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Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits
PURPOSE: 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. St...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232847/ https://www.ncbi.nlm.nih.gov/pubmed/34188472 http://dx.doi.org/10.2147/NDT.S315148 |
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author | Zhang, Lirong Zhao, Shaocong Lin, Qiong Song, Minmin Wu, Shouren Zheng, Hua |
author_facet | Zhang, Lirong Zhao, Shaocong Lin, Qiong Song, Minmin Wu, Shouren Zheng, Hua |
author_sort | Zhang, Lirong |
collection | PubMed |
description | PURPOSE: 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. |
format | Online Article Text |
id | pubmed-8232847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-82328472021-06-28 Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits Zhang, Lirong Zhao, Shaocong Lin, Qiong Song, Minmin Wu, Shouren Zheng, Hua Neuropsychiatr Dis Treat Original Research PURPOSE: 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. Dove 2021-06-21 /pmc/articles/PMC8232847/ /pubmed/34188472 http://dx.doi.org/10.2147/NDT.S315148 Text en © 2021 Zhang et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Zhang, Lirong Zhao, Shaocong Lin, Qiong Song, Minmin Wu, Shouren Zheng, Hua Algorithms to Predict Anxiety and Depression Among University Students in China After Analyzing Lifestyles and Sport Habits |
title | 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_short | 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 |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232847/ https://www.ncbi.nlm.nih.gov/pubmed/34188472 http://dx.doi.org/10.2147/NDT.S315148 |
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