Determining the relationship between sleep quality and reproductive health issues in fertile and infertile men and women: A cross-sectional study

Abstract

Background and objective

Reproductive health is an integral part of overall health. Sleep disorders and infertility may be reciprocally related. Therefore, the present study aims to investigate the association between sleep quality and reproductive health in fertile and infertile men and women.

Methods

In this cross-sectional study, the population consists of 736 men and women who visited health centers in Behshahr County and the infertility center at Imam Khomeini Hospital in Sari in 2024. Four questionnaires were utilized in this study: Socio-Demographic-Medical Questionnaire, Pittsburgh Sleep Quality Index, Sexual Quality of Life Questionnaire, and the Depression Anxiety Stress Scale. After data collection, the data were entered into SPSS version 22. If the data distribution was normal, independent two-sample t-tests and one-way analysis of variance were used, and if the data distribution was not normal, nonparametric equivalents, Mann-Whitney U and Kruskal-Wallis tests, were used.

Result

The results showed a considerable difference in sleep quality between fertile and infertile individuals, particularly among females. Among infertile females, 65.9 % reported poor sleep quality (PSQI ≥6), compared to 49.6 % of fertile females, which showed a statistically significant difference (p = 0.007). In contrast, although a higher proportion of infertile males (36.8 %) reported poor sleep quality compared to fertile males (29.1 %), this difference was not statistically significant (p = 0.077).

Conclusion

We found that sleep disturbance and reproductive health are linked. Therefore, it is essential to manage sleep quality in infertile individuals by reproductive health professionals.

Graphical abstract

Highlights

• •Poor sleep quality is significantly more common among infertile individuals.
• •Sleep quality independently predicts infertility risk in women.
• •Comparative four-group design reveals gender-specific sleep patterns.
• •Sleep disturbance may be a modifiable factor in reproductive health.
• •Findings support sleep screening in infertility care and policy planning.

1. Introduction

Reproductive health is an integral part of overall health, well-being, and quality of life [1]. It has always been a public health concern [2]. Infertility is a common clinical problem, defined as the inability to conceive after one year of regular unprotected intercourse [[3], [4], [5]]. Epidemiological reports estimate the prevalence of infertility in developed countries to range from 3.5 % to 16.7 %, with an average prevalence of 9 % [[6], [7], [8]]. Studies have shown that, on average, 1.9 % of women aged 20–44 years were unable to experience the birth of their first live child, and 10.5 % had only one child and were unable to have another [[9], [10], [11], [12]].

There has been a significant increase in the prevalence of infertility over the past few decades, which has been associated with the rising prevalence of suboptimal lifestyle factors [13] such as obesity [14] and tobacco or alcohol use [15]. Since these environmental and lifestyle factors are modifiable, they present pathways for improving infertility management. Additionally, beyond the well-studied unhealthy lifestyle parameters associated with infertility, sleep may also be a novel and innovative parameter to consider in this context [16].

While the purpose of sleep remains largely unknown, its importance is undeniable. One-third of a person's life is dedicated to sleep [9]. Sleep is essential for physical health [9], cognitive functioning, and mental health [[17], [18], [19], [20]]. Among both men and women, sleep disorders, particularly insomnia, have been well-documented as playing a role in or being associated with a myriad of diseases, including cardiovascular disease, hypertension, impaired glucose regulation, depression, anxiety disorders, asthma, stroke, coronary artery disease, infarction, arthritis, obesity, chronic obstructive pulmonary disease, and chronic kidney disease [21,22]. In women, sleep disorders may be linked to menstrual disorders, pregnancy, postpartum depression, and menopausal transition, although studies are limited [15]. Importantly, sleep may represent a significant modifiable target for improving reproductive health and infertility treatment outcomes [16,23,24]. Nearly one in three infertile women reports sleep disturbances [25], and one in three reports poor sleep quality [26]. A cross-sectional study in China, which investigated the association between night shift work and semen parameters in 1346 Chinese men, confirmed that sperm count was significantly lower in night shift workers compared to those who did not work at night [27].

The average sleep duration reported is 6.8 h per night, compared to the 9 h observed a century ago, a change that can be partly attributed to social and occupational trends [[16], [17], [18]].

The prevalence of infertility has paralleled the rise in sleep disorders over recent decades [24]. Dimensions of sleep disorders can include poor sleep quality, persistent sleep disturbances, long or short sleep durations, sleep chronotype, circadian rhythm disruption, and obstructive sleep apnea [28]. In humans, sleep is mostly studied through questionnaires. Sometimes, studies report objective sleep measurements obtained through polysomnography, which combines electroencephalography, electrooculography, and electromyography, along with recordings of heart rate, breathing rate, and leg movement, as well as actigraphy systems [16]. Ultimately, sleep disorders and infertility may be reciprocally related, such that sleep disturbances and their associated outcomes may not only arise from reproductive processes but may also play a role in reproductive health [29].

The results of a study showed that sleep disorders were present in 34 % of infertile individuals, and women with diminished ovarian reserve were 30 times more likely to have sleep disorders, while controlling for variables such as race, body mass index, and vasomotor symptoms [26]. Also another study has shown that over 35 % of women undergoing in vitro fertilization reported sleep disturbances [30].

A recently conducted systematic review highlighted the limited number of studies examining the association between sleep disorders and reproductive health, indicating a significant gap in current research [8]. Given the critical role of sleep in overall physiological functioning, including hormonal regulation and reproductive processes, further investigation is warranted to understand how sleep disturbances may impact fertility in both men and women. The present study seeks to address this gap by exploring the relationship between sleep quality and reproductive health in fertile and infertile individuals, providing valuable insights that could inform clinical interventions and improve reproductive outcomes. Therefore, the present study aims to investigate the association between sleep quality and reproductive health in fertile and infertile men and women.

2. Materials and methods

2.1. Study design and ethical considerations

This cross-sectional study included men and women visiting health centers in Behshahr County and the infertility center at Imam Khomeini Hospital in Sari. Ethical approval for the study was obtained from the Sexual and Reproductive Health Research Center at Mazandaran University of Medical Sciences (Approval Code: IR.MAZUMS.REC.1401.521). Before participation, all individuals provided informed consent after receiving comprehensive information about the study's objectives, procedures, potential benefits, and risks. To ensure confidentiality, all collected data were anonymized and securely stored, with access restricted to authorized researchers only. Participation was entirely voluntary, and subjects retained the right to withdraw from the study at any point without penalty.

2.2. Inclusion criteria, sample, and sampling strategy

The inclusion criteria for this study were men and women of reproductive aged who were willing to participate and had sufficient time to complete the questionnaire. Pregnant and breastfeeding individuals were excluded from the study.

Due to the absence of similar studies, a preliminary study with a sample size of 30 (15 women and 15 men) was conducted to determine the final sample size. Based on a significance level of 0.05, a power of 0.80, and sample size formulas for one-way ANOVA and correlation coefficient differences, the sample sizes were determined as follows:

Based on the average sleep score of fertile and infertile men and women:

$$n=\frac{{\delta }^2}{\left(g-1\right)\lambda }=60$$

Accordingly, 60 fertile men, 60 infertile men, 60 fertile women, and 60 infertile women were included in the study.

Note: The sample size calculation for one-way ANOVA was performed using complex formulas, and the final sample size was calculated using G-Power software version 3.1.9.2.

Based on the average sexual quality of life score of fertile and infertile men and women:

$$n=\frac{{\delta }^2}{\left(g-1\right)\lambda }=24$$

Accordingly, 24 fertile men, 24 infertile men, 24 fertile women, and 24 infertile women were included in the study.

Based on the correlation between sleep quality and sexual health in women:

$$\text{Effect}\text{size}:q=0.5-laerge\left(r\right)=0.35$$

$$n=\frac{2\times {\left(z_{1-\alpha /2}+z_{1-\beta }\right)}^2}{q^2}=132$$

Based on the correlation between sleep quality and sexual health in men:

$$\text{Effect}\text{size}:q=0.5-laerge\left(r\right)=0.26$$

$$n=\frac{2\times {\left(z_{1-\alpha /2}+z_{1-\beta }\right)}^2}{q^2}=236$$

Consequently, 132 fertile women, 132 infertile women, 236 fertile men, and 236 infertile men were included in this study.

2.3. Data collection

Four questionnaires were utilized for data collection in this study.

2.3.1. Socio-Demographic-Medical Questionnaire

This questionnaire was designed after an extensive review of the literature and consultations with the research team. It includes variables such as age, spouse's age, gender, education, occupation, duration of marriage, number of pregnancies, number of children, history of infertility, history of infertility treatment, history of miscarriage, history of stillbirth, history of low birth weight, history of preterm birth, type of delivery, history of abnormal bleeding, history of premenstrual syndrome, history of dysmenorrhea, contraceptive method, history of substance use, history of alcohol use, history of addiction, history of chronic diseases, history of psychiatric disorders diagnosed by a physician, history of specific medication use, history of infertility, history of infertility treatment, duration of infertility, use of ovulation-inducing drugs, infertility surgery, history of in vitro fertilization, history of endometriosis, and history of miscarriage prevention treatment [6,23,27,[31], [32], [33], [34]].

2.3.2. Pittsburgh Sleep Quality Index (PSQI)

The PSQI assesses individuals’ sleep quality over the past four weeks. In scoring the PSQI, seven component scores are derived, each scored 0 (no difficulty) to 3 (severe difficulty). The components include 1) overall subjective sleep quality, 2) sleep latency, 3) sleep duration, 4) sleep efficiency (based on the ratio of actual sleep time to time spent in bed), 5) sleep disturbances (measured by nocturnal awakenings), 6) use of sleep medication, and 7) daytime dysfunction (problems experienced during the day due to poor sleep). The questionnaire has nine items, but question 5 itself contains ten subitems, so the total questionnaire has 19 items, which are scored on a 4-point Likert scale from 0 to 3. Values of “0” and “3” indicate “better” and “worse” sleep, respectively. The values of all seven domains are summed to obtain a total score. The total score range of the PSQI is 0–21. A total score of ≤5 indicates poor sleep quality and >5 indicates good sleep quality [[35], [36], [37], [38], [39]]. The validity and reliability of the questionnaire have already been confirmed in the Iranian community [38,39].

2.3.3. Sexual Quality of Life Questionnaire

This tool was first introduced in 1998 and revised in 2005 by Symonds et al. to assess the impact of sexual dysfunction on sexual quality of life, particularly on variables such as self-esteem, emotional well-being, and interpersonal relationships. the sexual quality of life-female (SQOLF) consists 18 items and each item is rated on a six-point response (completely agree to completely disagree). The response categories could be scored either 1 to 6 or 0 to 5 giving a total score of 18–108 or 0–90. Higher score indicates better female Sexual quality of life [40,41]. The reliability and validity of this tool were established with a Cronbach's alpha of 0.95. In this study, the standardized version by Dr. Masoumi et al. (2013), which was validated with a Cronbach's alpha of 0.77 for Iranian women, was used [42].

2.3.4. The Depression Anxiety Stress Scale (DASS)

In the present study, the DASS-21 questionnaire with 21 items and three subscales (7 items for each subscale) was used. This instrument measures the prevalence of signs and symptoms of depression, anxiety, and stress during the past weeks. The items of this scale are rated on a four-point Likert scale, and the score of each item ranges from 0 (“does not apply to me at all”) to 3 (“applies to me most of the time”). The subscale scores are calculated by summing the scores of the individual items, and the maximum sum for each subscale is 21. Higher scores indicate higher psychological distress. The original study reported high reliability of the DASS-21, with Cronbach's alpha coefficients for depression, anxiety, and stress being 0.91, 0.84, and 0.90, respectively [43]. The validity and reliability of the questionnaire have already been confirmed in the Iranian community [44].

2.4. Statistical analysis

After data collection, all data were entered into SPSS software version 29. Descriptive statistics were used to summarize the variables: minimum, maximum, mean, and standard deviation for quantitative data, and frequencies and percentages for categorical data.

To assess the distribution of quantitative variables, the Kolmogorov-Smirnov test was applied. For normally distributed data, independent two-sample t-tests and one-way ANOVA were used to compare group means. For non-normally distributed data, the Mann-Whitney U test and Kruskal-Wallis test were employed. Associations between categorical variables were examined using the Chi-square test. Correlations between quantitative variables were analyzed using Pearson's correlation coefficient for normally distributed data and Spearman's rank correlation for non-normal data.

To assess the strength of association between sleep quality and reproductive health status, univariate logistic regression was initially conducted to estimate crude odds ratios (ORs) and 95 % confidence intervals (CIs). Subsequently, multivariate logistic regression was performed to adjust for potential confounders including age, BMI, and smoking status. The results were reported as adjusted odds ratios (aORs) with corresponding 95 % CIs and p-values, and a significance level of 0.05 was considered for all statistical tests.

3. Results

3.1. The sociodemographic characteristics of the participants

Table 1 presents the demographic and clinical characteristics of study participants, categorized by sex (female and male) and fertility status (fertile vs. infertile).

Table 1.

Demographic characteristics of the participants.

Variable	Female		Male
	Fertile	Infertile	Fertile	Infertile
Age (mean ± SD)	32.61 ± 5.38	36.67 ± 5.58	37.57 ± 4.25	36.58 ± 4.25
Education
<=diploma	63 (46.0)	93 (68.9)	156 (65.8)	131 (56.7)
University	74 (54.0)	42 (31.1)	81 (34.2)	100 (43.3)
Job-status
Housekeeper	76 (55.5)	111 (82.2)	–	–
Employed	61 (44.5)	24 (17.8)	56 (23.6)	49 (21.2)
Self-employed	–	–	154 (65.0)	125 (54.1)
With a fixed salary	–	–	13 (5.5)	40 (17.3)
Without a fixed salary	–	–	14 (5.9)	17 (7.4)
chronic diseases
Yes	23 (16.8)	25 (18.5)	23 (9.7)	27 (11.7)
No	114 (83.2)	110 (81.5)	214 (90.3)	204 (88.3)
Psychiatric diseases
Yes	7 (5.1)	20 (14.9)	7 (3.0)	7 (3.0)
No	130 (94.9)	114 (85.1)	230 (97.0)	224 (97.0)
Drug use
Yes	18 (13.1)	17 (12.6)	17 (7.2)	23 (10.0)
No	119 (86.9)	118 (87.4)	220 (92.8)	208 (90.0)
Smoking
Yes	6 (4.4)	8 (5.9)	69 (29.1)	57 (24.7)
No	131 (95.6)	127 (94.1)	168 (70.9)	174 (75.3)
Pregnancy number
0	0 (0)	80 (59.3)		–
1	98 (71.5)	35 (25.9)		–
2,3	39 (28.5)	20 (14.8)		–
Child number
0	16 (11.7)	121 (89.6)	2 (0.8)	223 (96.5)
1	99 (72.3)	14 (10.4)	123 (51.9)	8 (3.5)
2,3	22 (16.1)	0 (0)	112 (47.3)	0 (0)
Abortion
0	103 (75.2)	94 (69.6)	–	–
1	31 (22.6)	27 (20.0)	–	–
2	3 (2.2)	14 (10.4)	–	–
Drug use for abortion
Yes	25 (18.2)	39 (28.9)	–	–
No	112 (81.8)	96 (71.1)	–	–
Stillbirth history
0	133 (97.1)	130 (96.3)	–	–
1	4 (2.9)	5 (3.7)	–	–
Preterm labor
0	133 (97.1)	132 (97.8)	–	–
1	4 (2.9)	3 (2.2)	–	–
Low birth weight
Yes	15 (10.9)	18 (13.3)	–	–
No	122 (89.1)	117 (86.7)	–	–
Endometriosis history
Yes	19 (13.9)	28 (20.7)
No	118 (86.1)	107 (79.3)
Abnormal uterine bleeding
Yes	10 (7.3)	13 (9.6)
No	127 (92.7)	122 (90.4)
Menstrual disorders
Yes	37 (27.0)	43 (31.9)
No	100 (73.0)	92 (68.1)
Dysmenorrhea
Yes	98 (71.5)	97 (71.9)
No	39 (28.5)	38 (28.1)
Contraception methods
No	85 (62.0)	133 (98.5)
Yes	52 (38.0)	2 (1.5)
Type of infertility
Primary	–	119 (88.1)	–	215 (93.1)
Secondary	–	16 (11.9)	–	16 (6.9)
Infertility duration
1–5	–	79 (58.5)	–	116 (24.8)
5–10	–	18 (13.3)	–	76 (16.2)
10–15	–	28 (20.7)	–	26 (5.6)
≥15	–	10 (7.4)	–	13 (2.8)
Infertility treatment history
Yes	–	112 (83.0)	–	174 (37.2)
No	–	23 (17.0)	–	57 (12.2)
Surgery history
Yes	–	11 (8.1)	–	51 (10.9)
No	–	124 (91.9)	–	180 (38.5)
In Vitro Fertilization
Yes	–	59 (43.7)	–	88 (18.8)
No	–	76 (56.3)	–	143 (30.6)
Ovulation-stimulating drugs
Yes	–	116 (85.9)
No	–	19 (14.1)

The mean age varied across groups: fertile females were the youngest (32.61 ± 5.38 years), while infertile females were older (36.67 ± 5.58 years). Male participants had comparable mean ages in both fertile (37.57 ± 4.25 years) and infertile (36.58 ± 4.25 years) groups.

Educational attainment differed by fertility status. Among females, 54.0 % of fertile participants had university education compared to 31.1 % of infertile females. In contrast, infertile males had a higher rate of university education (43.3 %) than fertile males (34.2 %).

Regarding employment status, most infertile females were housekeepers (82.2 %), whereas 44.5 % of fertile females were employed. Among males, self-employment was the most common occupation (65.0 % in fertile, 54.1 % in infertile), with a notable increase in fixed-salary employment among infertile males (17.3 % vs. 5.5 %).

Chronic diseases were reported in 16.8 % of fertile and 18.5 % of infertile females, while prevalence among males was lower (approximately 10 %). Psychiatric disorders were more prevalent in infertile females (14.9 %) compared to fertile females (5.1 %) and males (3 %).

Drug use was slightly more common in infertile males (10.0 %) than fertile males (7.2 %). Smoking was significantly more prevalent among males (24.7–29 %) than females (5 %).

Reproductive history showed that 59.3 % of infertile females had no prior pregnancies, while 72.3 % of fertile females had one child. Among males, nearly all infertile participants (96.5 %) had no children.

Abortion history was similar between fertile and infertile females, although drug-induced abortion was more frequent among infertile females (28.9 %).

Gynecological conditions such as endometriosis (20.7 %), menstrual disorders (31.9 %), and dysmenorrhea (71.9 %) were more prevalent in infertile females.

Primary infertility was the most common type, reported by 88.1 % of infertile females and 93.1 % of infertile males.

Duration of infertility varied, with most infertile females (58.5 %) and a smaller proportion of infertile males (24.8 %) experiencing infertility for 1–5 years.

A high percentage of infertile females (83.0 %) and males (37.2 %) had undergone infertility treatment. IVF was more commonly used among females (43.7 %) than males (18.8 %), and ovulation-stimulating drugs were administered to 85.9 % of infertile females.

Multivariate logistic regression analysis was conducted to assess the association between sleep quality and infertility status, stratified by gender. As presented in Table 2, poor sleep quality was significantly associated with increased odds of infertility among women. Specifically, infertile women were more likely to report poor sleep quality (PSQI ≥6) compared to fertile women, with an adjusted odds ratio (OR) of 1.94 (95 % CI: 1.19–3.17, p = 0.007).

Table 2.

Association between sleep quality and infertility status: Multivariate analysis by gender.

Gender	Group	Sleep Quality	OR (95 % CI)	p-value
Female	Fertile	Good (<6)	Reference	–
Female	Infertile	Poor (≥6)	1.94 (1.19–3.17)	0.007
Male	Fertile	Good (<6)	Reference	–
Male	Infertile	Poor (≥6)	1.39 (0.96–2.01)	0.077

Among men, a similar trend was observed, with infertile men showing higher rates of poor sleep quality than their fertile counterparts. However, the association did not reach statistical significance (OR = 1.39, 95 % CI: 0.96–2.01, p = 0.077).

These findings suggest that poor sleep quality may be an independent predictor of infertility in women, while the relationship in men appears weaker and warrants further investigation in larger and more diverse samples.

Table 3 presents the crude and adjusted odds ratios (aORs) with 95 % confidence intervals (CIs) for factors associated with poor sleep quality (PSQI ≥6) among fertile and infertile women. Variables with a p-value <0.20 in the univariate analysis were included in the multivariate logistic regression model. Among fertile women, significant predictors of poor sleep quality included older age (aOR = 1.093, 95 % CI: 1.007–1.186, p = 0.033), presence of menstrual disorders (aOR = 3.799, 95 % CI: 1.463–9.865, p = 0.006), use of abortion-inducing drugs (aOR = 3.989, 95 % CI: 1.257–12.654, p = 0.019), and higher total sexual dysfunction scores (aOR = 1.107, 95 % CI: 1.035–1.184, p = 0.001). Conversely, marital duration of 1–5 years (compared to 15–20 years) was identified as a protective factor against poor sleep quality (aOR = 0.289, 95 % CI: 0.092–0.907, p = 0.033). Additionally, higher DASS scores were inversely associated with sleep quality (aOR = 0.925, 95 % CI: 0.889–0.963, p < 0.001), indicating that increased psychological distress was linked to poorer sleep outcomes.

Table 3.

Crude and Adjusted Odds Ratios for Predictors of Poor Sleep Quality Among Fertile and Infertile females.

Variables	Fertile		Infertile
	Crude OR (95 %CI)	Adjusted OR (95 %CI)	Crude OR (95 %CI)	Adjusted OR (95 %CI)
	P-value	P-value	P-value	P-value
Age	1.065 (0.998, 1.137), 0.058	1.093 (1.007–1.186), 0.033	1.016 (0.953, 1.083), 0.632	–
Husband's age	1.029 (0.967, 1.095), 0.372	–	1.054 (0.993, 1.118), 0.085	–
Education ( ≤ diploma)	0.535 (0.271, 1.057), 0.072	–	0.588 (0.263, 1.316), 0.196	–
Job (Housekeeper)	1.663 (0.843–3.281), 0.143	2.050 (0.897–4.683), 0.089	0.449 (0.156–1.294), 0.138	0.381 (0.115–1.262), 0.114
Chronic disease (yes)	1.728 (0.693, 4.313), 0.241	–	1.810 (0.668, 4.902), 0.244	–
Smoking (yes)	2.094 (0.371, 11.829), 0.403	–	0.286 (0.65, 1.255), 0.097	0.051 (0.007–0.401), 0.005
Substance abuse (yes)	2.094 (0.371, 11.829), 0.403	–	0.810 (0.249, 2.633), 0.726	–
Marital status (Years)		–		–
1–5	0.306 (0.107–0.877), 0.028		0.289 (0.0922–0.907), 0.033
5–10	0.554 (0.181–1.693), 0.300		0.440 (0.124–1.554), 0.202
10–15	0.433 (0.134–1.400), 0.162		0.196 (0.061–0.627), 0.006
15-20 (Ref.)
Dysmenorrhea (yes)	1.625 (0.767–3.445), 0.205	–	0.414 (0.172–0.998), 0.050	0.162 (0.051–0.518), 0.002
Menstrual disorders (yes)	3.264 (1.453–7.331), 0.004	3.799 (1.463–9.865), 0.006	1.514 (0.688–3.335), 0.303	–
Drug use	1.070 (0.620–4.710), 0.300	–	4.459 (0.974–20.428), 0.054	7.702 (1.320–44.953), 0.023
Abnormal uterine bleeding (yes)	4.467 (0.913–21.863), 0.065	–	3.103 (0.658–14.637), 0.153	4.299 (0.858–21.549), 0.076
Abortion drug (yes)	3.189 (1.234–8.238), 0.017	3.989 (1.257–12.654), 0.019	1.048 (0.477–2.303), 0.908	–
Stillbirth history (yes)	0.319 (0.032–3.142), 0.327	–	1.303 (0.210–8.088), 0.776	–
Surgery history (yes)	–	–	5.696 (0.706–45.960), 0.102	5.579 (0.633–49.148), 0.122
DASS	0.962 (0.933–0.993), 0.015	0.925 (0.889–0.963), 0 < 0.001	0.995 (0.962–1.029), 0.780	–
Total sexual dysfunction	1.054 (0.997–1.114), 0.063	1.107 (1.035–1.184), 0.001	1.059 (0.984–1.139), 0.228	–

Among infertile women, smoking was strongly protective against poor sleep quality (aOR = 0.051, 95 % CI: 0.007–0.401, p = 0.005), while dysmenorrhea (aOR = 0.162, 95 % CI: 0.051–0.518, p = 0.002) and drug use (aOR = 7.702, 95 % CI: 1.320–44.953, p = 0.023) were identified as significant risk factors.

Table 4 summarizes the crude and adjusted odds ratios for factors associated with poor sleep quality (PSQI ≥6) among fertile and infertile men. Variables with a p-value <0.20 in the univariate analysis were included in the multivariate model.

Table 4.

Crude and adjusted odds ratios for predictors of poor sleep quality among fertile and infertile men.

Variables	Fertile		Infertile
	Crude OR (95 %CI)	Adjusted OR (95 %CI)	Crude OR (95 %CI)	Adjusted OR (95 %CI)
	P-value	P-value	P-value	P-value
Age	1.009 (0.945–1.078), 0.792	–	0.964 (0.905–1.027), 0.259	–
Wife's age	0.997 (0.932–1.068), 0.942	–	0.945 (0.895–0.997), 0.037	–
Education ( ≤ diploma)	1.270 (0.695–2.319), 0.437	–	0.846 (0.494–1.451), 0.544	–
Job		–
Free	1.869 (0.500–6.994), 0.353		5.169 (1.133–23.584), 0.034	4.574 (0.953–21.944), 0.057
clerk	0.797 (0.187–3.392), 0.759		4.355 (0.892–21.261), 0.069	2.039 (0.368–11.296), 0.415
With a fixed salary	1.630 (0.287–9.256), 0.582		4.038 (0.806–20.247), 0.090	4.117 (0.772–21.945), 0.097
without a fixed salary	Ref.		Ref.
Chronic disease (yes)	0.483 (0.158–1.474), 0.201	–	0.565 (0.229–1.399), 0.217	–
Psychiatric disorders (ye)	0.000 (0.000–3.5), 0.999	–	4.500 (0.854–23.725), 0.076	6.463 (0.984–42.432), 0.052
Drug use (yes)	0.500 (0.139–1.798), 0.288	–	0.729 (0.287–1.851), 0.506	–
Smoking (yes)	0.655 (0.343–1.251), 0.200	0.425 (0.203–0.893), 0.024	1.635 (0.890–3.004), 0.113	–
Substance abuse (yes)	0.527 (0.111–2.506), 0.421		0.449 (0.122–1.657), 0.229	–
Marital status		–
1–5	0.578 (0.207–1.616), 0.296		7.674 (0.935–62.990), 0.058	5.593 (0.631–49.545), 0.122
5–10	0.523 (0.199–1.374), 0.189		6.346 (0.776–51.896), 0.085	3.971 (0.435, 36.244), 0.222
10–15	0.597 (0.219–1.628), 0.313		4.390 (0.522–36.907), 0.173	2.078 (0.215, 20.052), 0.527
15–20	Ref.		Ref.
DASS	1.044 (1.021–1.068), <0.001	1.023 (0.997–1.050), 0.079	1.034 (1.014–1.054), 0.001	1.033 (1.004–1.063), 0.027
Total sexual dysfunction	0.885 (0.841–0.931), <0.001	0.894 (0.846–0.946), <0.001	0.937 (0.903–0.971), <0.001	0.949 (0.906–0.0993), 0.025

In fertile men, smoking was significantly associated with better sleep quality (aOR = 0.425, 95 % CI: 0.203–0.893, p = 0.024). Although higher DASS scores showed a trend toward association with poor sleep, the result did not reach statistical significance (aOR = 1.023, 95 % CI: 0.997–1.050, p = 0.079). Interestingly, higher total sexual dysfunction scores were significantly associated with better sleep quality (aOR = 0.894, 95 % CI: 0.846–0.946, p < 0.001), suggesting an inverse relationship.

Among infertile men, higher DASS scores remained a significant predictor of poor sleep quality (aOR = 1.033, 95 % CI: 1.004–1.063, p = 0.027). Psychiatric disorders showed a borderline association with poor sleep quality (aOR = 6.463, 95 % CI: 0.984–42.432, p = 0.052). Similar to fertile men, higher sexual dysfunction scores were inversely associated with poor sleep quality (aOR = 0.949, 95 % CI: 0.906–0.993, p = 0.025).

Although job type initially showed a significant crude association with poor sleep quality (OR = 5.169, 95 % CI: 1.133–23.584, p = 0.034), this relationship did not remain significant in the adjusted model.

Multivariate logistic regression analysis was performed to assess the association between poor sleep quality and infertility status, adjusting for key demographic and lifestyle confounders including age, body mass index (BMI), and smoking status. As presented in Table 5, poor sleep quality (defined as PSQI ≥6) was significantly associated with increased odds of infertility (adjusted OR = 2.31; 95 % CI: 1.45–3.68; p = 0.001).

Table 5.

Multivariate logistic regression analysis of factors associated with infertility status.

Variable	Adjusted OR	95 % CI	p-value
Poor sleep quality	2.31	1.45–3.68	0.001
Age (years)	1.04	1.01–1.07	0.015
BMI (kg/m2)	1.08	1.02–1.15	0.008
Smoking (Yes vs No)	1.56	0.92–2.64	0.098

Age and BMI also showed statistically significant associations with infertility. Each additional year of age was associated with a 4 % increase in the odds of infertility (OR = 1.04; 95 % CI: 1.01–1.07; p = 0.015), and each unit increase in BMI was associated with an 8 % increase in infertility risk (OR = 1.08; 95 % CI: 1.02–1.15; p = 0.008).

Smoking status demonstrated a non-significant trend toward increased infertility risk (OR = 1.56; 95 % CI: 0.92–2.64; p = 0.098), suggesting a possible association that warrants further investigation in larger samples.

Overall, these findings indicate that poor sleep quality remains an independent predictor of infertility, even after controlling for age, BMI, and smoking. This underscores the potential role of sleep health as a modifiable behavioral factor in reproductive outcomes.

4. Discussion

This study aimed to explore the association between sleep disorders and reproductive health in fertile and infertile men and women. Our results suggested that sleep disorders were significantly associated with reproductive health. In this regard, the results of a study have shown that infertility and poor sleep quality are directly related, and sleep disorders are significantly associated with infertility, which is consistent with our study and also confirms that poor sleep quality is associated with poorer fertility treatment outcomes, such as reduced embryo quality, reduced number of retrieved egg and lower fertilization rate [23]. Also, another recent study found that sleep disorders were more common in the infertile group compared to the fertile group, and sleep disorders were positively associated with the risk of infertility, and considered sleep disorders to be an essential risk factor and modifiablfe behavioral pattern for infertility [45]. In another study conducted by our research team to investigate sleep disorders and the outcomes of in vitro fertilization, the results showed a direct and significant relationship between sleep quality and pregnancy rate and emphasizing the need for further studies [31]. A previous cross-sectional study also showed that sleep disorders were significantly associated with infertility, emphasizing that further studies are needed because sleep disturbance is a modifiable factor and could be a new strategy to deal with infertility, which is consistent with the results of our study [46]. A study of 1820 women of reproductive age identified sleep disorders as an independent risk factor for infertility. The study results showed that the risk of infertility in those with sleep disorders was 2.14 times higher than in those without sleep disorders [47]. The results of this study were also consistent with our study.

Our study revealed that infertile men have poorer sleep quality than fertile men. Studies in this regard have shown that insufficient sleep in adult men negatively affects the functioning of their reproductive system [4,48,49]. In the last decade, more researchers have looked at the link between sleep disorders and male reproductive health. Data shows that men with sleep disorders have reduced sperm concentration, progressive decreased motility, and reduced total sperm count that the results of the studies are consistent with our study [[50], [51], [52], [53]].

Also, in our study, factors affecting poor sleep quality in fertile women included: age factors, menstrual disorders, use of abortion drugs, sexual dysfunction, and higher depression scores and in infertile women included: drug use and dysmenorrhea. A study was conducted to investigate factors related to sleep disorders in women of reproductive age. The results of the studies showed that sleep disorders in women are affected by a complex interaction of hormonal fluctuations such as menstrual disorders, psychological factors such as sexual dysfunction and higher depression scores, and lifestyle choices, and that psychological distress caused by infertility, anxiety, and depression were considered important factors in sleep disorders in women which confirms the results of our study [29]. Another study aimed to investigate factors associated with sleep quality in women of reproductive age, and the results of the study revealed an association between substance use, age, depression, hormonal changes, and menstrual disorders with poor sleep quality, which is consistent with the results of our study [54].

Our study revealed that in fertile men, higher depression scores were associated with sleep disturbance. A study conducted in this regard identified depression as a major factor in sleep disorders in men [55]. Our study identified several important clinical implications that, based on these findings, can inform professionals about incorporating a modifiable strategy into existing approaches for the prevention and management of infertility.The World Health Organization has declared that infertility is a disease and is estimated to affect 14 to 25 percent of couples worldwide [56]. Although infertility is one of the significant global health challenges and issues, the risk factors for infertility are usually not modifiable, but recent studies have shown that among the factors affecting reproductive health, sleep can be considered a modifiable factor and addressed [30,46,57].

4.1. Limitations and future directions

This study is the first to simultaneously examine four distinct target groups—fertile women, infertile women, fertile men, and infertile men—within a single comparative framework and sample size. While this design offers a unique lens into gender- and fertility-specific sleep patterns, one limitation is the relatively small sample size in certain subgroups, which may affect statistical power and limit generalizability. Additionally, sleep quality was assessed using self-report questionnaires, which may introduce recall bias and underestimate the severity of sleep disturbances and their impact on reproductive health.

Future research should incorporate objective sleep assessment tools such as actigraphy and polysomnography to enhance diagnostic accuracy. However, due to their high cost and logistical constraints, these methods remain limited in large-scale epidemiological studies. Moreover, our study did not explore the underlying biological or psychological mechanisms linking sleep disturbances to infertility. These pathways are complex, multifactorial, and not yet fully elucidated, warranting further investigation through longitudinal and mechanistic studies.

4.2. Strategic and clinical implications

Despite these limitations, our findings underscore the potential role of sleep disturbance as a modifiable behavioral factor in reproductive health. By identifying sleep quality as a differentiating factor between fertile and infertile individuals, this study offers a new strategic perspective for both clinical practice and public health policy. Reproductive health professionals are encouraged to integrate sleep assessment into routine care for individuals of reproductive age, emphasizing prevention, early identification, and management of sleep disorders as part of comprehensive infertility care.

In sum, this study contributes novel insights to the literature by adopting a four-group comparative design and highlighting sleep disturbance as a potential target for intervention. Future research should build on these findings to develop multidisciplinary approaches that address sleep health as a critical component of reproductive well-being.

5. Conclusion

In conclusion, this study demonstrated that poor sleep quality is significantly more prevalent among infertile individuals and may increase the likelihood of infertility. By adopting a four-group comparative design fertile women, infertile women, fertile men, and infertile men this research offers a unique contribution to the literature, highlighting sleep disturbance as a potentially modifiable behavioral factor in reproductive health.

Unlike previous studies that often focused on isolated populations, our findings underscore the importance of integrating sleep assessment into infertility care. Sleep disorders, if identified and managed early, may serve as a strategic entry point for improving reproductive outcomes. This study not only reinforces the link between sleep and fertility but also opens new avenues for clinical intervention and public health planning.

We recommend that reproductive health professionals consider sleep quality as a core component of infertility screening and treatment. Future research should explore the underlying mechanisms through longitudinal and multidisciplinary approaches, and policymakers are encouraged to support sleep-focused initiatives within reproductive health programs. Ultimately, improving sleep may represent a cost-effective and impactful strategy in the prevention and management of infertility.

CRediT authorship contribution statement

Farangis habibi: Writing – review & editing, Writing – original draft, Data curation, Conceptualization. Marzieh Azizi: Writing – review & editing, Writing – original draft. Sepideh Peyvandi: Data curation. Roya Nikbakht: Formal analysis. Javad Setareh: Writing – review & editing, Data curation. Mohammad Ahmadi: Data curation. Zohreh Shahhosseini: Supervision, Project administration, Conceptualization.

Ethics approval and consent to participate

This study received approval from the Sexual and Reproductive Health Research Center, affiliated with Mazandaran University of Medical Sciences, Sari, Iran, with code number 17321 and the National Code of Biomedical Ethics, IR.MAZUMS.REC.1401.521 Moreover, necessary measures were taken to ensure data confidentiality, anonymity, the right to withdraw, and voluntary participation.

Funding

This study was granted by Mazandaran University of Medical Sciences, Sari, Iran.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

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