Tracking mood symptoms across the menstrual cycle in women with depression using ecological momentary assessment and heart rate variability - Glyn Lewis; Joseph F Hayes; Kyra Delray (BMJ Mental Health, 2025)
Background There is limited research on premenstrual exacerbation (PME) of depression. It is unclear how mood and fatigue fluctuate across the menstrual cycle, and whether heart rate variability (HRV) tracks these fluctuations.Objective To determine if there is PME of mood, energy and HRV in depressed women.Methods Cohort study in women with depression, using the mobile health platform, Juli, to track their menstrual cycle, HRV, mood and energy using ecological momentary assessment (EMA). We modelled the relationship between mood, energy, HRV and menstrual cycle with different lag times (0–3 days) using simple polynomial regression. Results are reported as the SD change from the average rating for an individual for each day across the menstrual cycle.Findings Women diagnosed with depression (N=352) tracked their menstrual cycle (≥2 periods), HRV and recorded ≥5 daily mood and energy levels (N=9393 entries). We found a gradual decline in mood beginning at 14 days before menstruation and continuing until 3 days before the next menstruation (β=0.0004, 95% CI 0.0001 to 0.0008, p<0.001). Mood ratings were lowest from 3 days before until 2 days after menstruation; 54.3% (95% CI 48.9% to 59.6%) had a lower mean score during this period than the rest of the cycle. Through the rest of the cycle, participants experienced improvement in mood. Mood rating was associated with HRV on the same day (β=−0.0022, 95% CI −0.0020 to −0.0026, p=0.005) and 1–3 days prior. Energy was not associated with the day of the menstrual cycle.Conclusions There is variation in mood across the menstrual cycle in women with depression, consistent with PME.Clinical implications EMA over two consecutive cycles could be useful for understanding menstrual cycle-related mood changes and diagnostic clarity may lead to alternative treatment and management options.