医学生潜在健康危险行为类别与抑郁症状

Yang Yajuan, XU Honglyu, W. Ying, Xie Yang, Zou Liwei, Li Tingting, Tao Shuman, wu xiaoyan
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Logistic regression\n was provided to further analyze the association between health risk behaviors and\n depressive symptoms for the groups.\n Results The health hazard behaviors of medical students could be divided into three separate\n classes: substance dependence group (8.4%), mobile phone dependence group (22.4%)\n and low-risk group (69.2%). The distribution of health risk behaviors of medical students\n with different gender, major, grade, only child, father’s educational level, monthly\n living expenses, academic achievement and number of friends were statistically significant\n (χ\n 2 = 99.37, 19.07, 12.05, 6.64, 14.28, 19.35, 20.61, 26.39, P<0.05). The results of Logistic regression analysis showed that after adjusting for\n gender, major, grade, only child, father’s educational level, monthly living expenses,\n academic achievement and number of friends, the mobile phone dependence group was\n positively correlated with depressive symptoms (β = 1.75, P<0.01).\n Conclusion Different ratent classes of health risk behaviors have different correlation with\n depressive symptoms in groups. lt is suggested that school health workers should make\n individualized intervention plan for different types of health hazard behavior of\n medical students in the future educational activities, carry out stratified intervention,\n alleviate the symptoms of depression and promote the mental health of medical students.\n 【摘要】 目的 识别医学生多种健康危害行为的潜在类别,并进一步分析不同潜类别与抑郁症状的关联。\n 方法 采用方便抽样的原则,于 2018 年 10 月在安徽医科大学整群抽取 2 014 名医学生开展横断面调査。使用自编手机问卷,调査内容 包括一般人口学资料、健康危害行为评价以及抑郁症状。采用潜在类别分析方法分析吸烟、饮酒、自杀、睡眠障碍、手机依\n 赖、缺乏运动等 6 种健康危害行为潜在类别,采用 Logistic 回归方法进一步分析不同类别与抑郁症状的关联。\n 结果 医学生健康危害行为分为物质依赖组 (8.4%)、手机依赖组 (22.4%) 和低危风险组 (69.2%) 3 个潜在类别。不同性别、专业、年 级、是否为独生子女、父亲文化程度、每月生活费、学习成绩及朋友个数之间医学生健康危害行为分布差异均有统计学意义\n (χ\n 2 值分别为 99.37, 19.07, 12.05, 6.64, 14.28, 19.35, 20.61, 26.39, P 值均<0.05)。Logistic 回归分析结果显示,调整性别、专 业、年级、独生子女及父亲文化程度、每月生活费、学习成绩以及朋友个数后,手机依赖组与抑郁症状呈正相关\n (β=1.75, P<0.01)。\n 结论 医学生健康危害行为呈现类别分布,各潜类别健康危害行为与抑郁症状关联不同。提示学校卫生工作者在 今后的教育活动中应针对医学生不同类别的健康危害行为制定个体化干预方案,进行分层干预,缓解抑郁症状,促进学生\n 心理健康。","PeriodicalId":106801,"journal":{"name":"Chinese Journal of School Health","volume":"326 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Latent classes of health risk behaviors in medical students and depressive symptoms\",\"authors\":\"Yang Yajuan, XU Honglyu, W. Ying, Xie Yang, Zou Liwei, Li Tingting, Tao Shuman, wu xiaoyan\",\"doi\":\"10.16835/J.CNKI.1000-9817.2021.04.024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective To identify the latent classes of various health risk behaviors among medical students\\n and further analyze the correlation between different classes and depression.\\n Methods Using the method of convenient sampling, 2 014 medical students were selected from\\n Anhui Medical University in October 2018. Self-designed online questionnaire were\\n used to collect demographic data, health risk behavior (including smoking, drinking,\\n suicide, sleep disorders, mobile phone dependence and lack of exercise) and depressive\\n symptoms. Latent Class Analysis was used to cluster individuals. Logistic regression\\n was provided to further analyze the association between health risk behaviors and\\n depressive symptoms for the groups.\\n Results The health hazard behaviors of medical students could be divided into three separate\\n classes: substance dependence group (8.4%), mobile phone dependence group (22.4%)\\n and low-risk group (69.2%). The distribution of health risk behaviors of medical students\\n with different gender, major, grade, only child, father’s educational level, monthly\\n living expenses, academic achievement and number of friends were statistically significant\\n (χ\\n 2 = 99.37, 19.07, 12.05, 6.64, 14.28, 19.35, 20.61, 26.39, P<0.05). The results of Logistic regression analysis showed that after adjusting for\\n gender, major, grade, only child, father’s educational level, monthly living expenses,\\n academic achievement and number of friends, the mobile phone dependence group was\\n positively correlated with depressive symptoms (β = 1.75, P<0.01).\\n Conclusion Different ratent classes of health risk behaviors have different correlation with\\n depressive symptoms in groups. lt is suggested that school health workers should make\\n individualized intervention plan for different types of health hazard behavior of\\n medical students in the future educational activities, carry out stratified intervention,\\n alleviate the symptoms of depression and promote the mental health of medical students.\\n 【摘要】 目的 识别医学生多种健康危害行为的潜在类别,并进一步分析不同潜类别与抑郁症状的关联。\\n 方法 采用方便抽样的原则,于 2018 年 10 月在安徽医科大学整群抽取 2 014 名医学生开展横断面调査。使用自编手机问卷,调査内容 包括一般人口学资料、健康危害行为评价以及抑郁症状。采用潜在类别分析方法分析吸烟、饮酒、自杀、睡眠障碍、手机依\\n 赖、缺乏运动等 6 种健康危害行为潜在类别,采用 Logistic 回归方法进一步分析不同类别与抑郁症状的关联。\\n 结果 医学生健康危害行为分为物质依赖组 (8.4%)、手机依赖组 (22.4%) 和低危风险组 (69.2%) 3 个潜在类别。不同性别、专业、年 级、是否为独生子女、父亲文化程度、每月生活费、学习成绩及朋友个数之间医学生健康危害行为分布差异均有统计学意义\\n (χ\\n 2 值分别为 99.37, 19.07, 12.05, 6.64, 14.28, 19.35, 20.61, 26.39, P 值均<0.05)。Logistic 回归分析结果显示,调整性别、专 业、年级、独生子女及父亲文化程度、每月生活费、学习成绩以及朋友个数后,手机依赖组与抑郁症状呈正相关\\n (β=1.75, P<0.01)。\\n 结论 医学生健康危害行为呈现类别分布,各潜类别健康危害行为与抑郁症状关联不同。提示学校卫生工作者在 今后的教育活动中应针对医学生不同类别的健康危害行为制定个体化干预方案,进行分层干预,缓解抑郁症状,促进学生\\n 心理健康。\",\"PeriodicalId\":106801,\"journal\":{\"name\":\"Chinese Journal of School Health\",\"volume\":\"326 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chinese Journal of School Health\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.16835/J.CNKI.1000-9817.2021.04.024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of School Health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.16835/J.CNKI.1000-9817.2021.04.024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

目的确定医学生各种健康危险行为的潜在类别,并进一步分析不同类别与抑郁症的相关性。方法采用方便抽样法,于2018年10月在安徽医科大学抽取2 014名医学生进行调查。使用自行设计的在线问卷收集人口统计数据、健康风险行为(包括吸烟、饮酒、自杀、睡眠障碍、手机依赖和缺乏运动)和抑郁症状。使用潜类分析对个体进行聚类。采用Logistic回归进一步分析各组健康危险行为与抑郁症状之间的关系。结果医学生健康危害行为可分为物质依赖组(8.4%)、手机依赖组(22.4%)和低危组(69.2%)。不同性别、专业、年级、独生子女、父亲受教育程度、月生活费、学业成绩、朋友人数的医学生健康风险行为分布均有统计学意义(χ 2 = 99.37、19.07、12.05、6.64、14.28、19.35、20.61、26.39,P<0.05)。Logistic回归分析结果显示,在调整性别、专业、年级、独生子女、父亲受教育程度、月生活费、学业成绩、朋友数量后,手机依赖组与抑郁症状呈正相关(β = 1.75, P<0.01)。结论不同类型的健康危险行为与抑郁症状的相关性存在差异。建议学校卫生工作者在今后的教育活动中针对医学生不同类型的健康危害行为制定个性化干预方案,分层干预,缓解医学生抑郁症状,促进医学生心理健康。【摘要】 目的 识别医学生多种健康危害行为的潜在类别,并进一步分析不同潜类别与抑郁症状的关联。 方法 采用方便抽样的原则,于 2018 年 10 月在安徽医科大学整群抽取 2 014 名医学生开展横断面调査。使用自编手机问卷,调査内容 包括一般人口学资料、健康危害行为评价以及抑郁症状。采用潜在类别分析方法分析吸烟,饮的酒,自杀,睡眠障碍,手机依赖,缺乏运动等6种健康危害行为潜在类别,采用物流回归方法进一步分析不同类别与抑郁症状的关联。结果医学生健康危害行为分为物质依赖组(8.4%)、手机依赖组(22.4%)和低危风险组(69.2%)3个潜在类别。不同性别,专业,年级,是否为独生子女,父亲文化程度,每月生活费,学习成绩及朋友个数之间医学生健康危害行为分布差异均有统计学意义(χ2值分别为99.37,19.07,12.05,6.64,14.28,19.35,20.61,26.39,P值均< 0.05)。物流回归分析结果显示,调整性别,专业,年级,独生子女及父亲文化程度,每月生活费,学习成绩以及朋友个数后,手机依赖组与抑郁症状呈正相关(β= 1.75,P < 0.01)。结论 医学生健康危害行为呈现类别分布,各潜类别健康危害行为与抑郁症状关联不同。提示学校卫生工作者在 今后的教育活动中应针对医学生不同类别的健康危害行为制定个体化干预方案,进行分层干预,缓解抑郁症状,促进学生 心理健康。
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Latent classes of health risk behaviors in medical students and depressive symptoms
Objective To identify the latent classes of various health risk behaviors among medical students and further analyze the correlation between different classes and depression. Methods Using the method of convenient sampling, 2 014 medical students were selected from Anhui Medical University in October 2018. Self-designed online questionnaire were used to collect demographic data, health risk behavior (including smoking, drinking, suicide, sleep disorders, mobile phone dependence and lack of exercise) and depressive symptoms. Latent Class Analysis was used to cluster individuals. Logistic regression was provided to further analyze the association between health risk behaviors and depressive symptoms for the groups. Results The health hazard behaviors of medical students could be divided into three separate classes: substance dependence group (8.4%), mobile phone dependence group (22.4%) and low-risk group (69.2%). The distribution of health risk behaviors of medical students with different gender, major, grade, only child, father’s educational level, monthly living expenses, academic achievement and number of friends were statistically significant (χ 2 = 99.37, 19.07, 12.05, 6.64, 14.28, 19.35, 20.61, 26.39, P<0.05). The results of Logistic regression analysis showed that after adjusting for gender, major, grade, only child, father’s educational level, monthly living expenses, academic achievement and number of friends, the mobile phone dependence group was positively correlated with depressive symptoms (β = 1.75, P<0.01). Conclusion Different ratent classes of health risk behaviors have different correlation with depressive symptoms in groups. lt is suggested that school health workers should make individualized intervention plan for different types of health hazard behavior of medical students in the future educational activities, carry out stratified intervention, alleviate the symptoms of depression and promote the mental health of medical students. 【摘要】 目的 识别医学生多种健康危害行为的潜在类别,并进一步分析不同潜类别与抑郁症状的关联。 方法 采用方便抽样的原则,于 2018 年 10 月在安徽医科大学整群抽取 2 014 名医学生开展横断面调査。使用自编手机问卷,调査内容 包括一般人口学资料、健康危害行为评价以及抑郁症状。采用潜在类别分析方法分析吸烟、饮酒、自杀、睡眠障碍、手机依 赖、缺乏运动等 6 种健康危害行为潜在类别,采用 Logistic 回归方法进一步分析不同类别与抑郁症状的关联。 结果 医学生健康危害行为分为物质依赖组 (8.4%)、手机依赖组 (22.4%) 和低危风险组 (69.2%) 3 个潜在类别。不同性别、专业、年 级、是否为独生子女、父亲文化程度、每月生活费、学习成绩及朋友个数之间医学生健康危害行为分布差异均有统计学意义 (χ 2 值分别为 99.37, 19.07, 12.05, 6.64, 14.28, 19.35, 20.61, 26.39, P 值均<0.05)。Logistic 回归分析结果显示,调整性别、专 业、年级、独生子女及父亲文化程度、每月生活费、学习成绩以及朋友个数后,手机依赖组与抑郁症状呈正相关 (β=1.75, P<0.01)。 结论 医学生健康危害行为呈现类别分布,各潜类别健康危害行为与抑郁症状关联不同。提示学校卫生工作者在 今后的教育活动中应针对医学生不同类别的健康危害行为制定个体化干预方案,进行分层干预,缓解抑郁症状,促进学生 心理健康。
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