NURSING PRACTICE AND HEALTH CARE

Association between use of Internet and Quality of Life (QOL) among Young People in Hong Kong

Mei-yi Siu*

The School of Nursing, Tung Wah College, Hong Kong SAR, People’s Republic of China, China

CitationCitation COPIED

Siu MY. Association between use of internet and quality of life (qol) among young people in Hong Kong. Nurs Pract Health Care. 2019 Feb;1(1):103

© 2019 Siu MY. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 international License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Increased prevalence of Internet use among youth in Hong Kong becomes an important social and health concern. The aim of the study was to examine the relationship between internet usage and quality of life (QOL) among young people in Hong Kong.

Method: This cross-section corelational study recruited a convenience sample of 273 young adults aged 18-30 in Hong Kong. The Internet Addiction Test (IAT) was adopted to assess the internet usage among participants and the World Health Organization Quality of Life Instrument (WHOQOL-BREF) was used to measure their QOL. Internet users were categorized into the “no”, “mild”, “moderate” and “severe” addiction groups according to their IAT scores.

Results: 9.9% of participants were classified as severe internet addiction group. Internet usage was negatively associated with QOL in young people (r=-0.17, p=0.004). Several domains of the WHOQOL-BREF, including Psychological (r=-0.19, p=0.001), and environmental (r=-0.20, p=0.001), correlated negatively with IAT score. Post hoc analysis on ANOVAs revealed that severe internet addiction group reduced significantly on QOL (p<0.001), compared to other groups. Further, male young adults were more susceptible to internet addiction than female (p=0.003). 

Conclusions: The study findings suggested that problematic internet use was serious among young people in Hong Kong. Compared with the young female, male youth might be more liable to internet overuse. Further study with large sample size was recommended to confirm the result. Early implementation of preventive and control measures on internet addictive use in the at-risk young aged group was suggested.

Keywords

Hong Kong; Internet addiction; Quality of life 

Introduction

In modern days, rapid development of information and communications technology has brought tremendous changes to the ways how people interact and communicate with each other. Cyber communication is a crucial part of daily living in many people worldwide. The internet is a substantial platform for academic enquiry, business transactions, recreational and social circles. Nowadays, 54.4% of the world’s population have internet access [1]. In Hong Kong, around 80% of households with personal computers connected to the Internet. Meanwhile, the Smartphone penetration rate reached to 85.8% in 2016. Yet, according to the thematic report, the rates of internet use were relatively higher among younger people than older people. The corresponding rate of internet access for young people aged between 10 to 44 has reached for almost 100% [2].

Undoubtedly, the internet brings a lot of conveniences and has been added values to everyday life. However, it carries with a dark side, which causes for a new age of internet addiction. Problematic internet use or internet addiction is referred to any online-related, compulsive, excessive or poor controlled behavior which interferes with normal daily living and causes distress [3]. Young people have been identified as a high-risk group for internet addiction. Young people usually show high interests in technology who are more likely to be hanged in the cyber world [4].

Numerous prevalence studies on internet addiction have been conducted all over the world to review the problem. A recent meta-analysis of 31 countries/major cities across seven regions worldwide, estimated for an overall prevalence of internet addiction as 6%. The average age of participants was 18.42 ± 5.02-year-old [5]. In the Asian region, the estimated prevalence of problematic internet uses by a nationwide survey among adolescents in Japan was 7.9% [6]. The prevalence of internet addiction among high school students in Taiwan was 17.4% [7]. Surveying on a total of 6468 adolescents aged from 10 to 18 years old in China, the overall prevalence of internet addiction was noted to be 26.50% [8]. A local 5-year longitudinal study on the prevalence of internet addiction in the Hong Kong school-aged children (age range: 10-18 years) stretched from 17% to 26.8% [9]. The large variance of prevalence rates stalked from different assessment tools and cut offs in use, and different criteria for sample selected across research [10]. Nevertheless, the problem of internet addiction appeared to be more serious in Asian samples than in other regions, which would need special attention.

Regarding the gender difference in problematic internet use in the literature, inconsistent results had been presented. Several researches suggested that the prevalence of internet addiction was higher in boys than in girls [9,11]. A recent large-scale cross-sectional survey of 5,538 Spanish adolescents between the ages of 12 and 20, found that the problematic use of the internet was associated with the female gender (Odd ratio=1.49) [12]. Then Chiu et al. [13] investigated on gender difference between Taiwan college students’ internet addiction, doesn’t showed any discrepancy among both the sexes. Another Canadian study on 3938 high school students from grades 9 to 11, had also reported that there was no difference in the proportion of internet addiction among boys and girls. However, boys would spend more time than the girls in the cyber world. While the girls made intensive use of social networks, boys had more frequently engaged in multi-player online role-playing games, online games, and played visit to adult sites [14].

Internet addiction would induce for different physical problems, including poor sleep, physical inactivity, obesity, and poor vision [15,16], as well as behavioral problems and mental disorders, such as substance abusers, depression, anxiety, and reduced life satisfaction [17,18]. People with problematic internet use were prone to be socially isolated [19].

It is no doubt that internet addiction affects one’s physical, psychological and social well-being, which can be expressed in term of “quality of life” (QOL). According to World Health Organization (WHO), QOL can be conceptualized as people’s perception of their position in life with reference to cultural beliefs and values in the community, influencing on their physical health, mental wellbeing, social interactions and their relationship to the living environment [20].

Internet usage is very popular in the 18-30 age group in Hong Kong, most of them are studying in university or at work who access to the internet regularly for the academy, for fun and for job fulfilling purposes. Previous local internet studies have targeted mainly on primary to secondary students [9,18,21]. Hence, there is limited study to investigate the relationship between the usage of internet and quality of life in this age cohort. The aim of this study is to examine the level of internet use and quality of life in young people aged 18-30 in Hong Kong.

Method

Study design

The study adopted a cross-sectional correlation design. Convenience sampling were sought for busy areas such as Mong Kok or other regions in Hong Kong, where young people were likely to visit. Participants were invited to fill-in a set of questionnaires including (1) a demographic data sheet, (2) the Internet Addiction Test (IAT), and (3) the World Health Organization Quality of Life Instruments (WHOQOL-BREF), after they consented to the study

Selection of sample

Inclusion criteria of the current study were Hong Kong Chinese people aged between 18 to 30 who were able to read and understand English language. The sample size was calculated by on-line calculator (http://www.sample-size.net/correlation-sample-size/). 259 participants were required to give 90% of detection power with expected correlation coefficient of 0.2, at a significance level of 0.05. Allowing for 10% of attrition, 285 participants would be recruited for the study.

Outcome measures

The Internet Addiction Test (IAT) was self-administered instrument developed by Young to measure internet addiction, which had 20 questions rated on a Likert scale from rarely (point 1) to always (point 5). It inquired about how Internet use affects an individual’s daily activities, social relations, productivity, sleep-rest patterns, and feelings. The IAT score ranged from 20 to 100, respondents could be classified into four levels of internet addiction, namely, no addiction (20-39 point), mild addiction (40-59 point), moderate addiction (60-79 point) and severe addiction (80-100 point). Thus, higher score represented for greater risk on internet addiction [22]. The assessment tool was proven with good internal consistency and concurrent validity, explained for 68% of variance [23]. A meta-analysis study on the reliability of IAT by Frangos and colleagues (2012) found that the overall Cronbach’s alpha from eleven studies reached 0.89, of which the instrument showing higher reliability in college students and in Asian [24]. Another study using IAT on Hong Kong Chinese adolescences had also evidently for excellent internal consistency of the tool, which Cronbach’s alpha was yielded at 0.93 [25].

The short form of World Health Organization Quality of Life Instrument (WHOQOL-BREF) was used to measure QOL of participants. It consisted of 26 questions, answering in 5-point Likert scale. Four domains of QOL assessed: physical health, psychological state, social life and environmental health. Higher scores revealed better QOL [26]. The WHOQOL-BREF showed generally good internal consistency, values for Cronbach’s alpha reported in four domains: 0.68 (social), 0.80 (environment), 0.81 (psychological) and 0.82 (physical) respectively. Construct validity of the instrument exemplified with acceptable item–total correlations, ranging from 0.45-0.70 [27]. 

Data analysis

The IBM SSS version 23 was used for data analysis. Descriptive statistics was done to summarize and presented the sample characteristics and outcome measures. Missing or extreme data would be discarded and would not be used in subsequent analysis.

The relationship between QOL and internet addiction was measured by Pearson’s correlation test. The independent t-test was utilized to detect for gender difference on IAT & WHOQOL scores. Further, one-way ANOVA with post hoc tests were computed to differentiate changes of QOL with different level of IAT classifications. All statistic tests were two-tailed and a p ≤ 0.05 was considered as significant for all tests.

Ethical consideration

Approval for the study was obtained from the School Research Committee from a tertiary education institution. All participants had signed for written consent with a full explanation on the research purpose. They could withdraw at any point of the study. Data confidentiality was assured.

Results   

Data collection spanned from January 2017 to February 2017, 300 questionnaires were distributed. Amongst all, 27 were rejected due to uncompleted forms and extreme data. The attrition rate was 9%. (Supplementary Figure). Out of 273 participants, 62.8% were female and 87.9% attained tertiary educational level. Their mean age was around 22-year-old. According to the IAT classification, over 80 % of participants were recognized as moderate (n=202, f=74%) to severe (n=27, f=9.9%) internet addiction. The “no” and “mild” internet addiction group would be regrouped into one group in the subsequent data analysis (n=44, f=16.1%) (Table 1).

Bi-variance analysis on IAT and WHOQOL-BREF demonstrated a significant negative relationship between QOL and internet addiction (r=-0.17, p=0.004). The psychological domain (r=-0.19, p=0.001) and the environmental domain (r=-0.20, p=0.001) of the WHOQOLBREF were negatively correlated with IAT, reached the statistical significance level (Table 2).

The independent t-test was done to detect if any, gender difference on internet usage and QOL. The result showed no substantial difference between male and female participants on QOL ratings. However, the male participants scored significantly higher in IAT than their female counterparts (p=0.003) (Table 3).

One-way ANOVA was conducted and had observed that there was a statistically significant difference in WHOQOL-BREF scores for the three IAT groups (p<0.001), except on the social domain (p=0.054). However, the effect sizes for such difference were small, not exceeding for 0.1 (Table 4).

Next, post hoc analysis revealed that the severe internet addiction group was significantly reduced in perceived QOL (p<0.001) among IAT groups. Further, they diminished significantly on physical health (p=0.001), psychological health (p<0.001) and environmental domain of WHOQOL (p<0.001), comparing to the “moderate” and “no or mild” internet addiction groups (Table 5).

Note: #Re-group of IAT class “No to mild internet addiction”: n=44 (16.1%)

Table 1: Baseline characteristics of the participants

* p-value ≤ 0.05

Table 2: Correlation results for IAT scores with WHOQOL-BREF scores

* p-value ≤ 0.05

Table 3: Independent t-test results of IAT and WHOQOL-BREF between male and female participants

* p-value ≤ 0.05

Table 4: One-way ANOVA results of IAT classifications and WHOQOLBREF scores

* p-value ≤ 0.05

Note: The IAT class sizes were unequal, type I error could not be excluded

Table 5: Post hoc analysis of IAT classifications and WHOQOL-BREF scores

Discussion

The primary findings of current study suggested a negative association between internet usage and QOL among Hong Kong Chinese young people aged 18-30. In the current study, 9.9% of our participants were referred to “severe” internet addiction group who exhibited reduction in self-rated QOL. Although the sample size was small, this study result was comparable with the recent meta-analysis by Cheng and Li (2014) for the estimated prevalence of internet addiction among young people (mean age: 18.42 ± 5.02) across 31 nations at 6.0% [5]. Meanwhile, prevalence of internet addiction in Hong Kong adolescents reported by Shek and Yu (2016) extending between 17% to 26.8% [9]. Future large scale territorywide survey is needed for more accurate estimation of the internet addiction prevalence rate in the young adult group. Nevertheless, there is a high risk of problematic internet usage among Hong Kong young generations requiring attention. 

The severe internet addiction group reported for diminishing in several aspects of QOL, namely physical health, psychological health and relationships with the environment, as measured by the WHOQOL-BREF. This finding echoed with a correlational study on 174 medical students in Iran, of which perceived QOL was lower in the internet addicted medical students in the physical, psychological, and social domains of WHOQOL measures, compared to normal healthy subjects [28].

The physical domain of the WHOQOL-BREF referred to energy level, physical comfort and sleep pattern. Worse ratings on physical health among the severe internet addicts provided support to previous studies on physical symptoms inflicted by internet overuse, including low physical energy level, musculoskeletal pain, headache and sleep disturbances [15,16].

Participants of the severe internet addiction group also reported the impairment on psychological aspects of QOL. Much research had already pointed out negative psychological consequence on internet addiction, the compulsive computer users were prone to develop psychiatric symptoms such as depression, anxiety and loneliness [18,29]. While severe internet addiction decreased physical and psychological well being, problematic internet user reduced life satisfaction [30] and might be exhibited self-disruptive behaviors such as pathological gambling and compulsive sexual activities [3,17].

Regarding negative relationship with the environment among severe internet addicts in this study, it could be partially explained by being occupied in the cyber world, young people came less accessible to outdoor activities. The problematic internet users tended to be socially isolated [19]. Another possible explanation might be the economic disadvantage of young participants in the current research. The mean age of participants of this correlational study was around 22-year-old, many of them were college students who had no income, relying on their parents’ financial support. Some of them had just left school for work, employed in junior posts who earned low salaries. They might not have much money to take part in social outdoor activities, but responded to internet which provided cybernetic social support and entertainment to them. In the current study, there showed non-significant negative trend of association between internet addiction and social aspect of QOL (p=0.054). Hence, internet addiction might considerably interrupt social and family connections, because compulsive online behaviors could be a substitution for social relationships. 

Despite there were no significant gender difference in QOL ratings, this study found that male participants scored higher than female participants on IAT. The result was coherence with a former local longitudinal survey of adolescents (age range: 10-18 years old), presenting that male students were more susceptible than female student to internet overuse [9]. With regard to the gender difference on internet addiction among new generations in Hong Kong, they require more empirical information to identify sex difference in patterns of internet use, so gender-specific strategies might be recommended to prevent internet addiction. 

Strengths and Limitations

The current study provides first-hand information on internet addiction and QOL among young people aged 18-30 in Hong Kong. Yet, several limitations of the study cannot be overlooked. Firstly, the Young’s 20-item IAT was used in this study, as well as in many other studies, for identifying internet addiction; it allows results comparison and interpretation. However, the clinical relevance of IAT is still questionable in evaluating the severity of Internet addiction [31]. There is no consensus on research among the clinical cutoff for internet addiction. The findings of current study need to be interpreted with cautions. Next, the sample size of the current study was small, it was hardly generalizable and representative of young generations in Hong Kong. The cross-sectional study allowed, data collection at one time-point, the trend of internet addiction among young people could not be obtained. Future large-scale longitudinal territory-wide study may use other reliable assessment tool with IAT to confirm the clinical condition of internet addiction among participants, and assesses the sex difference in patterns and modes of internet use, in order to provide more accurate and validated data to address the increasing public concerns about the problematic internet use.

Clinical Implications and Conclusion

This correctional research has shed light on emphasizing the negative association between internet overuse and QOL among young people in Hong Kong. The findings highlighted substantial amount of youth at risk of internet addiction, which adversely impacted on their physical and psychological well-being. To protect the young generations from internet addiction, multi-dimensional interventions are recommended. Early screening of internet addiction can be developed as a preventive health program for the youths. Health promotions of wise use of computers/mobile phones and education talks on the negative consequences of internet overuse should be delivered to primary/secondary schools and universities to students or through social media to the general public. Last but not the least, health care professionals can design varieties of meaningful activities for young people to enhance their social connections with community and draw their attentions out of the cyber world.

Conflicts of Interest

 The author declares no conflict of interest

1. Internet World Stats. Usage and Population Statistics. 2018, Jun. (Ref)

2. Hong Kong Monthly Digest of Statistics. Usage of Information Technology and the Internet by Hong Kong Residents, 2000 to 2016. Hong Kong: C & S D, HKSAR; 2017. (Ref)

3. Shaw M, Black DW. Internet addiction: definition, assessment, epidemiology and clinical management. CNS Drugs. 2008;22(5):353-365. (Ref)

4. Greydanus DE, Greydanus MM. Internet use, misuse, and addiction in adolescents: Current issues and challenges. Int J Adolesc Med Health. 2012;24 (4):283-289. (Ref)

5. Cheng C, Li AY. Internet Addiction Prevalence and Quality of (Real) Life: A Meta-Analysis of 31 Nations Across Seven World Regions. Cyberpsychol Behav Soc Netw. 2014 Dec;17(12):755-760. (Ref)

6. Mihara S, Osaki Y, Nakayama H, Sakuma H, Ikeda M, et al. Internet use and problematic Internet use among adolescents in Japan: A nationwide representative survey. Addict Behav Rep. 2016 Oct;4:58-64. (Ref)

7. Lin MP, Wu JY, You J, Hu WH, Yen CF. Prevalence of internet addiction and its risk and protective factors in a representative sample of senior high school students in Taiwan. J Adolesc. 2018 Jan;62:38-46. (Ref)

8. Xin M, Xing J, Pengfei W, Houru L, Mengcheng W, et al. Online activities, prevalence of Internet addiction and risk factors related to family and school among adolescents in China. Addict Behav Rep. 2017 Oct;7:14-18. (Ref)

9. Shek DT, Yu L. Adolescent Internet Addiction in Hong Kong: Prevalence, Change, and Correlates. J Pediatr Adolesc Gynecol. 2016 Feb;29(Suppl1):S22-S30. (Ref)

10. Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: A systematic review of epidemiological research for the last decade. Curr Pharm Des. 2014;20(25):4026-4052. (Ref)

11. Ha YM, Hwang WJ. Gender Differences in Internet Addiction Associated with Psychological Health Indicators Among Adolescents Using a National Web-based Survey. Int J Ment Health Addict. 2014 Oct;12(5):660-669. (Ref)

12. Muñoz-Miralles R, Ortega-González R, López-Morón MR, BatallaMartínez C, Manresa JM, et al. The problematic use of Information and Communication Technologies (ICT) in adolescents by the cross sectional JOITIC study. BMC Pediatr. 2016 Aug;16(1):140. (Ref)

13. Chiu SI, Hong FY, Chiu SL. An Analysis on the Correlation and Gender Difference between College Students’ Internet Addiction and Mobile Phone Addiction in Taiwan. ISRN Addict. 2013 Sep. (Ref)

14. Dufour M, Brunelle N, Tremblay J, Leclerc D, Cousineau MM, et al. Gender Difference in Internet Use and Internet Problems among Quebec High School Students. Can J Psychiatry. 2016 Oct;61(10):663-668. (Ref)

15. Eliacik K, Bolat N, Koçyiğit C, Kanik A, Selkie E, et al. Internet addiction, sleep and health-related life quality among obese individuals: a comparison study of the growing problems in adolescent health. Eat Weight Disord. 2016 Dec;21(4):709-717. (Ref)

16. Surís JC, Akre C, Piguet C, Ambresin AE, Zimmermann G, et al. Is Internet use unhealthy? A cross-sectional study of adolescent Internet overuse. Swiss Med Wkly. 2014;144:w14061. (Ref)

17. Evren C, Dalbudak E, Evren B, Demirci AC. High risk of Internet addiction and its relationship with lifetime substance use, psychological and behavioral problems among 10(th) grade adolescents. Psychiatr Danub. 2014 Dec ;26(4):330-339. (Ref)

18. Wu AMS, Li J, Lau JTF, Mo PKH, Lau MMC. Potential impact of internet addiction and protective psychosocial factors onto depression among Hong Kong Chinese adolescents – direct, mediation and moderation effects. Compr Psychiatry. 2016 Oct;70:41-52. (Ref)

19. Puri A, Sharma R. Internet usage, depression, social isolation and loneliness amongst adolescents. Indian J Health Wellbeing. 2016; 7(10):996-1003. (Ref)

20. World Health Organization. WHOQOL: Measuring Quality of Life. Switzerland. (Ref)

21. Lam LT. Parental mental health and Internet Addiction in adolescents. Addict Behav. 2015 Mar; 42:20-23. (Ref)

22. Young KS. Internet addiction: The emergence of a new clinical disorder. CyberPsychology and Behavior. 1998 Aug;1(3): 237- 244. (Ref)

23. Widyanto L, McMurran M. The psychometric properties of the Internet addiction test, Cyberpsychol Behav. 2004 Aug;7(4):443- 450. (Ref)

24. Frangos CC, Frangos CC, Sotiropoulos I. A Meta-analysis of the Reliabilty of Young’s Internet Addiction Test. WCE. 2012 Jul;1. (Ref)

25. Lai CM, Mak KK, Watanabe H, Ang RP, Pang JS, et al. Psychometric properties of the internet addiction test in Chinese adolescents. J Pediatr Psychol. 2013 Aug;38(7):794-807. (Ref)

26. World Health Organization. Programme on Mental Health: WHOQOL User Manual. 1998. (Ref)

27. Skevington SM, Lotfy M, O’Connell KA, WHOQOL Group. The World Health Organization’s WHOQOL-BREF quality of life assessment: Psychometric properties and results of the international field trial. A Report from the WHOQOL Group. QualLife Res. 2004 Mar;13(2):299-310. (Ref)

28. Fatehi F, Monajemi A, Sadeghi A, Mojtahedzadeh R, Mirzazadeh A. Quality of life in medical students with internet addiction. Acta Med Iran. 2016 Oct;54(10):662-666. (Ref)

29. Alavi SS, Maracy MR, Jannatifard F, Eslami M. The effect of psychiatric symptoms on the internet addiction disorder in Isfahan’s University students. J Res Med Sci. 2011 Jun; 16(6): 793-800. (Ref)

30. Cao H, Sun Y, Wan Y, Hao J, Tao F. Problematic Internet use in Chinese adolescents and its relation to psychosomatic symptoms and life satisfaction. BMC Public Health. 2011;11:802. (Ref)

31. Kim SJ, Park DH, Ryu SH, Yu J, Ha JH. Usefulness of Young’s Internet addiction test for clinical populations. Nord J Psychiatry. 2013 Dec;67(6):393-399. (Ref)

Supplementary Information