Effects of dysmenorrhea on work productivity and quality of life in Japanese women: A large-scale web-based cross-sectional study.

Dysmenorrhea, defined as intense abdominal pain occurring immediately before and during menstruation, has a prevalence of 67%–90% [ 1 ]. Dysmenorrhea is common in women of reproductive age [ 2 , 3 ]. This age group corresponds to the prime working years, and the overall impact of menstrual symptoms on work productivity is substantial. The annual economic burden of menstrual symptoms for Japanese women is estimated at USD 8.6 billion [ 4 ], indicating the necessity for effective interventions. Previous studies have reported that dysmenorrhea negatively impacts work productivity [ 5 – 7 ]. Additionally, its impact on quality of life (QoL) has been investigated in previous studies [ 8 – 10 ]. However, many previous studies have defined dysmenorrhea based on the self-reported presence of pain during menstruation or by using pain severity assessments, such as the Visual Analog Scale (VAS) or Numerical Rating Scale (NRS), through interviews [ 1 ], making it difficult to compare the results between different studies. Moreover, many Japanese women reported perceiving pain during menstruation as “natural,” and 46.8% cited “not feeling the need” as their reason for not seeking medical care [ 11 ]. Therefore, self-reported surveys of Japanese women may have underestimated the true prevalence of dysmenorrhea. Moreover, 94.8% Japanese women reported using analgesics for menstrual pain [ 11 ], suggesting that their self-assessment of pain severity may have been underestimated by the regular use of analgesics. The dysmenorrhea score [ 12 ], calculated as the sum of the pain and drug scores, can assess the severity of pain that individuals may not be consciously aware of, enabling a more appropriate evaluation of the disease. In previous studies, the dysmenorrhea score has been widely used in clinical trials in Japan for evaluating treatment efficacy [ 12 – 14 ]. However, few studies have assessed the validity of using dysmenorrhea scores to identify populations in untreated groups that require therapeutic intervention. Therefore, we aimed to analyze the impact of dysmenorrhea on work productivity and QoL using the dysmenorrhea score as well as the validity of using this score to assess the severity and need for therapeutic intervention in untreated populations.

In total, 2,974 and 3,677 women answered the first and second questionnaires, respectively. The total number of merged IDs was 3,723. After excluding women who were not menstruating and those who were already receiving hormonal therapy, data from 2,555 women were used in the final analysis. Among the participants who answered the WPAI-GH questionnaire and whose absenteeism data were available, 366/2,096 (17.5%) (95% confidence interval [CI]: 15.8% to 19.1%) experienced absenteeism due to health reasons. Among participants whose presenteeism data were available, 1,311/2,040 (64.3% [95% CI: 62.2% to 66.3%]) experienced presenteeism. Table 1 presents the descriptive statistics of the respondents’ demographics. A total of 2,064 women completed the dysmenorrhea questionnaire. The severe group, based on the dysmenorrhea score, consisted of 902 participants (43.7%), excluding those with missing scores. Younger participants tended to have a higher prevalence in the severe group. Approximately 70% of participants were office workers. Based on the dysmenorrhea score, the proportion of participants with asthma and allergies was significantly higher in the severe group than in the normal group. 1 Median (Q1, Q3). 2 Chi-squared test and Wilcoxon rank sum test. t-test for BMI. Abbreviations: HT, hypertension; DL, dyslipidemia; DM, diabetes mellitus. Table 2 shows the median values of each WPAI-GH indicator and summary scores of SF-36v2. Absenteeism, presenteeism, overall work impairment, and activity impairment in the WPAI-GH group were significantly higher than those in the severe group ( Fig 1A - 1D ). Regarding HRQoL, the scores for all three components of the SF-36v2, namely the PCS, MCS, and RCS, were significantly lower in the severe group than in the normal group ( Fig 1E - 1G ). A, absenteeism; B, presenteeism; C, overall work impairment; D, activity impairment; E, physical component summary (PCS); F, mental component summary (MCS); G, role/social component summary (RCS) scores. Statistical significance was determined using Wilcoxon rank-sum test. Asterisks (*) indicate statistically significant differences (p < 0.05). In the multiple regression analysis, even after adjusting cofounding factors, the severity of the dysmenorrhea score was significant factor of impaired absenteeism (beta [b]=2.14 [95% CI: 0.66 to 3.62]), presenteeism (b = 9.07 [95% CI: 6.92 to 11.22]), overall work impairment (b = 9.52 [95%CI: 7.24–11.80]) and activity impairment (b = 10.47 [95% CI: 7.24 to 11.80]) ( Fig 2A - 2D ). In addition, the absence of comorbidities (b = −3.04 [95%CI: −5.21 to −0.88]) was positively associated with absenteeism. Comorbidity with heart disease was slightly associated with greater impairment in presenteeism, overall work impairment, and activity impairment compared to no heart disease. Forest plots show point estimates of beta coefficients and 95% confidence intervals (CIs) for each variable in the multiple regression models. A, absenteeism; B, presenteeism; C, overall work impairment; D, activity impairment; E, physical component summary (PCS); F, mental component summary (MCS); G, role/social component summary (RCS) scores. Interestingly, low annual household income was associated with impaired absenteeism (b = 3.71 [95% CI: 1.29 to 6.13]), presenteeism (b = 3.51 [95% CI: 0.10 to 7.12]), overall work impairment (b = 4.42 [95% CI: 0.70 to 8.14]) and activity impairment (b = 3.00 [95% CI: 0.70 to 8.14]). Living in urban areas was significantly associated with greater impairment in presenteeism (b = 3.34 [95% CI: 1.16 to 5.52]), overall work impairment (b = 3.33 [95% CI: 1.02 to 5.64]), and activity impairment (b = 2.85 [95% CI: 1.02 to 5.64]) compared to living in rural areas. The severe dysmenorrhea score was significantly associated with reduced PCS (b = −2.76 [95% CI: −3.42 to −2.09]), MCS (b = −3.96 [95% CI: −4.77 to −3.16]), and RCS (b = −1.08 [95% CI: −1.98 to −0.17]). Absence of comorbidities was positively associated with higher physical QoL (b = 2.05 [95% CI: 1.07 to 3.02]) and higher mental QoL (b = 1.24 [95% CI: 0.06 to 2.42]). Asthma was associated with lower PCS scores, whereas heart disease was associated with lower MCS scores. Interestingly, low income was a significant factor for reduced MCS (b = −2.66 [95% CI: −3.99 to −1.33]) and RCS (b = −1.89 [95% CI: −3.38 to −0.40]) ( Fig 2E - 2G ).

Severe dysmenorrhea, defined as a dysmenorrhea score of ≥3, was associated with impaired work productivity and reduced QoL. QoL can be affected not only physically, but also mentally or socially by dysmenorrhea. Screening at a threshold of three points for the dysmenorrhea score and providing hormone therapy and/or workplace adjustments to those who meet this criterion may contribute to improvements in work productivity and QoL.

This study had several limitations. First, as this was a cross-sectional study, causality could not be established. Therefore, whether improvements in dysmenorrhea would truly contribute to improved work productivity or QoL remains unclear. Second, because the responses were based on patient recall, recall bias may have occurred; that is, individuals with more severe symptoms may be more likely to recall their degree of work impairment. This could result in those able to recall more conditions also reporting higher dysmenorrhea scores and greater impairment in QoL and work productivity, leading to a possible spurious correlation. Furthermore, since comorbidities were self-reported by the patients, the accuracy of these responses may be limited, and the severity or control status of the comorbidities could not be determined. For example, severe dysmenorrhea may be accompanied by severe anemia, which itself can negatively affect QoL and work productivity; if anemia is underreported, residual confounding may remain even after multivariate analysis. In addition, since recruitment for research participation was conducted through voluntary enrollment via smartphone app, there is a potential for selection bias among the participants. Specifically, individuals with higher health awareness and health literacy may be overrepresented. Therefore, caution is needed when generalizing the findings of this study to all Japanese women, and even more so when generalizing to women in other countries.

Participants were recruited via the smartphone application LunaLuna ( https://www.mti.co.jp/eng/?page_id=2755 ), which is an application for recording the menstrual cycle, managing menstrual schedule, predicting ovulation date, and tracking pill use, and is freely available on Android and iOS platforms [ 15 ]. Individuals who provided informed consent to participate in the study were administered two separate questionnaires. The first questionnaire assessed the dysmenorrhea score [ 12 ], while the second collected data on participants’ background factors and the Work Productivity and Activity Impairment Questionnaire: General Health (WPAI-GH) and SF-36v2 health survey responses. Questionnaires were distributed in two separate sessions from June 2022 to August 2022. The study protocol was approved by the Research Ethics Committee of the Graduate School of Medicine, University of Tokyo (ethics approval 2020374NI; approved on March 21, 2021). This study was performed in accordance with the principles of the Declaration of Helsinki. MTI Ltd. anonymized and transferred the data to the Graduate School of Medicine at the University of Tokyo. Inclusion in the study required participants to acknowledge the in-app notifications describing the research outline and data usage policy, followed by the selection of the Agreement button, thereby providing their informed consent electronically. The data used in this study were accessed for research purposes from February 2, 2025 to June 16, 2025. The authors did not have access to information that could identify individual participants during or after data collection; all data were fully anonymized prior to analysis. The dysmenorrhea score [ 12 ] is a composite measure used to assess the severity of menstrual pain and its impact on daily activities and medication use. The total dysmenorrhea score is calculated as the sum of pain and drug scores. Pain score ranges from 0 to 3 points: 0 = No pain 1 = Mild pain (pain is present but does not interfere with daily life) 2 = Moderate pain (pain causes some interference in activities of daily life, but not enough to require rest) 3 = Severe pain (daily life is difficult or rest is required due to pain) And the drug score ranges from 0 to 3 points: 0 = No use 1 = Once per menstrual cycle 2 = Twice per menstrual cycle 3 = Three or more times per menstrual cycle The total score ranges from 0 to 6, and participants with a total score of ≥3 were categorized in the severe group. WPAI-GH [ 16 ] is a self-reported instrument that measures the impact of health problems on work productivity and daily activities over the past 7 days. It comprises four domains: absenteeism, presenteeism, overall work impairment, and activity impairment. Each domain is scored from 0% (no impairment) to 100% (complete impairment). Absenteeism is the percentage of work time missed owing to health issues. Presenteeism is the percentage of work impairment due to health issues. Overall work impairment was calculated using the following formula: Absenteeism + [(1-absenteeism) × presenteeism]. Activity impairment is the percentage of impairment in daily activities outside of work owing to health issues. SF-36v2 [ 17 , 18 ] is a widely used self-administered questionnaire designed to measure general health-related (HR)QoL. The results consist of eight subscales [Physical Functioning (PF), Role Physical (RP), Bodily Pain (BP), General Health (GH), Vitality (VT), Social Functioning (SF), Role Emotional (RE), and Mental Health (MH)] and three summary scores [Physical Component Summary (PCS), Mental Component Summary (MCS), and role/social component summary (RCS)] [ 19 ]. Norm-based scores were used for evaluation; that is, each SF-36 domain and component summary score is standardized against the general Japanese population to have a mean of 50 and a standard deviation of 10. Many factors are associated with the severity of dysmenorrhea, the type of symptoms, work productivity, and HRQoL. Age, BMI, and comorbidities are known to be associated with the severity of dysmenorrhea [ 20 ]. In addition, psychological distress and dysmenorrhea are correlated [ 21 ], and the severity of PMS symptoms is related to the severity of dysmenorrhea [ 20 , 21 ]. Moreover, income and area of residence have been found to be correlated with QoL [ 22 , 23 ]. Therefore, to adjust for confounding factors, the following variables were collected using a questionnaire and were included in the model: Age ranged from 20 to 49 years and was included in the model as a continuous variable. BMI: Calculated from height and weight reported in the questionnaire Gravidity Area of residence: Based on the Japanese Resident Register as of 2024, prefectures ranked within the top 10 in population were defined as “urban areas,” while all other prefectures were defined as “rural areas.” Annual household income: 10,000,000 yen in the high-income group. Comorbidities: Participants indicated whether they had no comorbidities or had any of the following diseases: hypertension (HT), dyslipidemia (DL), diabetes mellitus (DM), asthma, allergy, liver disease, heart disease, tuberculosis, autoimmune disease, anemia, stroke, cancer, and other comorbidities. Comorbidities with a low prevalence were excluded from the regression analysis, and HT, DL, DM, asthma, allergy, heart disease, and anemia were included in the model. The two surveys were linked by ID, and if there were duplicate IDs, only the record with the later timestamp was retained. Participants were excluded if they had not yet experienced menarche, were postmenopausal, or were already receiving hormonal therapy, such as low-dose oral contraceptives or intrauterine devices. The WAPI-GH and SF-36v2 values were compared between the severe and normal groups, as defined by the dysmenorrhea score. To compare background factors, continuous variables were analyzed using the Wilcoxon rank-sum test, whereas categorical variables were assessed using Fisher’s exact test. For the multivariate analysis, a multiple regression model was constructed with absenteeism, presenteeism, overall work impairment, activity impairment, PCS, MCS, and RCS as dependent variables. Spearman’s correlation coefficient was used for correlation analysis. R version 4.3.3 [ 24 ] and RStudio 2024.12.0 [ 25 ] were used for statistical analysis, and a p-value <0.05 was considered statistically significant.

The blue curve represents a quartic polynomial fit, and the red dashed lines indicate the positions of the inflection points. For absenteeism, no clear inflection point was found and thus omitted. (JPG)