Compliance and alternative behaviors of heavy gamers in adolescents to Chinese online gaming restriction policy

Abstract

Background and aims
In 2021, China implemented a policy to prevent adolescents from excessive online gaming, with the goal of encouraging healthier leisure activities.

Methods
Three months after this policy was implemented, we conducted a study involving 430 Chinese adolescents who regularly played online games for over two hours daily before the policy. We collected their responses to the restriction, including their compliance with the policy, engagement in undesirable alternative behaviors (e.g., watching short videos), and engagement in desirable alternative behaviors (e.g., playing sports). We also collected data on individual factors, parental technology interference, and feelings of restriction to use as predictors for behaviors, including those related to violating the restriction or watching short videos.

Results
A small percentage of heavy gamers violated the restriction by renting others' game accounts (3%) or using a family member's identity (14%), while 59% of the sample shifted to watching short videos. Heavy gamers who lived in rural areas, spent more time on online games prior to the policy, did not feel restricted from playing online games, and experienced parental technology interference were more likely to violate the restriction. Females or those lacking stable hobbies were more inclined to watch short videos.

Conclusions
Although the policy restricted heavy gaming, it has also led to increased short video use. Policymakers could explore alternative approaches, such as developing infrastructure that supports outdoor leisure activities in rural areas, encouraging parents to model responsible technology use behaviors, and guiding adolescents to cultivate positive hobbies in their leisure time.

Full text

Compliance and alternative behaviors of heavy
gamers in adolescents to Chinese online gaming
restriction policy
XINYU ZHOU
1,2
, MIN LIAO
1,2
, MONIKA GOROWSKA
1,2
,
XIJING CHEN
1,2
pand YONGHUI LI
1,2
1
Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 100101,
Beijing, China
2
Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
Received: September 8, 2022 •Revised manuscript received: January 25, 2024; March 14, 2024 •Accepted: March 23,
2024
ABSTRACT
Background and aims: In 2021, China implemented a policy to prevent adolescents from excessive online
gaming, with the goal of encouraging healthier leisure activities. Methods: Three months after this policy
was implemented, we conducted a study involving 430 Chinese adolescents who regularly played online
games for over two hours daily before the policy. We collected their responses to the restriction, including
their compliance with the policy, engagement in undesirable alternative behaviors (e.g., watching short
videos), and engagement in desirable alternative behaviors (e.g., playing sports). We also collected data on
individual factors, parental technology interference, and feelings of restriction to use as predictors for
behaviors, including those related to violating the restriction or watching short videos. Results: Asmall
percentage of heavy gamers violated the restriction by renting others’game accounts (3%) or using a
family member’s identity (14%), while 59% of the sample shifted to watching short videos. Heavy gamers
who lived in rural areas, spent more time on online games prior to the policy, did not feel restricted from
playing online games, and experienced parental technology interference were more likely to violate the
restriction. Females or those lacking stable hobbies were more inclined to watch short videos. Conclusions:
Although the policy restricted heavy gaming, it has also led to increased short video use. Policymakers
could explore alternative approaches, such as developing infrastructure that supports outdoor leisure
activities in rural areas, encouraging parents to model responsible technology use behaviors, and guiding
adolescents to cultivate positive hobbies in their leisure time.
KEYWORDS
gaming restriction policy, adolescents, video game playing, addiction, China, regulation, policy, short videos,
alternative behaviors
INTRODUCTION
Online gaming is a popular pastime for Chinese adolescents (China Internet Network
Information Center, 2021), but excessive use can have negative effects on their physical
activity and education (Hong et al., 2019;Kocako
glu, Karao
glu, & Kutlu, 2021). While
moderate gaming can benefit emotional well-being and social interactions (Lobel, Engels,
Stone, & Granic, 2019;Russoniello, O’Brien, & Parks, 2009), it is important to find ways to
prevent harm caused by excessive use.
Limiting availability of online gaming through policy measures has been proposed as a
solution to prevent excessive gaming (Daniel, 2019;Kiraly et al., 2018). While such measures
would perhaps be criticized in the Western world for potentially violating civil liberties, some
Asian countries with strong government control have enforced bans on online gaming
(Kiraly et al., 2018). In August 2021, China imposed online gaming restrictions for minors
under 18, limiting gaming time to one hour on Fridays, Saturdays, Sundays, and holidays.
Journal of Behavioral
Addictions
DOI:
10.1556/2006.2024.00021
© 2024 The Author(s)
BRIEF REPORT
pCorresponding author.
E-mail: chenxijing@psych.ac.cn
Unauthenticated | Downloaded 05/20/24 02:47 AM UTC

This policy aimed to prevent adolescents from excessive
online gaming, thereby promoting healthier behaviors, such
as social interaction or physical activity, that foster their
overall development (The State Press and Publication
Administration, 2021).
Despite reports from gaming companies indicating that
adolescents are less engaged in online games following the
policy, it is unknown what alternative behaviors the heavy
gamers would take when the games are unavailable. Ac-
cording to Tencent’s earnings report, the percentage of time
spent and money consumed by minors among all players has
declined in September 2021 compared to the previous year
(Tencent, 2021). Some scholars believe that this policy seems
useful on the surface (Carrasp, Stavropoulos, Motti-Stefanidi,
Labrique, & Griffiths, 2021), but it remains unclear whether
any positive outcomes, such as increased physical exercise and
social activities, have resulted from the reduction of gaming
time and cost (Xiao, 2022). For heavy gamers who spent more
than two hours on games prior to the policy, such circum-
stances can be particularly challenging, as they may have a
strong motivation to violate the restriction and find other
ways to continue playing or engage in other online activities
such as watching short videos (Davies & Blake, 2016). Short
videos are becoming a new addictive medium (Lu, Liu, Ge,
Bai, & Liu, 2022;Xu, Gao, Wei, Liu, & Zhang, 2023) and may
become a common alternative behavior for heavy adolescent
gamers. These videos offer novel content and are recom-
mended based on user preferences (Nam & Jung, 2021).
Adolescents spend more time watching short videos than
young adults (Wu et al., 2021), and 73.6% of them watch such
content for entertainment purpose rather than for educational
or hobby-related activities (China Internet Network Infor-
mation Center, 2023). Therefore, examining differences
among adolescents and their relationship with the evolution
of online behaviors in this context may offer valuable insights
for preventing gaming addiction in the future (Carrasp et al.,
2021;Stavropoulos, Motti-Stefanidi, & Griffiths, 2022).
Considering the limited empirical studies on heavy
gamers in adolescents under this restriction (Kiraly, Browne,
& Demetrovics, 2022), the overall aims of this cross-
sectional study are:
1. To identify the compliance of heavy gamers with this
restriction and determine whether they would violate it.
2. To identify the desirable and undesirable alternative be-
haviors adopted by heavy gamers under the restriction.
3. To determine the factors (individual factors, feeling of
restriction, parent technology interference) that are
associated with the behaviors of violating the restriction
and watching short videos.
METHODS
Participants
In December 2021, three months after implementing the
online gaming restriction, we used convenience sampling to
collect data. Researchers contacted primary and middle
school teachers in their social network and introduced this
study. Then the teachers introduced this project to 2,846
adolescent students in the classroom, and their parents in
WeChat groups. Adolescents interested in participating in
this study completed the online questionnaires. All questions
were listed in “Questionnaire Star”, a professional online
survey platform widely used in China (Huang, 2021). To
investigate the response of heavy gamers to the policy, we
selected 430 individuals based on the following inclusion
criteria: (1) adolescents aged 9–18 years old; (2) who spent
two or more hours per day on online gaming prior to
the policy (Gromada, 2022). Exclusion criteria included:
(1) adolescents who refused to answer (n510); (2) adoles-
cents who reported game time exceeding 15 h per day (n57).
Measures
Behaviors in response to the policy. We used a following
multiple-choice question to measure behaviors in response
to the policy: "Do you take the following options when you
want to play but the game is restricted?". The options were
created based on a series of unstructured interviews with
adolescent gamers (n548). To ensure the representative-
ness of the data, we sampled students from various grades in
the middle and primary schools. The researchers conducted
individual interviews with each participant and recorded
their responses. We included all responses for a compre-
hensive set of options. From the responses, we identified two
types of behaviors that violated the gaming restriction.
Internet-focused activities were considered undesirable be-
haviors as they contradicted the original intention of the
policy, while those with potential mental or physical health
benefits were classified as desirable behaviors.
Individual factors. Individual factors included age, sex,
place of residence, stable hobbies, regular sports, and pre-
policy gaming time (Henchoz et al., 2016;Hong et al., 2019).
Stable hobbies and regular sports were scored using a two-
point system. For instance, hobbies were measured by the
question “Do you have stable hobbies? (yes, no)”. Partici-
pants were also asked about their daily online gaming
duration (minutes) before the policy implementation.
Feeling of restriction. Feeling of restriction was measured
by the question “After August 30, do you feel restricted in
playing online games? (yes, no)”.
Parent technology interference. Parent technology inter-
ference was measured by the question “When talking or
doing activities with your parents, were you frequently
interrupted because your parents use electronic devices
(mobile phone/TV/tablet/computer/game console/iPad)?”
with the same scoring rules as mentioned above (McDaniel
& Radesky, 2018).
Data analysis
The compliance was measured by subtracting the propor-
tion of participants who violated the restriction from the
2Journal of Behavioral Addictions
Unauthenticated | Downloaded 05/20/24 02:47 AM UTC

total sample. The prevalence of each undesirable and
desirable alternative behavior was determined by calcu-
lating their respective proportions among the participants.
The factors that related to the behaviors of violating the
restriction and watching short videos were tested by
multivariate logistic regression analysis. Data analysis was
performed using SPSS 26.0, and significance was tested at
the p< 0.05 level.
Ethics
The study was approved by the ethical committee of the
Institute of Psychology, Chinese Academy of Sciences
(reference No. H21099). Online informed consent was ob-
tained from all individual participants and the participant
has consented for data to be used in the research.
RESULTS
Compliance
84.7% of the heavy gamers complied with the online
gaming restriction, but 14% used a family member’s identity
(n562) and 3% (n511) rented the game accounts (see
Fig. 1). Additionally, 75.6% of the heavy gamer participants
(n5325) reported experiencing restrictions due to the
policy (see Table 1).
Alternative behaviors
Figure 1 illustrated the distribution of behaviors among
participants in response to the restriction. Among the
desirable behaviors, the participants reported listening to
music (45%; n5195), studying (41%; n5117), going out
with family or friends (38%; n5164), playing sports
(36%; n5155), sleeping (32%; n5136), and taking up
hobbies such as chess or dance (19%; n583). In terms of
undesirable behaviors, the participants engaged not only in
gaming-related activities, including playing other unlimited
games (23%; n598) and watching online game live (20%;
n586), but also in other internet activities, including
watching short videos (59%, n5255), watching anime or
TV series (51%; n5218), and reading internet novels (20%;
n586).
The most prevalent alternative behavior among heavy
gamers was watching short videos. Since it was considered
an undesirable behavior, we conducted the further analysis
to identify the influencing factors.
The factors influencing the behaviors of violating the
restriction and watching short videos
Place of residence, pre-policy gaming time, feeling of re-
striction, and parental technology interference were pre-
dictors of violating the restriction (see Table 1). Compared
to participants living in rural areas, those in urban areas
(aOR 50.43, CI: 0.25–0.76) had lower odds of violating the
restriction. Similarly, participants who felt restriction from
playing online games (aOR 50.52, CI: 0.28–0.98) had lower
odds of violating the restriction, and those who felt parental
technology interference (aOR 51.86, CI: 1.06–0.3.28) had
higher odds of violating the restriction. Participants who
spent more time on gaming prior to the policy were more
likely to violate the restriction.
Sex and stable hobbies were predictors of watching short
videos (see Table 1). Compared to females, male participants
(aOR 50.46, CI: 0.29–0.72) had lower odds of watching
short videos. Compared to participants who did not have
stable hobbies, those who did (aOR 50.63, CI: 0.42–0.95)
had lower odds of watching short videos.
Fig. 1. Proportions for violating the restriction and each alternative behavior. The left panel displayed the ratio for using a family
member’s identity or renting the game accounts to violate the restriction. The right panel showed the ratio for each alternative behavior,
including undesirable behaviors (gaming-related activities in red, other internet activities in orange), as well as desirable behaviors (in blue)
Journal of Behavioral Addictions 3
Unauthenticated | Downloaded 05/20/24 02:47 AM UTC

DISCUSSION
In our study, we found that the majority of heavy gamers
among adolescents felt restricted by the policy. Moreover, a
small percentage of them violated this restriction by renting
the game accounts (3%) or using a family member’s identity
to authenticate the game (14%). Since renting game accounts
was considered illegal, so heavy gamers rarely resorted to this
method. Therefore, using a family member’s identity for
authentication was a more common behavior (Zhan & Chan,
2012), despite companies actively adopting facial recognition
for login and during gameplay (May & Chien, 2021).
Surprisingly, more than half of heavy gamers used short
videos to pass the time when they were unable to engage in
gaming activities. This result coincided with the significant
presence of adolescents on short video platforms, as evi-
denced by the fact that 69.7% of adolescents in the United
States (Statista, 2023) and 54.1% of adolescents in China
(China Internet Network Information Center, 2023) were
active users in 2023. While short videos offer certain bene-
fits, their addictive nature can contribute to excessive screen
time, ultimately affecting adolescents’cognitive functions
and academic performance (Xu et al., 2023). That is, this
policy did not have a satisfactory impact on reducing screen
time. Nevertheless, we also found that more than 30% of
adolescents opted for desirable behaviors, such as engaging
in physical activities and improving social interaction, which
enhanced their overall well-being.
The regression analysis identified several influencing
factors for undesirable behaviors, including the place of
residence, sex, hobbies, and parental technology interfer-
ence. It revealed that adolescents in rural areas were more
likely to violate the restriction, consistent with previous
research that highlighted their vulnerability to online
gaming addiction (Pawlowska et al., 2015). There is an
observed tendency for rural parents to exercise less super-
vision over their children’s internet usage (Chang et al.,
2016) and the noted absence of community resources (Li,
Liu, Zhang, & Xu, 2015), highlighting the need for rural
infrastructure development. Furthermore, adolescents who
were distracted by their parents’electronic devices were
more likely to ignore game restrictions, indicating that
parents should be mindful of their device usage. Moreover,
adolescent girls were more inclined to consume short videos
as an alternative behavior compared to boys. This is
consistent with previous studies showing that adolescent
girls watch videos more frequently (Taverno Ross et al.,
2013). Besides, heavy gamers who reported having no stable
hobbies preferred to use short videos, highlighting the
importance of guiding adolescents in developing stable in-
terests (Auhuber, Vogel, Grafe, Kiess, & Poulain, 2019).
To our knowledge, this study was the first to examine the
compliance and the alternative behaviors of heavy gamers
when faced with gaming policy, as well as factors that
contribute to violating the restriction and watching short
videos. The results showed that, even though the policy
restricted heavy gaming, it also led to undesirable behaviors,
Table 1. Weighted adjusted logistic regression models of violating the restriction and watching short videos (n5430)
Total
Violate the restriction (n566) Watch short videos (n5255)
n(%)/M (SD) aOR (95%CI) pvalue n(%)/M (SD) aOR (95%CI) pvalue
Sex 0.229 <0.001
Male 295 (68.6%) 48 (72.7%) 1.46 (0.79, 2.71) 158 (62.0%) 0.46 (0.29, 0.72)
Female 135 (31.40%) 18 (27.3%) REF 97 (38.0%) REF
Age 12.75 (1.48) 12.70 (1.61) 0.99 (0.82, 1.20) 0.921 12.80 (1.55) 1.05 (0.92, 1.20) 0.473
Place of residence 0.003 0.708
Urban 242 (56.3%) 27 (40.9%) 0.43 (0.25, 0.76) 141 (55.3%) 0.93 (0.62, 1.38)
Rural 188 (43.7%) 39 (59.1%) REF 114 (44.7%) REF
Pre-policy gaming
time
210.91 (127.96) 264.83 (165.89) 1.01 (1.00, 1.01) <0.001 218.14 (133.078) 1.00 (0.99, 1.00) 0.327
Feeling of restriction 0.040 0.682
Yes 325 (75.6%) 46 (69.7%) 0.52 (0.28, 0.98) 195 (76.5%) 1.10 (0.69, 1.76)
No 105 (24.4%) 20 (30.3%) REF 60 (23.5%) REF
Stable hobby 0.594 0.028
Yes 250 (58.1%) 40 (60.6%) 1.17 (0.66, 2.06) 136 (53.3%) 0.63 (0.42, 0.95)
No 180 (41.9%) 26 (39.4%) REF 119 (46.7%) REF
Regular sports 0.537 0.492
Yes 279 (64.9%) 40 (60.6%) 0.83 (0.47, 1.48) 158 (60.2%) 0.86 (0.56, 1.32)
No 151 (35.1%) 26 (39.4%) REF 97 (38.0%) REF
Parental technology
interference
0.031 0.208
Yes 145 (33.7%) 29 (43.9%) 1.86 (1.06, 3.28) 91 (35.7%) 1.32 (0.86, 2.01)
No 285 (66.3%) 37 (56.1%) REF 164 (64.3%) REF
Notes. Continuous variables are presented using means (standard deviations), while categorical variables are presented using sample size
(weighted percentages); aOR 5adjusted odds ratio; 95% CI: 95% Confidence interval; REF is reference group.
4Journal of Behavioral Addictions
Unauthenticated | Downloaded 05/20/24 02:47 AM UTC

especially watching short videos. To foster the mental and
physical well-being of adolescents, governments should
prioritize the development of community infrastructure that
supports outdoor leisure activities in rural areas. Parents, on
the other hand, should demonstrate responsible technology
usage behaviors and maintain household discipline. While
adolescents should cultivate positive hobbies for resilience
against internet addiction.
Funding sources: This study was funded by a grant from
International Cooperation and Exchange of the National
Natural Science Foundation of China (32161133004).
Authors’contribution: Xinyu Zhou conceived of the study,
performed the statistical analysis, and drafted the manu-
script; Min Liao and Monika Gorowska conducted data
collection and helped to draft the manuscript; Xijing Chen
led the statistical analysis and interpretation, and helped in
drafting the manuscript. Yonghui Li helped to conceive the
present study with collected data, participated in its design
and coordination, All authors read and approved the final
manuscript.
Conflict of interest: All authors have no conflicts of interest
and did not receive any financial support from any other
organization regarding the content of this paper.
Data availability statement: The data that support the
findings of this study are available from the corresponding
author, XC, upon reasonable request.
REFERENCES
Auhuber, L., Vogel, M., Grafe, N., Kiess, W., & Poulain, T. (2019).
Leisure activities of healthy children and adolescents. Interna-
tional Journal of Environmental Research and Public Health,
16(12), 2078. https://doi.org/10.3390/ijerph16122078.
Carrasp, M. C., Stavropoulos, V., Motti-Stefanidi, F., Labrique, A.,
& Griffiths, M. D. (2021). Draconian policy measures are un-
likely to prevent disordered gaming. Journal of Behavioral
Addictions,10(4), 849–853. https://doi.org/10.1556/2006.2021.
00075.
Chang, F. C., Miao, N. F., Chiu, C. H., Chen, P. H., Lee, C. M.,
Chiang, J. T., & Chuang, H. Y. (2016). Urban–rural differences
in parental Internet mediation and adolescents’Internet risks in
Taiwan. Health, Risk and Society,18(3–4), 188–204. https://doi.
org/10.1080/13698575.2016.1190002.
China Internet Network Information Center (2021). Report on
Internet use of minors in China. Retrieved March 20, 2022,
from http://www.cnnic.cn/hlwfzyj/hlwxzbg/qsnbg/202107/
t20210720_71505.htm.
China Internet Network Information Center (2023). The 5th na-
tional survey report on internet use among Chinese minors.
Retrieved December 30, 2023, from https://www.cnnic.net.cn/
n4/2023/1225/c116-10908.html.
Daniel, T. L. S. (2019). The “ABCDE”of video gaming control:
Arguments, basic research, conceptual models, documented
lessons, and evaluation. Journal of Behavioral Addictions,8(1),
3–6. https://doi.org/10.1556/2006.8.2019.13.
Davies, B., & Blake, E. (2016). Evaluating existing strategies to limit
video game playing time. IEEE Computer Graphics and Appli-
cations,36(2), 47–57. https://doi.org/10.1109/MCG.2016.25.
Gromada, A. (2022). Moderate gaming and internet use show
positive association with online reading of 10-Year-Olds in
Europe. Computers and Education Open,3, 100109. https://doi.
org/10.1016/j.caeo.2022.100109.
Henchoz, Y., Studer, J., Deline, S., N’Goran, A. A., Baggio, S., &
Gmel, G. (2016). Video gaming disorder and sport and exercise
in emerging adulthood: A longitudinal study. Behavioral
Medicine,42(2), 105–111. https://doi.org/10.1080/08964289.
2014.965127.
Hong, J. S., Kim, S. M., Jung, J. W., Kim, S. Y., Chung, U. S., &
Han, D. H. (2019). A Comparison of risk and protective factors
for excessive Internet game play between Koreans in Korea and
immigrant Koreans in the United States. Journal of Korean
Medical Science,34(23), e162. https://doi.org/10.3346/jkms.
2019.34.e162.
Huang, T. (2021). Research on the use intention of potential de-
signers of unmanned cars based on technology acceptance
model. PLoS One,16(8), e0256570. https://doi.org/10.1371/
journal.pone.0256570.
Kiraly, O., Browne, D. T., & Demetrovics, Z. (2022). Developmental
and family implications of state-controlled video game play in
China. JAMA Pediatrics,176(6), 543–544. https://doi.org/10.
1001/jamapediatrics.2022.0322.
Kiraly, O., Griffiths, M. D., King, D. L., Lee, H. K., Lee, S. Y.,
Banyai, F., …Demetrovics, Z. (2018). Policy responses to
problematic video game use: A systematic review of current
measures and future possibilities. Journal of Behavioral Addic-
tions,7(3), 503–517. https://doi.org/10.1556/2006.6.2017.050.
Kocako
glu, U., Karao
glu, N., & Kutlu, R. (2021). The relationship
between computer game addiction and obesity in third and
fourth grade elementary school students. Gulhane Medical
Journal,63(2), 87–95. https://doi.org/10.4274/gulhane.galenos.
2020.1162.
Li, L. W., Liu, J., Zhang, Z., & Xu, H. (2015). Late‐life depression
in rural China: Do village infrastructure and availability
of community resources matter? International Journal of
Geriatric Psychiatry,30(7), 729–736. https://doi.org/10.1002/
gps.4217.
Lobel, A., Engels, R. C. M. E., Stone, L. L., & Granic, I. (2019).
Gaining a competitive edge: Longitudinal associations between
children’s competitive video game playing, conduct problems,
peer relations, and prosocial behavior. Psychology of Popular
Media Culture,8(1), 76–87. https://doi.org/10.1037/
ppm0000159.
Lu, L., Liu, M., Ge, B., Bai, Z., & Liu, Z. (2022). Adolescent
addiction to short video applications in the mobile internet era.
Frontiers in Psychology,13, 893599. https://doi.org/10.3389/
fpsyg.2022.893599.
May, T., & Chien, A. C. (2021). Game over: Chinese company
deploys facial recognition to limit youths’play. The New York
Times. Retrieved July 8, 2022, from https://www.nytimes.com/
Journal of Behavioral Addictions 5
Unauthenticated | Downloaded 05/20/24 02:47 AM UTC

2021/07/08/business/video-game-facial-recognition-tencent.
html.
McDaniel, B. T., & Radesky, J. S. (2018). Technoference: Parent
distraction with technology and associations with child
behavior problems. Child Development,89(1), 100–109. https://
doi.org/10.1111/cdev.12822.
Nam, J., & Jung, Y. (2021). Digital natives’snack content con-
sumption and their goals: A means-end chain approach. Tele-
matics and Informatics,63, 101664. https://doi.org/10.1016/j.
tele.2021.101664.
Pawlowska, B., Zygo, M., Potembska, E., Kapka-Skrzypczak, L.,
Dreher, P., & Kedzierski, Z. (2015). Prevalence of Internet
addiction and risk of developing addiction as exemplified by a
group of Polish adolescents from urban and rural areas. Annals
of Agricultural and Environmental Medicine,22(1), 129–136.
https://doi.org/10.5604/12321966.1141382.
Russoniello, C. V., O’Brien, K., & Parks, J. M. (2009). EEG, HRV and
psychological correlates while playing bejeweled II: A randomized
controlled study. Studies in Health Technology and Informatics,
144(1), 189–192. https://doi.org/10.3233/978-1-60750-017-9-189.
Statista (2023). Distribution of TikTok users in the United States in
2022, by age group. Retrieved March 01, 2024, from https://
www.statista.com/statistics/1095196/tiktok-us-age-gender-
reach/.
Stavropoulos, V., Motti-Stefanidi, F., & Griffiths, M. D. (2022).
Risks and opportunities for youth in the digital era a cyber-
developmental approach to mental health. European Psycholo-
gist,27(2), 86–101. https://doi.org/10.1027/1016-9040/a000451.
Taverno Ross, S. E., Byun, W., Dowda, M., McIver, K. L.,
Saunders, R. P., & Pate, R. R. (2013). Sedentary behaviors in
fifth-grade boys and girls: Where, with whom, and why?
Childhood Obesity,9(6), 532–539. https://doi.org/10.1089/chi.
2013.0021.
Tencent (2021). Tencent announces 2021 third quarter results.
Tencent Investor Relations. Retrieved November 10, 2021, from
https://static.www.tencent.com/uploads/2021/11/10/57d32da
50c1d7abe221d7f9ca9ec3dcb.pdf.
The State Press and Publication Administration (2021). Notice on
further strict management and practically preventing minors
from indulging in online games. Retrieved August 30, 2021,
from https://www.nppa.gov.cn/nppa/contents/719/98785.
shtml.
Wu, Y., Wang, X., Hong, S., Hong, M., Pei, M., & Su, Y. (2021). The
relationship between social short-form videos and youth’s well-
being: It depends on usage types and content categories. Psy-
chology of Popular Media,10(4), 467–477. https://doi.org/10.
1037/ppm0000292.
Xiao, L. Y. (2022). Reserve your judgment on “Draconian”Chinese
video gaming restrictions on children. Journal of Behavioral
Addictions,11(2), 249–255. https://doi.org/10.1556/2006.2022.
00022.
Xu, Z. Y., Gao, X. Q., Wei, J., Liu, H. Q., & Zhang, Y. (2023).
Adolescent user behaviors on short video application, cognitive
functioning and academic performance. Computers and Edu-
cation,203, 104865. https://doi.org/10.1016/j.compedu.2023.
104865.
Zhan, J. D., & Chan, H. C. (2012). Government regulation of online
game addiction. Communications of the Association for Infor-
mation Systems,30, 187–198. https://doi.org/10.17705/1CAIS.
03013.
Open Access statement. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License
(https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided
the original author and source are credited, a link to the CC License is provided, and changes –if any –are indicated.
6Journal of Behavioral Addictions
Unauthenticated | Downloaded 05/20/24 02:47 AM UTC