Sleep behaviors and time-to-pregnancy: results from a Guangzhou City cohort

Abstract

Introduction

Fertility outcomes are increasingly influenced by modern lifestyle factors, including sleep behaviors. However, the relationship between sleep and time to pregnancy (TTP) is underexplored.

Methods

We conducted a prospective cohort study of 1,684 couples in Guangzhou, China. Sleep behaviors were assessed via structured interviews. Cox proportional hazards models were used to estimate fecundability ratios (FRs), adjusting for potential confounders. Sleep-wake regularity was assessed for all women. Among those with regular patterns (n = 1506), we further analyzed sleep duration, bedtime, perceived sleep sufficiency, and insomnia.

Results

Among all participants, 178 (10.6%) had irregular sleep. Time-varying models revealed that compared to regular sleepers, irregular sleepers exhibited a decreasing fecundability ratio (FR < 1) after approximately 2.6 months of attempting pregnancy, with the association becoming statistically significant after 4.1 months. In women with regular sleep, longer sleep duration was associated with higher fecundability (adjusted FR = 1.18, 95% CI: 1.09–1.27; p < 0.001). Spline analysis indicated a linear increase in fecundability with sleep durations exceeding 7.5 h. Perceived insufficient sleep was linked to reduced fecundability (adjusted FR = 0.62, 95% CI: 0.48–0.81; p < 0.001), while later bedtime was associated with lower fecundability (adjusted FR = 0.91, 95% CI: 0.84–0.98; p = 0.045). Insomnia showed no significant effect (adjusted FR = 0.86, 95% CI: 0.67–1.11, p = 0.241).

Conclusions

Irregular sleep patterns may reduce fecundability over time. Among women with regular sleep, longer duration, earlier bedtime, and sufficient perceived sleep were associated with improved reproductive potential. Sleep optimization could be a modifiable behavioral target to enhance fertility.

Trial registration

ChiCTR2300068809 registered 1/3/2023.

Introduction

Reproductive health is a critical public health concern, with infertility affecting an estimated 12.6–17.5% of couples of reproductive age globally [1]. Infertility imposes substantial emotional, social, and economic burdens on individuals, families, and communities [2]. Time to Pregnancy (TTP), defined as the time it takes for a couple to achieve conception, is widely recognized as a practical and objective measure of fertility [3]. A shorter TTP generally reflects higher fertility potential [4]. TTP is increasingly accepted as a sensitive indicator of couple fecundity in epidemiological research. Compared with binary infertility outcomes, it enables detection of subtle variations in fertility and is less susceptible to recall bias when collected prospectively [5, 6]. It is widely acknowledged that fertility is influenced by a multitude of factors, encompassing age, lifestyle, environmental exposures, and underlying medical conditions [7, 8]. However, the role of sleep behavior—a fundamental component of a healthy lifestyle—has not been thoroughly investigated in relation to fertility outcomes.​.

Sleep is essential for overall health, playing a crucial role in hormonal regulation, metabolic processes, and immune function [9, 10]. Emerging research suggests that sleep disturbances may negatively affect reproductive health. A systematic review reported an association between sleep disturbances and female infertility, as well as suboptimal outcomes of fertility treatments [11]. However, findings on the relationship between sleep and fertility remain inconsistent. A secondary analysis of a preconception cohort study found no significant association between sleep duration, later sleep midpoint, or social jetlag and fertility [12]. Conversely, a cross-sectional study identified a significant link between sleep-wake behavior and female fertility, independent of sleep duration [13]. In a preconception cohort study conducted in North America, difficulty sleeping at night among women was associated with a slight decline in fecundity, and a weak inverse correlation was observed between shorter sleep duration and fecundity. These discrepancies highlight the need for further research to clarify the role of sleep behaviors in fertility outcomes. In men, inadequate sleep has been linked to lower sperm concentration and motility [14], reinforcing the relevance of sleep for reproductive health in both sexes.

Despite growing interest in sleep and reproductive health, the association between sleep behaviors and TTP remains understudied. Investigating this relationship is essential for identifying modifiable lifestyle factors that could potentially enhance fertility. Understanding the influence of different sleep parameters influence TTP may also inform targeted interventions to improve reproductive outcomes. In the Chinese context, rapid urbanization and lifestyle shifts have contributed to the growing prevalence of sleep insufficiency and delayed sleep onset, particularly among working-age women [15, 16]. These sociodemographic transitions may interact with reproductive patterns, such as delayed childbearing, underscoring the importance of modifiable lifestyle factors in fertility preservation. Given the increasing prevalence of delayed childbearing and infertility, this line of research is particularly relevant.

Therefore, this study aims to examine the relationship between sleep behaviors and TTP among women planning pregnancy in Guangzhou City. Specifically, we assessed how sleep duration, quality, and patterns are associated with conception timelines. We hypothesize that irregular sleep patterns, insomnia, short sleep duration, and late sleep onset time may prolong TTP. The findings of this study could contribute to public health recommendations and lifestyle guidelines for women preparing for pregnancy, ultimately supporting informed reproductive health decisions.

Materials and methods

Study sample

Data for this study were obtained from a cohort of couples preparing for pregnancy at Guangzhou Baiyun District Maternal and Child Health Hospital. The objective was to assess the impact of pre-pregnancy BMI on TTP. The study was registered with the China Clinical Trials Registry (ChiCTR) under registration number ChiCTR2300068809, with the initial registration on January 3, 2023. Further methodological details are available in previous publications [17].

Couples who participated in the National Free Pre-pregnancy Checkup Program (NFPCP) between January and June 2022 were included. Telephone follow-ups were conducted 13 to 15 months post-examination to inquire about their pregnancy preparation status and subsequent pregnancies.

The inclusion criteria were as follows: (1) Female partner aged between 20 and 49 years, and male partner aged 22 years or older; (2) Couples who were not pregnant at the time of the initial assessment; (3) Both partners self-reported an intention to conceive and were not using contraception during the examination.

The exclusion criteria included: (1) Female partners who tested positive for cytomegalovirus or Toxoplasma gondii IgM antibodies, or if either partner had syphilis, HIV, or any other medical condition requiring treatment that would delay conception; (2) Couples who declined participation or were unwilling to cooperate with the follow-up survey; (3) Couples who were already pregnant during the examination month; (4) Couples who were planning to undergo or had previously used assisted reproductive technology (ART).

After applying the exclusion criteria, a total of 1,684 couples were included in the study. A more detailed analysis was conducted on a subsample of 1,506 couples in which the female partner reported a regular sleep pattern (Fig. 1).

Fig. 1.

Study population creation

Ethical approval

The study was approved by the Medical Ethics Committee at Guangzhou Baiyun District Maternal and Child Health Hospital. Every participant provided written informed consent before enrolling in the study, and verbal consent was obtained again during follow-up for participation in the telephone interview.

Exposures and outcome

In this study, the exposure factors refer to various sleep behaviors, including irregular sleep patterns, sleep onset time, sleep duration, insomnia, and perceived insufficient sleep. Sleep behaviors during the preconception period were obtained through telephone follow-up by trained medical professionals. Definitions and measurements were as follows:

Irregular sleep patterns were defined as inconsistent or highly variable sleep and wake times in daily life during the preconception period. Specifically, participants were asked: “During your preconception period, did you have irregular sleep patterns (inconsistent bedtimes and wake times varying by >1 hour on ≥3 days per week)?” (Yes/No).

Sleep onset time was defined as the time women subjectively reported falling asleep during the preconception period, and was recorded in a decimal format in which 60 minutes equaled one unit (e.g., 23:30 was recorded as 23.5). If sleep occurs past midnight, each additional hour is incremented by 1 (e.g., a bedtime of 1:00 AM is recorded as 25). Participants were asked: “During your preconception period, what time did you usually fall asleep?”

Sleep duration refers to the average total sleep time per day during the preconception period. Participants were asked: “During your preconception period, how many hours of sleep did you get in a typical 24-hour period?”

Insomnia was defined as persistent difficulty initiating or maintaining sleep, or experiencing early morning awakenings, among women attempting to conceive. Participants were asked: “Did you experience persistent difficulty falling asleep, staying asleep, or waking too early ≥3 times/week?” (Yes/No). Perceived insufficient sleep was defined as a frequent self-reported experience of unrefreshing sleep or persistent fatigue upon awakening during the preconception period, irrespective of sleep duration. Participants were asked: “During your preconception period, did you wake up feeling unrefreshed or experience persistent fatigue despite adequate time in bed on ≥3 days per week?” (Yes/No).

The primary outcome was TTP.

1. TTP for pregnant couples = (date of last menstrual before pregnancy - date of last menstrual at examination)/30 + 1;

2. TTP for unpregnant couples = (date of last menstrual at follow-up - date of last menstrual at examination)/30.

In accordance with previous studies on TTP calculation methods [18, 19], if conception is confirmed and occurs within the following cycle, one cycle is added to the TTP to account for the actual time of conception. Periods during which couples paused pregnancy attempts were subtracted from the total. All reported pregnancies were confirmed via clinical tests at the hospital.

Covariates

Covariate selection was based on the Health Behavior Theory (HBT) [20], which focuses on the influence of individual behaviors on health outcomes. HBT is grounded in the understanding that lifestyle factors, such as diet, physical activity, sleep patterns and smoking, play a crucial role in health outcomes, including reproductive health. Guided by previous research [8, 21], we identified potential variables that may influence the study outcome. We then applied the Least Absolute Shrinkage and Selection Operator (LASSO) regression method for variable selection to reduce multicollinearity, thereby enhancing the robustness and predictive performance of the model. The final variables included in the model were BMI, age, occupation, education, tobacco exposure (no, yes), frequent consumption of takeaway food (no, yes), duration of electronic device use, regular menstruation (no, yes), and exercise frequency. The ages of the couples were recorded either at the time of examination or when they began preparing for pregnancy following the examination, and were categorized into four groups:"≤25 years,""26-29 years,""30-34 years,"and"≥35 years."This classification was based on both research regarding fertility and age, and actual reproductive policies and trends. BMI was calculated as weight (kg) divided by the square of height (m). Height and weight measurements were taken by trained medical professionals using a smart, interconnected height and weight measurement device. Participants were required to stand barefoot in a neutral posture, without shoes or outerwear, and weight data was recorded once it stabilized. According to the guidelines of the Chinese Obesity Working Group (WGOC), the BMI thresholds were defined as follows: underweight < 18.5 kg/m², normal weight 18.5–23.9 kg/m², overweight 24–27.9 kg/m², and obesity ≥28 kg/m². For analysis, BMI was categorized into three groups: underweight, normal weight, and overweight/obese. Occupation was classified into three categories:"Manual","Office", or"Others". Education referred to the highest level of education attained, including high school or vocational school and below, college, bachelor's degree, and postgraduate education. Tobacco exposure was defined as active smoking or passive exposure for an average of five minutes or more per day. Frequent takeaway consumption was defined as eating takeaway at least once a day. The duration of electronic device use was treated as a continuous variable, representing the average daily hours spent using mobile phones, tablets, computers, or watching television during the pre-pregnancy period. Regular menstruation status was determined based on the doctor's inquiry and judgment during the examination. Exercise frequency was categorized by the number of moderate-intensity physical activity sessions (lasting over 30 minutes) per week, with categories: “<1 time per week,” “1–3 times per week,” and “>3 times per week.

Statistical analysis

All statistical analyses were performed using R software (version 4.0.0). Group differences were assessed using the χ² test for categorical variables, the Wilcoxon rank-sum test for continuous variables with a non-normal distribution, and one-way analysis of variance (ANOVA) for continuous variables with a normal distribution. Normally distributed continuous variables were presented as mean ± standard deviation (SD), non-normally distributed continuous variables were described using the median and interquartile range (IQR), while categorical variables were described as frequencies and percentages.

The Cox proportional hazards regression model was employed to examine the association between various sleep behaviors and TTP, estimating fecundability ratios (FRs) and corresponding 95% confidence intervals (95% CIs). An FR > 1 indicated a shorter TTP and higher fertility, whereas an FR < 1 suggested prolonged TTP and reduced fertility.

During covariate selection, we first identified potential factors influencing TTP based on previous studies and theoretical frameworks. These variables included age, BMI, occupation, education level, tobacco exposure, and alcohol consumption for both partners. For women specifically, these included primiparity, frequent consumption of takeaway food, duration of electronic device use, coffee consumption, menstrual regularity, and exercise frequency. LASSO-Cox regression was then applied, incorporating an L1 penalty into the Cox proportional hazards model to shrink certain regression coefficients to zero, thereby automatically selecting the most important variables.

For each sleep behavior subgroup, the LASSO model was fitted using the following steps: First, a survival object was constructed, with TTP as the survival time and pregnancy success as the survival outcome. Categorical variables were then processed, and a predictor matrix for the Cox regression model was built. We used the glmnet() function to fit the LASSO-Cox regression model across a range of regularization parameters (λ), and the coefficient path was visualized. The optimal λ value was determined using 10-fold cross-validation, and the error curve was plotted. The final set of covariates included in the Cox regression model was composed of those with nonzero coefficients at the optimal λ value (lambda.min). The final covariates retained across most sleep behavior subgroups included BMI, age, occupation, and education level for both partners, as well as tobacco exposure, frequent consumption of takeaway food, duration of electronic device use, regular menstruation, and exercise frequency for women. Collinearity diagnostics showed that all VIF values for sleep variables and covariates were less than 5.

Regarding the missing data, the regular sleep group had 0.27% missing data for sleep onset time. These missing values were handled using multiple imputation by chained equations (MICE), with 50 imputations. The imputation model included the following covariates: BMI, age, occupation, and education level for both partners, as well as sleep onset time, sleep duration, insomnia, perceived insufficient sleep, tobacco exposure, frequent consumption of takeaway food, duration of electronic device use, regular menstruation, exercise frequency, and TTP for women.

The proportional hazards assumption of the Cox regression model was tested using Schoenfeld residuals. In the analysis, the variable"irregular sleep patterns"violated the proportional hazards assumption in the Cox regression model. Therefore, we applied an extended Cox proportional hazards model incorporating a time-dependent covariate to explore the time-varying effect of irregular sleep patterns on TTP. In this study, irregular sleep patterns were initially assessed as a binary categorical variable, with participants classified as having either a regular (coded as 0) or irregular (coded as 1) sleep-wake pattern based on self-reported data. To examine the potential time-varying effect of irregular routines on TTP, this variable was converted into numeric form and included as a time-dependent covariate in the Cox proportional hazards model. Specifically, to account for violation of the proportional hazards assumption, irregular sleep patterns were modeled using both a baseline main effect and a time-interaction term constructed via the tt() function in R, defined as a product of the variable and the natural logarithm of follow-up time (i.e., log(TTP + 1)). This allowed us to assess potential deviations from the proportional hazards assumption by capturing time-varying effects of irregular sleep patterns on time-to-pregnancy. After model fitting, the coefficients for both the main and interaction terms were extracted along with their variance-covariance matrix. These values were used to compute time-specific log hazard ratios and their standard errors across a 12-month interval. The log HRs were exponentiated to obtain corresponding hazard ratios and 95% confidence intervals, and the trend was visualized using the ggplot2 package in R.

In the regular sleep patterns group, we investigated the effects of sleep onset time, sleep duration, insomnia, and perceived insufficient sleep on TTP, while in the irregular sleep patterns group, we did not further analyze specific sleep behaviors due to the limited sample size (n = 178). In the regular sleep patterns group, three models were established: Model 1 without any adjustments, Model 2 adjusting for demographic characteristics of both partners, and Model 3 adjusting for all covariates. In the Cox regression model for the regular sleep group, Holm-Bonferroni correction was applied to adjust the p-values for sleep onset time, sleep duration, insomnia, and perceived insufficient sleep.

Restricted cubic spline (RCS) analysis was conducted to evaluate the nonlinear effects of different ranges of sleep duration and sleep onset time on TTP. The number of knots in the RCS model was selected based on Akaike Information Criterion (AIC). In our study, we compared the age and occupational characteristics between couples lost to follow-up and those included in the analysis, and found no statistically significant differences. Finally, sensitivity analyses were conducted to explore whether live birth impacted the findings. Statistical significance was defined as p < 0.05.

Results

Baseline characteristics of study participants

The 1,684 couples included in the study contributed a total of 11,973 conception cycles and 1,127 pregnancies. Among the women attempting to conceive, 178 (10.6%) reported irregular sleep patterns, while 1,506 (89.4%) reported regular sleep patterns. A total of 236 women (14.0%) self-reported symptoms of insomnia, and 262 (15.6%) reported perceived insufficient sleep. The median TTP was 6 months [IQR: 3-11]. Among the 1,506 women with regular sleep patterns, the median sleep onset time was 23:30 [IQR: 23:00–24:00], and the median sleep duration was 7.5 hours [IQR: 7.0–8.0]. When grouped by pregnancy status, significant differences were observed in women's BMI, their partners’ BMI, women's age, their partners’ age, frequent consumption of takeaway food, menstrual regularity, irregular sleep patterns, sleep duration, insomnia, perceived insufficient sleep, and TTP (Table 1).

Table 1.

Descriptive characteristics of study population by pregnancy status

Characteristic	Total	Pregnancy (no)	Pregnancy (yes)	p value
BMI (%)				0.004
normal weight	1147 (68.1)	363 (65.2)	784 (69.6)
overweight and obese	211 (12.5)	91 (16.3)	120 (10.6)
underweight	326 (19.4)	103 (18.5)	223 (19.8)
Spouse’s BMI (%)				0.028
normal weight	892 (53.0)	272(48.8)	620 (55.0)
overweight and obese	718 (42.6)	263 (47.2)	455 (40.4)
underweight	74 (4.4)	22(4.0)	52 (4.6)
Age (%)				< 0.001
≤ 25 years	272(16.2)	68 (12.2)	204 (18.1)
26–29 years	809(48.0)	245 (44.0)	564 (50.0)
30–34 years	474 (28.1)	178 (32.0)	296 (26.3)
≥ 35 years	129 (7.7)	66 (11.8)	63 (5.6)
Spouse’s Age (%)				< 0.001
≤ 25 years	146 (8.7)	34 (6.1)	112 (9.9)
26–29 years	633 (37.6)	188 (33.7)	445 (39.5)
30–34 years	663 (39.4)	231 (41.5)	432 (38.4)
≥ 35 years	242 (14.3)	104 (18.7)	138 (12.2)
Occupation (%)				0.560
Office	610 (36.2)	201 (36.1)	409 (36.3)
Manual	455 (27.0)	159 (28.5)	296 (26.3)
Others	619 (36.8)	197 (35.4)	422 (37.4)
Spouse’s Occupation (%)				0.573
Office	470 (27.9)	164 (29.5)	306 (27.1)
Manual	547 (32.5)	174 (31.2)	373 (33.1)
Others	667 (39.6)	219 (39.3)	448 (39.8)
Education (%)				0.553
high school or vocational school and below	292 (17.3)	106 (19.0)	186 (16.5)
college	609 (36.2)	203 (36.5)	406 (36.0)
bachelor’s degree	680 (40.4)	215 (38.6)	465 (41.3)
postgraduate education	103 (6.1)	33 (5.9)	70 (6.2)
Spouse’s Education (%)				0.961
high school or vocational school and below	347 (20.6)	115 (20.6)	232 (20.6)
college	578 (34.3)	188 (33.8)	390 (34.6)
bachelor’s degree	655 (38.9)	221 (39.7)	434 (38.5)
postgraduate education	104 (6.2)	33 (5.9)	71 (6.3)
Tobacco exposure = yes (%)	76(4.5)	19 (3.4)	57 (5.1)	0.160
Frequent consumption of takeaway food = yes (%)	503 (29.9)	141 (25.3)	362 (32.1)	0.005
Duration of electronic device use (median [IQR])	11.00 [8.50, 12.50]	11.00 [8.50, 13.00]	11.00 [8.50, 12.50]	0.195
Regular menstruation = yes (%)	1463 (86.9)	464 (83.3)	999 (88.6)	0.003
Exercise frequency (%)				0.056
<1 time per week	817 (48.5)	274 (49.2)	543 (48.2)
1–3 times per week	600 (35.6)	211 (37.9)	389 (34.5)
>3 times per week	267 (15.9)	72 (12.9)	195 (17.3)
Irregular sleep patterns = yes (%)	178 (10.6)	78 (14.0)	100 (8.9)	0.002
Sleep onset time (median [IQR])	23.50 [23.00, 24.00]	23.75 [23.25, 24.25]	23.50 [23.00, 24.00]	0.002
Sleep duration (median [IQR])	7.50 [7.00, 8.00]	7.50 [7.00, 8.00]	7.50 [7.50, 8.25]	< 0.001
Insomnia = yes (%)	236 (14.0)	113 (20.3)	123 (10.9)	< 0.001
Perceived insufficient sleep = yes (%)	262 (15.6)	143 (25.7)	119 (10.6)	< 0.001
TTP (median [IQR])	6.00 [3.00, 11.00]	12.00 [9.00, 13.00]	5.00 [3.00, 8.00]	< 0.001

The association between sleep parameters and TTP among women with regular sleep patterns

Table2 presents the association between sleep parameters and TTP among women with regular sleep patterns based on Cox regression analysis. Three models were used to assess these relationships: Model 1 (unadjusted), Model 2 (adjusted for partners'demographic characteristics), and Model 3 (adjusted for all covariates).

Table 2.

Association between sleep parameters and TTP among women with regular sleep patterns, Cox regression analysis

	Model11			Model22			Model33
	FR (95%CI)	p value	Adjusted p value4	FR (95%CI)	p value	Adjusted p value4	FR (95%CI)	p value	Adjusted p value4
Sleep onset time	0.96(0.89–1.03)	0.253	0.253	0.93(0.86,1.00)	0.046	0.093	0.91(0.84,0.98)	0.022	0.045
Sleep duration	1.19(1.11–1.28)	<0.001	<0.001	1.18(1.09,1.27)	<0.001	<0.001	1.18(1.09,1.27)	<0.001	<0.001
Insomnia
no	Reference			Reference			Reference
yes	0.82(0.64,1.04)	0.102	0.204	0.86(0.67,1,10)	0.269	0.270	0.86(0.67,1.11)	0.241	0.241
Perceived insufficient sleep
no	Reference			Reference			Reference
yes	0.64(0.49,0.82)	<0.001	0.001	0.62(0.48,0.80)	<0.001	<0.001	0.62(0.48,0.81)	<0.001	0.001

FR Fecundability ratio, 95%Cl 95% confidence interval

¹Model 1: No covariates were adjusted

²Model 2: Adjusted for the demographic characteristics of both partners, including age, occupation, and education

³Model 3: Adjusted for all covariates, including BMI, age, occupation, and education level for both members of the couple, as well as tobacco exposure, frequent consumption of takeaway food, duration of electronic device use, regularity of menstruation, and exercise frequency for women

⁴p value: The p-values were adjusted using the Holm-Bonferroni method for the four variables: “Sleep onset time”, “Sleep duration”, “Insomnia”, and “Perceived insufficient sleep”

In Model 1, sleep onset time did not show a statistically significant association with TTP (FR = 0.96, 95% CI: 0.89–1.03, p = 0.253). After adjustment for demographic factors in Model 2, sleep onset time was found to be marginally significant (FR = 0.93, 95% CI: 0.86–1.00, p = 0.046), although the Holm-Bonferroni adjusted p-value (p = 0.093) indicated that this result may not be statistically robust. In the fully adjusted Model 3, the association remained significant (FR = 0.91, 95% CI: 0.84–0.98, p = 0.022), with an adjusted p-value of 0.045, indicating a potential impact of sleep onset time on TTP. Notably, the association between sleep onset time and TTP became statistically significant only after full adjustment in Model 3. In contrast, both the crude model and Model 2 (adjusting for demographic variables) showed no significant association. This indicates that certain lifestyle and reproductive health factors included in Model 3—such as BMI, tobacco exposure, and electronic device use—may confound or suppress the relationship in earlier models.

Sleep duration was significantly associated with TTP across all models. In Model 1, longer sleep duration was associated with increased fecundability (FR = 1.19, 95% CI: 1.11–1.28, p < 0.001). This association remained stable after adjusting for demographic characteristics in Model 2 (FR = 1.18, 95% CI: 1.09–1.27, p < 0.001) and in the fully adjusted Model 3 (FR = 1.18, 95% CI: 1.09–1.27, p < 0.001). The Holm-Bonferroni adjusted p-values also confirmed the statistical significance of this association (p < 0.001 in all models).

No significant association was observed between insomnia and TTP across all models. In Model 1, women with insomnia had a slightly reduced fecundability ratio compared to those without insomnia (FR = 0.82, 95% CI: 0.64–1.04, p = 0.102), but this association was not statistically significant. After adjusting for demographic characteristics in Model 2 (FR = 0.86, 95% CI: 0.67–1.10, p = 0.269) and in Model 3 (FR = 0.86, 95% CI: 0.67–1.11, p = 0.241), the results remained nonsignificant, with adjusted p-values exceeding the significance threshold.

Perceived insufficient sleep was strongly associated with a longer TTP in all models. In Model 1, those who reported perceived insufficient sleep had a significantly reduced fecundability ratio (FR = 0.64, 95% CI: 0.49–0.82, p < 0.001), and this association remained significant in Model 2 (FR = 0.62, 95% CI: 0.48–0.80, p < 0.001) and Model 3 (FR = 0.62, 95% CI: 0.48–0.81, p < 0.001). The Holm-Bonferroni adjusted p-values (p = 0.001 in all models) confirmed the robustness of this finding.

Overall, sleep duration and perceived insufficient sleep exhibited significant associations with TTP, suggesting that both longer sleep duration and avoiding perceived insufficient sleep may be beneficial for fecundability. Sleep onset time showed a marginally significant association after full adjustment, while insomnia did not appear to have a meaningful impact on TTP.

The association between irregular and regular sleep patterns in relation to TTP

Figure2 illustrates the time-varying effect of irregular sleep patterns on fecundability by presenting FRs with corresponding 95% CIs. The analysis is derived from a Cox proportional hazards model incorporating time-varying effects, accounting for both the main effect of irregular sleep patterns and their interaction with time, based on data from 1,684 women attempting to conceive.

Fig. 2.

Time-dependent effect of irregular versus regular sleep patterns on TTP. Note: The red line represents the estimated fecundability ratio (FR) across different TTP months, while the shaded blue region indicates the corresponding 95% CIs. The black dashed line at FR = 1 serves as a reference, representing no effect. A No covariates were adjusted for in the time-dependent Cox regression model. The baseline effect of irregular routines was statistically significant (FR = 2.05; 95% CI: 1.10–3.84; p = 0.025), while the interaction with time was also significant and negative (FR = 0.56; 95% CI: 0.39–0.81; p = 0.002). B The covariates included in the time-dependent Cox regression model were BMI, age, occupation, and education level for both partners, as well as tobacco exposure, frequent consumption of takeaway food, duration of electronic device use, regular menstruation, exercise frequency for women. The baseline effect of irregular routines was statistically significant (FR = 2.05; 95% CI: 1.09–3.85; p = 0.025), while the interaction with time was also significant and negative (FR = 0.56; 95% CI: 0.39–0.81; p = 0.002)

Panel A shows the unadjusted association from a time-dependent Cox model, comparing women with irregular vs. regular sleep patterns. It compares women with irregular sleep patterns to those with regular sleep patterns. During the early phase of the observation period (TTP = 1 to 2.5 months), the FR remains above 1, although the difference is not statistically significant. As TTP extends beyond 2.5 months, the FR gradually decreases to below 1. When TTP exceeds 3.9 months, the association becomes statistically significant, suggesting a progressively detrimental effect of irregular sleep patterns on fecundability over time compared to regular sleep patterns.

Panel B presents the same association after adjusting for relevant covariates, comparing irregular and regular sleep patterns. In the initial stage (TTP = 1 to 2.6 months), the FR remains above 1, but the difference is not statistically significant. As TTP increases beyond 2.6 months, the FR steadily declines below 1, and becomes statistically significant when TTP exceeds 4.1 months. This consistent trend further underscores the robustness of the observed time-dependent effect of irregular sleep patterns relative to regular sleep patterns.

Restricted cubic spline model of the association between sleep duration and TTP

Figure3 illustrates the association between sleep duration and TTP using a restricted cubic spline model. Panel A presents the association between sleep duration and TTP based on a model adjusted for key sleep-related factors, including sleep onset time, insomnia, and perceived insufficient sleep. The overall p-value was statistically significant (P-overall < 0.001), suggesting a strong association between sleep duration and TTP. The p-value for nonlinearity (P-nonlinear = 0.210) indicates that the association was primarily linear. The FR increased with sleep duration, suggesting that longer sleep duration was associated with higher fecundability. At the extreme ends, the confidence intervals widened due to the lower number of observations in these ranges.

Fig. 3.

Restricted cubic spline model of the association between sleep duration and TTP. Note: A Restricted Cubic Spline Model for sleep duration with Adjusting for key sleep-related variables, including sleep onset time, insomnia, and perceived insufficient sleep for women. B Restricted Cubic Spline Model for sleep duration with Adjusting for all covariates, including BMI, age, occupation, and education level for both members of the couple, as well as tobacco exposure, frequent consumption of takeaway food, duration of electronic device use, regularity of menstruation, exercise frequency, sleep onset time, insomnia, and perceived insufficient sleep for women. In both panels, the x-axis denotes sleep duration, and the y-axis indicates the estimated fecundability ratio (FR) with 95% confidence intervals (CI). The gray histogram in the background represents the distribution of sleep duration in the study population, and the black vertical dashed line indicates the reference sleep duration. The black horizontal dashed line indicates the fecundability ratio (FR) = 1

Panel B shows the fully adjusted model with additional covariate adjustments beyond those included in Panel A. Similar to Panel A, the overall association remained statistically significant (P-overall < 0.001), reinforcing the robustness of the findings. However, the nonlinearity test yielded a P-nonlinear = 0.833, indicating that the relationship between sleep duration and TTP was even more linear in this comprehensively adjusted model. The upward trend in FR with increasing sleep duration remained consistent with the findings observed in Panel A.

These results suggest that longer sleep duration is positively associated with fecundability, and this relationship follows a predominantly linear trend. The findings highlight the potential importance of adequate sleep duration for reproductive health, with the most favorable fecundability observed beyond the reference sleep duration of 7.5 hours.

Restricted cubic spline model of the association between sleep onset time and TTP

Figure4 illustrates restricted cubic spline models examining the association between sleep onset time and TTP. Panel A displays the model adjusted for key sleep-related variables in women, including sleep duration, insomnia, and perceived insufficient sleep. A statistically significant overall association was observed (P-overall = 0.011), with marginal evidence of nonlinearity (P-nonlinear = 0.069). The curve shows that sleep onset time before approximately 23:30 (11:30 PM) is associated with higher fecundability, while later sleep onset time is linked to a reduced FR, indicating longer TTP. This pattern suggests a potentially protective role of earlier sleep onset time, even after accounting for key sleep-related confounders.

Fig. 4.

Restricted cubic spline model of the association between sleep onset time and TTP. Note: A Restricted Cubic Spline Model for sleep onset time with Adjusting for key sleep-related variables, including sleep duration, insomnia, and perceived insufficient sleep for women. B Restricted Cubic Spline Model for sleep onset time with Adjusting for all covariates, including BMI, age, occupation, and education level for both members of the couple, as well as tobacco exposure, frequent consumption of takeaway food, duration of electronic device use, regularity of menstruation, exercise frequency, sleep duration, insomnia, and perceived insufficient sleep for women. In both panels, the x-axis denotes sleep onset time, and the y-axis indicates the estimated fecundability ratio (FR) with 95% confidence intervals (CI). The gray histogram in the background represents the distribution of sleep onset time in the study population, and the black vertical dashed line indicates the reference sleep time. The black horizontal dashed line indicates the fecundability ratio (FR) = 1

Panel B reveals consistent findings in the fully adjusted model (P-overall = 0.027), although the nonlinearity becomes less pronounced (P-nonlinear = 0.187). The inverse relationship between delayed sleep onset time and fecundability remains evident, underscoring the robustness of the association.

Panel B reveals consistent findings in the fully adjusted model (P-overall = 0.027), although the nonlinearity becomes less pronounced (P-nonlinear = 0.187). The inverse relationship between delayed sleep onset time and fecundability remains evident, underscoring the robustness of the association.

Sensitivity analyses

Sensitivity analyses were presented in Table 3. The association between sleep behaviors and TTP remained robust after restricting the analysis to participants with live birth outcomes, excluding biochemical pregnancies, spontaneous abortions, or stillbirths.

Table 3.

Association between pre-pregnancy sleep behaviors and TTP in sensitivity analyses

	Model11			Model22
	FR (95%CI)	P value		FR (95%CI)	P value
Irregular sleep patterns
No	Reference			Reference
Yes	2.23(1.16,4.28)	0.016		2.20(1.14,4.23)	0.018
The interaction with time	0.53(0.36,0.78)	0.001		0.54(0.36,0.79)	0.002

	Model33			Model44			Model55
	FR(95%CI)	Pvalue	Adjusted pvalue6	FR(95%CI)	Pvalue	Adjusted pvalue6	FR(95%CI)	Pvalue	Adjusted pvalue6
Sleep onset time	0.95(0.87,1.03)	0.185	0.185	0.91(0.84,0.99)	0.034	0.069	0.89(0.81,0.97)	0.008	0.016
Sleep duration	1.22(1.13,1.32)	<0.001	<0.001	1.20(1.11,1.30)	<0.001	<0.001	1.20(1.10,1.30)	<0.001	<0.001
Insomnia
No	Reference			Reference			Reference
Yes	0.75(0.57,0.99)	0.040	0.081	0.79(0.60.1.05)	0.108	0.108	0.81(0.61,1.08)	0.148	0.148
Perceived insufficient sleep
No	Reference			Reference			Reference
Yes	0.55(0.41,0.74)	<0.001	<0.001	0.54(0.40,0.72)	<0.001	<0.001	0.53(0.40,0.72)	<0.001	<0.001

FR Fecundability ratio, 95% Cl 95% confidence interval

¹Model 1: No covariates were adjusted

²Model 2: Adjusted for all covariates, including BMI, age, occupation, and education level for both members of the couple, as well as tobacco exposure, frequent consumption of takeaway food, duration of electronic device use, regular menstruation, exercise frequency for women

³Model 3: No covariates were adjusted

⁴Model 4: Adjusted for the demographic characteristics of both partners, including age, occupation, and education

⁵Model 5: Adjusted for all covariates, including BMI, age, occupation, and education level for both members of the couple, as well as tobacco exposure, frequent consumption of takeaway food, duration of electronic device use, regularity of menstruation, and exercise frequency for women

⁶p value: The p-values were adjusted using the Holm-Bonferroni method for the four variables: “Sleep onset time”, “Sleep duration”, “Insomnia”, and “Perceived insufficient sleep”

Discussion

This study comprehensively examined the relationship between multiple dimensions of sleep behavior and female fecundability, as measured by TTP, using adjusted statistical models. After adjusting for covariates in all models, we found that irregular sleep patterns were consistently associated with longer TTP. Moreover, among women with regular sleep-wake patterns, certain specific sleep behaviors—including subjective sleep insufficiency, sleep duration, and sleep onset time—were significantly associated with TTP.

The time-dependent relationship between sleep regularity and TTP

In this study, the use of a time-varying Cox proportional hazards model allowed us to capture how the effect of sleep irregularity on TTP evolved over time. We found that irregular sleep patterns violated the proportional hazards assumption of the Cox regression model; instead, the negative impact of irregular sleep became increasingly pronounced as attempts to conceive continued. This suggests that chronic, rather than acute, circadian disruption may exert a more substantial influence on reproductive function. This time-sensitive relationship supports findings from chronobiological studies linking chronic circadian misalignment with disruptions in the hypothalamic-pituitary-gonadal (HPG) axis and altered menstrual cycle regularity [22, 23].

The observed associations are biologically plausible and supported by a growing body of chronobiological and reproductive literature. Circadian disruption from irregular or delayed sleep can impair the secretion patterns of reproductive hormones, such as luteinizing hormone (LH), follicle-stimulating hormone (FSH), and estradiol, which are crucial for ovulation and implantation [24]. Additionally, irregular sleep has been associated with increased systemic inflammation and metabolic dysregulation, both of which are linked to infertility and subfertility [25, 26].

Importantly, our analysis indicates that the influence of irregular sleep on fecundability is not only statistically robust but also dynamic over time. These findings underscore the cumulative nature of poor sleep habits, suggesting that their adverse effects may intensify with continued exposure and highlighting the potential of sleep regulation as a long-term strategy to enhance reproductive potential.

Moreover, even in the context of regular sleep-wake patterns, our data show that sleep onset time and total sleep duration are independently associated with conception probability. These associations were observed in models that simultaneously adjusted for key sleep-related factors, including insomnia and perceived insufficient sleep, suggesting that the observed effects are independent of other major dimensions of sleep. These results highlight the importance of circadian timing, suggesting that both sleep regularity and alignment with the body’s internal clock are critical for optimizing reproductive outcomes [24, 27].

The association between specific sleep behaviors and TTP

Among participants with regular sleep schedules, we examined the effects of key sleep dimensions—including sleep duration, bedtime, insomnia symptoms, and subjective sleep sufficiency—on TTP. Our findings indicate that certain sleep behavior patterns within this subgroup may independently influence fecundability.

Using restricted cubic spline models, we observed a predominantly linear relationship between sleep duration and fecundability, with longer sleep durations associated with increased fecundability. The fully adjusted model confirmed the robustness of this association, with the most favorable fecundability observed beyond the reference sleep duration of 7.5 h.

It is worth noting that while the spline model provided a flexible visualization of the dose–response relationship, the Cox proportional hazards model allowed for a formal test of associations with TTP. Interestingly, in the Cox model, the association between later sleep onset time and TTP did not reach statistical significance until full adjustment (Model 3), which accounted for a wide range of lifestyle and reproductive health variables. This suggests that residual confounding in less adjusted models may have masked the true association between sleep timing and fecundability.

One prospective cohort study reported that shorter sleep duration and delayed sleep timing were linked to an increased likelihood of cycle discontinuation prior to embryo transfer [28]. However, another study found an inverse association between short sleep duration (≤ 7 h) and fertility, while no statistically significant association was found for long sleep duration (≥ 9 h) [29]. Collectively, these findings underscore the potential role of adequate sleep duration in optimizing reproductive potential, and highlight the importance of proper model selection and covariate adjustment in uncovering these associations.

The linearity of the association (P-nonlinear = 0.833 in the fully adjusted model) aligns with the hypothesis that perceived insufficient sleep may negatively impact reproductive function, while incrementally longer sleep durations may support hormonal stability and ovarian function. While some previous studies have reported U-shaped or non-linear associations between sleep and fertility [11, 30], our findings suggest that in this population, the relationship is primarily linear, with no clear detrimental effect observed at longer sleep durations.

Studies have shown that sleep, particularly deep non-REM sleep, plays a critical role in modulating the pulsatile release of gonadotropin-releasing hormone (GnRH) and luteinizing hormone (LH), which are essential for ovulatory function [31, 32]. Experimental studies on sleep restriction have demonstrated its capacity to blunt this pulsatility, thereby potentially impairing follicular development and ovulation. Given that sleep duration is a modifiable behavior, lifestyle interventions targeting improved sleep hygiene could serve as a non-invasive and cost-effective strategy to enhance reproductive outcomes in women trying to conceive.

Our spline analysis of sleep onset time revealed that bedtimes earlier than 23:30 (11:30 PM) were significantly associated with shorter TTP, whereas later bedtimes did not demonstrate a significant effect on TTP. This suggests that circadian alignment, as indicated by earlier sleep onset, plays an important role in reproductive physiology. The significance of this relationship aligns with existing literature indicating that sleep onset time may influence fertility outcomes [11, 13, 33]. Previous studies have demonstrated that a delayed sleep phase (i.e., later bedtimes) can disrupt circadian rhythms, with potential negative effects on reproductive health. For instance, research has shown that delayed sleep onset time can impair melatonin synthesis [34, 35], which is critical for regulating the sleep-wake cycle and reproductive hormones. Melatonin has the potential to improve oocyte quality and fertilization rates. [36, 37]. Additionally, delayed sleep phases have been associated with increased oxidative stress, a factor that can negatively impact oocyte quality and embryonic development [38]. Oxidative stress is believed to damage cellular components such as DNA, proteins, and lipids, leading to functional impairments in reproductive tissues [39]. These mechanisms could underlie the observed relationship between sleep onset time and conception probability.

Although our study did not find significant effects of later onset times (after 23:30) on conception probability, the literature does suggest that consistent circadian alignment—reflected by earlier onset times—can enhance reproductive health. Further investigation is warranted to elucidate the specific mechanisms by which circadian rhythms and sleep timing influence fertility, particularly through melatonin regulation and oxidative stress pathways.

Self-reported insomnia symptoms were associated with longer TTP across all three models; however, these associations did not reach statistical significance. While this lack of significance may be attributable to limited power or potential misclassification bias, the consistently observed direction of the effect suggests that insomnia may still be a relevant factor. A systematic review highlights that accumulating evidence from the literature demonstrates the adverse effects of sleep disturbances on female reproductive health [25]. Chronic insomnia may impair female fertility by disrupting hormonal balance, activating the HPA axis, and altering circadian regulation of reproductive function [40]. Future studies using validated diagnostic tools and objective sleep measures are warranted to better evaluate this relationship.

Subjective sleep insufficiency was significantly and negatively associated with fecundability, suggesting that individuals’ perceptions of inadequate sleep—despite seemingly sufficient total sleep duration—may reflect underlying disturbances such as sleep fragmentation or reduced sleep efficiency. Although discrepancies exist between subjective and objective assessments of sleep [41], subjective perceptions may still capture meaningful dimensions of sleep quality that are not evident through objective measures alone. These results emphasize the importance of integrating both subjective and objective metrics of sleep in fertility research to comprehensively assess their impact on reproductive outcomes.

Strengths and limitation

This study presents several strengths. First, it includes a relatively large and demographically diverse sample, which enhances statistical power and improves the generalizability of the findings. Second, the incorporation of time-varying effects through the Cox model is a major methodological advantage, as it accounts for dynamic behavioral and biological changes over time. The use of three complementary statistical models provides a robust analytical framework for identifying true associations while adjusting for potential confounders. Additionally, the application of restricted cubic spline analyses allowed for visualization and quantification of potential non-linear relationships between sleep parameters and TTP, which are often overlooked in traditional regression frameworks.

However, several limitations should be acknowledged. First, all sleep parameters were self-reported, which may introduce recall bias and subjective interpretation. We did not utilize standardized sleep assessment tools such as the Pittsburgh Sleep Quality Index (PSQI) or the Insomnia Severity Index (ISI), which may have yielded more reliable and comprehensive evaluations of sleep disturbances. Considering the feasibility of telephone follow-up, our questions covered essential aspects of sleep, including timing, duration, insomnia symptoms, and subjective sleep quality. However, they were not derived from validated instruments and have not been independently tested for psychometric properties. Consequently, exposure misclassification is possible. Future studies should consider incorporating validated sleep questionnaires and investigation methods to improve the measurement validity and reliability of sleep-related exposures.

Furthermore, the generalizability of our findings may be constrained due to the specific demographic composition of our study cohort. The study exclusively recruited women from Guangzhou, China, who were actively attempting to conceive. This geographical and demographic specificity may limit the broader applicability of the results. Unique cultural, environmental, and genetic factors within this population could modulate sleep behaviors and their associations with fecundability. Therefore, caution is warranted when extrapolating these findings to women from different regions or cultural backgrounds.

Second, we did not differentiate between sleep patterns on weekdays and weekends. Emerging evidence suggests that discrepancies between workday and weekend sleep schedules (i.e., social jetlag) may significantly contribute to circadian misalignment and associated reproductive health risks [42, 43]. This omission may have led to underestimation of the impact of weekly sleep variability on fecundability.

Third, we acknowledge that sleep behaviors can change over time, and our study captured these behaviors only once. This limitation may affect the generalizability of our findings over longer periods. Future research could benefit from repeated assessments of sleep behaviors to better account for their dynamic nature and to further explore how changes in sleep patterns might influence TTP. Additionally, TTP was calculated based on standardized 30-day menstrual cycles, which may not reflect individual variability in actual cycle length. This approach, although commonly used in fecundability studies, could lead to exposure misclassification—potentially underestimating TTP for women with shorter cycles and overestimating it for those with longer cycles. Collecting detailed menstrual cycle data in future studies could improve the accuracy of TTP estimates and allow for more individualized assessment of fecundability.

Fourth, a small number of individuals did not use contraception before their check-up, which may shorten the TTP. Generally speaking, in China, participating in a free pre-pregnancy health check-up is considered the official start of preparing for pregnancy. In Guangzhou, preconception checkup clinics are adjacent to marriage registration offices, and over 60% of those attending preconception health checks are newly married couples who participate voluntarily. Therefore, we believe that this population represents those who are beginning to prepare for pregnancy. In future research, this should be addressed with more comprehensive data.

Fifth, although we focused on sleep behaviors and their potential impact on fertility, we did not include objective measures of sleep, such as actigraphy. We also did not assess light exposure, which can influence melatonin secretion and reproductive hormone rhythms. Furthermore, we did not evaluate sleep disorders such as obstructive sleep apnea, which may coexist with insomnia and independently affect fertility outcomes. Additionally, we did not collect data on the frequency of sexual intercourse, which is a key factor that could influence TTP. Although we included female exercise frequency and both partners’ BMI as proxy indicators of physical health, we lacked information on mental health status, which could act as a potential confounder. Future research in this area should consider incorporating these variables to provide a more comprehensive understanding of the relationship between sleep behaviors and fertility.

Lastly, male partner characteristics, such as sleep behaviors and semen quality, were not accounted for. Given the dyadic nature of conception, future research should adopt a couple-based framework to better understand the interplay between sleep and reproductive outcomes.

Conclusions

In this cohort study of women attempting natural conception, irregular sleep patterns were associated with a time-dependent decline in the probability of conception. Among participants with regular sleep–wake cycles, longer sleep duration, earlier sleep onset, and perceived sleep sufficiency were positively associated with fecundability. Specifically, both sleep duration and sleep timing showed linear relationships with TTP, while perceived sleep insufficiency was consistently linked to prolonged TTP. Although insomnia symptoms were assessed only in this subgroup, no significant association with fecundability was observed.

These findings highlight the relevance of sleep health—including both sleep quantity and circadian alignment—as a potentially modifiable factor in reproductive health counseling. Incorporating routine sleep assessment and targeted behavioral interventions into reproductive health strategies may offer a promising approach to improving fertility outcomes in couples planning pregnancy.

Authors’ contributions

YZ and BL designed the study. YZ collected, analyzed the data and drafted the manuscript. YX and DG collected the data and assisted in literature search. BL and DG gave suggestions, and BL revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

The study was funded by Guangzhou Baiyun District Medical and Health Science and Technology Project (2025-YL-019).

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study received approval from the Medical Ethics Committee at Guangzhou Baiyun District Maternal and Child Health Hospital. Every participant provided written informed consent before enrolling in the study. This study is registered with the China Clinical Trials Registry (www.clinicaltrials.gov) (registration number ChiCTR2300068809).

Competing interests

The authors declare no competing interests.