On any given day, more than million women worldwide are menstruating. In total, an estimated million lack access to menstrual products and adequate facilities for menstrual hygiene management MHM.

To effectively manage their menstruation, girls and women require access to water, sanitation and hygiene WASH facilities, affordable and appropriate menstrual hygiene materials, information on good practices, and a supportive environment where they can manage menstruation without embarrassment or stigma.

They understand the basic facts linked to the menstrual cycle and how to manage it with dignity and without discomfort or fear.

The challenges that menstruating girls, women, and other menstruators face encompass more than a basic lack of supplies or infrastructure. While menstruation is a normal and healthy part of life for most women and girls, in many societies, the experience of menstruators continues to be constrained by cultural taboos and discriminatory social norms.

The resulting lack of information about menstruation leads to unhygienic and unhealthy menstrual practices and creates misconceptions and negative attitudes, which motivate, among others, shaming, bullying, and even gender-based violence.

For generations of girls and women, poor menstrual health and hygiene is exacerbating social and economic inequalities, negatively impacting their education, health, safety, and human development. The multi-dimensional issues that menstruators face require multi-sectoral interventions.

WASH professionals alone cannot come up with all of the solutions to tackle the intersecting issues of inadequate sanitary facilities, lack of information and knowledge, lack of access to affordable and quality menstrual hygiene products, and the stigma and social norms associated with menstruation.

Research has shown that approaches that can effectively combine information and education with appropriate infrastructure and menstrual products, in a conducive policy environment, are more successful in avoiding the negative effects of poor MHH — in short, a holistic approach requiring collaborative and multi-dimensional responses.

Priority Areas. In low-income countries, half of the schools lack adequate water, sanitation, and hygiene services crucial to enable girls and female teachers to manage menstruation UNICEF Schools that have female-friendly facilities and incorporate information on menstruation into the curriculum for both girls and boys can reduce stigma and contribute to better education and health outcomes.

When girls and women have access to safe and affordable sanitary materials to manage their menstruation, they decrease their risk of infections. This can have cascading effects on overall sexual and reproductive health, including reducing teen pregnancy, maternal outcomes, and fertility.

Poor menstrual hygiene, however, can pose serious health risks, like reproductive and urinary tract infections which can result in future infertility and birth complications.

Neglecting to wash hands after changing menstrual products can spread infections, such as hepatitis B and thrush. Awareness of MHH contributes to building an enabling environment of nondiscrimination and gender equality in which female voices are heard, girls have choices about their future, and women have options to become leaders and managers.

In addition, feminine hygiene products are a multibillion-dollar industry, which, if properly tapped into, can generate income for many and significantly boost economic growth.

Disposable sanitary products contribute to large amounts of global waste. Ensuring women and girls have access to sustainable and quality products, and improving the management of the disposal of menstrual products, can make a big difference to the environment.

In India alone, roughly million women and girls use an average of eight disposable and non-compostable pads per month, generating 1. Country Examples. Enhancing opportunities for women to access adequate menstrual health and hygiene is central to the World Bank Group in achieving its development outcomes.

In addition, the project is facilitating behavior change sessions and training on the importance of menstrual hygiene and safely managed WASH facilities. Access to finance will be provided to women entrepreneurs to help them market and sell soaps, disinfectants and menstrual hygiene products at household doorsteps.

This will improve menstrual hygiene practices, especially among those who are too shy and reluctant to purchase them at public markets.

This includes gender-separated facilities with door locks, lighting, disposal bins, and handwashing stations with soap and water. Behavior changes and hygiene promotion campaigns incorporating MHH will be undertaken, targeting students, teachers, parents and the larger community.

Under the project, sanitation facilities were constructed at more than schools across the Greater Accra Metropolitan Area. The facilities all include separate toilets and changing rooms for girls, with locks on doors, handwashing facilities, and hygienic and safe spaces for disposal of used sanitary products.

This article looks at common side effects, risks, and…. A full-term pregnancy lasts about 40 weeks and has three trimesters. Here, learn about the changes for the person and their baby before and after….

People may experience cravings for substances that have little to no nutritional value, this is known as pica. Pica is common in pregnant people…. Xulane is prescribed to help prevent pregnancy. Find out what the recommended dosage is, how to apply the drug, and more.

My podcast changed me Can 'biological race' explain disparities in health? Why Parkinson's research is zooming in on the gut Tools General Health Drugs A-Z Health Hubs Health Tools Find a Doctor BMI Calculators and Charts Blood Pressure Chart: Ranges and Guide Breast Cancer: Self-Examination Guide Sleep Calculator Quizzes RA Myths vs Facts Type 2 Diabetes: Managing Blood Sugar Ankylosing Spondylitis Pain: Fact or Fiction Connect About Medical News Today Who We Are Our Editorial Process Content Integrity Conscious Language Newsletters Sign Up Follow Us.

Medical News Today. Health Conditions Health Products Discover Tools Connect. Can you get pregnant on your period? Medically reviewed by Wendy A. Satmary, MD — By Danielle Dresden and Tom Rush — Updated on July 12, How likely it is Right before or after your period Ovulation Birth control Summary People can get pregnant at any time during their menstrual cycle, though it is much less likely during their period.

Can you get pregnant during your period? What are the chances of getting pregnant during your period? Can you get pregnant right before or after your period? More about ovulation. Impact of birth control.

How we reviewed this article: Sources. Medical News Today has strict sourcing guidelines and draws only from peer-reviewed studies, academic research institutions, and medical journals and associations. We avoid using tertiary references. We link primary sources — including studies, scientific references, and statistics — within each article and also list them in the resources section at the bottom of our articles.

You can learn more about how we ensure our content is accurate and current by reading our editorial policy. Share this article. Latest news Ovarian tissue freezing may help delay, and even prevent menopause. RSV vaccine errors in babies, pregnant people: Should you be worried? How gastric bypass surgery can help with type 2 diabetes remission.

Atlantic diet may help prevent metabolic syndrome. How exactly does a healthy lifestyle help prevent dementia? Related Coverage. What types of birth control are there? Medically reviewed by Cynthia Cobb, DNP, APRN, WHNP-BC, FAANP.

Medically reviewed by Valinda Riggins Nwadike, MD, MPH. Pregnancy trimesters: A guide. What to know about pica in pregnancy.

The chorionic membrane is a part of the placenta. During some pregnancies, blood forms between the uterus and this membrane, resulting in vaginal bleeding. The heaviness of bleeding can range from light spotting to a heavy flow.

A small number of women with a subchorionic hemorrhage experience cramping along with bleeding. It's also possible to have a hemorrhage without experiencing any bleeding. When this happens, the hemorrhage is usually detected during a routine ultrasound.

Women diagnosed with a subchorionic hemorrhage will usually have a follow-up ultrasound to confirm that the hematoma is going away rather than enlarging.

Many women with a subchorionic hemorrhage in early pregnancy go on to have healthy, full-term pregnancies. Most women can safely continue with sexual activity during pregnancy. However, sexual intercourse and foreplay that includes vaginal penetration can sometimes cause irritation.

When this occurs, a pregnant woman may experience spotting, which typically resolves on its own. During pregnancy, the placenta can implant over any surface on the uterus, although typically the implantation is higher up. When the placenta implants too low, it can cover or closely approach the cervix, leading to a condition called placenta previa.

Painless, bright red vaginal bleeding that happens during the second half of pregnancy is the most common symptom of placenta previa. The condition is usually diagnosed at the time of your anatomy scan ultrasound around 20 weeks of pregnancy.

In many cases, the bleeding comes and goes. When bleeding occurs due to placenta previa, medical care is necessary to protect both the mother and baby during the remainder of pregnancy and delivery. Vaginal bleeding is the most common symptom of placental abruption, and in some cases, the bleeding is severe enough to pose a risk to the mother.

Placental abruption is usually associated with cramping pains or contractions. Like placenta previa, placental abruption requires medical care.

Your provider can conduct an examination and run tests to determine if there is a problem and then recommend treatments as needed. Home - The Thread Health Can you be pregnant and still have a period? Can you get your period during pregnancy? What are the potential causes of vaginal bleeding during pregnancy?

Implantation bleeding. Ectopic pregnancy. Subchorionic hemorrhage. Sexual activity. Placenta previa. Placental abruption. What to do about vaginal bleeding. Explore more. Can you get your period on birth control?

By Patricia Ann Convery, MD, Fellow, American College of Obstetrics and Gynecology. How often do you get your period? By Patricia Ann Convery, MD Fellow, American College of Obstetrics and Gynecology.

Top of each chart Original user observations as in Fig. Middle of each chart Colored squares HMM-labeled line represent the most likely sequence of HMM states given the observations Methods.

Bottom of each chart Normalized probabilities of each state on each day of the cycle Methods. b Top Cycle length and estimated ovulation day. Bottom Luteal phase duration, computed as the number of days between the ovulation day excluded and the 1st day of the next cycle excluded.

Vertical lines indicate median values. c Average estimated state probabilities by cycle-day counting from estimated ovulation aggregated by total cycle length in bins of 3 units for all cycles with reliable ovulation estimation. These estimations allowed the comparison, for cycles with reliable ovulation estimation , cycles, Methods , of the cycle length distribution to those of estimated day of ovulation and of the duration of the luteal phase i.

Cycle length distribution is asymmetrical around the typical 27 to 28 days, with a heavy tail on longer cycles. Similarly, the distribution of the follicular i. Luteal phase duration distribution, which is also asymmetrical, presents however a skew for smaller values and a smaller standard deviation Fig.

Median values were 12 K and 13 S days, which is in line with a previous study that used fertility monitors 31 but shorter than values reported in studies that used luteinizing hormone LH peak for timing of ovulation 14 days. Overall, the comparison with previous studies of the cycle phases duration and range shows that the follicular phase and the whole cycle length have higher mean values and larger ranges than what was previously observed, while the luteal phase duration and range was closer to those found in previous studies 28 , 29 , 31 , 32 , 33 Supplementary Fig.

The primary aim was to provide health practitioners with an overview of how and what FAM app users voluntarily track on these apps. Many, if not most clinicians are unfamiliar with the specifics of health-related apps, and thus the information from this study may provide clinically helpful information.

The secondary aim was to propose a mathematical framework to estimate the underlying hormonal states and most likely day of ovulation from FAM observation. This allowed a comparison of the duration of the menstrual cycle phases from the present digital study with reported values from previous clinical studies.

The typical FAM app user is about 30 years old, lives in a western country in Europe or Northern America and has a healthy BMI. The height, weight and BMI ranges reported by Sympto users are similar to those reported for the French population, 34 which is where most Sympto users are located.

Thus, to the extent that these users differ from the general population, our results may be more or less generalizable to other populations.

The tracking frequency of users that utilize the apps for FAM tracking, is on average higher than the minimum required to detect changes associated with ovulation. In particular, if users rely on the app for their family planning, i. The reported FAM observations BBT, cervical mucus changes, cervix openness, etc.

are overall aligned with expected patterns of FAM-related body signs, showing that these apps enable hundreds of thousands of users across Europe and North America to follow their fertility and ovulation patterns.

Temperature is found to increase by 0. The aggregated patterns of the reported menstrual body-signs are in good agreement between the two applications despite different app design, user experience and targeted populations Methods. Individual cycles often present noisy profiles, and missing data are a frequent concern.

To partly alleviate these issues, the mathematical framework HMM used in this study discretizes the menstrual cycle in independent successive biologically-relevant states and allows the estimation of ovulation timing along with uncertainty indicators. The variation range in the ovulation time and in the luteal phase duration was found to be larger than previously described in other studies 29 , 31 , 32 , 35 that relied on much smaller populations but that used biomarkers which offer a greater precision for the estimation of ovulation time.

Interestingly, the cycle phases distributions were slightly different when considering the data from the two apps.

These differences might be due to biases found in the user population, especially for users seeking pregnancy that could be at higher risk of sub-fertility if assumed that they start tracking after they have already tried to get pregnant for several months Supplementary Fig.

The strength of this study lies in the scale and precision of the datasets, as a variety of fertility patterns are captured, and as users track the evolution of their cycles at a high frequency over long intervals of time. It also provides a non-proprietary and replicable mathematical method to infer biological states, and in particular to estimate the timing of ovulation, from fertility awareness self-tracked data.

The most obvious potential limitation of this study comes from the origin of these retrospective data: a self-selected possibly biased population, limited medical and general information on users, irregular observation patterns and little control on assessing the validity of the observations, in particular with regard to cervical mucus tracking.

While the tracking frequency limitation can be alleviated through strict selection of users and cycles Methods , all other limiting factors might have introduced biases in the present analysis.

Prospective studies on selected cohorts with appropriate follow-up and information provided to users will provide higher quality data, which could then be used for comparison. While this study does not assess the benefits for users to use tracking apps compared to relying on their memory or charting their cycles on paper or in their personal calendars, it provides clinicians and digital epidemiologists with an overview of the expected tracking behaviors and body-signs patterns, so that they can evaluate the suitability and benefits of digital self-tracking for their clinical practice or for the design of prospective studies.

Based on the current findings, it appears that digital self-tracking of FAM-related body signs could provide a more accessible, although less precise, means to evaluate the status and evolution of menstrual health than traditional medical monitoring which requires frequent office visits for ultrasounds or hormonal testing from blood or disposable urinary tests.

The self-tracked observations presented here require only a standard thermometer with a 0. Digital self-tracking, compared to paper-based tracking or memory-relying surveys, supplies standardized records and scalable collection methods.

Typically, digital self-tracking of fertility-awareness body signs offers an interesting option for clinicians or researchers interested in changes of a variable of interest for example level of pain or occurrence of a given symptom across the menstrual cycle, or in the overall changes in menstrual rhythmicity.

For investigations requiring a precise assessment of hormonal levels or ovulation timing, additional tests would be necessary until the accuracy and precision of methods using FAM digital records can be established.

The long term and yet very precise recordings presented in this study support the idea that the menstrual cycle, like other biological rhythms, is a vital sign whose variations inform about overall health status.

Models could also be established to investigate potential sub-fertility causes anovulation, recurrent early pregnancy losses, etc. More generally, such data and tracking apps, combined with tracking of other coexisting symptoms, health indicators or behavioral markers, enable the exploration of the menstrual dimension of the course of chronic diseases.

Many menstrual symptoms associated with the pre-menstrual syndrome PMS , such as mastalgia breast pain , or disease, like migraine that can exist in a menstrual or non-menstrual form, have been shown to be associated with steroid hormones although the exact causes have not been elucidated yet.

distinct forms of symptom expression in the population. It is likely that users of such applications already have an increased awareness of their cycles, and this study suggests that these digitally self-tracked observations potentially present an opportunity to facilitate the dialog between patients and their clinicians, helping them to make informed decisions based on quantified indicators.

Extended Materials and methods can be found in the Supplementary Materials. To briefly summarize the methodology used in this study: datasets were first filtered to keep cycles of users using the apps for fertility awareness purposes, i. to self-identify their fertility window, for at least 4 cycles.

Data were then aggregated to describe the overall observation patterns. Finally, a Hidden Markov Model HMM was defined and used to detect ovulation time and assess the reliability of this estimation. Two de-identified retrospective datasets were acquired from the Symptotherm foundation www.

org ; Switzerland and Kindara www. com ; US upon receiving ethical approval from the Canton Geneva ethical commission CCER Genève, Switzerland , study number — These two apps were selected as they both ranked high in a study comparing the performances of apps marketed to avoid pregnancy using FAMs, 11 as their privacy policies specified the use of their de-identified datasets for research purposes and as their user pools were very large or diverse geographically and culturally.

Sympto was released in and is available worldwide in eight languages English, French, German, Italian, Spanish, Polish, Russian, and Bulgarian. Kindara has been released in and is available worldwide in English. Both organizations de-identified their datasets before transferring them to the authors.

Both apps are available on iOS and Android platforms and are available as free simplified or paid apps. All features used in this study are available in the free versions of the apps.

Kindara provided a random subset of their overall pool of users with at least 4 logged cycles users, 2,, cycles while Sympto provided observations from their long-term users at least 4 cycles tracked with the app and from users who provided their weight, height and menarche age 13, users, 79, cycles.

Both apps offer similar FAM tracking options but differ in their design and user experience Supplementary Fig. A description of the datasets fields is provided in Table 2.

Kindara K is primarily marketed to women who wish to achieve pregnancy and does not provide feedback to users in terms of the opening or closing of their fertile window.

Sympto S is marketed as a family planning tool that can be utilized to plan or avoid a pregnancy. The Sympto app provides feedback to their users based on their observations, indicating when they are potentially fertile, very fertile or infertile. The key differences between these two apps are i the algorithmic- S vs.

user- K interpretation of observations, ii the per-cycle S vs. per-user K definition of fertility goals users wish to achieve, iii the criteria for the onset of a new cycle, i. self-assessed or automatic, based on first day of reported bleeding K , and iv the resolution at which users can report their observations Table 2 , Supplementary Material.

Given that these are self-tracked data, missing data is a frequent issue, and many cycles within the datasets provided by the app were not suitable for the analyses of this study. We followed an iterative approach in which we first inspected the raw datasets and identified patterns or behavior that were inconsistent with the aims of the study for example, on-going cycles.

Finally, the HMM was used to estimate ovulation and, for the reports of cycle length, follicular and luteal phase durations, only cycles in which ovulation could reliably be estimated were kept Fig.

Sympto: 39, cycles; Kindara: , cycles denote cycles of regular users of the apps in which FAM body signs have been logged. Typically, cycles with long tracking gaps or in which only the period flow was logged were excluded. S defined as ovulatory cycles by the STM algorithm of Sympto, i.

K cycle length was at least 4 days longer than the total number of days in which bleeding was reported. Detected temperature shift was at least 0. The uncertainty on the ovulation estimation as provided by the HMM framework developed here was lower than ±1. For each standard cycle, the tracking frequency was computed as the number of days with observations in that cycle divided by the length of the cycle.

For both app, observations of all standard cycles were summarized by cycle-day. For the temperature, as the important feature to detect if ovulation has occurred is the relative rise in temperature, a reference temperature was computed for each cycle.

This reference temperature was identified as the 0. Relative temperature measurements were then computed as the difference between the logged temperature and this reference temperature. The distribution at a resolution of 0.

The FAM body-signs are considered to reflect the hormonal changes orchestrating the menstrual cycles. The study was focused on understanding the extent to which these tracked cycles were consistent with previously described menstrual cycle physiologic changes, and the extent to which it was thus possible for app users to estimate timing of ovulation.

Hidden Markov Models HMM are one of the most suitable mathematical frameworks to estimate ovulation timing, due to their ability to uncover, from observations, latent phenomenon, which in this use include the cascade of hormonal events across the menstrual cycle. HMM have also been previously used for analysis of menstrual periodicity.

The HMM as implemented in this study describes a discretization in 10 states of the successive hormonal events throughout an ovulatory menstrual cycle. The HMM definition includes the probabilities of observing the different FAM reported body signs in each state emission probabilities and the probabilities of switching from one state to another transition probabilities.

Emission probabilities were chosen to reflect observations previously made in studies that tested for ovulation with LH tests or ultrasounds, 6 , 8 , 27 while transition probabilities were chosen in a quasi-uniform manner Supplementary Material.

The ovulation estimations were robust to changes in transition probabilities but not to variations in emission probabilities Supplementary Fig. Once the model was defined, the Viterbi and the Backward—Forward algorithms 47 were used to calculate the most probable state sequence for each cycle Supplementary Material and thus to estimate ovulation timing, i.

Finally, a confidence score was defined to account for missing observations and variation in temperature taking time in a window of ~5 days around the estimated ovulation day Supplementary Material.

The ten states, defined as a discretization of the hormonal evolution across the cycle further details in Supplementary Material , are:. Further information on research design is available in the Nature Research Reporting Summary linked to this article. Data are however available from the authors upon reasonable request and with permission of Sympto and Kindara.

Lamprecht, V. Natural family planning effectiveness: evaluating published reports. Article CAS PubMed Google Scholar. Peragallo Urrutia, R. et al. Effectiveness of fertility awareness-based methods for pregnancy prevention.

Google Scholar. Marshall, J. Cervical mucus and basal body temperature method of regulating births field trial. Lancet , — Article Google Scholar. Moghissi, K. Prediction and detection of ovulation. In: Modern Trends in Infertility and Conception Control eds Wallach, E.

Cyclic changes of cervical mucus in normal and progestin-treated women. Billings, E. Symptoms and hormonal changes accompanying ovulation. Wilcox, A. BMJ , — Article CAS PubMed PubMed Central Google Scholar.

Frank-Herrmann, P. Determination of the fertile window: reproductive competence of women—European cycle databases. Article PubMed Google Scholar. Bigelow, J. Mucus observations in the fertile window: a better predictor of conception than timing of intercourse.

Duane, M. The performance of fertility awareness-based method apps marketed to avoid pregnancy. Board Fam. Dreaper, J.

Women warned about booming market in period tracker apps - BBC News. BBC Moglia, M. Evaluation of smartphone menstrual cycle tracking applications using an adapted applications scoring system.

Freis, A. Plausibility of menstrual cycle apps claiming to support conception. Public Health 6 , 1—9 Berglund Scherwitzl, E. Fertility awareness-based mobile application for contraception. Health Care 21 , — Article PubMed PubMed Central Google Scholar.

Perfect-use and typical-use Pearl Index of a contraceptive mobile app. Identification and prediction of the fertile window using Natural Cycles. Health Care 20 , — Alvergne, A. Do sexually transmitted infections exacerbate negative premenstrual symptoms? Insights from digital health.

Health , — Pierson, E. Modeling individual cyclic variation in human behavior. Liu, B. The World Wide Web Conference. Barron, M. Expert in fertility appreciation: the Creighton Model practitioner. Neonatal Nurs. Templeton, A.

Relation between the luteinizing hormone peak, the nadir of the basal body temperature and the cervical mucus score. BJOG Int. Article CAS Google Scholar. Case, A.

Menstrual cycle effects on common medical conditions. Spencer, E. Validity of self-reported height and weight in EPIC—Oxford participants. Public Health Nutr. Accuracy of basal body temperature for ovulation detection. A composite picture of the menstrual cycle.

Lenton, E. Normal variation in the length of the follicular phase of the menstrual cycle: effect of chronological age. Normal variation in the length of the luteal phase of the menstrual cycle: identification of the short luteal phase.

Presented at the Thirty-Second Annual Meeting of the American Fertility Society, April 5 to 9, , Las Vegas, Nev. Fehring, R. Variability in the phases of the menstrual cycle. Cole, L. The normal variabilities of the menstrual cycle. Harlow, S. Public Health.

Accessed 13 Mar Crawford, N. Prospective evaluation of luteal phase length and natural fertility. American Academy of Pediatrics and American College of Obstretricians and Gynecologists.

Menstruation in girls and adolescents: using the menstrual cycle as a vital sign. Is female health cyclical? Evolutionary perspectives on menstruation.

Trends Ecol. In Press. Salathé, M. Digital epidemiology. PLoS Comput. Grayson, M. Nature , S1 Pinkerton, J. Menstrual cycle-related exacerbation of disease. Yonkers, K.

Premenstrual disorders. Smith, R. Evaluation and management of breast pain.

Pinkerton, J. Menstrual cycle-related exacerbation of disease. Yonkers, K. Premenstrual disorders. Smith, R. Evaluation and management of breast pain. Mayo Clin. Vetvik, K. Symptoms of premenstrual syndrome in female migraineurs with and without menstrual migraine. Headache Pain. Kernich, C.

Migraine headaches. Neurologist 14 , — Allais, G. Treating migraine with contraceptives. Güven B. Clinical characteristics of menstrually related and non-menstrual migraine. Acta Neurol. Rabiner, L. A tutorial on hidden Markov models and selected applications in speech recognition.

IEEE 77 , — Download references. The authors are deeply grateful to all Kindara and Sympto users whose data have been used for this study and to the Symptotherm foundation and Kindara company. In particular, we thank Dr. Wettstein, C. Bourgeois, V.

Salonna, T. Newcomer, T. Baras, C. Allémann, P. Ducoeurjoly, and F. Goddyn for sharing their experience, references and for fruitful discussions. We thank S. Holmes, C. Droin and G. Lazzari for discussion on the mathematical modeling. Department of Surgery, Stanford School of Medicine, Stanford University, Pasteur Dr.

Digital Epidemiology Lab, Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne EPFL , Campus Biotech, Chemin des mines 9, , Geneva, Switzerland. Quality of Life Technologies lab, Institute of Services Science, Center for Informatics, University of Geneva, CUI Battelle bat A, Route de Drize 7, , Carouge, Switzerland.

DIKU, University of Copenhagen, Copenhagen, Denmark. HH, Stanford, CA, , USA. You can also search for this author in PubMed Google Scholar. initiated and conceived the study, analyzed the data and designed the figures, L. and M. wrote the manuscript. All authors discussed the results and implications and commented on the manuscript at all stages.

Correspondence to Laura Symul. discloses that she is a consultant and medical advisor to Clue by Biowink. The remaining authors declare no competing interests. Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions. Symul, L. Assessment of menstrual health status and evolution through mobile apps for fertility awareness.

npj Digit. Download citation. Received : 15 September Accepted : 08 May Published : 16 July Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature.

nature npj digital medicine articles article. Download PDF. Subjects Computational models Epidemiology Reproductive signs and symptoms. Abstract For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician.

Introduction A broad diversity of fertility awareness methods FAMs has been developed in the past century, 1 , 2 primarily designed to help couples manage fertility and family planning. Full size image.

Table 1 Number of observations, cycles and users Full size table. Reported fertility awareness body signs exhibit temporal patterns at the user population level Confident that users regularly logged observations Fig.

Methods Extended Materials and methods can be found in the Supplementary Materials. Mobile phone applications and data acquisition Two de-identified retrospective datasets were acquired from the Symptotherm foundation www.

Table 2 Reported observations Full size table. References Lamprecht, V. Article CAS PubMed Google Scholar Peragallo Urrutia, R. Google Scholar Marshall, J. Article Google Scholar Moghissi, K. Article CAS PubMed Google Scholar Billings, E.

Article Google Scholar Wilcox, A. Article CAS PubMed PubMed Central Google Scholar Frank-Herrmann, P. Article PubMed Google Scholar Bigelow, J. Article PubMed Google Scholar Frank-Herrmann, P. Article CAS PubMed Google Scholar Duane, M.

Article PubMed Google Scholar Dreaper, J. Article PubMed Google Scholar Moglia, M. Article Google Scholar Freis, A. Article Google Scholar Berglund Scherwitzl, E. Article PubMed PubMed Central Google Scholar Berglund Scherwitzl, E.

Article CAS PubMed PubMed Central Google Scholar Berglund Scherwitzl, E. Article PubMed Google Scholar Alvergne, A. Article Google Scholar Pierson, E. Article CAS PubMed Google Scholar Templeton, A. Article CAS Google Scholar Case, A.

Article CAS PubMed Google Scholar Spencer, E. Article PubMed Google Scholar Moghissi, K. Article CAS PubMed Google Scholar Moghissi, K. Article CAS PubMed Google Scholar Lenton, E. Article CAS PubMed Google Scholar Fehring, R. Article PubMed Google Scholar Cole, L.

Article CAS PubMed Google Scholar Harlow, S. Article CAS PubMed Google Scholar Eurostat. Article PubMed PubMed Central Google Scholar American Academy of Pediatrics and American College of Obstretricians and Gynecologists. Google Scholar Alvergne, A. Article PubMed Google Scholar Salathé, M. Article Google Scholar Grayson, M.

Article CAS PubMed Google Scholar Pinkerton, J. Article PubMed PubMed Central Google Scholar Yonkers, K. Article PubMed Google Scholar Smith, R. Article PubMed Google Scholar Vetvik, K. Article PubMed Google Scholar Allais, G. Article PubMed Google Scholar Güven B.

Article PubMed Google Scholar Rabiner, L. Article Google Scholar Download references. Acknowledgements The authors are deeply grateful to all Kindara and Sympto users whose data have been used for this study and to the Symptotherm foundation and Kindara company.

Author information Authors and Affiliations Department of Surgery, Stanford School of Medicine, Stanford University, Pasteur Dr. HH, Stanford, CA, , USA Paula Hillard Authors Laura Symul View author publications. View author publications. Ethics declarations Competing interests P. Supplementary information.

Supplementary Material. Reporting Summary. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4. About this article. Cite this article Symul, L. Copy to clipboard. Schantz Claudia S. Fernandez Anne Marie Z. Jukic Current Epidemiology Reports Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data Kathy Li Iñigo Urteaga Noémie Elhadad npj Digital Medicine About the journal Aims and scope Content types Journal Information About the Editors Contact Editorial policies Calls for Papers Journal Metrics About the Partner Open Access Early Career Researcher Editorial Fellowship Editorial Team Vacancies.

There was a U-shaped relation between cycle length and preterm birth with both short relative risk [RR] 1. Keywords: adverse pregnancy outcome; birth size; gestational diabetes mellitus; impaired glucose tolerance; menstrual cycle length; preterm birth.

Abstract Background: Retrospective studies suggest that menstrual cycle length may be a risk marker of adverse pregnancy outcomes, but this evidence is susceptible to recall bias. What are work schedule issues? Working at night, during your normal sleep hours, can change your circadian rhythms , which regulates your menstrual cycle and your pregnancy hormones.

Shift work and long working hours have been related to menstrual disorders, miscarriages, and preterm birth. Women who work at night, or who work long hours, often do not get enough sleep. Who works long hours and rotating or night shifts? The number of hours Americans work each week is more than Japan and most of western Europe.

Healthcare workers, flight attendants and pilots, law enforcement workers, and workers in the service industry commonly work long hours and rotating or night shifts.

What is not known? What can I do to make my work schedule better? If possible, avoid working during normal sleep time during pregnancy. Good sleep hygiene sleep practices and habits is always important, but especially for shift workers. Read about general recommendations on sleep hygiene.