Cycle Length Variability Research Articles

A Two-Stage Optimization Method for Schedule and Trajectory of CAVs at an Isolated Autonomous Intersection

Autonomous intersection management has become a state-of-the-art control strategy customized for connected and autonomous vehicles. Combining the advantages of tile-based and conflict point-based approaches, this paper proposes a two-stage optimization method based on a developed intersection modeling approach. The first stage is a timing schedule optimization model, assigning vehicle arrival times at an intersection. Based on the output of the first stage, the second stage is a trajectory optimization model, which gives the eco-driving strategies. Moreover, a rolling optimization with a variable cycle length is adopted to run the method continuously. Simulation results show that the proposed method outperforms the genetic algorithm-based method in terms of computation time, and can reduce vehicle delay and fuel consumption by 89.48% and 46.84%, respectively, under different traffic demands compared to the first-come-first-serve method. Furthermore, the performance of the proposed method under asymmetric traffic demand is discussed. Sensitivity analyses suggest that (1) a long cycle length benefits the proposed method within certain limits and (2) a proper deceleration within the intersection can balance traffic delay with fuel consumption. In addition, an additional model with a heuristic rule is compared with the original timing schedule optimization model. It is found that reducing binaries in the first stage can make a tradeoff between the quality of the solution and efficiency, which can be used in conjunction with long cycles.

Charting the solar cycle

Sunspot records reveal that whilst the Sun has an approximately 11 year cycle of activity, no two cycles are of the same duration. Since this activity is a direct driver of space weather at Earth, this presents an operational challenge to quantifying space weather risk. We recently showed that the Hilbert transform of the sunspot record can be used to map the variable cycle length onto a regular “clock” where each cycle has the same duration in Hilbert analytic phase. Extreme geomagnetic storms rarely occur within the quiet part of the cycle which is a fixed interval of analytic phase on the clock; there is a clear active-quiet switch-off and quiet-active switch-on of activity. Here we show how the times of the switch-on/off can be determined directly from the sunspot time-series, without requiring a Hilbert transform. We propose a method-charting-that can be used to combine observations, and reports of societal impacts, to improve our understanding of space weather risk.

3D spatial modeling of sinoatrial node cells reveals a critical role of subcellular ryanodine receptor distribution in pacemaker automaticity.

Ryanodine receptors (RyRs) represent a central component in sinoatrial node (SAN) pacemaker system, as dysregulated RyRs associated with catecholaminergic polymorphic ventricular tachycardia are linked with SAN dysfunction. Within each SAN cell (SANC), the subcellular distribution pattern of RyRs is region-dependent, varying from central SAN (mostly underneath the surface sarcolemma) to peripheral SAN (striated pattern). A similar distribution pattern is also reported for the PKA- and/or Ca2+/calmodulin-dependent protein kinase II (CaMKII)-dependent phosphorylation of RyRs. The impact and functional implication of their distribution patterns on the SANC pacemaker activity have not been fully elucidated. Here, we employed our recently developed 3D spatial model of mouse SANCs to investigate how the subcellar distribution and phosphorylation of RyRs affect spontaneous firing. Our model that features a high surface-to-center gradient of RyR expression well recapitulates the coupled-clock mechanisms, and the biomarkers of simulated spontaneous action potentials agree well with those from previous experimental studies. When abolishing the gradient without changing the whole-cell expression of RyRs in the SANCs, the model predicts 15.0% slowing of the firing rate. Reversing the surface-to-center gradient of RyR distribution (i.e., higher density in the cell center vs surface sarcolemma) further reduced the firing rate (by 25.3%) while increasing the beat-to-beat variation in cycle length, due to disrupted coupling between local Ca2+ releases and the membrane clock. Likewise, preventing RyR phosphorylation caused a substantial slowing in the firing rate (17.6%) while also augmenting beat-to-beat variability. Our simulations mechanistically uncover the functional significance of subcellar RyR distribution and its phosphorylation in maintaining robust automaticity of SANCs. The modeling framework will be applied to systematically delineate the impact of Ca2+-clock protein phosphorylation, distribution, and their interplay with the membrane-clock proteins on the SANC automaticity.

Cycle length oscillation during ventricular tachycardia: What is the mechanism?

A 55 y/o male with history of inferoapical myocardial infarction presented with symptomatic tolerated ventricular tachycardia (VT) and was referred for catheter ablation. A linear tridecapolar catheter (1-1-1 mm, T3, Access Point Technologies) was positioned along the region of endocardial isochronal crowding on the isochronal late activation map. Stable monomorphic VT with right bundle branch superior axis with V4 transition was induced and exhibited spontaneous variability in tachycardia cycle length (360-415ms). What is the mechanism of oscillation in tachycardia cycle length (TCL)? (Figure 1)

Pediatric pulse dose corticosteroid therapy dosing and administration in the treatment of alopecia areata: A review of literature.

The use of pulse dose corticosteroid therapy (PDCT) in children for treatment of alopecia areata (AA) has been reported, but dosing regimens are not well-established. We aim to evaluate the available literature regarding the utilization and various dosing regimens of PDCT, as well as associated side effects, in the treatment of AA in children. We performed a systematic review of studies describing the use of PDCT for the treatment of AA in children. Eight relevant studies were identified, five of which administered the treatment intravenously (IV) and three of which administered the treatment orally. Protocols with IV administration included two studies which used IV dexamethasone at 1.5mg/kg/day for 1-3 days monthly for a maximum of 12 cycles and three studies used IV methylprednisolone 8-30 mg/kg/day for 1-3 days monthly for a maximum of 3-10cycles. The three protocols with oral administration included variable doses of prednisolone at variable intervals and cycle lengths, betamethasone and dexamethasone at a prednisolone equivalent of 5mg/kg, and methylprednisolone 15 mg/kg for 3 days bimonthly for 12 cycles. In these studies, PDCT was generally well-tolerated and resulted in improvement of the AA. PDCT was found to be well-tolerated with few serious side effects reported. It appears to be beneficial early in disease course, especially for those with multifocal AA.

Multi-period dynamic pricing model for deteriorating products in a supply chain with preservation technology investment and carbon emission

In this study, a multi-period dynamic pricing model in a two-echelon supply chain consisting of a retailer and a manufacturer is developed for a deteriorating product with a fixed shelf life. The demand at the retailer in each period is considered to be a function of the retail price, inventory on stock, reference price, and product freshness. Preservation technology investment (PTI) is considered to retard the rate of deterioration with carbon emission to keep the process green. Centralised and decentralised models are developed considering dynamic pricing for the retailer and static pricing for the manufacturer with a variable replenishment cycle length. Algorithms have been developed to obtain the optimal replenishment cycle length, retailer’s end of inventory level, retail price, PTI and discounted total profit for the retailer/manufacturer/supply chain. We study the generated trade-off between PTI, benefits of reduced deterioration rate and cost of carbon emission. The numerical studies suggest that supply chain profit can be greatly improved with efficient PTI and dynamic pricing policy. We also find that the initial reference price plays a key role in improving the profit.

Intergenerational genotypic interactions drive collective behavioural cycles in a social insect

Many social animals display collective activity cycles based on synchronous behavioural oscillations across group members. A classic example is the colony cycle of army ants, where thousands of individuals undergo stereotypical biphasic behavioural cycles of about one month. Cycle phases coincide with brood developmental stages, but the regulation of this cycle is otherwise poorly understood. Here, we probe the regulation of cycle duration through interactions between brood and workers in an experimentally amenable army ant relative, the clonal raider ant. We first establish that cycle length varies across clonal lineages using long-term monitoring data. We then investigate the putative sources and impacts of this variation in a cross-fostering experiment with four lineages combining developmental, morphological and automated behavioural tracking analyses. We show that cycle length variation stems from variation in the duration of the larval developmental stage, and that this stage can be prolonged not only by the clonal lineage of brood (direct genetic effects), but also of the workers (indirect genetic effects). We find similar indirect effects of worker line on brood adult size and, conversely (but more surprisingly), indirect genetic effects of the brood on worker behaviour (walking speed and time spent in the nest).

Spatiotemporal signatures of response to atrial fibrillation ablation

Abstract Background Atrial fibrillation (AF) can have organized regions, in the form of consistent dominant frequency sites, focal or reentrant sites, but it is unclear how these overlap with or differ from focal atrial tachycardias (AT) or potential drivers. We set out to develop an intuitive method based on fundamental electrogram shape and timing to separate types of AF. Objective To test the hypothesis that spatial regions of electrogram (EGM) in AF that show similar shapes over time based on cross-correlation analysis may separate patients with differing response to ablation. Methods We recruited N=133 patients (63.8±12.1 Y, 32% women), (i) N=10 had AT, (ii) N=122 AF that was or was not terminated by ablation, and (iii) N=1 pacing. All patients had left atrial mapping by 64 pole baskets. We applied repetitive activity (REACT) mapping that correlates EGMs in contiguous 2x2 regions (Fig. 1A) over 4sec. To calibrate REACT, we introduced simulated variations in shape (gaussian noise) and timing (gaussian delay) to pacing EGMs and computed nomograph over 100 random trials (Fig. 1C). Results Fig. 1B shows that REACT in a 71-year-old man with AT is more organized than in a 65 YO man with AF (100% vs 40% mapped field). Overall, REACT was higher in AT than AF (0.63±0.15 vs 0.36±0.22, p<0.001). There were 24 cases in which global REACT between AF and AT groups had the overlapping range of values, indicating organized “islands” in AF analogous to AT. From nomograph in Fig. 1C we identified that this overlap reflects 15 ms variation in cycle length and 20% variation in EGM shape (labelled “x” in Fig. 1C). Conclusion Basic electrogram properties in AF of similar shapes in spatial areas over time can separate response to ablation and may represent “islands” of AT. Future studies should investigate the mechanisms for such islands and whether they may be targeted for therapy. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): US National Institutes of Health

Hormonal Agents for the Treatment of Depression Associated with the Menopause.

Perimenopause marks the transition from a woman’s reproductive stage to menopause. Usually occurring between 42 and 52 years of age, it is determined clinically by the onset of irregular menstrual cycles or variable cycle lengths. Women are at an increased risk of depression and anxiety during perimenopause and the menopausal transition. Depressive symptoms experienced in perimenopause are often more severe compared to pre- and post-menopause. During menopausal transition, the impact of fluctuating estrogen in the central nervous system (CNS) can have negative psychological effects for some women. Traditional first-line management of menopausal depression involves antidepressants, with modest outcomes. The positive effects of estrogen treatment in the CNS are becoming increasingly recognised, and hormonal therapy (HT) with estrogen may have a role in the treatment of menopausal depression. In this review we will outline the prevalence, impact and neurochemical basis of menopausal-associated depression, as well as hormone-based approaches that have increasing promise as effective treatments.

Coronary sinus electrogram characteristics predict termination of AF with ablation and long-term clinical outcome.

Markers predicting atrial fibrillation (AF) termination and freedom from AF/atrial tachycardia (AT) has been proposed. This study aimed to evaluate the role of novel coronary sinus (CS) electrogram characteristics in predicting the acute ablation response and freedom from AF/AT during follow-up. Patients undergoing ablation for persistent AF as part of the Stochastic Trajectory Analysis of Ranked signals mapping study were included. Novel CS electrogram characteristics including CS cycle length variability (CLV) and CS activation pattern stability (APS) and proportion of low voltage zones (LVZs) were reviewed as potential predictors for AF termination on ablation and freedom from AF/AT during follow-up. The relationship between localized driver characteristics and CS electrogram characteristics was also assessed. Sixty-five patients were included. AF termination was achieved in 51 patients and 80% of patients were free from AF/AT during a follow-up of 29.5 ± 3.7 months. CS CLV of <30 ms, CS APS of ≥30% and proportion of LVZ < 30% showed high diagnostic accuracy in predicting AF termination on ablation and freedom from AF/AT during follow-up (CS CLV odds ratio [OR] 25.6, area under the curve [AUC] 0.91; CS APS OR 15.9, AUC 0.94; proportion of LVZs OR 21.4, AUC 0.88). These markers were independent predictors of AF termination on ablation and AF/AT recurrence during follow-up. Ablation of a smaller number of drivers that demonstrate greater dominance strongly correlate with greater CS organization. Novel CS electrogram characteristics were independent predictors of AF termination and AF/AT recurrence during follow-up. These markers can potentially aid in predicting outcomes and guide ablation and follow-up strategies.

Computer-Aided Detection of Fiducial Points in Seismocardiography through Dynamic Time Warping.

Accelerometer-based devices have been employed in seismocardiography fiducial point detection with the aid of quasi-synchronous alignment between echocardiography images and seismocardiogram signals. However, signal misalignments have been observed, due to the heartbeat cycle length variation. This paper not only analyzes the misalignments and detection errors but also proposes to mitigate the issues by introducing reference signals and adynamic time warping (DTW) algorithm. Two diagnostic parameters, the ratio of pre-ejection period to left ventricular ejection time (PEP/LVET) and the Tei index, were examined with two statistical verification approaches: (1) the coefficient of determination (R2) of the parameters versus the left ventricular ejection fraction (LVEF) assessments, and (2) the receiver operating characteristic (ROC) classification to distinguish the heart failure patients with reduced ejection fraction (HFrEF). Favorable R2 values were obtained, R2 = 0.768 for PEP/LVET versus LVEF and R2 = 0.86 for Tei index versus LVEF. The areas under the ROC curve indicate the parameters that are good predictors to identify HFrEF patients, with an accuracy of more than 92%. The proof-of-concept experiments exhibited the effectiveness of the DTW-based quasi-synchronous alignment in seismocardiography fiducial point detection. The proposed approach may enable the standardization of the fiducial point detection and the signal template generation. Meanwhile, the program-generated annotation data may serve as the labeled training set for the supervised machine learning.

Associations between drinking water disinfection byproducts and menstrual cycle characteristics: A cross-sectional study among women attending an infertility clinic.

Disinfection byproducts (DBPs) have been shown to alter ovarian steroidogenesis and cause estrous cyclicity disturbance and prolongation in experimental studies, however human studies are lacking. We aimed to evaluate the cross-sectional associations between drinking water DBPs and menstrual cycle characteristics. A total of 1078 women attending an infertility clinic in Wuhan, China were included between December 2018 and January 2020. Characteristics of menstrual cycle were collected by questionnaires. Concentrations of dichloroacetic acid (DCAA) and trichloroacetic acid (TCAA) were measured in urine as biomarkers of drinking water DBPs. Multivariate logistic and linear regression models were used to evaluate the associations between urinary DCAA and TCAA concentrations and menstrual cycle characteristics. Higher urinary DCAA concentrations were associated with increased odds ratios (ORs) of irregular menstrual cycle (OR=1.80; 95% CI: 0.97, 3.33 for the highest vs. lowest quartile; P for trend=0.05) and long menstrual cycle (OR=1.62; 95% CI: 0.97, 2.70 for the highest vs. lowest quartile; P for trend=0.06), as well as prolonged variation in cycle length (β=1.27 days; 95% CI: -0.11, 2.66 for the highest vs. lowest quartile; P for trend=0.04). Higher urinary TCAA concentrations were associated with prolonged bleeding duration (β=0.23 days; 95% CI: -0.06, 0.51 for the highest vs. lowest quartile; P for trend=0.07). These results suggest that exposure to drinking water DBPs is associated with menstrual cycle disturbances. These findings are warranted to confirm in other studies.

Menstrual cycle abnormalities in women vaccinated against COVID-19

As more reports of menstrual cycle abnormalities after COVID-19 vaccine inoculation are emerging in media and in clinical practice as well, it was deemed necessary to investigate these claims. This review aims to gather current scientific evidence on COVID vaccination’s effect on menstrual cycle. Further and extensive research is undeniably needed to fully understand these phenomena, but current studies have demonstrated an undeniable link between some menstrual cycle abnormalities (especially heavy menstrual flow and variations in cycle length) and breakthrough bleeding in non-menstruating population and vaccination against SARS-CoV-2. Up to this point, these disturbances seem to be temporary, not lasting for more than a few cycles. The reported menstrual changes have appeared after inoculations of all brands of vaccines, and no particular technology (mRNA or adenovirus-vectored) was particularly associated with them. The greatest concerns raised were those regarding fertility. However, until now, there is no evidence of any of the COVID vaccines affecting fertility when comparing vaccinated and unvaccinated populations.

The Inhibition of the Small-Conductance Ca2+-Activated Potassium Channels Decreases the Sinus Node Pacemaking during Beta-Adrenergic Activation.

Sinus pacemaking is based on tight cooperation of intracellular Ca2+ handling and surface membrane ion channels. An important player of this synergistic crosstalk could be the small-conductance Ca2+-activated K+-channel (ISK) that could contribute to the sinoatrial node (SAN) pacemaking driven by the intracellular Ca2+ changes under normal conditions and beta-adrenergic activation, however, the exact role is not fully clarified. SK2 channel expression was verified by immunoblot technique in rabbit SAN cells. Ionic currents and action potentials were measured by patch-clamp technique. The ECG R-R intervals were obtained by Langendorff-perfusion method on a rabbit heart. Apamin, a selective inhibitor of SK channels, was used during the experiments. Patch-clamp experiments revealed an apamin-sensitive current. When 100 nM apamin was applied, we found no change in the action potential nor in the ECG R-R interval. In experiments where isoproterenol was employed, apamin increased the cycle length of the SAN action potentials and enhanced the ECG R-R interval. Apamin did not amplify the cycle length variability or ECG R-R interval variability. Our data indicate that ISK has no role under normal condition, however, it moderately contributes to the SAN automaticity under beta-adrenergic activation.

Identification of Circumferential Pulmonary Vein Isolation Gaps and Critical Atrial Substrate From HD Grid Maps in Atrial Fibrillation Patients: Insights From Omnipolar Technology

Identification of Circumferential Pulmonary Vein Isolation Gaps and Critical Atrial Substrate From HD Grid Maps in Atrial Fibrillation Patients: Insights From Omnipolar Technology

Preconception menstrual cycle disorder and antenatal depression: a cross-sectional study with prerecorded information

Objectives This study aimed to evaluate the association between pre-pregnancy menstrual cycle characteristics (i.e. cycle-length variability and cycle length) and antenatal depression. Methods This study retrieved the data from the baseline survey of the randomized controlled trial with pregnant women at 16–20 weeks gestation who used a pregnancy tracking app. The antenatal depression was assessed using the Edinburgh Postnatal Depression Scale (EPDS) at 16–20 weeks gestation, and the cutoff point of 10/11 was adopted. Pre-conceptional menstrual cycle information was obtained via a prerecorded period tracking app. Cycle-length variability was defined as the average difference in the lengths between cycles, dichotomized into regular (≤6 days) and irregular (>6 days). Cycle length was coded as a categorical variable: <25 days, 25–26 days, 27–29 days (regular), 30–31 days, 32–33 days, and 34+ days. The associations of cycle disorders with EPDS scores were examined using regression analysis and a multiple logistic regression model adjusted for covariates. Results The total of 3473 participants were analyzed in this study. Irregular cycle variability (>6 days) was significantly associated with high EPDS scores (adjusted: standardized beta = 0.049, p = 0.003) and with high cutoff EPDS scores (over 11) (adjusted OR = 1.40 [1.02–1.94], p = 0.038) but not with cycle length. Conclusion Irregular menstrual cycle before pregnancy was associated with depression during pregnancy. The health of the menstrual cycle during preconception needs to be further explored to promote healthy maternal mental health.

184 The presence of atrial fibrillation in acute myocardial infarction patients and hyperglycaemia

Abstract Aims In patients with acute myocardial infarction (AMI), hyperglycaemia is a common feature determining a worse prognosis. Few studies have examined the relationship between hyperglycemic status and atrial fibrillation in-hospital stay. To evaluate the relationship between admission hyperglycaemia (aHGL) and occurrence of atrial fibrillation (AF) in-hospital stay in patients with AMI, categorized as myocardial infarction with obstructive coronary artery disease (MIOCA) and myocardial infarction with nonobstructive coronary artery disease (MINOCA). Methods and results Hyperglycaemia was defined as a serum glucose level ≥140 mg/dl at the time of hospital admission. AF was defined as a beat-to-beat variability in cycle length and morphology with irregular fibrillatory waves on surface ECG. Among 2702 patients (32.1% were female, mean age was 70.1 ± 13.4 years), 24.2% were diabetic. Out of 2702 patients, 2457 were MIOCA and 245 (9.1%) were MINOCA patients. At admission, the mean value of serum glucose levels was 146 ± 66 mg/dl. At hospital admission, hyperglycemic status was present in 37.7% of patients and 8.3% presented atrial fibrillation. At hospital admission, atrial fibrillation (aAF) was significantly frequent in hyperglycemic than normoglycaemic patients (11.5% vs. 6.3%, respectively; P &amp;lt; 0.001). In aHGL MIOCA patients had a higher rate of aAF (10.7%) than normoglycaemic MIOCA (6.4%; P &amp;lt; 0.001). In aHGL MINOCA patients had 31% aAF than normoglycaemic MINOCA patients (7.5%; P &amp;lt; 0.001). During the hospital stay, the new onset of atrial fibrillation was higher in aHGL than normoglycaemic status in total population (6.3% vs. 2.9%, P &amp;lt; 0.001), in MIOCA subgroup (6.3% vs. 3.1%, P &amp;lt; 0.001) and MINOCA subgroup (7.3% vs. 1.5%, P = 0.003). Multivariate analysis adjusted for age, sex, and diabetes revealed that the presence of hyperglycaemia was an independent predictor for the onset of atrial fibrillation (OR: 1.7; 95% CI: 1.1–2.6; P = 0.02). Conclusions Hyperglycaemia was an independent predictor of new atrial fibrillation during hospitalization in patients with AMI. Moreover, at hospital admission, patients with hyperglycemic status presented a higher incidence of atrial fibrillation, both in MIOCA and MINOCA subgroups. Further studies are needed to understand the biological mechanisms involved in these associations.

Menstrual cycle length and modern living: a review.

Purpose of reviewThe aim of this review is to evaluate biological, life history, environmental, and lifestyle factors and exposures that cause variability in menstrual cycle length (MCL).Recent findingsRecent literature has detailed a number of factors that influence MCL, with particular emphasis placed on novel environmental exposures, such as air pollution and endocrine disrupting chemicals.SummaryMCL varies widely in response to intrinsic and extrinsic inputs and is a useful predictor of reproductive health and fecundability.Video abstract

Time to Pregnancy for Women Using a Fertility Awareness Based Mobile Application to Plan a Pregnancy.

Background: Time to pregnancy (TTP) is a biomarker of fecundability and has been associated with behavioral and environmental characteristics; however, these associations have not been examined in a large population-based sample of application (app) users.Materials and Methods: This observational study followed 5,376 women with an age range of 18 to 45 years who used an app to identify their fertile window. We included women who started trying to conceive between September 30, 2017 and August 31, 2018. TTP was calculated as the number of menstrual cycles from when the user switched to “Plan” mode up to and including the cycle in which they logged a positive pregnancy test. We examined associations with several characteristics, including age, gravidity, body mass index, cycle length and cycle length variation, frequency of sexual intercourse, and temperature measuring frequency. Discrete time fecundability models were used to estimate fecundability odds ratios.Results: For the complete cohort the 6-cycle and 12-cycle cumulative pregnancy probabilities were found to be 61% (95% confidence interval [CI]: 59–62) and 74% (95% CI: 73–76), respectively. The median TTP was four cycles. The highest fecundability was associated with an age of less than 35 years, with cycle length variation <5 days and logging sexual intercourse on at least 20% of days added (the proportion of days in which intercourse was logged) (11.5% [n = 613] of entire sample). This group achieved a 6- and 12-cycle cumulative pregnancy probability of 88% (95% CI: 85–91) and 95% (95% CI: 94–97), respectively, and a TTP of 2 cycles.Conclusions: Natural Cycles was an effective method of identifying the fertile window and a noninvasive educational option for women planning a pregnancy. Women under age 35 with regular cycles showed a high pregnancy rate.

Modelling menstrual cycle length in athletes using state-space models

The ability to predict an individual’s menstrual cycle length to a high degree of precision could help female athletes to track their period and tailor their training and nutrition correspondingly. Such individualisation is possible and necessary, given the known inter-individual variation in cycle length. To achieve this, a hybrid predictive model was built using data on 16,524 cycles collected from a sample of 2125 women (mean age 34.38 years, range 18.00–47.10, number of menstrual cycles ranging from 4 to 53). A mixed-effect state-space model was fitted to capture the within-subject temporal correlation, incorporating a Bayesian approach for process forecasting to predict the duration (in days) of the next menstrual cycle. The modelling procedure was split into three steps (1) a time trend component using a random walk with an overdispersion parameter, (2) an autocorrelation component using an autoregressive moving-average model, and (3) a linear predictor to account for covariates (e.g. injury, stomach cramps, training intensity). The inclusion of an overdispersion parameter suggested that 26.36%[23.68%,29.17%] of cycles in the sample were overdispersed. The random walk standard deviation for a non-overdispersed cycle is 27.41 pm 1.05 [1.00, 1.09] days while under an overdispersed cycle, the menstrual cycle variance increase in 4.78 [4.57, 5.00] days. To assess the performance and prediction accuracy of the model, each woman’s last observation was used as test data. The root mean square error (RMSE), concordance correlation coefficient and Pearson correlation coefficient (r) between the observed and predicted values were calculated. The model had an RMSE of 1.6412 days, a precision of 0.7361 and overall accuracy of 0.9871. In conclusion, the hybrid model presented here is a helpful approach for predicting menstrual cycle length, which in turn can be used to support female athlete wellness.