目的探讨群组孕期保健模式结合数字化营养管理对妊娠期糖尿病(GDM)孕妇体重管理及血糖控制的影响。方法选取2020年1月至2022年12月收治的110例GDM孕妇为研究对象,随机将其分为对照组和观察组,各55例。对照组实施常规护理,观察组实施群...目的探讨群组孕期保健模式结合数字化营养管理对妊娠期糖尿病(GDM)孕妇体重管理及血糖控制的影响。方法选取2020年1月至2022年12月收治的110例GDM孕妇为研究对象,随机将其分为对照组和观察组,各55例。对照组实施常规护理,观察组实施群组孕期保健模式结合数字化营养管理。比较两组的干预效果。结果干预后,观察组的身体质量指数(BMI)低于对照组,成年人健康自我管理能力测评量表(AHSMSRS)评分高于对照组(P<0.05)。干预后,观察组的餐后2 h血糖(2 h PG)、甘油三酯(TG)水平及糖化血红蛋白(HbA1c)均低于对照组(P<0.05)。观察组的妊娠和围生儿不良结局总发生率均低于对照组(P<0.05)。结论群组孕期保健模式结合数字化营养管理用于GDM孕妇中,不仅可提高孕妇的自我管理能力及体重管理质量,还可增强血糖控制效果,降低妊娠和围生儿不良结局发生率。展开更多
Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements;time,capacity,speed,and culture.Crosscultural variations are increasing the complexity level because each mass and...Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements;time,capacity,speed,and culture.Crosscultural variations are increasing the complexity level because each mass and event have different characteristics and challenges.However,no prior study has employed the six Hofstede Cultural Dimensions(HCD)for predicting crowd behaviors.This study aims to develop the Cultural Crowd-Artificial Neural Network(CC-ANN)learning model that considers crowd’s HCD to predict their physical(distance and speed)and social(collectivity and cohesion)characteristics.The model was developed towards a cognitive intelligent decision support tool where the predicted characteristics affect the estimated regulation plan’s time and capacity.We designed the experiments as four groups to analyze the proposed model’s outcomes and extract the interrelations between the HCD of crowd’s grouped individuals and their physical and social characteristics.Furthermore,the extracted interrelations were verified with the dataset’s statistical correlation analysis with a P-value<0.05.Results demonstrate that the predicted crowd’s characteristics were positively/negatively affected by their considered cultural features.Similarly,analyzing outcomes identified the most influential HCD for predicting crowd behavior.The results also show that the CC-ANN model improves the prediction and learning performance for the crowd behavior because the achieved accepted level of accuracy does not exceed 10 to 20 epochs in most cases.Moreover,the performance improved by 90%,93%respectively in some cases.Finally,all prediction best cases were related to one or more cultural features with a low error of 0.048,0.117,0.010,and 0.014 mean squared error,indicating a novel cultural learning model.展开更多
文摘目的探讨群组孕期保健模式结合数字化营养管理对妊娠期糖尿病(GDM)孕妇体重管理及血糖控制的影响。方法选取2020年1月至2022年12月收治的110例GDM孕妇为研究对象,随机将其分为对照组和观察组,各55例。对照组实施常规护理,观察组实施群组孕期保健模式结合数字化营养管理。比较两组的干预效果。结果干预后,观察组的身体质量指数(BMI)低于对照组,成年人健康自我管理能力测评量表(AHSMSRS)评分高于对照组(P<0.05)。干预后,观察组的餐后2 h血糖(2 h PG)、甘油三酯(TG)水平及糖化血红蛋白(HbA1c)均低于对照组(P<0.05)。观察组的妊娠和围生儿不良结局总发生率均低于对照组(P<0.05)。结论群组孕期保健模式结合数字化营养管理用于GDM孕妇中,不仅可提高孕妇的自我管理能力及体重管理质量,还可增强血糖控制效果,降低妊娠和围生儿不良结局发生率。
基金This project is funded by the Deanship of Scientific research(DSR),King Abdulaziz University,Jeddah,under Grant No.(DF-593-165-1441).Therefore,the authors gratefully acknowledge the technical and financial support of the DSR.
文摘Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements;time,capacity,speed,and culture.Crosscultural variations are increasing the complexity level because each mass and event have different characteristics and challenges.However,no prior study has employed the six Hofstede Cultural Dimensions(HCD)for predicting crowd behaviors.This study aims to develop the Cultural Crowd-Artificial Neural Network(CC-ANN)learning model that considers crowd’s HCD to predict their physical(distance and speed)and social(collectivity and cohesion)characteristics.The model was developed towards a cognitive intelligent decision support tool where the predicted characteristics affect the estimated regulation plan’s time and capacity.We designed the experiments as four groups to analyze the proposed model’s outcomes and extract the interrelations between the HCD of crowd’s grouped individuals and their physical and social characteristics.Furthermore,the extracted interrelations were verified with the dataset’s statistical correlation analysis with a P-value<0.05.Results demonstrate that the predicted crowd’s characteristics were positively/negatively affected by their considered cultural features.Similarly,analyzing outcomes identified the most influential HCD for predicting crowd behavior.The results also show that the CC-ANN model improves the prediction and learning performance for the crowd behavior because the achieved accepted level of accuracy does not exceed 10 to 20 epochs in most cases.Moreover,the performance improved by 90%,93%respectively in some cases.Finally,all prediction best cases were related to one or more cultural features with a low error of 0.048,0.117,0.010,and 0.014 mean squared error,indicating a novel cultural learning model.
