Association of Stress Levels and Sleep Quality with Menstrual Cycle Disorders among Reproductive-Age Indonesian Female Migrant Workers
DOI:
https://doi.org/10.32807/jkp.v21i2.2021Keywords:
Stress level, Sleep quality, Menstrual cycle disorders, Indonesian migrant workers, Kaigo, JapanAbstract
Female migrant workers in the kaigo sector in Japan experience occupational demands that may affect sleep quality, stress levels, and menstrual health. This study aimed to analyze the associations of stress levels and sleep quality with menstrual cycle disorders among reproductive-age Indonesian female migrant workers in Gunma Prefecture, Japan. An observational analytic study with a cross-sectional design was conducted involving 50 female migrant workers aged 18–35 years selected through purposive sampling. Stress levels were measured using the Perceived Stress Scale (PSS-10), sleep quality using the Pittsburgh Sleep Quality Index (PSQI), and menstrual cycle disorders according to the International Federation of Gynecology and Obstetrics (FIGO) classification. Data were analyzed using the Chi-square test or Fisher-Freeman-Halton exact test, as appropriate, with statistical significance set at p<0.05. Most respondents experienced menstrual cycle disorders (68.0%), poor sleep quality (64.0%), and moderate stress levels (48.0%). Sleep quality was significantly associated with menstrual cycle disorders (p<0.001). Compared with good sleep quality, fair sleep quality was associated with higher odds of menstrual cycle disorders (OR=4.67, 95% CI: 0.40–53.95), while poor sleep quality showed substantially higher odds (OR=67.67, 95% CI: 6.08–752.66). Stress levels were also significantly associated with menstrual cycle disorders (p=0.034), although sparse data across stress categories limited the stability of estimates. These findings suggest that stress and sleep quality may be associated with menstrual cycle disorders among Indonesian female migrant workers in the kaigo sector. However, the results should be interpreted cautiously because of the small sample size, sparse data, and potential unmeasured confounding. Larger longitudinal studies are needed to clarify temporal relationships and potential causal mechanisms.
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