Abstract
Our study aimed to assess the prevalence and determinants of sleep disturbances among pregnant women in Kolar District, Karnataka, India. It focused on specific disturbances, such as obstructive sleep apnea (OSA), insomnia, restless leg syndrome (RLS), excessive daytime sleepiness (EDS), and poor sleep quality across pregnancy trimesters. A cross-sectional community-based study among 251 pregnant women was conducted. Sleep disturbances using validated tools, including the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and Insomnia Severity Index (ISI), STOP-Bang and single-question RLS screener tool were measured. Bivariate logistic regression was followed by multivariate logistic regression identified significant predictors. Sleep disturbances were highly prevalent, with poor sleep quality being most common (39.84%), followed by OSA (13.55%), EDS-moderate and severe category (11.56%), insomnia (9.6%), and RLS (6.80%). Proportion of pregnant women who screened positive for any sleep disturbance was 49.4%. Disturbances increased as pregnancy progressed, particularly in the third trimester. Significant predictors included increased neck circumference (aOR 1.08; p = 0.003), high-risk pregnancy (aOR 3.37; p < 0.001), and pregnancy trimester. Primigravida women were less likely to experience sleep issues compared to multigravida women (aOR 0.54; p = 0.034). High-risk pregnancies were associated with increased odds of OSA, insomnia, and EDS. Our study highlights the significant burden of sleep disturbances among pregnant women, with poor sleep quality being the most prevalent. High-risk pregnancies, increased neck circumference, and multigravida status were key determinants. Our findings emphasize the need for targeted interventions to improve maternal sleep quality and reduce potential adverse outcomes.
Supplementary Information
The online version contains supplementary material available at 10.1007/s41105-024-00556-7.
Keywords: Sleep–wake disorders, Pregnant women, Epidemiology, Sleep quality, Factors, Determinants, Circadian rhythm
Introduction
Sleep disturbances are common, especially among women, and are often worsened by hormonal changes. This issue is more pronounced in pregnant women, with notable variations across populations and pregnancy stages. Studies show that up to 76.3% of pregnant women experience sleep disturbances, rising to 83.5% by the eighth month[1]. Another study reports that 50.43% face sleep problems, linked to factors like late-stage pregnancy, multiple pregnancies, unplanned pregnancies, and psychological issues such as depression and anxiety[2]. In Europe, 34% of women suffer poor sleep in the first trimester, 46% in the third, and 71% after childbirth due to insomnia and excessive daytime sleepiness (EDS) [3]. Sleep quality generally declines from the first trimester, peaking in the third, with insomnia, sleep-disordered breathing, and restless legs syndrome causing significant fragmentation and daytime fatigue.
Sleep disturbances in pregnant women arise from a complex mix of physiological, hormonal, and physical changes throughout pregnancy. Early on, hormonal fluctuations disrupt sleep patterns [4, 5]. As pregnancy progresses, increased body mass, fetal growth, and discomfort contribute to conditions like sleep-disordered breathing, restless legs syndrome, and leg cramps [6, 7]. Pre-existing conditions, including obesity, gestational diabetes, and preeclampsia, also heighten sleep disorder risks. Factors like anxiety, iron or magnesium deficiencies, and acid reflux exacerbate sleep issues [8] which not only reduce quality of life but increase risks of preterm birth, low birth weight, and neonatal complications [9, 10]. Postpartum, fragmented sleep due to infant care presents additional challenges. Maternal sleep problems can also affect infants, leading to feeding issues, sensory sensitivities, and socioemotional difficulties [11–13].
Sleep disturbances among pregnant women in India are a major concern, with studies showing a high prevalence. Factors, such as cultural, socioeconomic, environmental influences, and physiological changes, contribute to poor sleep. Nearly half of pregnant women in India experience poor sleep quality [14]. Challenges including limited healthcare access, societal stress, and nutritional deficiencies further exacerbate these issues. While sleep disturbances in pregnancy are well-studied in western populations, there is a research gap in India, limiting evidence-based healthcare strategies. This study aimed to estimate the prevalence and determinants of sleep disturbances, including OSA, insomnia, RLS, EDS, and poor sleep quality, among pregnant women in Kolar District, Karnataka. It is among the first comprehensive studies in India on this topic, offering valuable insights valuable insights to this under-researched field.
Materials and methods
Study setting and participants
A cross-sectional study was carried out in a community attached to Municipal Hospital, Urban Primary Health Centre (UPHC), Kolar district, Karnataka, a state in the southern part of India. Kolar, located on the southern plains, spans 46.56 km2 and had a population of 138,462 as per the 2011 census. The district comprises five talukas: Kolar, Bangarpet, Mulbagal, Malur, and Srinivasapura. Despite its small size, Kolar mirrors other Indian cities with mixed residential and commercial areas, unplanned urbanization, and decreasing population density toward the outskirts. As of October 2023, there were approximately 371 pregnant women residing in the study area. The inclusion criteria involved pregnant women who were permanent residents of the area for at least six months and who provided informed consent. Exclusion criteria included those currently hospitalized, residing in the area for less than six months, or unable to stand or verbally communicate with the investigator.
Sample size and sampling strategy
The sample size was calculated using the standard formula n = Z2*p*q/e2 for estimating prevalence of any of the sleep disturbances using previously published values where n is the sample size, Z (1.96) represents the 95% confidence level, p is the estimated prevalence, q = 1 − p, and e is the 5% margin of error. Among all, the prevalence of RLS among pregnant women, “p” is taken as 20.5% as it yielded a higher sample size and was considered final[15]. The absolute precision: “e” of 5% and 95% confidence limits were set for the estimation. The final estimated sample size was 251 after an addition of a 10% non-response error. Purposive sampling was applied to select the study participants due to logistical and practical reasons. The study focused on a specific population—pregnant women in a defined area within Kolar district—making purposive sampling efficient for selecting relevant participants. Additionally, the limited data collection period of two months required a focused approach, allowing researchers to efficiently access participants, including those in difficult-to-reach areas of the rural part of the district, ensuring comprehensive data collection within the constrained timeframe.
Data collection procedure
Data were collected over two months using a pre-designed, structured proforma that included socio-demographic information, such as age, education, occupation, marital status, and socio-economic status, classified per the Modified BG Prasad’s Classification, 2023. Anthropometric measurements carried out included height (in cm), weight (in kg), neck circumference (in cm) and body mass index (BMI) in kg/m2. Obstetric and morbidity profiles were recorded, with data on co-morbidities and complications obtained from the Mother and Child Protection card. High-risk pregnancies were defined based on Ministry of Health and Family Welfare guidelines [16]. Data were captured using the EpiCollect Version 5.0 smartphone application for quality assurance.
Assessment tools
A structured, pre-tested proforma collected data using validated tools translated into Indian languages by MAPI Research Trust. Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI), with a global score of 5 + indicating poor sleep. Excessive daytime sleepiness (EDS) was measured using the Epworth Sleepiness Scale (ESS), where a score of 10 + signifies significant EDS. RLS was screened with a single-question tool, and OSA risk was assessed using STOP-BANG, with scores of 5 + indicating high risk. Insomnia was measured with the Insomnia Severity Index (ISI), where higher scores (up to 28) indicate more severe insomnia. These tools (PSQI, ESS, ISI, STOP-BANG) have strong reliability and validity in pregnancy populations [17–19]. Although the RLS screening tool lacks validation in pregnant women, it was chosen for ease of use over the more complicated and difficult to use IRLSSG tool [20].
Statistical analysis
The data were analyzed using IBM SPSS Statistics for Windows, Version 28.0 (IBM Corp., Armonk, N.Y., USA). Continuous variables were presented as means/medians ± SD, while categorical variables were expressed as percentages. Bivariate logistic regression was used to calculate Unadjusted Odds Ratios (UOR) with 95% Confidence Intervals (CI) for all variables. The dependent variable was the presence of sleep disturbances (Any sleep disturbance, OSA, insomnia, RLS, EDS and poor sleep quality), while independent variables included demographic, obstetric, anthropometric, and health-related factors. All hypothesized exposure variables that were significantly associated with the outcome at a 10% level of significance (p < 0.010) in bivariate analysis were considered to be included in the multivariable logistic regression model. Adjusted Odds Ratios (AOR) with 95% CI were computed after adjusting for the effect of other variables using multivariable model. A p value of < 0.05 was considered as the criteria for statistical significance.The scatterplot matrix and boxplot of sleep assessment tools were generated using statistical software package R (http://www.R-project.org, The R Foundation).
Ethical considerations
The scientific and ethical clearance for this study was obtained from NIMHANS Ethics Committee vide letter no NO.NIMH/DO/IEC (BS & NS DIV)/2023. Detailed information pertaining to the nature, objectives of the study and test procedures was provided to the study participants and written informed consent (for participants aged ≥ 18) and assent followed by written informed consent from the guardian (for participants aged ≤ 18) was obtained. Anonymity of the study participants was ensured. Strict confidentiality of the information collected was maintained.
Results
Sociodemographic, obstetric, morbidity, and sleep disturbance profiles
Table 1 presents the sociodemographic, obstetric, and morbidity profiles of 251 participants. Most mothers are housewives (36.70%), with 8% employed. Majority completed Pre-University Course (38.60%) or high school (31.10%). Participants are almost evenly split between urban (48.20%) and rural (51.80%) residences, with 57% living in joint families. Nearly half (47%) belong to Class III of Modified BG Prasad's Classification, indicating a monthly income of 2630–4294 Rs. Obstetric data shows 40.6% in their second trimester, with 60.10% primigravida and 61.70% nulliparous. High-risk pregnancies affect 32.70%, while complications (4%) and comorbidities (4.80%) are relatively low. A significant 84.50% holds Below Poverty Line ration cards. Sleep disturbance data reveals poor sleep quality in 39.84%, with 13.55% experiencing OSA, 11.56% EDS, 9.60% insomnia, and 6.80% RLS (Fig. 1). Proportion of pregnant women who screened positive for any sleep disturbance was 49.40%.
Table 1.
Socio-demographic, obstetric and morbidity profile of the study participants (N = 251)
| Characteristics | N (%) | Characteristics | N (%) |
|---|---|---|---|
| Mother’s occupation | Trimester | ||
| House wife | 231 (92) | First | 60 (23.90) |
| Employed | 20 (8) | Second | 102 (40.60) |
| Mother’s education | Third | 89 (35.50) | |
| Illiterate | 10 (4) | Gravida | |
| Primary (till 4th standard) | 8 (3.20) | Primigravida | 151 (60.10) |
| Middle school (5th to 7th standard) | 25 (10) | Second | 78 (31.10) |
| High School (8th–10th standard) | 78 (31.10) | Third | 22 (8.80) |
| Pre-university course (11th–12th standard) | 97 (38.5) | Parity | |
| Graduate and above | 33 (13.20) | Nulliparous | 155 (61.70) |
| Residence | First | 77 (30.70) | |
| Urban | 121 (48.20) | Second | 18 (7.20) |
| Rural | 130 (51.80) | Third | 1 (0.40) |
| Type of family | High-risk pregnancy | ||
| Nuclear | 108 (43) | Yes | 82 (32.70) |
| Joint | 143 (57) | No | 169 (67.30) |
| Socioeconomic status (SES)a | Complications in current pregnancy | ||
| Class I 8763 and above) | 11 (4.40) | Yes | 10 (4) |
| Class II (4381.5–8675.3) | 73 (29.10) | No | 241 (96) |
| Class III (2630–4294) | 118 (47) | Comorbidity | |
| Class IV (1314.5–2541.27) | 42 (16.70) | Yes | 12 (4.8) |
| Class V (< 1314.5) | 7 (2.80) | No | 239 (95.20) |
| Ration card | Religion | ||
| Below poverty line | 212 (84.50) | Hindu | 204 (81.30) |
| Above poverty line | 8 (3.20) | Muslim | 44 (17.50) |
| None | 31 (12.30) | Christian | 3 (1.20) |
aAs per modified BG Prasad's classification, 2023. Values in Rs./month
Fig. 1.
Prevalence of various sleep disturbances (N = 251)
Sleep disturbance trends and correlations across pregnancy trimesters
Across all trimesters, the ESS, ISI, PSQI, and StopBang scores displayed overlapping distributions, indicating no significant trimester effect on sleep disturbance. Among these, the ISI exhibited the greatest variability in scores, with more outliers compared to the other tools, while the StopBang scores were more tightly clustered, reflecting greater consistency in sleep apnea risk assessment (Fig. 2). Figure 3 highlights the relationships between these tools, where ESS and ISI exhibited the strongest correlation (r = 0.616), suggesting that higher levels of daytime sleepiness are linked to more severe insomnia symptoms. Meanwhile, PSQI and ESS also showed a moderate positive correlation (r = 0.592), reflecting the relationship between poor sleep quality and daytime sleepiness. The correlation between StopBang and PSQI was relatively weak (r = 0.251), implying that general sleep quality and the risk of sleep apnea may not be as strongly associated.
Fig. 2.
Comparison of sleep disturbance assessment scores across trimesters (n = 251) using the Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), and the STOP-BANG Questionnaire
Fig.3.
Scatterplot matrix of various sleep disturbance assessment tools: Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), and Stop-Bang Questionnaire
Factors associated with sleep disturbances
The paper presents detailed bivariate and multivariate analyses of sleep disturbances among 251 pregnant women, with individual sleep disorder analyses in the supplementary tables 1–5. Bivariate regression identified several key factors associated with sleep disturbances (Table 2). Increased neck circumference significantly increased the odds of sleep disturbances (OR 1.10; 95% CI 1.04–1.16; p < 0.001). Women in the first and second trimesters had lower odds of sleep disturbances compared to those in the third trimester (OR 0.43, p = 0.014; OR 0.53, p = 0.032), and primigravida women had lower odds (OR 0.37; p < 0.001). High-risk pregnancy was strongly associated with increased odds of sleep disturbances (OR 4.10; p < 0.001). Multivariate analysis in Table 3 confirmed the significance of neck circumference, gravidity, and high-risk pregnancy as predictors, with high-risk pregnancy maintaining strong odds (aOR 3.37; p < 0.001) for any sleep disturbance. For specific sleep disturbance as highlighted in Table 4, high-risk pregnancy and neck circumference were key predictors of obstructive sleep apnea (aOR 2.20, p = 0.036; aOR 1.25, p < 0.001). Insomnia was strongly linked to high-risk pregnancy (aOR 6.24, p < 0.001) and lower maternal education. EDS (of moderate and severe category) was associated with high-risk pregnancy (aOR 2.64, p = 0.027) and neck circumference (aOR 1.12, p = 0.010). Poor sleep quality was more common in multigravida women (aOR 0.46, p = 0.007) and those with high-risk pregnancies (aOR 2.29, p < 0.001). Sleep duration was positively correlated with poor sleep quality (aOR 1.31, p = 0.011). Multivariate analysis could not be carried out for RLS as only one variable came out to be significant in the bivariate analysis.
Table 2.
Socio-demographic, obstetric and morbidity-related factors associated with any sleep disturbance among the study participants (N = 251)
| Variables | Any sleep disturbance | Unadjusted OR (95% CI) | p value* | |
|---|---|---|---|---|
| Yes (%) (n = 124) | No (%) (n = 127) | |||
| Age (in years) | ||||
| ≤ 25 | 72 (58.1) | 75 (59.1) | 0.96 (0.58–1.58) | 0.873 |
| ≥ 26 | 52 (41.9) | 52 (40.9) | REF | |
| Height (in cm)a | 0.98 (0.95–1.01) | 0.375 | ||
| Weight (in kg)a | 1.01 (0.98–1.03) | 0.426 | ||
| BMI (in kg/m2)a | 1.04 (0.98–1.12) | 0.174 | ||
| Neck circumference (in cm)a | 1.10 (1.04–1.16) | < 0.001* | ||
| Hours of sleepa | 1.16 (0.96–1.42) | 0.118 | ||
| Mother’s education | ||||
| High school and below | 61 (49.2) | 60 (47.2) | 1.08 (0.65–1.77) | 0.757 |
| PUC and above | 63 (50.8) | 67 (52.8) | REF | |
| Mother’s occupation | ||||
| House wife | 113 (91.1) | 118 (92.9) | 0.78 (0.31–1.96) | 0.602 |
| Employed | 11 (8.9) | 9 (7.1) | REF | |
| Residence | ||||
| Urban | 53 (42.7) | 68 (53.5) | 0.64 (0.39–1.06) | 0.087 |
| Rural | 71 (57.3) | 59 (46.5) | REF | |
| Type of family | ||||
| Nuclear | 48 (38.7) | 60 (47.2) | 0.70 (0.42–1.16) | 0.173 |
| Joint | 76 (61.3) | 67 (52.8) | REF | |
| Socioeconomic status | ||||
| Upper + middle | 101 (81.5) | 101 (79.5) | 1.13 (0.60–2.11) | 0.701 |
| Lower | 23 (18.5) | 26 (20.5) | REF | |
| Trimester | ||||
| First | 24 (19.4) | 36 (28.3) | 0.43 (0.22–0.84) | 0.014* |
| Second | 46 (37.1) | 56 (44.1) | 0.53 (0.29–0.94) | 0.032* |
| Third | 54 (43.4) | 35 (27.6) | REF | |
| Gravida | ||||
| Primigravida | 60 (48.4) | 91 (71.7) | 0.37 (0.22–0.62) | < 0.001* |
| Multigravida | 64 (51.6) | 36 (28.3) | REF | |
| Parity | ||||
| Nulliparous | 63 (50.8) | 92 (72.4) | 0.03 (0.23–0.66) | < 0.001* |
| Multiparous | 61 (49.2) | 35 (27.6) | REF | |
| Hemoglobin level (in g/dl) | ||||
| < 11 | 59 (47.6) | 55 (43.3) | 1.18 (0.72–1.95) | 0.497 |
| ≥ 11 | 65 (52.40 | 72 (56.7) | REF | |
| High-risk pregnancy | ||||
| Yes | 59 (47.6) | 23 (18.1) | 4.10 (2.31–7.27) | < 0.001* |
| No | 65 (52.4) | 104 (81.9) | REF | |
| Complication in current pregnancy | ||||
| Yes | 7 (5.6) | 3 (2.4) | 2.47 (0.62–9.78) | 0.197 |
| No | 117 (94.4) | 124 (97.6) | REF | |
| Comorbid condition | ||||
| Yes | 9 (7.3) | 3 (2.4) | 3.23 (0.85–12.24) | 0.084 |
| No | 115 (92.70 | 124 (97.6) | REF | |
| Type of delivery in past pregnancy | ||||
| FTND | 27 (42.9) | 27 (75) | 0.25 (0.10–0.61) | 0.003* |
| LSCS | 36 (57.1) | 9 (25.0) | REF | |
FTND full term normal delivery, LSCS lower segment cesarean section
a Represents the continuous variables, OR odds ratio, CI confidence Interval
*p value considered significant at < 0.05
Table 3.
Multivariable logistic regression analysis of the factors associated with any sleep disturbances among the study participants (N = 251)
| Variables | Any sleep disorder | Unadjusted OR (95% CI) | p value* | Adjusted OR (95% CI) | p value** | |
|---|---|---|---|---|---|---|
| No (%) (n = 127) | Yes (%) (n = 124) | |||||
| Trimester | ||||||
| First | 36 (28.3) | 24 (19.4) | 0.43 (0.22–0.84) | 0.014 | 0.48 (0.23–1.00) | 0.050 |
| Second | 56 (44.1) | 46 (37.1) | 0.53 (0.29–0.94) | 0.032 | 0.56 (0.30–1.04) | 0.071 |
| Third | 35 (27.6) | 54 (43.4) | REF | REF | ||
| Gravida | ||||||
| Primigravida | 91 (71.7) | 60 (48.4) | 0.37 (0.22–0.62) | < 0.001 | 0.54 (0.30–0.95) | 0.034 |
| Multigravida | 36 (28.3) | 64 (51.6) | REF | REF | ||
| High-risk pregnancy | ||||||
| Yes | 23 (18.1) | 59 (47.6) | 4.10 (2.31–7.27) | < 0.001 | 3.37 (1.84–6.16) | < 0.001 |
| No | 104 (81.9) | 65 (52.4) | REF | REF | ||
| Comorbid condition | ||||||
| Yes | 3 (2.4) | 9 (7.3) | 3.23 (0.85–12.24) | 0.084 | 3.25 (0.51–20.53) | 0.210 |
| No | 124 (97.6) | 115 (92.70 | REF | REF | ||
| Type of delivery | ||||||
| FTND | 27 (75) | 27 (42.9) | 0.25 (0.10–0.61) | 0.003 | 1.76 (0.17–18.37) | 0.633 |
| LSCS | 9 (25.0) | 36 (57.1) | REF | REF | ||
| Locality | ||||||
| Urban | 68 (53.5) | 53 (42.7) | 0.64 (0.39–1.06) | 0.087 | 0.70 (0.40–1.20) | 0.198 |
| Rural | 59 (46.5) | 71 (57.3) | REF | REF | ||
| Neck circumference (in cm) | – | 1.10 (1.04–1.16) | < 0.001 | 1.08 (1.02–1.15) | 0.003 | |
FTND full term normal delivery, LSCS lower segment cesarean section
a Represents the continuous variable, OR odds ratio, CI confidence interval
*Unadjusted p value from bivariate logistic regression (*p value considered significant at < 0.05)
**Adjusted p value from multivariable logistic regression (p value considered significant at < 0.05)
Table 4.
Multivariable logistic regression analysis of the factors associated with various sleep disorders among the study participants (N = 251)
| Variables | Unadjusted OR (95% CI) | p value* | Adjusted OR (95% CI) | p value** |
|---|---|---|---|---|
| 1. Obstructive Sleep Apnea | ||||
| High-risk pregnancy | ||||
| No | REF | 0.023 | REF | 0.036 |
| Yes | 2.33 (1.12–4.86) | 2.20 (1.05–4.63) | ||
| Neck circumference (in cm) | 1.26 (1.14–1.39) | < 0.001 | 1.25 (1.13–1.38) | < 0.001 |
| 2. Insomnia | ||||
| Mother’s education | ||||
| Pre-university college and above | REF | 0.008 | REF | 0.008 |
| High school and below | 0.25 (0.09–0.69) | 0.24 (0.08–0.68) | ||
| High-risk pregnancy | ||||
| No | REF | < 0.001 | REF | < 0.001 |
| Yes | 6.05 (2.39–15.28) | 6.24 (2.43–16.03) | ||
| 3. Excessive daytime sleepiness | ||||
| High-risk pregnancy | ||||
| No | REF | 0.002 | REF | 0.027 |
| Yes | 3.42 (1.54–7.56) | 2.64 (1.11–6.25) | ||
| Neck circumference (in cm)a | 1.14 (1.04–1.24) | 0.003 | 1.12 (1.02–1.22) | 0.010 |
| 4. Poor quality of sleep | ||||
| Gravida | ||||
| Multigravida | REF | < 0.001 | REF | 0.007 |
| Primigravida | 0.34 (0.20–0.58) | 0.46 (0.26–0.80) | ||
| High-risk pregnancy | ||||
| No | REF | < 0.001 | REF | < 0.001 |
| Yes | 3.71 (2.13–6.46) | 2.29 (1.67–5.32) | ||
| Hours of sleep | 1.31 (1.06–1.61) | 0.009 | 1.31 (1.06–1.63) | 0.011 |
*Unadjusted p value from bivariate logistic regression (p value considered significant at < 0.05)
**Adjusted p value from multivariable logistic regression (p value considered significant at < 0.05)
a Represents the continuous variables, OR odds ratio, CI confidence interval
Discussion
Pregnancy brings about numerous hormonal, physiological, and physical changes that can negatively impact sleep quality. Our study aimed to evaluate the prevalence of sleep disturbances and explore contributing factors among pregnant women in an Indian community. Almost half of the participants (49.40%) exhibited signs of sleep disturbances, which aligns with findings from similar studies. For instance, smaller studies in Poland reported a prevalence of sleep disorders as high as 94.3% among pregnant women, while Wołyńczyk-Gmaj et al. found that 84.2% of women in their third trimester experienced various sleep issues [21]. A 1998 survey by the US National Sleep Foundation similarly indicated that 78% of pregnant women faced sleep disruptions [22]. In our study, two common issues identified were excessive daytime sleepiness (EDS) of moderate and severe categories and poor sleep quality, with prevalence rates of 11.56% and 39.84%, respectively. These sleep disturbances frequently occur together during pregnancy, triggered by hormonal changes, physical discomfort, and heightened stress, leading to frequent night-time awakenings that prevent restorative sleep. This results in daytime fatigue and overall disruption of sleep patterns. Supporting evidence from other studies reveals similar trends; for example, a study in Nigeria found that 50% of pregnant women experienced poor sleep quality [23], while research from Poland reported poor sleep quality in 95.1% of women during the first trimester, 93% in the second, and 94.8% in the third trimester [24]. The prevalence of EDS varies from 6.20 to 32.1% across different studies [3, 25, 26]. The gap between subjective sleep assessments and objective measures, such as the PSQI, suggests that many women may underestimate the severity of their sleep problems. Another common sleep disturbance during pregnancy is OSA, with 13.55% of the participants in our study being at a higher risk of developing this condition. In mothers, OSA during pregnancy is linked to a higher risk of developing gestational hypertension, pre-eclampsia, and gestational diabetes. For infants, it is associated with lower birth weight, premature delivery, and the need for admission to a neonatal intensive care unit [27]. Studies on objectively diagnosed OSA in pregnant women remains limited, with rates ranging from 3 to 27%, depending on gestational age and diagnostic methods [28]. We also found that increased neck circumference was a significant predictor specifically for OSA and EDS. Increased neck circumference in pregnant women is a key risk factor for sleep disorders, particularly OSA, due to airway narrowing and compromised respiratory function. This leads to disrupted sleep, EDS, and poor sleep quality. The association between neck circumference and OSA is significant, and addressing it through weight management and other interventions may improve sleep outcomes in pregnancy[29]. It must be noted that Screening for OSA during pregnancy is challenging because its symptoms often overlap with typical pregnancy-related changes. While polysomnography remains the gold standard for diagnosis, its high cost and inconvenience contribute to underdiagnosis. As a result, screening tools like STOP-BANG and the Berlin Questionnaire are commonly used for assessing OSA risk.
The analysis of bivariate and multivariate regression models highlights the multifactorial nature of sleep disturbances among pregnant women, with high-risk pregnancies consistently identified as a key predictor of various sleep issues. Medical conditions associated with high-risk pregnancies, such as gestational diabetes, preeclampsia, and placental abnormalities, likely contribute to increased discomfort, anxiety, and disrupted sleep patterns. These conditions are often exacerbated by the frequent medical appointments and monitoring required during high-risk pregnancies [30]. The trimester of pregnancy plays an important role in sleep quality, with sleep disturbances worsening with each passing trimester. Research conducted among Japanese women found that sleep habits significantly worsened from the second trimester to the postpartum period, with the shortest sleep duration occurring during the first week after childbirth. As pregnancy progressed, subjective sleep problems, measured by the PSQI, became more pronounced, particularly in the third trimester and postpartum, where declines in sleep quality, longer sleep latency, and reduced sleep duration were commonly reported. Additionally, the prevalence of sleep-disordered breathing increased from 11.8% in the second trimester to 21.3% in the third trimester, slightly decreasing to 19.2% postpartum [31]. However, in our study, no significant escalation in sleep assessment scores was observed across trimesters. This discrepancy could be attributed to the small sample size or limited variability within the sample, which may have restricted the statistical power needed to detect more subtle changes in sleep disturbances. Multigravidity (a woman who has been pregnant two or more times) was notably linked with increased odds of sleep disturbances. This may stem from heightened physical discomfort and the added stress of caring for other children. This finding contrasts with some studies that suggest primigravida women, those experiencing their first pregnancy, are more likely to suffer from poor sleep [32]. Additionally, the demands of caring for other children during pregnancy could increase stress and contribute to sleep disruptions. Lower levels of maternal education and the presence of high-risk pregnancy emerged as significant contributors for insomnia, with the latter increasing the likelihood of insomnia by more than six times. Several studies indicate that while educational level may not directly correlate with insomnia among pregnant women, other socioeconomic factors and targeted interventions may play crucial role. Socioeconomic status, often tied to income rather than education alone, significantly impacts prenatal insomnia, increasing the risk of postpartum depression [33]. Additionally, lifestyle factors such as income have been shown to influence conditions like gestational diabetes, which indirectly affect sleep quality. Interventions focusing on sleep hygiene and behavioral education, rather than educational attainment, have proven effective in improving sleep quality among pregnant women, emphasizing the value of targeted health education [34].
Strengths and limitations
This study marks one of the earliest comprehensive investigations into sleep disturbances among pregnant women in India. Utilizing pre-tested and validated tools to screen for a wide range of sleep disturbances, we have strengthened the reliability of our findings. Conducting data collection within a community setting allowed for a more accurate representation of sleep disturbance among pregnant individuals. However, key limitations include the exclusion of key variables like substance use, depression, and caffeine intake due to time constraints, which may have omitted critical predictors. The small sample size or low variability within the sample might have restricted our ability to detect subtle changes across the trimesters. Additionally, the cross-sectional design prevents establishing causality or the direction of relationships between risk factors and sleep disturbances. Therefore, while our study provides important insights, future research should include a broader range of variables and employ longitudinal designs to better establish causal links and improve generalizability.
Conclusion and recommendations
It is evident that sleep disturbances were highly prevalent among pregnant women, particularly with advancing trimester. The most common issues were poor sleep quality, EDS, and insomnia. High-risk pregnancies were strongly associated with these sleep disturbances. Our findings recommend routine screening of sleep disorders during antenatal visits is crucial to identify at-risk women early. Additionally, research should explore the multifactorial determinants of sleep disorders—such as hormonal changes, lifestyle, and comorbidities—to develop targeted strategies for improving sleep quality throughout pregnancy and the postpartum period. Timely interventions, particularly for high-risk conditions like sleep-disordered breathing, can positively impact maternal and fetal health. Early identification and intervention, especially for high-risk pregnancies is important, to mitigate the adverse outcomes associated with poor sleep.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We would like to express our sincere appreciation to all the participants who generously dedicated their time and effort to take part in this study. We would like to sincerely acknowledge the encouragement and support of the Director of NIMHANS. We are immensely thankful to the faculty, staff and students of Department of Epidemiology for their guidance and support. We also offer our special thanks to District Health Officer, District Family Welfare Officer, Kolar and Administrative Medical Officer of Urban Primary Health Centre, Municipal Hospital, Kolar for their invaluable cooperation and support, which were critical to the successful completion of this study.
Author contributions
Akashanand, Pracheth Raghuveer and Ravi Yadav designed and conceptualized the study along with its methodology. Akashanand, Ravi Girikematha Shankar and Pracheth Raghuveer performed formal analysis and investigation for the study. Akashanand wrote the first draft of the manuscript. Pracheth Raghuveer, Ravi Yadav, Sudha Deepika Reddy and Ravi Girikematha Shankar critically reviewed, edited the manuscript. All authors read and approved the final manuscript.
Funding
No funding was received for conducting this study.
Declarations
Conflict of interest
The authors have no competing interests to declare that are relevant to the content of this article.
Ethical permission
The scientific and ethical clearance for this study was obtained from NIMHANS Ethics Committee vide letter no NO.NIMH/DO/IEC (BS & NS DIV)/2023.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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