Adherence to a Healthy Sleep Pattern is Associated with Enhanced Fertility: A Couple-Based Prospective Preconception Cohort Study

Adherence to a Healthy Sleep Pattern is Associated with Enhanced Fertility: A Couple-Based Prospective Preconception Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adherence to a Healthy Sleep Pattern is Associated with Enhanced Fertility: A Couple-Based Prospective Preconception Cohort Study Ying Tang, Baolin Wang, Rui Shan, Hong Gan, Tierong Liao, Jianhua Cao, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8596518/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Declining fertility has become a major global public-health concern. Healthy sleep patterns may improve reproductive capacity. However, accessible epidemiological studies concerning the association between couples' shared sleep pattern and fertility in humans are limited. Methods This study used the Reproductive Health of Childbearing Couples - Anhui Cohort (RHCC-AC) database in China, enrolling 18,715 couples. Participants completed baseline sleep and lifestyle questionnaires, with fertility follow-ups at 6, 12, and 24 months. Fertility was measured by time-to-pregnancy and infertility. Cox regression and logistic regression models were used to estimate fecundability odds ratios (FORs) and relative risks (RRs), along with their 95% confidence intervals (CIs). Results During the 24-month follow-up of 18,715 couples, 14,552 wives (77.8%) achieved pregnancy. The median time to pregnancy was 3.0 months ( IQR : 1.0-5.0), and the overall infertility rate was 15.6%. In individual-level analyses, healthy sleep patterns among wives were significantly associated with higher fecundability (adjusted fecundability ratio [ aFOR ], 1.120 [95% CI , 1.067-1.175]) and decreased risk of infertility (adjusted risk ratio [ aRR ], 0.845 [95% CI , 0.744-0.960]). Difficulty falling asleep among husbands was also associated with decreased fecundability (a FOR , 0.951 [95% CI , 0.909-0.995]). Couple-based analysis showed that fecundability was significantly reduced when both spouses had a sleep problem score ≥4 ( aFOR, 0.900 [95% CI , 0.838-0.966]). Couples with healthy sleep patterns exhibited increased fertility (a FOR , 1.068 [95% CI , 1.016-1.122]). In addition, the risk of infertility was significantly elevated in couples where only the wife had a sleep problem score ≥4 ( aRR , 1.238 [95% CI , 1.090-1.406]). Healthy sleep improved fertility in wives (especially older/overweight) and husbands (especially remarried); newlywed couples also benefited. Additionally, healthy sleep along with the absence of depressive symptoms enhanced fertility. Conclusions This cohort study revealed that concordant healthy sleep patterns in both spouses were associated with higher fecundability, whereas multiple sleep issues in wives alone were linked to lower fecundability and increased infertility risk. The decline in fertility was amplified when both partners experienced sleep problems, reflecting a "double burden" effect. These findings underscore the need for couple-based sleep interventions to improve reproductive outcomes. sleep time to pregnancy fecundability infertility couple Figures Figure 1 Figure 2 Figure 3 Figure 4 Background As infertility rates increase, decreasing fertility rates have become a significant global health issue, with time-to-pregnancy (TTP) being an essential clinical measure of human fecundability[1]. Infertility (TTP ≥ 12 months) is associated with profound personal and societal implications, including psychosocial distress, economic burden, and reduced quality of life[2–4]. Although ART has become increasingly accessible, effective, and safe in recent years, the overall clinical pregnancy rate continues to be low[5,6]. Thus, identifying infertility risk factors is of substantial public health significance. Recent research highlights a significant relationship between sleep and female infertility, suggesting that sleep may be a key factor in reproductive health[7,8]. Sleep characteristics among women is a well-known and strong risk factor for reproductive health, such as short sleep duration, insomnia, poor sleep quality and sleep apnea have been shown to disrupt female reproductive function through multiple pathways, including menstrual cycle irregularities, reduced fecundability, and lower success rates in assisted reproductive technologies (ART)[9–11]. The underlying mechanisms may involve sleep problems disrupting circadian rhythms, leading to reproductive hormone imbalances, and affecting follicular growth, differentiation, and ovulation, ultimately having a negative impact on female fertility[12,13]. However, some studies have found no significant association between self-reported sleep duration/difficulty and female fecundability, suggesting methodological variations or multifactorial influences[14]. Furthermore, recent findings had suggested that male sleep patterns might have affected fertility, possibly through effects on semen quality and sexual function[15–17]. An experimental study has shown that a short sleep duration combined with a late bedtime increases the negative impact on semen quality compared to having a short sleep duration with a regular or early bedtime[18]. Current research has several limitations: most studies examine isolated sleep parameters, focus predominantly on females, and lack analyses of the impact of couples' shared sleep on fertility[19–21]. Better knowledge about sleep patterns' impact on fertility across multiple parameters among both individuals and couples separately and combined in the general population is urgently needed for optimization of comprehensive study designs clarifying underlying mechanisms while controlling confounding factors, and for evidence-based integration of sleep health assessment and synchronized preventive strategies in clinical reproductive counseling for couples seeking conception. In this study, we hypothesize that preconception healthy sleep patterns in both partners independently and jointly predict enhanced fecundability and reduced infertility risk. Using a prospective cohort design, we will examine associations between sleep parameters (e.g., mean sleep midpoint) and reproductive outcomes (fecundability odds ratios, infertility) in a large, population-based sample of 18,715 preconception couples. Our findings may identify modifiable sleep-related risk factors, offering novel insights to optimize reproductive health at both individual and couple levels. Methods Study design In this study, we evaluated the effects of sleep patterns on fertility in couples of childbearing age using the Reproductive Health of Childbearing Couples-Anhui Cohort (RHCC-AC). Between April 2019 and June 2021, a total of 33,687 couples participated in a study conducted at 16 premarital examination centers across 16 cities and counties in Anhui Province, China[22,23]. Participants completed a baseline questionnaire with information regarding their demographic characteristics, lifestyle, depressive symptoms, and sleep characteristics, as well as providing their biological samples (e.g., urine). After enrolling, couples were contacted 6 months, 12 months, and 24 months later to determine their fertility status. Details about the cohort characteristics and study design are provided in another location[22,23]. In this study, infertility was defined as having a TTP longer than 12 months or requiring assisted reproductive technology. We excluded couples with incomplete basic information and sleep characteristics for either spouse; couples who had tried to conceive for more than 6 months or had been pregnant at enrollment; couples who were on continuous contraception after recruitment; and couples who were lost to follow-up. Finally, we included 18,715 couples in the fertility analysis and 15,489 couples in the infertility analysis. A total of 3,226 couples with a time to pregnancy (TTP) of less than 12 months who were not pregnant were excluded from the infertility analysis (eFigure 1 in Supplement 1). Ethics committee approval was obtained from Anhui Medical University (approval number 20189999), and informed consent was secured from all participants. The results were documented in accordance with the STROBE reporting guideline for observational studies[24]. Sleep exposures We used specific questions from the Munich Chronotype Questionnaire (MCTQ) and the Pittsburgh Sleep Quality Index (PSQI) to assess sleep patterns and circadian rhythm issues. Participants were asked about their sleep patterns over the past month. For both free days and working days, they were asked: "What time do you generally go to bed with the intention of falling asleep?" "What time did you wake up in the morning?" and "How many hours do you think you actually slept each day?". We assessed social jetlag and other circadian rhythm disturbances by combining sleep and wake-up times from both weekdays and free days. Additionally, sleep status over the last month was investigated, including the questions: "How long does it take you to fall asleep?" "How do you rate your sleep quality?" and "Do you snore during sleep?". The Epworth Sleepiness Scale (ESS), a validated instrument developed at Epworth Hospital in Melbourne, Australia, was used to assess daytime sleepiness among reproductive-age couples. This 8-item questionnaire asks respondents to rate their likelihood of dozing in various situations (e.g., reading, watching television, sitting in public) on a 4-point Likert scale (0 = never, 3 = often), yielding total scores from 0–24. Higher scores indicate greater sleepiness, with established cutoffs categorizing scores as normal (0–10), mild-to-moderate (11–15), or severe (16–24). In this study, the Cronbach's α coefficient of the ESS was 0.8. Sleep patterns were evaluated based on ten binary-scored (0 or 1) indicators: insufficient sleep, delayed sleep midpoint, large social jet lag, difficulty falling asleep, poor sleep quality, frequent snoring, frequent nocturnal awakenings, late chronotype, no afternoon nap, and excessive daytime sleepiness. The sum of these scores yielded a total sleep problem score, which was analyzed in five categories (0, 1, 2, 3, and ≥ 4). Participants were subsequently classified into one of three sleep pattern categories: healthy (0–1), intermediate (2–3), or poor (≥ 4). Fertility Couples' fertility was assessed using TTP as a continuous variable, along with two dichotomous measures: pregnancy status and infertility diagnosis. To ascertain these outcomes, researchers or medical personnel conducted follow-ups with all enrolled couples at 6, 12, and 24 months, gathering detailed information on pregnancy outcomes, contraceptive use, abortions, and the timing of these occurrences. In this study, infertility was defined as having a TTP longer than 12 months or requiring assisted reproductive technology. Details of this process have been elaborated on in earlier studies[6]. Covariates At baseline, we collected multidimensional covariate data for both spouses. Demographics included age, region, BMI (calculated as weight/height²), marital status, and age at first sex. Socioeconomic status (SES) was indexed via education, income, and employment, with each scored 0 or 1. The summed SES score was categorized into low, medium, and high levels. Lifestyle was assessed across eight domains: smoking, alcohol drinking, intake of pickled/smoked/barbecued/fried foods, sugary beverage consumption, takeaway fast food consumption, daily sitting time, physical activity level, and bedtime electronic device use. Each domain was dichotomously scored (0 or 1), and the sum constituted the unhealthy lifestyle score, which was categorized into four levels: 0–1, 2, 3, and ≥ 4. Physical activity was evaluated using the International Physical Activity Questionnaire-Short Form. Depressive symptoms were classified as either present or absent according to the total score from the Patient Health Questionnaire-9 (PHQ-9). In this study, the reliability coefficient Cronbach's α of the PHQ-9 was 0.8. Wife-specific covariates included adverse pregnancy history (miscarriage, stillbirth, or neonatal death), live birth history (yes/no), and age at menarche (< 13 vs. ≥13 years). Husband-specific covariates included age at first spermatogenesis (< 15 vs. ≥15 years). Statistical analysis All statistical analyses were performed using SPSS 23 (SPSS software for Windows, Version 23; SPSS Inc, Chicago, Illinois, USA) and GraphPad Prism software (GraphPad Prism for Windows,Version 8.0.2 ; GraphPad Software, Inc., La Jolla, CA, USA) was used for graphing. Continuous variables (e.g., age) are described as mean ± standard deviation, while categorical variables (e.g., socioeconomic status) are presented as frequencies and proportions, with group comparisons made using chi-square tests. Effect sizes ( φ / Cramer's V ) were calculated to quantify the association strength between sleep indicators and fertility. Spearman’s rank correlation coefficient was used to assess the correlation between the sleep characteristics of wives and husbands. The Kaplan–Meier method was employed to estimate the monthly probability of pregnancy, while a log-rank test was used to assess the differences. We fitted Cox proportional hazards regression models with time to pregnancy (TTP) as the time scale to estimate the fertility odds ratios ( FORs ) and 95% confidence intervals ( CIs ) for the associations between sleep characteristics, sleep problem scores, sleep patterns, and fertility among wives, husbands, and couples. FORs 1.0 indicate increased fertility (shorter TTP). Then, logistic regression analysis was utilized to explore the relationship between sleep characteristics, sleep problem score, sleep patterns, and infertility (dichotomous variable). The data are presented as risk ratios ( RRs ) with 95% confidence intervals ( CIs ). The couples who conceived through assisted reproductive technologies were included in the Cox regression analysis, while they were classified as the infertility group in the logistic regression analysis. For the couple-based analyses, we divided the couples sleep problem into the following four groups:(1) neither partner with a sleep problem, (2) man-only sleep problem, (3) woman-only sleep problem, and (4) both partners with a sleep problem. we divided the couples sleep patterns into the following four groups:(1) neither partner with a healthy sleep patterns, (2) man-only healthy sleep patterns, (3) woman-only healthy sleep patterns, and (4) both partners with healthy sleep patterns. Both unadjusted and adjusted regression models were established and implemented in individual-specific and couple-based analyses. In the adjusted regression analysis, Model 1 was adjusted for regional areas, both partners' ages, BMI, socioeconomic status, marital status, age of sexual debut, unhealthy lifestyle scores, wives' adverse pregnancy history, and live birth history. Model 2 was additionally adjusted for both partners' depressive symptoms based on the covariates in Model 1. The stratified analyses were conducted to assess the impact of study characteristics on the outcomes. We evaluated effect modification by established risk factors for fertility, including both partners' age (< 30 years vs. ≥30 years), BMI (< 24 vs. ≥24), and marital status (newlyweds vs. remarriage) through stratified analyses. Furthermore, we assessed the potential interaction between sleep patterns and depressive symptoms on fertility by including multiplicative interaction terms in both Cox and logistic regression models. Additionally, the sensitivity analyses was executed to verify the stability of our findings, reanalyzing the connection between sleep behaviors and infertility, excluding couples who conceived through assisted reproductive techniques. Finally, the log-rank test was performed again after excluding the upper 5% of values to address the right skewness in the time-to-pregnancy distribution. In all statistical analyses, P -values less than 0.05 were deemed statistically significant. Results Population and Sleep Characteristics Table presented the baseline characteristics of the couples, stratified by pregnancy status. The median ( IQR ) ages for wives and husbands were 26 [24–28] years and 27 [25–29] years, respectively. Significant differences between pregnant and non-pregnant couples were observed in age, body mass index (BMI), marital status, socioeconomic status, and age at sexual debut for both partners. Moreover, sleep characteristics such as a late sleep midpoint (after 3:30 a.m.) and poor sleep quality varied notably between the groups (eTable 1 in Supplement 1). During the 24-month follow-up, 14,552 of the 18,715 couples (77.8%) achieved pregnancy. Most pregnancies (11,182 [76.8%]) occurred within the first 6 months, followed by 2,213 (15.2%) between months 6 and 12. The median time to pregnancy (TTP) was 3.0 months ( IQR , 1.0–5.0). The overall infertility rate was 15.6%. Associations between preconception sleep in wives and husbands and fertility outcomes Survival analyses demonstrated that healthy sleep patterns were associated with significantly higher cumulative pregnancy rates in both wives (log-rank P < 0.001) and husbands (log-rank P = 0.002). Even after excluding the top 5% of couples with the longest time to pregnancy, healthy sleep remained a significant predictor of faster pregnancy (eFigures 3–4, Supplement 1). Figure 1 , eTable 2 and eTable 3 in Supplement 1 reveal that, healthy sleep patterns were significantly associated with increased odds of fertility in wives ( aFOR , 1.120 [95% CI , 1.067–1.175]), and with a reduced infertility risk in wives (aRR , 0.845 [95% CI , 0.744–0.960]). Certain sleep characteristics negatively impacted fertility, such as wives having average sleep duration of at least 8 hours, a sleep midpoint after 3:30 a.m., frequent snoring, a late-type chronotype, no napping, a sleep problem score of ≥ 4, and husbands experiencing difficulty falling asleep. Moreover, in wives, various negative sleep characteristics, such as a late sleep midpoint, frequent snoring, no napping and a high sleep problem score, were significantly linked to a higher risk of infertility (Fig. 1 and eTable 3 in Supplement 1). Stratified analyses revealed differential associations between healthy sleep patterns and fertility by age, BMI, and marital status (Fig. 2 and eTables 4–5 in Supplement 1). In wives, healthy sleep was positively associated with fertility across both age strata ( 1, P < 0.01), with a stronger effect in the older group ( aFOR , 1.102 vs 1.205). In husbands, this association was significant only among those < 30 years ( aFOR , 1.052 [95% CI , 1.001–1.106]). BMI in wives was significantly associated with fertility across all weight categories, with a greater effect observed in the overweight and obesity groups ( aFOR , 1.107 vs 1.173). Healthy sleep patterns were significantly associated with improved fertility among newlywed wives ( aFOR , 1.134 [95% CI , 1.078–1.193]), and among remarried husbands ( aFOR , 1.173 [95% CI , 1.002–1.373]). Furthermore, the findings indicated that wives under 30, with normal weight, or who were newly married showed a reduced risk of infertility linked to healthy sleep patterns. The interaction analysis showed that wives without depressive symptoms and with healthy sleep had significantly higher fecundability than those with depressive symptoms and poor sleep ( aFOR , 1.089 [95% CI , 1.045–1.135]; eFigure 5 and eTable 6). A similar pattern was observed for husbands ( aFOR , 1.050 [95% CI , 1.008–1.094]; eFigure 5 and eTable 6) .Consistently, the absence of depressive symptoms combined with a healthy sleep pattern was associated with a lower risk of infertility in both wives ( aRR , 0.870 [95% CI , 0.777–0.973]; eFigure 5 and eTable 7) and husbands ( aRR , 0.887 [95% CI , 0.794–0.992]; eFigure 5 and eTable 7). Sensitivity analyses excluding couples who underwent assisted reproductive treatment demonstrated that the results were largely consistent (eTables 8 and eTables 9). Associations between preconception sleep in couples and fertility outcomes According to eFigure 2 in Supplement 1, Spearman correlation analysis found significant associations between husbands' sleep characteristics and their wives' sleep characteristics(e.g., mean sleep midpoint). Survival analyses showed a notable difference in overall pregnancy rates among the four groups (log-rank P = 0.013). Couples where both partners had healthy sleep patterns were most likely to conceive, while those where neither partner had healthy sleep patterns were least likely (eFigure 3 and eFigure 4). In couple-based analyses, healthy sleep patterns in both partners were significantly associated with increased fertility (aFOR , 1.068 [95% CI , 1.016–1.122]) (Fig. 3 and eTable 10). Conversely, several specific sleep problems were associated with reduced fertility. Fertility was significantly lower when either wives alone or both partners exhibited adverse sleep characteristics, including a sleep midpoint after 3:30 a.m., late chronotype, no napping, or a sleep problem score ≥ 4. The association was strongest when both partners were affected, particularly when both reported frequent snoring ( aFOR , 0.816 [95% CI , 0.733–0.908]). Fertility was significantly lower when husbands experienced difficulty falling asleep ( aFOR , 0.946 [95% CI , 0.898–0.995]); eTable 10). Moreover, infertility risk was elevated when wives had a sleep midpoint after 3:30 a.m. or a sleep problem score ≥ 4 ( aRR , 1.238 [95% CI , 1.090–1.406]), and when both partners did not nap ( aRR , 1.184 [95% CI , 1.043–1.344]) or frequently snored ( aRR , 1.293 [95% CI , 1.014–1.649]; Fig. 3 and eTable 11). Stratified couple-based analyses revealed that the association between sleep patterns and fertility varied across demographic subgroups (Fig. 4 and eTable 12 in Supplement 1). Among group < 30 years, sleep problems reduced fertility (wives alone: aFOR , 0.937; both partners: aFOR , 0.916), while healthy sleep improved fertility (wives alone aFOR , 1.073, both partners aFOR , 1.079); In the ≥ 30 years group, sleep problems reduced fertility (wives: aFOR , 0.866; both: aFOR , 0.800). Among normal-weight group, fertility was higher when either the wife alone ( aFOR , 1.066) or both partners ( aFOR , 1.063) maintained healthy sleep patterns. However, in the overweight or obese group, this association was significant only for the wife's sleep pattern, where healthy sleep conferred a greater benefit ( aFOR , 1.133). By marital status, among newlywed couples, those in which both partners maintained healthy sleep patterns showed improved fertility ( aFOR , 1.073), whereas fertility was reduced when both partners experienced multiple sleep problems ( aFOR , 0.897). Besides, sleep problems were linked to an increased risk of infertility among specific subgroups: wives aged < 30 years ( aRR , 1.224), those with normal weight ( aRR , 1.201) or overweight/obesity ( aRR , 1.349), and newlywed wives ( aRR , 1.270); as well as among remarried husbands ( aRR , 1.458). In couple-based interaction analyses, the combination of depressive symptoms and unhealthy sleep patterns in both partners was associated with a significantly higher risk of infertility ( aRR , 1.294 [95% CI , 1.055–1.586]; eFigure 5 and eTable 7). By contrast, couples where both partners were free of depressive symptoms and adhered to healthy sleep patterns demonstrated significantly greater fecundability ( aFOR , 1.070 [95% CI , 1.015–1.128]; eFigure 5 and eTable 6). These findings remained consistent in sensitivity analyses excluding couples who underwent assisted reproductive treatment (eTables 8 and eTables 13 in Supplement 1). Discussion In this 24-month multicenter study of reproductive-age couples, we found that concordant healthy sleep patterns in both spouses were associated with higher fecundability. Conversely, multiple sleep issues in both spouses were linked to reduced fecundability. Notably, healthy sleep patterns in wives alone were also associated with higher fecundability, while multiple sleep issues in wives alone were associated with both lower fecundability and an increased risk of infertility. These findings underscore the importance of sleep as a modifiable factor in fertility counseling. We observed that maintaining a healthy sleep pattern was associated with enhanced fertility and a decreased risk of infertility in wives. Meanwhile, wives who suffered from sleep problems (e.g., a delayed sleep midpoint, snored frequently and took no naps) showed a significant decrease in fertility. These findings are consistent with previous in vitro fertilization (IVF) and preconception population based studies, which have linked prolonged or irregular sleep duration, evening chronotype, snoring, and poor sleep quality to reduced numbers of mature oocytes, diminished ovarian reserve, higher rates of unpredictable anovulation, impaired fertilization, lower clinical pregnancy rates, and increased risk of biochemical pregnancy loss following IVF[24–27]. Sleep disorders can induce oxidative stress and systemic inflammatory responses, which impair oocyte quality and interfere with the fertilization process, thereby reducing female fertility[28,29]. Furthermore, previous research indicates a non-linear relationship between nap duration and oocyte maturation, suggesting that post-lunch naps may mitigate negative effects on fertility by lowering cortisol and enhancing slow-wave sleep, indicating a causal link between sleep behavior and fertility[30]. In addition, recent studies have shown that reduced semen quality was significantly associated with shorter sleep durations, later bedtimes, later sleep midpoint, higher social jetlag and longer sleep latency[31,32]. The finding that husbands' difficulties falling asleep were linked to substantially diminished fertility aligns with this evidence. This effect may be driven by disruptions in sleep-related hormones, such as lower testosterone and melatonin levels, as well as circadian-driven imbalances in spermatogenic gene expression. These disturbances can collectively induce oxidative stress, sperm DNA damage, and testicular inflammation, ultimately impairing semen quality and reproductive function[33–39]. Our study reveals a substantial concordance in sleep characteristics among couples of reproductive age, particularly in sleep midpoints, demonstrating distinct gender-specific patterns and a strong dyadic interdependence in their sleep-wake cycles. Despite this, current research predominantly examines sleep as an individual factor, often overlooking the mutual influence of spousal sleep patterns[40,41]. Considering that differing sleep patterns can disrupt sexual timing and marital happiness, evaluating both partners' sleep at the same time provides a strong foundation for tailored pre-conception strategies to enhance relationship and behavior[42,43]. We provide novel evidence that the risk of fertility decline is significantly amplified when both partners, rather than just one, exhibit specific adverse sleep characteristics, such as frequent snoring, delayed sleep midpoint, or an elevated sleep problem score. This finding underscores a "double burden" effect. For instance, couples in which both partners frequently snore demonstrate a 30.5% increased risk of infertility, highlighting the compounded negative impact of mutual sleep-disordered breathing. This aligns with previous research linking obstructive sleep apnea (OSA) to individual-level fertility challenges and suggests a shared physiological pathway through which sleep disturbances may impair reproductive outcomes[10,44,45]. Concurrent sleep disturbances in partners may jointly diminish fertility through converging physiological and behavioral mechanisms. Physiologically, shared sleep disruptions can potentiate oxidative stress, systemic inflammation, and mitochondrial dysfunction, while dysregulating the hypothalamic-pituitary-gonadal (HPG) and hypothalamic-pituitary-adrenal (HPA) axes, leading to reproductive hormonal imbalance[46–48]. Behaviorally, sleep disturbances are associated with sexual dysfunction and reduced coital frequency, thereby directly lowering the probability of conception[49,50]. These findings suggest that interventions promoting healthy sleep patterns in both partners could enhance fertility by mitigating these intertwined physiological and behavioral pathways. Our detailed analysis revealed that the association between sleep and fertility is significantly modified by age, BMI, and marital status. Notably, wives aged ≥ 30 years with multiple sleep disturbances exhibited a significantly elevated risk of reduced fertility. This finding aligns with established evidence that advanced maternal age is associated with diminished ovarian reserve and oocyte quality, and suggests that circadian disruption may synergistically exacerbate these age-related declines, potentially through mechanisms involving altered gonadotropin pulsatility and hormonal balance[51–53]. Conversely, healthy sleep patterns were independently associated with improved fertility across all age groups, with a more pronounced effect observed among women aged 30 years and older. This age-specific benefit may operate through pathways beyond just improving ovarian reserve, including enhanced luteal function, and modulation of low-grade chronic inflammation, factors known to be critical for successful conception and implantation[9,54]. Overweight or obese women derived greater fertility benefits from healthy sleep compared to their normal-weight counterparts. This observation is biologically plausible, as sleep plays a pivotal role in regulating metabolic homeostasis. Healthy sleep may mitigate obesity-related metabolic disturbances, such as hyperinsulinemia, dyslipidemia, and elevated inflammatory markers, all of which are known to adversely affect ovulatory function, oocyte quality, and endometrial receptivity[54–56]. Among remarried husbands who are older and carry a higher metabolic burden, healthy sleep patterns were associated with improved fertility. This benefit is likely attributable to optimized testosterone regulation and improved semen quality, particularly reduced sperm DNA fragmentation[57–59]. These findings highlight the demographic specificity of sleep-fertility interactions and the need for targeted preconception counseling. Our interaction analysis revealed that wives without depressive symptoms and with healthy sleep exhibited significantly higher fecundability and lower infertility risk than those with depressive symptoms and poor sleep, with similar patterns observed for husbands. These results align with evidence that depression and sleep disturbances frequently co-occur and jointly disrupt the hypothalamic-pituitary-gonadal axis, impairing reproductive function through hormonal dysregulation, chronic inflammation, and behavioral changes such as reduced libido[22,60–61]. Notably, couples in which both partners presented with depressive symptoms and unhealthy sleep patterns exhibited a significantly higher risk of infertility, whereas those free of depression and adhering to healthy sleep demonstrated greater fecundability. This suggests that the reciprocal interplay between depression and sleep disorders creates a synergistic burden that jointly affects reproductive health within dyadic relationships. Given the high prevalence of both depression and sleep issues, integrated interventions targeting mental health and sleep hygiene in both partners may represent a promising strategy to optimize reproductive outcomes[61]. Methodological considerations This study has several strengths, including its prospective design, multicenter recruitment, large sample size, and extended follow-up period. The collection of exposure information prior to outcome ascertainment ensures a clear temporal sequence and enhances data reliability. We obtained comprehensive baseline information and systematically evaluated the impact of multiple sleep dimensions on fertility. Notably, we developed a composite “sleep score and pattern” indicator to characterize sleep among reproductive-age couples, thereby overcoming limitations associated with single-dimensional sleep assessments. The study further conducted in-depth analyses of heterogeneity across different age groups, BMI categories, and marital statuses, and also explored the interaction between sleep patterns and depressive symptoms. Importantly, beyond assessing individual effects of wives' or husbands' sleep, this study is among the first to investigate the combined influence of couples' sleep characteristics on fertility. However, several limitations should be considered. First, sleep measures were self-reported, which may introduce recall bias and subjective misclassification. Given the cohort scale, objective measures such as wearable devices or polysomnography were not feasible due to cost and logistical constraints. However, prior studies support a moderate correlation between self-reported and device-measured sleep duration, and self-report may better reflect long-term sleep patterns in large epidemiological research[62,63]. Secondly, the data on time-to-pregnancy was gathered through telephone follow-up and depended on participants' self-reports. Resource limitations led to lengthy follow-up intervals, meaning participants could have been pregnant for some time before follow-up, possibly causing recall bias about the timing of conception. Additionally, infertility is a sensitive matter, and participants may underreport because of social desirability bias or fear of discrimination, possibly leading to some infertility cases going undetected. Third, despite adjusting for numerous potential confounding variables, the study's observational design cannot fully exclude the effect of unmeasured or unknown residual confounders. Conclusion In this prospective cohort study, healthy sleep patterns in both spouses were associated with higher fecundability, while multiple sleep issues in wives correlated with reduced fecundability and increased infertility risk. The risk of fertility decline was amplified when both partners experienced sleep problems, revealing a "double burden" effect. This synergistic pattern was further pronounced when sleep problems co-occurred with depressive symptoms: couples with both risk factors faced significantly higher infertility risk, whereas those free of depression with healthy sleep demonstrated greater fecundability. The association between couples' sleep patterns and fertility exhibited notable heterogeneity, with stronger effects observed among wives aged ≥ 30, wives with overweight/obesity, remarried husbands, and newlywed couples. These findings underscore the importance of integrating couple-based sleep and mental health assessments into preconception care to improve reproductive outcomes. Abbreviations ART Assisted reproductive technologies TTP The time to pregnancy IVF In vitro fertilization OSA Obstructive sleep apnea MCTQ Munich Chronotype Questionnaire PSQI Pittsburgh Sleep Quality Index ESS Epworth Sleepiness Scale PHQ-9 Patient Health Questionnaire-9 BMI Body mass index SES Socioeconomic status FOR Fertility odds ratio RR Relative Risks CI Confidence interval Declarations Ethics approval and consent to participate Ethics committee approval was obtained from Anhui Medical University (approval number 20189999), and informed consent was secured from all participants. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This work was supported by grants from the Research Fund of National Natural Science Foundation of China (82504429);Natural Science Foundation of Anhui Province (2408085QH278); Key Program of Natural Science Research of Higher Education of Anhui Province (2022AH050672); the Research Fund of Anhui Institute of Translational Medicine(2022zhyx-C05) and the National Key Research and Development Program of China (2018YFC1004201). Author Contribution Drs Tang and Tao had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis; Drs Tang and Wang contributed equally. Concept and design: Tang, Wang, Shao, Tao; Acquisition, analysis, or interpretation of data: All authors; Drafting of the manuscript: Tang, Wang; Critical review of the manuscript for important intellectual content: Tang, Wang, Shan, Gan, Liao, Li, Geng, Bao, Pan, Zhu, Shao, Tao; Statistical analysis: Tang, Wang, Shao, Gan, Liao, Li, Geng, Cao, Zhang; Obtained funding: Tang, Zhu, Shao, Tao; Administrative, technical, or material support: Bao, Pan, Li, Shao; Supervision: Shao, Tao. All authors read and approved the final manuscript. Acknowledgement We sincerely thank the doctors and nurses of the 16 premarital examination centers, as well as the staff who provide technical support for our project. Data Availability See Supplement 2.Data Sharing StatementDataData available: NoAdditional InformationExplanation for why data not available: Data are available upon request to the corresponding author. Unrestricted data sharing is not allowed due to ethical consent and privacy restrictions. 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Baseline characteristics Total (N=18 715) No-pregnant (N=4 163) Pregnant (N=14 552) χ 2 φ/V Wife Age (years) 26.17 ± 3.39 26.80 ± 3.87 26.00 ± 3.22 205.08 ** 0.11 ** <25 6039(32.3) 1173(28.2) 4866(33.4) 25～29 9992(53.4) 2154(51.7) 7838(53.9) 30～34 2275(12.2) 653(15.7) 1622(11.1) ≥35 409(2.2) 183(4.4) 226(1.6) Regional areas 19.44 ** 0.03 ** Central 8257(44.1) 1825(43.8) 6432(44.2) North 4248(22.7) 858(20.6) 3390(23.3) South 6210(33.2) 1480(35.6) 4730(32.5) Body Mass Index(kg/m 2 ) 135.71 ** 0.09 ** <18.5 12113(64.7) 2455(59.0) 9658(66.4) 18.5～23.9 2581(13.8) 542(13.0) 2039(14.0) ≥24 4021(21.5) 1166(28.0) 2855(19.6) Socioeconomic status 19.76 ** 0.03 ** High 4728(25.3) 1029(24.7) 3699(25.4) Moderate 7452(39.8) 1563(37.5) 5889(40.5) Low 6535(34.9) 1571(37.7) 4964(34.1) Marital status 94.71 ** -0.07 ** Newlyweds 16887(90.2) 3592(86.3) 13295(91.4) Remarriage 1828(9.8) 571(13.7) 1257(8.6) Age of menarche (years) 0.93 0.01 <13 6809(36.4) 1541(37.0) 5268(36.2) ≥13 11906(63.6) 2622(63.0) 9284(63.8) Age of sexual debut(years) 47.64 ** 0.05 ** No sexual behavior 1135(6.1) 327(7.9) 808(5.6) <18 857(4.6) 224(5.4) 633(4.3) 18～20 4932(26.4) 1132(27.2) 3800(26.1) 21～23 6600(35.3) 1374(33.0) 5226(35.9) ≥24 5191(27.7) 1106(26.6) 4085(28.1) Adverse pregnancy history 5.67 * 0.02 * Do not have 17908(95.7) 4011(96.3) 13897(95.5) Have 807(4.30) 152(3.7) 655(4.5) History of live birth 5.98 * -0.02 * Do not have 17697(94.6) 3905(93.8) 13792(94.8) Have 1018(5.40) 258(6.2) 760(5.2) Unhealthy lifestyle score 19.80 ** 0.03 ** 0～1 6042(32.3) 1305(31.3) 4737(32.6) 2 6088(32.5) 1290(31.0) 4798(33.0) 3 4036(21.6) 924(22.2) 3112(21.4) ≥4 2549(13.6) 644(15.5) 1905(13.1) Baseline characteristics Total (N=18 715) No-pregnant (N=4 163) Pregnant (N=14 552) χ 2 φ/V Depressive symptom 0.30 -0.00 No 14145(75.6) 3133(75.3) 11012(75.7) Yes 4570(24.4) 1030(24.7) 3540(24.3) Husband Age (years) 27.15 ± 3.36 27.74 ± 3.77 26.98 ± 3.21 152.46 ** 0.09 ** <25 4000(21.4) 757(18.2) 3243(22.3) 25～29 10999(58.8) 2351(56.5) 8648(59.4) 30～34 3147(16.8) 835(20.1) 2312(15.9) ≥35 569(3.0) 220(5.3) 349(2.4) Body Mass Index(kg/m2) 16.51 ** 0.03 ** <18.5 9596(51.3) 2019(48.5) 7577(52.1) 18.5～23.9 842(4.50) 198(4.80) 644(4.40) ≥24 8277(44.2) 1946(46.7) 6331(43.5) Socioeconomic status 16.92 ** 0.03 ** High 8472(45.3) 1772(42.6) 6700(46.0) Moderate 6808(36.4) 1568(37.7) 5240(36.0) Low 3435(18.4) 823(19.8) 2612(17.9) Marital status 60.09 ** -0.06 ** newlyweds 17861(95.4) 3881(93.2) 13980(96.1) remarriage 854(4.6) 282(6.8) 572(3.9) Age of first spermatogenesis (years) 1.54 -0.01 <15 9433(50.4) 2063(49.6) 7370(50.6) ≥15 9282(49.6) 2100(50.4) 7182(49.4) Age of sexual debut(years) 35.58 ** 0.04 ** No sexual behavior 603(3.2) 184(4.4) 419(2.9) <18 1642(8.8) 411(9.9) 1231(8.5) 18～20 5764(30.8) 1259(30.2) 4505(31.0) 21～23 6066(32.4) 1287(30.9) 4779(32.8) ≥24 4640(24.8) 1022(24.5) 3618(24.9) Unhealthy lifestyle score 6.34 0.02 0～1 2894(15.5) 613(14.7) 2281(15.7) 2 4538(24.2) 971(23.3) 3567(24.5) 3 5199(27.8) 1181(28.4) 4018(27.6) ≥4 6084(32.5) 1398(33.6) 4686(32.2) Depressive symptom 3.56 -0.01 Yes 15155(81.0) 3329(80.0) 11826(81.3) No 3560(19.0) 834(20.0) 2726(18.7) * P ＜0.05, ** P ＜0.01. Pregnant and non-pregnant groups were compared using the Chi-square test for the categorical variables. Additional Declarations No competing interests reported. Supplementary Files Supplement1.docx Supplement2.doc SupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 May, 2026 Editor assigned by journal 06 May, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 18 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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17:47:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":444054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationships between Preconception Sleep in Both Partners and Fertility Outcomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusted fertility odds ratios (\u003cem\u003eFORs\u003c/em\u003e; with 95% \u003cem\u003eCIs\u003c/em\u003e) for time to pregnancy (TTP, modeled as a continuous variable) and adjusted risk ratios (\u003cem\u003eRRs\u003c/em\u003e; with 95%\u003cem\u003e CIs\u003c/em\u003e) for infertility (a dichotomous outcome defined as TTP \u0026gt;12 months or use of assisted reproductive technology [ART]) in relation to sleep characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFORs \u003c/em\u003e(left panel), estimated using Cox proportional hazards regression, represent fecundability per month: \u003cem\u003eFOR\u003c/em\u003e \u0026lt;1 indicates shorter TTP (higher fertility) and \u003cem\u003eFOR\u003c/em\u003e \u0026gt;1 indicates longer TTP (lower fertility) relative to the reference category (e.g., average sleep duration: 7-8 h). \u003cem\u003eRRs \u003c/em\u003e(right panel), estimated using logistic regression, reflect infertility risk: \u003cem\u003eRR\u003c/em\u003e \u0026gt;1 denotes increased risk and \u003cem\u003eRR\u003c/em\u003e\u0026lt;1 denotes decreased risk relative to the reference.\u003c/p\u003e\n\u003cp\u003eAll models were adjusted for regional areas, both partners' ages, BMI, socioeconomic status, marital status, age of sexual debut, unhealthy lifestyle scores, depressive symptoms, wives' adverse pregnancy history, and live birth history. Statistical significance is indicated as\u0026nbsp;\u003csup\u003e* \u003c/sup\u003e\u003cem\u003eP\u0026lt;\u003c/em\u003e0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u0026lt;\u003c/em\u003e0.01. The horizontal dashed line at 1.0 represents the null association.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8596518/v1/c741d409d7fcf1f0c3d742c6.jpg"},{"id":109337895,"identity":"f4835302-4510-4be9-9842-d59c3e4065c4","added_by":"auto","created_at":"2026-05-15 17:47:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":351413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationships between Preconception Sleep in Both Partners and Fertility Outcomes by Age, Marriage and Weight\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusted fecundability odds ratios (\u003cem\u003eFORs\u003c/em\u003e; with 95%\u003cem\u003e CIs\u003c/em\u003e) for time to pregnancy (TTP) as a continuous outcome, derived from Cox proportional hazards regression\u003cem\u003e \u003c/em\u003e(left panel). Adjusted risk ratios (\u003cem\u003eRRs\u003c/em\u003e; with 95%\u003cem\u003e CIs\u003c/em\u003e) for infertility (defined as TTP \u0026gt;12 months or need for assisted reproductive technology [ART]), derived from logistic regression(right panel).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFORs\u003c/em\u003e \u0026lt;1 indicate shorter TTP (higher fecundability); \u003cem\u003eFORs\u003c/em\u003e \u0026gt;1 indicate longer TTP (lower fecundability). \u003cem\u003eRRs\u003c/em\u003e \u0026gt;1 indicate increased infertility risk; \u003cem\u003eRRs\u003c/em\u003e \u0026lt;1 indicate decreased risk. All estimates are presented relative to their respective reference categories (e.g., age group or BMI classification) and were adjusted for geographic region, both partners’ age and BMI, socioeconomic status, marital status, age at sexual debut, wife’s unhealthy lifestyle score, history of adverse pregnancy, and prior live birth.\u003c/p\u003e\n\u003cp\u003eAll models were adjusted for regional areas, both partners' ages, BMI, socioeconomic status, marital status, age of sexual debut, unhealthy lifestyle scores, depressive symptoms, wives' adverse pregnancy history, and live birth history. Statistical significance is indicated as\u0026nbsp;\u003csup\u003e* \u003c/sup\u003e\u003cem\u003eP\u0026lt;\u003c/em\u003e0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u0026lt;\u003c/em\u003e0.01. The horizontal dashed line at 1.0 represents the null association.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8596518/v1/d8f85fbdc3f22aea9e4f35d8.jpg"},{"id":109337894,"identity":"470d046d-cb7f-4a88-8266-5483be6a8736","added_by":"auto","created_at":"2026-05-15 17:47:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":448966,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationships between Preconception Sleep in Couple and Fertility Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusted fertility odds ratios (\u003cem\u003eFORs\u003c/em\u003e; 95% \u003cem\u003eCIs\u003c/em\u003e) for time to pregnancy (TTP) and adjusted risk ratios (\u003cem\u003eRRs\u003c/em\u003e; 95% \u003cem\u003eCIs\u003c/em\u003e) for infertility (defined as TTP \u0026gt;12 months or requiring ART) in relation to sleep characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFORs\u003c/em\u003e were estimated from a Cox proportional hazards model (left panel) and represent continuous TTP: a \u003cem\u003eFOR\u003c/em\u003e \u0026lt;1 indicates shorter TTP (higher fecundability), and a \u003cem\u003eFOR\u003c/em\u003e \u0026gt;1 indicates longer TTP (lower fecundability), with “Neither partner” as the reference category for each sleep characteristic. \u003cem\u003eRRs \u003c/em\u003ewere estimated from a logistic regression model (right panel) and indicate dichotomous infertility risk: an \u003cem\u003eRR\u003c/em\u003e \u0026gt;1 corresponds to elevated risk and an \u003cem\u003eRR\u003c/em\u003e \u0026lt;1 to reduced risk, using the same “Neither partner” reference.\u003c/p\u003e\n\u003cp\u003eAll models were adjusted for regional areas, both partners' ages, BMI, socioeconomic status, marital status, age of sexual debut, unhealthy lifestyle scores, depressive symptoms, wives' adverse pregnancy history, and live birth history. Statistical significance is indicated as\u0026nbsp;\u003csup\u003e* \u003c/sup\u003e\u003cem\u003eP\u0026lt;\u003c/em\u003e0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u0026lt;\u003c/em\u003e0.01. The horizontal dashed line at 1.0 represents the null association.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8596518/v1/949a8b57d0c94d9652af4ba7.jpg"},{"id":109405437,"identity":"7b4da6f5-cec3-4a73-a1db-51f093bda7c9","added_by":"auto","created_at":"2026-05-17 13:18:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":342295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationships between Preconception Sleep in Couple and Fertility Outcomes by Age, Marriage and Weight\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusted fertility odds ratios (\u003cem\u003eFORs\u003c/em\u003e; 95%\u003cem\u003e CIs\u003c/em\u003e) for time to pregnancy and infertility, stratified by partner-specific sleep characteristics and demographic/anthropometric subgroups. \u003cem\u003eFORs\u003c/em\u003e in the fertility panel (left) were estimated from Cox proportional hazards models, with a \u003cem\u003eFOR\u003c/em\u003e \u0026lt;1 indicating shorter TTP (higher fecundability) and a \u003cem\u003eFOR\u003c/em\u003e \u0026gt;1 indicating longer TTP (lower fecundability), relative to the “Neither(Neither of the partners has this condition）” partner reference category. \u003cem\u003eRRs\u003c/em\u003e in the infertility panel (right) were derived from corresponding regression models, where an \u003cem\u003eRR\u003c/em\u003e \u0026gt;1 represents elevated infertility risk and an \u003cem\u003eRR\u003c/em\u003e \u0026lt;1 represents reduced risk, compared to the same “Neither” reference.\u003c/p\u003e\n\u003cp\u003eAll models were adjusted for regional areas, both partners' ages, BMI, socioeconomic status, marital status, age of sexual debut, unhealthy lifestyle scores, depressive symptoms, wives' adverse pregnancy history, and live birth history.\u0026nbsp;Statistical significance is indicated as\u0026nbsp;\u003csup\u003e* \u003c/sup\u003e\u003cem\u003eP\u0026lt;\u003c/em\u003e0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u0026lt;\u003c/em\u003e0.01. The horizontal dashed line at 1.0 represents the null association.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8596518/v1/f347d71d9beeeb43063879f1.jpg"},{"id":109405448,"identity":"ef0f3cf1-8dec-444a-8d51-d08938277a41","added_by":"auto","created_at":"2026-05-17 13:18:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2042193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8596518/v1/e06600ae-ade0-48fd-838b-d88c6eddf785.pdf"},{"id":109405512,"identity":"844c0c4c-1243-43f0-9d50-dfd5ae507a84","added_by":"auto","created_at":"2026-05-17 13:18:36","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1099408,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8596518/v1/9cd57cdccf3272a6f0a79ed8.docx"},{"id":109405417,"identity":"0b54f65e-f820-4fc0-8d0b-e24eb8dd7f90","added_by":"auto","created_at":"2026-05-17 13:17:55","extension":"doc","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":11776,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement2.doc","url":"https://assets-eu.researchsquare.com/files/rs-8596518/v1/d5c329e7374e229e9e27a5ce.doc"},{"id":109337898,"identity":"199adffa-5385-4042-b624-e8b7bd971241","added_by":"auto","created_at":"2026-05-15 17:47:44","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":13836,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8596518/v1/5bc69cc4120ff67103b55462.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adherence to a Healthy Sleep Pattern is Associated with Enhanced Fertility: A Couple-Based Prospective Preconception Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eAs infertility rates increase, decreasing fertility rates have become a significant global health issue, with time-to-pregnancy (TTP) being an essential clinical measure of human fecundability[1]. Infertility (TTP\u0026thinsp;\u0026ge;\u0026thinsp;12 months) is associated with profound personal and societal implications, including psychosocial distress, economic burden, and reduced quality of life[2\u0026ndash;4]. Although ART has become increasingly accessible, effective, and safe in recent years, the overall clinical pregnancy rate continues to be low[5,6]. Thus, identifying infertility risk factors is of substantial public health significance.\u003c/p\u003e \u003cp\u003eRecent research highlights a significant relationship between sleep and female infertility, suggesting that sleep may be a key factor in reproductive health[7,8]. Sleep characteristics among women is a well-known and strong risk factor for reproductive health, such as short sleep duration, insomnia, poor sleep quality and sleep apnea have been shown to disrupt female reproductive function through multiple pathways, including menstrual cycle irregularities, reduced fecundability, and lower success rates in assisted reproductive technologies (ART)[9\u0026ndash;11]. The underlying mechanisms may involve sleep problems disrupting circadian rhythms, leading to reproductive hormone imbalances, and affecting follicular growth, differentiation, and ovulation, ultimately having a negative impact on female fertility[12,13]. However, some studies have found no significant association between self-reported sleep duration/difficulty and female fecundability, suggesting methodological variations or multifactorial influences[14]. Furthermore, recent findings had suggested that male sleep patterns might have affected fertility, possibly through effects on semen quality and sexual function[15\u0026ndash;17]. An experimental study has shown that a short sleep duration combined with a late bedtime increases the negative impact on semen quality compared to having a short sleep duration with a regular or early bedtime[18]. Current research has several limitations: most studies examine isolated sleep parameters, focus predominantly on females, and lack analyses of the impact of couples' shared sleep on fertility[19\u0026ndash;21]. Better knowledge about sleep patterns' impact on fertility across multiple parameters among both individuals and couples separately and combined in the general population is urgently needed for optimization of comprehensive study designs clarifying underlying mechanisms while controlling confounding factors, and for evidence-based integration of sleep health assessment and synchronized preventive strategies in clinical reproductive counseling for couples seeking conception.\u003c/p\u003e \u003cp\u003eIn this study, we hypothesize that preconception healthy sleep patterns in both partners independently and jointly predict enhanced fecundability and reduced infertility risk. Using a prospective cohort design, we will examine associations between sleep parameters (e.g., mean sleep midpoint) and reproductive outcomes (fecundability odds ratios, infertility) in a large, population-based sample of 18,715 preconception couples. Our findings may identify modifiable sleep-related risk factors, offering novel insights to optimize reproductive health at both individual and couple levels.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eIn this study, we evaluated the effects of sleep patterns on fertility in couples of childbearing age using the Reproductive Health of Childbearing Couples-Anhui Cohort (RHCC-AC). Between April 2019 and June 2021, a total of 33,687 couples participated in a study conducted at 16 premarital examination centers across 16 cities and counties in Anhui Province, China[22,23]. Participants completed a baseline questionnaire with information regarding their demographic characteristics, lifestyle, depressive symptoms, and sleep characteristics, as well as providing their biological samples (e.g., urine). After enrolling, couples were contacted 6 months, 12 months, and 24 months later to determine their fertility status. Details about the cohort characteristics and study design are provided in another location[22,23]. In this study, infertility was defined as having a TTP longer than 12 months or requiring assisted reproductive technology. We excluded couples with incomplete basic information and sleep characteristics for either spouse; couples who had tried to conceive for more than 6 months or had been pregnant at enrollment; couples who were on continuous contraception after recruitment; and couples who were lost to follow-up. Finally, we included 18,715 couples in the fertility analysis and 15,489 couples in the infertility analysis. A total of 3,226 couples with a time to pregnancy (TTP) of less than 12 months who were not pregnant were excluded from the infertility analysis (eFigure 1 in Supplement 1). Ethics committee approval was obtained from Anhui Medical University (approval number 20189999), and informed consent was secured from all participants. The results were documented in accordance with the STROBE reporting guideline for observational studies[24].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSleep exposures\u003c/h3\u003e\n\u003cp\u003eWe used specific questions from the Munich Chronotype Questionnaire (MCTQ) and the Pittsburgh Sleep Quality Index (PSQI) to assess sleep patterns and circadian rhythm issues. Participants were asked about their sleep patterns over the past month. For both free days and working days, they were asked: \"What time do you generally go to bed with the intention of falling asleep?\" \"What time did you wake up in the morning?\" and \"How many hours do you think you actually slept each day?\". We assessed social jetlag and other circadian rhythm disturbances by combining sleep and wake-up times from both weekdays and free days. Additionally, sleep status over the last month was investigated, including the questions: \"How long does it take you to fall asleep?\" \"How do you rate your sleep quality?\" and \"Do you snore during sleep?\".\u003c/p\u003e \u003cp\u003eThe Epworth Sleepiness Scale (ESS), a validated instrument developed at Epworth Hospital in Melbourne, Australia, was used to assess daytime sleepiness among reproductive-age couples. This 8-item questionnaire asks respondents to rate their likelihood of dozing in various situations (e.g., reading, watching television, sitting in public) on a 4-point Likert scale (0\u0026thinsp;=\u0026thinsp;never, 3\u0026thinsp;=\u0026thinsp;often), yielding total scores from 0\u0026ndash;24. Higher scores indicate greater sleepiness, with established cutoffs categorizing scores as normal (0\u0026ndash;10), mild-to-moderate (11\u0026ndash;15), or severe (16\u0026ndash;24). In this study, the Cronbach's α coefficient of the ESS was 0.8.\u003c/p\u003e \u003cp\u003eSleep patterns were evaluated based on ten binary-scored (0 or 1) indicators: insufficient sleep, delayed sleep midpoint, large social jet lag, difficulty falling asleep, poor sleep quality, frequent snoring, frequent nocturnal awakenings, late chronotype, no afternoon nap, and excessive daytime sleepiness. The sum of these scores yielded a total sleep problem score, which was analyzed in five categories (0, 1, 2, 3, and \u0026ge;\u0026thinsp;4). Participants were subsequently classified into one of three sleep pattern categories: healthy (0\u0026ndash;1), intermediate (2\u0026ndash;3), or poor (\u0026ge;\u0026thinsp;4).\u003c/p\u003e\n\u003ch3\u003eFertility\u003c/h3\u003e\n\u003cp\u003eCouples' fertility was assessed using TTP as a continuous variable, along with two dichotomous measures: pregnancy status and infertility diagnosis. To ascertain these outcomes, researchers or medical personnel conducted follow-ups with all enrolled couples at 6, 12, and 24 months, gathering detailed information on pregnancy outcomes, contraceptive use, abortions, and the timing of these occurrences. In this study, infertility was defined as having a TTP longer than 12 months or requiring assisted reproductive technology. Details of this process have been elaborated on in earlier studies[6].\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eAt baseline, we collected multidimensional covariate data for both spouses. Demographics included age, region, BMI (calculated as weight/height\u0026sup2;), marital status, and age at first sex. Socioeconomic status (SES) was indexed via education, income, and employment, with each scored 0 or 1. The summed SES score was categorized into low, medium, and high levels. Lifestyle was assessed across eight domains: smoking, alcohol drinking, intake of pickled/smoked/barbecued/fried foods, sugary beverage consumption, takeaway fast food consumption, daily sitting time, physical activity level, and bedtime electronic device use. Each domain was dichotomously scored (0 or 1), and the sum constituted the unhealthy lifestyle score, which was categorized into four levels: 0\u0026ndash;1, 2, 3, and \u0026ge;\u0026thinsp;4. Physical activity was evaluated using the International Physical Activity Questionnaire-Short Form. Depressive symptoms were classified as either present or absent according to the total score from the Patient Health Questionnaire-9 (PHQ-9). In this study, the reliability coefficient Cronbach's α of the PHQ-9 was 0.8. Wife-specific covariates included adverse pregnancy history (miscarriage, stillbirth, or neonatal death), live birth history (yes/no), and age at menarche (\u0026lt;\u0026thinsp;13 vs. \u0026ge;13 years). Husband-specific covariates included age at first spermatogenesis (\u0026lt;\u0026thinsp;15 vs. \u0026ge;15 years).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS 23 (SPSS software for Windows, Version 23; SPSS Inc, Chicago, Illinois, USA) and GraphPad Prism software (GraphPad Prism for Windows,Version 8.0.2 ; GraphPad Software, Inc., La Jolla, CA, USA) was used for graphing. Continuous variables (e.g., age) are described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical variables (e.g., socioeconomic status) are presented as frequencies and proportions, with group comparisons made using chi-square tests. Effect sizes (\u003cem\u003eφ\u003c/em\u003e/\u003cem\u003eCramer's V\u003c/em\u003e) were calculated to quantify the association strength between sleep indicators and fertility. Spearman\u0026rsquo;s rank correlation coefficient was used to assess the correlation between the sleep characteristics of wives and husbands. The Kaplan\u0026ndash;Meier method was employed to estimate the monthly probability of pregnancy, while a log-rank test was used to assess the differences.\u003c/p\u003e \u003cp\u003eWe fitted Cox proportional hazards regression models with time to pregnancy (TTP) as the time scale to estimate the fertility odds ratios (\u003cem\u003eFORs\u003c/em\u003e) and 95% confidence intervals (\u003cem\u003eCIs\u003c/em\u003e) for the associations between sleep characteristics, sleep problem scores, sleep patterns, and fertility among wives, husbands, and couples. \u003cem\u003eFORs\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.0 indicate reduced fertility (longer TTP), whereas \u003cem\u003eFORs\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1.0 indicate increased fertility (shorter TTP). Then, logistic regression analysis was utilized to explore the relationship between sleep characteristics, sleep problem score, sleep patterns, and infertility (dichotomous variable). The data are presented as risk ratios (\u003cem\u003eRRs\u003c/em\u003e) with 95% confidence intervals (\u003cem\u003eCIs\u003c/em\u003e). The couples who conceived through assisted reproductive technologies were included in the Cox regression analysis, while they were classified as the infertility group in the logistic regression analysis.\u003c/p\u003e \u003cp\u003eFor the couple-based analyses, we divided the couples sleep problem into the following four groups:(1) neither partner with a sleep problem, (2) man-only sleep problem, (3) woman-only sleep problem, and (4) both partners with a sleep problem. we divided the couples sleep patterns into the following four groups:(1) neither partner with a healthy sleep patterns, (2) man-only healthy sleep patterns, (3) woman-only healthy sleep patterns, and (4) both partners with healthy sleep patterns. Both unadjusted and adjusted regression models were established and implemented in individual-specific and couple-based analyses. In the adjusted regression analysis, Model 1 was adjusted for regional areas, both partners' ages, BMI, socioeconomic status, marital status, age of sexual debut, unhealthy lifestyle scores, wives' adverse pregnancy history, and live birth history. Model 2 was additionally adjusted for both partners' depressive symptoms based on the covariates in Model 1.\u003c/p\u003e \u003cp\u003eThe stratified analyses were conducted to assess the impact of study characteristics on the outcomes. We evaluated effect modification by established risk factors for fertility, including both partners' age (\u0026lt;\u0026thinsp;30 years vs. \u0026ge;30 years), BMI (\u0026lt;\u0026thinsp;24 vs. \u0026ge;24), and marital status (newlyweds vs. remarriage) through stratified analyses. Furthermore, we assessed the potential interaction between sleep patterns and depressive symptoms on fertility by including multiplicative interaction terms in both Cox and logistic regression models.\u003c/p\u003e \u003cp\u003eAdditionally, the sensitivity analyses was executed to verify the stability of our findings, reanalyzing the connection between sleep behaviors and infertility, excluding couples who conceived through assisted reproductive techniques. Finally, the log-rank test was performed again after excluding the upper 5% of values to address the right skewness in the time-to-pregnancy distribution.\u003c/p\u003e \u003cp\u003eIn all statistical analyses, \u003cem\u003eP\u003c/em\u003e-values less than 0.05 were deemed statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePopulation and Sleep Characteristics\u003c/h2\u003e \u003cp\u003eTable presented the baseline characteristics of the couples, stratified by pregnancy status. The median (\u003cem\u003eIQR\u003c/em\u003e) ages for wives and husbands were 26 [24\u0026ndash;28] years and 27 [25\u0026ndash;29] years, respectively. Significant differences between pregnant and non-pregnant couples were observed in age, body mass index (BMI), marital status, socioeconomic status, and age at sexual debut for both partners. Moreover, sleep characteristics such as a late sleep midpoint (after 3:30 a.m.) and poor sleep quality varied notably between the groups (eTable 1 in Supplement 1). During the 24-month follow-up, 14,552 of the 18,715 couples (77.8%) achieved pregnancy. Most pregnancies (11,182 [76.8%]) occurred within the first 6 months, followed by 2,213 (15.2%) between months 6 and 12. The median time to pregnancy (TTP) was 3.0 months (\u003cem\u003eIQR\u003c/em\u003e, 1.0\u0026ndash;5.0). The overall infertility rate was 15.6%.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociations between preconception sleep in wives and husbands and fertility outcomes\u003c/h3\u003e\n\u003cp\u003eSurvival analyses demonstrated that healthy sleep patterns were associated with significantly higher cumulative pregnancy rates in both wives (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and husbands (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Even after excluding the top 5% of couples with the longest time to pregnancy, healthy sleep remained a significant predictor of faster pregnancy (eFigures 3\u0026ndash;4, Supplement 1). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, eTable 2 and eTable 3 in Supplement 1 reveal that, healthy sleep patterns were significantly associated with increased odds of fertility in wives (\u003cem\u003eaFOR\u003c/em\u003e, 1.120 [95% \u003cem\u003eCI\u003c/em\u003e, 1.067\u0026ndash;1.175]), and with a reduced infertility risk in wives \u003cem\u003e(aRR\u003c/em\u003e, 0.845 [95% \u003cem\u003eCI\u003c/em\u003e, 0.744\u0026ndash;0.960]). Certain sleep characteristics negatively impacted fertility, such as wives having average sleep duration of at least 8 hours, a sleep midpoint after 3:30 a.m., frequent snoring, a late-type chronotype, no napping, a sleep problem score of \u0026ge;\u0026thinsp;4, and husbands experiencing difficulty falling asleep. Moreover, in wives, various negative sleep characteristics, such as a late sleep midpoint, frequent snoring, no napping and a high sleep problem score, were significantly linked to a higher risk of infertility (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and eTable 3 in Supplement 1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStratified analyses revealed differential associations between healthy sleep patterns and fertility by age, BMI, and marital status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and eTables 4\u0026ndash;5 in Supplement 1). In wives, healthy sleep was positively associated with fertility across both age strata (\u0026lt;\u0026thinsp;30 and \u0026ge;\u0026thinsp;30 years; \u003cem\u003eaFOR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with a stronger effect in the older group (\u003cem\u003eaFOR\u003c/em\u003e, 1.102 vs 1.205). In husbands, this association was significant only among those\u0026thinsp;\u0026lt;\u0026thinsp;30 years (\u003cem\u003eaFOR\u003c/em\u003e, 1.052 [95% \u003cem\u003eCI\u003c/em\u003e, 1.001\u0026ndash;1.106]). BMI in wives was significantly associated with fertility across all weight categories, with a greater effect observed in the overweight and obesity groups (\u003cem\u003eaFOR\u003c/em\u003e, 1.107 vs 1.173). Healthy sleep patterns were significantly associated with improved fertility among newlywed wives (\u003cem\u003eaFOR\u003c/em\u003e, 1.134 [95% \u003cem\u003eCI\u003c/em\u003e, 1.078\u0026ndash;1.193]), and among remarried husbands (\u003cem\u003eaFOR\u003c/em\u003e, 1.173 [95% \u003cem\u003eCI\u003c/em\u003e, 1.002\u0026ndash;1.373]). Furthermore, the findings indicated that wives under 30, with normal weight, or who were newly married showed a reduced risk of infertility linked to healthy sleep patterns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe interaction analysis showed that wives without depressive symptoms and with healthy sleep had significantly higher fecundability than those with depressive symptoms and poor sleep (\u003cem\u003eaFOR\u003c/em\u003e, 1.089 [95% \u003cem\u003eCI\u003c/em\u003e, 1.045\u0026ndash;1.135]; eFigure 5 and eTable 6). A similar pattern was observed for husbands (\u003cem\u003eaFOR\u003c/em\u003e, 1.050 [95% \u003cem\u003eCI\u003c/em\u003e, 1.008\u0026ndash;1.094]; eFigure 5 and eTable 6) .Consistently, the absence of depressive symptoms combined with a healthy sleep pattern was associated with a lower risk of infertility in both wives (\u003cem\u003eaRR\u003c/em\u003e, 0.870 [95% \u003cem\u003eCI\u003c/em\u003e, 0.777\u0026ndash;0.973]; eFigure 5 and eTable 7) and husbands (\u003cem\u003eaRR\u003c/em\u003e, 0.887 [95% \u003cem\u003eCI\u003c/em\u003e, 0.794\u0026ndash;0.992]; eFigure 5 and eTable 7). Sensitivity analyses excluding couples who underwent assisted reproductive treatment demonstrated that the results were largely consistent (eTables 8 and eTables 9).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between preconception sleep in couples and fertility outcomes\u003c/h2\u003e \u003cp\u003eAccording to eFigure 2 in Supplement 1, Spearman correlation analysis found significant associations between husbands' sleep characteristics and their wives' sleep characteristics(e.g., mean sleep midpoint). Survival analyses showed a notable difference in overall pregnancy rates among the four groups (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). Couples where both partners had healthy sleep patterns were most likely to conceive, while those where neither partner had healthy sleep patterns were least likely (eFigure 3 and eFigure 4). In couple-based analyses, healthy sleep patterns in both partners were significantly associated with increased fertility \u003cem\u003e(aFOR\u003c/em\u003e, 1.068 [95% \u003cem\u003eCI\u003c/em\u003e, 1.016\u0026ndash;1.122]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and eTable 10). Conversely, several specific sleep problems were associated with reduced fertility. Fertility was significantly lower when either wives alone or both partners exhibited adverse sleep characteristics, including a sleep midpoint after 3:30 a.m., late chronotype, no napping, or a sleep problem score\u0026thinsp;\u0026ge;\u0026thinsp;4. The association was strongest when both partners were affected, particularly when both reported frequent snoring (\u003cem\u003eaFOR\u003c/em\u003e, 0.816 [95% \u003cem\u003eCI\u003c/em\u003e, 0.733\u0026ndash;0.908]). Fertility was significantly lower when husbands experienced difficulty falling asleep (\u003cem\u003eaFOR\u003c/em\u003e, 0.946 [95% \u003cem\u003eCI\u003c/em\u003e, 0.898\u0026ndash;0.995]); eTable 10). Moreover, infertility risk was elevated when wives had a sleep midpoint after 3:30 a.m. or a sleep problem score\u0026thinsp;\u0026ge;\u0026thinsp;4 (\u003cem\u003eaRR\u003c/em\u003e, 1.238 [95% \u003cem\u003eCI\u003c/em\u003e, 1.090\u0026ndash;1.406]), and when both partners did not nap (\u003cem\u003eaRR\u003c/em\u003e, 1.184 [95% \u003cem\u003eCI\u003c/em\u003e, 1.043\u0026ndash;1.344]) or frequently snored (\u003cem\u003eaRR\u003c/em\u003e, 1.293 [95% \u003cem\u003eCI\u003c/em\u003e, 1.014\u0026ndash;1.649]; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and eTable 11).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStratified couple-based analyses revealed that the association between sleep patterns and fertility varied across demographic subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and eTable 12 in Supplement 1). Among group\u0026thinsp;\u0026lt;\u0026thinsp;30 years, sleep problems reduced fertility (wives alone: \u003cem\u003eaFOR\u003c/em\u003e, 0.937; both partners: \u003cem\u003eaFOR\u003c/em\u003e, 0.916), while healthy sleep improved fertility (wives alone \u003cem\u003eaFOR\u003c/em\u003e, 1.073, both partners \u003cem\u003eaFOR\u003c/em\u003e, 1.079); In the \u0026ge;\u0026thinsp;30 years group, sleep problems reduced fertility (wives: \u003cem\u003eaFOR\u003c/em\u003e, 0.866; both: \u003cem\u003eaFOR\u003c/em\u003e, 0.800). Among normal-weight group, fertility was higher when either the wife alone (\u003cem\u003eaFOR\u003c/em\u003e, 1.066) or both partners (\u003cem\u003eaFOR\u003c/em\u003e, 1.063) maintained healthy sleep patterns. However, in the overweight or obese group, this association was significant only for the wife's sleep pattern, where healthy sleep conferred a greater benefit (\u003cem\u003eaFOR\u003c/em\u003e, 1.133). By marital status, among newlywed couples, those in which both partners maintained healthy sleep patterns showed improved fertility (\u003cem\u003eaFOR\u003c/em\u003e, 1.073), whereas fertility was reduced when both partners experienced multiple sleep problems (\u003cem\u003eaFOR\u003c/em\u003e, 0.897). Besides, sleep problems were linked to an increased risk of infertility among specific subgroups: wives aged\u0026thinsp;\u0026lt;\u0026thinsp;30 years (\u003cem\u003eaRR\u003c/em\u003e, 1.224), those with normal weight (\u003cem\u003eaRR\u003c/em\u003e, 1.201) or overweight/obesity (\u003cem\u003eaRR\u003c/em\u003e, 1.349), and newlywed wives (\u003cem\u003eaRR\u003c/em\u003e, 1.270); as well as among remarried husbands (\u003cem\u003eaRR\u003c/em\u003e, 1.458).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn couple-based interaction analyses, the combination of depressive symptoms and unhealthy sleep patterns in both partners was associated with a significantly higher risk of infertility (\u003cem\u003eaRR\u003c/em\u003e, 1.294 [95% \u003cem\u003eCI\u003c/em\u003e, 1.055\u0026ndash;1.586]; eFigure 5 and eTable 7). By contrast, couples where both partners were free of depressive symptoms and adhered to healthy sleep patterns demonstrated significantly greater fecundability (\u003cem\u003eaFOR\u003c/em\u003e, 1.070 [95% \u003cem\u003eCI\u003c/em\u003e, 1.015\u0026ndash;1.128]; eFigure 5 and eTable 6). These findings remained consistent in sensitivity analyses excluding couples who underwent assisted reproductive treatment (eTables 8 and eTables 13 in Supplement 1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this 24-month multicenter study of reproductive-age couples, we found that concordant healthy sleep patterns in both spouses were associated with higher fecundability. Conversely, multiple sleep issues in both spouses were linked to reduced fecundability. Notably, healthy sleep patterns in wives alone were also associated with higher fecundability, while multiple sleep issues in wives alone were associated with both lower fecundability and an increased risk of infertility. These findings underscore the importance of sleep as a modifiable factor in fertility counseling.\u003c/p\u003e \u003cp\u003eWe observed that maintaining a healthy sleep pattern was associated with enhanced fertility and a decreased risk of infertility in wives. Meanwhile, wives who suffered from sleep problems (e.g., a delayed sleep midpoint, snored frequently and took no naps) showed a significant decrease in fertility. These findings are consistent with previous in vitro fertilization (IVF) and preconception population based studies, which have linked prolonged or irregular sleep duration, evening chronotype, snoring, and poor sleep quality to reduced numbers of mature oocytes, diminished ovarian reserve, higher rates of unpredictable anovulation, impaired fertilization, lower clinical pregnancy rates, and increased risk of biochemical pregnancy loss following IVF[24\u0026ndash;27]. Sleep disorders can induce oxidative stress and systemic inflammatory responses, which impair oocyte quality and interfere with the fertilization process, thereby reducing female fertility[28,29]. Furthermore, previous research indicates a non-linear relationship between nap duration and oocyte maturation, suggesting that post-lunch naps may mitigate negative effects on fertility by lowering cortisol and enhancing slow-wave sleep, indicating a causal link between sleep behavior and fertility[30]. In addition, recent studies have shown that reduced semen quality was significantly associated with shorter sleep durations, later bedtimes, later sleep midpoint, higher social jetlag and longer sleep latency[31,32]. The finding that husbands' difficulties falling asleep were linked to substantially diminished fertility aligns with this evidence. This effect may be driven by disruptions in sleep-related hormones, such as lower testosterone and melatonin levels, as well as circadian-driven imbalances in spermatogenic gene expression. These disturbances can collectively induce oxidative stress, sperm DNA damage, and testicular inflammation, ultimately impairing semen quality and reproductive function[33\u0026ndash;39].\u003c/p\u003e \u003cp\u003eOur study reveals a substantial concordance in sleep characteristics among couples of reproductive age, particularly in sleep midpoints, demonstrating distinct gender-specific patterns and a strong dyadic interdependence in their sleep-wake cycles. Despite this, current research predominantly examines sleep as an individual factor, often overlooking the mutual influence of spousal sleep patterns[40,41]. Considering that differing sleep patterns can disrupt sexual timing and marital happiness, evaluating both partners' sleep at the same time provides a strong foundation for tailored pre-conception strategies to enhance relationship and behavior[42,43]. We provide novel evidence that the risk of fertility decline is significantly amplified when both partners, rather than just one, exhibit specific adverse sleep characteristics, such as frequent snoring, delayed sleep midpoint, or an elevated sleep problem score. This finding underscores a \"double burden\" effect. For instance, couples in which both partners frequently snore demonstrate a 30.5% increased risk of infertility, highlighting the compounded negative impact of mutual sleep-disordered breathing. This aligns with previous research linking obstructive sleep apnea (OSA) to individual-level fertility challenges and suggests a shared physiological pathway through which sleep disturbances may impair reproductive outcomes[10,44,45]. Concurrent sleep disturbances in partners may jointly diminish fertility through converging physiological and behavioral mechanisms. Physiologically, shared sleep disruptions can potentiate oxidative stress, systemic inflammation, and mitochondrial dysfunction, while dysregulating the hypothalamic-pituitary-gonadal (HPG) and hypothalamic-pituitary-adrenal (HPA) axes, leading to reproductive hormonal imbalance[46\u0026ndash;48]. Behaviorally, sleep disturbances are associated with sexual dysfunction and reduced coital frequency, thereby directly lowering the probability of conception[49,50]. These findings suggest that interventions promoting healthy sleep patterns in both partners could enhance fertility by mitigating these intertwined physiological and behavioral pathways.\u003c/p\u003e \u003cp\u003eOur detailed analysis revealed that the association between sleep and fertility is significantly modified by age, BMI, and marital status. Notably, wives aged\u0026thinsp;\u0026ge;\u0026thinsp;30 years with multiple sleep disturbances exhibited a significantly elevated risk of reduced fertility. This finding aligns with established evidence that advanced maternal age is associated with diminished ovarian reserve and oocyte quality, and suggests that circadian disruption may synergistically exacerbate these age-related declines, potentially through mechanisms involving altered gonadotropin pulsatility and hormonal balance[51\u0026ndash;53]. Conversely, healthy sleep patterns were independently associated with improved fertility across all age groups, with a more pronounced effect observed among women aged 30 years and older. This age-specific benefit may operate through pathways beyond just improving ovarian reserve, including enhanced luteal function, and modulation of low-grade chronic inflammation, factors known to be critical for successful conception and implantation[9,54]. Overweight or obese women derived greater fertility benefits from healthy sleep compared to their normal-weight counterparts. This observation is biologically plausible, as sleep plays a pivotal role in regulating metabolic homeostasis. Healthy sleep may mitigate obesity-related metabolic disturbances, such as hyperinsulinemia, dyslipidemia, and elevated inflammatory markers, all of which are known to adversely affect ovulatory function, oocyte quality, and endometrial receptivity[54\u0026ndash;56]. Among remarried husbands who are older and carry a higher metabolic burden, healthy sleep patterns were associated with improved fertility. This benefit is likely attributable to optimized testosterone regulation and improved semen quality, particularly reduced sperm DNA fragmentation[57\u0026ndash;59]. These findings highlight the demographic specificity of sleep-fertility interactions and the need for targeted preconception counseling. Our interaction analysis revealed that wives without depressive symptoms and with healthy sleep exhibited significantly higher fecundability and lower infertility risk than those with depressive symptoms and poor sleep, with similar patterns observed for husbands. These results align with evidence that depression and sleep disturbances frequently co-occur and jointly disrupt the hypothalamic-pituitary-gonadal axis, impairing reproductive function through hormonal dysregulation, chronic inflammation, and behavioral changes such as reduced libido[22,60\u0026ndash;61]. Notably, couples in which both partners presented with depressive symptoms and unhealthy sleep patterns exhibited a significantly higher risk of infertility, whereas those free of depression and adhering to healthy sleep demonstrated greater fecundability. This suggests that the reciprocal interplay between depression and sleep disorders creates a synergistic burden that jointly affects reproductive health within dyadic relationships. Given the high prevalence of both depression and sleep issues, integrated interventions targeting mental health and sleep hygiene in both partners may represent a promising strategy to optimize reproductive outcomes[61].\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMethodological considerations\u003c/h2\u003e \u003cp\u003eThis study has several strengths, including its prospective design, multicenter recruitment, large sample size, and extended follow-up period. The collection of exposure information prior to outcome ascertainment ensures a clear temporal sequence and enhances data reliability. We obtained comprehensive baseline information and systematically evaluated the impact of multiple sleep dimensions on fertility. Notably, we developed a composite \u0026ldquo;sleep score and pattern\u0026rdquo; indicator to characterize sleep among reproductive-age couples, thereby overcoming limitations associated with single-dimensional sleep assessments. The study further conducted in-depth analyses of heterogeneity across different age groups, BMI categories, and marital statuses, and also explored the interaction between sleep patterns and depressive symptoms. Importantly, beyond assessing individual effects of wives' or husbands' sleep, this study is among the first to investigate the combined influence of couples' sleep characteristics on fertility.\u003c/p\u003e \u003cp\u003eHowever, several limitations should be considered. First, sleep measures were self-reported, which may introduce recall bias and subjective misclassification. Given the cohort scale, objective measures such as wearable devices or polysomnography were not feasible due to cost and logistical constraints. However, prior studies support a moderate correlation between self-reported and device-measured sleep duration, and self-report may better reflect long-term sleep patterns in large epidemiological research[62,63]. Secondly, the data on time-to-pregnancy was gathered through telephone follow-up and depended on participants' self-reports. Resource limitations led to lengthy follow-up intervals, meaning participants could have been pregnant for some time before follow-up, possibly causing recall bias about the timing of conception. Additionally, infertility is a sensitive matter, and participants may underreport because of social desirability bias or fear of discrimination, possibly leading to some infertility cases going undetected. Third, despite adjusting for numerous potential confounding variables, the study's observational design cannot fully exclude the effect of unmeasured or unknown residual confounders.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this prospective cohort study, healthy sleep patterns in both spouses were associated with higher fecundability, while multiple sleep issues in wives correlated with reduced fecundability and increased infertility risk. The risk of fertility decline was amplified when both partners experienced sleep problems, revealing a \"double burden\" effect. This synergistic pattern was further pronounced when sleep problems co-occurred with depressive symptoms: couples with both risk factors faced significantly higher infertility risk, whereas those free of depression with healthy sleep demonstrated greater fecundability. The association between couples' sleep patterns and fertility exhibited notable heterogeneity, with stronger effects observed among wives aged\u0026thinsp;\u0026ge;\u0026thinsp;30, wives with overweight/obesity, remarried husbands, and newlywed couples. These findings underscore the importance of integrating couple-based sleep and mental health assessments into preconception care to improve reproductive outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eART Assisted reproductive technologies\u003c/p\u003e \u003cp\u003eTTP The time to pregnancy\u003c/p\u003e \u003cp\u003eIVF In vitro fertilization\u003c/p\u003e \u003cp\u003eOSA Obstructive sleep apnea\u003c/p\u003e \u003cp\u003eMCTQ Munich Chronotype Questionnaire\u003c/p\u003e \u003cp\u003ePSQI Pittsburgh Sleep Quality Index\u003c/p\u003e \u003cp\u003eESS Epworth Sleepiness Scale\u003c/p\u003e \u003cp\u003ePHQ-9 Patient Health Questionnaire-9\u003c/p\u003e \u003cp\u003eBMI Body mass index\u003c/p\u003e \u003cp\u003eSES Socioeconomic status\u003c/p\u003e \u003cp\u003eFOR Fertility odds ratio\u003c/p\u003e\u003cp\u003eRR\u0026nbsp; Relative Risks\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp;Confidence interval\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eEthics committee approval was obtained from Anhui Medical University (approval number 20189999), and informed consent was secured from all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the Research Fund of National Natural Science Foundation of China (82504429);Natural Science Foundation of Anhui Province (2408085QH278); Key Program of Natural Science Research of Higher Education of Anhui Province (2022AH050672); the Research Fund of Anhui Institute of Translational Medicine(2022zhyx-C05) and the National Key Research and Development Program of China (2018YFC1004201).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDrs Tang and Tao had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis; Drs Tang and Wang contributed equally. Concept and design: Tang, Wang, Shao, Tao; Acquisition, analysis, or interpretation of data: All authors; Drafting of the manuscript: Tang, Wang; Critical review of the manuscript for important intellectual content: Tang, Wang, Shan, Gan, Liao, Li, Geng, Bao, Pan, Zhu, Shao, Tao; Statistical analysis: Tang, Wang, Shao, Gan, Liao, Li, Geng, Cao, Zhang; Obtained funding: Tang, Zhu, Shao, Tao; Administrative, technical, or material support: Bao, Pan, Li, Shao; Supervision: Shao, Tao. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely thank the doctors and nurses of the 16 premarital examination centers, as well as the staff who provide technical support for our project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSee Supplement 2.Data Sharing StatementDataData available: NoAdditional InformationExplanation for why data not available: Data are available upon request to the corresponding author. Unrestricted data sharing is not allowed due to ethical consent and privacy restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHong X, Yin J, Wang W, Zhao F, Yu H, Wang B. The current situation and future directions for the study on time-to-pregnancy: a scoping review. Reprod Health. 2022;19(1):150. \u003c/li\u003e\n\u003cli\u003eDecker AN, Fischer AR, Gunn HE. 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Association of sleep disturbances with diminished ovarian reserve in women undergoing infertility treatment. Sci Rep-Uk. 2024;14(1):26279. \u003c/li\u003e\n\u003cli\u003eLi X, Zhang X, Hou X, Bing X, Zhu F, Wu X, et al. Obstructive sleep apnea-increased DEC1 regulates systemic inflammation and oxidative stress that promotes development of pulmonary arterial hypertension. Apoptosis. 2023;28(3-4):432-46. \u003c/li\u003e\n\u003cli\u003eSmits MAJ, Schomakers BV, van Weeghel M, Wever EJM, Wust RCI, Dijk F, et al. Human ovarian aging is characterized by oxidative damage and mitochondrial dysfunction. Hum Reprod. 2023;38(11):2208-20. \u003c/li\u003e\n\u003cli\u003eAitken RJ. Impact of oxidative stress on male and female germ cells: implications for fertility. Reproduction. 2020;159(4):R189-01. \u003c/li\u003e\n\u003cli\u003eBariya S, Tao Y, Zhang R, Zhang M. Impact of sleep characteristics on IVF/ICSI outcomes: a prospective cohort study. 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Commun Biol. 2025;8(1):644. \u003c/li\u003e\n\u003cli\u003eRiviere E, Rossi SP, Tavalieri YE, Munoz De Toro MM, Ponzio R, Puigdomenech E, et al. Melatonin daily oral supplementation attenuates inflammation and oxidative stress in testes of men with altered spermatogenesis of unknown aetiology. Mol Cell Endocrinol. 2020;515:110889. \u003c/li\u003e\n\u003cli\u003eLok R, Qian J, Chellappa SL. Sex differences in sleep, circadian rhythms, and metabolism: implications for precision medicine. Sleep Med Rev. 2024;75:101926. \u003c/li\u003e\n\u003cli\u003eDeng Q, Li Y, Sun Z, Gao X, Zhou J, Ma G, et al. Sleep disturbance in rodent models and its sex-specific implications. Neurosci Biobehav R. 2024;164:105810. \u003c/li\u003e\n\u003cli\u003eWilson SJ, Novak JR. The implications of being \u0026quot;in it together\u0026quot;: relationship satisfaction and joint health behaviors predict better health and stronger concordance between partners. Ann Behav Med. 2022;56(10):1014-25. \u003c/li\u003e\n\u003cli\u003eGunn HE, Lee S, Eberhardt KR, Buxton OM, Troxel WM. Nightly sleep-wake concordance and daily marital interactions. Sleep Health. 2021;7(2):266-72. \u003c/li\u003e\n\u003cli\u003eEisenberg E, Legro RS, Diamond MP, Huang H, O\u0026apos;Brien LM, Smith YR, et al. Sleep habits of women with infertility. J Clin Endocr Metab. 2021;106(11):e4414-26. \u003c/li\u003e\n\u003cli\u003eJhuang Y, Chung C, Wang I, Peng C, Meng E, Chien W, et al. Association of obstructive sleep apnea with the risk of male infertility in taiwan. Jama Netw Open. 2021;4(1):e2031846. \u003c/li\u003e\n\u003cli\u003eBeroukhim G, Esencan E, Seifer DB. Impact of sleep patterns upon female neuroendocrinology and reproductive outcomes: a comprehensive review. Reprod Biol Endocrin. 2022;20(1):16. \u003c/li\u003e\n\u003cli\u003eXin X, Li J, Zhang J, Wu H. Association of sleep, inflammation and female infertility: a cross-sectional survey and genetic approach. Brain Behav. 2025;15(6):e70627. \u003c/li\u003e\n\u003cli\u003eSadeghpour S, Ghasemnejad-Berenji M, Maleki F, Behroozi-Lak T, Bahadori R, Ghasemnejad-Berenji H. The effects of melatonin on follicular oxidative stress and art outcomes in women with diminished ovarian reserve: a randomized controlled trial. J Ovarian Res. 2025;18(1):5. \u003c/li\u003e\n\u003cli\u003ePigeon WR, Youngren W, Carr M, Bishop TM, Seehuus M. Relationship of insomnia to sexual function and sexual satisfaction: findings from the sleep and sex survey II. J Psychosom Res. 2023;175:111534. \u003c/li\u003e\n\u003cli\u003eKling JM, Kapoor E, Mara K, Faubion SS. Associations of sleep and female sexual function: good sleep quality matters. \u003cem\u003eMenopause\u003c/em\u003e. 2021;28(6):619-25. \u003c/li\u003e\n\u003cli\u003eMuraleedharan A, Pillai A, Nair BG, Krishnarajabhatt HS, Ramachandran C, Aji A, et L. 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The impact of obesity on reproductive health and metabolism in reproductive-age females. Fertil Steril. 2024;122(2):194-203. \u003c/li\u003e\n\u003cli\u003eBroughton DE, Moley KH. Obesity and female infertility: potential mediators of obesity\u0026apos;s impact. Fertil Steril. 2017;107(4):840-47. \u003c/li\u003e\n\u003cli\u003ePereira TA, Thaker N, Rubez AC, Lima VFN, Bernie HL, Esteves SC. Managing obesity-related male infertility: insights from weight loss intervention. Hum Reprod. 2025;40(11):2027-2037.\u003c/li\u003e\n\u003cli\u003eSigman M. Significance of sperm DNA fragmentation and paternal age. Fertil Steril. 2020;114(2):262. \u003c/li\u003e\n\u003cli\u003eKopalli SR, Cha K, Lee S, Hwang S, Lee Y, Koppula S, et al. Cordycepin, an active constituent of nutrient powerhouse and potential medicinal mushroom cordyceps militaris linn, ameliorates age-related testicular dysfunction in rats. Nutrients, 2019, 11(4):906.\u003c/li\u003e\n\u003cli\u003eJin B, Zhang H, Song F, Wu G, Yang H. Interaction of sleep duration and depression on cardiovascular disease: a retrospective cohort study. BMC Public Health. 2022;22(1):1752. \u003c/li\u003e\n\u003cli\u003eChen F, Zhao X, Qian X, Wang W, Zhou Y, Xu J. Relationships among sleep quality, anxiety, and depression among Chinese nurses: A network analysis. J Affect Disord. 2025;389:119587. \u003c/li\u003e\n\u003cli\u003eCespedes EM, Hu FB, Redline S, Rosner B, Alcantara C, Cai J, et al. Comparison of self-reported sleep duration with actigraphy: results from the hispanic community health study/study of latinos sueno ancillary study. Am J Epidemiol. 2016;183(6):561-73. \u003c/li\u003e\n\u003cli\u003eKim J, Jung HJ, Choi HG, Rhee C, Wee JH. Association between sleep duration and chronic rhinosinusitis among the korean general adult population: korea national health and nutrition examination survey. Sci Rep-Uk. 2019;9(1):7158. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv \u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable \u0026nbsp;Baseline characteristics of childbearing age couples by pregnancy status based on the follow-up of 24 months (n=18 715).\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=18 715)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo-pregnant\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=4 163)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePregnant\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=14 552)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003e\u0026phi;/V\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWife\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e26.17 \u0026plusmn; 3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e26.80 \u0026plusmn; 3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e26.00 \u0026plusmn; 3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e205.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.11\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6039(32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1173(28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e4866(33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e25～29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e9992(53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2154(51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e7838(53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e30～34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2275(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e653(15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1622(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026ge;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e409(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e183(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e226(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegional areas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e19.44\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8257(44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1825(43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e6432(44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4248(22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e858(20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e3390(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6210(33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1480(35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e4730(32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody Mass Index(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e135.71\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.09\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e12113(64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2455(59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e9658(66.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e18.5～23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2581(13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e542(13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e2039(14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026ge;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4021(21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1166(28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e2855(19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocioeconomic status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e19.76\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4728(25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1029(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e3699(25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp; Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e7452(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1563(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e5889(40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6535(34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1571(37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e4964(34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e94.71\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.07\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eNewlyweds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e16887(90.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3592(86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e13295(91.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eRemarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1828(9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e571(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1257(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of menarche (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026lt;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6809(36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1541(37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e5268(36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026ge;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e11906(63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2622(63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e9284(63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of sexual debut(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e47.64\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.05\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eNo sexual behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1135(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e327(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e808(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026lt;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e857(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e224(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e633(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e18～20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4932(26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1132(27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e3800(26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e21～23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6600(35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1374(33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e5226(35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026ge;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e5191(27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1106(26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e4085(28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdverse pregnancy history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5.67\u003csup\u003e*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003csup\u003e*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDo not have\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e17908(95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4011(96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e13897(95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHave\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e807(4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e152(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e655(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of live birth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5.98\u003csup\u003e*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.02\u003csup\u003e*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDo not have\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e17697(94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3905(93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e13792(94.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHave\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1018(5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e258(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e760(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnhealthy lifestyle score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e19.80\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0～1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6042(32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1305(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e4737(32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6088(32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1290(31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e4798(33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4036(21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e924(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e3112(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026ge;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2549(13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e644(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1905(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=18 715)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo-pregnant\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=4 163)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePregnant\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=14 552)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003e\u0026phi;/V\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepressive symptom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e14145(75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e3133(75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e11012(75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e4570(24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1030(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3540(24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"6\" style=\"width: 556px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHusband\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e27.15 \u0026plusmn; 3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e27.74 \u0026plusmn; 3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e26.98 \u0026plusmn; 3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e152.46\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.09\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e4000(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e757(18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3243(22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e25～29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e10999(58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e2351(56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e8648(59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e30～34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e3147(16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e835(20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e2312(15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026ge;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e569(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e220(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e349(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody Mass Index(kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e16.51\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.03\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e9596(51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e2019(48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e7577(52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e18.5～23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e842(4.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e198(4.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e644(4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026ge;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e8277(44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1946(46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e6331(43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocioeconomic status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e16.92\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.03\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e8472(45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1772(42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e6700(46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp; Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e6808(36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1568(37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e5240(36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e3435(18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e823(19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e2612(17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e60.09\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-0.06\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003enewlyweds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e17861(95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e3881(93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e13980(96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003eremarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e854(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e282(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e572(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of first spermatogenesis (years)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e9433(50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e2063(49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e7370(50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026ge;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e9282(49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e2100(50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e7182(49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of sexual debut(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e35.58\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.04\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNo sexual behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e603(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e184(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e419(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e1642(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e411(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1231(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e18～20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e5764(30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1259(30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e4505(31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e21～23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e6066(32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1287(30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e4779(32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026ge;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e4640(24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1022(24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3618(24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnhealthy lifestyle score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0～1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e2894(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e613(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e2281(15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e4538(24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e971(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3567(24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e5199(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1181(28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e4018(27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026ge;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e6084(32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1398(33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e4686(32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepressive symptom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e15155(81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e3329(80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e11826(81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e3560(19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 109px;\"\u003e\n \u003cp\u003e834(20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003e2726(18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"6\" style=\"width: 556px;\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e＜0.05,\u0026nbsp;\u003csup\u003e**\u0026nbsp;\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e＜0.01. Pregnant and non-pregnant groups were compared using the Chi-square test for the categorical variables.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sleep, time to pregnancy, fecundability, infertility, couple","lastPublishedDoi":"10.21203/rs.3.rs-8596518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8596518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eDeclining fertility has become a major global public-health concern. Healthy sleep patterns may improve reproductive capacity. However, accessible epidemiological studies concerning the association between couples' shared sleep pattern and fertility in humans are limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThis study used the Reproductive Health of Childbearing Couples - Anhui Cohort (RHCC-AC) database in China, enrolling 18,715 couples. Participants completed baseline sleep and lifestyle questionnaires, with fertility follow-ups at 6, 12, and 24 months. Fertility was measured by time-to-pregnancy and infertility. Cox regression and logistic regression models were used to estimate fecundability odds ratios (FORs) and relative risks (RRs), along with their 95% confidence intervals (CIs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e During the 24-month follow-up of 18,715 couples, 14,552 wives (77.8%) achieved pregnancy. The median time to pregnancy was 3.0 months (\u003cem\u003eIQR\u003c/em\u003e: 1.0-5.0), and the overall infertility rate was 15.6%. In individual-level analyses, healthy sleep patterns among wives were significantly associated with higher fecundability (adjusted fecundability ratio [\u003cem\u003eaFOR\u003c/em\u003e], 1.120 [95% \u003cem\u003eCI\u003c/em\u003e, 1.067-1.175]) and decreased risk of infertility (adjusted risk ratio [\u003cem\u003eaRR\u003c/em\u003e], 0.845 [95% \u003cem\u003eCI\u003c/em\u003e, 0.744-0.960]). Difficulty falling asleep among husbands was also associated with decreased fecundability (a\u003cem\u003eFOR\u003c/em\u003e, 0.951 [95% \u003cem\u003eCI\u003c/em\u003e, 0.909-0.995]). Couple-based analysis showed that fecundability was significantly reduced when both spouses had a sleep problem score ≥4 (\u003cem\u003eaFOR, \u003c/em\u003e0.900 [95% \u003cem\u003eCI\u003c/em\u003e, 0.838-0.966]). Couples with healthy sleep patterns exhibited increased fertility (a\u003cem\u003eFOR\u003c/em\u003e, 1.068 [95% \u003cem\u003eCI\u003c/em\u003e, 1.016-1.122]). In addition, the risk of infertility was significantly elevated in couples where only the wife had a sleep problem score ≥4 (\u003cem\u003eaRR\u003c/em\u003e, 1.238 [95%\u003cem\u003e CI\u003c/em\u003e, 1.090-1.406]). Healthy sleep improved fertility in wives (especially older/overweight) and husbands (especially remarried); newlywed couples also benefited. Additionally, healthy sleep along with the absence of depressive symptoms enhanced fertility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eThis cohort study revealed that concordant healthy sleep patterns in both spouses were associated with higher fecundability, whereas multiple sleep issues in wives alone were linked to lower fecundability and increased infertility risk. The decline in fertility was amplified when both partners experienced sleep problems, reflecting a \"double burden\" effect. These findings underscore the need for couple-based sleep interventions to improve reproductive outcomes.\u003c/p\u003e","manuscriptTitle":"Adherence to a Healthy Sleep Pattern is Associated with Enhanced Fertility: A Couple-Based Prospective Preconception Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 17:47:39","doi":"10.21203/rs.3.rs-8596518/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-06T12:16:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-06T12:01:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T11:42:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2026-03-19T02:37:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"72b97987-46ab-4f16-a6cd-72ba9dde90bc","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"30","date":"2026-05-06T12:16:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-06T12:01:36+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T17:47:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 17:47:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8596518","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8596518","identity":"rs-8596518","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}