Skip to main content

Menstrual Cycle Tracking Applications and the Potential for Epidemiological Research: a Comprehensive Review of the Literature

Abstract

Purpose of Review

We reviewed published studies on menstrual cycle tracking applications (MCTAs) in order to describe the potential of MCTAs for epidemiologic research.

Recent Findings

A search of PubMed, Web of Science, and Scopus for MCTA literature yielded 150 articles. After exclusions, there were 49 articles that addressed the primary interest areas: 1) characteristics of MCTA users in research, 2) reasons women use or continue using MCTAs, 3) accuracy of identifying ovulation and utility at promoting and preventing pregnancy, and 4) quality assessments of MCTAs across several domains.

Summary

MCTAs are an important tool for the advancement of epidemiologic research on menstruation. MCTA studies should describe the characteristics of their user base and missing data patterns. Describing the motivation for using MCTAs throughout a user’s life and validating the data collected should be prioritized in future research.

This is a preview of subscription content, access via your institution.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Carroll JK, Moorhead A, Bond R, LeBlanc WG, Petrella RJ, Fiscella K. Who uses mobile phone health apps and does use matter? A secondary data analytics approach. J Med Internet Res. 2017;19:1–9.

    Article  Google Scholar 

  2. Fox S, Duggan M. Mobile Health. Pew Research Center: Internet, Science & Tech. 2012. Available from: https://www.pewresearch.org/internet/2012/11/08/mobile-health-2012/. Accessed 20 Apr 2020

  3. Lupton D. Quantified sex: a critical analysis of sexual and reproductive self-tracking using apps. Cult Health Sex. 2015;17:440–53.

    Article  Google Scholar 

  4. Ford EA, Roman SD, McLaughlin EA, Beckett EL, Sutherland JM. The association between reproductive health smartphone applications and fertility knowledge of Australian women. BMC Womens Health. 2020;20:1–10.

    Article  Google Scholar 

  5. Bull J, Rowland S, Lundberg O, Berglund-Scherwitzl E, Gemzell-Danielsson K, Trussell J et al. Typical use effectiveness of Natural Cycles: postmarket surveillance study investigating the impact of previous contraceptive choice on the risk of unintended pregnancy. BMJ Open. 2019;9:e026474. https://doi.org/10.1136/bmjopen-2018-026474.

  6. Kleinschmidt TK, Bull JR, Lavorini V, Rowland SP, Pearson JT, Scherwitzl EB, et al. Advantages of determining the fertile window with the individualised Natural Cycles algorithm over calendar-based methods. European Journal of Contraception and Reproductive Health Care. 2019;24:457–63.

  7. Berglund Scherwitzl E, Lindén Hirschberg A, Scherwitzl R. Identification and prediction of the fertile window using NaturalCycles. Eur J Contracept Reprod Health Care. 2015;20:403–8.

    Article  Google Scholar 

  8. Berglund Scherwitzl E, Gemzell Danielsson K, Sellberg JA, Scherwitzl R. Fertility awareness-based mobile application for contraception. Eur J Contracept Reprod Health Care. 2016;21:234–41.

    Article  Google Scholar 

  9. Berglund Scherwitzl E, Lundberg O, Kopp Kallner H, Gemzell Danielsson K, Trussell J, Scherwitzl R. Perfect-use and typical-use Pearl Index of a contraceptive mobile app. Contraception. 2017;96:420–5.

    Article  CAS  Google Scholar 

  10. Ericson (2015) Mobility report: on the pulse of the networked society.

    Google Scholar 

  11. Vaz F, Silva RR, Bernardino J. Using data mining in a mobile application for the calculation of the female fertile period. In: A. F, J. F, editor. 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2018. Rua Pedro Nunes, Coimbra, Portugal: SciTePress, Polytechnic of Coimbra, ISEC; 2018. p. 359–66.

    Google Scholar 

  12. • Shattuck D, Haile L, Simmons R. Lessons from the dot contraceptive efficacy study: analysis of the use of agile development to improve recruitment and enrollment for mhealth research. JMIR mHealth and uHealth. 2018;6:e99. https://doi.org/10.2196/mhealth.9661. This paper demonstrates the importance of a strategic and iterative recruitment technique in order to have a diverse study sample.

  13. Jacobson AE, Vesely SK, Haamid F, Christian-Rancy M, O’Brien SH. Mobile application vs paper pictorial blood assessment chart to track menses in young women: a randomized cross-over design. J Pediatr Adolesc Gynecol. 2018;31:84–8.

    Article  Google Scholar 

  14. Alvergne A, Vlajic Wheeler M, Hogqvist Tabor V. Do sexually transmitted infections exacerbate negative premenstrual symptoms? Insights from digital health. Evolution, medicine, and public health. 2018;2018:138–50.

    Article  Google Scholar 

  15. Blödt S, Pach D, Eisenhart-Rothe S v, Lotz F, Roll S, Icke K, et al. Effectiveness of app-based self-acupressure for women with menstrual pain compared to usual care: a randomized pragmatic trial. Am J Obst Gynecol. 2018;218:227.e1–227.e9.

  16. Song M, Kanaoka H. Effectiveness of mobile application for menstrual management of working women in Japan: randomized controlled trial and medical economic evaluation. J Med Econ. 2018;21:1131–8.

    Article  Google Scholar 

  17. Simmons R, Shattuck D, Jennings V. Assessing the efficacy of an app-based method of family planning: the dot study protocol. JMIR Res Protocols. 2017;6:e5. https://doi.org/10.2196/resprot.8829.

  18. Faust L, Bradley D, Landau E, Noddin K, Farland LV, Baron A, et al. Findings from a mobile application–based cohort are consistent with established knowledge of the menstrual cycle, fertile window, and conception. Fertility and Sterility. 2019;112:450–457.e3.

  19. Soumpasis I, Grace B, Johnson S. Real-life insights on menstrual cycles and ovulation using big data. Human Reproduction Open. 2020;2020:1–9.

    Article  Google Scholar 

  20. Sohda S, Suzuki K, Igari I. Relationship between the menstrual cycle and timing of ovulation revealed by new protocols: analysis of data from a self-tracking health app. J Med Internet Res. 2017;19:e391.

    Article  Google Scholar 

  21. • Bull JR, Rowland SP, Scherwitzl EB, Scherwitzl R, Danielsson KG, Harper J. Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles. npj Digital Med. 2019;2:83. This paper has menstrual cycle data from more than 600,000 menstrual cycles with wide age and BMI ranges, showing how large the potential sample size can be with MCTAs.

    Article  Google Scholar 

  22. Bradley D, Landau E, Jesani N, Mowry B, Chui K, Baron A et al. Time to conception and the menstrual cycle: an observational study of fertility app users who conceived. Hum Fertil. 2019;1–9.

  23. Johnson S, Stanford JB, Warren G, Bond S, Bench-Capon S, Zinaman MJ. Increased likelihood of pregnancy using an app-connected ovulation test system: a randomized controlled trial. J Women's Health. 2020;29:84–90.

    Article  Google Scholar 

  24. Liu B, Thomas D, Shi S, Symul L, Leskovec J, Wu Y, et al. Predicting pregnancy using large-scale data from a women’s health tracking mobile application. In: 2019 World Wide Web Conference, WWW 2019. Stanford, United States: Association for Computing Machinery, Inc, Dept. of Computer Science; 2019. p. 2999–3005.

  25. Stanford JB, Willis SK, Hatch EE, Rothman KJ, Wise LA. Fecundability in relation to use of mobile computing apps to track the menstrual cycle. Hum Reprod (Oxford, England). 2020;35:2245–52.

    Article  Google Scholar 

  26. •• Haile LT, Fultz HM, Simmons RG, Shelus V. Market-testing a smartphone application for family planning: assessing potential of the CycleBeads app in seven countries through digital monitoring. mHealth. 2018;4:27–27. This paper highlights the potential for MCTAs to fill an unmet need for contraceptive in low-income countries. Additionally, the authors find that a social media campaign is an effective way to recruit a diverse sample.

    Article  Google Scholar 

  27. Shelus V, Ashcroft N, Burgess S, Giuffrida M, Jennings V. Preventing pregnancy in Kenya through distribution and use of the CycleBeads mobile application. Int Perspect Sex Reprod Health. 2017;43:131–41.

    Article  Google Scholar 

  28. Bretschneider RA. A goal- and context-driven approach in mobile period tracking applications. In: Antona M, Stephanidis C, editors. Universal access in human-computer interaction: access to learning, health and well-being, uahci 2015, pt III; 2015. p. 279–87.

    Chapter  Google Scholar 

  29. Levy J, Romo-Avilés N. “A good little tool to get to know yourself a bit better”: a qualitative study on users’ experiences of app-supported menstrual tracking in Europe. BMC Public Health. 2019;19:1213.

    Article  Google Scholar 

  30. Gambier-Ross K, McLernon DJ, Morgan HM. A mixed methods exploratory study of women’s relationships with and uses of fertility tracking apps. Digital Health. 2018;4:205520761878507.

    Article  Google Scholar 

  31. Johnson S, Marriott L, Zinaman M. Can apps and calendar methods predict ovulation with accuracy? Curr Med Res Opin. 2018;34:1587–94.

    Article  CAS  Google Scholar 

  32. Lee J, Kim J. Can menstrual health apps selected based on users’ needs change health-related factors? A double-blind randomized controlled trial. J Am Med Inform Assoc. 2019;26:655–66.

    Article  Google Scholar 

  33. Starling MS, Kandel Z, Haile L, Simmons RG. User profile and preferences in fertility apps for preventing pregnancy: an exploratory pilot study. mHealth. 2018;4:21–21.

    Article  Google Scholar 

  34. Eschler J, Menking A, Fox S, Backonja U. Defining menstrual literacy with the aim of evaluating mobile menstrual tracking applications. Computers, Informatics, Nursing : CIN. 2019;37:638–46.

    Article  Google Scholar 

  35. •• Epstein DA, Lee NB, Kang JH, Agapie E, Schroeder J, Pina LR, et al. Examining menstrual tracking to inform the design of personal informatics tools. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17. New York, New York, USA, United States: ACM Press; 2017. p. 6876–88. This paper provides qualitative data valuable for understanding user experiences with MCTAs.

  36. Hamper J. ‘Catching ovulation’: exploring women’s use of fertility tracking apps as a reproductive technology. Body & Society. 2020;26:3–30. https://doi.org/10.1177/1357034X19898259.

  37. Anderson K, Burford O, Emmerton L. Mobile health apps to facilitate self-care: a qualitative study of user experiences. PLoS One. 2016;11:e0156164.

  38. Vanya M, Jakó M, Füle G, Fidrich M, Surányi A, Bitó T, et al. Use of infertility handling among women of reproductive age. International Summit on eHealth. 2017;360°, 2016 181 LNICST:497–501.

  39. Mangone ER, Lebrun V, Muessig KE. Mobile phone apps for the prevention of unintended pregnancy: a systematic review and content analysis. JMIR mHealth and uHealth. 2016;4:e6.

    Article  Google Scholar 

  40. Moglia ML, Nguyen HV, Chyjek K, Chen KT, Castaño PM. Evaluation of smartphone menstrual cycle tracking applications using an adapted applications scoring system. Obstet Gynecol Surv. 2016;71:713–4.

    Article  Google Scholar 

  41. Zwingerman R, Chaikof M, Jones C. A critical appraisal of fertility and menstrual tracking apps for the iPhone. J Obstet Gynaecol Canada. 2020;42:583–590.

  42. Li D, Heyer L, Jennings VH, Smith CA, Dunson DB. Personalised estimation of a woman’s most fertile days. European Journal of Contraception and Reproductive Health Care. 2016;21:323–8.

    Article  Google Scholar 

  43. Handel P, Wahlstrom J. Digital contraceptives based on basal body temperature measurements. BIOMEDICAL SIGNAL PROCESSING AND CONTROL. 2019;52:141–51.

    Article  Google Scholar 

  44. Duane M, Contreras A, Jensen ET, White A. The performance of fertility awareness-based method apps marketed to avoid pregnancy. J Am Board Fam Med : JABFM. 2016;29:508–11.

    Article  Google Scholar 

  45. Manhart M. A comparison of user behaviors for a fertility-tracking app: does training in an NFP method improve persistence and use? The Linacre Quarterly. 2019;87:53–59.

  46. • Symul L, Wac K, Hillard P, Salathé M. Assessment of menstrual health status and evolution through mobile apps for fertility awareness. npj Digital Med. 2019;2:64. This study is selected as important because of the focus on capturing user tracking behavior, which is key to understanding in order to promote good-quality data from MCTAs.

    Article  Google Scholar 

  47. Jennings V, Haile LT, Simmons RG, Spieler J, Shattuck D. Perfect- and typical-use effectiveness of the Dot fertility app over 13 cycles: results from a prospective contraceptive effectiveness trial. Eur J Contracept Reprod Health Care. 2019;24:148–53.

    Article  Google Scholar 

  48. Freis A, Freundl-Schütt T, Wallwiener L-M, Baur S, Strowitzki T, Freundl G, et al. Plausibility of menstrual cycle apps claiming to support conception. Front Public Health. 2018;6:98.

  49. • Hutcherson T, Cieri-Hutcherson N, Donnelly P, Feneziani M, Grisanti K. Evaluation of mobile applications intended to aid in conception using a systematic review framework. Annals of Pharmacotherapy. 2019;54:178–186. This paper scored MCTAs based on an adapted tool for evaluation of applications, thus contributing to the literature on evidence-based strategy for comparing MCTAs.

  50. U.S. Food and Drug Administration. FDA allows marketing of first direct-to-consumer app for contraceptive use to prevent pregnancy. 2018. Available from: https://www.fda.gov/news-events/press-announcements/fda-allows-marketing-first-direct-consumer-app-contraceptive-use-prevent-pregnancy. Accessed 14 Oct 2020.

  51. ACOG. Menstruation in girls and adolescents: using the menstrual cycle as a vital sign. https://www.acog.org/clinical/clinical-guidance/committee-opinion/articles/2015/12/menstruation-in-girls-and-adolescents-using-the-menstrual-cycle-as-a-vital-sign. Accessed 4 Apr 2020.

  52. Medical Dictionary Pearl Index. https://medical-dictionary.thefreedictionary.com/Pearl+index. Accessed 20 Apr 2020.

  53. Levy J. “It’s your period and therefore it has to be pink and you are a girl”: users’ experiences of (de-)gendered menstrual app design. In: 4th Conference on Gender and IT, GenderIT 2018. Granada, Spain: Association for Computing Machinery, University of Granada; 2018. p. 63–5.

    Google Scholar 

  54. Fowler L, Gillard C, Morain S. Readability and accessibility of terms of service and privacy policies for menstruation-tracking smartphone applications. Health Promotion Pract. 2020;21:679–683.

  55. • Earle S, Marston H, Hadley R, Banks D. Use of menstruation and fertility app trackers: a scoping review of the evidence. BMJ Sex Reprod Health. 2020. https://doi.org/10.1017/CBO9781107415324.004. This literature review strengthens our findings because of the similar conclusions reached by reviewing a similar body of literature.

Download references

Funding

National Institute of Environmental Health Sciences.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anne Marie Z. Jukic.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Reproductive and Perinatal Epidemiology

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schantz, J.S., Fernandez, C.S.P. & Jukic, A.M.Z. Menstrual Cycle Tracking Applications and the Potential for Epidemiological Research: a Comprehensive Review of the Literature. Curr Epidemiol Rep 8, 9–19 (2021). https://doi.org/10.1007/s40471-020-00260-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40471-020-00260-3

Keywords