Evidence map›Paper›PMID 39929936›Full record

ArticleScientific reports2025

The prevalence and associated risk factors of primary dysmenorrhea among women in Beijing: a cross-sectional study.

Yi-Ling Wang, Hong-Lan Zhu

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yi-Ling WangDepartment of Obstetrics and Gynecology, Peking University People's Hospital, Peking University, Beijing, 100044, China.
Hong-Lan ZhuDepartment of Obstetrics and Gynecology, Peking University People's Hospital, Peking University, Beijing, 100044, China. honglanzhu01@163.com.

Funding

Beijing Municipal Commission of Education 7222206
6 · The paper itself

Abstract

Primary dysmenorrhea is a common and troublesome gynecological condition. The disease seriously affects the daily life of young and middle-aged women and is a major public health problem. The aim of the research was to conduct an assessment of the prevalence of primary dysmenorrhea and associated factors in Beijing area, and to describe and compare the association between different lifestyles and the degree of dysmenorrhea. We conducted a cross-sectional study at Peking University People's Hospital in the form of an online questionnaire. Out of the total of 516 women who took part in the study, 450 were included in the analysis. The questionnaire consisted of 39 questions that were grouped into three parts. The first concerned characteristics of the study subjects such as age, weight and qualifications. The second part of the questionnaire pertained to the menstruation-related conditions, such as age at menarche, knowledge of menstruation, menstrual cycle, menstrual period, menstrual flow, and so on. In the third part, the women were asked about their lifestyle habits and emotional factors. Dysmenorrhea affected 86.4% of the interviewed women. Significant factors associated with dysmenorrhea were age of menarche (p < 0.001), body mass index (p = 0.023), smoking (p ˂0.001), eating sweet foods frequently (p = 0.042), sedentary time (p = 0.037) and self-esteem (p = 0.043). Chi-square test further found a statistically significant correlation between BMI (p = 0.032), alcohol consumption (p = 0.046), sleep duration (p = 0.006) and the severity of dysmenorrhea. This study shows that the prevalence of dysmenorrhea is high enough for medical workers and public health administrators to pay attention to it. Women with dysmenorrhea were characterized with a family history of dysmenorrhea, early age of menarche, frequent smoking, eating sweet foods frequently, sedentary time, abnormal BMI and low self-esteem. We suggest that it is necessary to pay attention to the related factors of dysmenorrhea.

Indexed as

DysmenorrheaAdolescentAdultBeijingBody Mass IndexCross-Sectional StudiesFemaleHumansLife StyleMenarcheMiddle AgedPrevalenceRisk FactorsSurveys and QuestionnairesYoung AdultDysmenorrheaLifestyleMenstruationSedentary timeSelf-esteem

Identifiers

PMID39929936
PMCPMC11811143

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.