Exploring Stress and Problematic Use of Short-Form Video Applications among Middle-Aged Chinese Adults: The Mediating Roles of Duration of Use and Flow Experience
Abstract
:1. Introduction
1.1. Stress and Problematic SVAs Use
1.2. Duration of Use as a Mediator
1.3. Flow Experience as a Mediator
1.4. A Serial Mediation Model
2. Materials and Methods
2.1. Participants
2.2. Measures
2.3. Analytical Strategy
3. Results
3.1. Preliminary Results
3.2. Primary Results
4. Discussion
4.1. Theoretical Implications
4.2. Practical Implications
4.3. Limitations and Future Research
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Li, X.; Chan, M. Smartphone Uses and Emotional and Psychological Well-Being in China: The Attenuating Role of Perceived Information Overload. Behav. Inf. Technol. 2021, 1–11. [Google Scholar] [CrossRef]
- Chan, M. Mobile Phones and the Good Life: Examining the Relationships among Mobile Use, Social Capital and Subjective Well-Being. New Media Soc. 2015, 17, 96–113. [Google Scholar] [CrossRef]
- China Internet Network Information Center The 48th Statistical Report on China’s Internet Development. 2021. Available online: http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/202109/t20210915_71543.htm (accessed on 23 October 2021).
- Wang, X.; Zhao, S.; Zhang, M.X.; Chen, F.; Chang, L. Life History Strategies and Problematic Use of Short-Form Video Applications. Evol. Psychol. Sci. 2021, 7, 39–44. [Google Scholar] [CrossRef]
- Cong, P.; Li, L.; Zhou, J.; Cao, K.; Wei, T.; Chen, M.; Hu, S. Developing User Perceived Value Based Pricing Models for Cloud Markets. IEEE Trans. Parallel Distrib. Syst. 2018, 29, 2742–2756. [Google Scholar] [CrossRef]
- Mou, X.; Xu, F.; Du, J.T. Examining the Factors Influencing College Students’ Continuance Intention to Use Short-Form Video APP. Aslib J. Inf. Manag. 2021, 73, 992–1013. [Google Scholar] [CrossRef]
- Zhang, X.; Wu, Y.; Liu, S. Exploring Short-Form Video Application Addiction: Socio-Technical and Attachment Perspectives. Telemat. Inform. 2019, 42, 101243. [Google Scholar] [CrossRef]
- School of Journalism and Communication; Tsinghua University; Social Sciences Academic Press (China) Media and Economic Management Committee of Chinese Association for History of Journalismn and Communication Report on China’s Media Industry Development. Available online: https://www.163.com/dy/article/GI68PUQ40531B4B8.html (accessed on 22 December 2021).
- QuestMobile QuestMobile Annual Report on the Silver Economy in China 2020. Available online: https://www.questmobile.com.cn/research/report-new/115 (accessed on 22 December 2021).
- Guangming Daily Addiction to Short-Form Video Applications among the Middle-Aged and Old Adults. Available online: https://baijiahao.baidu.com/s?id=1682438047500676383&wfr=spider&for=pc (accessed on 22 December 2021).
- China Business Daily Risks of Internet Addiction among the Elderly. Available online: https://baijiahao.baidu.com/s?id=1709663773108348049&wfr=spider&for=pc (accessed on 22 December 2021).
- Jun, S. The Reciprocal Longitudinal Relationships between Mobile Phone Addiction and Depressive Symptoms among Korean Adolescents. Comput. Hum. Behav. 2016, 58, 179–186. [Google Scholar] [CrossRef]
- Sahin, S.; Ozdemir, K.; Unsal, A.; Temiz, N. Evaluation of Mobile Phone Addiction Level and Sleep Quality in University Students. Pak. J. Med. Sci. 2013, 29, 913–918. [Google Scholar] [CrossRef] [PubMed]
- Yang, Z.; Griffiths, M.D.; Yan, Z.; Xu, W. Can Watching Online Videos Be Addictive? A Qualitative Exploration of Online Video Watching among Chinese Young Adults. Int. J. Environ. Res. Public Health 2021, 18, 7247. [Google Scholar] [CrossRef]
- Elhai, J.D.; Dvorak, R.D.; Levine, J.C.; Hall, B.J. Problematic Smartphone Use: A Conceptual Overview and Systematic Review of Relations with Anxiety and Depression Psychopathology. J. Affect. Disord. 2017, 207, 251–259. [Google Scholar] [CrossRef] [PubMed]
- Widyanto, L.; Griffiths, M. ‘Internet Addiction’: A Critical Review. Int. J. Ment. Health Addict. 2006, 4, 31–51. [Google Scholar] [CrossRef]
- Kardefelt-Winther, D. Problematizing Excessive Online Gaming and Its Psychological Predictors. Comput. Hum. Behav. 2014, 31, 118–122. [Google Scholar] [CrossRef]
- Kwon, M.; Lee, J.-Y.; Won, W.-Y.; Park, J.-W.; Min, J.-A.; Hahn, C.; Gu, X.; Choi, J.-H.; Kim, D.-J. Development and Validation of a Smartphone Addiction Scale (SAS). PLoS ONE 2013, 8, e56936. [Google Scholar] [CrossRef]
- Li, J.; Zhan, D.; Zhou, Y.; Gao, X. Loneliness and Problematic Mobile Phone Use among Adolescents during the COVID-19 Pandemic: The Roles of Escape Motivation and Self-Control. Addict. Behav. 2021, 118, 106857. [Google Scholar] [CrossRef] [PubMed]
- Billieux, J.; Maurage, P.; Lopez-Fernandez, O.; Kuss, D.J.; Griffiths, M.D. Can Disordered Mobile Phone Use Be Considered a Behavioral Addiction? An Update on Current Evidence and a Comprehensive Model for Future Research. Curr. Addict. Rep. 2015, 2, 156–162. [Google Scholar] [CrossRef] [Green Version]
- Young, K.S. Internet Addiction: A New Clinical Phenomenon and Its Consequences. Am. Behav. Sci. 2004, 48, 402–415. [Google Scholar] [CrossRef]
- Young, K.S. Internet Addiction: The Emergence of a New Clinical Disorder. Cyberpsychol. Behav. 1998, 1, 237–244. [Google Scholar] [CrossRef] [Green Version]
- Shaw, M.; Black, D.W. Internet Addiction. CNS Drugs 2008, 22, 353–365. [Google Scholar] [CrossRef]
- Chen, L.; Yan, Z.; Tang, W.; Yang, F.; Xie, X.; He, J. Mobile Phone Addiction Levels and Negative Emotions among Chinese Young Adults: The Mediating Role of Interpersonal Problems. Comput. Hum. Behav. 2016, 55, 856–866. [Google Scholar] [CrossRef]
- Dwyer, R.J.; Kushlev, K.; Dunn, E.W. Smartphone Use Undermines Enjoyment of Face-to-Face Social Interactions. J. Exp. Soc. Psychol. 2018, 78, 233–239. [Google Scholar] [CrossRef] [Green Version]
- Davis, R.A. A Cognitive-Behavioral Model of Pathological Internet Use. Comput. Hum. Behav. 2001, 17, 187–195. [Google Scholar] [CrossRef]
- Kardefelt-Winther, D. A Conceptual and Methodological Critique of Internet Addiction Research: Towards a Model of Compensatory Internet Use. Comput. Hum. Behav. 2014, 31, 351–354. [Google Scholar] [CrossRef] [Green Version]
- Dong, G.; Lu, Q.; Zhou, H.; Zhao, X. Precursor or Sequela: Pathological Disorders in People with Internet Addiction Disorder. PLoS ONE 2011, 6, e14703. [Google Scholar] [CrossRef] [Green Version]
- Blanchflower, D.G. Is Happiness U-Shaped Everywhere? Age and Subjective Well-Being in 145 Countries. J. Popul. Econ. 2021, 34, 575–624. [Google Scholar] [CrossRef]
- Lovibond, P.F.; Lovibond, S.H. The Structure of Negative Emotional States: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav. Res. Ther. 1995, 33, 335–343. [Google Scholar] [CrossRef]
- Kuru, T.; Celenk, S. The Relationship Among Anxiety, Depression, and Problematic Smartphone Use in University Students: The Mediating Effect of Psychological Inflexibility. Alpha Psychiatry 2021, 22, 159–164. [Google Scholar] [CrossRef]
- Chiu, S.-I. The Relationship between Life Stress and Smartphone Addiction on Taiwanese University Student: A Mediation Model of Learning Self-Efficacy and Social Self-Efficacy. Comput. Hum. Behav. 2014, 34, 49–57. [Google Scholar] [CrossRef]
- DeLongis, A.; Folkman, S.; Lazarus, R.S. The Impact of Daily Stress on Health and Mood: Psychological and Social Resources as Mediators. J. Pers. Soc. Psychol. 1988, 54, 486–495. [Google Scholar] [CrossRef]
- Brand, M.; Wegmann, E.; Stark, R.; Müller, A.; Wölfling, K.; Robbins, T.W.; Potenza, M.N. The Interaction of Person-Affect-Cognition-Execution (I-PACE) Model for Addictive Behaviors: Update, Generalization to Addictive Behaviors beyond Internet-Use Disorders, and Specification of the Process Character of Addictive Behaviors. Neurosci. Biobehav. Rev. 2019, 104, 1–10. [Google Scholar] [CrossRef] [PubMed]
- Zhou, H.; Dang, L.; Lam, L.W.; Zhang, M.X.; Wu, A.M.S. A Cross-Lagged Panel Model for Testing the Bidirectional Relationship between Depression and Smartphone Addiction and the Influences of Maladaptive Metacognition on Them in Chinese Adolescents. Addict. Behav. 2021, 120, 106978. [Google Scholar] [CrossRef]
- Seo, S.Y.; Lee, Y.H. Relationship between Everyday Stress, Social Support, Tendency towards Absorption and Internet Addiction. Korean Psychol. Assoc. J. Clin. 2007, 26, 391–405. [Google Scholar]
- Oh, Y.-K.; Kim, K.-E. The Influence of Worker’s Stress and Anxiety on Work Commitment: Focused on Mediating Effect of Smartphone Addiction. J. Digit. Converg. 2017, 15, 487–495. [Google Scholar] [CrossRef]
- Cho, H.-Y.; Kim, D.J.; Park, J.W. Stress and Adult Smartphone Addiction: Mediation by Self-Control, Neuroticism, and Extraversion. Stress Health 2017, 33, 624–630. [Google Scholar] [CrossRef] [PubMed]
- Yang, H.; Liu, B.; Fang, J. Stress and Problematic Smartphone Use Severity: Smartphone Use Frequency and Fear of Missing Out as Mediators. Front. Psychiatry 2021, 12, 1–5. [Google Scholar] [CrossRef] [PubMed]
- Annoni, A.M.; Petrocchi, S.; Camerini, A.-L.; Marciano, L. The Relationship between Social Anxiety, Smartphone Use, Dispositional Trust, and Problematic Smartphone Use: A Moderated Mediation Model. Int. J. Environ. Res. Public. Health 2021, 18, 2452. [Google Scholar] [CrossRef]
- Leung, L. Stressful Life Events, Motives for Internet Use, and Social Support Among Digital Kids. Cyberpsychol. Behav. 2007, 10, 204–214. [Google Scholar] [CrossRef] [PubMed]
- Kim, D.H. The Moderating Effects of the Way of Coping in between Perceived Stress and Smartphone Use in Adolescent. Korean J. Stress Res. 2016, 24, 57–64. [Google Scholar] [CrossRef] [Green Version]
- Toda, M.; Ezoe, S.; Nishi, A.; Mukai, T.; Goto, M.; Morimoto, K. Mobile Phone Dependence of Female Students and Perceived Parental Rearing Attitudes. Soc. Behav. Personal. 2008, 36, 765–770. [Google Scholar] [CrossRef]
- Young, K.S. Cognitive Behavior Therapy with Internet Addicts: Treatment Outcomes and Implications. Cyberpsychol. Behav. 2007, 10, 671–679. [Google Scholar] [CrossRef] [Green Version]
- Gökçearslan, Ş.; Mumcu, F.K.; Haşlaman, T.; Çevik, Y.D. Modelling Smartphone Addiction: The Role of Smartphone Usage, Self-Regulation, General Self-Efficacy and Cyberloafing in University Students. Comput. Hum. Behav. 2016, 63, 639–649. [Google Scholar] [CrossRef]
- Haug, S.; Castro, R.P.; Kwon, M.; Filler, A.; Kowatsch, T.; Schaub, M.P. Smartphone Use and Smartphone Addiction among Young People in Switzerland. J. Behav. Addict. 2015, 4, 299–307. [Google Scholar] [CrossRef] [Green Version]
- Swann, C.; Crust, L.; Vella, S.A. New Directions in the Psychology of Optimal Performance in Sport: Flow and Clutch States. Curr. Opin. Psychol. 2017, 16, 48–53. [Google Scholar] [CrossRef]
- Csikszentmihalyi, M. Happiness, Flow, and Economic Equality. Am. Psychol. 2000, 55, 1163–1164. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.; Yang, X.; Zhang, X. Relationships among Boredom Proneness, Sensation Seeking and Smartphone Addiction among Chinese College Students: Mediating Roles of Pastime, Flow Experience and Self-Regulation. Technol. Soc. 2020, 62, 101319. [Google Scholar] [CrossRef]
- Agarwal, R.; Karahanna, E. Time Flies When You’re Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage. MIS Q. 2000, 24, 665–694. [Google Scholar] [CrossRef]
- Guo, Y.M.; Klein, B.D. Beyond the Test of the Four Channel Model of Flow in the Context of Online Shopping. Commun. Assoc. Inf. Syst. 2009, 24, 837–856. [Google Scholar] [CrossRef]
- Peifer, C.; Schächinger, H.; Engeser, S.; Antoni, C.H. Cortisol Effects on Flow-Experience. Psychopharmacology 2015, 232, 1165–1173. [Google Scholar] [CrossRef]
- Landsbergis, P.A. The Changing Organization of Work and the Safety and Health of Working People: A Commentary. J. Occup. Environ. Med. 2003, 45, 61–72. [Google Scholar] [CrossRef] [PubMed]
- Lee, S.; Wickrama, K.K.A.S.; Lee, T.K.; O’Neal, C.W. Long-Term Physical Health Consequences of Financial and Marital Stress in Middle-Aged Couples. J. Marriage Fam. 2021, 83, 1212–1226. [Google Scholar] [CrossRef]
- Volkow, N.D.; Fowler, J.S.; Wang, G.-J.; Goldstein, R.Z. Role of Dopamine, the Frontal Cortex and Memory Circuits in Drug Addiction: Insight from Imaging Studies. Neurobiol. Learn. Mem. 2002, 78, 610–624. [Google Scholar] [CrossRef]
- de Ruiter, M.B.; Veltman, D.J.; Goudriaan, A.E.; Oosterlaan, J.; Sjoerds, Z.; van den Brink, W. Response Perseveration and Ventral Prefrontal Sensitivity to Reward and Punishment in Male Problem Gamblers and Smokers. Neuropsychopharmacology 2009, 34, 1027–1038. [Google Scholar] [CrossRef] [Green Version]
- Dong, G.; Hu, Y.; Lin, X. Reward/Punishment Sensitivities among Internet Addicts: Implications for Their Addictive Behaviors. Prog. Neuropsychopharmacol. Biol. Psychiatry 2013, 46, 139–145. [Google Scholar] [CrossRef] [PubMed]
- Leung, L. Net-Generation Attributes and Seductive Properties of the Internet as Predictors of Online Activities and Internet Addiction. Cyberpsychol. Behav. 2004, 7, 333–348. [Google Scholar] [CrossRef]
- Chou, T.-J.; Ting, C.-C. The Role of Flow Experience in Cyber-Game Addiction. Cyberpsychol. Behav. 2003, 6, 663–675. [Google Scholar] [CrossRef]
- Leung, L. Exploring the Relationship between Smartphone Activities, Flow Experience, and Boredom in Free Time. Comput. Hum. Behav. 2020, 103, 130–139. [Google Scholar] [CrossRef]
- Chen, D.; Cheng, C.; Urpelainen, J. Support for Renewable Energy in China: A Survey Experiment with Internet Users. J. Clean. Prod. 2016, 112, 3750–3758. [Google Scholar] [CrossRef]
- Huang, Q. How Does News Media Exposure Amplify Publics’ Perceived Health Risks About Air Pollution in China? A Conditional Media Effect Approach. Int. J. Commun. 19328036 2020, 14, 1705–1724. [Google Scholar]
- Zhou, Z.; Wu, J.P.; Zhang, Q.; Xu, S. Transforming Visitors into Members in Online Brand Communities: Evidence from China. J. Bus. Res. 2013, 66, 2438–2443. [Google Scholar] [CrossRef]
- Justine, M.; Azizan, N.; Hassan, V.; Salleh, Z.; Manaf, H. Barriers to Participation in Physical Activity and Exercise among Middle-Aged and Elderly Individuals. Singapore Med. J. 2013, 54, 581–586. [Google Scholar] [CrossRef]
- Nagarkar, A.; Gadkari, R.; Kulkarni, S. Correlates of Functional Limitations in Midlife: A Cross-Sectional Study in Middle-Aged Men (45-59 Years) from Pune. J.-Life Health 2020, 11, 144–148. [Google Scholar] [CrossRef]
- Zhang, R.; Fu, J.S. Privacy Management and Self-Disclosure on Social Network Sites: The Moderating Effects of Stress and Gender. J. Comput.-Mediat. Commun. 2020, 25, 236–251. [Google Scholar] [CrossRef]
- Hong, W.; Liu, R.-D.; Ding, Y.; Zhen, R.; Jiang, R.; Fu, X. Autonomy Need Dissatisfaction in Daily Life and Problematic Mobile Phone Use: The Mediating Roles of Boredom Proneness and Mobile Phone Gaming. Int. J. Environ. Res. Public. Health 2020, 17, 5305. [Google Scholar] [CrossRef]
- Karsay, K.; Schmuck, D.; Matthes, J.; Stevic, A. Longitudinal Effects of Excessive Smartphone Use on Stress and Loneliness: The Moderating Role of Self-Disclosure. Cyberpsychol. Behav. Soc. Netw. 2019, 22, 706–713. [Google Scholar] [CrossRef]
- Bentler, P.M. On the Fit of Models to Covariances and Methodology to the Bulletin. Psychol. Bull. 1992, 112, 400–404. [Google Scholar] [CrossRef]
- Hassim, S.R.; Arifin, W.N.; Kueh, Y.C.; Yaacob, N.A. Confirmatory Factor Analysis of the Malay Version of the Smartphone Addiction Scale among Medical Students in Malaysia. Int. J. Environ. Res. Public. Health 2020, 17, 3820. [Google Scholar] [CrossRef]
- Kim, H.-J.; Min, J.-Y.; Min, K.-B.; Lee, T.-J.; Yoo, S. Relationship among Family Environment, Self-Control, Friendship Quality, and Adolescents’ Smartphone Addiction in South Korea: Findings from Nationwide Data. PLoS ONE 2018, 13, e0190896. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yu, H.-G.; Son, C. Effects of ACT on Smartphone Addiction Level, Self-Control, and Anxiety of College Students with Smartphone Addiction. J. Digit. Converg. 2016, 14, 415–426. [Google Scholar] [CrossRef] [Green Version]
- Heo, Y.; Lee, K. Smartphone Addiction and School Life Adjustment Among High School Students: The Mediating Effect of Self-Control. J. Psychosoc. Nurs. Ment. Health Serv. 2018, 56, 28–36. [Google Scholar] [CrossRef]
- Tangney, J.P.; Baumeister, R.F.; Boone, A.L. High Self-Control Predicts Good Adjustment, Less Pathology, Better Grades, and Interpersonal Success. J. Pers. 2004, 72, 271–324. [Google Scholar] [CrossRef] [PubMed]
- Busch, P.A.; Hausvik, G.I.; Ropstad, O.K.; Pettersen, D. Smartphone Usage among Older Adults. Comput. Hum. Behav. 2021, 121, 106783. [Google Scholar] [CrossRef]
- Bolin, J.H.; Hayes, A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. J. Educ. Meas. 2014, 51, 335–337. [Google Scholar] [CrossRef]
- Preacher, K.J.; Hayes, A.F. Asymptotic and Resampling Strategies for Assessing and Comparing Indirect Effects in Multiple Mediator Models. Behav. Res. Methods 2008, 40, 879–891. [Google Scholar] [CrossRef] [PubMed]
- MacKinnon, D.P.; Lockwood, C.M.; Williams, J. Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods. Multivar. Behav. Res. 2004, 39, 99–128. [Google Scholar] [CrossRef] [Green Version]
- Peifer, C.; Schulz, A.; Schächinger, H.; Baumann, N.; Antoni, C.H. The Relation of Flow-Experience and Physiological Arousal under Stress—Can u Shape It? J. Exp. Soc. Psychol. 2014, 53, 62–69. [Google Scholar] [CrossRef]
- Omar, B.; Dequan, W. Watch, Share or Create: The Influence of Personality Traits and User Motivation on TikTok Mobile Video Usage. Int. J. Interact. Mob. Technol. IJIM 2020, 14, 121–137. [Google Scholar] [CrossRef]
- Cervi, L.; Marín-Lladó, C. What are political parties doing on TikTok? The Spanish case. Prof. Inf. EPI 2021, 30, 1–17. [Google Scholar] [CrossRef]
- Andrews, S.; Ellis, D.A.; Shaw, H.; Piwek, L. Beyond Self-Report: Tools to Compare Estimated and Real-World Smartphone Use. PLoS ONE 2015, 10, e0139004. [Google Scholar] [CrossRef] [Green Version]
Measure | Item | Frequency | Percentage(%) |
---|---|---|---|
Gender | Male | 103 | 53.0% |
Female | 91 | 46.9% | |
Area | Beijing, Shanghai, Tianjin, Chongqing, Shenzhen | 43 | 22.1% |
Capital city of province | 59 | 30.4% | |
Prefecture-level cities | 49 | 25.2% | |
Counties and towns | 39 | 20.1% | |
Administrative villages | 4 | 2.0% | |
Monthly income | Less than RMB 1500 | 3 | 1.5% |
RMB 1501–2000 | 4 | 2.0% | |
RMB 2001–3000 | 11 | 5.6% | |
RMB 3001–5000 | 38 | 19.5% | |
RMB 5001–8000 | 75 | 38.6% | |
RMB 8001–12,000 | 33 | 17.0% | |
RMB 12,001–20,000 | 22 | 11.3% | |
More than RMB 20,000 | 8 | 4.1% | |
Education level | Never attend to school | 0 | 0% |
Primary school | 6 | 3.0% | |
Middle school | 15 | 7.7% | |
High school | 30 | 15.4% | |
Vocational high school | 16 | 8.2% | |
Higher vocational school | 55 | 28.3% | |
Bachelor | 70 | 36.0% | |
Master | 2 | 1.0% | |
PhD | 0 | 0% |
Variables | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|---|---|
1. Stress | 2.46 | 0.86 | - | ||||||||
2. Duration | 5.25 | 1.76 | 0.23 ** | - | |||||||
3. Flow experience | 3.83 | 0.52 | 0.04 | 0.40 ** | - | ||||||
4. Problematic SVAs use | 3.02 | 0.75 | 0.45 ** | 0.43 ** | 0.52 ** | - | |||||
5. Gender | 1.47 | 0.50 | 0.07 | 0.12 | 0.10 | 0.09 | - | ||||
6. Age | 49.93 | 3.05 | −0.08 | −0.03 | −0.07 | 0.09 | −0.06 | - | |||
7. Education level | 5.63 | 1.47 | −0.15 * | −0.13 | −0.12 | −0.18 * | −0.06 | 0.04 | - | ||
8. Monthly Income | 5.09 | 1.36 | −0.25 ** | 0.02 | 0.03 | −0.16 * | −0.13 | −0.04 | 0.54 ** | - | |
9. Self-control | 2.87 | 0.77 | −0.55 ** | −0.24 ** | −0.21 ** | −0.58 ** | −0.20 ** | 0.06 | 0.31 ** | 0.28 ** | - |
Independent Variables | Problematic SVAs Use | |||
---|---|---|---|---|
Block 1: Control variables | ||||
Gender (male = 1, female = 2) | –0.02 | –0.01 | –0.05 | –0.05 |
Age | 0.12 * | 0.14 * | 0.13 * | 0.15 ** |
Education level | –0.002 | –0.02 | 0.02 | 0.05 |
Monthly income | 0.01 | 0.04 | –0.02 | –0.05 |
Self-control | –0.59 *** | –0.47 *** | –0.44 *** | –0.36 *** |
Adjusted R2 | 33.0% | |||
Block2 | ||||
Stress | 0.22 ** | 0.16 * | 0.22 *** | |
Incremental adjusted R2 | 3.1% | |||
Block3 | ||||
Duration of use | 0.30 *** | 0.15 ** | ||
Incremental adjusted R2 | 7.7% | |||
Block4 | ||||
Flow experience | 0.40 *** | |||
Incremental adjusted R2 | 12.6% | |||
Total adjusted R2 | 33.0% | 35.9% | 43.6% | 56.5% |
Predictors | Duration of use | Flow Experience | Problematic SVAs Use |
---|---|---|---|
β | β | β | |
Gender | 0.10 | 0.03 | –0.05 |
Age | 0.01 | –0.05 | 0.15 ** |
Education level | –0.16 | –0.08 | 0.05 |
Monthly income | 0.21 * | 0.08 | –0.05 |
Self-control | –0.13 | –0.20 * | –0.36 *** |
Stress | 0.18 * | –0.16 * | 0.22 *** |
Duration of use | — | 0.37 *** | 0.15 ** |
Flow experience | — | — | 0.40 *** |
F | (6, 187) = 3.86 | (7, 186) = 6.86 | (8, 185) = 32.30 |
R2 | 11.0% | 20.5% | 58.3% |
Paths | Standardized (β) | 95% Cl | |
---|---|---|---|
Low | High | ||
Stress→Duration of use→Problematic SVAs use | 0.03 | 0.0003 | 0.06 |
Stress→Flow experience→Problematic SVAs use | –0.06 | –0.13 | 0.004 |
Stress→Duration of use→Flow experience→Problematic SVAs use | 0.03 | 0.002 | 0.06 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Huang, Q.; Hu, M.; Chen, H. Exploring Stress and Problematic Use of Short-Form Video Applications among Middle-Aged Chinese Adults: The Mediating Roles of Duration of Use and Flow Experience. Int. J. Environ. Res. Public Health 2022, 19, 132. https://doi.org/10.3390/ijerph19010132
Huang Q, Hu M, Chen H. Exploring Stress and Problematic Use of Short-Form Video Applications among Middle-Aged Chinese Adults: The Mediating Roles of Duration of Use and Flow Experience. International Journal of Environmental Research and Public Health. 2022; 19(1):132. https://doi.org/10.3390/ijerph19010132
Chicago/Turabian StyleHuang, Qing, Mingxin Hu, and Hongliang Chen. 2022. "Exploring Stress and Problematic Use of Short-Form Video Applications among Middle-Aged Chinese Adults: The Mediating Roles of Duration of Use and Flow Experience" International Journal of Environmental Research and Public Health 19, no. 1: 132. https://doi.org/10.3390/ijerph19010132