Relationship between Fatigue, Sleep Disturbance, and Gestational Stress among Pregnant Women in the Late Stages

An explanatory model of quality of life in high-risk pregnant women in Korea: a structural equation model

PURPOSE

This study aimed to develop and validate a structural model for the quality of life (QoL) among high-risk pregnant women, based on Roy's adaptation model.

METHODS

This cross-sectional study collected data from 333 first-time mothers diagnosed with a high-risk pregnancy in two obstetrics and gynecology clinics in Cheonan, Korea, or participating in an online community, between October 20, 2021 and February 20, 2022. Structured questionnaires measured QoL, contextual stimuli (uncertainty), coping (adaptive or maladaptive), and adaptation mode (fatigue, state anxiety, antenatal depression, maternal identity, and marital adjustment).

RESULTS

The mean age of the respondents was 35.29±3.72 years, ranging from 26 to 45 years. The most common high-risk pregnancy diagnosis was gestational diabetes (26.1%). followed by preterm labor (21.6%). QoL was higher than average (18.63±3.80). Above-moderate mean scores were obtained for all domains (psychological/baby, 19.03; socioeconomic, 19.00; relational/spouse-partner, 20.99; relational/family-friends, 19.18; and health and functioning, 16.18). The final model explained 51% of variance in QoL in high-risk pregnant women, with acceptable overall model fit. Adaptation mode (β=-.81, p=.034) and maladaptive coping (β=.46 p=.043) directly affected QoL, and uncertainty (β=-. 21, p=.004), adaptive coping (β=.36 p=.026), and maladaptive coping (β=-.56 p=.023) indirectly affected QoL.

CONCLUSION

It is essential to develop nursing interventions aimed at enhancing appropriate coping strategies to improve QoL in high-risk pregnant women. By reinforcing adaptive coping strategies and mitigating maladaptive coping, these interventions can contribute to better maternal and fetal outcomes and improve the overall well-being of high-risk pregnant women.

Poor sleep quality and associated factors among pregnant women on antenatal care follow up at Nekemte Referral Hospital and Wollega University Hospital, Nekemte, Ethiopia, 2019: a cross-sectional study

Sleep disturbances are common in women, especially during pregnancy. This can result in emotional and psychological consequences for pregnant women, and it could lead to some serious complications for both mothers and their babies. However, it is not well recognized and has not been studied in developing countries, including Ethiopia.

Objective

To assess the quality of sleep and associated factors among pregnant women on antenatal care follow-up at Nekemte Referral Hospital and Wollega University Hospital, Nekemte, Ethiopia 2019.

Methods

An institution-based cross-sectional study was conducted from May to June 2019 at Nekemte Referral Hospital and Wollega University Hospital. A systematic random sampling technique was used to get 408 samples. Sleep quality was assessed using structured questionnaires of the Pittsburgh Sleep Quality Index tool. Then, the collected data was coded and entered into Epi-Data 3.1 version and analyzed using SPSS version 20. A logistic regression analysis was computed to determine the association between independent variables and sleep quality. Statistically significant was considered at P-value < 0.05.

Results

With 96.4% response rate, the magnitude of poor sleep quality was found to be 59.1% [95% CI: (54.2, 64)]. Poor sleep quality was high among participants with unplanned pregnancy [AOR = 4.25,95%CI:(1.47,12.23)],poor sleep hygiene[AOR = 2.93,95%CI:(1.41,6.09)],depressed women[AOR = 5.73,95%CI:(2.49,13.21)], anxiety disorder[AOR = 6.62,95%CI:(2.61,16.82)] and third trimester participants [AOR = 5.84,95% CI:(2.49,13.21)].

Conclusion

This study demonstrated that poor sleep quality among pregnant women is high. Factors like depression, anxiety, poor sleep hygiene, first time pregnancy, unplanned pregnancy, and late gestational age were found to be associated with poor sleep quality. This underlines health care planners’ needs to incorporate screening for poor sleep quality into routine ANC services.

Depression remains a neglected public health problem among pregnant women in Northwest Ethiopia

BACKGROUND

Antenatal depression is highly prevalent but a neglected public health problem in low income countries. It has serious effects on the general health of women, birth outcomes and child health. However, there has been limited substantial evidence on the prevalence and predictors of antenatal depression in Ethiopia. This lack of evidence potentiates the consequences of the problem and can limit the attention to intervention. Thus, this study aimed to assess the prevalence and potential predictors of antenatal depression at Debre Tabor and Woreta towns, Northeast Ethiopia.

METHODS

A community-based cross-sectional study was employed on 548 pregnant women recruited by a cluster sampling method. Depressive symptoms were measured using the Edinburgh Postnatal Depression Scale (EPDS). The List of Threatening Experiences Questionnaire (LTE-Q), the Oslo-3 Social Support Scale (OSSS-3), Intimate Partner Violence (IPV) Scale and Fast Alcohol Screening Test (FAST) were also used to measure stressful events, social support, intimate partner violence (IPV) and hazardous alcohol use respectively. Bivariable and multivariable logistic regression analyses were carried out to identify factors associated with antenatal depression.

RESULTS

The prevalence of antenatal depression was found to be 24.45% (95% CI: 21.20, 28.30%). Being single (AOR =3.32, 95% CI = 1.36, 8.09); fear of pregnancy complication (AOR = 3.84, 95% CI = 1.53,9.62); history of chronic illness (AOR = 8.14, 95% CI = 2.14, 30.91); unplanned pregnancy (AOR = 2.99, 95% CI = 1.36,6.55); history of stillbirth (AOR = 3.56, 95% CI = 1.23, 10.29),one or more negative life events (AOR = 4.06, 95% CI = 1.71, 9.66) and intimate partner violence (AOR = 3.91, 95% CI = 1.65, 9.26) were factors significantly associated with antenatal depression.

CONCLUSION

Nearly a quarter of pregnant women suffer from depressive symptoms during pregnancy. Being single; fear of pregnancy complication; history of chronic illness; unplanned pregnancy; history of stillbirth; one or more negative life events and intimate partner violence were important predictors of antenatal depression in this study. Health care workers should consider addressing these risk factors during a routine antenatal care. Also, integrating early screening, detection, and treatment of antenatal depression into routine antenatal care is warranted to improve the quality of life of pregnant women and pregnancy outcomes as well.

[Multiple Factors in the Second Trimester of Pregnancy on Preterm Labor Symptoms and Preterm Birth]

PURPOSE

The aim of this study was to determine the influence of various factors on preterm labor symptoms (PLS) and preterm birth (PB).

METHODS

This prospective cohort study included 193 women in the second stage of pregnancy. Multiple characteristics including body mass index (BMI), smoking, and pregnancy complications were collected through a self-report questionnaire. Pregnancy stress and PLS were each measured with a related scale. Cervical length and birth outcome were evaluated from medical charts. Multiple regression was used to predict PLS and logistic regression was used to predict PB.

RESULTS

Multiple regression showed smoking experience, pregnancy complications and pregnancy specific stress were predictors of PLS and accounted for 19.2% of the total variation. Logistic regression showed predictors of PB to be twins (OR=13.68, CI=3.72~50.33, p<.001), shorter cervix (<25mm) (OR=5.63, CI=1.29~24.54, p<.05), BMI >25 (kg/m²) (OR=3.50, CI=1.35~9.04, p<.01) and a previous PB (OR=4.15, CI=1.07~16.03, p<.05).

CONCLUSION

The results of this study show that the multiple factors affect stage II pregnant women can result in PLS or PB. And preterm labor may predict PB. These findings highlight differences in predicting variables for pretrm labor and for PB. Future research is needed to develop a screening tool to predict the risk of preterm birth in pregnant women.