Predictors for Overweight/Obesity of Chinese Healthcare Workers

Abstract

Background Healthcare workers have played a crucial role in preventing and controlling the COVID-19 pandemic. However, the heightened risk of infection and intense work schedules have not only induced occupational burnout among them but also significantly impacted their mental health and lifestyles. A large number of foreign studies have shown that the COVID-19 pandemic has led to unreasonable diet, reduced exercise, irregular work and rest, and decreased sleep quality among HCWs, increasing the risk of overweight and obesity. Despite this, research on weight and lifestyle changes among Chinese healthcare workers during the pandemic is limited, and the key lifestyle factors contributing to these weight changes remain unclear. Objective To analyze the predictors of overweight and obesity in Chinese healthcare workers by constructing a Bayesian network model, and to provide a scientific basis for the prevention and control of overweight and obesity. Methods In August 2022, Chinese healthcare workers in 100 medical institutions from five provinces/autonomous regions/municipalities were randomly sampled, and the questionnaire (Cronbach's α=0.820, AVCR=63.55%) was prepared by the researchers to collect data. All respondents were required to scan QR code generated by the "Wenjuanxing" to answer the e-questionnaire and submit. The "bnlearn" package of R 4.3.0 software was used to construct a Bayesian network model, and Netica 6.09 software was used for Bayesian network risk prediction. Results The study surveyed a total of 20 261 healthcare workers, of whom females accounted for 67.57% (13 690/20 261) ; The average age was (40.2±9.2) years old; 73.28% (14 848/20 261) had a college or undergraduate education level. In 2019 and 2022, the overweight/obesity rates were 43.06% (8 726/20 261) and 45.71% (9 262/20 261), respectively. From 2019 to 2022, 12.64% (1 458/11 535) of survey respondents' BMI changed from underweight/normal to overweight/obese. The Bayesian network model included a total of 15 nodes, and the amount of consumption of vegetables and fruits, breakfast frequency, alcohol drinking, and appetite were the parent nodes of BMI changing from underweight/normal to overweight/obesity, and when there were "a reduction" in the consumption of vegetables and fruits, "no change" in frequency of eating breakfast, alcohol drinking consumption "no change", and "a great increase" in the appetite the risk of BMI changing from underweight/normal to overweight/obese was the highest (75.00%). And when there were "a great increase" in consumption of vegetables and fruits, "an increase" in the frequency of eating breakfast, "never or rarely" in alcohol drinking and "a reduction" in appetite, the risk of becoming overweight/obese was the lowest (2.04%) . Conclusion Consumption of vegetables and fruits, eating breakfast frequently, drinking alcohol and appetite are the direct predictors of overweight/obesity of Chinese healthcare workers. During the epidemic of major infectious diseases such as the COVID-19, on the premise of ensuring the normal operation of medical and health institutions, a reasonable rotation system is implemented to provide psychological support and lifestyle behavior intervention services, which is conducive to the prevention and control of obesity of healthcare workers.

Keywords

• overweight|obesity|healthcare worker|lifestyle|bayesian network