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Article(id=1324078324766606099, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078175000605624, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202503387, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1742400000000, receivedDateStr=2025-03-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614472810, onlineDateStr=2026-11-02, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614472810, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614472810, creator=13701087609, updateTime=1793614472810, updator=13701087609, issue=Issue{id=1324078175000605624, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='2', pageStart='193', pageEnd='384', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614437103, creator='13701087609', updateTime=1793614526046, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324078548130082942, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078175000605624, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324078548130082943, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078175000605624, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=245, endPage=251, ext={EN=ArticleExt(id=1324078326578545428, articleId=1324078324766606099, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of latent profile of depressive symptoms and influencing factors of food delivery rider, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the latent categories of depressive symptoms among food delivery riders and analyze the influencing factors of different categories.

Methods

Participants were selected through cluster randomized sampling from six food delivery service providers in Chengdu. Data were collected using a general information questionnaire, the Patient Health Questionnaire (PHQ), and the Core Occupational Stress Scale. Latent profile analysis (LPA) was employed to classify depressive symptoms, and multinomial logistic regression was conducted to identify associated risk factors.

Results

Among the 560 food delivery riders, the depressive symptoms were divided into three latent categories: the no-depression-risk group (28.21%), the low-risk group of emotional depression (61.43%), and the high-risk group of emotional depression with medium-risk of cognitive depression (10.36%). Divorce was a risk factor for high-risk emotional depression-medium-risk cognitive depression (OR=2.22, 95% CI: 1.58-3.12); undergraduate degree or above was a risk factor for high-risk emotional depression-medium-risk cognitive depression (OR=3.17, 95% CI: 2.70-3.71); long working hours were a risk factor for depression (OR=2.70, 95% CI: 2.17-3.36); and social support was a protective factor for depression (OR=0.83, 95% CI: 0.76-0.91).

Conclusion

Timely and effective intervention measures should be formulated for different latent categories to relieve the depressive emotions of food delivery riders.

, authors=Han WANG, Xiao-song YANG, Fei YANG, Yuan-feng LI, Qi PENG, authorsList=Han WANG, Xiao-song YANG, Fei YANG, Yuan-feng LI, Qi PENG, authorCompany=null, correspAuthors=Qi PENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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=1324078328243684127, articleId=1324078324766606099, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=外卖骑手抑郁症状潜在剖面分析及其影响因素研究, columnId=1228016571273113811, journalTitle=现代预防医学, columnName=环境与职业卫生, runingTitle=null, highlight=null, articleAbstract=
目的

探讨外卖骑手抑郁症状的潜在类别，并分析不同类别的影响因素。

方法

采用整群随机方法，抽取成都市6家企业的外卖骑手，使用一般资料调查表、患者健康问卷和职业紧张测量核心量表进行调查，运用潜在剖面分析划分抑郁症状潜在类别，并通过无序多分类logistic回归分析其影响因素。

结果

共纳入560名外卖骑手，外卖骑手抑郁症状分为无抑郁风险组（28.21%）、情绪抑郁低风险组（61.43%）和情绪抑郁高风险-认知抑郁中风险组（10.36%）。离异是情绪抑郁高风险-认知抑郁中风险的危险因素（OR=2.22，95% CI：1.58~3.12）；本科及以上是情绪抑郁高风险-认知抑郁中风险的危险因素（OR=3.17，95% CI：2.70~3.71）;长时间工作是抑郁的危险因素（OR=2.70，95% CI：2.17~3.36）;社会支持是抑郁的保护因素（OR=0.83，95% CI：0.76~0.91）。

结论

针对不同潜在类别制定及时有效的干预措施，以缓解外卖骑手抑郁情绪。

, authors=王涵, 杨晓松, 杨非, 李元锋, 彭琦, authorsList=王涵, 杨晓松, 杨非, 李元锋, 彭琦, authorCompany=null, correspAuthors=彭琦, authorNote=

王涵（1994—），女，硕士，主管医师，研究方向：职业卫生

, correspAuthorsNote=
彭琦，E-mail:
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Association of occupational stress, job burnout and depressive symptoms of employees in labor-intensive enterprises[J]. China Occupational Medicine, 2022, 49(1): 29-33, 40. (In Chinese), articleTitle=Association of occupational stress, job burnout and depressive symptoms of employees in labor-intensive enterprises, refAbstract=null), Reference(id=1324078345100591994, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, doi=null, pmid=null, pmcid=null, year=2023, volume=7, issue=5, pageStart=789, pageEnd=801, url=null, language=null, rfNumber=[20], rfOrder=34, authorNames=Schiller B, Yu Q, Tei S, journalName=Nat Hum Behav, refType=null, unstructuredReference=Schiller B, Yu Q, Tei S, et al. How perceived meaning in work buffers stress responses: A neural mechanism of cognitive reappraisal[J]. Nat Hum Behav, 2023, 7(5): 789-801., articleTitle=How perceived meaning in work buffers stress responses: A neural mechanism of cognitive reappraisal, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1324078328516313888, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, xref=null, ext=[AuthorCompanyExt(id=1324078328528896801, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, companyId=1324078328516313888, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Occupational Disease Prevention and Control Department, Chengdu Center for Disease Control and Prevention(Chengdu Institute of Health Supervision), Chengdu, Sichuan 610041, China), AuthorCompanyExt(id=1324078328537285410, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, companyId=1324078328516313888, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=成都市疾病预防控制中心（成都市卫生监督所）职业病防制科，四川 成都 610041)])], figs=[ArticleFig(id=1324078333729833800, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=EN, label=Fig.1, caption=Variation of evaluation indicators with the number of categories, figureFileSmall=9DcNhgBe4RPWeggKkokhZA==, figureFileBig=VJNqxH/lgTl+kJzH+hSxNQ==, tableContent=null), ArticleFig(id=1324078333826302793, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=CN, label=图1, caption=评价指标随类别数变化关系, figureFileSmall=9DcNhgBe4RPWeggKkokhZA==, figureFileBig=VJNqxH/lgTl+kJzH+hSxNQ==, tableContent=null), ArticleFig(id=1324078335969592138, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=EN, label=Fig.2, caption=Distribution of depressive symptoms in 3 latent profiles of food delivery riders, figureFileSmall=oOEnPlbMqdFuWPkkPUzPrw==, figureFileBig=0USMNY/4kkzQz4XLknlH3A==, tableContent=null), ArticleFig(id=1324078336229638987, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=CN, label=图2, caption=外卖骑手3个潜在剖面抑郁症状分布, figureFileSmall=oOEnPlbMqdFuWPkkPUzPrw==, figureFileBig=0USMNY/4kkzQz4XLknlH3A==, tableContent=null), ArticleFig(id=1324078336342885196, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=EN, label=Fig.3, caption=Radar chart of depressive symptom distribution in food delivery riders with moderate depressive symptoms, figureFileSmall=jrYPVuyRcYWykzrNGsBZ6A==, figureFileBig=hGw9HIL89PI8zZn1yDvLzQ==, tableContent=null), ArticleFig(id=1324078336409994061, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=CN, label=图3, caption=中度抑郁症状外卖骑手抑郁症状分布雷达图, figureFileSmall=jrYPVuyRcYWykzrNGsBZ6A==, figureFileBig=hGw9HIL89PI8zZn1yDvLzQ==, tableContent=null), ArticleFig(id=1324078336653263694, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=EN, label=Fig.4, caption=Forest plot of logistic regression analysis on latent profiles of depressive symptoms in food delivery riders, figureFileSmall=/opzIJnUn17XukNEVDkIoQ==, figureFileBig=ZwXNw3E1rZNZGNdLOy6sag==, tableContent=null), ArticleFig(id=1324078336728761167, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=CN, label=图4, caption=外卖骑手抑郁症状潜在剖面logistic回归分析森林图, figureFileSmall=/opzIJnUn17XukNEVDkIoQ==, figureFileBig=ZwXNw3E1rZNZGNdLOy6sag==, tableContent=null), ArticleFig(id=1324078336821035856, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=EN, label=Table 1, caption=

Fitting results of the latent profile analysis model for the depressive symptoms of food delivery riders

, figureFileSmall=null, figureFileBig=null, tableContent=
类别数AICBICaBICEntropyP值
(LMR)		P值
(BLRT)		类别概率
(%)
110 802.2410 880.1410 823.00----
29 449.369 570.549 481.650.830.01<0.00152.68/47.32
38 427.388 591.848 471.210.940.01<0.00161.43/10.36/28.21
48 157.458 365.198 212.810.930.01<0.00153.93/6.25/10.00/29.82
57 970.888 221.908 037.780.920.01<0.00122.86/9.64/2.86/39.10 /25.54
), ArticleFig(id=1324078337093665617, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=CN, label=表1, caption=

外卖骑手抑郁症状潜在剖面分析模型拟合结果

, figureFileSmall=null, figureFileBig=null, tableContent=
类别数AICBICaBICEntropyP值
(LMR)		P值
(BLRT)		类别概率
(%)
110 802.2410 880.1410 823.00----
29 449.369 570.549 481.650.830.01<0.00152.68/47.32
38 427.388 591.848 471.210.940.01<0.00161.43/10.36/28.21
48 157.458 365.198 212.810.930.01<0.00153.93/6.25/10.00/29.82
57 970.888 221.908 037.780.920.01<0.00122.86/9.64/2.86/39.10 /25.54
), ArticleFig(id=1324078337177551698, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=EN, label=Table 2, caption=

Comparison between latent profile categories of depressive symptoms and PHQ-9 classifications in food delivery riders

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潜在剖面PHQ-9无抑郁症状PHQ-9
轻度抑郁症状		PHQ-9
中度抑郁症状		PHQ-9
中重度抑郁症状		PHQ-9
重度抑郁症状
无抑郁风险组1571000
情绪抑郁低风险组212685500
情绪抑郁高风险-认知抑郁中风险组0021370
), ArticleFig(id=1324078337609565011, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=CN, label=表2, caption=

外卖骑手抑郁症状潜在剖面类别与PHQ-9分类比较

, figureFileSmall=null, figureFileBig=null, tableContent=
潜在剖面PHQ-9无抑郁症状PHQ-9
轻度抑郁症状		PHQ-9
中度抑郁症状		PHQ-9
中重度抑郁症状		PHQ-9
重度抑郁症状
无抑郁风险组1571000
情绪抑郁低风险组212685500
情绪抑郁高风险-认知抑郁中风险组0021370
), ArticleFig(id=1324078337689256788, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=EN, label=Table 3, caption=

Univariate analysis of latent profiles of depressive symptoms in food delivery riders

, figureFileSmall=null, figureFileBig=null, tableContent=
变量无抑郁风险组情绪抑郁低风险组情绪抑郁高风险-认知抑郁中风险组检验统计量P值
性别[n(%)]11.16b0.004
男91(57.59)164(47.67)40(68.97)
女67(42.41)180(52.33)18(31.03)
年龄分段（岁）[n(%)]21.79b0.001
≤3020(12.66)35(10.17)5( 8.62)
31~3939(24.68)124(36.05)23(39.66)
40~4921(13.29)64(18.60)17(29.31)
≥5078(49.37)121(35.17)13(22.41)
婚姻状况[n(%)]11.18b0.02
未婚14(8.86)40(11.63)3( 5.17)
已婚138(87.34)279(81.10)46(79.31)
离异6(3.80)25( 7.27)9(15.52)
文化程度[n(%)]21.16b0.002
初中及以下98(62.03)150(43.60)23(39.66)
高中/职高/中专29(18.35)69(20.06)17(29.31)
大专12(7.59)49(14.24)6(10.34)
大学本科及以上19(12.03)76(22.09)12(20.69)
户口类型[n(%)]2.69b0.26
非农业户口81(51.27)187(54.36)37(63.79)
农业户口77(48.73)157(45.64)21(36.21)
每日工作时间（小时）[n(%)]12.06b0.02
≤838(24.05)58(16.86)4( 6.90)
9~1059(37.34)139(40.41)20(34.48)
≥1161(38.61)147(42.73)34(58.62)
工龄分段（年）[n(%)]6.60b0.58
<1127(80.38)254(73.84)39(67.24)
1~215( 9.49)43(12.50)11(18.97)
3~511( 6.96)35(10.17)7(12.07)
6~94( 2.53)9( 2.62)1( 1.72)
≥101( 0.63)3( 0.87)0( 0.00)
职业紧张（）
社会支持18.73(4.11)18.51(3.11)16.24(4.47)11.32a<0.001
组织与回报20.12(5.03)19.83(3.96)18.53(4.65)2.88a0.06
要求与付出10.72(3.59)11.00(3.85)13.90(3.96)16.48a<0.001
自主性6.72(1.99)6.51(1.97)6.72(2.20)0.74a0.48
), ArticleFig(id=1324078337945109333, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=CN, label=表3, caption=

外卖骑手抑郁症状潜在剖面单因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量无抑郁风险组情绪抑郁低风险组情绪抑郁高风险-认知抑郁中风险组检验统计量P值
性别[n(%)]11.16b0.004
男91(57.59)164(47.67)40(68.97)
女67(42.41)180(52.33)18(31.03)
年龄分段（岁）[n(%)]21.79b0.001
≤3020(12.66)35(10.17)5( 8.62)
31~3939(24.68)124(36.05)23(39.66)
40~4921(13.29)64(18.60)17(29.31)
≥5078(49.37)121(35.17)13(22.41)
婚姻状况[n(%)]11.18b0.02
未婚14(8.86)40(11.63)3( 5.17)
已婚138(87.34)279(81.10)46(79.31)
离异6(3.80)25( 7.27)9(15.52)
文化程度[n(%)]21.16b0.002
初中及以下98(62.03)150(43.60)23(39.66)
高中/职高/中专29(18.35)69(20.06)17(29.31)
大专12(7.59)49(14.24)6(10.34)
大学本科及以上19(12.03)76(22.09)12(20.69)
户口类型[n(%)]2.69b0.26
非农业户口81(51.27)187(54.36)37(63.79)
农业户口77(48.73)157(45.64)21(36.21)
每日工作时间（小时）[n(%)]12.06b0.02
≤838(24.05)58(16.86)4( 6.90)
9~1059(37.34)139(40.41)20(34.48)
≥1161(38.61)147(42.73)34(58.62)
工龄分段（年）[n(%)]6.60b0.58
<1127(80.38)254(73.84)39(67.24)
1~215( 9.49)43(12.50)11(18.97)
3~511( 6.96)35(10.17)7(12.07)
6~94( 2.53)9( 2.62)1( 1.72)
≥101( 0.63)3( 0.87)0( 0.00)
职业紧张（）
社会支持18.73(4.11)18.51(3.11)16.24(4.47)11.32a<0.001
组织与回报20.12(5.03)19.83(3.96)18.53(4.65)2.88a0.06
要求与付出10.72(3.59)11.00(3.85)13.90(3.96)16.48a<0.001
自主性6.72(1.99)6.51(1.97)6.72(2.20)0.74a0.48
), ArticleFig(id=1324078338096104278, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=EN, label=Table 4, caption=

Assignment of independent variables

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变量赋值情况
性别男=0，女=1
年龄分段30岁及以下=0，31~39岁=1，40~49岁=2,50岁及以上=3
婚姻状况未婚=0，已婚=1，离婚=2
文化程度初中及以上=0，高中/职高/中专=1，大专=2，大学及以上=3
每日工作时间8小时及以下=0，9~10小时=1,11小时及以上=2
社会支持原始值
要求与付出原始值
), ArticleFig(id=1324078338318402391, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078324766606099, language=CN, label=表4, caption=

自变量赋值情况

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变量赋值情况
性别男=0，女=1
年龄分段30岁及以下=0，31~39岁=1，40~49岁=2,50岁及以上=3
婚姻状况未婚=0，已婚=1，离婚=2
文化程度初中及以上=0，高中/职高/中专=1，大专=2，大学及以上=3
每日工作时间8小时及以下=0，9~10小时=1,11小时及以上=2
社会支持原始值
要求与付出原始值
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外卖骑手抑郁症状潜在剖面分析及其影响因素研究
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王涵 , 杨晓松 , 杨非 , 李元锋 , 彭琦
现代预防医学 | 环境与职业卫生 2026,53(2): 245-251
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现代预防医学 |环境与职业卫生 2026 , 53 (2) : 245 -251
外卖骑手抑郁症状潜在剖面分析及其影响因素研究
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王涵, 杨晓松, 杨非, 李元锋, 彭琦
作者信息
  • 成都市疾病预防控制中心（成都市卫生监督所）职业病防制科，四川 成都 610041
通讯作者:
彭琦，E-mail:
作者简介:

王涵（1994—），女，硕士，主管医师，研究方向：职业卫生

Analysis of latent profile of depressive symptoms and influencing factors of food delivery rider
Han WANG, Xiao-song YANG, Fei YANG, Yuan-feng LI, Qi PENG
Affiliations
  • Occupational Disease Prevention and Control Department, Chengdu Center for Disease Control and Prevention(Chengdu Institute of Health Supervision), Chengdu, Sichuan 610041, China
出版时间: 2026-01-25 doi: 10.20043/j.cnki.MPM.202503387
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摘要
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目的

探讨外卖骑手抑郁症状的潜在类别，并分析不同类别的影响因素。

方法

采用整群随机方法，抽取成都市6家企业的外卖骑手，使用一般资料调查表、患者健康问卷和职业紧张测量核心量表进行调查，运用潜在剖面分析划分抑郁症状潜在类别，并通过无序多分类logistic回归分析其影响因素。

结果

共纳入560名外卖骑手，外卖骑手抑郁症状分为无抑郁风险组（28.21%）、情绪抑郁低风险组（61.43%）和情绪抑郁高风险-认知抑郁中风险组（10.36%）。离异是情绪抑郁高风险-认知抑郁中风险的危险因素（OR=2.22，95% CI：1.58~3.12）；本科及以上是情绪抑郁高风险-认知抑郁中风险的危险因素（OR=3.17，95% CI：2.70~3.71）;长时间工作是抑郁的危险因素（OR=2.70，95% CI：2.17~3.36）;社会支持是抑郁的保护因素（OR=0.83，95% CI：0.76~0.91）。

结论

针对不同潜在类别制定及时有效的干预措施，以缓解外卖骑手抑郁情绪。

外卖骑手  /  抑郁症状  /  潜在剖面分析  /  影响因素
Objective

To explore the latent categories of depressive symptoms among food delivery riders and analyze the influencing factors of different categories.

Methods

Participants were selected through cluster randomized sampling from six food delivery service providers in Chengdu. Data were collected using a general information questionnaire, the Patient Health Questionnaire (PHQ), and the Core Occupational Stress Scale. Latent profile analysis (LPA) was employed to classify depressive symptoms, and multinomial logistic regression was conducted to identify associated risk factors.

Results

Among the 560 food delivery riders, the depressive symptoms were divided into three latent categories: the no-depression-risk group (28.21%), the low-risk group of emotional depression (61.43%), and the high-risk group of emotional depression with medium-risk of cognitive depression (10.36%). Divorce was a risk factor for high-risk emotional depression-medium-risk cognitive depression (OR=2.22, 95% CI: 1.58-3.12); undergraduate degree or above was a risk factor for high-risk emotional depression-medium-risk cognitive depression (OR=3.17, 95% CI: 2.70-3.71); long working hours were a risk factor for depression (OR=2.70, 95% CI: 2.17-3.36); and social support was a protective factor for depression (OR=0.83, 95% CI: 0.76-0.91).

Conclusion

Timely and effective intervention measures should be formulated for different latent categories to relieve the depressive emotions of food delivery riders.

Food delivery riders  /  Depressive symptoms  /  Latent profile analysis  /  Influencing factors
王涵, 杨晓松, 杨非, 李元锋, 彭琦. 外卖骑手抑郁症状潜在剖面分析及其影响因素研究. 现代预防医学, 2026 , 53 (2) : 245 -251 . DOI: 10.20043/j.cnki.MPM.202503387
Han WANG, Xiao-song YANG, Fei YANG, Yuan-feng LI, Qi PENG. Analysis of latent profile of depressive symptoms and influencing factors of food delivery rider[J]. Modern Preventive Medicine, 2026 , 53 (2) : 245 -251 . DOI: 10.20043/j.cnki.MPM.202503387
正文
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随着数字经济的发展，外卖送餐、快递物流等新型行业不断涌现。截止2022年，仅美团平台外卖骑手数量超过600万，且呈逐年上升趋势[1]。近年来，“外卖骑手抑郁症”“外卖骑手猝死事件”等社会热点频发，引发了全社会对外卖骑手职业心理健康问题的广泛关注[2]。劳动时间长、配送任务重、工作压力大以及劳动收入不稳定等因素，易导致外卖骑手产生职业紧张、抑郁等心理健康问题[3]。职业紧张与抑郁症状呈高度正相关[4]，而社会支持、组织与回报等职业相关因素显著影响职业人群抑郁症状的发生[5]。目前关于外卖骑手抑郁症状的研究仍较有限，其他职业人群抑郁症状的研究[6-8]主要根据抑郁症状量表总分判断抑郁状况或抑郁严重程度，但忽视了抑郁症状的个体异质性，难以提供针对性和个性化的干预措施。因此本研究基于潜在剖面分析(Latent Profile Analysis，LPA)方法，识别外卖骑手不同抑郁症状群体，并探究不同群体特征和抑郁症状影响因素，为制定个性化的干预措施提供理论依据。
1　对象与方法
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1.1　调查对象
2024年6-10月，采用整群随机抽样的方法，随机抽取成都市外卖配送业6家企业的所有外卖骑手。纳入标准：年龄≥18 岁；工龄超过半年；无精神病史。排除标准：调查前1周有精神药物使用史。共发放590份问卷，回收560份有效问卷，有效回收率为94.92%。
本研究经中国疾病预防控制中心职业卫生与中毒控制所伦理委员会批准(批号:NIOHP201914),调查对象均知情同意且自愿参与。
1.2　调查工具
1.2.1　基本情况调查
基本情况包括性别、年龄、户口类型、文化程度、婚姻状况等社会人口学资料和工龄、每日工作时间、是否需要上夜班等工作相关资料。
1.2.2　抑郁症状调查
采用患者健康问卷（PHQ-9）[9]评估近半年出现抑郁症状的频率。该量表共9个条目，采用Likert 4级评分法，完全不会、偶尔、一半以上的日子、一直如此分别计0~3分。以总分0~4、5~9、10~14、15~19、20~27分别判定为无抑郁症状和轻度、中度、中重度、重度抑郁症状。该量表在本研究的Cronbach α系数为0.908。
1.2.3　职业紧张调查
采用职业紧张测量核心量表[10]评估近半年出现职业紧张的情况。该量表共17个条目，包含社会支持、组织与回报、要求与付出和自主性4个维度。采用Likert 5级评分法，完全不同意、不同意、基本同意、同意、完全同意分别计1~5分。组织与回报和自主性反向计分条目经过转换（6-实际得分）后再计算。社会支持得分越高，意味着社会支持水平越高;组织与回报得分越高，表示获得的组织支持与回报水平越高;要求与付出得分越高，说明工作中要求与所需付出越多;自主性得分越高，反映工作中自主程度越高。该量表社会支持、组织与回报、要求与付出和自主性4个维度在本研究的Cronbach α系数分别为0.897、0.825、0.798和0.892。
1.3　调查方法
调查前对调查员进行统一培训，考核通过后方可开展调查。调查对象通过扫描二维码匿名填写调查问卷，填写困难者可由调查员根据调查对象回答代为填写。问卷回收后进行人工审核，剔除同质、作答时间过短、逻辑有误的问卷。
1.4　统计学分析
采用R 4.2.1进行统计分析。潜在剖面分析（Latent Profile Analysis，LPA）通过逐步增加潜在剖面个数，结合多项模型拟合指标确定最佳群体数。LPA模型拟合优度评价指标包括：(1)艾凯克信息准则(Akaike information criterion，AIC)、贝叶斯信息准则(Bayesian Information Criterion，BIC)和调整贝叶斯信息准则(Adjusted Bayesian Information Criterion，aBIC)，数值越小表明模型拟合度越高；(2)熵(Entropy)用于评价模型分类准确度，熵值越接近1表明分类准确度越高；(3)似然比检验包括罗-梦戴尔-鲁本校正似然比检验(Lo Mendell Rubin, LMR)和基于 bootstrap 的似然比检验 (bootstrap likelihood ratio test, BLRT)检验，用于比较模型之间的拟合差异。当P<0.05 时，表明k个类别模型优于k-1个类别模型。方差分析和χ2检验用于类别间比较，无序多分类logistic回归进行影响因素分析。检验水准α=0.05。
2　结果
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2.1　外卖骑手一般资料和抑郁现状
本研究共纳入560名外卖骑手，其中男性295名(52.68%)，女性265名(47.32%)；年龄范围为18~58(43.63±11.30)岁；抑郁得分为0~27(6.79±4.84)分，68.21%的外卖骑手存在不同程度的抑郁症状。
2.2　外卖骑手抑郁症状潜在剖面分析
以PHQ-9量表9个条目为外显指标，共建立5个潜在剖面模型，见表1。AIC、BIC和aBIC值随着类别数增加而下降，且在类别数为3时下降幅度明显趋缓，见图1。同时Entropy值在类别数为3时最大，因此最终选择3个潜在类别数：类别1共158名（28.21%），9个条目得分均值接近0，界定为“无抑郁风险组”；类别2共344名（61.43%），自杀倾向平均得分接近0，其余8个条目得分均值在1分左右，界定为“情绪抑郁低风险组”；类别3共58名（10.36%），疲倦无力平均得分为2.24，自杀倾向平均得分为1.24，其余7个条目得分均值在2分左右，界定为“情绪抑郁高风险-认知抑郁中风险组[11]”，见图2。
2.3　外卖骑手抑郁症状潜在剖面类别与PHQ-9分类比较
PHQ-9界定为无抑郁症状的外卖骑手，潜在剖面分析主要归类为无抑郁风险组（88.20%）；PHQ-9界定为轻度抑郁症状的外卖骑手，潜在剖面分析主要归类为情绪抑郁低风险组（99.63%）；PHQ-9界定为中度抑郁症状的外卖骑手，潜在剖面分析主要归类为情绪抑郁低风险组（72.37%）和情绪抑郁高风险-认知抑郁中风险组（27.63%）；PHQ-9界定为中重度抑郁症状的外卖骑手，潜在剖面分析全部归类为情绪抑郁高风险-认知抑郁中风险组,见表2。
此外，PHQ-9界定为中度抑郁症状的外卖骑手存在明显的异质性，部分外卖骑手（27.63%）在疲倦无力、自杀倾向等症状得分较高，表现出“情绪抑郁高风险-认知抑郁中风险”；部分外卖骑手（72.37%）各抑郁症状得分在1左右,表现出“情绪抑郁低风险”，见图3。
2.4　外卖骑手抑郁症状潜在剖面的单因素分析
3个潜在剖面类别的外卖骑手在性别、年龄、婚姻状况、文化程度和每日工作时间方面的差异具有统计学意义（P<0.05），而在户口类型、工龄的差异无统计学意义（P>0.05）。此外，3个潜在剖面类别的外卖骑手在感知社会支持以及要求与付出方面差异显著（P<0.05），但在感知组织与回报、自主性水平方面差异不具有统计学意义（P>0.05）。见表3。
2.5　外卖骑手抑郁症状潜在剖面的多因素分析
以外卖骑手抑郁症状的3个潜在类别为因变量（以无抑郁风险组为参考组），将单因素分析中有统计学意义的变量（性别、年龄、婚姻状况、文化程度、每日工作时间、社会支持以及要求与付出）作为自变量，构建多因素无序多分类logistic回归模型。自变量赋值情况见表4。结果显示，年龄分段、婚姻状况、文化程度、每日工作时间以及职业紧张相关因素的社会支持、要求与付出，均为外卖骑手抑郁症状潜在类别的影响因素，见图4。
3　讨论
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3.1　外卖骑手抑郁症状存在异质性
本研究中外卖骑手抑郁症状检出率为68.21%，显著高于我国国民总体抑郁症状检出率10.6%[12]，亦高于北京市非京籍快递及外卖从业人员抑郁症状检出率30.75%[13]。
PHQ-9分类基于总分对抑郁严重程度进行分级，掩盖了外卖骑手抑郁症状的不同组合模式和亚型。而潜在剖面分析能识别出外卖骑手抑郁症状明显的个体异质性：类别1为无抑郁风险组，该类别外卖骑手无明显抑郁风险；类别2为情绪抑郁低风险组，自杀倾向极低，其他抑郁症状表现比较均衡，提示应同时关注该人群的心理和生理健康状态；类别3为情绪抑郁高风险-认知抑郁中风险组，以疲倦无力为主要症状，生理相关症状比心理相关症状更严重，对该人群应优先从缓解疲倦入手，重点关注生理相关症状，从而进一步缓解心理相关症状。总体而言，外卖骑手整体抑郁风险水平较高，且症状表现存在个体差异，我们应关注抑郁的个体表现，制定有针对性的干预措施。
3.2　年龄分段、婚姻状况、文化程度影响外卖骑手抑郁症状潜在类别
本研究发现,与无抑郁风险组相比，40~49岁的外卖骑手更有可能归为情绪抑郁低风险组和情绪抑郁高风险-认知抑郁中风险组，可能是该年龄段人群普遍面临着较大的生活压力，承受着职业、健康、家庭等方面带来的困扰和焦虑[14]。相比未婚，已婚的外卖骑手更有可能归为无抑郁风险组，离异的外卖骑手更有可能归为情绪抑郁低风险组和情绪抑郁高风险-认知抑郁中风险组，揭示良好的婚姻关系对外卖骑手心理健康具有积极的保护作用[15]。本科及以上的外卖骑手更有可能归为情绪抑郁低风险组和情绪抑郁高风险-认知抑郁中风险组，可能是因为该人群对职业前景往往有着较高期望，在面对外卖配送这种高强度体力劳动和简单重复的工作内容时，心理上更容易产生失落感和挫败感，进而增加抑郁风险，本科及以上的外卖骑手应建立职业认同感，正确看待该职业的工作价值和社会贡献。
3.3　每日工作时间影响外卖骑手抑郁症状潜在类别
本研究发现，每日工作时间明显影响外卖骑手抑郁症状潜在类别分布。与每日工作时间≤8小时者相比，9~10小时工作者更可能归为情绪抑郁低风险组，11小时及以上工作者更可能归为情绪抑郁高风险-认知抑郁中风险组。长时间工作不仅增加身体疲劳，也容易导致心理疲惫，由此产生的负性事件及负性情绪可能诱发抑郁症状[16]。因此，平台应设计灵活的工作制度，明确规定骑手每日最长工作时长，并通过技术手段进行监控和提醒；同时提供弹性工作时段，允许外卖骑手可以根据自身身体状况和生活安排灵活调整工作时间。
3.4　社会支持和要求与付出影响外卖骑手抑郁症状潜在类别
本研究结果表明,社会支持是抑郁症状的保护因素，社会支持水平低的外卖骑手更可能归为情绪抑郁高风险-认知抑郁中风险组。高水平的社会支持能够帮助外卖骑手缓解压力，觉察积极情绪[17-18]。要求与付出是抑郁症状的危险因素，要求与付出高的外卖骑手更可能归为情绪抑郁低风险组和情绪抑郁高风险-认知抑郁中风险组，外卖骑手工作付出较多却得不到相应回报时，可严重影响其心理健康，增加抑郁风险[19]。但如果个体认同其工作价值，脑岛和默认模式网络会重构压力、要求与付出的感知，减少消极情绪反应，从而要求与付出对外卖骑手抑郁水平的负面效应存在个体差异高[20]。因此增加外卖骑手工作中的社会支持与工作回报有助于降低抑郁水平。
4　结论
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外卖骑手抑郁症状存在异质性，可归为无抑郁风险组、情绪抑郁低风险组和为情绪抑郁高风险-认知抑郁中风险组。破碎的婚姻关系、本科及以上学历、较长工作时间、较低的社会支持和较高的要求与付出都是外卖骑手抑郁风险的危险因素。同时，外卖骑手的抑郁情绪并非一成不变，其可能随着社会政策的调整、平台运营模式的优化以及骑手自身健康状况的改变而发生动态变化。因此，在后续研究中有必要进一步开展追踪调查，以便实时、动态地评估外卖骑手抑郁症状的变化情况，从而为相关干预措施提供更具时效性和针对性的数据支持。

文献
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2026年第53卷第2期
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doi: 10.20043/j.cnki.MPM.202503387
  • 接收时间：2025-03-20
  • 首发时间：2026-11-02
  • 出版时间：2026-01-25
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  • 收稿日期：2025-03-20
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    成都市疾病预防控制中心（成都市卫生监督所）职业病防制科，四川 成都 610041

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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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