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Cited by 115Reproductive Health and ContraceptionReproductive Health and TechnologiesMobile Health and mHealth Applications

Can apps and calendar methods predict ovulation with accuracy?

Open access
Johnson, Sarah, Marriott, Lorrae, Zinaman, Michael·2018·Current Medical Research and Opinion
DOI 10.1080/03007995.2018.1475348
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Abstract

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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  • The 10-day spread of observed ovulation days observed for all cycle lengths examined agrees with the findings of Johnson et al. (2018).

    “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

    The Results report ovulation probabilities across days 11–20 for 28-day cycles and a similarly broad spread for shorter and longer cycles. They do not establish a 10-day spread for every cycle length examined; Table 1 shows that the spreads vary by cycle length. The claim’s 'all cycle lengths' qualifier is therefore too specific. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Several studies have shown that calendar apps are not accurate in identifying the fertile window.

    “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]

  • Many fertility tracking apps purport to predict ovulation based on cycle length characteristics alone, which can have extremely low predictive accuracy.

    “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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Research works printed but not linked: 2

  1. Clay R. Does stress hinder conception? Monitor Psychol 2006;37:46
  2. Godbert S, Miro F, Shreeves C, et al. Comparison between the dif- ferent methods developed for determining the onset of the LH surge in urine during the human menstrual cycle. Arch Gynecol Obstet 2015;292:1153-61

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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
ORCID
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
DOI
10.1080/03007995.2018.1475348
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 · 2016: 1
FWCI
8.10
Type
journal article
Language
en
Open access
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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