Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic - Wang, Qihe; Chu, Haiyun; Qu, Pengfeng; Fang, Haiqin; Liang, Dong; Liu, Sana; Li, Jinliang; Liu, Aidong (Frontiers in Nutrition, 2023)
Objective: The COVID-19 pandemic has become a major public health concern over the past three 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. Methods: The present study was a cross-sectional study conducted in North China, during May-August 2022. A total of 5400 doctors and nurses were randomly recruited from thirty-nine COVID-19 designated hospitals and 5271 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. Results: Of participants, only 2216 (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 R2, 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. Conclusions: 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.