Abnormal uterine bleeding patterns determined through menstrual tracking among participants in the Apple Women’s Health Study
Open accessAbstract
BACKGROUND: Use of menstrual tracking data to understand abnormal bleeding patterns has been limited because of lack of incorporation of key demographic and health characteristics and confirmation of menstrual tracking accuracy. OBJECTIVE: This study aimed to identify abnormal uterine bleeding patterns and their prevalence and confirm existing and expected associations between abnormal uterine bleeding patterns, demographics, and medical conditions. STUDY DESIGN: Apple Women's Health Study participants from November 2019 through July 2021 who contributed menstrual tracking data and did not report pregnancy, lactation, use of hormones, or menopause were included in the analysis. Four abnormal uterine bleeding patterns were evaluated: irregular menses, infrequent menses, prolonged menses, and irregular intermenstrual bleeding (spotting). Monthly tracking confirmation using survey responses was used to exclude inaccurate or incomplete digital records. We investigated the prevalence of abnormal uterine bleeding stratified by demographic characteristics and used logistic regression to evaluate the relationship of abnormal uterine bleeding to a number of self-reported medical conditions. RESULTS: There were 18,875 participants who met inclusion criteria, with a mean age of 33 (standard deviation, 8.2) years, mean body mass index of 29.3 (standard deviation, 8.0), and with 68.9% (95% confidence interval, 68.2-69.5) identifying as White, non-Hispanic. Abnormal uterine bleeding was found in 16.4% of participants (n=3103; 95% confidence interval, 15.9-17.0) after accurate tracking was confirmed; 2.9% had irregular menses (95% confidence interval, 2.7-3.1), 8.4% had infrequent menses (95% confidence interval, 8.0-8.8), 2.3% had prolonged menses (95% confidence interval, 2.1-2.5), and 6.1% had spotting (95% confidence interval, 5.7-6.4). Black participants had 33% higher prevalence (prevalence ratio, 1.33; 95% confidence interval, 1.09-1.61) of infrequent menses compared with White, non-Hispanic participants after controlling for age and body mass index. The prevalence of infrequent menses was increased in class 1, 2, and 3 obesity (class 1: body mass index, 30-34.9; prevalence ratio, 1.31; 95% confidence interval, 1.13-1.52; class 2: body mass index, 35-39.9; prevalence ratio, 1.25; 95% confidence interval, 1.05-1.49; class 3: body mass index, >40; prevalence ratio, 1.51; 95% confidence interval, 1.21-1.88) after controlling for age and race/ethnicity. Those with class 3 obesity had 18% higher prevalence of abnormal uterine bleeding compared with healthy-weight participants (prevalence ratio, 1.18; 95% confidence interval, 1.02-1.38). Participants with polycystic ovary syndrome had 19% higher prevalence of abnormal uterine bleeding compared with participants without this condition (prevalence ratio, 1.19; 95% confidence interval, 1.08-1.31). Participants with hyperthyroidism (prevalence ratio, 1.34; 95% confidence interval, 1.13-1.59) and hypothyroidism (prevalence ratio, 1.17; 95% confidence interval, 1.05-1.31) had a higher prevalence of abnormal uterine bleeding, as did those reporting endometriosis (prevalence ratio, 1.28; 95% confidence interval, 1.12-1.45), cervical dysplasia (prevalence ratio, 1.20; 95% confidence interval, 1.03-1.39), and fibroids (prevalence ratio, 1.14; 95% confidence interval, 1.00-1.30). CONCLUSION: In this cohort, abnormal uterine bleeding was present in 16.4% of those with confirmed menstrual tracking. Black or obese participants had increased prevalence of abnormal uterine bleeding. Participants reporting conditions such as polycystic ovary syndrome, thyroid disease, endometriosis, and cervical dysplasia had a higher prevalence of abnormal uterine bleeding.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Where reported, the mean age of participants in mobile app studies ranged from 24.70 ± 5.45 years to 33.0 ± 8.2 years, and mean BMI ranged from 24.2 kg/m2 to 29.3 ± 8.00 kg/m2.
“Where reported, the mean age (±standard deviation) of participants ranged from 24.70 ± 5.45 years [56] to 33.0 ± 8.2 years [61], whilst the mean body mass index (BMI) ranged from 24.2 kg/m2 [43] to 29.3 ± 8.00 kg/m2 [61].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [61]
One phone-based study implies collection of health data and menstrual cycle tracking through apps from iPhone users with an iCloud account.
“One study, previously described in the context of mobile apps, implies the collection of health data and menstrual cycle tracking through apps from iPhone users with an iCloud account [61].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [61]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The “All of Us” Research Program
The All of Us Research Program Investigators (2019) · New England Journal of Medicine · doi:10.1056/nejmsr1809937 · Cited by 2,440 · see citing works
Azziz, R., Carmina, E., Dewailly, D., Diamanti-Kandarakis, E., Escobar-Morreale, H. F., Futterweit, W., Janssen, O. E., Legro, R. S., Norman, R. J., Taylor, A. E., et al. (2009) · Fertility and Sterility · doi:10.1016/j.fertnstert.2008.06.035 · Cited by 2,132 · see citing works
Munro, M. G., Critchley, H., & Fraser, I. S. (2018) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.12666 · Cited by 1,093 · see citing works
VARIATION OF THE HUMAN MENSTRUAL CYCLE THROUGH REPRODUCTIVE LIFE
TRELOAR, A. E., BOYNTON, R. E., BEHN, B. G., & BROWN, B. W. (1968) · Obstetrical & Gynecological Survey · doi:10.1097/00006254-196801000-00019 · Cited by 1,018 · see citing works
Variation in the Incidence of Uterine Leiomyoma Among Premenopausal Women by Age and Race
Marshall, L. M. (1997) · Obstetrics and Gynecology · doi:10.1016/s0029-7844(97)00534-6 · Cited by 888 · see citing works
The “All of Us” Research Program
Sullivan, F., McKinstry, B., & Vasishta, S. (2019) · New England Journal of Medicine · doi:10.1056/nejmc1912496 · Cited by 855 · see citing works
Menstruation in girls and adolescents: using the menstrual cycle as a vital sign
American Academy of Pediatrics, Committee on Adolescence, American College of Obstetricians and Gynecologists, & Committee on Adolescent Health Care (2006) · PEDIATRICS · doi:10.1542/peds.2006-2481 · Cited by 651 · see citing works
Menstrual cycle irregularity and risk for future cardiovascular disease
Solomon, C. G., Hu, F. B., Dunaif, A., Rich-Edwards, J. E., Stampfer, M. J., Willett, W. C., Speizer, F. E., & Manson, J. E. (2002) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.87.5.8471 · Cited by 528 · see citing works
Salathé, M., Bengtsson, L., Bodnar, T. J., Brewer, D. D., Brownstein, J. S., Buckee, C., Campbell, E. M., Cattuto, C., Khandelwal, S., Mabry, P. L., et al. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002616 · Cited by 500 · see citing works
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 · see citing works
Liu, Z., Doan, Q. V., Blumenthal, P. D., & Dubois, R. W. (2007) · Value in Health · doi:10.1111/j.1524-4733.2007.00168.x · Cited by 400 · see citing works
The FIGO recommendations on terminologies and definitions for normal and abnormal uterine bleeding
Fraser, I., Critchley, H., Broder, M., & Munro, M. (2011) · Seminars in Reproductive Medicine · doi:10.1055/s-0031-1287662 · Cited by 384 · see citing works
Long or Highly Irregular Menstrual Cycles as a Marker for Risk of Type 2 Diabetes Mellitus
Solomon, C. G. (2001) · JAMA · doi:10.1001/jama.286.19.2421 · Cited by 362 · see citing works
Examining menstrual tracking to inform the design of personal informatics tools
Epstein, D. A., Lee, N. B., Kang, J. H., Agapie, E., Schroeder, J., Pina, L. R., Fogarty, J., Kientz, J. A., & Munson, S. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025635 · Cited by 346 · see citing works
(1967) · PubMed · Cited by 257 · see citing works
The Burden of Uterine Fibroids for African-American Women: Results of a National Survey
Stewart, E. A., Nicholson, W. K., Bradley, L., & Borah, B. J. (2013) · Journal of Women s Health · doi:10.1089/jwh.2013.4334 · Cited by 255 · see citing works
Petersen, R., Pan, L., & Blanck, H. M. (2019) · Preventing Chronic Disease · doi:10.5888/pcd16.180579 · Cited by 222 · see citing works
An epidemiological survey of symptoms of menstrual loss in the community.
Shapley, M., Jordan, K. P., & Croft, P. R. (2004) · Europe PMC (PubMed Central) · Cited by 193 · see citing works
Length and variation in the menstrual cycle--a cross-sectional study from a Danish county
MÜNSTER, K., SCHMIDT, L., & HELM, P. (1992) · BJOG: An International Journal of Obstetrics & Gynaecology · doi:10.1111/j.1471-0528.1992.tb13762.x · Cited by 193 · see citing works
Health-related quality of life and economic burden of abnormal uterine bleeding
Fraser, I. S., Langham, S., & Uhl-Hochgraeber, K. (2009) · Expert Review of Obstetrics & Gynecology · doi:10.1586/17474108.4.2.179 · Cited by 185 · see citing works
Menstrual pattern and common menstrual disorders among university students in Turkey
Çakır, M., Mungan, İ., Karakaş, T., et al. (2007) · Pediatrics International · doi:10.1111/j.1442-200x.2007.02489.x · Cited by 183 · see citing works
Wang, Y., Arvizu, M., Rich-Edwards, J. W., Stuart, J. J., Manson, J. E., Missmer, S. A., Pan, A., & Chavarro, J. E. (2020) · BMJ · doi:10.1136/bmj.m3464 · Cited by 179 · see citing works
How regular is regular? An analysis of menstrual cycle regularity
Creinin, M. D., Keverline, S., & Meyn, L. A. (2004) · Contraception · doi:10.1016/j.contraception.2004.04.012 · Cited by 168 · see citing works
Accuracy of Reporting of Menstrual Cycle Length
Jukic, A. M. Z., Weinberg, C. R., Wilcox, A. J., et al. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwm265 · Cited by 148 · see citing works
Self-citationDisturbances of Menstruation in Thyroid Disease
Koutras, D. A. (1997) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1997.tb52152.x · Cited by 137 · see citing works
The analysis of menstrual bleeding patterns: A review
Belsey, E. M. (1988) · Contraception · doi:10.1016/0010-7824(88)90035-2 · Cited by 105 · see citing works
Li, K., Urteaga, I., Wiggins, C. H., Druet, A., Shea, A. A., Vitzthum, V. J., & Elhadad, N. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0269-8 · Cited by 100 · see citing works
Harlow, S. D., Lin, X., & Ho, M. J. (2000) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(99)00202-4 · Cited by 97 · see citing works
Menstrual bleeding patterns in untreated women
Belsey, E. M. (1997) · Contraception · doi:10.1016/s0010-7824(96)00273-9 · Cited by 95 · see citing works
A mixed methods exploratory study of women’s relationships with and uses of fertility tracking apps
Gambier-Ross, K., McLernon, D. J., & Morgan, H. M. (2018) · Digital Health · doi:10.1177/2055207618785077 · Cited by 94 · see citing works
Menstrual irregularity and asthma and lung function
Real, F. G., Svanes, C., Omenaas, E. R., Antó, J. M., Plana, E., Janson, C., Jarvis, D., Zemp, E., Wjst, M., Leynaert, B., et al. (2007) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2007.04.041 · Cited by 90 · see citing works
Assessment of menstrual health status and evolution through mobile apps for fertility awareness
Symul, L., Wac, K., Hillard, P. J. A., & Salathé, M. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0139-4 · Cited by 80 · see citing works
Nguyen, B. T., Pang, R. D., Nelson, A. L., Pearson, J. T., Noccioli, E. B., Reissner, H., von Schwarzenfeld, A. K., & Acuna, J. M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258314 · Cited by 71 · see citing works
The prevalence of menstrual disorders in Iran: A systematic review and meta-analysis.
Samani, R. O., Almasi‐Hashiani, A., Razavi, M., Vesali, S., Rezaeinejad, M., Maroufizadeh, S., & Sepidarkish, M. (2018) · Europe PMC (PubMed Central) · Cited by 60 · see citing works
Cirillo, P. M., Wang, E. T., Cedars, M. I., Chen, L.-M., & Cohn, B. A. (2016) · International Journal of Cancer · doi:10.1002/ijc.30144 · Cited by 57 · see citing works
Real world data on symptomology and diagnostic approaches of 27,840 women living with endometriosis
Becker, K., Heinemann, K., Imthurn, B., Marions, L., Moehner, S., Gerlinger, C., Serrani, M., & Faustmann, T. A. (2021) · Scientific Reports · doi:10.1038/s41598-021-99681-3 · Cited by 56 · see citing works
Menstrual cycle characteristics and fecundability in a North American preconception cohort
Wesselink, A. K., Wise, L. A., Hatch, E. E., Rothman, K. J., Mikkelsen, E. M., Stanford, J. B., McKinnon, C. J., & Mahalingaiah, S. (2016) · Annals of Epidemiology · doi:10.1016/j.annepidem.2016.05.006 · Cited by 54 · see citing works
Self-citationEthnic Differences in the Length of the Menstrual Cycle during the Postmenarcheal Period
Harlow, S. D., Campbell, B., Lin, X., & Raz, J. A. (1997) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a009316 · Cited by 53 · see citing works
Kazemijaliseh, H., Tehrani, F. R., Behboudi‐Gandevani, S., et al. (2017) · PubMed · Cited by 48 · see citing works
Jacobson, A. E., Vesely, S. K., Haamid, F. W., Christian‐Rancy, M., & O’Brien, S. H. (2017) · Journal of Pediatric and Adolescent Gynecology · doi:10.1016/j.jpag.2017.09.009 · Cited by 44 · see citing works
Paramsothy, P., Harlow, S., Greendale, G. A., et al. (2014) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.12768 · Cited by 43 · see citing works
Menstrual irregularity as a biological limit to early pregnancy awareness
Nobles, J. E., Cannon, L. M., & Wilcox, A. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113762118 · Cited by 42 · see citing works
Design and methods of the Apple Women’s Health Study: a digital longitudinal cohort study
Mahalingaiah, S., Fruh, V., Rodriguez, E., Konanki, S. C., Onnela, J.-P. “., Veiga, A. D. F., Lyons, G. R., Ahmed, R., Li, H., Gallagher, N. J., et al. (2021) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2021.09.041 · Cited by 41 · see citing works
Self-citationThe epidemiology of self‐reported intermenstrual and postcoital bleeding in the perimenopausal years
Shapley, M., Bucknall, M., Jordan, K. P., & Croft, P. R. (2013) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.12218 · Cited by 19 · see citing works
Hypothyroidism and its Effect on Menstrual Pattern and Fertility.
Urmi, S. J., Begum, S. R., Fariduddin, M., et al. (2015) · PubMed · Cited by 13 · see citing works
Menorrhagia, active component service women, U.S. Armed Forces, 1998-2012.
Dorsey, K. A. (2013) · PubMed · Cited by 11 · see citing works
Is the Menstrual Cycle Truly a Vital Sign?
Sanfilippo, J. S. (2014) · Journal of Pediatric and Adolescent Gynecology · doi:10.1016/j.jpag.2014.10.004 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review
Graca, S., Alloh, F., Lagojda, L., Dallaway, A., Kyrou, I., Randeva, H. S., & Kite, C. (2024) · Healthcare · doi:10.3390/healthcare12161671 · Cited by 14 · see citing works
- Authors
- Zhang, C. Y., Li, H., Zhang, S., Suharwardy, S., Chaturvedi, U., Fischer-Colbrie, T., Maratta, L. A., Onnela, J.-P., Coull, B. A., Hauser, R., Williams, M. A., Baird, D. D., Jukic, A. M. Z., Mahalingaiah, S., & Curry, C. L.
- Affiliations
- Apple (United States) (US)
- Harvard University (US)
- Stanford Medicine (US)
- National Institutes of Health (US)
- Corresponding author
- Curry, Christine L.
- ORCID
- Venue
- American Journal of Obstetrics and Gynecology
- Publisher
- Elsevier BV
- Published
- 2023-02
- Volume
- 228
- Issue
- 2
- Pages
- 213.e1-213.e22
- ISSN
- 0002-9378 (ISSN-L)
- PMC
- PMC9877138
- OpenAlex
- W4307821025
- Cited by
- 25
- References
- 47
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 7 · 2023: 2
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- American Journal of Obstetrics and Gynecology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- Funders
- U.S. Environmental Protection Agency
- National Science Foundation
- Apple Inc
- National Institutes of Health
- National Institute of Environmental Health Sciences (Z0ES049003, Z0ES1033333)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Menstrual Health and Disorders
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Reproductive Health and Contraception
- Keywords
- abnormal uterine bleeding, menstrual tracking, menstrual irregularity, metrorrhagia, spotting, prevalence, obesity, body mass index, racial disparities, Black women, Apple Watch, menstrual health, hormonal contraception, oral contraceptives, hormonal IUD
- MeSH terms
- Adult, Endometriosis, Female, Humans, Menorrhagia, Menstruation Disturbances, Obesity, Polycystic Ovary Syndrome, Pregnancy, Women's Health
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
