--- title: What factors affect how men and women view premenstrual syndrome? description: Education, male gender, and being aged 18-45 are linked to more positive views of PMS. Rural residence is linked to less positive views. However, this is a sm url: https://niyora.com/research/papers/breaking-the-silence-a-community-based-cross-sectional-study-exploring-the-2025/ --- - [Niyora](https://niyora.com/) - [Research](https://niyora.com/research/) - [Library](https://niyora.com/research/library/) - What factors affect how men and women view premenstrual syndrome? Survey study, 2025 # What factors affect how men and women view premenstrual syndrome? Education, male gender, and being aged 18-45 are linked to more positive views of PMS. Rural residence is linked to less positive views. However, this is a small study in one district of India. Early or limited evidence 281 adults (male and female) in Perambalur District, Tamil Nadu, India, mean age 32.21 years ## What they did Community-based cross-sectional study of 281 adults in Perambalur District, Tamil Nadu, India, conducted from August to October 2024. Participants completed a pre-tested structured questionnaire about their attitudes toward PMS. Researchers used multivariable linear regression to identify independent predictors of attitude score. ## What they found The mean attitude score was 7.12 (±2.17). Positive predictors of higher attitude scores included age groups 18-45 years, male gender (B=0.77), and having school education (B=0.74). Rural area of residence had a negative impact on attitude score (B=-0.53). ## What it means for you Education and male gender are linked to more positive views of PMS, while rural residence is associated with less positive views. The main limitation is that this study was in one specific district, so findings may not apply to other regions or countries. 281 adult participants 7.12 mean attitude score 0.77 attitude score increase for males versus females ## About the study Original title Breaking the Silence: A Community-Based Cross-Sectional Study Exploring the Attitude Towards Premenstrual Syndrome Among Adults in Perambalur District of Tamil Nadu, India Authors Anand V V, Mohandas NV, Swarna CB et al. Journal Cureus Year 2025 Study design Survey study Participants 281 What was tested None Open access Yes Read the paper [doi.org/10.7759/cureus.76847](https://doi.org/10.7759/cureus.76847) Original abstract Background Menstruation is a natural process crucial for female reproductive health. Premenstrual syndrome (PMS) is characterized by physical and emotional symptoms occurring before menstruation. Social and cultural factors significantly influence menstrual attitudes and PMS perception. Cultural norms and a lack of confidence during this phase may impede women's ability to lead innovation. This is where men can play a crucial role in driving change by actively engaging in menstrual health advocacy. By challenging restrictive practices and negative perceptions surrounding menstruation, men can contribute to better menstrual hygiene management. The objective of this study was to determine the independent predictors of attitude towards PMS among both male and female adult populations in Perambalur district, Tamil Nadu, India. Materials and methods A community-based cross-sectional study was conducted from August 2024 to October 2024. A pre-tested, structured questionnaire was utilized to assess the attitude towards PMS. The questions on attitude towards PMS were scored as per the recommendation of an expert committee. The data were entered into Microsoft Excel, numerically coded, and analyzed using IBM SPSS version 26. Descriptive statistics was used to characterize the study participants and was expressed in frequencies, percentages, and mean (±standard deviation (SD)). Independent samples t-test and one-way ANOVA were used to determine the significant difference between the mean attitude score and the independent variables. A multivariable linear regression model was used to determine the independent predictors of attitude score. A p-value <0.05 was considered statistically significant. Results The study included a total of 281 study participants. The mean (±SD) age of the study participants' age was 32.21 (±8.64) years. The mean (±SD) attitude score among the study participants was 7.12 (±2.17). The independent predictors, which had a positive impact on attitude score, were as follows: (1) age groups (18-25 years, 26-35 years, and 36-45 years), (2) male gender (B = 0.77; 95% CI 0.28-1.27), (3) school education (B = 0.74; 95% CI 0.21-1.28). The independent predictor, which had a negative impact on attitude scores, was a rural area of residence (B = -0.53; 95% CI -1.07 to -0.26). Conclusion The attitude towards PMS is multifaceted. Interventions should address the gaps through multi-pronged approaches, including education, community awareness, and policy changes, while also considering broader social and environmental factors impacting menstrual health. Reproduced under CC-BY. ## Topics [Premenstrual syndrome (PMS)](https://niyora.com/research/topics/condition-pms/)[Menstrual cycle and mood](https://niyora.com/research/topics/condition-cycle-mood/)[Dysmenorrhea and period pain](https://niyora.com/research/topics/condition-dysmenorrhea/)[Stigma, attitudes and culture](https://niyora.com/research/topics/life-area-stigma-culture/) Research tells you what helps on average. Niyora helps on the day it hits. Calm down, see it another way, and decide what to say, with a PMS and PMDD app built by a doctor. ## Related studies Survey study, 2023 What factors make premenstrual syndrome worse in nursing students? Survey study, 2022 Do female athletes get enough knowledge and support about their period? Survey study, 2011 How common is PMS in working women, and do they know about it? Survey study, 2006 What menstrual problems do adolescent girls experience? Study, 1995 Do women who exercise have fewer PMS symptoms? Survey study, 2025 How do menstrual health and mental health affect each other? Ask AI about this page Copy prompt ChatGPT ↗ Claude ↗ Perplexity ↗ Google AI Mode ↗ Grok ↗