VOGUE: Receptivity of Women during Menstrual Cycle

We are pleased to present a research project that aims to explore the relationship between a woman’s menstrual cycle and her receptivity to just-in-time adaptive interventions. This pilot study seeks to investigate how a woman’s menstrual cycle stage and associated symptoms, including cyclical emotional changes, may impact her engagement and response to digital health interventions.

The menstrual cycle is a natural process experienced by women from an average age of 12.4 years until menopause, typically occurring between the ages of 44-51. This cycle, with an average duration of 28 days, consists of follicular and luteal phases accompanied by various hormonal fluctuations, emotional states, physiological variables, and changes in productivity.

Despite the importance of receptivity to digital health interventions for their effectiveness, the potential link between a woman’s menstrual cycle stage and her state of receptivity remains largely unexplored. This project aims to fill this research gap by investigating whether different stages of the menstrual cycle, along with associated symptoms, have an impact on a woman’s ability to receive and benefit from just-in-time adaptive interventions.

To conduct this study, we will recruit five regularly menstruating women aged 20-35 who are not using hormone-based contraception, planning pregnancy, or suffering from endometriosis. The participants will be equipped with a Garmin fitness tracker and will engage in a 90-day digital health intervention built on our existing MobileCoach Platform. This intervention will include stress management practices, period tracking, and daily emotional assessments using the Affective Slider.

During the study, we will collect physiological variables such as heart rate, heart rate variability, stress levels, respiration rate, and sleep patterns using the Garmin fitness tracker. Participants will also provide self-reported emotional assessments, beginning from the first day of their menstrual period. Additionally, participants will receive daily notifications at random times to measure their baseline receptivity based on the duration of their response.

By analyzing the collected data and utilizing machine learning techniques, we aim to develop models that predict the optimal timing for notifications tailored to each participant’s receptivity. We will evaluate the accuracy of these models in predicting the participant’s receptivity to notifications, taking into account their menstrual cycle stage and associated physiological variables.

We expect that this study will reveal valuable insights into the interplay between the menstrual cycle, emotional states, and receptivity to digital health interventions. Furthermore, we anticipate that the findings will support the development of personalized interventions that consider the menstrual cycle stage and its impact on a woman’s receptivity. By optimizing the tonality and content of notifications based on the menstrual cycle stage, we aim to enhance adherence to digital health interventions, ultimately making them more effective in improving women’s health and well-being.

We invite you to join us in this groundbreaking research project as we explore the fascinating connection between the menstrual cycle and receptivity to digital health interventions. Your participation will contribute to advancing knowledge in this field and pave the way for more tailored and effective healthcare interventions for women.

Related Work

  1. Mishra, V., Künzler, F., Kramer, J.-N., Fleisch, E., Kowatsch, T., & Kotz, D. (2021). Detecting Receptivity for mHealth Interventions in the Natural Environment. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 5(2), Article 74. https://doi.org/10.1145/3463492
  2. Künzler, F., Mishra, V., Kramer, J.-N., Kotz, D., Fleisch, E., & Kowatsch, T. (2019). Exploring the State-of-Receptivity for mHealth Interventions. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(4), 1-27, Article 140. https://doi.org/10.1145/3369805
  3. Keller, R., Wangenheim, F. v., Mair, J., & Kowatsch, T. (2023). Receptivity to mobile health interventions. In N. Jacobson, T. Kowatsch, & L. Marsch (Eds.), Digital Therapeutics for Mental Health and Addiction (pp. 65-77). Academic Press. https://doi.org/10.1016/B978-0-323-90045-4.00006-X
  4. Pierson, E., Althoff, T., Thomas, D. et al. Daily, weekly, seasonal and menstrual cycles in women’s mood, behaviour and vital signs. Nat Hum Behav 5, 716–725 (2021). https://doi.org/10.1038/s41562-020-01046-9
  5. Shilaih M, Goodale BM, Falco L, Kübler F, De Clerck V, Leeners B. Modern fertility awareness methods: wrist wearables capture the changes in temperature associated with the menstrual cycle. Biosci Rep. 2018 Nov 30;38(6). https://doi.org/10.1042/bsr20171279
  6. Goodale BM, Shilaih M, Falco L, Dammeier F, Hamvas G, Leeners B. Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study. J Med Internet Res. 2019 Apr 18;21(4). https://doi.org/10.2196/13404

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In Brief

Our study explores how a woman’s menstrual cycle stage and symptoms influence her receptivity to digital health interventions, aiming to develop personalized approaches for enhanced effectiveness.

Research Team

Marc-Robin Grüner, Anja Bischof, Davinny Sou, Prof. Dr. Tobias Kowatsch, Dr. Marcia Nißen

External Advisors

Dr. Larissa Greive
Prof. Dr. Varun Mishra
Prabhakaran “Prabhu” Santhanam
Chang Siang Lim


June 2023 – December 2024

Marc-Robin Grüner, M.A. Candidate
Marc-Robin Grüner, M.A. CandidateResearch Assistant, Centre for Digital Health Interventions; University of St. Gallen