Background: With the continuous large-scale development of the COVID-19 vaccine, the acceptance of vaccination and its influencing factors at the individual level have become crucial to stemming the pandemic. This study aims to explore the factors that influence the acceptance of the COVID-19 vaccine among international college students. Methods: The target population constituted international students pursuing various degrees in Jiangsu Province through an online cross-sectional study. A cluster random sampling was performed using a self-administered questionnaire. The Health Belief Model and Knowledge, Attitude/Beliefs, and Practice Theory served as the underlying theories to understanding the factors that influence vaccine acceptance. Results: We received 330 responses. About 36.4% intended to accept the vaccine. The acceptance varied across respondents’ place of residence, program of study, continent of origin, knowledge, susceptibility, severity, benefits, and cues to action (p < 0.05). A multivariable logistics regression revealed cues to action (p < 0.001), the perception of COVID-19 vaccination benefits (p = 0.002), and the perception of barriers (p < 0.001) that were associated with vaccine acceptance. Conclusions: The acceptance of the COVID-19 vaccine was low among international students. The correct and comprehensive beliefs of the target groups regarding the benefits and barriers of the vaccination must be raised. Various effective social strategies must be adopted to trigger the intention of COVID-19 vaccination. The study findings will inform the decisions of public health campaigners, aimed at reducing vaccine hesitation when the COVID-19 vaccine is widely available.

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Inappropriate gestational weight gain has become a public health concern that threatens maternal and child health. Pregnant women's ability to manage their weight during pregnancy directly impacts their weight gain. In this study, we integrated the protection motivation theory and the information-motivation-behavioral skills model to develop an integrative theoretical model suitable for pregnancy weight management and reveal significant explainable factors of weight management behaviors during pregnancy. Based on a cross-sectional survey of 550 pregnant women from Jiangsu province, we came up with our findings. The results showed that several factors influenced pregnancy weight management behavior. According to the research, information, self-efficacy, response costs, and behavioral skills were significantly associated with weight management behaviors during pregnancy, while behavioral skills were also significant mediators of information, self-efficacy, and behavior. Furthermore, the information related to pregnancy weight management had the biggest impact on weight management behavior during pregnancy. The results of the model fit were acceptable and the integrative model could explain 30.6% of the variance of weight management behavior during pregnancy, which implies that the integrative theoretical model can effectively explain and predict weight management behaviors during pregnancy. Our study provides practical implications for the integrative model in improving pregnancy weight management behavior and offers a theoretical base for the weight management of pregnant women.

An undesirable psychological state may deteriorate individual's weight management-related behaviors. This study aims to see if ineffective weight control measures were linked to depressive symptoms during pregnancy. We conducted a cross-sectional questionnaire survey of 784 pregnant women and collected information on sociodemographic factors, maternal characteristics, depression, and weight management activities throughout pregnancy (exercise management, dietary management, self-monitoring regulation, and management objectives). About 17.5% of pregnant women exhibited depressive symptoms. The mean score on dietary management was upper-middle, exercise management and self-monitoring regulation were medium, and management objectives were lower-middle. Multivariable linear regression analysis revealed that pregnant women with depressive symptoms had lower levels of exercise management (β = −1.585, p = 0.005), dietary management (adjusted β = −0.984, p = 0.002), and management objectives (adjusted β = −0.726, p = 0.009). However, there was no significant relationship between depressive symptoms and pregnant women's self-monitoring regulating behavior (p &gt; 0.05). The findings indicated the inverse association between depressive symptoms and gestational weight management behaviors. These results offer important indications for pregnancy weight management professionals by highlighting the need for mental health interventions for pregnant women experiencing depressive symptoms.

Background. The rising incidence of hypertension and diabetes calls for a global response. Hypertension and diabetes will rise in Ghana as the population ages, urbanization increases, and people lead unhealthy lives. Our goal was to create a time series algorithm that effectively predicts future increases to help preventative medicine and health care intervention strategies by preparing health care practitioners to control health problems. Methods. Data on hypertension and diabetes from January 2016 to December 2020 were obtained from three health facilities. To detect patterns and predict data from a particular time series, three forecasting algorithms (SARIMAX (seasonal autoregressive integrated moving average with exogenous components), ARIMA (autoregressive integrated moving average), and LSTM (long short-term memory networks)) were implemented. We assessed the model’s ability to perform by calculating the root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE). Results. The RMSE, MSE, MAE, and MAPE for ARIMA (5, 2, 4), SARIMAX 1 , 1 ,   1 × 1 , 1 , 1 ,   7 , and LSTM was 28, 769.02, 22, and 7%, 67, 4473, 56, and 14%, and 36, 1307, 27, and 8.6%, respectively. We chose ARIMA (5, 2, 4) as a more suitable model due to its lower error metrics when compared to the others. Conclusion. All models had promising predictability and predicted a rise in the number of cases in the future, and this was essential for administrative and management planning. For appropriate and efficient strategic planning and control, the prognosis was useful enough than would have been possible without it.