PMPU-CAT Platform: Development of a Computerized Adaptive Testing Platform for Problematic Mobile Phone Use Among Adolescents

doi:10.12139/j.1672-0628.2026.01.012

Abstract

Abstract:

This study aimed to develop a computerized adaptive testing (CAT) platform for assessing problematic mobile phone use (PMPU), implemented using the Java programming language. The platform integrated the Bayesian expected a posteriori method for latent trait estimation, the maximum Fisher information criterion for item selection, and a standard error threshold as the termination criterion. To evaluate the platform’s performance, a series of simulation studies were conducted by comparing the PMPU-CAT platform with both an R-based CAT implementation and the conventional paper and pencil PMPU assessment. The comparison focused on item usage, measurement error, and test reliability. The results indicate that the PMPU-CAT platform achieves high measurement precision while maintaining superior testing efficiency. Overall, the platform provides an effective technical tool for applications in educational and psychological assessment.

Key words: problematic mobile phone use, computerized adaptive testing, psychological assessment, PMPU-CAT

摘要：

本研究旨在开发一个基于JAVA语言的问题性手机使用的计算机化自适应测验平台。在平台开发过程中，采用贝叶斯期望后验估计作为能力估计的算法、最大信息量选题算法进行项目选择、标准误阈限值作为终止规则。通过模拟实验，将PMPU-CAT平台与R平台和问题性手机使用传统纸笔测验的题目使用数量、测量误差以及测验信度进行对比，结果表明，PMPU-CAT平台具有良好的测验精度，并且其测验效率较高。该平台可以为教育与心理测评领域提供良好的技术支持。

关键词: 问题性手机使用, 计算机化自适应测验, 心理测评, PMPU-CAT

Yunxiao QI, Yuanqiu MA, Yaojie GAO, Tuo LIU. PMPU-CAT Platform: Development of a Computerized Adaptive Testing Platform for Problematic Mobile Phone Use Among Adolescents[J]. Studies of Psychology and Behavior, 2026, 24(1): 103-112.

齐运晓, 马苑秋, 高垚杰, 刘拓. PMPU-CAT Platform：面向青少年的问题性手机使用计算机自适应平台的研发[J]. 心理与行为研究, 2026, 24(1): 103-112.

Figures/Tables 14

References 0

	陈欢, 王丽, 乔宁宁, 曹玉萍, 张亚林. 成年人智能手机成瘾量表的初步编制. 中国临床心理学杂志, 2017, 25 (4): 645- 649. DOI
	李宇斌, 蔡艳, 涂冬波. 手机依赖的计算机化自适应测量及其效果评估. 心理科学, 2020, 43 (3): 748- 755. DOI
	任世秀, 古丽给娜, 刘拓. 中文版无手机恐惧量表的修订. 心理学探新, 2020, 40 (3): 247- 253.
	苏双, 潘婷婷, 刘勤学, 陈潇雯, 王宇静, 李明月. 大学生智能手机成瘾量表的初步编制. 中国心理卫生杂志, 2014, 28 (5): 392- 397.
	王孟成, 刘拓. (2023). 心理与行为定量研究手册. 重庆: 重庆大学出版社.
	向模军, 岐世峰. 利用Rserve实现Java调用R脚本. 现代信息科技, 2019, 3 (7): 90- 91,94. DOI
	熊婕, 周宗奎, 陈武, 游志麒, 翟紫艳. 大学生手机成瘾倾向量表的编制. 中国心理卫生杂志, 2012, 26 (3): 222- 225. DOI
	于晓琪, 李哲能, 方圆, 董妍. 青少年智能手机问题性使用与焦虑的交叉滞后分析. 心理科学, 2021, 44 (4): 866- 872. DOI
	张龙飞, 刘凯, 宋鸽, 涂冬波. 计算机化自适应测验技术在情绪智力智能测评中的初步应用——基于项目反应理论. 江西师范大学学报(自然科学版), 2020, 44 (5): 454- 461. DOI
	中国互联网络信息中心. (2025). 第55次《中国互联网络发展状况统计报告》. 2025-04-13取自https://www3.cnnic.cn/n4/2025/0117/c88-11229.html
	Akaike, H. Stochastic theory of minimal realization. IEEE Transactions on Automatic Control, 1974, 19 (6): 667- 674. DOI
	American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association.
	Bianchi, A., & Phillips, J. G. Psychological predictors of problem mobile phone use. CyberPsychology & Behavior, 2005, 8 (1): 39- 51. DOI
	Billieux, J. Problematic use of the mobile phone: A literature review and a pathways model. Current Psychiatry Reviews, 2012, 8 (4): 299- 307. DOI
	Billieux, J., van Der Linden, M., & Rochat, L. The role of impulsivity in actual and problematic use of the mobile phone. Applied Cognitive Psychology, 2008, 22 (9): 1195- 1210. DOI
	Chang, H. H., & Ying, Z. L. A global information approach to computerized adaptive testing. Applied Psychological Measurement, 1996, 20 (3): 213- 229. DOI
	Chang, H. H., & Ying, Z. L. To weight or not to weight? Balancing influence of initial items in adaptive testing. Psychometrika, 2008, 73 (3): 441- 450. DOI
	Chu, G. C. H., Chan, L. Y. L., Do, C. W., Tse, A. C. Y., Cheung, T., Szeto, G. P. Y., ... Lee, P. H. Association between time spent on smartphones and digital eye strain: A 1-year prospective observational study among Hong Kong children and adolescents. Environmental Science and Pollution Research, 2023, 30 (20): 58428- 58435. DOI
	Digital Reports. (2025). Digital 2025: Global overview report. Retrieved April 13, 2025, from https://datareportal.com/reports/digital-2025-global-overview-report
	Guo, L., Liu, X. R., & Liu, T. Generalizing computerized adaptive testing for problematic mobile phone use from Chinese adults to adolescents. Current Psychology, 2024, 43 (16): 14148- 14158. DOI
	Jun, S. The reciprocal longitudinal relationships between mobile phone addiction and depressive symptoms among Korean adolescents. Computers in Human Behavior, 2016, 58, 179- 186. DOI
	Kalaitzaki, A., Laconi, S., Spritzer, D. T., Hauck, S., Gnisci, A., Sergi, I., ... Sahlan, R. N. The prevalence and predictors of problematic mobile phone use: A 14-country empirical survey. International Journal of Mental Health and Addiction, 2024, 22 (1): 746- 765. DOI
	Kim, D., Lee, Y., Lee, J., Nam, J. K., & Chung, Y. Development of Korean smartphone addiction proneness scale for youth. PLoS One, 2014, 9 (5): e97920. DOI
	Kwon, M., Lee, J. Y., Won, W. Y., Park, J. W., Min, J. A., Hahn, C., ... Kim, D. J. Development and validation of a Smartphone Addiction Scale (SAS). PLoS One, 2013, 8 (2): e56936. DOI
	Leung, L. Linking psychological attributes to addiction and improper use of the mobile phone among adolescents in Hong Kong. Journal of Children and Media, 2008, 2 (2): 93- 113. DOI
	Lin, Y. H., Chang, L. R., Lee, Y. H., Tseng, H. W., Kuo, T. B. J., & Chen, S. H. Development and validation of the Smartphone Addiction Inventory (SPAI). PLoS One, 2014, 9 (6): e98312. DOI
	Liu, K., Zhang, L. F., Tu, D. B., & Cai, Y. (2022). Developing an item bank of computerized adaptive testing for eating disorders in Chinese university students. SAGE Open. Advance online publication. https://doi.org/10.1177/21582440221141273
	Liu, Q. Q., Xu, X. P., Yang, X. J., Xiong, J., & Hu, Y. T. Distinguishing different types of mobile phone addiction: Development and validation of the Mobile Phone Addiction Type Scale (MPATS) in adolescents and young adults. International Journal of Environmental Research and Public Health, 2022, 19 (5): 2593. DOI
	Liu, X. R., Lu, H., Zhou, Z., Chao, M., & Liu, T. Development of a computerized adaptive test for problematic mobile phone use. Frontiers in Psychology, 2022, 13, 892387. DOI
	Ma, W. A., Richie-Halford, A., Burkhardt, A. K., Kanopka, K., Chou, C., Domingue, B. W., & Yeatman, J. D. ROAR-CAT: Rapid online assessment of reading ability with computerized adaptive testing. Behavior Research Methods, 2025, 57 (1): 56. DOI
	Muhammed, N. A., & Taha, M. A. Psychophysical and cognitive adverse effects of smart phones overuse on children and adolescent. Medical Journal of Babylon, 2024, 21 (1): 46- 51. DOI
	Muraki, E. A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 1992, 16 (2): 159- 176. DOI
	Oppl, S., Reisinger, F., Eckmaier, A., & Helm, C. A flexible online platform for computerized adaptive testing. International Journal of Educational Technology in Higher Education, 2017, 14 (1): 2. DOI
	Özyurt, H., Özyurt, Ö., Baki, A., & Güven, B. Integrating computerized adaptive testing into UZWEBMAT: Implementation of individualized assessment module in an e-learning system. Expert Systems with Applications, 2012, 39 (10): 9837- 9847. DOI
	Petersen, M. A., Vachon, H., Giesinger, J. M., & Groenvold, M. Development of standard computerised adaptive test (CAT) settings for the EORTC CAT Core. Quality of Life Research, 2024, 33 (4): 951- 961. DOI
	Rathakrishnan, B., Singh, S. S. B., Kamaluddin, M. R., Yahaya, A., Nasir, M. A. M., Ibrahim, F., & Ab Rahman, Z. Smartphone addiction and sleep quality on academic performance of university students: An exploratory research. International Journal of Environmental Research and Public Health, 2021, 18 (16): 8291. DOI
	Samaha, M., & Hawi, N. S. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior, 2016, 57, 321- 325. DOI
	Samejima, F. Estimation of latent ability using a response pattern of graded scores. Psychometrika, 1969, 34 (1): 1- 97. DOI
	Scalise, K., & Allen, D. D. Use of open-source software for adaptive measurement: Concerto as an R-based computer adaptive development and delivery platform. British Journal of Mathematical and Statistical Psychology, 2015, 68 (3): 478- 496. DOI
	Schwarz, G. Estimating the dimension of a model. The Annals of Statistics, 1978, 6 (2): 461- 464. DOI
	Segall, D. O. Computerized adaptive testing. Encyclopedia of Social Measurement, 2005, 1 (4): 429- 438.
	World Health Organization. (2022). International classification of diseases (11th revision). Retrieved April 13, 2025, from https://icd.who.int/browse/2025-01/mms/en
	Xu, L. L., Jin, R. Y., Huang, F. F., Zhou, Y. H., Li, Z. L., & Zhang, M. Q. Development of computerized adaptive testing for emotion regulation. Frontiers in Psychology, 2020, 11, 561358. DOI
	Zhang, Y., Wang, D. X., Gao, X. L., Cai, Y., & Tu, D. B. Development of a computerized adaptive testing for internet addiction. Frontiers in Psychology, 2019, 10, 1010. DOI

终止规则	题目使用数量(R)		M_SE(R)	MR(R)	r(R & True)
终止规则	M	SD	M_SE(R)	MR(R)	r(R & True)
SE(θ)=0.30	14.17	1.91	0.30	0.91	0.96
SE(θ)=0.50	4.15	0.70	0.47	0.78	0.88
SE(θ)=0.70	2.01	0.13	0.61	0.63	0.79

终止规则	题目使用数量(R)		M_SE(R)	MR(R)	r(R & True)
终止规则	M	SD	M_SE(R)	MR(R)	r(R & True)
SE(θ)=0.30	14.17	1.91	0.30	0.91	0.96
SE(θ)=0.50	4.15	0.70	0.47	0.78	0.88
SE(θ)=0.70	2.01	0.13	0.61	0.63	0.79

终止规则	题目使用数量 (JAVA)		M_SE(JAVA)	MR (JAVA)	r (JAVA & True)	r (R & JAVA)
终止规则	M	SD	M_SE(JAVA)	MR (JAVA)	r (JAVA & True)	r (R & JAVA)
SE(θ)=0.30	15.04	1.97	0.30	0.91	0.96	0.98
SE(θ)=0.50	4.59	0.67	0.48	0.77	0.88	0.94
SE(θ)=0.70	2.16	0.37	0.65	0.58	0.76	0.82

终止规则	题目使用数量 (JAVA)		M_SE(JAVA)	MR (JAVA)	r (JAVA & True)	r (R & JAVA)
终止规则	M	SD	M_SE(JAVA)	MR (JAVA)	r (JAVA & True)	r (R & JAVA)
SE(θ)=0.30	15.04	1.97	0.30	0.91	0.96	0.98
SE(θ)=0.50	4.59	0.67	0.48	0.77	0.88	0.94
SE(θ)=0.70	2.16	0.37	0.65	0.58	0.76	0.82

终止规则	MAE (R)	MAE (JAVA)	MSE (R)	MSE (JAVA)	RMSE (R)	RMSE (JAVA)
SE(θ)=0.30	0.24	0.24	0.09	0.09	0.30	0.30
SE(θ)=0.50	0.38	0.38	0.23	0.23	0.48	0.48
SE(θ)=0.70	0.49	0.52	0.38	0.43	0.61	0.65