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Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic

OBJECTIVE: 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 pandem...

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Autores principales: Wang, Qihe, Chu, Haiyun, Qu, Pengfeng, Fang, Haiqin, Liang, Dong, Liu, Sana, Li, Jinliang, Liu, Aidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908761/
https://www.ncbi.nlm.nih.gov/pubmed/36776607
http://dx.doi.org/10.3389/fnut.2023.1019827
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author Wang, Qihe
Chu, Haiyun
Qu, Pengfeng
Fang, Haiqin
Liang, Dong
Liu, Sana
Li, Jinliang
Liu, Aidong
author_facet Wang, Qihe
Chu, Haiyun
Qu, Pengfeng
Fang, Haiqin
Liang, Dong
Liu, Sana
Li, Jinliang
Liu, Aidong
author_sort Wang, Qihe
collection PubMed
description OBJECTIVE: 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. METHODS: 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. RESULTS: 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 R(2), 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. CONCLUSION: 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.
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spelling pubmed-99087612023-02-10 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 Front Nutr Nutrition OBJECTIVE: 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. METHODS: 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. RESULTS: 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 R(2), 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. CONCLUSION: 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. Frontiers Media S.A. 2023-01-26 /pmc/articles/PMC9908761/ /pubmed/36776607 http://dx.doi.org/10.3389/fnut.2023.1019827 Text en Copyright © 2023 Wang, Chu, Qu, Fang, Liang, Liu, Li and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Nutrition
Wang, Qihe
Chu, Haiyun
Qu, Pengfeng
Fang, Haiqin
Liang, Dong
Liu, Sana
Li, Jinliang
Liu, Aidong
Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic
title Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic
title_full Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic
title_fullStr Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic
title_full_unstemmed Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic
title_short Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic
title_sort machine-learning prediction of bmi change among doctors and nurses in north china during the covid-19 pandemic
topic Nutrition
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908761/
https://www.ncbi.nlm.nih.gov/pubmed/36776607
http://dx.doi.org/10.3389/fnut.2023.1019827
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