Article(id=1325535706818564512, tenantId=1146029695717560320, journalId=1256213402557890569, issueId=1325535161890398372, articleNumber=null, orderNo=null, doi=10.16835/j.cnki.1000-9817.2025365, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755792000000, receivedDateStr=2025-08-22, revisedDate=1760371200000, revisedDateStr=2025-10-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1793961939767, onlineDateStr=2026-11-06, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793961939767, onlineIssueDateStr=2026-11-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793961939767, creator=13701087609, updateTime=1793961939767, updator=13701087609, issue=Issue{id=1325535161890398372, tenantId=1146029695717560320, journalId=1256213402557890569, year='2025', volume='46', issue='12', pageStart='1673', pageEnd='1824', issueExtLink='null', onlineDate='null', pubDate='1766592000000', pubDateStr='2025-12-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793961809846, creator='13701087609', updateTime=1793961809846, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1716, endPage=1720, ext={EN=ArticleExt(id=1325535713160352161, articleId=1325535706818564512, tenantId=1146029695717560320, journalId=1256213402557890569, language=EN, title=Clustering and network analysis of the association between food intake and physical fitness in junior and senior high school students, columnId=1259565859148804482, journalTitle=Chinese Journal of School Health, columnName=Health Education and Promotion, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the network structure of dietary intake and physical fitness subtypes among junior and senior high school students, so as to provide precise and efficient intervention guidance for improving abnormal physical health issues in adolescents.

Methods

Based on the 2020-2021 Database of Youth Health (DYH), a total of 9 730 junior and senior high school students in Shandong Province were included for analysis. Dietary intake was assessed using a modified Chinese Dietary Quality Questionnaire, and physical fitness was evaluated according to the "2014 National Student Physical Fitness Standard". The K-means clustering algorithm was employed to identify potential subtypes of physical fitness in junior and high school students. Network analysis was used to construct a network linking dietary intake and physical fitness symptoms and to identify clusters of dietary behaviors and physical fitness symptoms among adolescents of different genders.

Results

The physical fitness among junior and senior high school students of different genders were categorized into two subtypes: the baseline steady type (3 427 male students, 4 039 female students) and the morphological deviation type (1 294 male students, 970 female students). Statistically significant differences were observed in network strength and network connectivity strength among different genders and physical fitness subtypes (all P < 0.05). Connections between food intake and physical health symptom clusters across different genders and physical health types among junior and senior high school students were primarily achieved through instant noodle intake and physical fitness (males of morphological deviation type, weight=0.06), fruit intake and physical fitness (males of baseline steady type, weight=-0.07), potato intake and vital capacity (females of morphological deviation type, weight=0.09), and processed meat intake and vital capacity (females of baseline steady type, weight=0.05).

Conclusions

Dietary intake serves as a significant modifiable risk factor for the physical fitness of junior and high school students. Interventions should focus on promoting healthy eating habits.

, authors=Yisong YAO1, Xiangli ZHONG2, Xianxian DENG2, Baoyi LIAO2, Dajun YANG2, Linwei LI2, Gongzhuoran LIANG2, Wenwen YIN*, 3, authorsList=Yisong YAO, Xiangli ZHONG, Xianxian DENG, Baoyi LIAO, Dajun YANG, Linwei LI, Gongzhuoran LIANG, Wenwen YIN, authorCompany=null, correspAuthors=Wenwen YIN, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2025 Chinese Journal of School Health. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1325535731502043560, articleId=1325535706818564512, tenantId=1146029695717560320, journalId=1256213402557890569, language=CN, title=中学生饮食摄入与体质健康关联的聚类与网络分析, columnId=1259565859903779205, journalTitle=中国学校卫生, columnName=健康教育与健康促进, runingTitle=null, highlight=null, articleAbstract=
目的

探索中学生饮食摄入与体质健康亚型的网络结构,为青少年改善异常体质健康问题提供精准化、高效的干预指导。

方法

基于2020—2021年“青少年健康主题”数据库(DYH),共纳入山东省9 730名初高中学生数据进行分析。采用改良的中国饮食质量问卷评估中学生饮食摄入情况,采用《2014年国家学生体质健康标准》评估中学生体质健康情况。使用K均值聚类算法识别中学生体质健康潜在亚型,使用网络分析构建中学生饮食摄入与体质健康症状网络、识别不同性别青少年的饮食行为与体质健康症状群。

结果

中学生体质健康均可分为基准稳态型(男生3 427名,女生4 039名)与形态偏离型(男生1 294名,女生970名),不同性别、体质健康亚型的网络强度与网络连接强度差异均有统计学意义(P值均 < 0.05)。不同性别、体质中学生的饮食摄入-体质健康症状群间的连接主要通过方便面摄入与身体素质(形态偏离型男生,权重为0.06),水果摄入与身体素质(稳态基准型男生,权重为-0.07),土豆摄入与肺活量(形态偏离型女生,权重为0.09)以及加工肉类摄入与肺活量(稳态基准型女生,权重为0.05)实现。

结论

饮食摄入是中学生体质健康的重要可调控风险因素,应将健康饮食摄入作为重要的干预切入点。

, authors=姚依松1, 钟香利2, 邓仙仙2, 廖宝仪2, 杨大军2, 李林蔚2, 梁龚卓然2, 尹雯雯*, 3, authorsList=姚依松, 钟香利, 邓仙仙, 廖宝仪, 杨大军, 李林蔚, 梁龚卓然, 尹雯雯, authorCompany=null, correspAuthors=尹雯雯, authorNote=

姚依松与钟香利为共同第一作者

姚依松(2000-),男,山东烟台人,在读硕士,主要研究方向为健康行为与健康促进

钟香利(2005-),女,四川成都人,在读本科,主要研究方向为饮食失调与饮食健康

, correspAuthorsNote=
尹雯雯,E-mail:
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注:体质健康中,B为BMI,T为身体素质,F为肺活量;饮食情况中,a~q分别为绿叶蔬菜、红/橙色蔬菜、土豆、其他蔬菜、薯类、水果、大豆制品、新鲜肉类、海鲜、蛋类、奶类、加工肉类、方便面、西式快餐、含糖饮料、零食、油炸食品;边线越粗代表相关性越强。

, figureFileSmall=JpcfRLbVvKLgv/vkiUIpMw==, figureFileBig=S+PlXegXBOeyuHpJvuGGAw==, tableContent=null), ArticleFig(id=1325535782710301161, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325535706818564512, language=EN, label=Fig.2, caption=Centrality indicators of symptom network of physical health and food intake among middle school students, figureFileSmall=ltELNe0zERSfLcijEagpzw==, figureFileBig=jqrVPeKTpHigbgZuEth15Q==, tableContent=null), ArticleFig(id=1325535783175868906, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325535706818564512, language=CN, label=图2, caption=中学生体质健康与饮食摄入网络的中心性指标

注:体质健康中,B为BMI,T为身体素质,F为肺活量;饮食情况中,a~q分别为绿叶蔬菜、红/橙色蔬菜、土豆、其他蔬菜、薯类、水果、大豆制品、新鲜肉类、海鲜、蛋类、奶类、加工肉类、方便面、西式快餐、含糖饮料、零食、油炸食品。

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中学生饮食摄入与体质健康关联的聚类与网络分析
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姚依松 1 , 钟香利 2 , 邓仙仙 2 , 廖宝仪 2 , 杨大军 2 , 李林蔚 2 , 梁龚卓然 2 , 尹雯雯 *, 3
中国学校卫生 | 健康教育与健康促进 2025,46(12): 1716-1720
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中国学校卫生 |健康教育与健康促进 2025 , 46 (12) : 1716 -1720
中学生饮食摄入与体质健康关联的聚类与网络分析
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姚依松1, 钟香利2, 邓仙仙2, 廖宝仪2, 杨大军2, 李林蔚2, 梁龚卓然2, 尹雯雯*, 3
作者信息
  • 1青岛大学附属烟台毓璜顶医院耳鼻咽喉头颈外科,山东 烟台 264000
  • 2川北医学院四川省基层卫生事业发展研究中心
  • 3湖北文理学院理工学院
通讯作者:
尹雯雯,E-mail:
作者简介:

姚依松与钟香利为共同第一作者

姚依松(2000-),男,山东烟台人,在读硕士,主要研究方向为健康行为与健康促进

钟香利(2005-),女,四川成都人,在读本科,主要研究方向为饮食失调与饮食健康

Clustering and network analysis of the association between food intake and physical fitness in junior and senior high school students
Yisong YAO1, Xiangli ZHONG2, Xianxian DENG2, Baoyi LIAO2, Dajun YANG2, Linwei LI2, Gongzhuoran LIANG2, Wenwen YIN*, 3
Affiliations
  • 1Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, Shandong Province, China
出版时间: 2025-12-25 doi: 10.16835/j.cnki.1000-9817.2025365
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目的

探索中学生饮食摄入与体质健康亚型的网络结构,为青少年改善异常体质健康问题提供精准化、高效的干预指导。

方法

基于2020—2021年“青少年健康主题”数据库(DYH),共纳入山东省9 730名初高中学生数据进行分析。采用改良的中国饮食质量问卷评估中学生饮食摄入情况,采用《2014年国家学生体质健康标准》评估中学生体质健康情况。使用K均值聚类算法识别中学生体质健康潜在亚型,使用网络分析构建中学生饮食摄入与体质健康症状网络、识别不同性别青少年的饮食行为与体质健康症状群。

结果

中学生体质健康均可分为基准稳态型(男生3 427名,女生4 039名)与形态偏离型(男生1 294名,女生970名),不同性别、体质健康亚型的网络强度与网络连接强度差异均有统计学意义(P值均 < 0.05)。不同性别、体质中学生的饮食摄入-体质健康症状群间的连接主要通过方便面摄入与身体素质(形态偏离型男生,权重为0.06),水果摄入与身体素质(稳态基准型男生,权重为-0.07),土豆摄入与肺活量(形态偏离型女生,权重为0.09)以及加工肉类摄入与肺活量(稳态基准型女生,权重为0.05)实现。

结论

饮食摄入是中学生体质健康的重要可调控风险因素,应将健康饮食摄入作为重要的干预切入点。

饮食习惯  /  摄食行为  /  体质  /  网络分析  /  学生
Objective

To explore the network structure of dietary intake and physical fitness subtypes among junior and senior high school students, so as to provide precise and efficient intervention guidance for improving abnormal physical health issues in adolescents.

Methods

Based on the 2020-2021 Database of Youth Health (DYH), a total of 9 730 junior and senior high school students in Shandong Province were included for analysis. Dietary intake was assessed using a modified Chinese Dietary Quality Questionnaire, and physical fitness was evaluated according to the "2014 National Student Physical Fitness Standard". The K-means clustering algorithm was employed to identify potential subtypes of physical fitness in junior and high school students. Network analysis was used to construct a network linking dietary intake and physical fitness symptoms and to identify clusters of dietary behaviors and physical fitness symptoms among adolescents of different genders.

Results

The physical fitness among junior and senior high school students of different genders were categorized into two subtypes: the baseline steady type (3 427 male students, 4 039 female students) and the morphological deviation type (1 294 male students, 970 female students). Statistically significant differences were observed in network strength and network connectivity strength among different genders and physical fitness subtypes (all P < 0.05). Connections between food intake and physical health symptom clusters across different genders and physical health types among junior and senior high school students were primarily achieved through instant noodle intake and physical fitness (males of morphological deviation type, weight=0.06), fruit intake and physical fitness (males of baseline steady type, weight=-0.07), potato intake and vital capacity (females of morphological deviation type, weight=0.09), and processed meat intake and vital capacity (females of baseline steady type, weight=0.05).

Conclusions

Dietary intake serves as a significant modifiable risk factor for the physical fitness of junior and high school students. Interventions should focus on promoting healthy eating habits.

Food habits  /  Feeding behavior  /  Body constitution  /  Network analysis  /  Students
姚依松, 钟香利, 邓仙仙, 廖宝仪, 杨大军, 李林蔚, 梁龚卓然, 尹雯雯. 中学生饮食摄入与体质健康关联的聚类与网络分析. 中国学校卫生, 2025 , 46 (12) : 1716 -1720 . DOI: 10.16835/j.cnki.1000-9817.2025365
Yisong YAO, Xiangli ZHONG, Xianxian DENG, Baoyi LIAO, Dajun YANG, Linwei LI, Gongzhuoran LIANG, Wenwen YIN. Clustering and network analysis of the association between food intake and physical fitness in junior and senior high school students[J]. Chinese Journal of School Health, 2025 , 46 (12) : 1716 -1720 . DOI: 10.16835/j.cnki.1000-9817.2025365
《健康中国行动(2019—2030年)》将2030年学生体质健康优良率目标设定为高于60%,但截至2019年学生体质健康优良率仅为23.8%[1]。研究表明,饮食对未来体质健康影响至关重要,饮食结构合理性越强,青少年的体质健康等级越理想[2-4]。也有一些研究对特定饮食摄入与青少年体质健康的关系展开分析,例如增加蛋白质、碳水化合物的摄入能够增加青少年的肌肉密度,钙、钾等微量元素的摄入则与青少年下肢力量有关[5]。然而,目前尚无研究系统评估各类特定饮食摄入与青少年体质健康的关系[6-7]。本研究使用K均值聚类算法归纳山东省青少年体质健康的不同亚型,并使用网络分析全面评估饮食摄入与体质健康亚型的关系,为青少年生命健康促进提供理论依据。
数据来源于“国家人口健康科学数据中心数据仓储”(https://www.ncmi.cn)中的青少年健康主题数据库(Database of Youth Health,DYH)。数据库采用概率比例规模抽样方法,对山东省17座城市186所学校(排除每个年级学生少于100名或总名数少于300名的学校)的99 327名初高中学生进行多轮健康现状和健康相关行为的面对面调查[8]。考虑到数据的时效性,本研究选取2020—2021学年数据进行分析。依据研究目的确定纳入标准:(1)2020—2021年期间参与该研究的初高中生,同时参与2020—2021年度体质健康测试、营养与膳食问卷调查;(2)资料完整;(3)问卷质量控制合格,无逻辑错误、信息缺失等问题;(4)教师、家长和学生均已填写知情同意书。最终纳入9 730名符合条件的研究对象,其中男生4 721名(48.5%),女生5 009名(51.5%);年龄15~20岁。
主要包括年龄、性别、民族、年级、户籍、居住地、家庭经济情况等。
使用改良的中国饮食质量问卷(Dietary Quality Questionnaire,DQQ)评估青少年近一周内各种食物摄入频率[9]。DQQ包括29种食物,经整理后归为17组食物,分别为绿叶蔬菜、红/橙色蔬菜、土豆、其他蔬菜类、薯类、水果、大豆制品、新鲜肉类、鱼虾/其他海鲜、蛋类、奶类、加工肉类、方便面、西式快餐、含糖饮料、加糖/盐的零食、油炸食品。询问每个参与者以下问题:“您在过去一周内,吃此类食物的频率是?”,回答分为5个等级(1=“0次”,2=“1~2次”,3=“大约每2 d 1次”,4=“大约每天1次”,5=“每天不止1次”)。本研究中该问卷的Cronbach α系数为0.72。
所有参与者的体质健康测试和评估均按照国家学生体质健康测试标准操作流程进行[10]。(1)身体形态:体质量指数(body mass index,BMI)=体重(kg)/[身高(m)]2;(2)身体功能:肺活量;(3)身体素质:50 m跑;坐位体前屈;立定跳远;引体向上(仅男生);1 min仰卧起坐(仅女生);1 000(男)/800 m(女)跑。根据美国疾病预防控制中心标准[11],按照BMI百分位数将青少年体重划分为低体重(BMI < P5)、正常体重(P5≤BMI < P85)、超重(P85≤BMI < P95)、肥胖(BMI≥P95)4个等级。该标准已被证明适用于中国青少年群体[3]。其余项目均按是否合格的标准来判定分数。
采用SPSS 26.0软件进行描述统计分析,利用R-studio程序(版本4.5.1)进行K均值聚类算法与网络分析,以识别不同性别青少年的饮食行为与体质健康症状群。最佳聚类结局通过肘部检验法确定[12-13]。根据K均值聚类结局,使用高斯图模型(gaussian graphical model, GGM)[14],结合最小绝对收缩和选择运算符(least absolute shrinkage and selection operator, LASSO)方法估计网络结构[15]。通过JASP(版本0.19.3)可视化网络中各节点的中心性指标,包括强度中心性、接近中心性、中介中心性和预期影响(expected influence,EI)评估网络中每个节点的重要性。采用非参数Bootstrap方法(nBoots=1 000)估计边缘权重和中心性指标的稳定性系数(correlation stability coefficient,CS),该系数至少大于0.25,最好大于0.5[16]。以P<0.05表示有统计学意义。
中学生绿叶蔬菜的摄入次数最多,一周内每天摄入>1次者4 014名;摄入次数最少的为西式快餐,一周内每天摄入>1次者658名。
结果显示,不同性别聚类结果的肘部图的拐点均位于k=2处,不同性别群体的最优聚类数均为2类。男生类别1为形态偏离型(n=1 294),该群体中学生明显超重,1 000 m跑时长超标,引体向上表现较弱,立定跳远距离较短。男生类别2为稳态基准型(n=3 427),该群体中学生大部分体质健康指标优于形态偏离型。女生类别1为稳态基准型(n=4 039),该群体大部分体质健康指标优于形态偏离型。中学生形态偏离型体质发生率为23.27%(2 264名)。
网络分析揭示了不同体质健康指标与特定饮食行为之间的独特关联模式,并且在不同性别中学生之间差异有统计学意义。体质健康的3个指标间和不同的饮食情况间关联均有统计学意义(P值均 < 0.05)。在男生体质健康与饮食情况的网络结构图1A中,关联最强的是身体素质与水果摄入,呈现负相关(权重为-0.08)。与男生相比,在女生体质健康与饮食情况的网络结构图1D中,体质健康与饮食情况的关联较弱,其中肺活量与加工肉类摄入的关联最强,存在正相关关系(权重为0.05)。而在男生形态偏离型亚组图2B中,身体素质与方便面摄入关联最强,呈现正相关(权重为0.06)。在男生稳态基准型亚组的网络结构图2C中,关联最强的也是身体素质与水果摄入,呈现负相关(权重为-0.07)。而在女生形态偏离型亚组图1E中,关联最强的是肺活量和土豆摄入,呈正相关(权重为0.09)。在女生稳态基准型亚组图1F中,肺活量与加工肉类摄入的关联也最强,呈正相关(权重为0.05)。
在网络中心性的分析图2中,不同体质类型群体中核心饮食摄入的影响力差异有统计学意义(P值均 < 0.05)。在图2(A)中,形态偏离型男生的薯类、加工肉类、方便面摄入的中介中心性高于稳态基准型;稳态基准型男生接近中心性最强的是薯类摄入(效应值=1.10),而在形态偏离型男生中,海鲜摄入的接近中心性最强(效应值=1.62);男生稳态基准型海鲜摄入的预期影响最大(EI=1.01),在形态偏离型中,西式快餐摄入的预期影响最大(EI=1.03)。在图2(B)中,女生形态偏离型的海鲜摄入的中介中心性远高于稳态基准型;女生稳态基准型中,海鲜摄入的接近中心性最强(效应值=1.11),而在形态偏离型中,西式快餐摄入的接近中心性最强(效应值=1.15);女生稳态基准型和形态偏离型中,西式快餐摄入的预期影响最大(EI值分别为1.32和1.02)。在不同体质类型群体中,西式快餐摄入的强度中心性均为最强。
本研究按照体质健康指标聚类分析的结果将中学生群体分成形态偏离型和稳态基准型2种类型。本研究结果显示,23.27%的中学生为形态偏离型体质,存在超重与耐力素质等体质健康异常,与全国学生体质健康调研的研究相符[17]。体质健康的3个指标间和不同的饮食情况间均关联,说明饮食摄入会极大影响中学生的体质健康。因此,改善饮食摄入状况是预防异常体质健康发生,获得更广泛健康效益的关键性途径。
本研究结果显示,体质健康类型与饮食状态在性别间差异有统计学意义。男生受饮食行为影响的健康指标为身体素质,其与水果摄入呈负相关,与之前的研究结果相反[5, 18]。青春期男生对能量和蛋白质需求极高[19],若水果挤占了其他营养密度更高的食物(如肉类、乳制品),可能暂时影响肌肉增长和爆发力[20-22]。在中国饮食文化中,水果大多作为餐前或餐后甜点而非主食,处于发育关键期的男生更依赖高碳水的饮食模式[23-24],可能间接影响到本次研究结果的呈现。在形态偏离型组中,男生身体素质与方便面摄入存在正相关。对于低体重或营养不良的男生,方便面的高能量可能帮助维持基础代谢和肌肉合成[23]。在本研究所涉及的群体中,女生体质健康与饮食摄入状态的关联性相比男生较低。受饮食行为影响的健康指标为肺活量,其与加工肉类的摄入存在正相关。加工肉类(如火腿、香肠)富含优质蛋白质和血红素铁[25-26],可能改善贫血或营养不足女性的肌肉功能和氧运输能力,从而间接支持肺活量。考虑到中学生饮食受家庭饮食习惯的影响,加工肉类的价格和易储存的优点更受家庭欢迎,加工肉类的摄入频率也会受到家庭与经济状况的影响。在形态偏离型组中,女生肺活量与土豆摄入存在正相关。土豆富含钾元素,而钾有助于调节体内电解质平衡[27],可能改善心血管功能和氧气运输效率,间接支持肺活量。
在不同体质类型群体中,西式快餐摄入接近中心性最强,其对中学生体质健康影响力在众多饮食行为中最大。本研究发现,西式快餐在中学生饮食结构中占据核心地位,在社交、文化、营养摄入等表现出广泛影响,而西餐采用的高盐高糖高脂肪配餐方式,会激发大脑奖赏回路,强迫反复进食,形成饮食依赖。而且其标准化供应链和快速服务,也使其成为时间紧张的青少年首选[28]。而在单独亚组网络分析中发现,在稳态基准型的男生群体内,海鲜的预期影响度最高,海鲜富含优质蛋白质与氨基酸[29],吸收利用率高。中学生处于快速生长期,肌肉合成依赖于蛋白质等营养素[19],提示饮食摄入与中学生体质健康密切关联,因此家校应当优化青少年饮食摄入结构,调整饮食行为,改善体质健康状况;考虑到家庭经济状况和社会因素对饮食摄入选择的影响,家校和社会应当加强公共卫生宣传,开展饮食健康教育和餐饮经济支持等活动来创造健康的生态环境。
本研究发现中学生饮食摄入与体质健康是具有相互作用的整体,饮食摄入干预有助于帮助中学生提升体质健康水平,预防相关疾病发生。但中学生饮食摄入受家庭经济条件、流行文化、作息时间、运动状况的影响[3]。因此,制定饮食摄入干预措施时,需结合其运动状况及家庭情况,制定个性化干预方案,保障实施可持续性,为解决中学生异常体质健康问题提供精准化、高效的干预指导。
研究局限性:(1)研究采用横断面研究设计,无法证实因果关系。(2)研究未考虑日常运动情况、饮食偏好、青春期等对中学生饮食摄入和体质健康可能产生的潜在协变量,而使结果产生偏差。(3)研究主要通过自我报告方法,容易产生回忆偏差。今后研究可通过纵向研究,观察变量随时间变化的规律和可能存在因果关系的路径;加入饮食摄入的重要数据和相关生理指标等方式进行质量控制。
  • 四川省大学生创新创业训练项目(S202310634117)
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2025年第46卷第12期
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doi: 10.16835/j.cnki.1000-9817.2025365
  • 接收时间:2025-08-22
  • 首发时间:2026-11-06
  • 出版时间:2025-12-25
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  • 收稿日期:2025-08-22
  • 修回日期:2025-10-14
基金
四川省大学生创新创业训练项目(S202310634117)
作者信息
    1青岛大学附属烟台毓璜顶医院耳鼻咽喉头颈外科,山东 烟台 264000
    2川北医学院四川省基层卫生事业发展研究中心
    3湖北文理学院理工学院

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尹雯雯,E-mail:
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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