Guideline, 2021
How should researchers study the menstrual cycle to get reliable and comparable results?
This guideline provides detailed recommendations for consistent research methods across studies, covering study design, identifying naturally cycling women, measuring hormones and ovulation, and data analysis.
What they did
Expert guideline presenting recommendations and tools for researchers studying the menstrual cycle. Covers study design, sampling, data collection (including measuring ovulation and hormone levels), data coding, and statistical analysis.
What they found
The guideline recommends: selecting appropriate study designs and sampling strategies, using consistent definitions of cycle phases, measuring ovulation and hormone levels carefully, coding cycle data consistently, and using proper statistical methods to analyze cycle effects.
What it means for you
Researchers need to use more consistent and rigorous methods when studying the menstrual cycle so findings from different studies can be combined and compared. This will help build better understanding of how the cycle affects health faster.
About the study
- Original title
- How to study the menstrual cycle: Practical tools and recommendations
- Authors
- Schmalenberger KM, Tauseef HA, Barone JC et al.
- Journal
- Psychoneuroendocrinology
- Year
- 2021
- Study design
- Guideline
- What was tested
- Not applicable. This is a methodology guideline.
- Open access
- Yes
- Read the paper
- doi.org/10.1016/j.psyneuen.2020.104895
Original abstract
Despite decades of research on the physiological and psychological effects of the menstrual cycle, studies have not sufficiently adopted consistent methods for operationalizing the menstrual cycle. This has resulted in substantial confusion in the literature and limited possibilities to conduct systematic reviews and meta-analyses. In order to facilitate more rapid accumulation of knowledge on cycle effects, the present paper offers a set of integrative guidelines and standardized tools for studying the menstrual cycle as an independent variable. We begin with (1) an overview of the menstrual cycle and (2) premenstrual disorders, followed by (3) recommendations and tools regarding data collection in cycle studies. These recommendations address selecting the appropriate study design and sampling strategy, managing demand characteristics, identifying a sample of naturally-cycling individuals, and measuring menstrual bleeding dates, ovarian hormones, and ovulation. We proceed with suggestions for (4) data preparation and coding of cycle day and phases, as well as (5) data visualization, statistical modeling, and interpretation of menstrual cycle associations. We also provide (6) recommendations for using menses start day and ovulation testing to schedule visits in laboratory studies and end with a (7) comprehensive summary and conclusion. Regardless of whether the influence of the menstrual cycle is of central interest in a study or should be controlled to accurately assess the effects of another variable, the use of these recommendations and tools will help make study results more meaningful and replicable.
Reproduced under public-domain.