Association of smartphone addiction with daily behaviors and mental health during the COVID-19 pandemic among medical college students
-
摘要:
目的 了解新型冠状病毒肺炎疫情期间大学生手机成瘾状况及与日常生活行为和心理健康的关联，为高校开展大学生健康教育及心理疏导工作提供基础数据参考。 方法 于2020年2月24日—3月4日，对广东省和山西省2所省属医药类院校10 357名在读大学生进行网络问卷调查，问卷包含人口学特征、手机成瘾、生活作息、身体活动、体重变化、焦虑状态等，基于倾向性评分逆概率加权法使用Logistic回归分析手机成瘾与行为及心理健康关联。 结果 大学生手机成瘾率为59.42%。研究生、本科生高年级(三至五年级)、非医护类专业、居住在广东、寒假前无规律运动习惯等的大学生手机成瘾率较高(χ2值分别为47.91，17.78，42.75，138.58，P值均 < 0.05)。逆概率加权后，手机成瘾大学生更倾向于晚睡(OR=1.82, 95%CI=1.66~1.98)、晚起床(OR=1.55, 95%CI=1.44~1.68)、久坐时间较长(OR=1.21, 95%CI=1.12~1.31)、焦虑(OR=2.98, 95%CI=2.52~3.40)、中等剧烈强度身体活动时间较少(OR=1.33, 95%CI=1.22~1.44)、体重增加(OR=1.27，95%CI=1.17~1.37)和其他不良生活行为感受(P值均 < 0.05)。 结论 新型冠状病毒肺炎疫情期间大学生手机成瘾情况较严重，伴随不良生活行为和情绪状态。 Abstract:Objective To investigate the smartphone addiction among college students during the Coronavirus Disease 2019 (COVID-19) pandemic and its association with daily behaviors and mental health, and to provide reference for heath education and psychological counseling for college students. Methods An observational study using online quyestionnaire was conducted among 10 357 college students of two provincial medical schools in Guangdong and Shanxi Province from February 24th to March 4th in 2020. Participants were investigated on demographic information, smartphone addiction, daily routine, physical activity, weight status, anxiety, and other health information. Logistic regression with inverse probability treatment weighting (IPTW) based on propensity score was used to analyze the association between smartphone addiction with daily behavior and mental health. Results The prevalence of smartphone addiction was 59.42%. The prevalence of phone addiction was higher in postgraduates, senior undergraduates, students with non-medical major, students living in GuangDong and those without regular exercise habit before vacation(χ2=47.91, 17.78, 42.75, 138.58, P < 0.05). With IPTW, there were significant associations between smartphone addiction and late bedtimes (OR=1.82, 95%CI=1.66-1.98) and wake-up times (OR=1.55, 95%CI=1.44-1.68), more sedentary behaviors (OR=1.21, 95%CI=1.12-1.31), less moderate to vigorous physical activity (OR=1.33, 95%CI=1.22-1.44), anxiety (OR=2.98, 95%CI=2.52-3.40), weight gain(OR=1.27, 95%CI=1.17-1.37) and other detrimental daily behavior and feelings. Conclusion High prevalence of smartphone addiction has been observed during the COVID-19 pandemic, with impaired daily behavior and mental health. -
Key words:
- Coronavirus /
- Cellular phone /
- Behavior, addictive /
- Mental health /
- Regression analysis /
- Students
-
表 1 不同组别大学生手机成瘾率比较
Table 1. Prevalence of phone addiction in college students with different groups
组别 人数 手机成瘾人数 χ2值 P值 性别 男 2 998 1 782(59.44) 0.00 0.98 女 7 359 4 372(59.41) 年级 本科低年级(一至二年级) 5 799 3 274(56.46) 47.91 < 0.01 本科高年级(三至五年级) 4 043 2 554(63.17) 硕士 515 326(63.30) 专业 医护 6 397 3 699(57.82) 17.78 < 0.01 非医护 3 959 2 455(62.01) 现居住省份 广东 3 095 1 959(63.30) 42.75 < 0.01 山西 4 935 2 927(59.31) 其他 2 327 1 268(54.49) 居住地类型 乡村 4 303 2 651(61.61) 14.63 < 0.01 城镇 6 054 3 503(57.86) 父亲文化程度 小学及以下 1 718 1 067(62.11) 55.25 < 0.01 初中/高中/中专/高职 6 740 4 100(60.83) 本科/专科 1 806 943(52.21) 硕士及以上 93 44(47.31) 母亲文化程度 小学及以下 2 616 1 687(64.49) 68.77 < 0.01 初中/高中/中专/高职 6 152 3 646(59.27) 本科/专科 1 522 782(51.38) 硕士及以上 67 39(58.21) 既往规律运动习惯 有 4 233 2 226(52.59) 138.58 < 0.01 无 6 124 3 928(64.14) 注：()内数字为成瘾率/%。 
下载: 导出CSV
表 2 倾向性评分IPTW前后手机成瘾组和非手机成瘾组的标准化差异(n=10 357)
Table 2. Standardized difference of propensity score before and after IPTW between mobile phone addiction group and non-mobile phone addiction group(n=10 357)
组别 选项 加权前 加权后 性别 男 -0.001 0.003 女 0.001 -0.003 年级 本科低年级(一至二年级) 0.139 -0.003 本科低年级(三至五年级) -0.125 0.003 研究生 -0.037 0.000 专业 医护类 0.085 -0.001 非医护类 -0.085 0.001 现居住省份 广东 -0.105 0.003 山西 0.004 -0.002 其他 0.110 0.000 居住地类型 乡村 -0.077 -0.001 城镇 0.077 0.001 父亲文化程度 小学及以下 -0.050 0.001 初中/高中/中专/高职 -0.080 -0.001 本科/专科 0.136 -0.001 硕士及以上 0.048 0.001 母亲文化程度 小学及以下 -0.123 0.002 初中/高中/中专/高职 0.008 -0.002 本科/专科 0.137 0.000 硕士及以上 0.005 0.001 既往规律运动习惯 有 0.236 -0.001 无 -0.236 0.001
下载: 导出CSV
表 3 大学生是否手机成瘾的生活作息情况
Table 3. Association between phone addiction and college students' daily lifestyle during the epidemic
手机成瘾 人数 起床时间 睡觉时间 午睡时间 每周早餐频次/次 8:00前 8：00后 24:00前 24：00后 < 1 h 不午睡/过长午睡 6~7 ≤5 否 4 203 2 273(54.08) 1 930(45.92) 3 202(76.18) 1 001(23.82) 1 948(46.35) 2 255(53.65) 2 549(60.65) 1 654(39.35) 是 6 514 2 509(40.77) 3 645(59.23) 3 830(62.24) 2 324(37.76) 2 192(35.62) 3 962(64.38) 2 969(48.25) 3 185(51.75) χ2值 178.02 222.89 119.80 154.31 P值 < 0.01 < 0.01 < 0.01 < 0.01 注：()内数字为构成比/%。
下载: 导出CSV
表 4 大学生是否手机成瘾的久坐行为、身体活动和体重情况
Table 4. Association between phone addiction and sedentary behavior, physical activity and weight during the epidemic
手机成瘾 人数 久坐时间/(h·d-1) 轻强度身体活动时间/(h·d-1) 每周中等强度身体活动时间/min < 8 ≥8 ≥8 < 8 ≥150 < 150 否 4 135 2 513(60.77) 1 622(39.23) 2 027(49.02) 2 108(50.98) 1 389(33.59) 2 746(66.41) 是 5 996 3 370(56.20) 2 626(43.80) 2 822(47.06) 3 174(52.94) 1 649(27.50) 4 347(72.50) χ2值 20.99 3.75 43.23 P值 < 0.01 0.05 < 0.01 手机成瘾 人数 每周剧烈强度身体活动时间/min 每周中等剧烈强度身体活动时间/min 疫情期间较寒假前体重变化 ≥75 < 75 ≥150 < 150 无增加 增加 否 4 135 1 206(29.17) 2 929(70.83) 2 187(52.89) 1 948(47.11) 2 394(57.84) 1 745(42.16) 是 5 996 1 364(22.75) 4 632(77.25) 2 650(44.20) 3 346(55.80) 3 150(51.98) 2 910(48.02) χ2值 53.23 74.14 33.58 P值 < 0.01 < 0.01 < 0.01 注：()内数字为构成比/%。
下载: 导出CSV
表 5 疫情期间是否手机成瘾大学生的焦虑与自我感受情况
Table 5. Association between phone addiction and anxiety as well as feelings during the epidemic
手机成瘾 人数 焦虑情况 疫情阻碍学习或工作进度 长期宅在家中无事可做 与家人关系紧张 无焦虑 焦虑 否 是 否 是 否 是 否 4 203 3 974(94.55) 229(5.45) 2 379(56.60) 1 824(43.40) 2 648(63.00) 1 555(37.00) 4 074(96.93) 129(3.07) 是 6 514 5 252(85.34) 902(14.66) 2 413(39.21) 3 741(60.79) 2 241(36.42) 3 913(63.58) 5 699(92.61) 455(7.39) χ2值 217.70 303.86 708.35 87.77 P值 < 0.01 < 0.01 < 0.01 < 0.01 手机成瘾 人数 娱乐休闲时间增加 居家作息良好 与家人相处时间增加做 与朋友联系时间增加 否 是 否 是 否 是 否 是 否 4 203 2 436(57.96) 1 767(42.04) 2 638(62.76) 1 565(37.24) 1 190(28.31) 3 013(71.69) 3 427(81.54) 776(18.46) 是 6 514 3 130(50.86) 3 024(49.14) 4 778(77.64) 1 376(22.36) 2 011(32.68) 4 143(67.32) 5 300(86.12) 854(13.88) χ2值 50.60 271.80 22.28 39.60 P值 < 0.01 < 0.01 < 0.01 < 0.01 注：()内数字为构成比/%。
下载: 导出CSV
-
[1] World Health Organization. WHO Coronavirus (COVID-19) Dashboard[EB/OL]. [2021-05-12]. https://covid19.who.int/table. [2] 刘勤学, 杨燕, 林悦, 等. 智能手机成瘾: 概念、测量及影响因素[J]. 中国临床心理学杂志, 2017, 25(1): 82-87. https://www.cnki.com.cn/Article/CJFDTOTAL-ZLCY201701019.htmLIU Q X, YANG Y, LIN Y, et al. Smartphone addiction: concepts, measurements, and factors[J]. Chin J Clin Psychol, 2017, 25(1): 82-87. https://www.cnki.com.cn/Article/CJFDTOTAL-ZLCY201701019.htm [3] XIE H, TAO S, ZHANG Y, et al. Impact of problematic mobile phone use and insufficient physical activity on depression symptoms: a college-based follow-up study[J]. BMC Public Health, 2019, 19(1): 1640. doi: 10.1186/s12889-019-7873-z [4] LUK T T, WANG M P, SHEN C, et al. Short version of the smartphone addiction scale in chinese adults: psychometric properties, sociodemographic, and health behavioral correlates[J]. J Behav Add, 2018, 7(4): 1157-1165. doi: 10.1556/2006.7.2018.105 [5] CHEN B, LIU F, DING S, et al. Gender differences in factors associated with smartphone addiction: a cross-sectional study among medical college students[J]. BMC Psychiatry, 2017, 17(1): 341. doi: 10.1186/s12888-017-1503-z [6] EXELMANS L, VAN DEN BOLCK J. Bedtime mobile phone use and sleep in adults[J]. Soc Sci Med, 2016, 148: 93-101. DOI: 10.1016/j.socscimed.2015.11.037. [7] 卫生部疾病预防控制局. 中国成人身体活动指南[M]. 北京: 人民卫生出版社, 2011: 11.Bureau of Disease Prevention and Control, National Health Commission of the People's Republic of China. Chinese physical activity guidelines for adults[M]. Beijing: People's Medical Publishing House, 2011: 11. [8] 弓宇婧, 张艺帆, 杨馨怡, 等. 运动对焦虑女大学生睡眠质量及负性情绪的干预效果[J]. 中国学校卫生, 2019, 40(4): 542-545. doi: 10.16835/j.cnki.1000-9817.2019.04.018GONG Y J, ZHANG Y F, YANG X Y, et al. Effectiveness of exercise intervention on sleep quality and negative emotion among female college students with anxiety[J]. Chin J Sch Health, 2019, 40(4): 542-545. doi: 10.16835/j.cnki.1000-9817.2019.04.018 [9] ALI M S, PRIETO-ALHAMBRA D, LOPES L C, et al. Propensity score methods in health technology assessment: principles, extended applications, and recent advances[J]. Front Pharmacol, 2019, 10: 973. DOI: 10.3389/fphar.2019.00973. [10] AUSTIN P C. An Introduction to propensity score methods for reducing the effects of confounding in observational studies[J]. Multivariate Behavioral Research, 2011, 46(3): 399-424. doi: 10.1080/00273171.2011.568786 [11] SOHN S, REES P, WILDRIDGE B, et al. Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence[J]. BMC Psychiatry, 2019, 19(7): 1650-1664. [12] LONG J, LIU T Q, LIAO Y H, et al. Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates[J]. BMC Psychiatry, 2016, 16(1): 408. doi: 10.1186/s12888-016-1083-3 [13] 潘继红. "新冠疫情期间国人睡眠"白皮书发布[N]. 健康报, 2020-03-18(004).PAN J H. The white paper-sleep of Chinese people during the Coronavirus Disease 2019 pandemic-has released[N]. Health Daily, 2020-03-18(004). [14] FOERSTER M, HENNEKE A, CHETTY-MHLANGA S, et al. Impact of adolescents' screen time and nocturnal mobile phone-related awakenings on sleep and general health symptoms: a prospective cohort study[J]. Int J Environ Res Public Health, 2019, 16(3): 518. doi: 10.3390/ijerph16030518 [15] JNIENE A, ERRGUIG L, ELHANGOUCHE A J, et al. Perception of sleep disturbances due to bedtime use of blue light-emitting devices and its impact on habits and sleep quality among young medical students[J]. Bio Med Res Int, 2019, 7012350. DOI: 10.1155/2019/7012350. [16] LEPP A, BARKLEY J E, SANDERS G J, et al. The relationship between cell phone use, physical and sedentary activity, and cardiorespiratory fitness in a sample of U.S. college students[J]. Int J Behav Nutr Physi Act, 2013, 10(1): 79. doi: 10.1186/1479-5868-10-79 [17] TOWNE S D, ORY M G, SMITH M L, et al. Accessing physical activity among young adults attending a university: the role of sex, race/ethnicity, technology use, and sleep[J]. BMC Public Health, 2017, 17(1): 721. doi: 10.1186/s12889-017-4757-y [18] XIANG M Q, LIN L, WANG Z R, et al. Sedentary behavior and problematic smartphone use in Chinese adolescents: the moderating role of self-control[J]. Front Psychol, 2019, 10: 3032. DOI: 10.3389/fpsyg.2019.03032. [19] KIM S E, KIM J W, JEE Y S. Relationship between smartphone addiction and physical activity in Chinese international students in Korea[J]. J Behav Addict, 2015, 4(3): 200-205. doi: 10.1556/2006.4.2015.028 [20] 陈娟娟, 李惠萍, 杨娅娟, 等. 大学生心理韧性在手机成瘾与心理健康间的中介作用[J]. 现代预防医学, 2020, 47(1): 93-96. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF202001028.htmCHEN J J, LI H P, YANG Y J, er al. Mediation effect of psychological resilience in the relationship between mobile phone addiction and mental health in college students[J]. Modern Prevent Med, 2020, 47(1): 93-96. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF202001028.htm [21] CARBONELL X, CHAMARRO A, OBERST U, et al. Problematic use of the internet and smartphones in university Students: 2006-2017[J]. Int J Environ Res Public Health, 2018, 15(3): 475. doi: 10.3390/ijerph15030475 [22] EXTREMERA N, QUINTANA-ORTS C, SáNCHEZ-LVAREZ N, et al. The role of cognitive emotion regulation strategies on problematic smartphone use: comparison between problematic and non-problematic adolescent users[J]. Int J Environ Res Public Health, 2019, 16(17): 3142. doi: 10.3390/ijerph16173142 [23] HERSHNER S, O'BRIEN L M. The impact of a randomized sleep education intervention for college students[J]. J Clin Sleep Med, 2018, 14(3): 337-347. doi: 10.5664/jcsm.6974 [24] CAO W, FANG Z, HOU G, et al. The psychological impact of the COVID-19 epidemic on college students in China[J]. Psychi Res, 2020, 287: 112934. DOI: 10.1016/j.psychres.2020.112934. [25] ZHANG Y, ZHANG H, MA X, et al. Mental health problems during the COVID-19 pandemics and the mitigation effects of exercise: a longitudinal study of college students in China[J]. Int J Environ Res Public Health, 2020, 17(10): 3722. doi: 10.3390/ijerph17103722 [26] PEANHA T, GOESSLER K F, ROSCHEL H, et al. Social isolation during the COVID-19 pandemic can increase physical inactivity and the global burden of cardiovascular disease[J]. Am J Physiol Heart Circulatory Physiol, 2020, 318(6): H1441-H1446. doi: 10.1152/ajpheart.00268.2020 [27] HALLAL P C, ANDERSEN L B, BULL F C, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects[J]. Lancet, 2012, 380(9838): 247-257. doi: 10.1016/S0140-6736(12)60646-1 [28] 韩拓, 马维冬, 巩红, 等. 新冠肺炎疫情居家隔离期间大学生负性情绪及影响因素分析[J/OL]. [2020-06-02]. http://kns.cnki.net/kcms/detail/61.1399.R.20200426.0824.002.html.HAN T, MA W D, GONG H, et al. Investigation and analysis of negative emotion among university students during home quarantine of COVID-19[J/OL]. [2020-06-02]. http://kns.cnki.net/kcms/detail/61.1399.R.20200 [29] RAIBER L, CHRISTENSEN R A, JAMNIK V K, et al. Accelerometer thresholds: accounting for body mass reduces discrepancies between measures of physical activity for individuals with overweight and obesity[J]. Appl Physiol Nutr Metab, 2017, 42(1): 53-58. doi: 10.1139/apnm-2016-0303 [30] CANNING K L, BROWN R E, JAMNIK V K, et al. Individuals underestimate moderate and vigorous intensity physical activity[J]. PLoS One, 2014, 9(5): e97927. doi: 10.1371/journal.pone.0097927 -
点击查看大图
表(5)
计量
- 文章访问数: 1005
- HTML全文浏览量: 409
- PDF下载量: 133
- 被引次数: 0
