Author(s):

Katie Gabier Ross

David J Mclernon

Heather M Morgan

Abstract:

Digital self-tracking is rising, including tracking of menstrual cycles by women using fertility tracking apps (FTAs). However, little is known about users’ experiences of FTAs and their relationships with them. The aim of this study was to explore women’s uses of and relationships with FTAs. This exploratory study employed a mixed methods approach, involving the collection and analysis of an online survey and follow-up interviews. Qualitative analysis of survey and interview data informed hypothesis development. Online surveys yielded 241 responses and 11 follow-up interviews were conducted. Just over a third of women surveyed had experience of using FTAs (89/241) and follow-up interviews were conducted with a proportion of respondents (11/241). Four main motivations to use FTAs were identified: (a) to observe cycle (72%); (b) to conceive (34%); (c) to inform fertility treatment (12%); and (d) as contraception (4%). Analysis of the free-text survey questions and interviews using grounded theory methodology highlighted four themes underpinning women’s relationships with FTAs: (a) medical grounding; (b) health trackers versus non-trackers; (c) design; and (d) social and ethical aspects. Participants who used other health apps were more likely to use FTAs (p = 0.001). Respondents who used contraception were less likely to use FTAs compared with respondents who did not use contraception (p = 0.002). FTA usage also decreases (p = 0.001) as age increases. There was no association between FTA usage and menstrual status (p = 0.259). This research emphasises the differing motivations for FTA use. Future research should further explore the diverse relationships between different subgroups of women and FTAs.

Document:

https://journals.sagepub.com/doi/10.1177/2055207618785077

References
1. Lupton D. Towards critical digital health studies: reflections on two decades of research in health and the way forward. Health 2016; 20(1): 49–61.
Google Scholar

2. Lupton, D . Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Crit Public Health 2013; 23(4): 393–403.
Google Scholar | Crossref | ISI

3. Krebs, P, Duncan, DT. Health app use among US mobile phone owners: a national survey. JMIR mHealth uHealth 2015; 3(4): e101.
Google Scholar | Crossref | Medline | ISI

4. Lupton, D . Apps as artefacts: towards a critical perspective on mobile health and medical apps. Societies 2014; 4(4): 606–622.
Google Scholar | Crossref

5. Morgan H. ‘Pushed’ self-tracking using digital technologies for chronic health condition management: a critical interpretive synthesis. Digital Health 2016; 2: 1–41.
Google Scholar

6. Caddy B. The quantified woman: fertility tracking and the future of our reproductive health. 2015. Available at: https://www.wareable.com/health-and-wellbeing/the-quantified-woman-fertility-tracking-1914 (accessed 16 February 2017).
Google Scholar

7. Weigel M. ‘Fitbit for your period’: the rise of fertility tracking. The Guardian, 2016. Available at: https://www.theguardian.com/technology/2016/mar/23/fitbit-for-your-period-the-rise-of-fertility-tracking (accessed 16 February 2017).
Google Scholar

8. Grimes DA, Gallo MF, Halpern V, et al. Fertility awareness-based methods for contraception. Cochrane Database of Systematic Reviews, 2004. Available at: http://cochranelibrary-wiley.com/doi/10.1002/14651858.CD004860.pub2/pdf.
Google Scholar

9. US Department of Health and Human Services. Effectiveness of family planning methods. https://www.cdc.gov/reproductivehealth/contraception/unintendedpregnancy/pdf/Contraceptive_methods_508.pdf (2004, accessed 17 March 2018).
Google Scholar

10. Berglund Scherwitzl, E, Gemzell Danielsson, K, Sellberg, JA Fertility awareness-based mobile application for contraception. Eur J Contracept Reprod Health Care 2016; 21(3): 234–241.
Google Scholar | Medline

11. Berglund Scherwitzl E, Lundberg O, Kopp Kallner H, et al. Perfect-use and typical-use Pearl Index of a contraceptive mobile app. Contraception 2017; 6, 420–425.
Google Scholar

12. Smith AD, Smith JL. billingsMentor: Adapting natural family planning to information technology and relieving the user of unnecessary tasks. Linacre Q 2014; 81(3): 219–238.
Google Scholar

13. Lupton D. Digital health now and in the future: findings from a participatory stakeholder workshop. Digital Health 2017; 3, 1–17.
Google Scholar

14. Setton RMD, Tierney CMD and Tsai TMD. The accuracy of web sites and cellular phone applications in predicting the fertile window. Obstet Gynecol 2016; 128(1): 58–63.
Google Scholar

15. Brown S, Blackwell LF and Cooke DG. Online fertility monitoring: some of the issues. Int J Open Inf Technol 2017; 5(4): 85–91.
Google Scholar

16.Moglia ML, Nguyen HV, Chyjek K, et al. Evaluation of smartphone menstrual cycle tracking applications using an adapted APPLICATIONS scoring system. Obstet Gynecol 2016; 127(6): 1153–1160.
Google Scholar

17. Wettstein H, Al-Shemmery Z and Bourgeois C. Natural family planning: when smartphone and iPhones are used for contraception. A comparison study of 7 symptothermal apps. Eur J Contracept Reprod Health Care 2014; 19: S201.
Google Scholar

18. Magistretti B. Natural Cycles is first contraceptive app to get EU approval. Venture Beat, 2017. Available at: http://venturebeat.com/2017/02/09/natural-cycles-is-first-contraceptive-app-to-get-eu-approval/ (accessed 20 March 2017).
Google Scholar

19. Lupton, D . Digital companion species and eating data: implications for theorising digital data-human assemblages. Big Data & Society 2016; 3(1): 1–5.
Google Scholar | SAGE Journals | ISI

20. Casler, K, Bickel, L, Hackett, E. Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing. Comput Hum Behav 2013; 29(6): 2156–2160.
Google Scholar | Crossref | ISI

21. Tuten TL, Urban DJ and Bosnjak M. Internet surveys and data quality: a review. 2002. Available at: https://ub-madoc.bib.uni-mannheim.de/8492/ (accessed 22 March 2017).
Google Scholar

22. King, DB, O’Rourke, N, DeLongis, A. Social media recruitment and online data collection: a beginner’s guide and best practices for accessing low-prevalence and hard-to-reach populations. Can Psychol 2014; 55(4): 240–249.
Google Scholar

23. INVOLVE. Do I need to apply for ethical approval to involve the public in my research? 2018; 201. http://www.invo.org.uk/posttypefaq/do-i-need-to-apply-for-ethical-approval-to-involve-the-public-in-my-research/# (accessed 17 March 2018).
Google Scholar

24. Glaser, BG, Strauss, AL. The discovery of grounded theory. Int J Qual Methods 1967; 5: 1–10.
Google Scholar

25. Francis, JJ, Johnston, M, Robertson, C What is an adequate sample size? Operationalising data saturation for theory-based interview studies. Psychol Health 2010; 25(10): 1229–1245.
Google Scholar | Crossref | Medline | ISI

26. Mason, M . Sample size and saturation in PhD studies using qualitative interviews. Forum: Qualitative Sozialforschung / Forum: Qualitative Social Research 2010; 11(3): Article 8.
Google Scholar

27. Baker SE and Edwards R. How many qualitative interviews is enough? National Centre for Research Methods Review Paper, 2012.
Google Scholar

28. Lanham M and Christensen MA. Fertility-related smartphone application use among patients seeking treatment for infertility. Fertil Steril 2015; 104(3): e354.
Google Scholar

29. Lange A, Yeh J, Messerlian C, et al. Smartphone fertility app use among couples of reproductive age: potential use of big data to improve fertility care and advance reproductive health research. Fertil Steril 2016; 106(3): e111.
Google Scholar

30. Freundl G and Frank-Herrmann P. Natürliche Familienplanung. In: Gnoth C and Mallmann P (eds) Perikonzeptionelle Frauenheilkunde. Berlin, Heidelberg: Springer-Verlag, 2014, pp.13–28.
Google Scholar

31. McCartney P. Nursing practice with menstrual and fertility mobile apps. MCN Am J Matern Child Nurs 2016; 41(1): 61.
Google Scholar

32. Lupton D. The use and value of digital media for information about pregnancy and early motherhood: a focus group study. BMC Pregnancy Childbirth 2016; 16: 171.
Google Scholar

33. Johnson, RB, Onwuegbuzie, AJ. Mixed methods research: a research paradigm whose time has come. Educ Res 2004; 33(7): 14–26.
Google Scholar | SAGE Journals

34. Epstein DA, Lee NB, Kang JH, et al. Examining menstrual tracking to inform the design of personal informatics tools. Proc SIGCHI Conf Hum Factor Comput Syst 2017; 2017:6876–6888.
Google Scholar