青少年手机依赖的纵向分析及其影响因素
A Longitudinal Analysis of Mobile Phone Dependence in Chinese Adolescents: The Risk and Promotive Factors of Mobile Phone Dependence Trajectories

DOI: 10.12677/AP.2021.111002, PP. 9-19

Keywords: 手机依赖，学业压力，心理痛苦，学业韧性，纵向设计
Mobile Phone Dependence, Academic Stress, Psychological Distress, Academic Resilience, Longitudinal Design

Full-Text   Cite this paper   Add to My Lib

Abstract:

本研究的目的是探索青少年手机依赖的发展轨迹，以及性别、学业压力、心理痛苦、学业韧性和手机依赖之间的纵向关系。方法：采用手机依赖指数量表(Mobile Phone Dependence Index, MPAI)中文版、学业压力量表(Academic Stress Scale, ASS)、简要症状清单(Brief Symptom Inventory, BSI)和学业韧性量表(Academic Resilience Scale, ARS)，每六个月施测一次，连续施测三次，共有140人(32.3%)没有参加完整的三次测试，最后得到293名学生，其中男生109人(37.2%)，女生184人(62.8%)，平均年龄为17.46。结果：潜类别增长模型结果显示手机依赖的发展轨迹存在显著的个体差异，表现为三条异质亚组发展轨迹，分别为低水平下降组(72.7%)、中等稳定组(19.4%)和高手机依赖组(7.9%)。Logistic回归分析结果显示，高水平依赖组和中度水平稳定组的心理痛苦水平显著高于低水平下降组。中等水平稳定组里女生显著多于男生。研究结果为发现青少年手机依赖的风险群体以及促进更有效的干预提供了启示。
The purpose of the present study was to identify the latent classes of mobile phone dependence trajectory patterns, and to examine the associations between gender, academic stress, psychological distress and academic resilience and these patterns. The participants were 293 10th grade students and they filled in the questionnaires over 3 waves, with an interval of 6 months between each wave (April 2017 wave 1, November 2017 wave 2, and June 2018 wave 3). Latent Class Growth Modeling was used to identify sub-populations and three latent classes were observed: High-dependency class (7.9%), Moderate stable class (19.4%), and Low decreasing class (72.7%). Multinomial logistic regression analysis showed that psychological distress was significantly higher in the High-dependency class and Moderate stable class, compared with the Low decreasing class. In addition, girls were more likely to be involved in the Moderate stable class than boys. Our findings provided implications for discovering the risk groups of MPD and facilitating a more effective intervention.

References

[1]	高燕, 李兆良(2009). 论提升大学生心理韧性与应对网络成瘾的发生. 吉林省经济管理干部学院学报, 23(3), 115-117.
[2]	巩彦平, 胡瑜(2015). 大学生网络成瘾及其与社会支持、心理韧性的关系. 晋中学院学报, (6), 91-95.
[3]	郝传慧(2008). 青少年生活事件、心理弹性与互联网使用的关系. 硕士学位论文, 北京: 首都师范大学.
[4]	黄海, 牛露颖, 周春燕, 吴和鸣(2014). 手机依赖指数中文版在大学生中的信效度检验. 中国临床心理学杂志, 5, 16.
[5]	郎艳, 贾福军, 李恒芬, 苏林雁, 赵山明(2008). 初中生网络成瘾状况调查及相关因素分析. 中国临床心理学杂志, 16(4), 417-419.
[6]	廖雅琼, 叶宝娟, 金平, 许强, 李爱梅(2017). 心理韧性对汉区少数民族预科生手机依赖的影响: 有调节的中介效应. 心理发展与教育, 33(4), 487-495.
[7]	王孟成(2014). 潜变量建模与Mplus应用, 基础篇. 重庆: 重庆大学出版社.
[8]	许婷婷(2017). 学业压力对青少年手机问题性使用的影响: 消极情绪的中介作用和保护性因素的调节效应. 硕士学位论文, 重庆: 西南大学.
[9]	张光珍, 梁宗保, 邓慧华, 陆祖宏(2014). 学校氛围与青少年学校适应: 一项追踪研究. 心理发展与教育, 30(4), 371-379.
[10]	Agnew, B., & White, H. R. (1992). An Empirical Test of General Strain Theory. Criminology, 30, 475-500. https://doi.org/10.1111/j.1745-9125.1992.tb01113.x
[11]	Agnew, R. (1992). Foundation for a General Strain Theory of Crime and Delinquency. Criminololgy, 30, 47-87. https://doi.org/10.1111/j.1745-9125.1992.tb01093.x
[12]	Alva, S. A. (1991). Academic Invulnerability among Mexican-American Students: The Importance of Protective Resources and Appraisals. Hispanic Journal of Behavioral Sciences, 13, 18-34. https://doi.org/10.1177/07399863910131002
[13]	Augner, C., & Hacker, G. W. (2012). Associations between Problematic Mobile Phone Use and Psychological Parameters in Young Adults. International Journal of Public Health, 57, 437-441. https://doi.org/10.1007/s00038-011-0234-z
[14]	Bianchi, A., & Phillips, J. G. (2005). Psychological Predictors of Problem Mobile Phone Use. CyberPsychology & Behavior, 8, 39-51. https://doi.org/10.1089/cpb.2005.8.39
[15]	Billieux, J. (2012). Problematic Use of the Mobile Phone: A Literature Review and a Path Ways Model. Current Psychiatry Reviews, 8, 299-307. https://doi.org/10.2174/157340012803520522
[16]	Billieux, L., & Linden, M. V. A. N. D. E. R. (2008). The Role of Impulsivity in Actual and Problematic Use of the Mobile Phone. Applied Cognitive Psychology, 22, 1195-1210. https://doi.org/10.1002/acp.1429
[17]	Block, J. J. (2008). Issues for DSM-V: Internet Addiction. American Journal of Psychiatry, 165, 306-307. https://doi.org/10.1176/appi.ajp.2007.07101556
[18]	Cassidy, S. (2016). The Academic Resilience Scale (ARS-30): A New Multidimensional Construct Measure. Frontiers in Psychology, 7, 1787. https://doi.org/10.3389/fpsyg.2016.01787
[19]	Chiu, S. (2014). The Relationship between Life Stress and Smart-phone Addiction on Taiwanese University Student？: A Mediation Model of Learning Self-Efficacy and Social Self-Efficacy. Computers in Human Behavior, 34, 49-57. https://doi.org/10.1016/j.chb.2014.01.024
[20]	Chóliz, M. (2012). Mobile-Phone Addiction in Adolescence: The Test of Mobile Phone Dependence (TMD). Progress in Health Sciences, 12, 33-44.
[21]	CNNIC (2019). The 43rd China Statistical Report on Internet Development. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201902/P020190318523029756345.pdf
[22]	Coyne, S. M., Padil-la-Walker, L. M., Holmgren, H. G., & Stockdale, L. A. (2018). Instagrowth: A Longitudinal Growth Mixture Model of Social Media Time Use across Adolescence. Journal of Research on Adolescence, 29, 897-907. https://doi.org/10.1111/jora.12424
[23]	Derogatis, L. R., & Melisaratos, N. (1983). The Brief Symptom Inventory: An Introductory Report. Psychological Medicine, 13, 595-605, https://doi.org/10.1017/S0033291700048017
[24]	Derogatis, L. R., & Spencer, P. M. (1982). The Brief Symptom Inventory (BSI): Administration and Procedures. Manual I. Baltimore, MD: Clinical Psychometric Research. https://doi.org/10.1037/t00789-000
[25]	Elhai, J. D., Tiamiyu, M. F., Weeks, J. W., Levine, J. C., Picard, K. J., & Hall, B. J. (2018). Depression and Emotion Regulation Predict Objective Smartphone Use Measured over One Week. Personality and Individual Differences, 133, 21-28. https://doi.org/10.1016/j.paid.2017.04.051
[26]	Goldschmidt, A. B., Wonderlich, S. A., Crosby, R. D., Engel, S. G., Lavender, J. M., Peterson, C. B. et al. (2014). Ecological Momentary Assessment of Stressful Events and Negative Affect in Bulimia Nervosa. Journal of Consulting and Clinical Psychology, 82, 30-39. https://doi.org/10.1037/a0034974
[27]	Haynos, A. F., & Fruzzetti, A. E. (2011). Anorexia Nervosa as a Disorder of Emotion Dysregulation: Evidence and Treatment Implications. Clinical Psychology: Science and Practice, 18, 183-202. https://doi.org/10.1111/j.1468-2850.2011.01250.x
[28]	Hou, J., Ndasauka, Y., Jiang, Y., Ye, Z., Wang, Y., Yang, L., Xu, F. et al. (2017). Excessive Use of Wechat, Social Interaction and Locus of Control among College Students in China. PLoS ONE, 12, e0183633. https://doi.org/10.1371/journal.pone.0183633
[29]	Jun, S., & Choi, E. (2015). Academic Stress and Internet Addiction from General Strain Theory Framework. Computers in Human Behavior, 49, 282-287. https://doi.org/10.1016/j.chb.2015.03.001
[30]	Jung, T., & Wickrama, K. A. S. (2008). An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling. Social and Personality Psychology Compass, 2, 302-317. https://doi.org/10.1111/j.1751-9004.2007.00054.x
[31]	Kardefelt-Winther, D. (2014). A Conceptual and Methodological Critique of Internet Addiction Research: Towards a Model of Compensatory Internet Use. Computers in Human Behavior, 31, 351-354. https://doi.org/10.1016/j.chb.2013.10.059
[32]	Khang, H., Kim, J. K., & Kim, Y. (2013). Self-Traits and Motivations as Antecedents of Digital Media Flow and Addiction: The Internet, Mobile Phones, and Video Games. Computers in Human Behavior, 29, 2416-2424. https://doi.org/10.1016/j.chb.2013.05.027
[33]	King, A. L. S., Valen？a, A. M., Silva, A. C. O., Baczynski, T., Carvalho, M. R., & Nardi, A. E. (2013). Nomophobia: Dependency on Virtual Environments or Social Phobia? Computers in Human Behavior, 29, 140-144. https://doi.org/10.1016/j.chb.2012.07.025
[34]	Kwon, M., Lee, J. Y., Won, W. Y., Park, J. W., Min, J. A., Hahn, C., Kim, D. J. et al. (2013). Development and Validation of a Smartphone Addiction Scale (SAS). PLoS ONE, 8, e83558. https://doi.org/10.1371/journal.pone.0083558
[35]	Lam, L. T., Peng, Z., Mai, J., & Jing, J. (2009). Factors Asso-ciated with Internet Addiction among Adolescents. CyberPsychology & Behavior, 12, 551-555. https://doi.org/10.1089/cpb.2009.0036
[36]	Lee, U., Lee, J., Ko, M., Lee, C., Kim, Y., Yang, S., Song, J. et al. (2014). Hooked on Smartphones: An Exploratory Study on Smartphone Overuse among College Students. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, April 2014, 2327-2336. https://doi.org/10.1145/2556288.2557366
[37]	Lemish, D., & Cohen, A. A. (2005). On the Gendered Nature of Mobile Phone Culture in Israel. Sex Roles, 52, 511-521. https://doi.org/10.1007/s11199-005-3717-7
[38]	Lepp, A., Barkley, J. E., & Karpinski, A. C. (2014). The Relationship between Cell Phone Use, Academic Performance, Anxiety, and Satisfaction with Life in College Students. Computers in Human Behavior, 31, 343-350. https://doi.org/10.1016/j.chb.2013.10.049
[39]	Leung, L. (2008). Linking Psychological Attributes to Addiction and Improper Use of the Mobile Phone among Adolescents in Hong Kong. Journal of Children and Media, 2, 93-113. https://doi.org/10.1080/17482790802078565
[40]	Li, D., Zhang, W., Li, X., Zhen, S., & Wang, Y. (2010). Stressful Life Events and Problematic Internet Use by Adolescent Females and Males: A Mediated Moderation Model. Computers in Human Behavior, 26, 1199-1207. https://doi.org/10.1016/j.chb.2010.03.031
[41]	Liu, R. D., Hong, W., Ding, Y., Oei, T. P., Zhen, R., Jiang, S., & Liu, J. (2019). Psychological Distress and Problematic Mobile Phone Use among Adolescents: The Mediating Role of Maladaptive Cognitions and the Moderating Role of Effortful Control. Frontiers in Psychology, 10, 1589. https://doi.org/10.3389/fpsyg.2019.01589
[42]	Liu, Y., & Lu, Z. (2011). The Chinese High School Student’s Stress in the School and Academic Achievement. Educational Psychology, 31, 27-35. https://doi.org/10.1080/01443410.2010.513959
[43]	Liu, Y., & Lu, Z. (2012). Chinese High School Students’ Academic Stress and Depressive Symptoms: Gender and School Climate as Moderators. Stress and Health, 28, 340-346. https://doi.org/10.1002/smi.2418
[44]	Livingstone, S. M. (1998). Making Sense of Television: The Psychology of Audience Interpretation. London: Routledge. https://books.glgoo.com/books?hl=zh-CN&lr=&id=0iIbwhcu1r4C&oi=fnd&pg=PR3&dq=Livingstone,+S., +1998.+Making+Sense+of+Television:+The+Psychology+of+Audience+Interpretation+ second+ed..+Routledge,+London.&ots=Fho8YN_gnr&sig=NeuB5EEqP_ bzvnsYkP0akHuGCYc&redir_esc =y#v=onepage&q&f=false
[45]	Lo, Y. (2001). Testing the Number of Components in a Normal Mixture. Biometrika, 88, 767-778. https://doi.org/10.1093/biomet/88.3.767
[46]	Luthar, S. S., Cicchetti, D., & Becker, B. (2000). The Construct of Resilience: A Critical Evaluation and Guidelines for Future Work. Child Development, 71, 543-562. https://doi.org/10.1111/1467-8624.00164
[47]	Martin, A. J. (2013). Academic Buoyancy and Academic Resilience: Exploring “Everyday” and “Classic” Resilience in the Face of Academic Adversity. School Psychology International, 34, 488-500. https://doi.org/10.1177/0143034312472759
[48]	Martin, A. J., & Marsh, H. W. (2006). Academic Resilience and Its Psychological and Educational Correlates: A Construct Validity Approach. Psychology in the Schools, 43, 267-281. https://doi.org/10.1002/pits.20149
[49]	McLachlan, G., & Peel, D. (2000). Finite Mixture Models. Wiley Series in Probability and Statistics, Hoboken, NJ: John Wiley & Sons, Inc. https://doi.org/10.1002/0471721182
[50]	McNicol, M. L., & Thorsteinsson, E. B. (2017). Internet Addiction, Psychological Distress, and Coping Responses among Adolescents and Adults. Cyberpsychology, Behavior, and Social Networking, 20, 296-304. https://doi.org/10.1089/cyber.2016.0669
[51]	Ng, B. D., & Wiemer-Hastings, P. (2005). Addiction to the Internet and Online Gaming. CyberPsychology & Behavior, 8, 110-113. https://doi.org/10.1089/cpb.2005.8.110
[52]	Nylund, K. L., Asparouhov, T., & Muthén, B. (2007). Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Structural Equation Modeling: A Multidisciplinary Journal, 14, 535-569. https://doi.org/10.1080/10705510701575396
[53]	Sellers, R. M., Caldwell, C. H., Schmeelk-Cone, K. H., & Zimmerman, M. A. (2003). Racial Identity, Racial Discrimination, Perceived Stress, and Psychological Distress among African American Young Adults. Journal of Health and Social Behavior, 44, 302. https://doi.org/10.2307/1519781
[54]	Seo, M., & Choi, E. (2018). Classes of Trajectory in Mobile Phone Depen-dency and the Effects of Negative Parenting on Them during Early Adolescence. School Psychology International, 39, 156-169. https://doi.org/10.1177/0143034317745946
[55]	Snodgrass, J. G., Lacy, M. G., Dengah II, H. J. F., Eisenhauer, S., Batchelder, G., & Cookson, R. J. (2014). A Vacation from Your Mind: Problematic Online Gaming Is a Stress Response. Computers in Human Behavior, 38, 248-260. https://doi.org/10.1016/j.chb.2014.06.004
[56]	Steinberg, L., Albert, D., Cauffman, E., Banich, M., Graham, S., & Woolard, J. (2008). Age Differences in Sensation Seeking and Impulsivity as Indexed by Behavior and Self-Report: Evidence for a Dual Systems Model. Developmental Psychology, 44, 1764-1778. https://doi.org/10.1037/a0012955
[57]	Thom, S. (2018). Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure. International Journal of Environmental Research and Public, 15, 2692. https://doi.org/10.3390/ijerph15122692
[58]	Young, K. S. (1996). Psychology of Computer Use: XL. Addictive Use of the Internet: A Case That Breaks the Stereotype. Psychological Reports, 79, 899-902. https://doi.org/10.2466/pr0.1996.79.3.899
[59]	Young, K. S. (2007). Cognitive Behavior Therapy with Internet Addicts: Treatment Outcomes and Implications. CyberPsychology & Behavior, 10, 671-679. https://doi.org/10.1089/cpb.2007.9971
[60]	Yu, J. J., & Park, S. J. (2017). Predictors and the Distal Outcome of General Internet Use: The Identification of Children’s Developmental Trajectories. British Journal of Developmental Psychology, 35, 483-497. https://doi.org/10.1111/bjdp.12188
[61]	Yu, L., Tan, D., & Shek, L. (2013). Original Study Internet Addiction in Hong Kong Adolescents: A Three-Year Longitudinal Study. Journal of Pediatric and Adolescent Gynecology, 26, S10-S17. https://doi.org/10.1016/j.jpag.2013.03.010
[62]	Zhang, G. H., Yang, X., Tu, X. L., Ding, N. N., & Lau, J. T. F. (2020). Prospective Relationships between Mobile Phone Dependence and Mental Health Status among Chinese Undergraduate Students with College Adjustment as a Mediator. Journal of Affective Disorders, 260, 498-505. https://doi.org/10.1016/j.jad.2019.09.047

Full-Text