A Comparison of Two Hormonal Fertility Monitoring Systems for Ovulation Detection: A Pilot Study.

Background and Objectives: Accuracy in detecting ovulation and estimating the fertile window in the menstrual cycle is essential for women to avoid or achieve pregnancy. There has been a rapid growth in fertility apps and home ovulation testing kits in recent years. Nevertheless, there lacks information on how well these apps perform in helping users understand their fertility in the menstrual cycle. This pilot study aimed to evaluate and compare the beginning, peak, and length of the fertile window as determined by a new luteinizing hormone (LH) fertility tracking app with the Clearblue Fertility Monitor (CBFM). Materials and Methods: A total of 30 women were randomized into either a quantitative Premom or a qualitative Easy@Home (EAH) LH testing system. The results of the two testing systems were compared with the results from the CBFM over three menstrual cycles of use. Potential LH levels for estimating the beginning of the fertile window were calculated along with user acceptability and satisfaction. Results: The estimates of peak fertility by the Premom and EAH LH testing were highly correlated with the CBFM peak results (R = 0.99, p < 0.001). The participants had higher satisfaction and ease-of-use ratings with the CBFM compared to the Premom and EAH LH testing systems. LH 95% confidence levels for estimating the beginning of the fertile window were provided for both the Premom and EAH LH testing results. Conclusions: Our pilot study findings suggest that the Premom and EAH LH fertility testing app can accurately detect impending ovulation for women and are easy to use at home. However, successful utilization of these low-cost LH testing tools and apps for fertility self-monitoring and family planning needs further evaluation with a large and more diverse population.

[Fertility Awareness Based Methods]. / Natürliche Familienplanung.

Fertility Awareness Based Methods Abstract. The so called natural family planning methods are based on the prospective detection of the few fertile days (fertile window) during the menstrual cycle. The existence of such a fertile window was originally based on observation of the menstrual cycle which showed that that the lowest fertlity was at the beginning and in the seccond half of the cycle. On this observation the calendar method was based. Later other signs of ovulation were detected (temperature, cervical mucus and even later the LH peak) which allowed to better predict the fertile phase. It became evident that the contraceptive efficacy was dependent on the number of ovulation related variables and the continuity and regularity of the measurements (user adherence). The new data collecting technologies facilitate correct and regular data collection and may such offer new opportunities.

A comparison of app-defined fertile days from two fertility tracking apps using identical cycle data.

OBJECTIVE: The Natural Cycles app employs daily basal body temperature to define the fertile window via a proprietary algorithm and is clinically established effective in preventing pregnancy. We sought to (1) compare the app-defined fertile window of Natural Cycles to that of CycleProGo, an app that uses BBT and cervical mucus to define the fertile window and (2) compare the app-defined fertile windows to the estimated physiologic fertile window. STUDY DESIGN: Daily BBT were entered into Natural Cycles from 20 randomly selected regularly cycling women with at least 12 complete cycles from the CycleProGo database. The proportion of cycles with equivalent (±1 cycle day) fertile-window starts and fertile-window ends was determined. The app-defined fertile windows were then compared to the estimated physiologic fertile window using Peak mucus to estimate ovulation. RESULTS: Fifty seven percent of cycles (136/238) had equivalent fertile-window starts and 36% (72/181) had equivalent fertile-window end days. The mean overall fertile-window length from Natural Cycles was 12.8 days compared to 15.1 days for CycleProGo (p < 0.001). The Natural Cycles algorithm declared 12% to 30% of cycles with a fertile-window start and 13% to 38% of cycles with a fertile-window end within the estimated physiologic fertile window. The CycleProGo algorithm declared 4% to 14% of cycles with a fertile-window start and no cycles with a fertile-window end within the estimated physiologic fertile window. CONCLUSIONS: Natural Cycles designated a higher proportion of cycles days as infertile within the estimated physiologic fertile window than CycleProGo. IMPLICATIONS: Use of cervical mucus in addition to BBT may improve the accuracy of identifying the fertile window. Additional studies with other markers of ovulation and the fertile window would give additional insight into the clinical implications of app-defined fertile window differences.

Soft Candy as an Electronic Material Suitable for Salivary Conductivity-Based Medical Diagnostics in Resource-Scarce Clinical Settings.

Soft candy was discovered to be an excellent electronic material and was used to fabricate electrodes for salivary conductivity-based diagnostics. Using a simple molding process, a soft candy (Tootsie Roll) was made into 20 × 20 × 5 mm electrodes with a stable frequency response (0.1-100 kHz). The soft candy electrode-liquid interface circuit model was also developed for the first time. Using 0.01, 0.05, and 0.1 M phosphate-buffered saline and artificial saliva of varying conductivities, the performance of the soft candy (Tootsie Roll) electrode was evaluated. The electrode has a low temperature coefficient of ∼0.02 V/C, and the evaporation-induced mass change during measurement (<3 min) was negligible. Using a trenched surface, a limit of detection (LOD) of ∼1630 µS/cm was obtained and was lower than the saliva conductivity of a healthy adult at ∼3500 µS/cm. Thus, it is suitable for monitoring the ovulation cycle for natural family planning as well as chronic kidney disease diagnosis. Given the ubiquity of soft candy, the simplicity of the molding process, and the negligible medical waste stream, it is a more appropriate approach to diagnostics design for resource-scarce clinical settings, such as those in developing countries. The broader impact of this work will be the paradigm shift of soft candy from food to a new class of edible, moldable, high-resistivity, and stable electronic materials.

Direct measurement of pregnanediol 3-glucuronide (PDG) in dried urine spots by liquid chromatography-mass spectrometry to detect ovulation.

BACKGROUND: Non-invasive self-testing using an objective chemical method to detect ovulation is valuable for women planning conception, practising contraception or undergoing infertility investigations or treatment. METHODS: Based on luteal phase secretion of progesterone (P4) and excretion of its major metabolite, pregnanediol glucuronide (PDG), we developed a novel direct liquid chromatography-mass spectrometry (LCMS) method to measure PDG and other steroid glucuronides in urine and in dried urine spots (DUS) without deconjugation or derivatization. Urine PDG by LCMS and immunoassay (P3G) and P4 by immunoassay with and without adjustment for creatinine were evaluated in daily first void urine samples from 10 women through a single menstrual cycle in which ovulation was confirmed by serial transvaginal ultrasound. RESULTS: Urine PDG with and without creatinine adjustment was stable during the follicular phase with the expected striking rise in the luteal phase peaking at 5 days after ovulation. Using a single spot urine sample (100 µL) or a DUS (<20 µL urine) and an optimal threshold to distinguish pre- from post-ovulatory samples, in ROC analysis urine PDG adjusted for creatinine accurately identified ovulation in 92 % of samples was comparable with P3G immunoassay and superior to urine P4 with or without adjustment for creatinine. Extending the analysis to two or three consecutive daily samples reduced the false negative rate from 8% to 2.6 % for two and 1.9 % for three urine samples. CONCLUSIONS: This method holds promise as a non-invasive self-test method for women to determine by an objective chemical method their ovulatory status.

Método de Ovulação Billings: entre eficácia e desconhecimento / Billings Ovulation Method: between efficacy and lack of knowledge / Método de Ovulación Billings: entre la eficacia y el desconocimiento

Resumo Apesar dos grandes investimentos, os métodos artificiais de planejamento familiar ainda apresentam limitações. Desde a década de 1950, cientistas trabalham para desenvolver um método natural confiável. Este artigo revisa a literatura sobre o Método de Ovulação Billings, apontando sua lógica, sua eficácia, seus benefícios, seus desafios e suas regras. Trata-se de método natural, embasado em mais de cinquenta anos de pesquisas. Sua chave de leitura é o muco cervical, confiável indicador da fertilidade, e sua eficácia está entre 97% e 99%, comparável aos métodos mais eficazes. O método também é indicado para todo tipo de ciclo, pois não se baseia em cálculos, e sim na observação do muco. Conclui-se que no Brasil é preciso fortalecer programas que ofereçam educação sexual mais global à população, capacitando ainda profissionais da saúde a respeito dos métodos naturais de planejamento familiar.

Abstract Despite large investments, artificial family planning methods still have limitations. Since the 1950s, scientists have worked to develop a reliable natural method. This article reviews the literature on the Billings Ovulation Method, pointing out its logic, efficacy, benefits, challenges, and rules. It is a natural method, based on more than fifty years of research. Its reading key is the cervical mucus, a reliable indicator of fertility, and its efficacy is between 97 and 99%, comparable to most effective methods. Billings is also suitable for all types of cycles, as it is not based on calculations, but on the observation of the mucus. In conclusion, Brazil needs to strengthen programs that offer more global sexual education to the population, also training health professionals on natural family planning methods.

Resumen A pesar de las grandes inversiones, los métodos artificiales de planificación familiar todavía tienen limitaciones. Desde la década de 1950, los científicos vienen trabajando para desarrollar un método natural confiable. Este artículo revisa la literatura sobre el Método de Ovulación Billings, presentando su lógica, efectividad, beneficios, desafíos y reglas. Se trata de un método natural, basado en más de cincuenta años de investigación. Su clave es el moco cervical, un indicador confiable de fertilidad, y su efectividad está entre el 97% y el 99%, comparable a los métodos más efectivos. El Método de Ovulación Billings también es adecuado para todo tipo de ciclos, ya que no se fundamenta en cálculos, sino en la observación de mocos. Se concluye que en Brasil es necesario fortalecer programas que ofrezcan una educación sexual más global a la población, además de capacitar a los profesionales de la salud sobre métodos naturales de planificación familiar.

Fertility Awareness-Based Methods for Family Planning: A Literature Review.

A review of 23 research articles to examine fertility awareness-based methods revealed biologic indicators and tracking methods to identify the fertile window in reproductive-aged women. This literature review indicated that a woman's cycle regularity is a major determinant of which method is best. Additionally, the woman's desire to achieve a pregnancy and her preference regarding the intensity of training are factors in method choice. Some evidence suggests that use of at least two biologic indicators is most effective for determining the fertility window. Recommended web and mobile applications also are discussed.

Technologies of time: women's practices of trying to conceive with ovulation biosensing.

Ovulation biosensors are devices worn on or used with the body, which can help women detect ovulation. The manufacturers of such devices claim that if women know when they ovulate, couples can arrange heterosexual intercourse during this time, and thus increase their chances of conceiving. Within the contemporary UK context, in which becoming pregnant is presented in the popular media, and in medical discourses, as more difficult for women in their thirties and forties, manufacturers' claims are attractive for those trying to conceive. Yet few sociological studies have examined women's practices of ovulation biosensing. Drawing on women's accounts of tracking ovulation, this paper explores how such practices fit into their trajectories of trying to conceive. It examines why ovulation biosensing seemingly becomes helpful, relevant or important during this time. Ovulation biosensors, it argues, alter the landscape of trying to become pregnant by introducing new stages and materialities which seemingly place women closer to conception. Women engage in ovulation biosensing, not only to help them become pregnant, but also as a way to manage the complexities of fertility and the uncertainties of becoming pregnant in contemporary society.

Modulation of saliva pattern and accurate detection of ovulation using an electrolyte pre-deposition-based method: a pilot study.

We developed an electrolyte pre-deposition-based saliva pattern modulation method to detect ovulation with high accuracy and reliability. Ovulation tests using human saliva have advantages in terms of the earlier ovulation detection and more convenient sample collection procedure; however, accuracy is low, which is a critical limitation given that the concentrations of salivary constituents can vary depending on the health status of the tested individual and subjective user judgement of the test result. In this study, we quantitatively analyzed saliva patterns according to the concentrations of electrolytes and proteins in the ovulation test and found that changes in the saliva pattern during the ovulatory period can be controlled by sodium chloride (NaCl) pre-deposition, which directly affects the accuracy of ovulation detection. The 100 nmol NaCl pre-deposition condition proved optimal, being two-fold more sensitive to changes in saliva pattern versus the non-pre-deposition condition (accuracy of ovulation detection = 66.6% and 33.3%, respectively). Although accuracy remained insufficient for actual applications compared to the urine-based ovulation detection method, we expect that the electrolyte pre-deposition method will greatly contribute to enhancing the performance of saliva-based ovulation detection tests, toward a commercially satisfactory level of accuracy.

A Critical Appraisal of Fertility and Menstrual Tracking Apps for the iPhone.

OBJECTIVE: This study sought to review and appraise Apple App Store applications (apps) designed for menstrual cycle tracking, ovulation prediction, and other topics related to fertility or the management of infertility. METHODS: The Apple App Store was systematically searched using the keywords "period tracker," "menstrual tracker," "fertility," "ovulation," "IVF," and "in vitro fertilization." Apps were downloaded after being screened against pre-defined inclusion criteria. Included apps were independently reviewed by two authors. Data were extracted, and an app quality score (AQS) was calculated according to a premade rubric. Apps were assessed on the basis of their general features, menstrual cycle tracking and prediction features, infertility-related features, and usability. RESULTS: A total of 140 apps were included in the final analysis, of which 90 (64.3%) were free. The average AQS was 32.5%. There was no significant difference in AQS between free and paid apps (31.5% vs. 34.2%; P = 0.491). A total of 62 (44.3%) apps made dynamic cycle predictions on the basis of cumulatively input data. Thirty-one apps (22.1%) contained serious inaccuracies in content, tools, or both. Only 25 apps (17.9%) contained information or functions specifically related to infertility or its management. CONCLUSION: Some high-quality menstrual cycle tracking apps are available; however, many more are of low quality, and users should be wary of relying on their predictions to avoid pregnancy or to maximize chances of conception. Few apps specifically address the needs of patients struggling with infertility.

Detection and Prediction of Ovulation From Body Temperature Measured by an In-Ear Wearable Thermometer.

OBJECTIVE: We present a non-invasive wearable device for fertility monitoring and propose an effective and flexible statistical learning algorithm to detect and predict ovulation using data captured by this device. METHODS: The system consists of an earpiece, which measures the ear canal temperature every 5 min during night sleep hours, and a base station that transmits data to a smartphone application for analysis. We establish a data-cleaning protocol for data preprocessing and then fit a Hidden Markov Model (HMM) with two hidden states of high and low temperature to identify the more probable state of each time point via the predicted probabilities. Finally, a post-processing procedure is developed to incorporate biorhythm information to form a time-course biphasic profile for each subject. RESULTS: The performance of the proposed algorithms applied to data collected by the device are compared with traditional methods in terms of match rate with self-reported ovulation days confirmed with an ovulation test kit. Empirical study results from a group of 34 users yielded significant improvements over the traditional methods in terms of detection accuracy (with sensitivity 92.31%) and prediction power (23.07-31.55% higher). CONCLUSION: We demonstrated the feasibility for reliable ovulation detection and prediction with high-frequency temperature data collected by a non-invasive wearable device. SIGNIFICANCE: Traditional fertility monitoring methods are often either inaccurate or inconvenient. The wearable device and learning algorithm presented in this paper provide a user friendly and reliable platform for tracking ovulation, which may have a broad impact on both fertility research and real-world family planning.

Increased Likelihood of Pregnancy Using an App-Connected Ovulation Test System: A Randomized Controlled Trial.

Background: Women trying to conceive are increasingly using fertility-tracking software applications to time intercourse. This study evaluated the difference in conception rates between women trying to conceive using an application-connected ovulation test system, which measures urinary luteinizing hormone and an estrogen metabolite, versus those trying without using ovulation testing. Materials and Methods: This home-based study involved 844 volunteers aged 18-40 years seeking to conceive. Volunteers randomized to the test arm were required to use the test system for the duration of the study while those randomized to the control arm were instructed not to use ovulation testing. Pregnancy rate differences across one and two cycles between the two groups were examined. Results: Volunteers in the test (n = 382) and control arms (n = 403) had similar baseline demographics. The proportion of women pregnant after one cycle was significantly greater in the test arm (25.4%) compared with the control arm (14.7%; p < 0.001). After two cycles, there continued to be a greater proportion of women pregnant in the test arm compared with the control arm (36.2% vs. 28.6%; p = 0.026). In the test arm, volunteers had intercourse less frequently per cycle compared with those not using ovulation testing (9 [range: 1-60] vs. 10 [range: 1-50]; p = 0.027), but were more likely to target intercourse to a particular part of their cycle compared with those not using ovulation testing (88.5% vs. 57.8%; p < 0.001). Conclusion: Using the test system to time intercourse within the fertile window increases the likelihood of conceiving within two menstrual cycles.

Serum microRNA profiling for the identification of predictive molecular markers of the response to controlled ovarian stimulation.

OBJECTIVE: To identify potential microRNA (miRNA) biomarkers of poor, normal and hyperresponse to controlled ovarian stimulation (COS). METHODS: In the present study, we assessed 40 serum samples from patients undergoing COS. We used ten samples to standardize miRNAs detection in the serum. The remaining 30 samples were split into three groups depending on the patient's response to COS: poor response (PR group, n=10), normal response (NR group, n=10), and hyperresponse (HR group, n=10). Aberrantly expressed miRNAs were identified using a large-scale expression analysis platform. Gene set enrichment analysis was performed to assess the biological processes potentially modulated by the identified miRNAs. RESULTS: Twenty-two miRNAs were detected only in the PR or HR groups when compared with the NR group. From those, 11 presented poor dissociation curves and were excluded from further analysis. A bioinformatics analysis revealed that the selected 11 miRNAs target several genes involved in GnRH, estrogen and prolactin signaling, oocyte maturation, female pregnancy, and meiosis. CONCLUSION: The large-scale analysis of miRNA expression identified distinct miRNA profiles for poor and hyperresponse to COS, which potentially modulate key processes for human assisted reproduction. All evidence suggests that the serum microRNA profiling may discriminate patients who will respond in an exacerbated manner from those who will respond insufficiently to COS. Further studies may validate these miRNAs, enabling the individualization of treatment and more successful outcomes.

Comparison of progesterone assay by chemiluminescence or radioimmunoassay for clinical decision-making in canine reproduction.

The Coat-A-Count® radioimmunoassay has been long and widely used to determine the concentration of progesterone in serum or plasma of bitches (progRIA), but was discontinued in 2014. The Immulite® 1000 LKPG1 chemiluminescence immunoassay has gained prominence since 2003 to determine the concentration of progesterone in serum of bitches, but the assay changed in 2012 (Immulite® 1000 LKPW1). This study assessed the feasibility of using Immulite® 1000 LKPW1 (progImm) to estimate the time of clinically relevant events during oestrus and compared progRIA and progImm 2 and 3 days after the first or only day of the luteinizing hormone surge (LH1). ProgImm first exceeded 5.1 nmol/L on the same day that progRIA first exceeded 6 nmol/L, a proxy for the occurrence of the LH surge, or the day before in 28 of 31 (90%) of oestrous periods. ProgImm first exceeded 13.6 nmol/L on the same day that progRIA first exceeded 16 nmol/L (a proxy for the day of ovulation) or the day before in 34 of 35 (97%) oestrous periods. ProgImm first exceeded 5.4 nmol/L on LH1 or the day before in 24 of 25 (95%) of oestrous periods. The median of progImm 2 days after LH1 was 1.2 nmol/L lower than the 10.7 nmol/L of progRIA (p = 0.001). The mean of progImm 3 days after LH1 was 2.2 nmol/L lower than the 19.0 nmol/L of progRIA (p 0.001). In conclusion, the days on which progImm first exceeded 5.1 nmol/L, 13.6 nmol/L and 5.4 nmol/L effectively estimate the days on which progRIA reached 6 nmol/L or 16 nmol/L or LH1.

Advantages of determining the fertile window with the individualised Natural Cycles algorithm over calendar-based methods.

Purpose: This study aims to compare the accuracy of fertile window identification with the contraceptive app Natural Cycles against the Rhythm Method and Standard Days Method (SDM).Materials and methods: Menstruation dates, basal body temperature (BBT), and luteinising hormone (LH) test results were collected anonymously from Natural Cycles app users. The fraction of green days (GDs) and wrong green days (WGDs) allocated by the various algorithms was determined over 12 cycles. For comparison of Natural Cycles and the Rhythm Method, 26,626 cycles were analysed.Results: Natural Cycles' algorithms allocated 59% GDs (LH, BBT) in cycle 12, while the fraction of WGDs averaged 0.08%. The Rhythm Method requires monitoring of six cycles, resulting in no GDs or WGDs in cycle 1-6. In cycle 7, 49% GDs and 0.26% WGDs were allocated. GDs and WGDs decreased to 43% and 0.08% in cycle 12. The probabilities of WGDs on the day before ovulation with Natural Cycles were 0.31% (BBT) and 0% (LH, BBT), and 0.80% with the Rhythm Method. The probability of WGDs on the day before ovulation was 6.90% with the SDM.Conclusions: This study highlights that individualised algorithms are advantageous for accurate determination of the fertile window and that static algorithms are more likely to fail during the most fertile days.

An objective volumetric method for assessment of ovarian follicular and luteal vascular flow using colour Doppler ultrasonography.

Our goal was to develop an objective computer-assisted volumetric method of assessing vascular flow from colour Doppler ultrasound data of ovarian structures recorded by free-hand movement. We hypothesized that a vascularity index (ratio of the region of blood flood to the region of ovarian structure) obtained from the three-dimensional volumetric analysis would be more precise (less variable) than conventional two-dimensional analysis of single images in estimating the functional status of the preovulatory follicles and corpus luteum. Doppler ultrasound cineloops of water buffaloes (Bubalus bubalis; n = 22) ovaries were recorded daily from 12 h before GnRH treatment to four days after ovulation. Cineloops were processed using Fiji and Imaris software packages for segmenting the area (two-dimensional analysis) and the volume (three-dimensional analysis) occupied by the blood-flow and associated tissue to calculate the vascularity index. For volumetric measurement, all images in a cineloop were used (i.e., no a-priori selection of images) while for two-dimensional analysis, three images from the region with apparent maximum vascularity were selected. The volumetric method was verified with theoretical ellipsoidal volume of the follicle (r = 0.96 P < 0.01) or corpus luteum (r = 0.58 P = 0.02). The variability in the follicular vascularity index among animals was lower using the volumetric method than two-dimensional analysis (0.018 ±â€¯0.002 vs 0.030 ±â€¯0.005, P < 0.01), while the variability for CL vascularity was similar between methods (P = 0.23). An increase in the follicular vascularity index was detected at 12 h after GnRH treatment using both methods (two-dimensional: 0.030 ±â€¯0.008, P < 0.01; three-dimensional: 0.016 ±â€¯0.006, P < 0.02). Buffaloes that ovulated tended to have a greater increase in 3D vascularity index than non-responding buffaloes (P = 0.06); the two-dimensional method was not able to detect these changes. Using the three-dimensional method, a moderate positive correlation (r = 0.59; P = 0.02) was evident between the follicular vascularity index at 14-16 h after GnRH treatment and follicular diameter. In conclusion, an objective volumetric method for assessing relative ovarian blood flow changes was developed using Doppler ultrasound cineloops recorded by free-hand movement. The 3-dimensional method eliminates the need for a-priori selection of images and is more precise as a result of decreased technical variability.

Findings from a mobile application-based cohort are consistent with established knowledge of the menstrual cycle, fertile window, and conception.

OBJECTIVE: To investigate the validity of self-reported fertility data generated by a mobile application-based cohort in comparison with data collected by traditional clinical methodologies. DESIGN: Data were collected from July 2013 to July 2018 through a mobile application designed to track fertility. Bayesian hierarchical models were used to assess day-specific pregnancy probabilities. Descriptive statistics were used to estimate differences in day of ovulation and lengths of menstrual phases and to assess changes in the cervix and ovulation-related symptoms drawing closer to the day of ovulation. SETTING: Not applicable. PATIENT(S): Data consisted of 225,596 menstrual cycles from 98,903 women. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Day-specific probabilities of pregnancy, variability in lengths of the follicular and luteal phases, trends in prevalence of symptoms and cervix changes across the fertile window. RESULT(S): Analyses were consistent with established clinical knowledge. Probability of conception was highest during the 5 days before and day of ovulation, with the highest probability occurring the day before ovulation. The average cycle length was 29.6 days, and average lengths of the follicular and luteal phases were 15.8 and 13.7 days, respectively. Closer to day of ovulation, women were more likely to report changes in the cervix corresponding to fluid consistency, feel, position, and openness and symptoms associated with ovulation, including pelvic pain, tender breasts, increased sex drive, and cramps. CONCLUSION(S): Components of the menstrual cycle and fertile window, when re-evaluated with a mobile application-based cohort, were found to be consistent with established clinical knowledge, suggesting an agreement between traditional and modern data collection methodologies.

Menstrual cyclicity is predictably unpredictable.

"Time moves slowly but passes quickly" - Alice Walker.