上海交通大学学报（医学版）

上海交通大学学报(医学版), 2023, 43(4): 487-494 doi: 10.3969/j.issn.1674-8115.2023.04.012

综述

睡眠时型与技术成瘾的关系及作用机制研究进展

吴玉婧,, 郭茜, 刘晓华, 李冠军,

上海交通大学医学院附属精神卫生中心临床三科，上海 200030

Progress in relationship between chronotype and technology addiction and its mechanism

WU Yujing,, GUO Qian, LIU Xiaohua, LI Guanjun,

Clinical Department 3, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China

通讯作者: 李冠军，电子信箱：liguanjun66@126.com。

编委: 邢宇洋

收稿日期: 2022-11-08   接受日期: 2023-02-05   网络出版日期: 2023-04-28

基金资助: 国家自然科学基金.  81971273
上海申康医院发展中心临床科技创新项目.  SHDC12019X09

Corresponding authors: LI Guanjun, E-mail:liguanjun66@126.com.

Received: 2022-11-08   Accepted: 2023-02-05   Online: 2023-04-28

作者简介 About authors

吴玉婧（1999—），女，硕士生；电子信箱：wuyj22@sjtu.edu.cn。 E-mail：wuyj22@sjtu.edu.cn。

摘要

数字化时代，互联网改变了人们的生活方式和昼夜节律，也带来了技术成瘾的全球性问题。众多研究表明睡眠时型与特定技术成瘾（如网络、手机、游戏和社交媒体）显著相关，这使得分析睡眠时型成为了探讨技术成瘾发生、发展和维持的新视角。睡眠时型包括清晨型、中间型和夜晚型，其中夜晚型是个体过度使用和依赖技术的风险因素。目前，有关睡眠时型与技术成瘾的研究多集中在两者的关系方面，鲜少有针对其内在作用机制的分析。基于此，该文从生理因素（如奖赏系统）、心理因素（如抑郁情绪）、个人因素（如性别、年龄、人格特质和睡眠模式）和环境因素（如父母教养风格）等多个角度进行探讨，并采用人-情绪-认知-执行交互模型和进化生命史理论从病因和进化的角度进行分析，对睡眠时型和技术成瘾的关系及其作用机制进行综述。

关键词： 睡眠时型 ; 技术成瘾 ; 奖赏系统 ; 人-情绪-认知-执行交互模型 ; 进化生命史理论

Abstract

In the era of digitalization, the Internet has changed people's lifestyle and circadian rhythm, and has also brought the global problem of technology addiction. Many studies have shown that chronotype is significantly related to specific technology addiction (such as Internet, smartphones, video games and social media), which makes chronotype become a new perspective to explore the occurrence, development and maintenance of technology addiction. Individuals can be classified into three chronotypes: morning type (M-type), neither type (N-type) and evening type (E-type). Most studies showed that E-type was the risk factor in the onset and maintenance of problematic technology use. At present, most of the prior research focused on the relationship between chronotype and technology addiction, and there were few studies on the mechanism. Based on this situation, this paper discusses physiological factors (such as reward system), psychological factors (such as depression), individual factors (such as gender, age, personality traits and sleep patterns) and environmental factors (such as parental style), analyzes the relationship with Interaction of Person-Affect-Cognition-Execution (I-PACE) model and life history theory from the perspectives of etiology and evolution, and reviews the relationship between chronotype and technology addiction and its mechanism.

Keywords： chronotype ; technology addiction ; reword system ; Interaction of Person-Affect-Cognition-Execution model ; life history theory

PDF (1361KB) 元数据 多维度评价 相关文章 导出 EndNote| Ris| Bibtex  收藏本文

本文引用格式

吴玉婧, 郭茜, 刘晓华, 李冠军. 睡眠时型与技术成瘾的关系及作用机制研究进展. 上海交通大学学报(医学版)[J], 2023, 43(4): 487-494 doi:10.3969/j.issn.1674-8115.2023.04.012

WU Yujing, GUO Qian, LIU Xiaohua, LI Guanjun. Progress in relationship between chronotype and technology addiction and its mechanism. Journal of Shanghai Jiao Tong University (Medical Science)[J], 2023, 43(4): 487-494 doi:10.3969/j.issn.1674-8115.2023.04.012

当前，信息技术已深度嵌入我们的生活，改变着个体的生活方式。第49次《中国互联网络发展状况统计报告》［1］显示，我国互联网普及率达73.0%，网民规模达10.32亿，其中手机网民高达10.29亿。技术的普及和应用在推动社会发展的同时，也满足了个体的个性化需求。但个体对技术的习惯和依赖可能会导致其行为失控，从而损害其情绪健康、工作绩效、人际关系等［2］。GRIFFITHS［3］认为，技术成瘾是涉及人机交互的行为成瘾。TUREL等［4］将技术成瘾定义为非适应性依赖的心理状态，具有突显性、情绪调节、耐受性、戒断、冲突和易复发等特征。作为全球性的成瘾问题，技术成瘾已成为行为成瘾领域的热点话题。

昼夜节律是一种内源性周期波动的生命过程［5］，在个体表达间存在差异，这种差异被称为睡眠时型。睡眠时型一般分为3种，即清晨型、中间型和夜晚型［6］。清晨型个体通常在一日中偏早时间的唤醒水平更高，夜晚型个体则在下午和晚上的唤醒水平更高，中间型处于上述两种情况之间［6］。近年来随着电子产品的普及，其屏幕蓝光可延后个体的昼夜节律，使人群中夜晚型的比例逐年上升［7］。研究［8］显示，夜晚型的倾向与较多的技术使用相关，且使用时长、频率的增加是技术成瘾的重要预测因子。在新型冠状病毒感染流行期间，居家隔离会减少人们的身体活动和日光暴露，增加由社会隔离和工作失序带来的压力，从而影响其对时间流逝的感知。而互联网技术可以提供一个改善社交隔离、缓解焦虑的机会，因此人们在这一时期出现了睡前电子设备使用水平显著上升和昼夜节律延迟的现象［9］。这种改变不仅使个体的睡眠时型倾向于夜晚型，还降低了其睡眠质量，增加精神健康问题的风险。目前，尚未有关于睡眠时型和技术成瘾关系的系统总结。鉴于此，本文通过分析睡眠时型与技术成瘾间的关系，阐述可能影响其关系的生理、心理、个人和环境因素，并试图通过人-情绪-认知-执行交互（Interaction of Person-Affect-Cognition-Execution，I-PACE）模型和进化生命史理论对两者的关系进行理解，以期为降低夜晚型的技术成瘾风险提供参考。

1 睡眠时型与技术成瘾的关系

目前，技术成瘾主要包括网络成瘾（如社交媒体成瘾、网络游戏成瘾、网络购物成瘾、网络色情成瘾、网络赌博成瘾等）和手机成瘾2个方面。而有关睡眠时型与技术成瘾的研究多集中在社交媒体成瘾、网络游戏成瘾等网络成瘾和手机成瘾［10-11］。通过文献检索，我们对睡眠时型与技术成瘾间的关系进行整理并阐述如下。

1.1 睡眠时型与网络成瘾的关系

网络成瘾包括2种类型，即广泛性网络成瘾和特异性网络成瘾；前者是指个体在互联网上无目的地使用不同应用程序且花费过多时间，后者涉及对特定应用程序（如游戏、社交、购物、色情等）的成瘾［12］。有研究［13-17］表明，夜晚型与广泛性网络成瘾呈显著正相关。与清晨型、中间型相比，夜晚型个体的网络成瘾得分更高［18］。在网络成瘾群体中，网络成瘾高危组个体的夜晚型占比亦较高［13］。在调整了性别、年龄、人格、父母学历、睡眠质量等变量后，夜晚型这一睡眠时型仍可显著预测出更高的网络成瘾水平［17，19-20］。同时，也有研究者获得了不同睡眠时型个体的网络成瘾水平不存在显著差异的结果，这可能与该研究所选样本相关；有沉重学业压力的韩国中学生，其在线娱乐时间几乎被学习和补课占据，导致网络成瘾水平在不同睡眠时型中的差异不显著［21］。

然而，有关夜晚型与特异性网络成瘾的研究主要集中在与社交媒体成瘾、网络游戏成瘾方面。有研究［22-25］表明，夜晚型与社交媒体成瘾、社交媒体使用强度和使用侵入性均呈显著正相关。针对Facebook的研究［23］结果显示，夜晚型个体使用目的更为多元，用户体验更加积极，也更具参与性；且纵向研究［22，24］表明，个体夜晚型的偏好程度可以正向预测其2个月后社交媒体成瘾水平，在调整了年龄、性别、人格、睡眠时间后，夜晚型仍是Facebook成瘾、使用强度和侵入性的重要预测因子。作为另一种特异性网络成瘾，网络游戏成瘾的相关研究主要偏向于儿童和青少年群体。有研究发现，参与网络游戏的频率和时间更多、养成睡前玩游戏习惯的儿童会越倾向于夜晚型［26］；夜晚型的青少年玩家也会花费更多的时间在电脑游戏上［27-28］。也有研究［29-31］证实夜晚型与网络游戏成瘾呈显著正相关，且在调整了性别、年龄和人格后，夜晚型仍能显著预测个体的网络游戏成瘾水平。

1.2 睡眠时型与手机成瘾的关系

横向研究［27，32-35］表明，夜晚型与手机成瘾呈显著正相关。在使用逐步回归模型调整了性别、年龄、人格、睡眠时长等因素后，研究发现睡眠时型仍是手机成瘾的独立预测因子［32-33，36］。纵向研究［34，37］则表明，手机成瘾可以预测个体之后的睡眠时型。交叉滞后模型的结果显示，手机成瘾可以单向预测个体1年后偏晚的睡眠时型，反之则无预测作用［34］。一项使用潜变量混合增长模型对大学生开展为期2年的追踪研究［37］的结果显示，与低问题性手机使用水平（低水平）发展轨迹组和中等问题性手机使用水平（中等水平）发展轨迹组相比，高问题性手机使用水平（高水平）发展轨迹组的个体中清晨型显著较少、夜晚型显著较多；在调整了性别、健康状态和睡眠质量等协变量后，多元逻辑回归结果显示具有中等水平、高水平发展轨迹的个体的睡眠时型更偏向于夜晚型，这表明长期的问题性手机使用可作为个体昼夜节律紊乱的预测因素。

鉴于睡眠时型受基因遗传的直接影响［38-39］，现有研究多倾向于将睡眠时型概念化为驱动技术成瘾的风险因素。目前，横向研究的结果展现出一种趋势，即夜晚型是技术成瘾的风险因素；相比于清晨型和中间型，夜晚型与网络成瘾、手机成瘾水平呈显著正相关，且夜晚型可以预测网络成瘾和手机成瘾水平。纵向研究则发现，夜晚型无法单向预测个体未来一段时间的手机成瘾水平，反之可以预测［34，37］，这与横向研究结果不一致。相关文献总结见表1。对于睡眠时型与技术成瘾的关系，横向研究方法限制了因果关系的解释力，而纵向研究的证据相对较少。因此，结合横向和纵向研究我们推测，该两者之间可能存在一种双向因果关系，但仍需要进一步的研究加以分析。

表 1   睡眠时型与技术成瘾关系的文献总结

Tab 1  Literature summary of the relationship between chronotype and technology addiction

Technology addictionLiterature

Basic information of subjects

(age/year; male proportion)

Measure

Comment

(when individuals are more

inclined to evening type)

ChronotypeAddiction
Internet addictionLIN et al., 2013[14]

2 731 college students

(19.40±3.60; 52.4%)

MESYBOCS-IU↑ Compulsive Internet use
RANDLER et al., 2014[17]

616 college students

(20.81±1.97; 27.9%)

CSMIA Scale↑ Internet addiction
KANG et al., 2015[13]

325 adults

(20‒49; 48.9%)

MEQYIAS↑ Incidence of Internet addiction
OH et al., 2016[15]

2 632 college students

(19.16±0.91; 48.9%)

CSMKIAT↑ Internet addiction
XU et al., 2018[19]

3 572 college students①

(freshmen and juniors; 34.5%)

MESIAT

↑ Internet addiction

↑ Incidence of Internet addiction

CHUNG et al., 2020[21]

765 adolescents

(15.07±1.36; 60.9%)

MESYIASNo significant difference in Internet addiction between different chronotypes
KOO et al., 2021[18]

8 565 high-school students

(16.77±0.85; 52.1%)

MEQIAPS↑ Internet addiction

PRZEPIORKA

et al., 2021[16]

398 college students

(20.37±2.29; 28.9%)

CSMIAT

↑ Problematic Internet use mental disorder

↑ Problematic Internet use time management disorder

YI et al., 2021[20]

7 457 college students①

[≤17 (15.1%), 18 (64.6%) and ≥19 (20.2%); 53.5%]

MEQIAT↑ Internet addiction
Social media addictionLIN et al., 2021[25]

1 791 young adults

(27.2±10.1; 30.1%)

rMEQBSMAS↑ Problematic social media use

BLACHNIO

et al., 2015[22]

633 Facebook users

(20.78±4.80; 35.9%)

CSM

FBI Scale;

FIQ

↑ Intensity and frequency of Facebook usage

↑ Facebook intrusion

HORZUM et al., 2022[24]

981 college students

(20.82±2.39; 26.9%)

CSMFAS↑ Facebook addiction
Internet gaming addictionVOLLMER et al., 2014[31]

741 adolescents

(12.89±1.05; 60.2%)

CSMCGAS

↑ Computer game usage time

↑ Computer game addiction

KÜÇÜKTURAN et al., 2022[30]

922 high-school students

(16.0±1.04; 42.7%)

CSMCASfA↑ Computer game addiction
DAĞ et al., 2022[29]

109 children

(9.20±1.45; 49.5%)

CCTQCASfC

↑ Computer game addiction

↑ "Can not give up playing games" subscale scores

↑ "Neglecting responsibilities because of computer games" subscale scores

Smartphone addictionTODA et al., 2015[27]

182 college students

(male: 21.70±2.60, female: 21.0±2.1; 67.0%)

MEQMPDQ↑ Smartphone dependence
RANDLER et al., 2016[36]

342 younger adolescents

(13.39±1.77; 51.5%);

208 older adolescents

(17.07±4.28; 29.8%)

CSM

SAPS for younger adolescents;

SAS-SV for older adolescents

↑ Smartphone addiction

DEMIRHAN

et al., 2016[33]

902 college students

(20.42±1.90; 27.0%)

CSMMPPUS↑ Problematic mobile phone usage
RANDJELOVIC et al., 2021[35]

77 college students①

(20‒22; both genders)

MEQSAS-SV↑ Problematic smartphone use
BAĞCI et al., 2022[32]

346 college students

(19.60±2.56; 32.7%)

CSMSAS-SF↑ Smartphone addiction
KANG et al., 2020[34]

Baseline: 940 college students

(19.10±0.90; 36.8%)

One-year follow-up: 902 college students

(19.10±0.90; 36.5%)

rMEQMPIQ

↑ Mobile phone involvement

↑ Mobile phone addiction behaviors

LI et al., 2022[37]

999 college students

(18.80±1.20; 37.7%)

MEQSQAPMPU↑ Score trajectories of problematic mobile phone use

Note: ① represents that no age or gender information was included in the original literature, so we use relevant information to supplement. ↑ represents a significant positive correlation. MEQ/MES—Morningness-Eveningness Questionnaire/Scale; rMEQ—reduced Morningness-Eveningness Questionnaire; CSM—Composite Scale of Morningness; CCTQ—Children's Chronotype Questionnaire; YBOCS-IU—Yale-Brown Obsessive Compulsive Scale for Internet Use; IA Scale—Internet Addiction Scale; YIAS—Young's Internet Addiction Scale; IAT—Internet Addiction Test; KIAT—Korean version of the Internet Addiction Test; IAPS—Internet Addiction Proneness Scale for Youth; BSMAS—Bergen Social Media Addiction Scale; FBI Scale—Facebook Intensity Scale; FIQ—Facebook Intrusion Questionnaire; FAS—Facebook Addiction Scale; CGAS—Computer Game Addiction Scale; CASfA—Computer Game Addiction Scale for Adolescents; CASfC—Computer Game Addiction Scale for Children; MPDQ—Mobile Phone Dependence Questionnaire; SAPS—Smartphone Addiction Proneness Scale; SAS-SV—Smartphone Addiction Scale-Short Version; SAS-SF—Smartphone Addiction Scale-Short Form; MPPUS—Mobile Phone Problem Usage Scale; MPIQ—Mobile Phone Involvement Questionnaire; SQAPMPU—Self-rating Questionnaire for Adolescent Problematic Mobile Phone Use.

新窗口打开| 下载CSV


2 睡眠时型与技术成瘾关系的作用机制

当前，相关研究仅集中在睡眠时型与技术成瘾的关系上，较少针对其中的内在机制进行探索［14-16，25］。近年来，神经科学研究发现技术成瘾与物质成瘾的病理路径趋同［40-41］。我们推测，是否可从睡眠时型与物质成瘾的研究中进行借鉴，寻找睡眠时型与技术成瘾关系的影响因素。基于此，如下从生理、心理、个人和环境这4个方面，对两者关系的作用机制进行分析。

2.1 生理因素

在研究睡眠时型与技术成瘾关系的生理影响因素中，奖赏通路是其具有代表性的因素之一。BYRNE等 ［42］发现，在奖励线索的背景下奖赏通路是能够引导动机、行为和愉悦情绪的神经网络，是成瘾的重要生物基础。有研究［38-39］显示，与睡眠时型相对应的昼夜节律基因（如Clock、Per1、Per2）的多态性表达会影响关键奖赏中枢活动和多巴胺能活动。与清晨型相比，夜晚型与奖励中枢中更高的多巴胺受体可用性相关，存在更强的奖赏敏感性［43］，在奖赏通路核心区域（如内侧前额叶、腹侧纹状体）中表现出更强的奖赏反应性［42］，且在涉及认知控制的脑区中默认网络功能连接性更弱［44］。脑科学研究表明，技术成瘾个体的参与认知控制、奖励处理的大脑皮层及皮层下区域可发生灰质体积、脑区间功能连接水平的显著改变［40-41，45-46］，总体表现出多巴胺奖赏系统的激活和执行控制网络的失效［47］。综上，夜晚型个体可能拥有一个成瘾易感性较高的奖赏系统，对技术使用可能存在更高的奖赏敏感性、奖赏反应性和更低效的认知控制，从而可促进其成瘾行为的形成和维持；同时，与奖赏系统相关的功能改变可能也是睡眠时型与技术成瘾关联的重要生物基础。

2.2 心理因素

在研究睡眠时型与技术成瘾关系的心理影响因素中，抑郁是其最受关注的因素之一。相关研究［15-16］表明，抑郁在夜晚型与网络成瘾之间起到了中介作用。夜晚型个体具有较低的未来时间洞察力，更倾向使用非适应性的情绪调节策略，而这些特点会加剧个体的负性体验，被认为是夜晚型与抑郁关联的行为机制［5］。与夜晚型个体的低情绪调节能力相关的脑机制改变，如较高的情绪加工脑区激活水平、较低的默认网络功能连接强度等，被认为是夜晚型与抑郁关联的神经基础［5，48-49］。且抑郁的严重程度与技术成瘾相关［50-51］。夜晚型个体具有生理和心理上的抑郁易感性，该类人群会将技术使用作为一种回避和调节抑郁情绪的策略，当技术使用为其提供了持续的奖励（如满足感、沉浸感）时，个体就会从正常技术使用过渡为技术成瘾。

2.3 个人因素

2.3.1 性别与年龄

睡眠时型与技术成瘾间的关联强度可能存在性别和年龄的差异。有研究表明，男性更偏向于夜晚型且更易沉迷于网络、游戏、社交媒体，女性则更偏向于清晨型和手机成瘾［13-14，17，24，27，30-32，36］，产生差异的原因可能包括高睾酮水平的性激素影响［52］、网络使用动机［17］以及父母对男孩持更放纵的教养风格［53］，但也有研究［14，20，28，35］发现了与之相反或无性别差异的结果。对于年龄方面，年轻人比成年人、老年人更偏向夜晚型且存在更多的技术使用问题［6，22，54-55］，年纪小的孩子比年长的孩子存在更多的网络游戏成瘾风险［31］和手机成瘾风险［56］。

2.3.2 人格特质

人格是个体稳定的心理特质。不同睡眠时型的个体存在特定的人格特质，使得技术成瘾也存在个体差异。元分析表明，清晨型与高尽责性、低神经质存在中等的相关性，夜晚型与低尽责性、高外向性和开放性存在中等的相关性［57］。而高尽责性和宜人性是技术使用过程中的保护因素，高神经质是促进技术成瘾的风险因素［58-61］。综上，夜晚型更偏向于低尽责性，该较为不利的人格特质增加了个体发展为技术成瘾的可能；且尽责性是与自我调节技能相关的特质，低尽责性意味着自我调节能力较低［62］，偏向即时满足，从而易导致技术成瘾［10，63］。

2.3.3 睡眠模式

研究表明，睡眠质量和失眠症状在夜晚型与社交媒体成瘾之间起到中介作用［25］，不利的睡眠模式可能在其中产生重要影响。睡前，人们的感觉更为放松，暴露在屏幕蓝光中的时间较长，从而延迟了个体的昼夜节律［64］。且密切的夜晚人机互动会导致长期的社交时差，在夜晚型个体中产生睡眠债务，甚至导致慢性睡眠剥夺［65］。而这也会在神经层面上增强夜晚型个体的奖励回路敏感性和冲动性，增加技术成瘾的发生风险［39］。

2.4 环境因素

家庭环境是青少年最为亲密的社会环境。在睡眠时型与技术成瘾关系研究中，父母教养风格是最受关注的环境影响因素。有研究［14］表明，父母教养风格可在睡眠时型和强迫性网络使用间发挥调节作用。父母对青少年的监督、与之交流形成的良好亲子关系是睡眠和技术成瘾的主要保护因素［66］，而缺乏父母支持和监督的青少年可能无法控制自己的就寝时间和日常安排，从而有更高的风险发展为夜晚型和网络成瘾［67］。研究发现，不良教养风格（如侵入性控制、自主性限制等）往往会塑造出焦虑型、回避型依恋的孩子［68］；该类子女易产生逆反心理、激发亲子冲突，常会通过使用网络和手机来抵抗和回避父母，以寻求补偿与替代性的社会支持［10］。

3 睡眠时型与技术成瘾关系的理论理解

基于上述分析，我们尝试使用I-PACE模型和进化生命史理论，从病因和进化的角度对睡眠时型与技术成瘾的关系进行理论分析。

3.1 夜晚型发展和维持技术成瘾的病理过程

BRAND等［69］于2016年提出I-PACE模型，用以解释特定网络使用障碍发展和维持的过程。该团队认为，特定网络使用障碍是易感性因素、调节或中介因素、执行功能因素相互作用的结果。根据I-PACE，睡眠时型及其相关的奖赏系统、人格特质、父母教养风格等均为技术成瘾的易感性因素，这些因素可能增加个体的冲动应对策略、对内外部刺激的抑郁反应等调节或中介因素的程度，再结合自我调节等执行控制能力的下降，从而可使夜晚型个体更易发展和维持技术成瘾行为。在特定技术使用障碍的早期阶段，夜晚型个体在不断延后的睡眠时间里通过网络和手机体验到的满足感会导致正强化，从而加强夜晚型与技术使用间的关联。随着技术使用向着失控和强迫性发展，夜晚型个体可能会经历人际冲突、抑郁焦虑等负面状况，进而通过网络和手机体验到的补偿感会继续维持其技术成瘾行为。

3.2 夜晚型倾向选择技术成瘾的进化视角

进化生命史理论认为，个体分配有限资源的倾向可反映其特有的生命史策略（life history strategy，LHS）。对于成长于恶劣环境的个体而言，其对未来的预期不佳，认为长期投入的风险较大，而将未来收益折现是资源利用最大化的方式，因此该类个体强调短期效应，倾向于即时满足，称为快策略者［70］。从进化视角来讲，夜晚型是一种快策略的表达，这可能与人类祖先使用火向夜间环境扩张相关。黑暗带来的适应性挑战选择了那些更加冲动、偏执以及社交性较高的人，且黑暗导致的能见度降低也更利于隐藏身份，从而可降低个体抑制不良行为的可能性，增加冲动行事的风险［71］。因此，危险且隐秘的夜间活动所选择和塑造的夜晚型个体会更加冲动，倾向于感觉寻求，看重短期效应［6］。对于快策略的夜晚型个体，成瘾行为是其为了获得即时满足的选择。沉迷于技术会减少长期投资，获得更多即时的回报和享受，这对快策略个体来说具有一定的进化适应性意义。

4 总结与展望

回顾现有横向研究可以发现，夜晚型个体具有更高的技术成瘾水平和技术成瘾风险，且夜晚型倾向还是个体技术成瘾重要且独特的预测因子。纵向研究结果表明个体的技术成瘾问题会增加未来夜晚型倾向的可能性，但反之个体的睡眠时型无法预测之后的技术成瘾。从上述两类研究获得的结果发现，睡眠时型与技术成瘾或存在双向因果关系。

在生理和心理层面上，夜晚型个体具有成瘾易感性更高的奖赏系统和抑郁易感性更高的情绪状态，这可能会增加其技术成瘾的风险。在个人和环境层面上，睡眠时型与技术成瘾的关系存在年龄和性别差异，年轻男性是高风险人群。此外，具有低尽责性的人格特质、不利的睡眠模式、接受了控制和忽视的教养风格特点的夜晚型个体可能更易技术成瘾。为了更加深入和全面地理解睡眠时型与技术成瘾的关系，我们使用I-PACE模型对夜晚型个体技术成瘾的发生、发展和维持过程进行理论解释，还从进化的新视角发现采用快策略的夜晚型个体选择技术成瘾是具有功能意义和进化适应性的。

综合上述研究我们发现，睡眠时型与技术成瘾关系相互促进，但由于该两者的关系证据多来自问卷调查，其结果或局限了对两者因果关系的推断。未来，或可考虑使用更客观的“数字时型”来衡量个体的内源性睡眠时型和技术使用水平［64］，增加前瞻性队列研究以进一步确定两者之间的因果关系；同时，借鉴睡眠时型与物质成瘾研究领域的方向和方法，以丰富睡眠时型与技术成瘾的内在机制研究，从而可以对具有可塑性的影响因素进行干预，降低技术成瘾的可能性。

作者贡献声明

吴玉婧负责论文的撰写与修改，郭茜、刘晓华、李冠军指导论文写作。所有作者均阅读并同意了最终稿件的提交。

AUTHOR's CONTRIBUTIONS

The manuscript was drafted and revised by WU Yujing. The manuscript was guided by GUO Qian, LIU Xiaohua and LI Guanjun. All the authors have read the last version of paper and consented for submission.

利益冲突声明

所有作者声明不存在利益冲突。

COMPETING INTERESTS

All authors disclose no relevant conflict of interests.

参考文献

中国互联网络信息中心. 第49次《中国互联网络发展状况统计报告》[EB/OL]. (2022-02-25)[2022-06-10]. http://www.cnnic.net.cn/n4/2022/0401/c88-1131.html.

[本文引用: 1]

China Internet Network Information Center. The 49th statistical report on China's internet development[EB/OL]. (2022-02-25) [2022-06-10]. http://www.cnnic.net.cn/n4/2022/0401/c88-1131.html.

[本文引用: 1]

MENG S Q, CHENG J L, LI Y Y, et al. Global prevalence of digital addiction in general population: a systematic review and meta-analysis[J]. Clin Psychol Rev, 2022, 92: 102128.

[本文引用: 1]

GRIFFITHS M. Internet addiction: fact or fiction? [J]. The Psychologist, 1999, 12(5): 246-250.

[本文引用: 1]

TUREL O, SERENKO A, GILES P. Integrating technology addiction and use: an empirical investigation of online auction users[J]. MIS Q, 2011, 35(4): 1043-1062.

[本文引用: 1]

陈永进, 黄惠珍, 支愧云, 等. 睡眠时型与抑郁的关系及其机制[J]. 心理科学进展, 2020, 28(10): 1713-1722.

[本文引用: 3]

CHEN Y J, HUANG H Z, ZHI K Y, et al. Relationship between chronotype and depression and its mechanism[J]. Advances in Psychological Science, 2020, 28(10): 1713-1722.

[本文引用: 3]

ADAN A, ARCHER S N, HIDALGO M P, et al. Circadian typology: a comprehensive review[J]. Chronobiol Int, 2012, 29(9): 1153-1175.

[本文引用: 4]

EUGENIA T, PAULINA S, JUSTYNA M. Disruption of circadian rhythmicity: an exploratory study in Poland[J]. Pers Individ Differ, 2014, 66: 98-101.

[本文引用: 1]

VOLLMER C, MICHEL U, RANDLER C. Outdoor light at night (LAN) is correlated with eveningness in adolescents[J]. Chronobiol Int, 2012, 29(4): 502-508.

[本文引用: 1]

CELLINI N, CANALE N, MIONI G, et al. Changes in sleep pattern, sense of time and digital media use during COVID-19 lockdown in Italy[J]. J Sleep Res, 2020, 29(4): e13074.

[本文引用: 1]

BUSCH P A, MCCARTHY S. Antecedents and consequences of problematic smartphone use: a systematic literature review of an emerging research area[J]. Comput Hum Behav, 2021, 114: 106414.

[本文引用: 3]

ZOU Z, WANG H, D′OLEIRE UQUILLAS F, et al. Definition of substance and non-substance addiction[J]. Adv Exp Med Biol, 2017, 1010: 21-41.

[本文引用: 1]

DAVIS R A. A cognitive-behavioral model of pathological Internet use[J]. Comput Hum Behav, 2001, 17(2): 187-195.

[本文引用: 1]

KANG D W, SOH M, LEE T K. Relationship between Internet addiction and circadian rhythm in adults[J]. Sleep Med Psychophysiol, 2015, 22(2): 57-63.

[本文引用: 4]

LIN Y H, GAU S S. Association between morningness-eveningness and the severity of compulsive Internet use: the moderating role of gender and parenting style[J]. Sleep Med, 2013, 14(12): 1398-1404.

[本文引用: 5]

OH K S, LEE H K, LEE K. The effect of circadian types and depressive mood on Internet addiction in Korean college students[J]. J Korean Soc Biol Psychiatry, 2016, 23(4): 173-178.

[本文引用: 2]

PRZEPIORKA A, BLACHNIO A, CUDO A. Relationships between morningness, Big Five personality traits, and problematic Internet use in young adult university students: mediating role of depression[J]. Chronobiol Int, 2021, 38(2): 248-259.

[本文引用: 3]

RANDLER C, HORZUM M B, VOLLMER C. Internet addiction and its relationship to chronotype and personality in a Turkish university student sample[J]. Soc Sci Comput Rev, 2014, 32(4): 484-495.

[本文引用: 5]

KOO D L, YANG K I, KIM J H, et al. Association between morningness-eveningness, sleep duration, weekend catch-up sleep and depression among Korean high-school students[J]. J Sleep Res, 2021, 30(1): e13063.

[本文引用: 2]

许浩迪, 张璐, 孙晋斌, 等. 大学生日周期类型与网络成瘾的关系[J]. 中华行为医学与脑科学杂志, 2018, 27(11): 1035-1038.

[本文引用: 2]

XU H D, ZHANG L, SUN J B, et al. Relationship between circadian typology and internet addiction in college students[J]. Chinese Journal of Behavioral Medicine and Brain Science, 2018, 27(11): 1035-1038.

[本文引用: 2]

易伶俐, 陶煜杰, 杨霞, 等. 大学新生网络成瘾、睡眠质量及日周期类型的现况调查[J]. 中华精神科杂志, 2021, 54(2): 132-137.

[本文引用: 3]

YI L L, TAO Y J, YANG X, et al. Internet addiction, sleep quality and sleep-wake chronotype among first-year undergraduate students[J]. Chinese Journal of Psychiatry, 2021, 54(2): 132-137.

[本文引用: 3]

CHUNG J S, CHOI E, LEE A R, et al. The difference in sleep, depression, anxiety, and Internet addiction between Korean adolescents with different circadian preference[J]. Indian J Psychiatry, 2020, 62(5): 524-530.

[本文引用: 2]

BLACHNIO A, PRZEPIORKA A, DÍAZ-MORALES J F. Facebook use and chronotype: results of a cross-sectional study[J]. Chronobiol Int, 2015, 32(9): 1315-1319.

[本文引用: 4]

HORZUM M B, DEMIRHAN E. The role of chronotype on Facebook usage aims and attitudes towards Facebook and its features[J]. Comput Hum Behav, 2017, 73: 125-131.

[本文引用: 1]

HORZUM M B, CANAN GÜNGÖREN Ö, GÜR ERDOĞAN D. The influence of chronotype, personality, sex, and sleep duration on Facebook addiction of university students in Turkey[J]. Biol Rhythm Res, 2022, 53(7): 1105-1115.

[本文引用: 3]

LIN C Y, IMANI V, GRIFFITHS M D, et al. Temporal associations between morningness/eveningness, problematic social media use, psychological distress and daytime sleepiness: mediated roles of sleep quality and insomnia among young adults[J]. J Sleep Res, 2021, 30(1): e13076.

[本文引用: 4]

KREJCI M, KAI W D, NAKADE M, et al. Effects of video game playing on the circadian typology and mental health of young Czech and Japanese children[J]. Psychology, 2011, 2(7): 674-680.

[本文引用: 1]

TODA M, TAKESHITA T, NISHIO N, et al. Chronotype and smartphone use among Japanese medical students[J]. Int J Cyber Behav Psychol Learn, 2015, 5(2): 75-80.

[本文引用: 4]

URBÁN R, MAGYARÓDI T, RIGÓ A. Morningness-eveningness, chronotypes and health-impairing behaviors in adolescents[J]. Chronobiol Int, 2011, 28(3): 238-247.

[本文引用: 2]

DAĞ Y S, YAYAN Y Ö, YAYAN E H. Computer game addictions of children: chronotype sleep[J]. JTSM, 2022, 9(1): 73-78.

[本文引用: 2]

KÜÇÜKTURAN A G, HORZUM M B, KORKMAZ G, et al. Investigating the relationship between personality, chronotype, computer game addiction, and sleep quality of high school students: a structural equation modelling approach[J]. Chronobiol Int, 2022, 39(4): 590-601.

[本文引用: 2]

VOLLMER C, RANDLER C, HORZUM M B, et al. Computer game addiction in adolescents and its relationship to chronotype and personality[J]. SAGE Open, 2014, 4(1): 215824401351805.

[本文引用: 3]

BAĞCI H, HORZUM M B. The relationship of smartphone addiction with chronotype and personality structures in university students[J]. Biol Rhythm Res, 2022, 53(12): 1917-1931.

[本文引用: 4]

DEMIRHAN E, RANDLER C, HORZUM M B. Is problematic mobile phone use explained by chronotype and personality?[J]. Chronobiol Int, 2016, 33(7): 821-831.

[本文引用: 2]

KANG Y Z, LIU S, YANG L L, et al. Testing the bidirectional associations of mobile phone addiction behaviors with mental distress, sleep disturbances, and sleep patterns: a one-year prospective study among Chinese college students[J]. Front Psychiatry, 2020, 11: 634.

[本文引用: 4]

RANDJELOVIC P, STOJILJKOVIC N, RADULOVIC N, et al. Problematic smartphone use, screen time and chronotype correlations in university students[J]. Eur Addict Res, 2021, 27(1): 67-74.

[本文引用: 3]

RANDLER C, WOLFGANG L, MATT K, et al. Smartphone addiction proneness in relation to sleep and morningness-eveningness in German adolescents[J]. J Behav Addict, 2016, 5(3): 465-473.

[本文引用: 3]

LI T T, ZHANG D, QU Y, et al. Association between trajectories of problematic mobile phone use and chronotype among Chinese college students[J]. Addict Behav, 2022, 134: 107398.

[本文引用: 4]

ADAN A N. A chronobiological approach to addiction[J]. J Subst Use, 2013, 18(3): 171-183.

[本文引用: 2]

LOGAN R W, HASLER B P, FORBES E E, et al. Impact of sleep and circadian rhythms on addiction vulnerability in adolescents[J]. Biol Psychiatry, 2018, 83(12): 987-996.

[本文引用: 3]

SCHETTLER L, THOMASIUS R, PASCHKE K. Neural correlates of problematic gaming in adolescents: a systematic review of structural and functional magnetic resonance imaging studies[J]. Addict Biol, 2022, 27(1): e13093.

[本文引用: 2]

SEPEDE G, TAVINO M, SANTACROCE R, et al. Functional magnetic resonance imaging of Internet addiction in young adults[J]. World J Radiol, 2016, 8(2): 210-225.

[本文引用: 2]

BYRNE J E M, TREMAIN H, LEITAN N D, et al. Circadian modulation of human reward function: is there an evidentiary signal in existing neuroimaging studies?[J]. Neurosci Biobehav Rev, 2019, 99: 251-274.

[本文引用: 2]

HASLER B P, MCCLUNG C A. Delayed circadian rhythms and substance abuse: dopamine transmission's time has come[J]. J Clin Invest, 2021, 131(18): e152832.

[本文引用: 1]

FACER-CHILDS E R, CAMPOS B M, MIDDLETON B, et al. Circadian phenotype impacts the brain's resting-state functional connectivity, attentional performance, and sleepiness[J]. Sleep, 2019, 42(5): zsz033.

[本文引用: 1]

HORVATH J, MUNDINGER C, SCHMITGEN M M, et al. Structural and functional correlates of smartphone addiction[J]. Addict Behav, 2020, 105: 106334.

[本文引用: 1]

PYEON A, CHOI J, CHO H, et al. Altered connectivity in the right inferior frontal gyrus associated with self-control in adolescents exhibiting problematic smartphone use: a fMRI study[J]. J Behav Addict, 2021, 10(4): 1048-1060.

[本文引用: 1]

NASSER N S, SHARIFAT H, RASHID A A, et al. Cue-reactivity among young adults with problematic instagram use in response to instagram-themed risky behavior cues: a pilot fMRI study[J]. Front Psychol, 2020, 11: 556060.

[本文引用: 1]

HORNE C M, NORBURY R. Late chronotype is associated with enhanced amygdala reactivity and reduced fronto-limbic functional connectivity to fearful versus happy facial expressions[J]. NeuroImage, 2018, 171: 355-363.

[本文引用: 1]

NIELSON D M, KEREN H N, O'CALLAGHAN G, et al. Great expectations: a critical review of and suggestions for the study of reward processing as a cause and predictor of depression[J]. Biol Psychiatry, 2021, 89(2): 134-143.

[本文引用: 1]

ELHAI J D, DVORAK R D, LEVINE J C, et al. Problematic smartphone use: a conceptual overview and systematic review of relations with anxiety and depression psychopathology[J]. J Affect Disord, 2017, 207: 251-259.

[本文引用: 1]

KO C H, YEN J Y, YEN C F, et al. The association between Internet addiction and psychiatric disorder: a review of the literature[J]. Eur Psychiatry, 2012, 27(1): 1-8.

[本文引用: 1]

RANDLER C, EBENHÖH N, FISCHER A, et al. Chronotype but not sleep length is related to salivary testosterone in young adult men[J]. Psychoneuroendocrinology, 2012, 37(10): 1740-1744.

[本文引用: 1]

HORZUM M B, BEKTAŞ M. Examining the Internet use aim and Internet parental style of primary school students in terms of various variables[J]. Croat J Educ, 2014, 16: 745-778.

[本文引用: 1]

FESTL R, SCHARKOW M, QUANDT T. Problematic computer game use among adolescents, younger and older adults[J]. Addiction, 2013, 108(3): 592-599.

[本文引用: 1]

SOULE L C, SHELL L W, KLEEN B A. Exploring Internet addiction: demographic characteristics and stereotypes of heavy Internet users[J]. J Comput Inf Syst, 2003, 44(1): 64-73.

[本文引用: 1]

PARK C, PARK Y R. The conceptual model on smart phone addiction among early childhood[J]. Int J Soc Sci Humanity, 2014, 4(2): 147-150.

[本文引用: 1]

LIPNEVICH A A, CREDÈ M, HAHN E, et al. How distinctive are morningness and eveningness from the Big Five factors of personality? A meta-analytic investigation[J]. J Pers Soc Psychol, 2017, 112(3): 491-509.

[本文引用: 1]

CHEW P K H. A meta-analytic review of Internet gaming disorder and the Big Five personality factors[J]. Addict Behav, 2022, 126: 107193.

[本文引用: 1]

HUANG C. Social media addiction and personality: a meta-analysis[J]. Asian J Social Psycho, 2022, 25(4): 747-761.

KAYIŞ A R, SATICI S A, YILMAZ M F, et al. Big Five-personality trait and Internet addiction: a meta-analytic review[J]. Comput Hum Behav, 2016, 63: 35-40.

MARENGO D, SINDERMANN C, HÄCKEL D, et al. The association between the Big Five personality traits and smartphone use disorder: a meta-analysis[J]. J Behav Addict, 2020, 9(3): 534-550.

[本文引用: 1]

KADZIKOWSKA-WRZOSEK R. Self-regulation and bedtime procrastination: the role of self-regulation skills and chronotype[J]. Pers Individ Differ, 2018, 128: 10-15.

[本文引用: 1]

GÁMEZ-GUADIX M, CALVETE E, ORUE I, et al. Problematic Internet use and problematic alcohol use from the cognitive-behavioral model: a longitudinal study among adolescents[J]. Addict Behav, 2015, 40: 109-114.

[本文引用: 1]

LIN Y H, WONG B Y, LIN S H, et al. Development of a mobile application (App) to delineate "digital chronotype" and the effects of delayed chronotype by bedtime smartphone use[J]. J Psychiatr Res, 2019, 110: 9-15.

[本文引用: 2]

CALIANDRO R, STRENG A A, VAN KERKHOF L W M, et al. Social jetlag and related risks for human health: a timely review[J]. Nutrients, 2021, 13(12): 4543.

[本文引用: 1]

BRANDHORST I, RENNER T, BARTH G M. Parental factors in Internet and computer game addiction in adolescence: an overview[J]. Z Kinder Jugendpsychiatr Psychother, 2021, 50(1): 37-50.

[本文引用: 1]

YOUNG K S, DE ABREU C N. Internet addiction: a handbook and guide to evaluation and treatment[M]. Hoboken: John Wiley & Sons, 2010.

[本文引用: 1]

D'ARIENZO M C, BOURSIER V, GRIFFITHS M D. Addiction to social media and attachment styles: a systematic literature review[J]. Int J Ment Health Addiction, 2019, 17(4): 1094-1118.

[本文引用: 1]

BRAND M, YOUNG K S, LAIER C, et al. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: an Interaction of Person-Affect-Cognition-Execution (I-PACE) model[J]. Neurosci Biobehav Rev, 2016, 71: 252-266.

[本文引用: 1]

王燕, 林镇超, 侯博文, 等. 生命史权衡的内在机制: 动机控制策略的中介作用[J]. 心理学报, 2017, 49(6): 783-793.

[本文引用: 1]

WANG Y, LIN Z C, HOU B W, et al. The intrinsic mechanism of life history trade-offs: the mediating role of control striving[J]. Acta Psychological Sinica, 2017, 49(6): 783-793.

[本文引用: 1]

VARELLA M A C, LUOTO S, SOARES R B D S, et al. COVID-19 pandemic on fire: evolved propensities for nocturnal activities as a liability against epidemiological control[J]. Front Psychol, 2021, 12: 646711.

[本文引用: 1]

/

〈 〉