The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Can apps and calendar methods predict ovulation with accuracy?
Open accessAbstract
OBJECTIVE: The accuracy of prediction of ovulation by cycle apps and published calendar methods was determined by comparing to true probability of ovulation. METHODS: A total of 949 volunteers collected urine samples for one entire menstrual cycle. Luteinizing hormone was measured to assign surge day, enabling probability of ovulation to be determined across different cycle lengths. Cycle-tracking apps were downloaded. As none provided their methodology, four published calendar-based methods were also examined: standard days, rhythm, alternative rhythm and simple calendar method. The volunteer ovulation data was applied to the app/calendar methods to determine their accuracy. RESULTS: Mean cycle length was 28 days (range: 23-35); 34% of women believed they had a 28-day cycle, but only 15% did. No LH surge was seen for 99 women. Most likely day of ovulation for a 28-day cycle was day 16 (21%). Accuracy of ovulation prediction was no better than 21% by the apps. The standard days and rhythm methods were most likely to predict ovulation (70% and 89%, respectively) but had very low accuracy. CONCLUSIONS: Ovulation day varies considerably for any given menstrual cycle length, thus it is not possible for calendar/app methods that use cycle-length information alone to accurately predict the day of ovulation. National Clinical Trial Code: NCT01577147. Registry website: www.clinicaltrials.gov .
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“A 10-day spread of observed ovulation days was observed for all cycle lengths examined; this also agrees with the findings of Johnson et al. (2018).”
From Real-life insights on menstrual cycles and ovulation using big data (2020) · cites this paper as Johnson et al. 2018
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“It is, therefore, unsurprising that several studies have shown that calendar apps are not accurate in identifying the fertile window.35–38”
From Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles (2019) · cites this paper as [38]
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“Apps to track fertility are increasingly being used among women who are seeking to conceive, and many purport to predict ovulation based on cycle length characteristics alone, which can have extremely low predictive accuracy (Moglia et al. 2016; Setton et al. 2016; Johnson et al. 2018).”
From Real-life insights on menstrual cycles and ovulation using big data (2020) · cites this paper as Johnson et al. 2018
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- Institute for Human Data Science., Patient adoption of mHealth: use, evidence and remaining barriers to mainstream acceptance
Cited by
Sorted by citations
Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles
Bull, J. R., Rowland, S. P., Scherwitzl, E. B., Scherwitzl, R., Danielsson, K. G., & Harper, J. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0152-7 · Cited by 404
Colenso‐Semple, L. M., D’Souza, A. C., Elliott-Sale, K. J., & Phillips, S. M. (2023) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2023.1054542 · Cited by 201
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Optimizing natural fertility: a committee opinion
Pfeifer, S., Butts, S., Fossum, G., Gracia, C., Barbera, A. L., Mersereau, J., Odem, R., Paulson, R., Penzias, A. S., Pisarska, M. D., et al. (2016) · Fertility and Sterility · doi:10.1016/j.fertnstert.2016.09.029 · Cited by 123
CE: An Evidence-Based Update on Contraception
Britton, L. E., Alspaugh, A., Greene, M. Z., & McLemore, M. R. (2020) · AJN American Journal of Nursing · doi:10.1097/01.naj.0000654304.29632.a7 · Cited by 122
Metadata
- Authors
- Johnson, S., Marriott, L., & Zinaman, M.
- Affiliations
- SPD Development Company Ltd, Clinical and Regulatory Affairs, Bedford, UK;
- SPD Development Company Ltd, Statistics and Data Management, Bedford, UK;
- Albert Einstein College of Medicine (US)
- Corresponding author
- Johnson, Sarah
- Venue
- Current Medical Research and Opinion
- Publisher
- Informa UK Limited
- Published
- 2018-05-25
- Volume
- 34
- Issue
- 9
- Pages
- 1587-1594
- ISSN
- 0300-7995 (ISSN-L)
- 1473-4877
- OpenAlex
- W2801130706
- Cited by
- 115
- References
- 25
- Citations by year
- 2026: 16 · 2025: 18 · 2024: 16 · 2023: 18 · 2022: 12 · 2021: 16 · 2020: 10 · 2019: 4 · 2018: 4
- FWCI
- 8.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Current Medical Research and Opinion (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Reproductive Health and Contraception
- Health Sciences › Medicine › Reproductive Medicine › Reproductive Health and Technologies
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- ovulation prediction, calendar method, luteinizing hormone surge, menstrual cycle length, fertility awareness, rhythm method, Standard Days Method
- MeSH terms
- Data Accuracy, Calendars as Topic, Adult, Female, Humans, Menstrual Cycle, Ovulation, Predictive Value of Tests, Pregnancy, Prognosis, Natural Family Planning Methods, Fertile Period, Mobile Applications
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