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Fear of childbirth and sleep quality among pregnant women: a generalized additive model and moderated mediation analysis

BMC Psychiatry volume 23, Article number: 931 (2023) Cite this article

Abstract

Objectives

This study aims to examine the associations among fear of childbirth, psychological distress, resilience, and sleep quality among Chinese pregnant women.

Methods

A cross-sectional survey was carried out between January 2022 to March 2022 among pregnant women who met the inclusion criteria and sought healthcare services at The First Affiliated Hospital of Guangzhou University of Chinese Medicine in Guangdong Province, Southern China. Data was collected using a structured questionnaire that included sociodemographic characteristics, childbirth attitudes questionnaires (CAQ), hospital anxiety and depression scale (HADS), Connor-Davidson resilience scale (CD-RISC), and Pittsburgh sleep quality index (PSQI). A generalized additive model and moderated mediation analysis were employed for data analysis.

Results

A non-linear and negative association between fear of childbirth and sleep quality was found in the second trimester and antenatal period. Psychological distress significantly mediated the relationship between fear of childbirth and sleep quality (first trimester: β = 0.044, 95%CI:0.022–0.071; second trimester: β = 0.029, 95%CI:0.009–0.056; third trimester: β = 0.064, 95%CI:0.046–0.088; antenatal period: β = 0.050, 95%CI:0.037–0.063). The moderating role of resilience between fear of childbirth and sleep quality was significant (second trimester: β=-0.006, 95%CI:-0.012–0.001, P = 0.025; antenatal period: β=-0.004, 95%CI:-0.007–-0.001, P = 0.014), as well as between fear of childbirth and psychological distress (first trimester: β=-0.016, 95%CI:-0.026–-0.005, P = 0.004; antenatal period: β=-0.005, 95%CI:-0.009–-0.001, P = 0.014).

Conclusions

Fear of childbirth, psychological distress, and resilience are three important factors affecting sleep quality in Chinese pregnant women.

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Introduction

Pregnancy is a critical period marked by significant physiological and psychological changes that can impact sleep quality and mental health [1, 2]. Sleep quality is often used to assess sleep continuity, including sleep latency, sleep efficiency, sleep duration, and waking after falling asleep [3]. Poor sleep quality during pregnancy has been confirmed as a risk factor for pregnancy-related complications such as gestational diabetes and premature birth [4, 5]. In addition, Vizzini et al. conducted a birth cohort study that revealed a significant association between maternal sleep disturbances during pregnancy and the manifestation of ADHD symptoms in preschool-aged children [6]. Thus, it is essential to investigate factors that influence sleep quality in pregnant women.

One contributing factor is the phenomenon known as fear of childbirth, which refers to a complex feeling of distress before childbirth and commonly experienced by pregnant women, with symptoms of worry, extreme anxiety, and even the desire to avoid childbirth [7]. The prevalence varies across countries, with about 20% of pregnant women experiencing this fear in Sweden and Italy [8, 9], while a recent survey in China reported a prevalence of 67.1% [10]. This fear has been linked to adverse outcomes, including the progression of birth and an increased likelihood of elective cesarean Sects [11, 12]. In extreme cases, they will choose to terminate their pregnancy [13]. Moreover, studies have shown a positive relationship between fear of childbirth and sleep disturbance or disorders [14, 15].

In addition, psychological distress may also influence sleep quality [16, 17], and can be exacerbated by the presence of fear of childbirth [18, 19]. However, no previous research has investigated the mediating role of psychological distress in the relationship between fear of childbirth and sleep quality. Moreover, research has consistently shown that pregnant women with high resilience exhibit better-coping abilities in managing childbirth-related concerns and report lower levels of fear of childbirth [10, 20]. Furthermore, resilience has been confirmed as a crucial protective factor for mental health and better sleep quality among pregnant women [18, 21,22,23]. Thus, resilience may have an important role in the relationships among fear of childbirth, psychological distress, and sleep quality.

What’s more, research has shown that fear of childbirth, psychological state, and sleep quality are dynamic and change/fluctuate across trimesters [15, 24, 25]. Nevertheless, to date, no studies have employed stratified analyses by trimester to explore the relationship among these factors at different stages of pregnancy. By conducting stratified analyses based on trimesters of gestation, critical periods for intervention can be identified, and a comprehensive understanding of the complex relationships among these factors will be achieved. Given the existing knowledge gaps, this study aims to explore the association between fear of childbirth and sleep quality, considering the potential mediating role of psychological distress and the moderating role of resilience. We hypothesize the following:

H1: Fear of childbirth is a significant predictor of sleep quality across different trimesters and throughout the antenatal period.

H2: The relationship between fear of childbirth and sleep quality is non-linear across different trimesters and throughout the antenatal period.

H3: Psychological distress may mediate the relationship between fear of childbirth and sleep quality at different trimesters and throughout the antenatal period.

H4: Resilience may moderate the relationship between fear of childbirth, psychological distress, and sleep quality at different trimesters and throughout the antenatal period.

Methods

Design and participants

This study was conducted from January 2022 to March 2022 at the First Affiliated Hospital of Guangzhou University of Chinese Medicine in Guangdong Province, southern China. We employed a convenience sampling method to collect data from pregnant women attending regular prenatal check-ups at the hospital. The target population eligible to participate in this study should meet the following inclusion criteria: (1) be at least 20 years old (legal marriage age for Chinese women is 20); (2) have a confirmed pregnancy; (3) be able to communicate fluently in Mandarin. Pregnant women with diagnosed mental illness or a history of mental health illness were excluded from the study. The questionnaires were independently completed by recruited participants and collected by a fixed team of three trained enumerators to ensure data quality. To estimate the minimum sample size required for detecting a significant moderated mediation effect, we utilized the R-based “pwr” package. Based on the assumptions of a mediation effect of 0.05, a moderator-dependent variable relationship of 0.3, a mediator-dependent variable relationship of 0.4, an alpha level of 0.05, and a desired statistical power of 0.8, we determined that at least 503 participants were needed for the study.

Instruments

Demographics

Based on prior research [13, 26], this study collected demographics, including age, academic degree, employment, income, and place of residence, as well as clinical information on weeks of pregnancy.

Childbirth attitudes questionnaires (CAQ)

The CAQ was developed by Tanglakmankhong et al [27], and its Chinese version was validated by Zhou et al [28]. It consisted of 16 items, ranging from 16 to 64, with higher scores indicating higher levels of fear of childbirth. The Cronbach’s α for CAQ in the current study was 0.945.

Hospital anxiety and depression scale (HADS)

The HADS was developed by Zigmond and Snaith [29], and validated in Chinese by Leung et al [30]. It has 14 items and ranges from 0 to 42, with higher scores indicating higher levels of anxiety and depression (psychological distress). In this study, Cronbach’s α was 0.745 for the anxiety domain, 0.700 for the depression domain, and 0.820 for the total scale.

10-item connor-davidson resilience scale (CD-RISC-10)

The CD-RISC-10 was validated by Campbell-Sills and Stein [31]. It ranges from 0 to 40, with higher scores indicating higher levels of resilience. Ye validated the Chinese version [32], and it was previously used in our previous research [18]. The Cronbach’s α in this study was 0.915.

Pittsburgh sleep quality index (PSQI)

The PSQI was developed by Buysse et al [33], and its Chinese version has been commonly used to evaluate sleep quality among pregnant women in China [26, 34]. It has seven domains, including sleep duration, sleep latency, sleep disturbances, subjective sleep quality, use of sleep medication, habitual sleep efficiency, and daytime dysfunction. The total score ranges from 0 to 21, with higher scores meaning poorer sleep quality. The Cronbach’s α was 0.715 in the present study.

Data analysis

First, independent samples t-test and one-way ANOVA were used to investigate differences in demographic factors of participants’ sleep quality (continuous variable). Demographic factors that exhibited statistical significance for each pregnancy were incorporated as covariates in subsequent mediated moderation models. Then, based on a cut-off of 7 [35], sleep quality was categorized into binary data (poor sleep was coded as 1 while good sleep was coded as 0). The relationship between fear of childbirth and sleep quality (category variable) was analyzed.

Second, the potential non-linear correlation between fear of childbirth and sleep quality in different trimesters of pregnancy was estimated by a generalized additive model [36].

Third, Pearson’s correlation analysis was conducted to ascertain the relationships among fear of childbirth, psychological distress, resilience, and sleep quality.

Fourth, the potential presence of common method variance was estimated by Harman’s one-factor model [37]. Subsequently, a comprehensive statistical approach was employed to examine both mediation and moderation effects, taking into account different covariates (confounders) in each trimester. The mediating role of psychological distress between fear of childbirth and sleep quality was investigated separately for each trimester of pregnancy. To achieve this, we utilized the PROCESS macro (model 4) for SPSS, developed by Hayes [38]. Then, model 5, model 7, and model 14 from the PROCESS macro were used to examine the moderating role of resilience on the direct relationship between fear of childbirth and sleep quality, as well as the indirect relationship through psychological distress.

Fifth, the Johnson-Neyman test was used to further probe the interaction pattern and identify statistically significant cut-off values for the moderating effect [39].

All statistical analyses were conducted using SPSS (version 26.0) and Empower Stats (version 2.2).

Ethical considerations

This study was approved by the ethics review committee of the participating hospital (No: K-2022-024) and was part of Be Resilient to Postpartum Depression (BRPD, Registration number: ChiCTR2100048465). Written consent was obtained before a formal investigation. Besides, the participants were informed that their data would be kept private and used anonymously for academic research.

Results

Sample characteristics

A total of 768 pregnant women were included in this study after excluding 48 individuals due to missing questionnaires, resulting in a participation rate of 94.1%. The mean age of the included pregnant women was 29.26 years (SD = 4.57), and one-third (35.2%) of the women had tertiary education. More than half (51.6%) were in the third trimester of pregnancy. Other details are demonstrated in Table 1.

Table 1 Demographic and relevant characteristics differences in the score of sleep quality (continuous variable)
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The relationship between fear of childbirth and sleep quality

The binary logistic regression analysis revealed that fear of childbirth significantly affected sleep quality in all trimesters and the overall antenatal period, as represented in Table 2. Pregnant women exhibiting elevated levels of fear of childbirth were more likely to experience suboptimal sleep quality, which can be attributed to heightened sleep disturbances stemming from anxiety and concerns related to childbirth. Besides, the results of the generalized additive model analysis (Fig. 1) showed that fear of childbirth had a linear relationship with sleep quality in the first trimester and third trimester, while this association became nonlinear in the second trimester and antenatal period.

Fig. 1
figure 1

The smooth fitting curves of fear of childbirth and sleep quality

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Table 2 The relationship between fear of childbirth and sleep quality (category variable)
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The meditation analysis

The first factor accounted for 24.47% (first trimester), 28.48% (second trimester), 27.34% (third trimester), and 26.73% (antenatal period) of the total variances and the common method bias was negligible. In the first trimester, PSQI showed a positive correlation with fear of childbirth (r = 0.330, P < 0.01) and psychological distress (r = 0.453, P < 0.01), while PSQI was negatively associated with resilience (r=-0.320, P < 0.01). Similarly, the associations among fear of childbirth, psychological distress, resilience, and PSQI were significant in the second trimester, third trimester, and antenatal period. Other correlation-related information is summarized in Table 3.

Table 3 Correlations between variables
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The univariate analyses revealed that employment and income during the second trimester, age and academic degree during the third trimester, and age and pregnancy period during the antenatal phase had a significant impact on sleep quality. Consequently, these factors were incorporated into the mediated moderation model as confounding variables. In Table 4, the significant mediation role of psychological distress between fear of childbirth and sleep quality was recognized, including first trimester (β = 0.044, 95%CI:0.022, 0.071), second trimester (β = 0.029, 95%CI:0.009, 0.056), third trimester (β = 0.064, 95%CI:0.046, 0.088), and antenatal period (β = 0.050, 95% CI:0.037, 0.063).

Table 4 Mediation analysis results for the relationship between fear of childbirth and sleep quality
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Moreover, in model 1, 95%CI of indirect effect (0.022, 0.071), direct effect (0.010, 0.093), and total effect (0.054, 0.136) implied psychological distress partially significantly mediated the relationship between fear of childbirth and sleep quality, with a mediating effect of 46.31%. Likewise, the mediating effects of psychological distress between fear of childbirth and sleep quality were 26.61% and 52.63% in the second trimester and antenatal period, respectively (model 2 and model 4). However, in model 3, 95%CI of the direct effect contained 0, so psychological distress fully mediated the relationship between fear of childbirth and sleep quality. The full details of the mediation analyses are presented in Supplementary Tables 1–4.

The moderation role of resilience

In Supplementary Table 5, the moderating effects of resilience were not significant in model 1 (β=-0.003, P = 0.306, 95%CI:-0.009, 0.004) and model 3 (β=-0.002, P = 0.788, 95%CI:-0.014, 0.011). However, in model 2, the interaction of fear of childbirth and resilience was significant (β=-0.016, P = 0.004, 95%CI:-0.026, -0.005), signifying that resilience did moderate the relationship between fear of childbirth and psychological distress. Simple slope tests (Fig. 2-A) showed that the effect of fear of childbirth on psychological distress was statistically significant at a low level of resilience (β = 0.298, 95%CI:0.197, 0.400) and a moderate level of resilience (β = 0.192, 95%CI:0.126, 0.258); The effect was not significant for those with high resilience (β = 0.086, 95%CI:-0.008, 0.179). Then, the Johnson-Neyman test (Fig. 2-B) indicated that there was a positive correlation between fear of childbirth and psychological distress when resilience was below 32.814, whereas the correlation gradually weakened and eventually disappeared as the value exceeded this threshold (95%CI of conditioned effect value of fear of childbirth on sleep quality included zero).

Fig. 2
figure 2

Simple slopes test and Johnson-Neyman test (First trimester and second trimester)

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In Supplementary Table 6, the significant moderation effect of resilience (model 1) was recognized and visualized in the simple slopes test (Fig. 2-C), suggesting that resilience significantly moderated the relationship between fear of childbirth and sleep quality. Also, the boundary value of the moderation effect of resilience is 31.356 (Fig. 2-D). Nevertheless, the moderation effects of resilience were not significant in model 2 (β=-0.002, P = 0.570, 95%CI:0.010, 0.005) and model 3 (β=-0.003, P = 0.564, 95%CI:0.014, 0.008).

In Supplementary Table 7, the interaction terms in the path from fear of childbirth to sleep quality (model 1, β=-0.004, P = 0.100, 95%CI:-0.008, 0.001), fear of childbirth to psychological distress (model 2, β=-0.003, P = 0.381, 95%CI:-0.008, 0.003), and psychological distress to sleep quality (model 3, β=-0.003, P = 0.519, 95%CI:-0.011, 0.006) were not significant.

In Supplementary Table 8, resilience acted as a significant moderator in the direct connection between fear of childbirth and sleep quality (model 1, β=-0.004, P = 0.014, 95%CI:-0.007, -0.001), and the association between fear of childbirth and psychological distress (model 2, β=-0.004, P = 0.014, 95%CI:-0.009, -0.001). However, the interaction between psychological distress and resilience was not significant (model 3, β=-0.003, P = 0.371, 95%CI:-0.009, -0.003). Then, Fig. 3-A illustrates that the relationship between fear of childbirth and sleep quality strengthens in the case of low resilience (β = 0.069, 95%CI:0.039, 0.095), as compared with moderate resilience (β = 0.043, 95%CI:0.021, 0.066) and high resilience (β = 0.018, 95%CI:-0.013, 0.049). Similarly, as shown in Fig. 3-C, compared to moderate resilience (β = 0.174, 95%CI:0.145, 0.203) and high resilience (β = 0.138, 95%CI:0.096, 0.180), the association between fear of childbirth and psychological distress was strengthened in case of low resilience (β = 0.210, 95%CI:0.171, 0.249). Additionally, the Johnson-Neyman test (Fig. 3-B) indicated the relationship of fear of childbirth was more strongly related to sleep quality for those with a resilience score of 31.211 or less.

Fig. 3
figure 3

Simple slopes test and Johnson-Neyman test (Antenatal period)

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Discussion

The current investigation aimed to examine the relationship between fear of childbirth and sleep quality while considering the potential mediating role of psychological distress and the moderating role of resilience. Findings substantiated the proposed associations and enriched our comprehension of these variables across different trimesters. First, as expected, fear of childbirth was negatively related to sleep quality, which was in line with previous studies [40,41,42]. A high level of fear of childbirth can lead to severe anxiety and fear in pregnant women. In this state, it can cause restlessness, sleeplessness, and lack of sleep time at night, resulting in poor sleep quality. Emerging studies indicated that cognitive-behavioral training and psychoeducation seemed to be effective in reducing fear of childbirth and improving sleep quality among pregnant women [43, 44]. These interventions can be adapted locally and applied to improve pregnant women’s physical and mental health worldwide.

Second, a non-linear association between fear of childbirth and sleep quality was identified in the second trimester and antenatal period, which had not been fully explored in previous research [42], contributing valuable insights to the existing fear of childbirth-sleep quality literature. The curvilinear relationship suggested that heterogeneity still exists within the different trimesters, and future studies could be enriched by conducting latent profile analysis or qualitative studies on pregnant women in both the second trimester and antenatal period.

Third, the mediation role of psychological distress was confirmed between fear of childbirth and sleep quality in all trimesters and the overall antenatal period, suggesting that fear of childbirth could have an indirect effect on sleep quality through psychological distress. Prior research supports the current finding by showing that fear of childbirth can lead to the development of psychological distress [18, 45], which subsequently has detrimental effects on the sleep quality of pregnant women [46, 47]. Anxiety/depression reduction interventions (i.e., mindfulness) could be developed and utilized in this population to improve their sleep quality. For example, interpersonal psychotherapy has been confirmed to be effective in preventing and treating psychological distress during pregnancy and postpartum [48,49,50], which could also be adapted to handle the fear of childbirth-induced sleep problems. In addition, the mediating effect of psychological distress was strongest in the third trimester, so future studies may focus on anxiety-depression interventions in this period to achieve a better effect.

Fourth, resilience did play a significant moderation role in fear of childbirth, psychological distress, and sleep quality. In other words, fear of childbirth would have a weaker association with sleep quality as resilience increased, especially among pregnant women with resilience scores greater than 31 in the second trimester and antenatal period. Besides, the correlation between fear of childbirth and psychological distress in those with high resilience levels was weaker than in those with low resilience levels (first trimester and antenatal period). This may be attributed to the fact that resilience plays a significant role in buffering against the fear of childbirth and psychological distress among pregnant women [10, 51]. Compared to those with low levels of resilience, pregnant women with high resilience levels better handle with fear of childbirth-induced psychological distress and maintain better sleep quality [52,53,54]. Furthermore, resilience-enhancing interventions have demonstrated positive effects on resilience and the preservation of mental well-being among pregnant women [55, 56]. These successful programs could also be adapted for wider application to more pregnant women to improve their resilience, so more research should be warranted in the future. Based on these findings, pregnant women with high levels of fear of childbirth, psychological distress, and low resilience levels may be vulnerable to sleep disturbance or disorder and should be given more attention.

To be brief, the current study contributes to our understanding of the relationships among fear of childbirth, psychological distress, resilience, and sleep quality based on a sample of Chinese pregnant women. It demonstrates that (1) fear of childbirth is negatively correlated with sleep quality; (2) The association between fear of childbirth and sleep quality is significantly mediated by psychological distress and moderated by resilience.

Strengths, limitations, and further Scopes

The primary strength of this study lies in the implementation of stratified analyses, which effectively identified variations across trimesters, enhanced result precision, and offered valuable insights for targeted interventions and support. Moreover, the examination of the mediating influence of psychological distress and the moderating effect of resilience contributed to a comprehensive comprehension of the intricate relationship among these factors. However, several limitations should be contemplated. First, the incidence of fear of childbirth among pregnant women in China is higher than those in other countries. Therefore, the findings and conclusions of the present study may not be generalized to pregnant women of different backgrounds. Besides, a causal relationship couldn’t be well settled because of the cross-sectional nature of this study, and a fixed cohort study with 4 waves of follow-up during the entire gestation period ought to be further performed to validate these findings. Third, in the moderated mediation model, a few potential confounders, i.e., social support, intimate partner violence, etc., are not considered because of the heavy scale burden, which will affect the association estimation.

Conclusions

Fear of childbirth, psychological distress, and resilience are three important factors affecting sleep quality in Chinese pregnant women.

Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

References

  1. Bedaso A, Adams J, Peng W, Sibbritt D. The relationship between social support and mental health problems during pregnancy: a systematic review and meta-analysis. Reprod Health. 2021;18(1):162.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Pauley AM, Moore GA, Mama SK, Molenaar P, Symons Downs D. Associations between prenatal sleep and psychological health: a systematic review. J Clin Sleep Med. 2020;16(4):619–30.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Nelson KL, Davis JE, Corbett CF. Sleep quality: an evolutionary concept analysis. Nurs Forum. 2022;57(1):144–51.

    Article  PubMed  Google Scholar 

  4. Du M, Liu J, Han N, Zhao Z, Yang J, Xu X, Luo S, Wang H. Maternal sleep quality during early pregnancy, risk factors and its impact on pregnancy outcomes: a prospective cohort study. Sleep Med. 2021;79:11–8.

    Article  PubMed  Google Scholar 

  5. Polo-Kantola P. Sleep disturbances in pregnancy: why and how should we manage them? Acta Obstet Gynecol Scand. 2022;101(3):270–2.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Vizzini L, Popovic M, Zugna D, Vitiello B, Trevisan M, Pizzi C, Rusconi F, Gagliardi L, Merletti F, Richiardi L. Maternal anxiety, depression and sleep disorders before and during pregnancy, and preschool ADHD symptoms in the NINFEA birth cohort study. Epidemiol Psychiatr Sci. 2019;28(5):521–31.

    Article  PubMed  CAS  Google Scholar 

  7. Wijma K, Wijma B, Zar M. Psychometric aspects of the W-DEQ; a new questionnaire for the measurement of fear of Childbirth. J Psychosom Obstet Gynaecol. 1998;19(2):84–97.

    Article  PubMed  CAS  Google Scholar 

  8. Poggi L, Goutaudier N, Séjourné N, Chabrol H. When fear of Childbirth is pathological: the fear Continuum. Matern Child Health J. 2018;22(5):772–8.

    Article  PubMed  Google Scholar 

  9. Hildingsson I, Haines H, Karlström A, Nystedt A. Presence and process of fear of birth during pregnancy-findings from a longitudinal cohort study. Women Birth. 2017;30(5):e242–7.

    Article  PubMed  Google Scholar 

  10. Huang J, Huang J, Li Y, Liao B. The prevalence and predictors of fear of Childbirth among pregnant Chinese women: a hierarchical regression analysis. BMC Pregnancy Childbirth. 2021;21(1):643.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ryding EL, Lukasse M, Parys AS, Wangel AM, Karro H, Kristjansdottir H, Schroll AM, Schei B. Fear of Childbirth and risk of cesarean delivery: a cohort study in six European countries. Birth. 2015;42(1):48–55.

    Article  PubMed  Google Scholar 

  12. Slade P, Balling K, Sheen K, Houghton G. Establishing a valid construct of fear of Childbirth: findings from in-depth interviews with women and midwives. BMC Pregnancy Childbirth. 2019;19(1):96.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. O’Connell MA, Khashan AS, Leahy-Warren P, Stewart F, O’Neill SM. Interventions for fear of childbirth including tocophobia. Cochrane Database Syst Rev 2021, 7(7): Cd013321.

  14. Liu L, Su G, Wang S, Zhu B. The prevalence of obstructive sleep apnea and its association with pregnancy-related health outcomes: a systematic review and meta-analysis. Sleep Breath. 2019;23(2):399–412.

    Article  PubMed  Google Scholar 

  15. Sedov ID, Cameron EE, Madigan S, Tomfohr-Madsen LM. Sleep quality during pregnancy: a meta-analysis. Sleep Med Rev. 2018;38:168–76.

    Article  PubMed  Google Scholar 

  16. Gao M, Hu J, Yang L, Ding N, Wei X, Li L, Liu L, Ma Y, Wen D. Association of sleep quality during pregnancy with stress and depression: a prospective birth cohort study in China. BMC Pregnancy Childbirth. 2019;19(1):444.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Felder JN, Laraia B, Coleman-Phox K, Bush N, Suresh M, Thomas M, Adler N, Epel E, Prather AA. Poor sleep quality, psychological distress, and the Buffering Effect of Mindfulness Training during pregnancy. Behav Sleep Med. 2018;16(6):611–24.

    Article  PubMed  Google Scholar 

  18. Mei X, Mei R, Liu Y, Wang X, Chen Q, Lei Y, Ye Z. Associations among fear of Childbirth, resilience and psychological distress in pregnant women: a response surface analysis and moderated mediation model. Front Psychiatry. 2022;13:1091042.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Molgora S, Fenaroli V, Prino LE, Rollè L, Sechi C, Trovato A, Vismara L, Volpi B, Brustia P, Lucarelli L, et al. Fear of Childbirth in primiparous Italian pregnant women: the role of anxiety, depression, and couple adjustment. Women Birth. 2018;31(2):117–23.

    Article  PubMed  Google Scholar 

  20. Handelzalts JE, Zacks A, Levy S. The association of birth model with resilience variables and birth experience: home versus hospital birth. Midwifery. 2016;36:80–5.

    Article  PubMed  Google Scholar 

  21. García-León M, Caparrós-González RA, Romero-González B, González-Perez R, Peralta-Ramírez I. Resilience as a protective factor in pregnancy and puerperium: its relationship with the psychological state, and with hair cortisol concentrations. Midwifery. 2019;75:138–45.

    Article  PubMed  Google Scholar 

  22. Baattaiah BA, Alharbi MD, Babteen NM, Al-Maqbool HM, Babgi FA, Albatati AA. The relationship between fatigue, sleep quality, resilience, and the risk of postpartum depression: an emphasis on maternal mental health. BMC Psychol. 2023;11(1):10.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Reshadat S, Zakiei A, Karami J, Ahmadi E. A study of the psychological and family factors associated with sleep quality among pregnant women. Sleep and Hypnosis. 2018;20(1):17–24.

    Google Scholar 

  24. Sagalova V, Le Dain AS, Bärnighausen T, Zagre NM, Vollmer S. Does early childbearing affect utilization of antenatal care services and infant birth weight: evidence from West and Central African Region. J Glob Health. 2021;11:13003.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Laursen M, Johansen C, Hedegaard M. Fear of Childbirth and risk for birth Complications in nulliparous women in the Danish National Birth Cohort. BJOG. 2009;116(10):1350–5.

    Article  PubMed  CAS  Google Scholar 

  26. Zhang X, Cao D, Sun J, Shao D, Sun Y, Cao F. Sleep heterogeneity in the third trimester of pregnancy: correlations with depression, memory impairment, and fatigue. Psychiatry Res. 2021;303:114075.

    Article  PubMed  Google Scholar 

  27. Tanglakmankhong K, Perrin NA, Lowe NK. Childbirth Self-Efficacy Inventory and Childbirth attitudes Questionnaire: psychometric properties of Thai language versions. J Adv Nurs. 2011;67(1):193–203.

    Article  PubMed  Google Scholar 

  28. Zhou X, Liu H, Li X, Zhang S. Fear of Childbirth and Associated Risk factors in healthy pregnant women in Northwest of China: a cross-sectional study. Psychol Res Behav Manag. 2021;14:731–41.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983;67(6):361–70.

    Article  PubMed  CAS  Google Scholar 

  30. Leung CM, Ho S, Kan CS, Hung CH, Chen CN. Evaluation of the Chinese version of the hospital anxiety and Depression Scale. A cross-cultural perspective. Int J Psychosom. 1993;40(1–4):29–34.

    PubMed  CAS  Google Scholar 

  31. Campbell-Sills L, Stein MB. Psychometric analysis and refinement of the Connor-Davidson Resilience Scale (CD-RISC): validation of a 10-item measure of resilience. J Trauma Stress. 2007;20(6):1019–28.

    Article  PubMed  Google Scholar 

  32. Ye ZJ, Qiu HZ, Li PF, Chen P, Liang MZ, Liu ML, Yu YL, Wang SN, Quan XM. Validation and application of the Chinese version of the 10-item Connor-Davidson Resilience Scale (CD-RISC-10) among parents of children with cancer diagnosis. Eur J Oncol Nurs. 2017;27:36–44.

    Article  PubMed  Google Scholar 

  33. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.

  34. Xu K, Zhang Y, Zhang Y, Xu Q, Lv L, Zhang J. Mental health among pregnant women under public health interventions during COVID-19 outbreak in Wuhan, China. Psychiatry Res. 2021;301:113977.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Yu Y, Zhu X, Xu H, Hu Z, Zhou W, Zheng B, Yin S. Prevalence of depression symptoms and its influencing factors among pregnant women in late pregnancy in urban areas of Hengyang City, Hunan Province, China: a cross-sectional study. BMJ Open. 2020;10(9):e038511.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hastie T, Tibshirani R. Generalized additive models for medical research. Stat Methods Med Res. 1995;4(3):187–96.

    Article  PubMed  CAS  Google Scholar 

  37. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879–903.

    Article  PubMed  Google Scholar 

  38. Hayes AF. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. New York, NY, US: Guilford Press; 2013.

    Google Scholar 

  39. Johnson PO, Fay LC. The Johnson-Neyman technique, its theory and application. Psychometrika. 1950;15(4):349–67.

    Article  PubMed  CAS  Google Scholar 

  40. Dencker A, Nilsson C, Begley C, Jangsten E, Mollberg M, Patel H, Wigert H, Hessman E, Sjöblom H, Sparud-Lundin C. Causes and outcomes in studies of fear of Childbirth: a systematic review. Women Birth. 2019;32(2):99–111.

    Article  PubMed  Google Scholar 

  41. Hall WA, Hauck YL, Carty EM, Hutton EK, Fenwick J, Stoll K. Childbirth fear, anxiety, fatigue, and sleep deprivation in pregnant women. J Obstet Gynecol Neonatal Nurs. 2009;38(5):567–76.

    Article  PubMed  Google Scholar 

  42. UÇAr T, Karakayali Ay Ç, Aksoy Derya Y, KarataŞ Okyay E. Determination of Sleep Quality in pregnant women with severe level fear of birth. Volume 7. GEVHER NESIBE JOURNAL OF MEDICAL AND HEALTH SCIENCES; 2022. pp. 193–202. 21.

  43. Webb R, Bond R, Romero-Gonzalez B, Mycroft R, Ayers S. Interventions to treat fear of Childbirth in pregnancy: a systematic review and meta-analysis. Psychol Med. 2021;51(12):1964–77. https://doi.org/10.1017/S0033291721002324. Epub 2021 Jun 25.

    Article  PubMed Central  Google Scholar 

  44. Khojasteh F, Afrashte M, Khayat S, Navidian A. Effect of cognitive-behavioral training on fear of Childbirth and sleep quality of pregnant adolescent slum dwellers. J Educ Health Promot. 2022;11:311.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Rouhe H, Salmela-Aro K, Gissler M, Halmesmäki E, Saisto T. Mental health problems common in women with fear of Childbirth. BJOG: An International Journal of Obstetrics & Gynaecology. 2011;118(9):1104–11.

    Article  CAS  Google Scholar 

  46. Sahin BM, Eren HK. The relationship between Sleep Quality and anxiety in pregnant women. Int J Caring Sci 2022, 15(3).

  47. Takelle GM, Muluneh NY, Biresaw MS. Sleep quality and associated factors among pregnant women attending antenatal care unit at Gondar, Ethiopia: a cross-sectional study. BMJ Open. 2022;12(9):e056564.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Bright KS, Charrois EM, Mughal MK, Wajid A, McNeil D, Stuart S, Hayden KA, Kingston D. Interpersonal psychotherapy to reduce psychological distress in Perinatal women: a systematic review. Int J Environ Res Public Health 2020, 17(22).

  49. Nakku JEM, Nalwadda O, Garman E, Honikman S, Hanlon C, Kigozi F, Lund C. Group problem solving therapy for perinatal depression in primary health care settings in rural Uganda: an intervention cohort study. BMC Pregnancy Childbirth. 2021;21(1):584.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Evans J, Ingram J, Law R, Taylor H, Johnson D, Glynn J, Hopley B, Kessler D, Round J, Ford J, et al. Interpersonal counselling versus perinatal-specific cognitive behavioural therapy for women with depression during pregnancy offered in routine psychological treatment services: a phase II randomised trial. BMC Psychiatry. 2021;21(1):504.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Puertas-Gonzalez JA, Mariño-Narvaez C, Romero-Gonzalez B, Vilar-López R, Peralta-Ramirez MI. Resilience, stress and anxiety in pregnancy before and throughout the pandemic: a structural equation modelling approach. Curr Psychol 2022:1–11.

  52. Li G, Kong L, Zhou H, Kang X, Fang Y, Li P. Relationship between prenatal maternal stress and sleep quality in Chinese pregnant women: the mediation effect of resilience. Sleep Med. 2016;25:8–12.

    Article  PubMed  CAS  Google Scholar 

  53. Sheerin CM, Lind MJ, Brown EA, Gardner CO, Kendler KS, Amstadter AB. The impact of resilience and subsequent stressful life events on MDD and GAD. Depress Anxiety. 2018;35(2):140–7.

    Article  PubMed  Google Scholar 

  54. Ma X, Wang Y, Hu H, Tao XG, Zhang Y, Shi H. The impact of resilience on prenatal anxiety and depression among pregnant women in Shanghai. J Affect Disord. 2019;250:57–64.

    Article  PubMed  Google Scholar 

  55. Walker AL, Witteveen AB, Otten RHJ, Verhoeven CJ, Henrichs J, de Jonge A. Resilience-enhancing interventions for antepartum depressive symptoms: systematic review. BJPsych Open. 2022;8(3):e89.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Van Haeken S, Braeken MAKA, Horsch A, Oosterman M, Bogaerts A. Development of a resilience-enhancing intervention during and after pregnancy: a systematic process informed by the behaviour change wheel framework. BMC Psychol. 2023;11(1):267.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to thank nurses from the participating hospital for supporting data collection and all the participants for completing the survey.

Funding

This research was funded by grants from National Natural Science Foundation of China (No. 72274043 and 71904033), Young Elite Scientists Sponsorship Program by CACM (No.2021-QNRC2-B08), Humanity and Social Science Foundation of Department of Education of Guangdong Province (No. 2020WTSCX009), Humanity and Social Science Foundation of Guangzhou (No. 2021GZGJ57), Guangdong Research Center for TCM Service and Industrial Development, Guangzhou University of Chinese Medicine (No. 2022ZDA03), and Humanity and Social Science Foundation of Guangzhou University of Chinese Medicine (No. 2021SKYB07).

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Authors and Affiliations

  1. School of Nursing, Hong Kong Polytechnic University, Hong Kong, China

    Xiaoxiao Mei, Yan Li & Zengjie Ye

  2. School of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China

    Ping Du, Xinqin Wang & Qianwen Chen

  3. Breast Oncology Department, Guangzhou Medical University Cancer Hospital, Guangzhou, China

    Ranran Mei

  4. School of Nursing, Guangzhou Medical University, Guangzhou, China

    Zengjie Ye

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  1. Xiaoxiao Mei
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  3. Yan Li
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Contributions

Xiaoxiao Mei: Conceptualization, Data curation, Methodology, Software, Writing - original draft. Ping Du: Investigation, Resources, Software. Yan Li: Methodology, Writing - Review and Editing. Ranran Mei: Investigation, Resources. Xinqin Wang: Investigation, Resources. Qianwen Chen: Investigation, Resources. Zengjie Ye: Supervision , Writing - Review and Editing. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Zengjie Ye.

Ethics declarations

Ethics approval and consent to participate

Informed consent was obtained from all participants and they were ensured that their information would remain confidential. Participants were allowed to withdraw from the study at any desired time. The is part of the Be Resilient to Postpartum Depression (BRPD, Registration number: ChiCTR2100048465) and was approved by the ethics review committee of the participating hospital (No: K-2022-024). The study was performed per the national legislation and institutional requirements. All methods were performed per the Declaration of Helsinki.

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Not applicable.

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The authors declare no competing interests.

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The results of mediated moderated analysis

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Mei, X., Du, P., Li, Y. et al. Fear of childbirth and sleep quality among pregnant women: a generalized additive model and moderated mediation analysis. BMC Psychiatry 23, 931 (2023). https://doi.org/10.1186/s12888-023-05435-y

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Keywords

BMC Psychiatry

ISSN: 1471-244X

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