Effect of Gestational Diabetes Mellitus History on Future Pregnancy Behaviors: The Mutaba'ah Study

Evaluation of Prenatal Comfort, Sleep, and Quality of Life in Pregnant Women with Cholestasis: A Cross-Sectional Study

BACKGROUND

Associated with adverse pregnancy outcomes, intrahepatic cholestasis of pregnancy is the most prevalent liver disease that women typically experience during pregnancy. This study aimed to evaluate prenatal comfort, sleep, and quality of life in pregnant women with cholestasis.

METHODS

This cross-sectional study was implemented between November 2022 and June 2023 at Mardin Training and Research Hospital with 150 pregnant women who received a diagnosis of pregnancy-induced intrahepatic cholestasis and agreed to participate. The following tools were utilized to collect data: A personal information form exploring socio-demographic and obstetric characteristics of participants, the Prenatal Comfort Scale (PCS), the Pittsburgh Sleep Quality Index (PSQI), and the World Health Organization Quality of Life-Brief Form (WHOQOL-BREF).

RESULTS

The mean age of participants was 27.79 ± 6.33 years. The mean PCS and PSQI scores were 61.20 ± 5.84 and 9.52 ± 3.02, respectively. The mean scores of "physical health, psychological health, social relationships, and environmental health" sub-dimensions in WHOQOL-BREF were 10.63 ± 2.18, 10.48 ± 2.10, 11.31 ± 3.28, and 11.27 ± 2.10, respectively. A significant difference was found for PSQI regarding hospitalization status and change in sleep quality variables (p = 0.025 and p = 0.035, respectively).

CONCLUSIONS

Cholestasis of pregnancy creates problems such as pruritus, body image changes, hospitalization, and poor sleep quality in women. This study showed that pregnant women with cholestasis had low levels of sleep quality and quality of life, implying that cholestasis affects their sleep quality, prenatal comfort levels, and quality of life in general. In addition, it is seen that women with this problem do not want to fall pregnant again.

Development and acceptability of a gestational diabetes mellitus prevention system (Better pregnancy) based on a user-centered approach: A clinical feasibility study

Background

Gestational diabetes mellitus (GDM) can increase the risk of adverse outcomes for both mothers and infants. Preventive interventions can effectively assist pregnant women suffering from GDM. At present, pregnant women are unaware of the importance of preventing GDM, and they possess a low level of self-management ability. Recently, mHealth technology has been used worldwide. Therefore, developing a mobile health app for GDM prevention could potentially help pregnant women reduce the risk of GDM.

Objective

To design and develop a mobile application, evaluate its acceptance, and understand the users'using experience and suggestions, thus providing a valid tool to assist pregnant women at risk of GDM in enhancing their self-management ability and preventing GDM.

Methods

An evidence-based GDM prevent app (Better pregnancy) was developed using user-centered design methods, following the health belief model, and incorporating GDM risk prediction. A convenient sampling method was employed from June to August 2022 to select 102 pregnant women at risk of GDM for the pilot study. After a week, the app's acceptability was evaluated using an application acceptance questionnaire, and we updated the app based on the feedback from the women. We used SPSS 26.0 for data analysis.

Results

The application offers various functionalities, including GDM risk prediction, health management plan, behavior management, health information, personalized guidance and consultation, peer support, family support, and other functions. In total, 102 pregnant women consented to participate in the study, achieving a retention rate of 98%; however, 2% (n = 2) withdrew. The Better pregnancy app's average acceptability score is 4.07 out of 5. Additionally, participants offered several suggestions aimed at enhancing the application.

Conclusions

The Better pregnancy app developed in this study can serve as an auxiliary management tool for the prevention of GDM, providing a foundation for subsequent randomized controlled trials.

Determinants of childbearing intentions among pregnant women with a suspected fetal congenital heart disease

BACKGROUND

The Saudi population is characterized by a high fertility rate, a conservative sociocultural context, and an ongoing societal and lifestyle transition. Yet, data regarding childbearing intentions and the associated factors is scarce. Furthermore, childbearing intention may be impacted by abnormal pregnancy events, such as the clinical suspicion or diagnosis of a fetal congenital disease and the negative experience that may result from it.

OBJECTIVES

The present study explored childbearing intention and determined the sociodemographic and health-related factors discouraging from future pregnancies among women visiting the cardiology clinic for antenatal screening of congenital heart diseases (CHD).

METHODS

A one-year cross-sectional study involved 150 consecutive pregnant women aged between 18 and 47 years old, who presented for antenatal screening of CHD at the fetal echocardiography clinic for, of a teaching hospital in Jeddah, Saudi Arabia. An interview-based questionnaire was administered before the clinic visit and collected sociodemographic data, obstetrical history, risk factors of congenital diseases, mothers' knowledge about fetal echocardiography and CHDs, and childbearing intention (the primary outcome).

RESULTS

Fetal echocardiography and CHD were correctly described by 60 % and 31.3 % of the participants respectively, while 40.7 % and 12 % recognized the association of congenital diseases with consanguinity and advanced maternal age respectively. Childbearing intentions showed 68.0 % of the participants declared desiring a future baby while the remaining 32.0 % declared having no further pregnancy expectations. Attitudes towards a hypothetical baby with CHD were positive in 97.3 % of the participants, and only 30 % declared that the fetal echocardiography results will impact their future childbearing decision. However, childbearing intention showed no significant association with knowledge or attitudes towards CHD. On the other hand, childbearing intention was independently associated with the number of children (OR=0.34; p<0.001), consanguinity with husband (OR=3.64; p = 0.010), and history of gestational diabetes (OR=0.28; p = 0.040).

CONCLUSION

Fertility expectations among Saudi mothers are more likely to be impacted by the personal experience and judgment of own pregnancy fitness, while no significant impact of fetal risks and events. This demonstrates a certain level of autonomy balanced with deeply rooted religious ethics yet combined with a lack of awareness about fetal and maternal risks associated with advanced maternal age and consanguinity. We emphasize the unmet need of education and family planning.

Results from the United Arab Emirates 2022 report card on physical activity for children and adolescents

Objective

The United Arab Emirates (UAE) 2022 Report Card provides a systematic evaluation of the physical activity (PA) levels of children and adolescents in the UAE.

Methods

The 2022 Report Card utilized data from 2017 to 2021 to inform 10 core PA indicators that were common to the Global Matrix 4.0.

Results

One in five (19%) UAE school children achieved the recommended amount of moderate-to-vigorous PA (i.e. ≥60 min/d; Total Physical Activity Grade F). Less than 1% of school children used active transport to and from school (Active Transportation Grade F). One in four (26%) secondary school children achieved the recreational screen time recommendations (i.e. ≤2 h/d; Sedentary Behaviours Grade D-). A quarter of adults reported achieving the recommended PA level (i.e. ≥150 min of moderate-intensity PA per week, or equivalent) (Family and Peers Grade D-). All school children are taught physical education (PE) by a specialist with at least a bachelor's degree in PE; however, the duration of weekly PE classes varied between schools (School Grade A-). The UAE Government has invested significant funds and resources into developing and implementing strategies and facilities that will increase PA across the entire population (Government Grade B+). Organised Sport and Physical Activity, Active Play, Physical Fitness, and Community and Environment indicators were graded 'Incomplete' (INC) due to a lack of available data.

Conclusions

Overall, PA levels remain low and sedentary behaviours remain high amongst UAE children and adolescents. The UAE Government has sustained investment in further developing PA opportunities for all children and adults which should translate to increased PA and health improvements at a population level.

Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach

Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes. The dataset was obtained from the Kurdistan region laboratories, which collected information from pregnant women with and without diabetes. The suggested model uses the clustering KMeans technique for data reduction and the elbow method to find the optimal k value and the Mahalanobis distance method to find more related cluster to new samples, and the classification methods such as decision tree, random forest, SVM, KNN, logistic regression, and Naïve Bayes are used for prediction. The results showed that using a mix of KMeans clustering, elbow method, Mahalanobis distance, and ensemble technique significantly improves prediction accuracy.

Diabetes Detection System by Mixing Supervised and Unsupervised Algorithms

Diabetes mellitus is also called gestational diabetes when a woman has high blood sugar while pregnant. It can show up at any time during pregnancy and cause problems for the mother and baby during or after the pregnancy. If the risks are found and dealt with as soon as possible, there is a chance that they can be reduced. The healthcare system is one of the many parts of our daily lives that are being rethought thanks to the creation of intelligent systems by machine learning algorithms. In this article, a hybrid prediction model is suggested to determine if a woman has gestational diabetes. The recommended model reduces the amount of data using the K-means clustering method. Predictions are made using several classification methods, such as decision trees, random forests, SVM, KNN, logistic regression, and naive Bayes. The results show that accuracy increases when clustering and classification are used together.

Insulin Aspart Combined with Exercise Therapy in Spleen Deficiency Type Gestational Diabetes Mellitus: The Effect on Disease Control and Pregnancy Outcomes

Objective. To investigate the effect of insulin aspart combined with exercise therapy on the disease control and pregnancy outcomes of spleen deficiency type gestational diabetes mellitus patients. Methods. In this prospective study, a total of 102 patients with spleen deficiency type gestational diabetes mellitus admitted to our hospital from January 2019 to December 2019 were selected and assigned at a ratio of 1 : 1 via the random number table method to receive insulin aspart (control group) or insulin aspart plus exercise therapy (observation group). Outcome measures include blood sugar, clinical efficacy, adverse pregnancy outcomes, and complications. Results. Insulin aspart plus exercise therapy was associated with significantly lower blood glucose and glycosylated hemoglobin levels versus insulin aspart alone ( $P<0.05$ ). Insulin aspart plus exercise therapy resulted in significantly higher total efficacy (96.08%) versus insulin aspart (74.51%) ( $P<0.05$ ). Patients receiving insulin aspart plus exercise therapy showed a significantly lower incidence of adverse pregnancy outcomes (3.92%) versus those given insulin aspart alone (37.25%) ( $P<0.05$ ). Insulin aspart plus exercise therapy resulted in a lower incidence of complications (5.88%) versus insulin aspart (41.17%) ( $P<0.05$ ). Conclusion. Exercise therapy plus insulin aspart might offer a viable treatment alternative for patients with spleen deficiency-type gestational diabetes mellitus given its promising effects in disease control and pregnancy outcomes, with good efficacy and safety profiles.

Views and needs of people who at high-risk of gestational diabetes mellitus for the development of mobile health applications: A descriptive qualitative research (Preprint)

Background

Early prevention of gestational diabetes mellitus (GDM) can reduce the incidence of not only GDM, but also adverse perinatal pregnancy outcomes. Moreover, it is of great significance to prevent or reduce the occurrence of type 2 diabetes. Mobile health (mHealth) apps can help pregnant women effectively prevent GDM by providing risk prediction, lifestyle support, peer support, professional support, and other functions. Before designing mHealth apps, developers must understand the views and needs of pregnant women, and closely combine users’ needs to develop app functions, in order to better improve user experience and increase the usage rate of these apps in the future.

Objective

The objective of this study was to understand the views of the high-risk population of gestational diabetes mellitus on the development of mobile health apps and the demand for app functions, so as to provide a basis for the development of gestational diabetes mellitus prevention apps.

Methods

Fifteen pregnant women with at least one risk factor for gestational diabetes were recruited from July to September 2021, and were interviewed via a semistructured interview using the purpose sampling method. The transcribed data were analyzed by the traditional content analysis method, and themes were extracted.

Results

Respondents wanted to develop user-friendly and fully functional mobile apps for the prevention of gestational diabetes mellitus. Pregnant women's requirements for app function development include: personalized customization, accurate information support, interactive design, practical tool support, visual presentation, convenient professional support, peer support, reasonable reminder function, appropriate maternal and infant auxiliary function, and differentiated incentive function.These function settings can encourage pregnant women to improve or maintain healthy living habits during their use of the app

Conclusions

This study discusses the functional requirements of target users for gestational diabetes mellitus prevention apps, which can provide reference for the development of future applications.