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Article

Predicting the Time Spent Playing Computer and Mobile Games among Medical Undergraduate Students Using Interpersonal Relations and Social Cognitive Theory: A Cross-Sectional Survey in Chongqing, China

by
Li Chen
1,2,3,†,
Ruiyi Liu
1,2,3,†,
Huan Zeng
1,2,3,
Xianglong Xu
1,2,3,
Rui Zhu
1,2,3,
Manoj Sharma
4,5,6 and
Yong Zhao
1,2,3,*
1
School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
2
Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China
3
The Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing 400016, China
4
Department of Behavioural and Environmental Health, Jackson State University, Jackson, MS 39213, USA
5
Health for All, Omaha, NE 68144, USA
6
College of Health Sciences, Walden University, Minneapolis, MN 55401, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Environ. Res. Public Health 2018, 15(8), 1664; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15081664
Submission received: 10 July 2018 / Revised: 30 July 2018 / Accepted: 4 August 2018 / Published: 6 August 2018
(This article belongs to the Special Issue Internet and Mobile Phone Addiction: Health and Educational Effects)
Versions Notes

Abstract

:
Background: Computer and mobile games are widely used among undergraduate students worldwide, especially in China. Our objective was to predict the time spent playing computer and mobile games based on interpersonal relations and social cognitive theory constructs (i.e., expectation, self-efficacy, and self-control). Methods: The cross-sectional survey was conducted in two medical universities using a sample of 1557 undergraduate students recruited by cluster sampling. The five-point Likert questionnaire was jointly developed by researchers from Chongqing Medical University and Jackson State University. Results: Approximately 30% and 70% of the students played computer and mobile games, respectively. The daily times spent by participants on computer games were 25.61 ± 73.60 min (weekdays) and 49.96 ± 128.60 min (weekends), and 66.07 ± 154.65 min (weekdays) and 91.82 ± 172.94 min (weekends) on mobile games. Students with high scores of interpersonal relations but low scores of self-efficacy spent prolonged time playing computer games on weekdays and weekends (p < 0.05 for all). Students with low scores of expectation spent prolonged time playing computer games on weekdays (p < 0.05). Students with high scores of interpersonal relations but low scores of self-efficacy and self-control spent prolonged time playing mobile games on weekdays and weekends (p < 0.05 for all). Conclusions: The prevalence and duration of playing mobile games were higher than those of playing computer games among medical undergraduate students in Chongqing, China. This study determined the interpersonal relations, self-efficacy, self-control, and expectation of the students at the time of playing computer and mobile games. Future studies may consider studying the interaction among game-related behaviours, environments, and personality characteristics.

1. Introduction

Globally, computer and mobile games are widely played by undergraduate students and have been identified to cause serious public health problems. The use of computer and mobile games is especially prevalent in China. By the end of December 2015, the number of Chinese teenage netizens (18 to 25 years old) reached 287 million, and of these, 66.5% play computer and mobile games [1]. By 2017, China had more than 390 million students playing computer games, accounting for 25.4% of all Internet users [2]. With the development of portable devices, the number of players shifting to mobile phones from computers has increased. Since 2013, 15.6% of users have not played games on their computers [3]. According to a 2015 survey, 90% of teenagers used mobile phones to surf the Internet, and 51.0% of them played mobile games online [1]. Playing computer games for a long period increases the screen viewing time of undergraduate students, causes dry eye syndrome and visual fatigue [4,5] and raises the risk of sedentary behaviour and obesity [6,7,8]. Smartphone overuse can lead to neck, wrist and back pains [9]. Excessive smartphone use at night can also shorten sleep time and lead to stress and depression [10]. In addition, a positive correlation was noted between the severity of Internet addiction and depression in adolescents, but no such relationship was found between time spent using social networks and depression [11]. Moreover, being in a state of negative tension, such as anxiety and depression, can promote the use of smartphones and the Internet and even lead to addiction [12,13].
In the study on Internet or smartphone addiction (computer and mobile games), several models of Internet and smartphone addiction are developed, like the IPACE model of Brand or the model concerning problematic smartphone use of Bilieux [12,14,15]. These models suggest that personality characteristics or psychopathological phenomena (depression and anxiety) significantly influence Internet and smartphone addiction. According to Montag’s research in 2017, the personality characteristics of Internet and smartphone addiction overlap, and the relationship between personality and Internet addiction is closer, with self-efficacy or self-control as potential precursors of addiction [16]. Empathy and life satisfaction are also linked to Internet and smartphone addiction [17]. In addition, a 2015 study in South Korea found that psychopathological phenomenon, such as anxiety, is a risk factor for Internet and smartphone addiction [13]. Preoccupation and conflict are risk factors for smartphone addiction [18]. Depression and attention deficit both play a critical role in Internet addiction [19]. Internet addiction can also predict stress, depression, anxiety, and loneliness [20]. Personality characteristics, such as self-esteem and resilience, also regulate depression and Internet and smartphone addiction [21]. Moreover, a considerable overlap exists between Internet and smartphone addiction [16,22]. This overlap may explain why studying the factors that influence Internet-use disorders (e.g., computer and mobile games) is crucial. Game disorder has progressed and is now officially included in the ICD-11. However, the inclusion of game disorder in the manual is controversial. The theoretical link between game disorders and personality traits and their role as factors of resilience or vulnerability (e.g., self-directedness, extraversion, impulsivity, and empathy) needs clarification.
Time spent on computer and mobile games is affected by many factors. In terms of gender, males are more interested in playing computer and mobile games than females [23]. A Chinese study found that teenage females spend more time on other extra-curricular and leisure activities, whereas teenage males spend more time playing video games [24]. Previous research showed that the length of time spent playing video games varies with age [25]. Smoking and drinking are associated with video games [26,27]. Family situation is also linked to playing computer and mobile games [6], whereby game behavior can enhance family cohesion to a certain extent [28]. Although game behavior is exhibited separately, most players prefer to share activities with friends or parents [28]. This preference may relatively extend game time. Many studies identified factors that influence the amount of time students spend playing computer and mobile games, but few studies focused on interpersonal relations and social cognitive theory constructs at the time of playing computer and mobile games, especially among Chinese medical undergraduate students.
Interpersonal relations and social cognitive theory constructs (i.e., expectation, self-efficacy, and self-control) are associated with game-playing behaviour [28,29,30]. Interpersonal relations measure the relationship between a person and those around him/her. A previous study found that people who play video games possess a good friendship network [28]. The social nature of certain video games can extend playing time whilst allowing players to gain new and other relationships [31]. Social cognition theory is a dynamic and reciprocal model based on the interaction among behaviour, personal factors, and environmental influences [32]. In this theory, expectation, self-efficacy and self-control are the core determinants for achieving a goal [33]. Social cognition theory was applied to study Chinese people for smoking cessation [34] and obesity prevention [35,36]. Expectations include the anticipation of the outcome of a particular action and the importance of the value of these results [37]. Self-efficacy describes a person’s confidence in exhibiting a particular behaviour at a given moment [38]. Self-control describes a person’s capability to regulate behaviour and includes strategies that encourage proximal and distal goal setting and self-rewards [37,38]. Another study found that self-efficacy is a comparatively robust predictor of involvement in massively multiplayer online role-playing game community [29]. In addition, self-control is negatively correlated with online game addiction [30], and students with high self-control spend lesser playing time on video games than those with low self-control. A study on Iranian students revealed that self-control when playing video games differs between male and female students [39].
Despite these efforts, previous studies paid little attention to medical undergraduate students, especially in China. Medical undergraduate students have more academic pressure and longer education years than other college students. Computer and mobile games provide a way for medical undergraduate students to relax. The video game time of medical students and the factors influencing this time may differ from those influencing students of other majors. To the best of our knowledge, this study is the first to use social cognitive theory in predicting the time spent playing computer and mobile games among medical undergraduate students in China. The objectives of this study are to examine the factors influencing students’ behaviours in playing computer and mobile games and the effects of interpersonal relations and social cognitive theory constructs (i.e., expectation, self-efficacy, and self-control) on the time spent by students playing computer and mobile games.

2. Materials and Methods

2.1. Study Design

A cross-sectional study on medical undergraduate students was conducted in Chongqing in March 2018. Each medical university/college in Chongqing consists of a comparable number of students. A total of 1557 students were chosen from Chongqing Medical University and Chongqing Medical and Pharmaceutical College by cluster sampling. Cluster sampling was used to select the classes in school, and 27 classes (6 classes from Chongqing Medical and Pharmaceutical College and 19 from Chongqing Medical University) were selected from the two medical schools in Chongqing. The 27 classes were from Grades 1 to 3 (10 classes from Grade 1, 9 classes from Grade 2, and 8 classes from Grade 3). Prior to the investigation, we conducted a pilot survey in March 2018, involving 50 students from Chongqing Medical University and Chongqing Medical and Pharmaceutical College. With the pilot survey as basis, the questionnaire was distributed by a trained student helper to students who gave written consent to participate. For the questionnaire distribution, the student helper was trained on the purpose and method of the research and was the one who explained the research objectives to the students. The students who signed the informed consent filled out the questionnaire ‘Study on the video games behaviour of undergraduate students in Chongqing, China’, and the participants did not receive incentives. The students were asked to answer the questionnaire in the classroom within 15–20 min. This study was approved by Chongqing Medical University (Reference Number: 2016001), and the ethical approval includes allowing anonymous surveys of minors and adults with the consent of the class counsellor. Written informed consent for processing personal data was obtained from each participant.

2.2. Instruments

Questionnaire refers specifically to the measurement instrument used to obtain games related information in this study. The questionnaire was designed by Chongqing Medical University and Jackson State University researchers. The part on interpersonal relations was developed by research analysts from Chongqing Medical University. The part on social cognitive theory was developed by research analysts from Jackson State University. The questionnaire was translated from English into Chinese. We also checked the readability of the translated questionnaire in the pilot survey. The internal consistency of the total questionnaire was 0.954 (Cronbach’s alpha). The games in this study refer to all multiplayer cooperative or independent games that operate on electronic device platforms, including online and offline variants.
The demographic information in this survey included gender, age (15–18, 19–20, 21–28 years old), ethnic group (Han nationals or Minority), grade level (Grade 1, Grade 2, Grade 3) and without siblings (Yes or No). Questions about living habits were also asked, including smoking (Smoker or Non-smoker) and drinking (Drinker or Non-drinker) preferences. This study measured the daily time spent playing computer and mobile games (on weekdays or weekends) with four questions. The participants were asked to answer the questions in min. We considered the score as a game time value which was zero, instead of not playing games when the self-reported game time was zero. The questions about interpersonal relations and social cognitive theory constructs were as follows: 6 items were used to measure interpersonal relations, 4 items were used to measure self-efficacy, 4 items were used to measure the self-control, and 10 items were used to measure the expectation (see Table 1). The internal consistency of the interpersonal relations was 0.929 (see Table 2). The internal consistencies of the self-efficacy, self-control, and expectation subscales were 0.887, 0.915, and 0.944, respectively.

2.3. Data Analysis

Frequencies and percentages were calculated to summarize the distributions of the categorical variables. A t-test was employed to compare the differences in the continuous variables between males and females. Generalized linear models were developed using social cognitive theory constructs (i.e., expectations, self-efficacy and self-control), interpersonal relations, healthy habits (smoking and drinking status), gender, age group, grade level, lack of siblings, and nationality as independent variables and time spent playing computer and mobile games on weekdays and weekends as dependent variables. Statistical tests included a two-sided test, and statistical significance was at p < 0.05. All data were analyzed using SPSS22.0 for Windows (SPSS Inc., Chicago, IL, USA).

3. Results

3.1. Characteristics of the Sample

This survey involved 1557 undergraduate students. Of the 1241 persons who answered all the questions, 458 (36.9%) were males and 783 (63.0%) were females. All the participants were 15–28 years old, and the average age of the participants was 19.76 ± 1.30 years old. A total of 89.2% were Han nationals; 10.8%, minorities. Of the 1241 participants, 35.1% were Grade 1, 27.0% were Grade 2, and 38.0% were Grade 3. A total of 43.4% had no siblings, and 56.6% had siblings. Moreover, 6% and 11% were smokers and drinkers, respectively, and 94.0% and 89% did not smoke and drink (see Table 3).

3.2. Daily Time Spent Playing Computer and Mobile Games among Undergraduate Students

Participants reported spending an average of 25.61 ± 73.60 min playing computer games per day on weekdays. A total of 898 (72.4%) reported spending 0 min playing computer games on weekdays, and 31 participants reported spending over 180 min playing computer games on weekdays. A total of 206 participants played computer games from 0 to 60 min on weekdays daily.
Participants reported an average of 49.96 ± 128.60 min playing computer games per day on weekends. A total of 853 (68.7%) reported spending 0 min playing computer games on weekends, and 85 participants reported spending over 180 min playing computer games on weekends. A total of 144 participants played computer games from 0 to 60 min, and 101 participants played computer games from 90 to 120 min on weekends daily.
Participants reported spending an average of 66.07 ± 154.65 min playing mobile games per day on weekdays. A total of 387 (31.2%) reported spending 0 min playing mobile games on weekdays, and 66 participants reported spending over 180 min playing mobile games on weekdays. The majority of participants spent between 0 to 60 min (546 participants) and 90 to 120 min (181 participants) playing mobile games on weekdays daily.
Participants reported spending an average of 91.82 ± 172.94 min playing mobile games per day on weekends. A total of 400 (32.3%) reported spending 0 min playing mobile games on weekends, and 151 participants reported spending over 180 min on playing mobile games on weekends. The majority of participants spent between 0 to 60 min (360 participants) and 90 to 120 min (208 participants) playing mobile games on weekends daily (see Table 2).

3.3. Descriptive Statistics of Interpersonal Relations and Social Cognitive Theory Constructs

Compared with males, females had a significantly higher mean score of interpersonal relations for playing video games (p = 0.041). However, no significant differences between males and females were observed in the mean scores of self-efficacy, self-control, and expectation for playing video games (see Table 4).

3.4. Generalised Linear Model Analysis for Factors Affecting the Time Spent Playing Video Games

In multivariable analyses, interpersonal relations, self-efficacy, and expectation were associated with the time spent playing computer games (see Table 4). Male students spent considerable time playing computer games on weekdays (p < 0.001) and weekends (p < 0.001). Students with a high score in interpersonal relations spent a long time playing computer games on weekdays (p < 0.001) and weekends (p < 0.001). Students with a high score in self-efficacy spent limited time playing computer games on weekdays (p = 0.010) and weekends (p = 0.011). Students with a high score in expectation spent limited time playing computer games on weekdays (p = 0.003).

3.5. Generalised Linear Model Analysis for Factors Affecting the Time Spent Playing Mobile Games

In multivariable analyses, interpersonal relations, self-efficacy, expectations, and self-control were associated with the time spent playing mobile games (see Table 5 and Table 6). Students with a high score in interpersonal relations spent a long time playing mobile games on weekdays (p < 0.001) and weekends (p < 0.001). Students with a high score in self-efficacy spent limited time playing mobile games on weekdays (p = 0.002) and weekends (p < 0.001). Students with a high score of self-control spent limited time playing mobile games on weekdays (p = 0.017) and weekends (p = 0.029).

4. Discussion

The aim of this study is to determine the interpersonal relations, self-efficacy, self-control, and expectation of the students at the time of playing computer and mobile games among Chinese medical undergraduates. Our results indicate that high interpersonal relations, low self-efficacy, and low expectations lead to longer computer games. We find also that high interpersonal relationship, low self-efficacy, and low self-control lead to longer mobile game time.
Approximately 30% and 70% of the participants played computer and mobile games, respectively. According to a previous study, roughly 63.7% participants are active players of computer and mobile games [40]. Our study reported high gaming rates among medical undergraduate students. In addition, mobile games are more popular than computer games among these undergraduate students. This finding implies that an increasing number of medical undergraduate students are replacing computer games with mobile games. A Chinese study found that 22% of medical undergraduates play mobile games or read online novels in class. The use of mobile phones is not limited by space and time and can even penetrate the classroom. The heavy and tedious courses in medical colleges may lead to high mobile game usage in class. This situation may be one reason why mobile gamers outnumber computer gamers in this setting. Medical schools should strengthen classroom management and enhance interest in the curriculum. Attracting students’ attention in class should be done as much as possible to improve quality of teaching and reduce use of mobile games.
Medical undergraduate students with high interpersonal relations and low self-efficacy showed increased times spent playing computer games on weekdays and weekends. A previous study found that gamers who spent more time playing computer games display more prosocial behavior [41] and wider friendship networks [28]. Games have gradually become a way of maintaining interpersonal relationships in real life among college students. Extended game time may be due to games’ ability to enhance users’ social attributes in real life. Extending game time may help maintain a good relationship and enhance users’ social attributes in real life. Good social conditions may also further increase game time. If students’ interpersonal communication is low, then they tend to avoid social interaction, and the use of games as a popular social way will be reduced. However, people with social difficulties play games for social comfort [42]. Students with poor relationships may also immerse themselves in a game, which is related to the type of game. People with good relationships may be inclined to choose social games, such as massively multiplayer games. People with poor relationships may be inclined to choose games that make them comfortable to ease social difficulties.
A previous study found that self-efficacy in the real world is negatively related to game addiction [43], which is similar to our finding. Self-efficacy may be enhanced or amplified by environmental encouragement. However, our study marks the first time the relationship between social cognition theory and playing time is tested among a specific segment of the Chinese population. Health education workers can enhance self-efficacy for playing a computer game to reduce screen time among undergraduate students, especially students from higher grade levels. A previous study showed that motivational interviewing to enhance self-efficacy through repetition, reinforcement, and encouragement can strengthen the intrinsic motivations of subjects [44]. Short-term goals and rewards can also improve self-efficacy [45].
Students with low expectation reported spending long times spent playing computer games on weekdays. A previous study found that adolescents’ expectation for health behaviour can influence the establishment of individual trajectories of health [46]. Therefore, teenagers can achieve healthy goals by raising expectations and controlling playing time. Students with high expectations are more health conscious and willing to take the initiative to reduce their playing time and gain health. Their parents or peers can encourage healthy behaviours, and such an action can lead to higher expectations. Building high expectations for playing video games for less than 3 h a day is important for adolescents. Health educators can enhance the expectation for playing video games to less than 3 h a day to decrease the time spent playing computer games. Medical undergraduate students who are also drinkers and smokers reported spending long time playing computer games on weekdays. This finding is supported by previous studies [27,47,48]. Previous studies have linked interpersonal relations with social behaviours such as smoking and drinking, and we have found that interpersonal relations are also linked to gaming behaviours, which may suggest that games are a social tool in some way [49,50].
For mobile games, medical undergraduate students with high interpersonal relations, low self-efficacy, and low self-control reported spending long time playing mobile games on weekdays and weekends. Playing mobile games can enhance friendships among peers. A study based on massively multiplayer online role-playing games showed that social interaction is essential in gaming experience. A mobile phone-based social network is a major tool for college students to socialize with others. This feature may extend the time spent by undergraduate students on mobile games. A previous study found that mental health self-efficacy influences the symptom outcomes of a mobile phone user. This study also linked self-efficacy with reduced depression, anxiety, and stress symptoms and reported that this improved work and social functioning [51]. We found that students with high self-control spent lesser time on mobile games than on computer games. Self-control is negatively correlated with mobile phone use [52], which supports our finding. The current study determined that self-control is negatively associated with mobile games. Students with low self-control are unable to control themselves from playing mobile games. Health education workers can enhance the self-efficacy and self-control of undergraduate students for playing mobile games to reduce time spent playing mobile games.
However, gender, age, ethic group, and grade levels are not significantly associated with the time spent playing mobile games. No space or time limit is found for mobile games, and mobile games are more popular than computer ones for males and females. A 2014 research found that when surfing and playing games become the main functions of mobile phones, males are more likely to use mobile phones than females [24]. In recent years, an increasing number of mobile games aimed at female groups have been developed, such as ‘Love and Producer’, which is popular among young Chinese women. This activity may be the reason why no significant difference was observed between genders in mobile games in this study. Moreover, age is not significantly associated with the time spent playing mobile games. The participants in this study were mostly 18–22 years old. Age is insignificant, possibly due to the concentration of participants in the age group. More age groups should be included to determine the relationship between age and mobile games.
This study has notable limitations. Firstly, the cross-sectional survey data cannot determine the direction of causality. We cannot clarify if constructs precede behaviour. Secondly, the time spent playing games was self-reported by the participants, which may not be accurate for game time. Self-reported game time of zero is probably the average time of the last month of zero, and this finding does not mean the participant has never played a game. This study also does not distinguish between excessive games because no clear standard for excessive game time is available. The self-reported game times may have introduced information and measurement bias. Future studies can collect the time spent playing games with Internet technologies. Thirdly, the questionnaire on interpersonal relations was developed by researchers in Chongqing Medical University. The interpersonal relationship scale was only tested for internal consistency and readability. It lacks wide recognition and use. Fourthly, our study was conducted on medical universities, which do not accurately represent all the undergraduates in China. Future research requires a larger sample size to cover more students with different majors. Fifthly, self-efficacy, self-control, and expectation are strictly focused on computer and mobile games, and this focus may have inflated correlations with self-reported gaming. Future studies may consider studying the relationship between self-efficacy, which is not strictly focused on computer and mobile games, and the time spent playing computer and mobile games. Finally, the types of games are not distinguished in this study, and different types of games may have different playing times.

5. Conclusions

The prevalence and duration of playing mobile games are higher than those of playing computer games among medical undergraduate students in Chongqing, China. This study determined the interpersonal relations, self-efficacy, self-control, and expectation on the time spent playing computer and mobile games. This study reported on students’ gender, age, grade level, and drinking and smoking status at the time of playing computer and mobile games. Future studies may consider studying the interaction among game-related behaviours, environments, and personality characteristics.

Author Contributions

All authors contributed to the design of the survey. L.C. designed the study, interpreted and analysed the data and drafted the manuscript. R.L. contributed to data analysis and data interpretation as well as drafted the manuscript. H.Z., X.X., R.Z., M.S. and Y.Z. helped draft the manuscript. All authors have read and approved the final manuscript. All authors have seen and approved the final version of the manuscript.

Funding

This research was funded by the Humanities and Social Science Project Fund of the Ministry of Education of the People’s Republic of China grant number 15YJA860020.

Acknowledgments

We wish to thank the research students from Chongqing Medical University for their help in the data collection.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. China Internet Network Information Center. Research Report on Internet Behavior of Chinese Teenagers in 2015. Available online: http://www.cnnic.cn/hlwfzyj/hlwxzbg/qsnbg/201608/P020160812393489128332.pdf (accessed on 10 July 2018).
  2. China Culture and Entertainment Industry Association. China Game Industry Development Report in 2017. Available online: http://www.cnccea.com/index.php?m=newscon&id=408&aid=770 (accessed on 10 July 2018).
  3. China Internet Network Information Center. 2013 China Mobile Game User Research Report. Available online: http://www.cnnic.cn/hlwfzyj/hlwxzbg/wybg/201409/P020140911538720279158.pdf (accessed on 10 July 2018).
  4. Dumery, B.; Grounauer, P.A.; van Toi, V. Eyestrain, Blink Rate and Dry Eye Syndromes of Video Display Terminal Users; Springer: Berlin/Heidelberg, Germany, 2010; pp. 270–273. [Google Scholar]
  5. Rajeev, A.; Gupta, A.; Sharma, M. Visual Fatigue and Computer Use Among College Students. Indian J. Community Med. 2006, 31, 192–193. [Google Scholar]
  6. He, M.Z.; Piché, L.; Beynon, C.; Harris, S. Screen-related sedentary behaviors: Children’s and parents’ attitudes, motivations, and practices. J. Nutr. Educ. Behav. 2010, 42, 17–25. [Google Scholar] [CrossRef] [PubMed]
  7. Bhadoria, A.S.; Kapil, U.; Kaur, S. Association of Duration of Time Spent on Television, Computer and Video Games with Obesity amongst Children in National Capital Territory of Delhi. Int. J. Prev. Med. 2015, 6, 80. [Google Scholar] [CrossRef] [PubMed]
  8. Xu, X.; Pu, Y.; Sharma, M.; Rao, Y.; Cai, Y.; Zhao, Y. Predicting Physical Activity and Healthy Nutrition Behaviors Using Social Cognitive Theory: Cross-Sectional Survey among Undergraduate Students in Chongqing, China. Int. J. Environ. Res. Public Health 2017, 14. [Google Scholar] [CrossRef] [PubMed]
  9. Cha, S.S.; Seo, B.K. Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use. Health Psychol. Open 2018, 5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Lemola, S.; Perkinson-Gloor, N.; Brand, S.; Dewald-Kaufmann, J.F.; Grob, A. Adolescents’ electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age. J. Youth Adolesc. 2015, 44, 405–418. [Google Scholar] [CrossRef] [PubMed]
  11. Banjanin, N.; Banjanin, N.; Dimitrijevic, I.; Panticd, I. Relationship between internet use and depression: Focus on physiological mood oscillations, social networking and online addictive behavior. Comput. Hum. Behav. 2015, 43, 308–312. [Google Scholar] [CrossRef]
  12. Billieux, J. Problematic Use of the Mobile Phone: A Literature Review and a Pathways Model. Curr. Psychiatry Rev. 2012, 8, 299–306. [Google Scholar] [CrossRef]
  13. Choi, S.W.; Kim, D.J.; Choi, J.S.; Ahn, H.; Choi, E.J.; Song, W.Y.; Kim, S.; Youn, H. Comparison of risk and protective factors associated with smartphone addiction and Internet addiction. J. Behav. Addict. 2015, 4, 308–314. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Brand, M. Theoretical Models of the Development and Maintenance of Internet Addiction; Springer International Publishing: Cham, Switzerland, 2017; pp. 19–34. [Google Scholar]
  15. Brand, M.; Young, K.S.; Laier, C.; Wölfling, K.; Potenza, M.N. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Neurosci. Biobehav. Rev. 2016, 71, 252–266. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Lachmann, B.; Duke, É.; Sariyska, R.; Montag, C. Who’s Addicted to the Smartphone and/or the Internet? Psychol. Pop. Media Cult. 2017. [Google Scholar] [CrossRef]
  17. Lachmann, B.; Sindermann, C.; Sariyska, R.Y.; Luo, R.; Melchers, M.C.; Becker, B.; Cooper, A.J.; Montag, C. The Role of Empathy and Life Satisfaction in Internet and Smartphone Use Disorder. Front. Psychol. 2018, 9, 398. [Google Scholar] [CrossRef] [PubMed]
  18. Lee, H.; Kim, J.W.; Choi, T.Y. Risk Factors for Smartphone Addiction in Korean Adolescents: Smartphone Use Patterns. J. Korean Med. Sci. 2017, 32, 1674–1679. [Google Scholar] [CrossRef] [PubMed]
  19. Sariyska, R.; Reuter, M.; Lachmann, B.; Montag, C. Attention Deficit/Hyperactivity Disorder is a Better Predictor for Problematic Internet use than Depression: Evidence from Germany. J. Addict. Res. Ther. 2015, 6, 209. [Google Scholar] [CrossRef]
  20. Ostovar, S.; Allahyar, N.; Aminpoor, H.; Moafian, F.; Nor, M.B.M.; Griffiths, M.D. Internet Addiction and its Psychosocial Risks (Depression, Anxiety, Stress and Loneliness) among Iranian Adolescents and Young Adults: A Structural Equation Model in a Cross-Sectional Study. Int. J. Mental Health Addict. 2016, 14, 257–267. [Google Scholar] [CrossRef] [Green Version]
  21. Park, H.; Choi, E. Smartphone Addiction and Depression: The Mediating Effects of Self-esteem and Resilience among Middle School Students. J. Korean Acad. Community Health Nurs. 2017, 28, 280–290. [Google Scholar] [CrossRef]
  22. Min, K.; Kim, D.-J.; Cho, H.; Yang, S. The Smartphone Addiction Scale: Development and Validation of a Short Version for Adolescents. PLoS ONE 2013, 8, e83558. [Google Scholar] [CrossRef]
  23. Yang, D.-J.; Chiu, J.-Z.; Chen, Y.-K. Examining the Social Influence on College Students for Playing Online Game: Gender Differences and Implications. Turk. Online J. Educ. Technol. 2011, 10, 115–122. [Google Scholar]
  24. Jiang, X.X.; Hardy, L.L.; Ding, D.; Baur, L.A.; Shi, H.-J. Recreational screen-time among Chinese adolescents: A cross-sectional study. J. Epidemiol. 2014, 24, 397–403. [Google Scholar] [CrossRef] [PubMed]
  25. Greenberg, B.S.; Sherry, J.; Lachlan, K.; Lucas, K.; Holmstrom, A. Orientations to Video Games Among Gender and Age Groups. Simul. Gaming 2010, 41, 238–259. [Google Scholar] [CrossRef]
  26. Forsyth, S.R.; Malone, R.E. Smoking in Video Games: A Systematic Review. Nicotine Tob. Res. Off. J. Soc. Res. Nicotine Tob. 2016, 18, 1390–1398. [Google Scholar] [CrossRef] [PubMed]
  27. Bőthe, B.; Tóth-Király, I.; Orosz, G. Clarifying the Links among Online Gaming, Internet Use, Drinking Motives, and Online Pornography Use. Games Health J. 2015, 4, 107–112. [Google Scholar] [CrossRef] [PubMed]
  28. Durkin, K.; Barber, B. Not so doomed: Computer game play and positive adolescent development. J. Appl. Dev. Psychol. 2002, 23, 373–392. [Google Scholar] [CrossRef]
  29. Hopp, T.; Barker, V.; Schmitz, A.W. Interdependent Self-Construal, Self-Efficacy, and Community Involvement as Predictors of Perceived Knowledge Gain among MMORPG Players. Cyberpsychol. Behav. Soc. Netw. 2015, 18, 468–473. [Google Scholar] [CrossRef] [PubMed]
  30. Kim, E.J.; Namkoong, K.; Ku, T.; Kim, S.J. The relationship between online game addiction and aggression, self-control and narcissistic personality traits. Eur. Psychiatry 2008, 23, 212–218. [Google Scholar] [CrossRef] [PubMed]
  31. Smyth, J.M. Beyond self-selection in video game play: An experimental examination of the consequences of massively multiplayer online role-playing game play. Cyberpsychol. Behav. 2007, 10, 717–721. [Google Scholar] [CrossRef] [PubMed]
  32. Bandura, A. Social Foundations of Thought and Action: A Social Cognitive Theory/Albert Bandura; American Psychological Association: Washington, DC, USA, 1986. [Google Scholar]
  33. Glanz, K.; Lewis, F.M.; Rimer, B.K. Health Behavior and Health Education: Theory, Research, and Practice; Jossey-Bass: San Francisco, CA, USA, 1997; pp. 344–353. [Google Scholar]
  34. Zheng, P.; Guo, F.; Chen, Y.; Fu, Y.; Ye, T.; Fu, H. A randomized controlled trial of group intervention based on social cognitive theory for smoking cessation in China. J. Epidemiol. 2007, 17, 147–155. [Google Scholar] [CrossRef] [PubMed]
  35. Murnan, J.; Sharma, M.; Lin, D. Predicting childhood obesity prevention behaviors using social cognitive theory: Children in China. Int. Q. Community Health Educ. 2006, 26, 73–84. [Google Scholar] [CrossRef] [PubMed]
  36. Chen, Y.; Ma, L.; Ma, Y.; Wang, H.; Luo, J.; Zhang, X.; Luo, C.; Wang, H.; Zhao, H.; Pan, D.; et al. A national school-based health lifestyles interventions among Chinese children and adolescents against obesity: Rationale, design and methodology of a randomized controlled trial in China. BMC Public Health 2015, 15, 210. [Google Scholar] [CrossRef] [PubMed]
  37. Bandura, A. Health Promotion by Social Cognitive Means. Health Educ. Behav. 2004, 31, 143–164. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Tougas, M.E.; Hayden, J.A.; McGrath, P.J.; Huguet, A.; Rozario, S. A Systematic Review Exploring the Social Cognitive Theory of Self-Regulation as a Framework for Chronic Health Condition Interventions. PLoS ONE 2015, 10, e0134977. [Google Scholar] [CrossRef] [PubMed]
  39. Haghbin, M.; Shaterian, F.; Hosseinzadeh, D.; Griffiths, M.D. A brief report on the relationship between self-control, video game addiction and academic achievement in normal and ADHD students. J. Behav. Addict. 2013, 2, 239–243. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Mohamud, S. Effect of Video Games on Medical Students’ Academic Performance: A Two-Institutions, Cross-Sectional Study. Int. J. Sci. Eng. Res. 2017, 8, 653–658. [Google Scholar]
  41. Mengel, F. Computer games and prosocial behavior. PLoS ONE 2014, 9, e94099. [Google Scholar] [CrossRef] [PubMed]
  42. Kowert, R.; Oldmeadow, J.A. Playing for social comfort: Online video game play as a social accommodator for the insecurely attached. Comput. Hum. Behav. 2015, 53, 556–566. [Google Scholar] [CrossRef]
  43. Jeong, E.J.; Kim, D.H. Social activities, self-efficacy, game attitudes, and game addiction. Cyberpsychol. Behav. Soc. Netw. 2011, 14, 213–221. [Google Scholar] [CrossRef] [PubMed]
  44. Walpole, B.; Dettmer, E.; Morrongiello, B.A.; McCrindle, B.W.; Hamilton, J. Motivational interviewing to enhance self-efficacy and promote weight loss in overweight and obese adolescents: A randomized controlled trial. J. Pediatr. Psychol. 2013, 38, 944–953. [Google Scholar] [CrossRef] [PubMed]
  45. Schunk, D.H. Enhancing Self-Efficacy and Achievement through Rewards and Goals: Motivational and Informational Effects. J. Educ. Res. 1984, 78, 29–34. [Google Scholar] [CrossRef]
  46. Mcdade, T.W.; Chyu, L.; Duncan, G.J.; Hoyt, L.T.; Doane, L.D.; Adam, E.K. Adolescents’ expectations for the future predict health behaviors in early adulthood. Soc. Sci. Med. 2011, 73, 391–398. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Frangos, C.C.; Frangos, C.C.; Sotiropoulos, I. Problematic Internet Use among Greek university students: An ordinal logistic regression with risk factors of negative psychological beliefs, pornographic sites, and online games. Cyberpsychol. Behav. Soc. Netw. 2011, 14, 51. [Google Scholar] [CrossRef] [PubMed]
  48. Ko, C.H.; Yen, J.Y.; Yen, C.F.; Chen, C.S.; Weng, C.C.; Chen, C.C. The association between Internet addiction and problematic alcohol use in adolescents: The problem behavior model. Cyberpsychol. Behav. 2008, 11, 571–576. [Google Scholar] [CrossRef] [PubMed]
  49. Liang, Y.; Zheng, X.; Zeng, D.D.; Zhou, X.; Leischow, S.J.; Chung, W. Characterizing Social Interaction in Tobacco-Oriented Social Networks: An Empirical Analysis. Sci. Rep. 2015, 5, 1–11. [Google Scholar] [CrossRef] [PubMed]
  50. Monahan, J.L.; Lannutti, P.J. Alcohol as social lubricant. Hum. Commun. Res. 2000, 26, 175–202. [Google Scholar] [CrossRef]
  51. Jiang, Z.; Zhao, X. Self-control and problematic mobile phone use in Chinese college students: The mediating role of mobile phone use patterns. BMC Psychiatry 2016, 16, 416. [Google Scholar] [CrossRef] [PubMed]
  52. Clarke, J.; Proudfoot, J.; Birch, M.R.; Whitton, A.E.; Parker, G.; Manicavasagar, V.; Harrison, V.; Christensen, H.; Hadzi-Pavlovic, D. Effects of mental health self-efficacy on outcomes of a mobile phone and web intervention for mild-to-moderate depression, anxiety and stress: Secondary analysis of a randomised controlled trial. BMC Psychiatry 2014, 14, 272. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Specific problems in interpersonal relationships and social cognitive theory constructs.
Table 1. Specific problems in interpersonal relationships and social cognitive theory constructs.
ConstructsSpecific Problems
Interpersonal relations 1How much do you agree with the following statement? I have good relationship with…
(1) my classmates, (2) my roommates, (3) everyone around me, (4) my parents, (5) my teachers, (6) anyone. I don’t often have conflicts with people.
Self-efficacy 2How sure are you that you will…
(1) play computer or mobile games for less than 3 h daily? (2) reduce the time spent playing computer or mobile games, even if you enjoy playing games? (3) reduce the time spent playing computer or mobile games if you have to hand in your homework? (4) reduce the time spent playing computer or mobile games if you have to do something important?
Self-control 3How sure are you that you will…
(1) set a goal to play computer or mobile games for less than 3 h daily? (2) reward yourself for insisting on reducing the time spent playing computer or mobile games daily? (3) remind yourself to insist on playing computer or mobile games for less than 3 h daily? (4) constantly check progress to make sure you play computer or mobile games for less than 3 h daily?
ExpectationIf I play computer games or mobile games for less than 3 h daily, I will … 4
(1) have additional friends, (2) have more spare time, (3) enjoy more, (4) feel more relaxed, (5) be able to study well.
Which of the following changes are important to you? 5
(6) have additional friends, (7) have more spare time, (8) enjoy more, (9) feel more relaxed, (10) be able to study well
1 Response options were “Not At All Agree”, “Slightly Agree”, “Moderately Agree”, “Very Agree”,” Completely Agree”. 2 Response options were “Not At All Sure”, “Slightly Sure”, “Moderately Sure”, “Very Sure”, “Completely Sure”. 3 Response options were “Not At All Sure”, “Slightly Sure”, “Moderately Sure”, “Very Sure”, “Completely Sure”. 4 Response options were “Never”, “Hardly Ever”, “Sometimes”, “Almost Always”, “Always”. 5 Response options were “Not At All Important”, “Slightly Important”, “Moderately Important”, “Very Important”, “Extremely Important”.
Table
Table 2. Times spent playing games by undergraduate students in Chongqing, China (N, %).
Table 2. Times spent playing games by undergraduate students in Chongqing, China (N, %).
Total Time (min)Time Spent Playing Computer Game on WeekdaysTime Spent Playing Computer Game on WeekendsTime Spent Playing Mobile Game on WeekdaysTime Spent Playing Mobile Game on Weekends
Mean ± SD25.61 ± 73.6049.96 ± 128.6066.07 ± 154.6591.82 ± 172.94
0 1898 (72.4)853 (68.7)387 (31.2)400 (32.3)
(0–30] 298 (7.9)52 (4.2)282 (22.7)159 (12.8)
(30–60]108 (8.7)92 (7.4)264 (21.3)201 (16.2)
(60–90]21 (1.7)19 (1.5)21 (1.7)36 (2.9)
(90–120]72 (5.8)101 (8.1)181 (14.6)208 (16.8)
(120–150]2 (0.2)3 (0.2)2 (0.2)14 (1.1)
(150–180]11 (0.9)36 (2.9)38 (3.1)71 (5.7)
>18031 (2.5)85 (6.8)66 (5.3)151 (12.2)
1 0 min; 2 greater than 0 min is less than or equal to 30 min.
Table
Table 3. Demographic characteristics of undergraduate students in Chongqing, China.
Table 3. Demographic characteristics of undergraduate students in Chongqing, China.
VariablesDaily Time Playing Computer GamesDaily Time Playing Mobile GamesTotal
On WeekdaysOn WeekendsOn WeekdaysOn Weekends
0 min>0 min0 min>0 min0 min>0 min0 min>0 min
N (%)N (%)N (%)N (%)N (%)N (%)N (%)N (%)N (%)
Gender
 Male109 (8.8)349(28.1)114 (9.2)344 (27.7)202 (16.3)256 (20.6)172 (13.9)286 (23.0)458 (36.9)
 Female278 (22.4)505 (40.7)287 (23.1)496 (40.0)696 (56.1)87 (7.0)681 (54.9)102 (8.2)783 (63.0)
Age
 15–18 years old63 (5.1)460 (12.9)67 (5.4)156 (12.6)158 (12.7)65 (5.2)140 (11.3)83 (6.7)223 (18.0)
 19–20 years old207 (16.7)458 (36.9)215 (17.3)450 (36.3)493 (39.7)172 (13.9)467 (37.6)198 (16.0)665 (53.6)
 21–28 years old117 (9.4)236 (19.0)119 (9.6)234 (18.9)247 (19.9)106 (8.5)246 (19.8)107 (8.6)353 (28.4)
Nationality
 Han nationals334 (23.9)773 (62.3)348 (28.0)759 (61.2)793 (63.9)314 (25.3)753 (60.7)354 (28.5)1107 (89.2)
 Minority53 (4.3)81 (6.5)53 (4.3)81 (6.5)105 (8.5)29 (2.3)100 (8.1)34 (2.7)134 (10.8)
Grade levels
 Grade 1139 (11.2)332 (26.8)144 (11.6)327 (26.3)349 (28.1)122 (9.8)327 (26.3)144 (11.6)435 (35.1)
 Grade 2109 (8.8)226 (18.2)111 (8.9)224 (18.0)245 (19.7)90 (7.3)222 (17.9)113 (9.1)335 (27.0)
 Grade 3139 (11.2)296 (23.9)146 (11.8)289 (23.3)304 (24.5)131 (10.6)304 (24.5)131 (10.6)471 (38.0)
Without siblings
 Yes162 (13.1)376 (30.3)175 (14.1)363 (29.3)364 (29.3)174 (14.0)342 (27.6)196 (15.8)538 (43.4)
 No225 (18.1)478 (38.5)226 (18.2)477 (38.4)534 (43.0)169 (13.6)511 (41.2)192 (15.5)703 (56.6)
Smoking status
 Smoker21 (1.7)54 (4.4)20 (1.6)55 (4.4)31 (2.5)44 (3.5)27 (2.2)48 (3.9)75 (6.0)
 Non-smoker366 (29.5)800 (64.5)381 (30.7)785 (63.3)867 (69.9)299 (24.1)826 (66.6)340 (27.4)1166 (94.0)
Drinking status
 Drinker36 (2.9)100 (8.1)41 (3.3)95 (7.7)78 (6.3)58 (4.7)70 (5.6)66 (5.3)136 (11.0)
 Non-drinker351 (28.3)754 (60.8)360 (29.0)745 (60.0)820 (66.1)285 (23.0)783 (63.1)322 (25.9)1105 (89.0)
Table
Table 4. Interpersonal relations and social cognitive theory constructs among undergraduate students in Chongqing, China.
Table 4. Interpersonal relations and social cognitive theory constructs among undergraduate students in Chongqing, China.
Interpersonal Relations and Social Cognitive Theory ConstructsMinMaxMean (SD)Standardized Cronbach AlphaMalesFemalesp-Value
Interpersonal relations0.0024.0015.56 (5.40)0.92915.98 (5.81)15.31 (5.14)0.041 *
Self-efficacy0.0016.0010.36 (4.59)0.88710.16 (4.36)10.49 (4.71)0.208
Self-control0.0016.009.13 (4.72)0.9158.82 (4.57)9.31 (4.81)0.068
Expectation0.0040.0025.00 (9.58)0.94424.58 (9.32)25.24 (9.72)0.238
Total---0.954
* Statistically significant (p < 0.05).
Table
Table 5. Generalised linear model analysis of factors that affect interpersonal relations and social cognitive theory constructs of time spent playing computer games among all participants in Chongqing, China.
Table 5. Generalised linear model analysis of factors that affect interpersonal relations and social cognitive theory constructs of time spent playing computer games among all participants in Chongqing, China.
ParameterTime Spent Playing Computer Games on WeekdaysTime Spent Playing Computer Games on Weekends
BSEp-ValueBSEp-Value
Males vs. Females39.1004.186<0.001 **79.7987.214<0.001 **
19–20 years old vs. 15–18 years old–15.1155.7280.008 *–27.6469.8720.005 *
21–28 years old vs. 15–18 years old–30.5357.816<0.001 **–56.25613.471<0.001 **
Han nationals vs. Minority5.1296.2930.415–3.66610.8470.735
Grade 2 vs. Grade 15.685.3070.2774.5149.1470.622
Grade 3 vs. Grade 120.9176.2660.001 *17.87610.8010.098
Without siblings vs. with siblings–3.1493.9990.4313.7116.8920.590
Smoker vs. Non-smoker1.2888.5320.88051.14514.7060.001 *
Drinker vs. Non-drinker17.2016.5430.009 *14.15811.2770.209
Interpersonal relations and Social Cognitive Theory constructs
Interpersonal relations2.3580.465<0.001 **3.2110.801<0.001 **
Self-efficacy–1.6190.6270.010 *–2.7561.0810.011 *
Self-control–0.6580.5600.239–0.8780.9640.362
Expectation–0.8920.2960.003 *–0.8810.5100.084
* Statistically significant (p < 0.05); ** Statistically significant (p < 0.001).
Table
Table 6. Generalised linear model analysis of factors that affect interpersonal relations and social cognitive theory constructs of time spent playing mobile games among all participants in Chongqing, China.
Table 6. Generalised linear model analysis of factors that affect interpersonal relations and social cognitive theory constructs of time spent playing mobile games among all participants in Chongqing, China.
ParameterTime Spent Playing Computer Games on WeekdaysTime Spent Playing Computer Games on Weekends
BSEp-ValueBSEp-Value
Males vs. Females6.5699.2640.47819.57310.3140.058
19–20 years old vs. 15–18 years old–8.58112.6760.498–11.78014.1140.404
21–28 years old vs. 15–18 years old–15.33217.2980.375–12.04719.2600.532
Han nationals vs. Minority17.00813.9280.22216.72315.5070.281
Grade 2 vs. Grade 1–8.53711.7460.467–0.44213.0780.973
Grade 3 vs. Grade 1–1.09513.8690.937–1.76215.4420.909
Without siblings vs. with siblings2.4538.8500.782–4.4439.8540.652
Smoker vs. Non-smoker–12.80318.8830.498–5.61321.0250.789
Drinker vs. Non-drinker21.08614.4800.1459.46516.1230.557
Interpersonal relations and Social Cognitive Theory constructs
Interpersonal relations3.7831.028<0.001 **4.6711.145<0.001 **
Self-efficacy–4.2081.3880.002 *–6.1571.546<0.001 **
Self-control–2.9541.2380.017 *–3.0111.3790.029 *
Expectation–0.4900.6550.454–0.4770.7290.513
* Statistically significant (p < 0.05); ** Statistically significant (p < 0.001).

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Chen, L.; Liu, R.; Zeng, H.; Xu, X.; Zhu, R.; Sharma, M.; Zhao, Y. Predicting the Time Spent Playing Computer and Mobile Games among Medical Undergraduate Students Using Interpersonal Relations and Social Cognitive Theory: A Cross-Sectional Survey in Chongqing, China. Int. J. Environ. Res. Public Health 2018, 15, 1664. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15081664

AMA Style

Chen L, Liu R, Zeng H, Xu X, Zhu R, Sharma M, Zhao Y. Predicting the Time Spent Playing Computer and Mobile Games among Medical Undergraduate Students Using Interpersonal Relations and Social Cognitive Theory: A Cross-Sectional Survey in Chongqing, China. International Journal of Environmental Research and Public Health. 2018; 15(8):1664. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15081664

Chicago/Turabian Style

Chen, Li, Ruiyi Liu, Huan Zeng, Xianglong Xu, Rui Zhu, Manoj Sharma, and Yong Zhao. 2018. "Predicting the Time Spent Playing Computer and Mobile Games among Medical Undergraduate Students Using Interpersonal Relations and Social Cognitive Theory: A Cross-Sectional Survey in Chongqing, China" International Journal of Environmental Research and Public Health 15, no. 8: 1664. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15081664

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