Do sexually transmitted infections exacerbate negative premenstrual symptoms? Insights from digital health

Effect of COVID-19 on Menstruation and Lower Reproductive Tract Health

Background

To evaluate the dynamically impact of the coronavirus disease 2019 (COVID-19) on the female reproductive system.

Methods

An online survey was shared to women of reproductive age who had been infected with COVID-19 and recovered in China between January and March 2023.

Results

In total, 610 women of childbearing age completed the menstrual component of the survey and 82.6% (n=504) women self-purchased medications without hospitalization. 254 women were menstruating during COVID-19 infection. 66.9% of them reported changes in menstruation, including cycle length (64.7%), menstrual flow (72.4%), and duration (53%), compared to pre-COVID-19. COVID-19-related chest tightness (OR: 9.5; 95% CI: 1.9-46.3), COVID-19-related stress (OR: 18.4; 95% CI: 1.4-249.7), and COVID-19-related low mood (OR: 6.2; 95% CI: 1.4-28.2) were associated with these menstrual changes. However, over 73% of women who menstruated during and after COVID-19 regained their pre-infection menstrual cycle (73%), duration (79.6%), and flow (75.2%) during their first menstruation after COVID-19 recovery. Compared to pre-infection, 19.7% (n=124) women reported changes in lower reproductive tract during COVID-19, including volume and color of vaginal discharge, vulvar pruritus, and vaginitis. These changes were significantly increased in those with a history of pelvic inflammatory disease (OR: 12.1; 95% CI: 3.1-48.2), ovarian cysts (OR: 4.9; 95% CI: 1.2-19.4), and vaginitis (OR: 5.5; 95% CI: 2.1-14.4) prior to COVID-19. However, 52.4% reported that their lower reproductive tract health had returned to its pre-infection within the first month after recovery from COVID-19, while 73.5% reported a return to the pre-infection within 2 months.

Conclusion

Changes in menstruation and lower reproductive tract associated with COVID-19 are transient. Menstruation and lower reproductive tract health will gradually return to pre-COVID-19 status within 2 months of recovery, which can help alleviate excessive concerns about the effects of COVID-19 on the reproductive system.

Effects of SARS-CoV-2 Vaccination on Menstrual Cycle: An Italian Survey-Based Study

Vaccination against SARS-CoV-2 has played a critical role in controlling the spread of the pandemic. The main side effects of SARS-CoV-2 vaccination include fever and fatigue; however, the potential impacts on menstrual cycles are to be determined. Given the limited number of studies suggesting menstrual changes post vaccination, this study investigates the correlation between COVID-19 vaccines and menstrual cycle changes in fertile-aged Italian women. A questionnaire was distributed from 1 October to 31 November 2022, focusing on menstrual rhythm and flow changes post vaccination. The analysis involved 471 participants. The study observed a shift from a regular to an irregular menstrual rhythm (p < 0.001), and changes in menstrual duration (p = 0.008 and p < 0.001 for first and second doses, respectively) and flow volume (p < 0.001). Most patients with irregular rhythms were vaccinated in the proliferative phase of their cycle. Within six months post vaccination, 74.2% of women with irregular post-vaccination rhythms reported a return to normality. These findings indicate primarily transient menstrual changes following mRNA COVID-19 vaccination, suggesting the vaccines' safety for women of reproductive age.

"GONE WITH THE WIND": The Transitory Effects of COVID-19 on the Gynecological System

The coronavirus disease no longer seems to represent an insurmountable global problem. This is thanks to the advent of coronavirus vaccines, which have alleviated the most serious symptoms associated with this disease. On the other hand, there are still many extrapulmonary symptoms of COVID-19, and among these also those of a gynecological nature. At the moment, there are several questions in this field, one above all concerns the causal link between COVID-19, vaccines and gynecological alterations. Furthermore, another important aspect is represented by the clinical impact of post-COVID-19 gynecological alterations on the female population which, to date, would seem to be mainly due to their duration, even if the extent of these symptoms is still poorly understood. Furthermore, it is not possible to foresee eventual long-term aggravations, or more serious symptoms caused by other viral variants that may arrive in the future. In this review, we focus on this theme and attempt to reorganize the different pieces of a puzzle which, to date, does not seem to have shown us its complete picture.

The DIY IVF cycle-harnessing the power of deeptech to bring ART to the masses

The emergence of telehealth including telemedicine, at-home testing, and mobile health applications has enabled patients to self-manage their reproductive care, especially during the COVID-19 pandemic. Reproduction is rapidly changing and embracing deeptech initiatives that can improve outcomes and facilitate personalized fertility solutions in the near future. This so-called DIY IVF informed by deeptech and moderated by femtech not only holds a tremendous amount of promise, but also challenges and possible pitfalls. This review discusses the current status of deeptech and femtech for IVF care in a post-Roe v. Wade environment.

Menstrual cycle changes and mental health states of women hospitalized due to COVID-19

PURPOSE

Many studies have evaluated the impact of the COVID-19 pandemic on women's mental health and menstrual changes. However, most of these studies only included nonhospitalized COVID-19 patients, while information on hospitalized women is very limited. Thus, this study aimed to examine the mental health status and menstrual changes in hospitalized female COVID-19 patients.

METHODS

A survey was administered to female COVID-19 patients in the isolation ward of a national referral hospital in Indonesia between January and August 2021, and the women were followed up 3 months after discharge. The survey evaluated menstrual patterns and mental health using the Self Reporting Questionnaire-29 (SRQ-29).

RESULTS

The study enrolled 158 female patients. There was an increase in patients who had a cycle length of > 32 or < 24 days, and significant increases in menstrual irregularity and heavy menstrual bleeding were noted. Overall, 37.3% of the patients reported a change in menstrual pattern after infection with COVID-19. Based on SRQ-29 scores, 32.3% of the women had neurotic symptoms, 12.7% had psychotic symptoms, and 38.0% had symptoms of posttraumatic stress disorder. Patients with symptoms of mental health disorders were twice as likely to report a menstrual change (OR 2.17, 95% CI 1.12-4.22; p = 0.021).

CONCLUSION

Menstrual changes and increased symptoms of mental health disorders occur in hospitalized female COVID-19 patients. The length of isolation was the key factor affecting overall menstrual changes and mental health in hospitalized female COVID-19 patients.

COVID-19-Associated Mental Health Impact on Menstrual Function Aspects: Dysmenorrhea and Premenstrual Syndrome, and Genitourinary Tract Health: A Cross Sectional Study among Jordanian Medical Students

The physiology of reproduction is affected by psychological distress through neuroendocrine pathways. Historically, COVID-19 is one of the most stressful events with devastating consequences. This research aims to investigate the relationship between dysmenorrhea, PMS, and reproductive tract health on one hand, and COVID-19-related anxiety, depression, and stress on the other among medical students in Jordan. Medical students were invited through teaching platforms and social media to complete an online survey. SPSS software was used to analyze data. A total of 385 medical students participated in this research. Hence, 49.9% of the study population reported severe dysmenorrhea during COVID-19 compared to 36.9% before COVID-19 (p = 0.000). Dysmenorrhea was significantly associated with disruptions of sport and daily activities during COVID-19 (p = 0.015 and p = 0.002, respectively). The prevalence of PMS components, e.g., mastalgia, fatigue, headache, palpitation, and emotional and sleep disturbances, was raised during COVID-19 compared with before (p < 0.05). Symptoms of genitourinary tract infections, such as lower abdominal pain, vaginal discharge, genitalia rash/ulcers and itching, and urgency, were significantly increased after COVID-19 (p < 0.05). Positive Pearson correlations between COVID-19-associated mental health disorders and dysmenorrhea severity, PMS, and genital tract health abnormalities were observed (p < 0.05). The multiple linear regression model revealed that dysmenorrhea severity, PMS symptoms like palpitation, and genitourinary symptoms like lower abdominal pain and urgency were associated with worsening of depression, while dysuria was associated with a protective effect against depression. Moreover, it was observed that dysmenorrhea severity, PMS symptoms, such as headache and palpitation, and urinary urgency were associated with aggravation of anxiety. However, food craving and dysuria were protective against anxiety. Finally, dysmenorrhea severity, PMS symptoms of headache and palpitation, lower abdominal pain, and urgency were related to worsening of stress, whereas the premenstrual symptom of breast pain was a protective factor against stress. This work showed that COVID-19 pandemic-related psychological distress and menstrual, premenstrual, and genitourinary symptoms are closely related. Further future work is required to evaluate the long lasting-effects of the pandemic on mental health and the physiology of reproduction.

Menstrual Cycle Tracking Applications and the Potential for Epidemiological Research: A Comprehensive Review of the Literature

PURPOSE OF REVIEW

We reviewed published studies on menstrual cycle tracking applications (MCTAs) in order to describe the potential of MCTAs for epidemiologic research.

RECENT FINDINGS

A search of PubMed, Web of Science, and Scopus for MCTA literature yielded 150 articles. After exclusions, there were 49 articles that addressed the primary interest areas: 1) characteristics of MCTA users in research, 2) reasons women use or continue using MCTAs, 3) accuracy of identifying ovulation and utility at promoting and preventing pregnancy, and 4) quality assessments of MCTAs across several domains.

SUMMARY

MCTAs are an important tool for the advancement of epidemiologic research on menstruation. MCTA studies should describe the characteristics of their user-base and missing data patterns. Describing the motivation for using MCTAs throughout a user's life and validating the data collected should be prioritized in future research.

Daily, weekly, seasonal and menstrual cycles in women's mood, behaviour and vital signs

Dimensions of human mood, behaviour and vital signs cycle over multiple timescales. However, it remains unclear which dimensions are most cyclical, and how daily, weekly, seasonal and menstrual cycles compare in magnitude. The menstrual cycle remains particularly understudied because, not being synchronized across the population, it will be averaged out unless menstrual cycles can be aligned before analysis. Here, we analyse 241 million observations from 3.3 million women across 109 countries, tracking 15 dimensions of mood, behaviour and vital signs using a women's health mobile app. Out of the daily, weekly, seasonal and menstrual cycles, the menstrual cycle had the greatest magnitude for most of the measured dimensions of mood, behaviour and vital signs. Mood, vital signs and sexual behaviour vary most substantially over the course of the menstrual cycle, while sleep and exercise behaviour remain more constant. Menstrual cycle effects are directionally consistent across countries.

Assessment of menstrual health status and evolution through mobile apps for fertility awareness

For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician. While these evaluations provide critical information on an individual's reproductive health status, they typically rely on memory-based self-reports, and the results are rarely, if ever, assessed at the population level. In recent years, mobile apps for menstrual tracking have become very popular, allowing us to evaluate the reliability and tracking frequency of millions of self-observations, thereby providing an unparalleled view, both in detail and scale, on menstrual health and its evolution for large populations. In particular, the primary aim of this study was to describe the tracking behavior of the app users and their overall observation patterns in an effort to understand if they were consistent with previous small-scale medical studies. The secondary aim was to investigate whether their precision allowed the detection and estimation of ovulation timing, which is critical for reproductive and menstrual health. Retrospective self-observation data were acquired from two mobile apps dedicated to the application of the sympto-thermal fertility awareness method, resulting in a dataset of more than 30 million days of observations from over 2.7 million cycles for two hundred thousand users. The analysis of the data showed that up to 40% of the cycles in which users were seeking pregnancy had recordings every single day. With a modeling approach using Hidden Markov Models to describe the collected data and estimate ovulation timing, it was found that follicular phases average duration and range were larger than previously reported, with only 24% of ovulations occurring at cycle days 14 to 15, while the luteal phase duration and range were in line with previous reports, although short luteal phases (10 days or less) were more frequently observed (in up to 20% of cycles). The digital epidemiology approach presented here can help to lead to a better understanding of menstrual health and its connection to women's health overall, which has historically been severely understudied.

Predicting pregnancy using large-scale data from a women's health tracking mobile application

Predicting pregnancy has been a fundamental problem in women's health for more than 50 years. Previous datasets have been collected via carefully curated medical studies, but the recent growth of women's health tracking mobile apps offers potential for reaching a much broader population. However, the feasibility of predicting pregnancy from mobile health tracking data is unclear. Here we develop four models - a logistic regression model, and 3 LSTM models - to predict a woman's probability of becoming pregnant using data from a women's health tracking app, Clue by BioWink GmbH. Evaluating our models on a dataset of 79 million logs from 65,276 women with ground truth pregnancy test data, we show that our predicted pregnancy probabilities meaningfully stratify women: women in the top 10% of predicted probabilities have a 89% chance of becoming pregnant over 6 menstrual cycles, as compared to a 27% chance for women in the bottom 10%. We develop a technique for extracting interpretable time trends from our deep learning models, and show these trends are consistent with previous fertility research. Our findings illustrate the potential that women's health tracking data offers for predicting pregnancy on a broader population; we conclude by discussing the steps needed to fulfill this potential.