Evaluation of smartphone menstrual cycle tracking applications using an adapted APPLICATIONS scoring system
Abstract
OBJECTIVE: To identify smartphone menstrual cycle tracking applications (apps) and evaluate their accuracy, features, and functionality. METHODS: In this systematic evaluation, we searched the Apple iTunes store for free menstrual cycle tracking apps for patient use. We considered an application accurate if menstrual cycle predictions were based on average cycle lengths of at least three previous cycles, ovulation (when included) was predicted at 13-15 days before the start of the next cycle, and the application contained no misinformation. We modified the APPLICATIONS Scoring System to evaluate the features and functionality of accurate apps. RESULTS: Our search criteria yielded 1,116 apps; 108 remained after excluding duplicate, non-English, nonmenstrual cycle tracking, and priced apps. We further eliminated 88 that did not meet inclusion or accuracy criteria. Of the 20 accurate, free apps, 80% contained information for conception and 50% for contraception. Common features and functionality included password protection (55%); no requirement for Internet connectivity (80%); no advertisements (65%); in-application technical support (70%); medical disclaimers (65%); health education (55%); tracking of menstrual flow (70%), symptoms (70%), and intercourse (75%); alerts for next menses (65%) and fertility (55%); and cycle length information (75%). Forty percent were available for Android. Usefulness for fertility medications (15%), professional involvement (5%), and cited literature (5%) were rare. CONCLUSION: Most free smartphone menstrual cycle tracking apps for patient use are inaccurate. Few cite medical literature or health professional involvement. We list accurate apps to aid health care providers in understanding the key components they can use to evaluate and recommend apps for patients.
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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 Moglia et al. 2016
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- Authors
- Moglia, M. L., Nguyen, H. V., Chyjek, K., Chen, K. T., & Castaño, P. M.
- Affiliations
- Columbia University Irving Medical Center (US)
- Venue
- Obstetrics & Gynecology
- Published
- 2016-06
- Volume
- 127
- Issue
- 6
- Pages
- 1153-1160
- ISSN
- 0029-7844 (ISSN-L)
- OpenAlex
- W2358427619
- Cited by
- 185
- References
- 16
- Citations by year
- 2026: 19 · 2025: 24 · 2024: 13 · 2023: 16 · 2022: 16 · 2021: 31 · 2020: 26 · 2019: 20 · 2018: 12 · 2017: 5 · 2016: 3
- FWCI
- 24.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Obstetrics and Gynecology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Menstrual Health and Disorders
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Reproductive Health and Contraception
- Keywords
- menstrual cycle tracking apps, mobile application, ovulation prediction, fertility monitoring, menstrual cycle prediction, free apps, fertility awareness
- MeSH terms
- Smartphone, Female, Humans, Menstrual Cycle, Predictive Value of Tests, Natural Family Planning Methods, Women's Health, Mobile Applications
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