Wang Qihe, Chu Haiyun, Qu Pengfeng, Fang Haiqin, Liang Dong, Liu Sana, Li Jinliang, Liu Aidong
Department of Nutrition Division I, China National Center for Food Safety Risk Assessment, Beijing, China.
Public Health Institute of Harbin Medical University, Harbin, China.
Front Nutr. 2023 Jan 26;10:1019827. doi: 10.3389/fnut.2023.1019827. eCollection 2023.
The COVID-19 pandemic has become a major public health concern over the past 3 years, leading to adverse effects on front-line healthcare workers. This study aimed to develop a Body Mass Index (BMI) change prediction model among doctors and nurses in North China during the COVID-19 pandemic, and further identified the predicting effects of lifestyles, sleep quality, work-related conditions, and personality traits on BMI change.
The present study was a cross-sectional study conducted in North China, during May-August 2022. A total of 5,400 doctors and nurses were randomly recruited from 39 COVID-19 designated hospitals and 5,271 participants provided valid responses. Participants' data related to social-demographics, dietary behavior, lifestyle, sleep, personality, and work-related conflicts were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a BMI change prediction model among doctors and nurses during the COVID-19 pandemic.
Of participants, only 2,216 (42.0%) individuals kept a stable BMI. Results showed that personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions had effects on the BMI change among doctors and nurses. The prediction model for BMI change was developed with a 33-26-20-1 network framework. The DNN model achieved high prediction efficacy, and values of , MAE, MSE, and RMSE for the model were 0.940, 0.027, 0.002, and 0.038, respectively. Among doctors and nurses, the top five predictors in the BMI change prediction model were unbalanced nutritional diet, poor sleep quality, work-family conflict, lack of exercise, and soft drinks consumption.
During the COVID-19 pandemic, BMI change was highly prevalent among doctors and nurses in North China. Machine learning models can provide an automated identification mechanism for the prediction of BMI change. Personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions have contributed to the BMI change prediction. Integrated treatment measures should be taken in the management of weight and BMI by policymakers, hospital administrators, and healthcare workers.
在过去3年里，新冠疫情已成为一个主要的公共卫生问题，给一线医护人员带来了不利影响。本研究旨在建立华北地区医护人员在新冠疫情期间体重指数（BMI）变化的预测模型，并进一步确定生活方式、睡眠质量、工作相关状况和人格特质对BMI变化的预测作用。
本研究为2022年5月至8月在华北地区开展的一项横断面研究。从39家新冠定点医院随机招募了5400名医护人员，5271名参与者提供了有效回复。通过问卷收集了参与者的社会人口统计学、饮食行为、生活方式、睡眠、人格以及工作相关冲突等方面的数据。应用深度神经网络（DNN）建立新冠疫情期间医护人员BMI变化的预测模型。
在参与者中，仅有2216人（42.0%）的BMI保持稳定。结果表明，人格特质、饮食行为、生活方式、睡眠质量、职业倦怠和工作相关状况对医护人员的BMI变化有影响。采用33-26-20-1网络框架建立了BMI变化的预测模型。DNN模型具有较高的预测效能，该模型的 、平均绝对误差（MAE）、均方误差（MSE）和均方根误差（RMSE）值分别为0.940、0.027、0.002和0.038。在医护人员中，BMI变化预测模型中排名前五的预测因素为营养饮食不均衡、睡眠质量差、工作-家庭冲突、缺乏运动和饮用软饮料。
在新冠疫情期间，华北地区医护人员中BMI变化非常普遍。机器学习模型可为BMI变化的预测提供一种自动识别机制。人格特质、饮食行为、生活方式、睡眠质量、职业倦怠和工作相关状况有助于BMI变化的预测。政策制定者、医院管理人员和医护人员在体重和BMI管理中应采取综合治疗措施。
