Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits

Lirong Zhang,1 Shaocong Zhao,1 Qiong Lin,2 Minmin Song,3 Shouren Wu,2 Hua Zheng4 1Department of Physical Education, Xiamen University of Technology, Xiamen, Fujian, People’s Republic of China; 2Department of Physical Education, JiMei University, Xiamen, Fujian, People’s Republic...

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Autores principales:	Zhang L, Zhao S, Lin Q, Song M, Wu S, Zheng H
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	students public health anxiety depression risk factors algorithms Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/020eeb5b823f4908b944f2605a1f5953
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spelling	oai:doaj.org-article:020eeb5b823f4908b944f2605a1f59532021-12-02T18:01:33ZAlgorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits1178-2021https://doaj.org/article/020eeb5b823f4908b944f2605a1f59532021-06-01T00:00:00Zhttps://www.dovepress.com/algorithms-to-predict-anxiety-and-depression-among-university-students-peer-reviewed-fulltext-article-NDThttps://doaj.org/toc/1178-2021Lirong Zhang,1 Shaocong Zhao,1 Qiong Lin,2 Minmin Song,3 Shouren Wu,2 Hua Zheng4 1Department of Physical Education, Xiamen University of Technology, Xiamen, Fujian, People’s Republic of China; 2Department of Physical Education, JiMei University, Xiamen, Fujian, People’s Republic of China; 3Department of Physical Education, Fujian Agriculture and Forestry University, Fuzhou, Fujian, People’s Republic of China; 4College of Physical Education and Health Sciences, Chongqing Normal University, Chongqing, People’s Republic of ChinaCorrespondence: Shaocong ZhaoDepartment of Physical Education, Xiamen University of Technology, Xiamen, Fujian, People’s Republic of ChinaTel +86-13806063882Email 358945018@qq.comPurpose: This study aims to identify potential risk factors associated with anxiety or depression and propose algorithms to predict anxiety and depression especially among university students.Methods: We included and analyzed 881 university students from eight colleges in China in November 2020. Student’s basic information, lifestyles, sport habits, comorbidities, and mental health conditions were collected. Anxiety and depression were measured using the generalized anxiety disorder 7 (GAD-7) and the patient health questionnaire 9 (PHQ-9), respectively. A multiple linear regression analysis was used to assess the ability of 25 potential risk factors for predicting anxiety and depression, and significant risk factors were included in the algorithms.Results: Of all the included students, 44.27% lived with mild or above anxious symptoms and 50.62% had mild or above depressive symptoms. According to the multiple linear regression model, grade levels (P< 0.01), member of college sports dance team (P=0.05), sedentary time (P=0.02), exercise frequency (P< 0.01), only child status (P=0.05), addiction of drinking (P< 0.01), and prefer eating vegetable (P< 0.01) were significantly associated with anxiety; grade levels (P< 0.01), member of college sports dance team (P< 0.01), sedentary time (P< 0.01), exercise frequency (P< 0.01), academic study period during free time (P=0.03), only child status (P< 0.01), addiction of drinking (P< 0.01), prefer eating vegetables (P< 0.01), and main types of drinking water (P< 0.01) were significantly associated with depression. Based on these significant factors, two algorithms were successfully developed, and two risk groups were created according to the algorithms.Conclusion: The study proposed two algorithms to calculate anxiety and depression, respectively, which can be useful tools to identify students with different risk of anxiety or depression. Effective measures are warranted to improve student’s sport habits and healthy lifestyles in order to mitigate anxiety and depression, especially among students in the high risk group.Keywords: students, public health, anxiety, depression, risk factors, algorithmsZhang LZhao SLin QSong MWu SZheng HDove Medical Pressarticlestudentspublic healthanxietydepressionrisk factorsalgorithmsNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 17, Pp 2011-2025 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	students public health anxiety depression risk factors algorithms Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	students public health anxiety depression risk factors algorithms Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Zhang L Zhao S Lin Q Song M Wu S Zheng H Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits
description	Lirong Zhang,1 Shaocong Zhao,1 Qiong Lin,2 Minmin Song,3 Shouren Wu,2 Hua Zheng4 1Department of Physical Education, Xiamen University of Technology, Xiamen, Fujian, People’s Republic of China; 2Department of Physical Education, JiMei University, Xiamen, Fujian, People’s Republic of China; 3Department of Physical Education, Fujian Agriculture and Forestry University, Fuzhou, Fujian, People’s Republic of China; 4College of Physical Education and Health Sciences, Chongqing Normal University, Chongqing, People’s Republic of ChinaCorrespondence: Shaocong ZhaoDepartment of Physical Education, Xiamen University of Technology, Xiamen, Fujian, People’s Republic of ChinaTel +86-13806063882Email 358945018@qq.comPurpose: This study aims to identify potential risk factors associated with anxiety or depression and propose algorithms to predict anxiety and depression especially among university students.Methods: We included and analyzed 881 university students from eight colleges in China in November 2020. Student’s basic information, lifestyles, sport habits, comorbidities, and mental health conditions were collected. Anxiety and depression were measured using the generalized anxiety disorder 7 (GAD-7) and the patient health questionnaire 9 (PHQ-9), respectively. A multiple linear regression analysis was used to assess the ability of 25 potential risk factors for predicting anxiety and depression, and significant risk factors were included in the algorithms.Results: Of all the included students, 44.27% lived with mild or above anxious symptoms and 50.62% had mild or above depressive symptoms. According to the multiple linear regression model, grade levels (P< 0.01), member of college sports dance team (P=0.05), sedentary time (P=0.02), exercise frequency (P< 0.01), only child status (P=0.05), addiction of drinking (P< 0.01), and prefer eating vegetable (P< 0.01) were significantly associated with anxiety; grade levels (P< 0.01), member of college sports dance team (P< 0.01), sedentary time (P< 0.01), exercise frequency (P< 0.01), academic study period during free time (P=0.03), only child status (P< 0.01), addiction of drinking (P< 0.01), prefer eating vegetables (P< 0.01), and main types of drinking water (P< 0.01) were significantly associated with depression. Based on these significant factors, two algorithms were successfully developed, and two risk groups were created according to the algorithms.Conclusion: The study proposed two algorithms to calculate anxiety and depression, respectively, which can be useful tools to identify students with different risk of anxiety or depression. Effective measures are warranted to improve student’s sport habits and healthy lifestyles in order to mitigate anxiety and depression, especially among students in the high risk group.Keywords: students, public health, anxiety, depression, risk factors, algorithms
format	article
author	Zhang L Zhao S Lin Q Song M Wu S Zheng H
author_facet	Zhang L Zhao S Lin Q Song M Wu S Zheng H
author_sort	Zhang L
title	Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits
title_short	Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits
title_full	Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits
title_fullStr	Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits
title_full_unstemmed	Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits
title_sort	algorithms to predict anxiety and depression among university students in china after analyzing lifestyles and sport habits
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/020eeb5b823f4908b944f2605a1f5953
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Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits

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