of the respondents had personal monthly expenditures between 5000\u0026ndash;10000. 68.8% of respondents had normal menstrual flow whereas only 1.4% had a heavy flow of menstrual bleeding. 64.4% of respondents experienced pain during menstruation i.e. dysmenorrhea, 95.5% did not use any kind of contraceptive methods and 87% of respondents did not consume menstrual delaying medicine. While moving to tea/coffee consumption 26% of the respondents did not consume tea or coffee and 34.7% consumed it daily. About 96.1% of the respondents did not consume alcohol and 96.5% of respondents did not smoke. The majority of the respondents 59.6% exercised sometimes and 49.1% meditated sometimes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of the Categorical Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal Female\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStatus of PMS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Females n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114(40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDemographic Variables\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMarital Status, n (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236(82.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148(86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88(77.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(21.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-economic variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259(90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154(90.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105(92.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation status of Mother, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(30.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(31.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82(28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(31.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation of Mother, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouse Wife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213(74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131(76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82(71.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeacher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(8.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment Employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Income (Monthly) \u003csup\u003e(\u003c/sup\u003eᶬ\u003csup\u003e)\u003c/sup\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20000\u0026ndash;30000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(21.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30000\u0026ndash;40000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(26.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40000\u0026ndash;50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(14.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(33.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal Expenditure (Monthly) \u003csup\u003e(\u003c/sup\u003eᶬ\u003csup\u003e)\u003c/sup\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(17.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5000\u0026ndash;10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(34.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10000\u0026ndash;15000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(25.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15000\u0026ndash;20000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(13.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual Health-Related Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual Flow, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(21.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196(68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109(63.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87(76.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysmenorrhea, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(21.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184(64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94(55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90(78.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContraceptive Use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272 (95.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164(95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108(94.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual Delaying Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248(87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165(96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83(72.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(27.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea/coffee Consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74(26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(21.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(29.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(37.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274(96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168(98.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106(93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275(96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165(96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110(96.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise/Workout, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170(59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103(60.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67(58.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(21.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeditation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(35.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140(49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82(48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58(50.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(11.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197(69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144(84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(46.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(25.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMissing value\u003csup\u003e(\u003c/sup\u003eᶬ\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean age and weight of the respondents were found to be 24.97 years and 52.74 kg with respective standard errors of mean 0.088 years and 0.434 kg. The mean of working hours was 3.23 hours with a standard error of 0.21 hours. The average age at menarche was 12.97 years with the standard error 0.077 years. The mean menstrual cycle was 29.49 days with a standard error of 0.18 days. The average number of pads used by the respondents per day was 3.29 pads with a standard error of 0.07. The average number of days of bleeds last during menstruation was 4.91 days with a standard error of 0.077 days. The mean sleeping hours was 7.47 hours in a day with a standard error of 0.066 hours which is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of the Continuous Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal Female\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStatus of PMS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years) Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (Kgs.) Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking Hours\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche(years)\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.61 \u0026plusmn; 0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emenstrual cycle (days)\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumbers of pads use per day\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBleeds lasts (days)\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esleeping hours Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBivariate Analysis\u003c/h2\u003e \u003cp\u003eIn the bivariate analysis, the age at menarche, family income, menstrual flow, dysmenorrhea, menstrual delaying medicine, alcohol consumption, and stress were found to be significant at a 5% level of significance whereas other variables such as marital status, employment status, occupation of mother, education qualification of mother, personal expenditure, contraceptive use, tea/coffee consumption, smoking habit, exercise/workout, meditation, age of the respondents, the weight of the respondents, working hours, menstrual cycle, number of pads used per day, bleeds last and sleeping hours in a day were found to be insignificant at the same 5% level of significant, which is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of Significant Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche(Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.030 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysmenorrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Income (Monthly)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.029 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumption of Menstrual Delaying Medicine (within 1 year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual Flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0001 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChi-square Test(\u003csup\u003e*\u003c/sup\u003e), Fisher exact Test(\u003csup\u003e**\u003c/sup\u003e), Mann-Whitney U test (\u003csup\u003e***\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the multiple binary logistic regression only four variables age at menarche [OR\u0026thinsp;=\u0026thinsp;0.689, 95% CI = (0.544, 0.872)], respondent with dysmenorrhea [OR\u0026thinsp;=\u0026thinsp;2.032, 95% CI = (1.073, 3.847)], respondents who consume menstrual delaying medicine [OR\u0026thinsp;=\u0026thinsp;11.015, 95% CI = (4.075, 29.774)], and respondents having mild stress [OR\u0026thinsp;=\u0026thinsp;2.713, 95% CI = (1.174, 6.271)], moderate stress [OR\u0026thinsp;=\u0026thinsp;3.934, 95% CI = (1.488, 10.399)], severe stress [OR\u0026thinsp;=\u0026thinsp;19.267, 95% CI = (5.343, 69.474)] were found to be significant at a 5% level of significance. It states that when one year increases in the age at menarche the chances of occurrence of PMS is decreased by 0.371 years. It was found that the likelihood of experiencing PMS was 2.032 times higher in respondents who experienced dysmenorrhea compared to those who did not. This suggests that dysmenorrhea is moderately associated with an increased risk of developing PMS. Similarly, the chances of occurrence of PMS was found to be 11.015 times greater in those respondents who consumed menstrual delaying medicine than those who did not consume it. The chances of occurrence of PMS was found to be 2.713 times greater in respondents who had mild stress, 3.934 times greater in those who had moderate stress, and 19.267 times greater in those who had severe stress than those with normal stress which is shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Analysis of PMS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΒ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% C.I. for Odds Ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDysmenorrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u0026reg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMenstrual delaying medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u0026reg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u0026reg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u0026reg; denotes the reference group\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate the factors associated with premenstrual syndrome (PMS) among female students at the Institute of Science and Technology, Tribhuvan University (IOST, T.U.). The results showed that the prevalence of PMS was 40%, which is similar to a study conducted on working women in various sectors (such as healthcare workers, advocates, bankers, teachers, and engineers) in Kochi city, South India (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) However, a study at KIST Medical College in Lalitpur reported a higher prevalence of PMS, possibly due to differences in the methods used to measure PMS (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The study found that participants who reported dysmenorrhea had a higher likelihood of experiencing PMS compared to those who did not report dysmenorrhea. This finding is consistent with a study conducted at Lumbini Medical College and Teaching Hospital in Palpa (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and another study among secondary-level students in Arba Minch town, Ethiopia, which also linked dysmenorrhea with PMS (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In our cultural context, women often avoid participating in festivals or rituals during menstruation due to traditional beliefs. This has led to the misconception that menstruation should be \"delayed\" when it coincides with a festival or event. As a result, some women use menstrual-delaying medication, which can lead to symptoms like weight gain, mood changes, and headaches during the luteal phase. These symptoms are similar to those experienced during PMS, and are thought to be side effects of hormonal contraceptives, which also delay menstruation and contribute to PMS (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, in this study, the use of contraceptives did not show a significant association with PMS, possibly due to limited data on contraceptive use. The study also highlighted that the use of medications like norethisterone and dydrogesterone to delay menstruation could worsen PMS symptoms due to hormonal changes. These medications affect progesterone levels, which are crucial in managing mood swings and physical discomfort associated with PMS. Norethisterone, in particular, has been linked to emotional distress and irritability due to hormonal fluctuations, and altering the menstrual cycle with these medications can disrupt the balance of estrogen and progesterone, intensifying PMS symptoms (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The study found a strong association between stress (mild, moderate, and severe) and the likelihood of PMS, which is also consistent with a study conducted at the Faculty of Public Health, Airlangga University in Indonesia (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The higher association between stress and PMS may be due to confounding factors, such as academic stress, workload, financial problems, relationship issues, health concerns, and other internal factors of stress. However, detailed information on these confounding factors was not explored in this study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of the study show that the most common physical symptoms of PMS among the female students of IOST, T.U. are joint or muscle pain (56.49%), breast tenderness (45.26%), abdominal pain (44.91%), acne flare-ups (40.35%), and fatigue (35.09%). The overall prevalence of PMS was 40% among the 285 female respondents. The study identified several factors associated with PMS, including stress, the use of menstrual-delaying medication, dysmenorrhea, and the age at which menstruation began. The findings suggest that higher stress levels increase the likelihood of experiencing PMS. Therefore, it is recommended that women manage their stress through medication or by finding ways to divert their minds from stressful situations.This study did not address dietary factors or the specific treatment approaches for PMS. For future research, it is suggested that the consumption patterns of menstrual-delaying medication be explored in greater detail, with the support of expert clinicians.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe study was conducted without a dedicated clinician to assess the clinical aspects, and detailed information about the use of menstrual delay medicine was not gathered. Additionally, dietary factors like food intake and nutrition were not considered in the study. The data was entirely based on the respondents' perceptions, which means there is a possibility of recall bias affecting the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACOG \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmerican College of Obstetricians and Gynecologists\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC.I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDASS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDepression, Anxiety and Stress Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD.F.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDegree of Freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDSM-V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiagnostic and Statistical Manual of Mental Disorder Edition fifth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIOST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInstitute of Science and Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePolycystic Ovarian Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePolycystic ovary syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePMDD \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePremenstrual Dysphoric Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePMS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePremenstrual Syndrome (Symptoms)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePSST \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePremenstrual Symptom Screening Tool\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT.U. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTribhuvan University\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI want to express gratitude to all the faculties of the Central Departments of Statistics (CDS), Tribhuvan University, for their helpful feedback and useful advice during this article, all the respondents who provide data and the Kathmandu Center for Research and Education (KCRE) for providing a grant for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Author\u0026apos;s Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSrisa Rijal prepared this article under the supervision of Prof. Dr. Gauri Shrestha, who guided the research, data analysis, and manuscript preparation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kathmandu Center for Research and Education (KCRE) provides funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe actual data used in this study are provided by the correspondence author in need.\u003c/p\u003e\n\u003cp\u003eEthical approval and consent\u003c/p\u003e\n\u003cp\u003eIn accordance with the declaration of the Institutional Review Committee of the Institute of Science and Technology, this study was approved as it maintained ethical principles set by the Nepal Health Research Council. The approved registration number is IRCIOST-24-0002. After obtaining ethical approval from the Institutional Review Committee, a written consent form and questionnaire were provided to each participant. Each participant signed a consent form before filling out the questionnaire. The information of participants was kept confidential.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eCompeting interest\u003c/p\u003e\n\u003cp\u003eNo competing financial interests exist.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWoods NF. Women\u0026rsquo;s health: The menstrual cycle. Premenstrual symptoms: Another look. Public Health Rep. 1987;102(4 SUPPL. JULY/AUG.):106\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eManandhar SA, Pramanik T, Amatya M, Silvanus V. Effect of Premenstrual Stress on Reaction Time among Medical and Dental Students of a Medical College in Kathmandu, Nepal. Nepal Med Coll J. 2022 Dec 23;24(4):283\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eFrackiewicz EJ, Shiovitz TM. Evaluation and management of premenstrual syndrome and premenstrual dysphoric disorder. J Am Pharm Assoc (Wash) [Internet]. 2001;41(3):437\u0026ndash;47. Available from: http://dx.doi.org/10.1016/S1086-5802(16)31257-8\u003c/li\u003e\n\u003cli\u003eKov\u0026aacute;cs Z, Hegyi G, Szőke H. Premenstrual syndrome and premenstrual dysphoric disorder. II. Diagnosis and treatment. Orv Hetil. 2022;163(26):1023\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eSahin S, Ozdemir K, Unsa A. Evaluation of premenstrual syndrome and quality of life in university students [Internet]. Article in Journal of the Pakistan Medical Association. 2014. Available from: https://www.researchgate.net/publication/266151457\u003c/li\u003e\n\u003cli\u003eDirekvand-Moghadam A, Sayehmiri K, Delpisheh A, Satar K. Epidemiology of premenstrual syndrome, a systematic review and meta-analysis study. J Clin Diagnostic Res. 2014;8(2):106\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003ePoudel E, Bist A, K. C. A. Knowledge, attitude, experience and management on premenstrual syndrome among secondary level adolescence girl students of Kirtipur Municipality, Kathmandu, Nepal. Int J Sci Reports. 2023;9(5):137\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003eChen Y, Yang X, Li X, Wei X, Bai L. Factors associated with premenstrual syndrome of emergency nurse: A multicenter study in China. Gynecol Obstet Clin Med [Internet]. 2022;2(4):199\u0026ndash;202. Available from: https://doi.org/10.1016/j.gocm.2022.10.007\u003c/li\u003e\n\u003cli\u003eRezende APR, Alvarenga FR, Ramos M, Franken DL, Dias Da Costa JS, Pattussi MP, et al. Prevalence of Premenstrual Syndrome and Associated Factors among Academics of a University in Midwest Brazil. Rev Bras Ginecol e Obstet. 2022;44(2):133\u0026ndash;41. \u003c/li\u003e\n\u003cli\u003eFarahmand M, Ramezani Tehrani F, Khalili D, Amin G, Negarandeh R. Factors associated with the severity of premenstrual syndrome among Iranian college students. J Obstet Gynaecol Res. 2017;43(11):1726\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eNgo VDT, Bui LP, Hoang LB, Tran MTT, Nguyen HVQ, Tran LM, et al. Associated factors with Premenstrual syndrome and Premenstrual dysphoric disorder among female medical students: A cross-sectional study. PLoS One [Internet]. 2023;18(1 January):1\u0026ndash;19. Available from: http://dx.doi.org/10.1371/journal.pone.0278702\u003c/li\u003e\n\u003cli\u003eShrestha DB, Shrestha S, Dangol D, Aryal, Barun B, Shrestha S, Sapkota B, et al. Premenstrual Syndrome in Students of a Teaching Hospital. J Nepal Health Res Counc. 2019 Aug 4;17(2):253\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eMahat A, Dhungana R, Amatya A. Pre-menstrual Syndrome and Pre-menstrual Dysphoric Disorder in Female Medical Students of Nepal. Kathmandu Univ Med J. 2023;21(1):46\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eSteiner M, Macdougall M, Brown E. The premenstrual symptoms screening tool (PSST) for clinicians. Arch Womens Ment Health. 2003 Aug;6(3):203\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003ea guide to depression-anxiety-and-stress-scale-dass21.pdf. \u003c/li\u003e\n\u003cli\u003eMaheshwari P, Bindu Menon AJ, Bhaskaran R. Indian nursing students\u0026rsquo; attitudes toward mental illness and persons with mental illness. Ind Psychiatry J. 2023;(32):255-2\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eBhandari S, Dwa Y, Maharjan M, Maskey S, Thakur M, Sharma S. Premenstrual Syndrome among Medical Students of a Medical college: A Descriptive Cross-sectional Study. J Nepal Med Assoc. 2023 Apr 1;61(260):347\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eAryal S, Thapa B, Pant SB. Premenstrual Syndrome and Premenstrual Dysphoric Disorder in Medical and Nursing Students of a Tertiary Care Teaching Hospital in Nepal. Nepal J Obstet Gynaecol. 2018 Jan 15;12(1):12\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eAli M, Dejene Y, Gultie T, Gebreselassie R, Hailu M, Kebede A. Premenstrual Syndrome and Associated Factors among Students in Secondary Schools in Arba Minch Town , Southern Ethiopia : A Cross - Sectional Study , 2021. 2023;(4):23\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eMartin D, Sale C, Cooper SB ESKI. atric Exercise Science. The article appears here in its accepted, peer-re-viewed form, as it was provided by the submitting author. It has not been copyedited, proofread, or formatted by the publisher. Jsep [Internet]. 2017;28:588\u0026ndash;95. Available from: https://research.rug.nl/files/30892751/Den_Hartigh_et_al._Short_and_long_term_PM_JSEP_accepted_version_Pure.pdf\u003c/li\u003e\n\u003cli\u003eAndani RW. Relationship Between Degree of Stress and Physical Activity of Female Students With Premenstrual Syndrome. J Berk Epidemiol. 2020;8(2):125. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multiple Binary Logistic Regression, Physical symptoms of PMS, Premenstrual Symptoms, Proportion of PMS","lastPublishedDoi":"10.21203/rs.3.rs-5115166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5115166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePremenstrual Symptoms refer to the psychological, physical, and behavioral symptoms that recur during the luteal phase of menstruation, impacting the day-to-day activities of females. However, there is limited information on the prevalence and factors associated with Premenstrual Symptoms among graduate-level female students at the Institute of Science and Technology, Tribhuvan University.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study employed institutional-based cross-sectional research methods. Data were gathered from all female participants of Institute of Science and Technology, Tribhuvan University and only 285 female participants fulfill the inclusion criteria. The data was collected using a self-administered questionnaire for demographic, socio-economic, behavioral and menstrual-health related factors and to measure the Premenstrual Syndrome a standard tool Premenstrual Symptoms Screening Tool (PSST) was used. Factors associated with PMS were analyzed using Multiple Binary Logistic Regression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResult\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong the 285 respondents, 40% reported experiencing Premenstrual Symptoms. The most common physical symptoms were joint or muscle pain (56.49%), breast tenderness (45.26%), abdominal pain (44.91%), acne flare-ups (40.35%), and fatigue (35.09%). The factors such as age at menarche [OR\u0026thinsp;=\u0026thinsp;0.689, 95% CI = (0.544, 0.872)], respondents with dysmenorrhea [OR\u0026thinsp;=\u0026thinsp;2.032, 95% CI = (1.073, 3.847)], respondents who consume menstrual delaying medicine [OR\u0026thinsp;=\u0026thinsp;11.015, 95% CI = (4.075, 29.774)] and respondents having mild stress [OR\u0026thinsp;=\u0026thinsp;2.713, 95% CI = (1.174, 6.271)], moderate stress [OR\u0026thinsp;=\u0026thinsp;3.934, 95% CI = (1.488, 10.399)], severe stress [OR\u0026thinsp;=\u0026thinsp;19.267, 95% CI = (5.343, 69.474)] were found to be significant at a 5% level of significance with the Premenstrual Symptoms.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe factors associated with Premenstrual Symptoms are Stress, Menstrual delaying medicine, dysmenorrhea, and age at menarche of respondents. The result suggests a potential relationship between menstrual delaying medicine and PMS symptoms.\u003c/p\u003e","manuscriptTitle":"Factors Associated with Premenstrual Symptoms: A Study among Graduate-Level Students of the Institute of Science and Technology, Tribhuvan University","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 06:50:31","doi":"10.21203/rs.3.rs-5115166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-12T00:28:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-03T07:33:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214463077090083266912627012474130231907","date":"2025-03-03T06:53:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-01T10:36:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29206319908749908069639833837676653547","date":"2025-03-01T10:02:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145186289871100813097209748890544464510","date":"2025-02-26T10:01:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-18T19:48:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176484713472419225450134643592073629231","date":"2025-02-18T19:25:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194898593491677029492539238678803329219","date":"2025-02-16T23:58:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-14T10:16:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-23T16:30:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-30T11:00:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-28T08:18:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2024-09-28T08:17:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"947994d4-2828-4efe-9097-650441c0b37c","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:40:30+00:00","versionOfRecord":{"articleIdentity":"rs-5115166","link":"https://doi.org/10.1186/s12905-025-03925-7","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2025-07-28 16:21:01","publishedOnDateReadable":"July 28th, 2025"},"versionCreatedAt":"2025-03-27 06:50:31","video":"","vorDoi":"10.1186/s12905-025-03925-7","vorDoiUrl":"https://doi.org/10.1186/s12905-025-03925-7","workflowStages":[]},"version":"v1","identity":"rs-5115166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5115166","identity":"rs-5115166","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}