Dataset Information

Higher Depressive Symptoms in Irregular Menstrual Cycles: Converging Evidence from Cross-Sectional and Prospective Assessments.

ABSTRACT:

Background

Menstrual cycle regularity is an important marker of reproductive health and associated with physiological and psychological illnesses, as well as experiencing stress. We hypothesized that individuals with irregular menstrual cycles report higher depressive symptom severity, after controlling for stress occurrence.

Methods

The hypothesis was examined through two measurement approaches: a cross-sectional and a prospective, longitudinal study. In the cross-sectional study, participants (n = 394) reported depressive symptoms and their overall menstrual cycle regularity. In the longitudinal study, participants (n = 77) completed questionnaires on depressive symptoms and stress during the mid-follicular and periovulatory phase of one menstrual cycle. Depressive symptoms were compared between participants with regular and irregular cycles through a Welch t test and an ANCOVA.

Results

Participants with irregular menstrual cycles reported more depressive symptoms in the cross-sectional analysis. Similarly, in the longitudinal analysis, the group with a current irregular menstrual cycle reported more depressive symptoms after controlling for stress occurrence. When including only complete data sets without multiple imputation (n = 52), the direction of the effects remained but did not reach statistical significance.

Conclusions

The results indicate an association between depressive symptoms and menstrual cycle irregularity. Limitations were that although we investigated the menstrual cycle prospectively, it would have been more precise to include two or more cycles and daily sex hormone measurements. Further limitations were the suboptimal statistical power and the data collection during the COVID pandemic. We give recommendations on how to incorporate the association of depressive symptoms and cycle irregularity in future study designs on women's mental health.

SUBMITTER: Klusmann H

PROVIDER: S-EPMC11326524 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Higher Depressive Symptoms in Irregular Menstrual Cycles: Converging Evidence from Cross-Sectional and Prospective Assessments.

Klusmann Hannah H Kapp Claudia C Engel Sinha S Schumacher Tabea T Bücklein Elise E Knaevelsrud Christine C Schumacher Sarah S

Psychopathology 20240109

<h4>Background</h4>Menstrual cycle regularity is an important marker of reproductive health and associated with physiological and psychological illnesses, as well as experiencing stress. We hypothesized that individuals with irregular menstrual cycles report higher depressive symptom severity, after controlling for stress occurrence.<h4>Methods</h4>The hypothesis was examined through two measurement approaches: a cross-sectional and a prospective, longitudinal study. In the cross-sectional study ...[more]

PMID: 38194941

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Project description:BackgroundDespite its importance to women's reproductive health and its impact on women's daily lives, the menstrual cycle, its regulation, and its impact on health remain poorly understood. As conventional clinical trials rely on infrequent in-person assessments, digital studies with wearable devices enable the collection of longitudinal subjective and objective measures.ObjectiveThe study aims to explore the technical feasibility of collecting combined wearable and digital questionnaire data and its potential for gaining biological insights into the menstrual cycle.MethodsThis prospective observational cohort study was conducted online over 12 weeks. A total of 42 cisgender women were recruited by their local gynecologist in Berlin, Germany, and given a Fitbit Inspire 2 device and access to a study app with digital questionnaires. Statistical analysis included descriptive statistics on user behavior and retention, as well as a comparative analysis of symptoms from the digital questionnaires with metrics from the sensor devices at different phases of the menstrual cycle.ResultsThe average time spent in the study was 63.3 (SD 33.0) days with 9 of the 42 individuals dropping out within 2 weeks of the start of the study. We collected partial data from 114 ovulatory cycles, encompassing 33 participants, and obtained complete data from a total of 50 cycles. Participants reported a total of 2468 symptoms in the daily questionnaires administered during the luteal phase and menses. Despite difficulties with data completeness, the combined questionnaire and sensor data collection was technically feasible and provided interesting biological insights. We observed an increased heart rate in the mid and end luteal phase compared with menses and participants with severe premenstrual syndrome walked substantially fewer steps (average daily steps 10,283, SD 6277) during the luteal phase and menses compared with participants with no or low premenstrual syndrome (mean 11,694, SD 6458).ConclusionsWe demonstrate the feasibility of using an app-based approach to collect combined wearable device and questionnaire data on menstrual cycles. Dropouts in the early weeks of the study indicated that engagement efforts would need to be improved for larger studies. Despite the challenges of collecting wearable data on consecutive days, the data collected provided valuable biological insights, suggesting that the use of questionnaires in conjunction with wearable data may provide a more complete understanding of the menstrual cycle and its impact on daily life. The biological findings should motivate further research into understanding the relationship between the menstrual cycle and objective physiological measurements from sensor devices.

Project description:BackgroundReproductive mood disorders indicate that within-person variation in depressive symptoms across the menstrual cycle can be related to ovarian hormone changes. Until now, such cycle-related symptom changes have been measured once daily, even though depression research indicates systematic diurnal changes in symptoms. Further, previous research often focused on aggregated depression scores. This study examined whether three daily assessments of depressive symptoms follow similar trajectories across the menstrual cycle and investigated within-person cyclical fluctuation of all individual symptoms and the aggregated score.Methods77 naturally-cycling participants (35 with and 42 without depressive disorder) provided three daily ratings of depressive symptoms across one menstrual cycle to evaluate individual and summarized symptoms.ResultsReliability estimates (w) of the three diurnal measurements ranged from 0.56 to 0.78. Cyclicity showed statistically significant interindividual differences for all symptoms, and individual symptoms differed significantly from each other in their magnitude of cyclicity.LimitationsOnly one menstrual cycle was assessed to reduce participant burden. Further, ovulation testing dates were based on self-reported cycle lengths, and only LH (luteinizing hormone) peaks were tested without subsequent progesterone rises.ConclusionsThe results highlight the need for a symptom-specific approach to assess individual variance in cyclicity of depressive symptoms. Reliability for one daily assessment can be improved by using the afternoon value, a sum score for depressiveness, or multiple items per symptom. Furthermore, this study emphasizes, that depressive symptoms can systematically change across the menstrual cycle, and it is, therefore, important to include it in depression research. Exploring female-specific risk factors of depression will enable the development of person-tailored treatments.Trial registrationThe study was preregistered at ClinicalTrials.gov (NCT04086316) with the first registration on 27/08/2019.

Dataset Information

Higher Depressive Symptoms in Irregular Menstrual Cycles: Converging Evidence from Cross-Sectional and Prospective Assessments.

Background

Methods

Results

Conclusions

Publications

Higher Depressive Symptoms in Irregular Menstrual Cycles: Converging Evidence from Cross-Sectional and Prospective Assessments.

Similar Datasets

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