Addictive Behaviors 42 (2015) 20–23
Contents lists available at ScienceDirect
Addictive Behaviors
Short Communication
Parental mental health and Internet Addiction in adolescents Lawrence T. Lam ⁎ Discipline of Paediatrics and Child Health, The Sydney Medical School, The University of Sydney, Australia Department of Health and Physical Education, The Hong Kong Institute of Education, Hong Kong, China
H I G H L I G H T S • • • •
Dyad studies on parental risk factors of Internet Addiction in adolescents are few. This is a unique dyad study on the topic. Parental depression is related to the Internet Addition status of their children. Results are useful for early intervention of youth Internet Addiction.
a r t i c l e
i n f o
Available online 1 November 2014 Keywords: Internet Addiction Parental mental health Parent Child Adolescents Dyad study
a b s t r a c t Purpose: This study aimed to investigate the relationship between parental mental health, particularly depression, and Internet Addiction (IA) among adolescents. Methods: This was a population-based parent-and-child dyad health survey utilising a random sampling technique. Adolescent IA was measured by the Internet Addiction Test (IAT) designed by Young. The mental health status of the parents was assessed using the Depression, Anxiety, Stress Scale (DASS). Data were analysed using logistic regression modelling techniques with adjustment for potential confounding factors. Results: A total of 1098 parent-and-child dyads were recruited and responded to the survey providing usable information. For IA, 263 (24.0%) students could be classified as at risk of moderate to severe IA. About 6% (n = 68), 4% (n = 43), and 8% (n = 87) of parents were categorised to be at risk of moderate to severe depression, anxiety, and stress respectively. Regression analysis results suggested a significant association between parental depression at the level of moderate to severe and IA in adolescents after adjusting for potential confounding factors (OR = 3.03, 95% C.I. = 1.67–5.48). On the other hand, no associations between parental anxiety and stress and child's IA were observed. Conclusions: The result suggested that there was a significant relationship between parental mental health, particularly depression, and the IA status of their children. These results have direct implications on the treatment and prevention of Internet Addiction among young people. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction Internet Addiction (IA), although not yet recognised as an established disorder, has been considered as an emerging behavioural problem particularly among adolescents for decades (American Psychiatric Association, 2013; Lenihan, 2007; Young, 2010). A number of factors, based on the Problem Behaviour Theory, have been suggested to be associated with IA among adolescents (Hopley & Nicki, 2010; Jessor, Costa, Kruger, & Turbin, 2006). Among these, there is a growing volume of studies in the literature on the relationship between familial and parental factors and IA among adolescents (Huang ⁎ The Hong Kong Institute of Education, 10 Lo Ping Road, Tai Po, N.T., Hong Kong, China. Tel.: +852 2948 6685; fax: +852 2948 7848. E-mail address: [email protected].
http://dx.doi.org/10.1016/j.addbeh.2014.10.033 0306-4603/© 2014 Elsevier Ltd. All rights reserved.
et al., 2010; Jang & Ji, 2012; Kalaitzaki & Birtchnell, 2014; Kwon, Chung, & Lee, 2011; Lam, Peng, Mai, & Jing, 2009; Li, Garland, & Howard; 2014; Lin, Lin, & Wu, 2009; Liu, Fang, Deng, & Zhang, 2012; Park, Kim, & Cho, 2008; Tsitsika et al., 2011; Van den Eijnden, Spijkerman, Vermulst, & Van Rooij, 2010; Xu et al., 2014; Yang, Sato, Yamawaki, & Miyata, 2013; Yen, Yen, Chen, Chen, & Ko, 2007). Various familial and parental factors were studied including: family relationship (Lam et al., 2009; Liu et al., 2012; Park, Kim, & Cho, 2008; Van den Eijnden et al., 2010; Yen et al., 2007); dysfunction family (Jang, & Ji, 2012; Lam et al., 2009; Tsitsika et al., 2011; Xu et al., 2014; Yen et al., 2007); parental attitudes, supervision or monitoring (Kwon, Chung, & Lee, 2011; Lin, Lin, & Wu, 2009; Park, Kim, & Cho, 2008; Van den Eijnden et al., 2010; Yang et al., 2013; Yen et al., 2007); and parenting styles (Huang et al., 2010; Kalaitzaki & Birtchnell, 2014; Liu et al., 2012; Xu et al., 2014). Results of a recent review study on familial factors and IA in youth suggested
L.T. Lam / Addictive Behaviors 42 (2015) 20–23
that there were significant relationships between divorced parents, single parent household, and being the only child in the family and adolescent IA (Xu et al., 2014). It was also highlighted that there were methodological shortcomings in the studies included in the review, and all these studies suffered from the same drawback with parental information collected through the report of the child, not from the parents per se. It has been well established that mental health problems, such as Attention Deficit disorder, obsessive–compulsive disorder, depression, anxiety, and hostility, are co-morbidities of IA among adolescents (Huang et al., 2010; Ko et al., 2012; Yen et al., 2008) However, in the search for potential risk or protective factors of IA among young people, particularly among familial and parental factors, none of the studies in the current literature attempted to examine the role of parental mental health in adolescent IA. This study aims to bridge the knowledge gap through examining the relationship between parental mental health, with parental information obtained directly from parents and the IA among adolescents. 2. Methods 2.1. Study design and recruitment of the parent-and-child sample This cross-sectional health survey was conducted among parentand-child dyads in Hong Kong in March 2014 among 13–17 year old high school students. The sample was generated from the total student population of adolescents who attended high schools within a specific local school region. Two schools were randomly selected from the list of registered high schools to be the target schools. A class was also randomly selected from each grade, from grade 7 to 11, with all students and parents in the class invited to participate in the study. Informed consent was obtained from participants with a signed consent form indicating the wilful participation of the parent-and-child dyad. Institute ethics approval for the study was granted by the Hong Kong Institute of Education. 2.2. Measurements The Parent's and Student's Health Survey Questionnaires included similar questions with some specifically designed for parents or students. Internet Addiction was assessed by the Internet Addiction Test (IAT, Young, 2014). The IAT is a 20 item self-reported scale and the design was based on the concepts and behaviours exhibited by pathological gamblers as definite by the DSM-IV diagnostic criteria. It includes questions that reflect typical behaviours of addiction. An example question is: “How often do you feel depressed, moody, or nervous when you are off-line, which goes away once you are back on-line?” Respondents were asked to indicate the propensity of their responses on a Likert scale ranging from 1 (rarely) to 5 (always). A study on the psychometric properties of the IAT suggested good reliability with Cronbach's alpha values ranged from 0.82 to 0.54 for various factors (Widyanto & McMurran, 2004). Based on the total scores calculated, the severity of addiction was then classified according to the suggested cut-off scores with 20–49 points as “normal”, 50–79 points as “moderate”, and 80–100 points as “severe” (Young, 2010). For ease of analysis, the variable was dichotomised into two categories: “Severe/moderate” and “Mild/normal” for both parent and child. Parent's and child's mental health was measured using the Depression, Anxiety, Stress Scale (DASS), a fully validated and commonly used instrument designed for the assessment of stress, depressive symptoms, and anxiety with good psychometric properties including strong reliability and validity (Antony et al., 1988). The DASS was designed as a quantitative measure of distress along three axes, however, it was not meant to be a categorical assessment of clinical diagnosis (Antony et al., 1988). Nevertheless, the scale could be useful for identifying individuals who were of high risk of mental health problems. In
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this study the stress score of the individual was classified according to the recommended categorisation with 0–7 as “Normal”; 8–9 as “Mild”; 10–12 as “Moderate”; and 13 or above as “Severe or Extremely severe”. For ease of analysis, the variable was also dichotomised into two categories: “Mild/normal” or “Moderate or above”. The depression, anxiety, and stress of child were also assessed using the DASS. The validity of DASS has been demonstrated and has also been recommended for use among children and adolescents (Szabó & Lovibond, 2006). Other information collected in the students' survey included demographics, location of family residence, whether the child was born in Hong Kong, and some detail on the means and patterns of accessing the Internet. For parents, questions on sex, age, occupation, and access to the Internet were also included in the questionnaire. 2.3. Data analysis Data were analysed using the Stata V10.0 statistical software program. Descriptive analyses were conducted using percentages, means, and standard deviation. Bivariate analyses were conducted to examine unadjusted relationships between student's IA, all parent-and-child variables of interest, child's mental health, and parental mental health. With the main focus of analyses on the relationship between parental mental health and their children's IA, potential confounding variables identified from the bivariate analyses were included in further logistic regression analyses. A significant bivariate result with a p b 0.10 was used as the selection criteria of potential confounding variables. The adjusted relationship between parental mental health and child IA was further examined using the multiple logistic regression approach with the calculation of the 95% Confident Intervals (C.I.). To further examine the possible effect of modification between parental mental health and other variables on child's IA, in particular child's stress levels, the interaction terms were tested using a Type I error rate of 1%. 3. Results A total of 1098 parent-and-child dyads were recruited and responded to the survey providing usable information and allowed matching of parent-and-child data. This represented a response rate of 95.3% of parents completing the questionnaire. Comparisons between those students with a respondent parent and those whose parent did not respond indicated no statistically significant differences in all demographics, including age, sex, grade, and place of birth. The parent-and-child characteristics and outcome measures of the respondents were summarised in Table 1. In terms of mental health, about 19% (n = 209), 25% (n = 251), and 14% (n = 157) of students could be classified with moderate to severe depression, anxiety, and stress respectively. For parents, about 6% (n = 68), 4% (n = 43), and 8% (n = 87) were categorised as moderate to severe depression, anxiety, and stress respectively. In terms of the IA, 263 (24.0%) children could be classified as moderate and severe users. The bivariate relationships between child IA, variables of interest and parental mental health were examined. The results were also summarised in Table 1. As shown, child IA was significantly associated with parental mental health, including depression, anxiety, and stress unadjusted for other potential confounding factors (χ21 = 30.37, p b 0.001; χ21 = 15.93, p b 0.001; χ21 = 10.25, p = 0.001). However, no other parental variables were related to the child's IA. Results suggested that age and sex of the child were significantly associated with their IA (χ21 = 5.33, p = 0.021; χ21 = 8.54, p = 0.003). Other students' variables, including owning a smartphone, daily access to the Internet, spending more than 3 h on the net, playing online games more than 3 h per day, visiting pornographic sites, sending or receiving offensive texts to images, short duration of sleep, and the their mental health, were significantly associated with student IA.
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L.T. Lam / Addictive Behaviors 42 (2015) 20–23 Table 1
Table 1 (continued)
T- Frequency (%) of the characteristics of child-and-parent dyad and unadjusted associations with child's IA (N = 1098). he Frequency r(%) Results of association e- Characteristics of respondents s- Child characteristics u- Sex Male 483 (44.0) χ21 = 5.33, p = 0.021 lts Female 614 (56.0) o- Age group b15 years or older 620 (56.5) χ21 = 8.54, p = 0.003 b15 years 478 (43.5) ta- Born locally Yes 767 (69.9) χ21 = 0.04, p = 0.843 iNo 331 (30.1) n- Place of residence ed Local 1056 (96.3) χ21 = 0.66, p = 0.418 Nearby city 41 (3.7) fro- Own a room with Internet access Yes 576 (52.5) χ21 = 0.51, p = 0.476 m No 522 (47.5) t- Own a computer with Internet access Yes 694 (63.2) χ21 = 1.30, p = 0.255 he No 404 (46.8) rOwn a smart phone with Internet access eYes 982 (89.4) χ21 = 4.08, p = 0.043 dNo 116 (10.6) u- Days accessing the Internet in the past week cEveryday 764 (69.8) χ22 = 47.38, p b 0.001 ed 4–6 days 3 days or less Main device used to access the Internet Computer Mobile Others Average time spent on the Internet 3 h or more/day b3 h/day Average time spent playing online games 3 h or more/day b3 h/day Visit pornographic site more than 5 min Yes No Sent or received offensive texts or images Yes No Duration of sleep per day 7–8 h b7 h N8 h Depression Moderate/severe Normal/mild Anxiety Moderate/severe Normal/mild Stress Moderate/severe Normal/mild Parent's characteristics Sex Male Female Age group 45 years or older b45 years Born locally Yes No Occupation Professional Semi/non-professional Others Access to the Internet at the residence
129 (11.8) 202 (18.4)
508 (46.3) χ22 = 2.53, p = 0.282 501 (45.7) 88 (8.0) 409 (37.9) χ21 = 67.50, p b 0.001 607 (62.1) 197 (18.0) χ21 = 46.47, p b 0.001 900 (82.0) 70 (6.4) χ21 = 5.65, p = 0.017 1027 (93.6) 96 (8.8) χ21 = 15.85, p b 0.001 998 (91.2) 708 (66.1) χ22 = 25.65, p b 0.001 232 (21.7) 131 (12.2) 209 (19.1) χ21 = 128.28, p b 0.001 888 (80.1) 251 (22.9)
χ21
= 111.87, p b 0.001
846 (77.1) 157 (14.3) χ21 = 111.81, p b 0.001 940 (85.7)
279 (25.5) χ21 = 2.07, p = 0.150 814 (74.5) 602 (56.5) 464 (43.5)
χ21
= 0.31, p = 0.557
455 (41.5) χ21 = 3.06, p = 0.080 641 (58.5) 160 (14.6) 157 (14.4) 776 (71.0)
χ22
= 5.13, p = 0.077
Characteristics of respondents Yes No Depression Moderate/severe Normal/mild Anxiety Moderate/severe Normal/mild Stress Moderate/severe Normal/mild
Frequency (%)
Results of association
1073 (98.0) χ21 = 1.91, p = 0.167 22 (2.0) 68 (6.2) χ21 = 30.37, p b 0.001 1025 (93.8) 87 (8.0) χ21 = 15.93, p b 0.001 1006 (92.0) 43 (3.9) χ21 = 10.25, p = 0.001 1049 (96.1)
model of the multivariate logistic regression analyses were presented in Table 2. These results indicated that, among the parental mental health variables, depression was still significantly associated with the child's IA. After adjusting for potential confounding factors, adolescents in the moderate to severe IA group were three times (OR = 3.03, 95% C.I. = 1.67–5.48) as likely to have their parents classified with moderate to severe depression when compared to those in the mild or normal group. No other parental mental health variables were related to the child's IA. Tests on the interaction terms between parental depression and child's mental health variables indicated none were significant. These results suggested that there was an independent relationship between parental depression and their children's IA.
4. Discussion The results of the study have provided some empirical evidence for a significant association between parental depression and the IA status of their children. This is the first attempt to investigate such a relationship and the results could be considered as unique. The finding provides an opportunity for researchers to gain a better insight into the dynamics of parental and personal factors in the IA of adolescents. A possible explanation of the results is that, since parental depression and the depression of their children are correlated as suggested by some
Table 2 Odd ratios (95% C.I.) of variables retained in the final logistic regression model. Variables
OR (95% C.I.)
Parent's depression Moderate/severe Mild/normal
3.03 (1.67–5.48) 1.00⁎
Child characteristics Days accessing the Internet in the past week Everyday 4–6 days 3 days or less Average time spent on the Internet 3 h or more/day b3 h/day Average time spent playing online games 3 h or more/day b3 h/day Depression Moderate/severe Mild/normal Stress Moderate/severe Mild/normal Anxiety level Moderate/severe Mild/normal ⁎ Reference group.
3.16 (1.76–5.67) 1.90 (0.89–4.05) 1.00 2.54 (1.80–3.57) 1.00 2.07 (1.41–3.05) 1.00 2.56 (1.65–3.99) 1.00 2.07 (1.26–3.40) 1.00 2.32 (1.50–3.60) 1.00
L.T. Lam / Addictive Behaviors 42 (2015) 20–23
gene–environment studies (Wilkinson et al., 2013), the association between parental depression and adolescent IA might be a reflection of the relationship of the children's depression state and their own IA. In other words, there is a parental depression effect on the child's IA and such an effect consists of direct and indirect components on the child's depression. Such interpretation assumes a directional pathway between depression and IA in which depression precedes IA. However, as far as the study design is concerned there is no substantiating evidence for a directional pathway interpretation of the results. The results of the study indicate that IA is not an individual matter of young people, but a familial problem. Parental mental health also plays a significant role in the problem. Hence, any treatment options that clinicians may consider, such as Cognitive Behavioural Therapy or other alternative approaches (Greydanus & Greydanus, 2012) need to take into consideration familial and parental components and address these factors as part of the treatment regime. Whilst applying family therapy, parental mental health status should also be examined and, if necessary, treatment should also be provided to parents as part of a more holistic approach. For prevention, parents need to be informed and educated on the impact of their own mental health, particularly depression, on their children in terms of their Internet use behaviour. Limitations identified in this study included: first, information on the exposure and outcome is obtained via a self-reported questionnaire that potentially constitutes a report bias in these variables although it would most likely be a non-differential bias; second, a cross-sectional study and the evidence provided from such a study can only be considered as associative and is insufficient to draw any causal inference (Rothman & Greenland, 1998). Future studies could be conducted with a better design such as a longitudinal cohort study, which includes important potential confounding factors, such as family function and parent–child relationship, to elucidate whether the association is of a causal nature. Role of Funding Sources The author declares the study has not received any funding support from any organisations. Contributors Lawrence T. Lam is the principal investigator who formulated the research question, developed the study protocol, obtained institutional ethics approval, designed and piloted the survey questionnaire, conducted data analyses, and wrote the manuscript. Conflict of Interest The author declares no conflict of interest of any kind in the production of this publication. Acknowledgements The author would like to acknowledge the valuable assistance of Dr. Li Yang in supervising the field work during data collection.
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Greydanus, D.E., & Greydanus, M.M. (2012). Internet use, misuse, and addiction in adolescents: Current issues and challenges. International Journal of Adolescence Mental Health, 24, 283–289. Hopley, A.A., & Nicki, R.M. (2010). Predictive factors of excessive online poker playing. Cyberpsychology, Behavior and Social Networking, 13, 379–385. Huang, X., Zhang, H., Li, M., Wang, J., Zhang, Y., & Tao, R. (2010). Mental health, personality, and parental rearing styles of adolescents with Internet addiction disorder. Cyberpsychology, Behavior and Social Networking, 13, 401–406. Jang, M.H., & Ji, E.S. (2012). Gender differences in associations between parental problem drinking and early adolescents' internet addiction. Journal for Specialists in Pediatric Nursing, 17, 288–300. Jessor, R., Costa, F.M., Kruger, P.M., & Turbin, M.S. (2006). A developmental study of heavy episodic drinking among college students: The role of psychosocial and behavioural protective and risk factors. Journal of Studies on Alcohol, 67, 86–94. Kalaitzaki, A.E., & Birtchnell, J. (2014). The impact of early parenting bonding on young adults' Internet addiction, through the mediation effects of negative relating to others and sadness. Addictive Behaviors, 39, 733–736. Ko, C.H., Yen, J.Y., Yen, C.F., Chen, C.S., & Chen, C.C. (2012). The association between Internet addiction and psychiatric disorder: A review of the literature. European Psychiatry, 27, 1–8. Kwon, J.H., Chung, C.S., & Lee, J. (2011). The effects of escape from self and interpersonal relationship on the pathological use of Internet games. Community Mental Health Journal, 47, 113–121. Lam, L.T., Peng, Z.W., Mai, J.C., & Jing, J. (2009). Factors associated with Internet addiction among adolescents. Cyberpsychology, Behavior and Social Networking, 12, 551–555. Lenihan, F. (2007). Computer addiction — A sceptical view: Invited commentary on: Lost online. Advances in Psychiatric Treatment, 13, 31. Li, W., Garland, E.I., & Howard, M.O. (2014). Family factors in Internet addiction among Chinese youth: A review of English-and Chinese-language studies. Computers in Human Behavior, 31, 392–411. Lin, C.H., Lin, S.L., & Wu, C.P. (2009). The effects of parental monitoring and leisure boredom on adolescents' Internet addiction. Adolescence, 44, 993–1004. Liu, Q.X., Fang, X.Y., Deng, L.Y., & Zhang, J.T. (2012). Parent–adolescent communication, parental Internet use and Internet-specific norms and pathological Internet use among Chinese adolescents. Computers in Human Behavior, 28, 1269–1275. Park, S.K., Kim, J.Y., & Cho, C.B. (2008). Prevalence of Internet addiction and correlations with family factors among South Korean adolescents. Adolescence, 43, 895–909. Rothman, K.J., & Greenland, S. (1998). Morden epidemiology (2nd ed.). Philadelphia, PA: Lippincott Williams & Wilkins. Szabó, M., & Lovibond, P.F. (2006). Anxiety, depression and tension/stress in children. Journal of Psychopathology and Behavioral Assessment, 28, 195–205. Tsitsika, A., Critselis, E., Louizou, A., Janikian, M., Freskou, A., Marangou, E., et al. (2011). Determinants of Internet addiction among adolescents: A case-control study. Scientific World Journal, 11, 866–874. Van den Eijnden, R.J., Spijkerman, R., Vermulst, A.A., & Van Rooij, A.J. (2010). Compulsive Internet use among adolescents: Bidirectional parent–child relationships. Journal of Abnormal Child Psychology, 38, 77–89. Widyanto, L., & McMurran, M. (2004). The psychometric properties of the pathological use of the Internet test. Cyberpsychology & Behavior, 7, 443–450. Wilkinson, P.O., Trzaskowski, M., Haworth, C.M., & Eley, T.C. (2013). The role of gene–environment correlations and interactions in middle childhood depressive symptoms. Developmental Psychobiology, 25, 93–104. Xu, J., Shen, L.X., Yan, C.H., Hu, H., Yang, F., Wang, L., et al. (2014). Parent–adolescent interaction and risk of adolescent internet addiction: A population-based study in Shanghai. BMC Psychiatry, 14, 112. Yang, C.Y., Sato, T., Yamawaki, N., & Miyata, M. (2013). Prevalence and risk factors of problematic Internet use: A cross-national comparison of Japanese and Chinese university students. Transcultural Psychiatry, 50, 263–279. Yen, J.Y., Ko, C.H., Yen, C.F., Chen, S.H., Chung, W.L., & Chen, C.C. (2008). Psychiatric symptoms in adolescents with Internet addiction: Comparison with substance use. Psychiatry and Clinical Neurosciences, 62, 9–16. Yen, J.Y., Yen, C.F., Chen, C.C., Chen, S.H., & Ko, C.H. (2007). Family factors of Internet addiction and substance use experience in Taiwanese adolescents. Cyberpsychology, Behavior and Social Networking, 10, 323–329. Young, K. (2010). Internet addiction over the decade: A personal look back. World Psychiatry, 9, 91. Young, K.S. (2014). The pathological use of the Internet test. Center for On-Line Addictions (Retrieved from http://www.netaddiction.com/index.php?option=com_ bfquiz&view=onepage&catid=46&Itemid=106).
Contents lists available at ScienceDirect
Addictive Behaviors
Short Communication
Parental mental health and Internet Addiction in adolescents Lawrence T. Lam ⁎ Discipline of Paediatrics and Child Health, The Sydney Medical School, The University of Sydney, Australia Department of Health and Physical Education, The Hong Kong Institute of Education, Hong Kong, China
H I G H L I G H T S • • • •
Dyad studies on parental risk factors of Internet Addiction in adolescents are few. This is a unique dyad study on the topic. Parental depression is related to the Internet Addition status of their children. Results are useful for early intervention of youth Internet Addiction.
a r t i c l e
i n f o
Available online 1 November 2014 Keywords: Internet Addiction Parental mental health Parent Child Adolescents Dyad study
a b s t r a c t Purpose: This study aimed to investigate the relationship between parental mental health, particularly depression, and Internet Addiction (IA) among adolescents. Methods: This was a population-based parent-and-child dyad health survey utilising a random sampling technique. Adolescent IA was measured by the Internet Addiction Test (IAT) designed by Young. The mental health status of the parents was assessed using the Depression, Anxiety, Stress Scale (DASS). Data were analysed using logistic regression modelling techniques with adjustment for potential confounding factors. Results: A total of 1098 parent-and-child dyads were recruited and responded to the survey providing usable information. For IA, 263 (24.0%) students could be classified as at risk of moderate to severe IA. About 6% (n = 68), 4% (n = 43), and 8% (n = 87) of parents were categorised to be at risk of moderate to severe depression, anxiety, and stress respectively. Regression analysis results suggested a significant association between parental depression at the level of moderate to severe and IA in adolescents after adjusting for potential confounding factors (OR = 3.03, 95% C.I. = 1.67–5.48). On the other hand, no associations between parental anxiety and stress and child's IA were observed. Conclusions: The result suggested that there was a significant relationship between parental mental health, particularly depression, and the IA status of their children. These results have direct implications on the treatment and prevention of Internet Addiction among young people. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction Internet Addiction (IA), although not yet recognised as an established disorder, has been considered as an emerging behavioural problem particularly among adolescents for decades (American Psychiatric Association, 2013; Lenihan, 2007; Young, 2010). A number of factors, based on the Problem Behaviour Theory, have been suggested to be associated with IA among adolescents (Hopley & Nicki, 2010; Jessor, Costa, Kruger, & Turbin, 2006). Among these, there is a growing volume of studies in the literature on the relationship between familial and parental factors and IA among adolescents (Huang ⁎ The Hong Kong Institute of Education, 10 Lo Ping Road, Tai Po, N.T., Hong Kong, China. Tel.: +852 2948 6685; fax: +852 2948 7848. E-mail address: [email protected].
http://dx.doi.org/10.1016/j.addbeh.2014.10.033 0306-4603/© 2014 Elsevier Ltd. All rights reserved.
et al., 2010; Jang & Ji, 2012; Kalaitzaki & Birtchnell, 2014; Kwon, Chung, & Lee, 2011; Lam, Peng, Mai, & Jing, 2009; Li, Garland, & Howard; 2014; Lin, Lin, & Wu, 2009; Liu, Fang, Deng, & Zhang, 2012; Park, Kim, & Cho, 2008; Tsitsika et al., 2011; Van den Eijnden, Spijkerman, Vermulst, & Van Rooij, 2010; Xu et al., 2014; Yang, Sato, Yamawaki, & Miyata, 2013; Yen, Yen, Chen, Chen, & Ko, 2007). Various familial and parental factors were studied including: family relationship (Lam et al., 2009; Liu et al., 2012; Park, Kim, & Cho, 2008; Van den Eijnden et al., 2010; Yen et al., 2007); dysfunction family (Jang, & Ji, 2012; Lam et al., 2009; Tsitsika et al., 2011; Xu et al., 2014; Yen et al., 2007); parental attitudes, supervision or monitoring (Kwon, Chung, & Lee, 2011; Lin, Lin, & Wu, 2009; Park, Kim, & Cho, 2008; Van den Eijnden et al., 2010; Yang et al., 2013; Yen et al., 2007); and parenting styles (Huang et al., 2010; Kalaitzaki & Birtchnell, 2014; Liu et al., 2012; Xu et al., 2014). Results of a recent review study on familial factors and IA in youth suggested
L.T. Lam / Addictive Behaviors 42 (2015) 20–23
that there were significant relationships between divorced parents, single parent household, and being the only child in the family and adolescent IA (Xu et al., 2014). It was also highlighted that there were methodological shortcomings in the studies included in the review, and all these studies suffered from the same drawback with parental information collected through the report of the child, not from the parents per se. It has been well established that mental health problems, such as Attention Deficit disorder, obsessive–compulsive disorder, depression, anxiety, and hostility, are co-morbidities of IA among adolescents (Huang et al., 2010; Ko et al., 2012; Yen et al., 2008) However, in the search for potential risk or protective factors of IA among young people, particularly among familial and parental factors, none of the studies in the current literature attempted to examine the role of parental mental health in adolescent IA. This study aims to bridge the knowledge gap through examining the relationship between parental mental health, with parental information obtained directly from parents and the IA among adolescents. 2. Methods 2.1. Study design and recruitment of the parent-and-child sample This cross-sectional health survey was conducted among parentand-child dyads in Hong Kong in March 2014 among 13–17 year old high school students. The sample was generated from the total student population of adolescents who attended high schools within a specific local school region. Two schools were randomly selected from the list of registered high schools to be the target schools. A class was also randomly selected from each grade, from grade 7 to 11, with all students and parents in the class invited to participate in the study. Informed consent was obtained from participants with a signed consent form indicating the wilful participation of the parent-and-child dyad. Institute ethics approval for the study was granted by the Hong Kong Institute of Education. 2.2. Measurements The Parent's and Student's Health Survey Questionnaires included similar questions with some specifically designed for parents or students. Internet Addiction was assessed by the Internet Addiction Test (IAT, Young, 2014). The IAT is a 20 item self-reported scale and the design was based on the concepts and behaviours exhibited by pathological gamblers as definite by the DSM-IV diagnostic criteria. It includes questions that reflect typical behaviours of addiction. An example question is: “How often do you feel depressed, moody, or nervous when you are off-line, which goes away once you are back on-line?” Respondents were asked to indicate the propensity of their responses on a Likert scale ranging from 1 (rarely) to 5 (always). A study on the psychometric properties of the IAT suggested good reliability with Cronbach's alpha values ranged from 0.82 to 0.54 for various factors (Widyanto & McMurran, 2004). Based on the total scores calculated, the severity of addiction was then classified according to the suggested cut-off scores with 20–49 points as “normal”, 50–79 points as “moderate”, and 80–100 points as “severe” (Young, 2010). For ease of analysis, the variable was dichotomised into two categories: “Severe/moderate” and “Mild/normal” for both parent and child. Parent's and child's mental health was measured using the Depression, Anxiety, Stress Scale (DASS), a fully validated and commonly used instrument designed for the assessment of stress, depressive symptoms, and anxiety with good psychometric properties including strong reliability and validity (Antony et al., 1988). The DASS was designed as a quantitative measure of distress along three axes, however, it was not meant to be a categorical assessment of clinical diagnosis (Antony et al., 1988). Nevertheless, the scale could be useful for identifying individuals who were of high risk of mental health problems. In
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this study the stress score of the individual was classified according to the recommended categorisation with 0–7 as “Normal”; 8–9 as “Mild”; 10–12 as “Moderate”; and 13 or above as “Severe or Extremely severe”. For ease of analysis, the variable was also dichotomised into two categories: “Mild/normal” or “Moderate or above”. The depression, anxiety, and stress of child were also assessed using the DASS. The validity of DASS has been demonstrated and has also been recommended for use among children and adolescents (Szabó & Lovibond, 2006). Other information collected in the students' survey included demographics, location of family residence, whether the child was born in Hong Kong, and some detail on the means and patterns of accessing the Internet. For parents, questions on sex, age, occupation, and access to the Internet were also included in the questionnaire. 2.3. Data analysis Data were analysed using the Stata V10.0 statistical software program. Descriptive analyses were conducted using percentages, means, and standard deviation. Bivariate analyses were conducted to examine unadjusted relationships between student's IA, all parent-and-child variables of interest, child's mental health, and parental mental health. With the main focus of analyses on the relationship between parental mental health and their children's IA, potential confounding variables identified from the bivariate analyses were included in further logistic regression analyses. A significant bivariate result with a p b 0.10 was used as the selection criteria of potential confounding variables. The adjusted relationship between parental mental health and child IA was further examined using the multiple logistic regression approach with the calculation of the 95% Confident Intervals (C.I.). To further examine the possible effect of modification between parental mental health and other variables on child's IA, in particular child's stress levels, the interaction terms were tested using a Type I error rate of 1%. 3. Results A total of 1098 parent-and-child dyads were recruited and responded to the survey providing usable information and allowed matching of parent-and-child data. This represented a response rate of 95.3% of parents completing the questionnaire. Comparisons between those students with a respondent parent and those whose parent did not respond indicated no statistically significant differences in all demographics, including age, sex, grade, and place of birth. The parent-and-child characteristics and outcome measures of the respondents were summarised in Table 1. In terms of mental health, about 19% (n = 209), 25% (n = 251), and 14% (n = 157) of students could be classified with moderate to severe depression, anxiety, and stress respectively. For parents, about 6% (n = 68), 4% (n = 43), and 8% (n = 87) were categorised as moderate to severe depression, anxiety, and stress respectively. In terms of the IA, 263 (24.0%) children could be classified as moderate and severe users. The bivariate relationships between child IA, variables of interest and parental mental health were examined. The results were also summarised in Table 1. As shown, child IA was significantly associated with parental mental health, including depression, anxiety, and stress unadjusted for other potential confounding factors (χ21 = 30.37, p b 0.001; χ21 = 15.93, p b 0.001; χ21 = 10.25, p = 0.001). However, no other parental variables were related to the child's IA. Results suggested that age and sex of the child were significantly associated with their IA (χ21 = 5.33, p = 0.021; χ21 = 8.54, p = 0.003). Other students' variables, including owning a smartphone, daily access to the Internet, spending more than 3 h on the net, playing online games more than 3 h per day, visiting pornographic sites, sending or receiving offensive texts to images, short duration of sleep, and the their mental health, were significantly associated with student IA.
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L.T. Lam / Addictive Behaviors 42 (2015) 20–23 Table 1
Table 1 (continued)
T- Frequency (%) of the characteristics of child-and-parent dyad and unadjusted associations with child's IA (N = 1098). he Frequency r(%) Results of association e- Characteristics of respondents s- Child characteristics u- Sex Male 483 (44.0) χ21 = 5.33, p = 0.021 lts Female 614 (56.0) o- Age group b15 years or older 620 (56.5) χ21 = 8.54, p = 0.003 b15 years 478 (43.5) ta- Born locally Yes 767 (69.9) χ21 = 0.04, p = 0.843 iNo 331 (30.1) n- Place of residence ed Local 1056 (96.3) χ21 = 0.66, p = 0.418 Nearby city 41 (3.7) fro- Own a room with Internet access Yes 576 (52.5) χ21 = 0.51, p = 0.476 m No 522 (47.5) t- Own a computer with Internet access Yes 694 (63.2) χ21 = 1.30, p = 0.255 he No 404 (46.8) rOwn a smart phone with Internet access eYes 982 (89.4) χ21 = 4.08, p = 0.043 dNo 116 (10.6) u- Days accessing the Internet in the past week cEveryday 764 (69.8) χ22 = 47.38, p b 0.001 ed 4–6 days 3 days or less Main device used to access the Internet Computer Mobile Others Average time spent on the Internet 3 h or more/day b3 h/day Average time spent playing online games 3 h or more/day b3 h/day Visit pornographic site more than 5 min Yes No Sent or received offensive texts or images Yes No Duration of sleep per day 7–8 h b7 h N8 h Depression Moderate/severe Normal/mild Anxiety Moderate/severe Normal/mild Stress Moderate/severe Normal/mild Parent's characteristics Sex Male Female Age group 45 years or older b45 years Born locally Yes No Occupation Professional Semi/non-professional Others Access to the Internet at the residence
129 (11.8) 202 (18.4)
508 (46.3) χ22 = 2.53, p = 0.282 501 (45.7) 88 (8.0) 409 (37.9) χ21 = 67.50, p b 0.001 607 (62.1) 197 (18.0) χ21 = 46.47, p b 0.001 900 (82.0) 70 (6.4) χ21 = 5.65, p = 0.017 1027 (93.6) 96 (8.8) χ21 = 15.85, p b 0.001 998 (91.2) 708 (66.1) χ22 = 25.65, p b 0.001 232 (21.7) 131 (12.2) 209 (19.1) χ21 = 128.28, p b 0.001 888 (80.1) 251 (22.9)
χ21
= 111.87, p b 0.001
846 (77.1) 157 (14.3) χ21 = 111.81, p b 0.001 940 (85.7)
279 (25.5) χ21 = 2.07, p = 0.150 814 (74.5) 602 (56.5) 464 (43.5)
χ21
= 0.31, p = 0.557
455 (41.5) χ21 = 3.06, p = 0.080 641 (58.5) 160 (14.6) 157 (14.4) 776 (71.0)
χ22
= 5.13, p = 0.077
Characteristics of respondents Yes No Depression Moderate/severe Normal/mild Anxiety Moderate/severe Normal/mild Stress Moderate/severe Normal/mild
Frequency (%)
Results of association
1073 (98.0) χ21 = 1.91, p = 0.167 22 (2.0) 68 (6.2) χ21 = 30.37, p b 0.001 1025 (93.8) 87 (8.0) χ21 = 15.93, p b 0.001 1006 (92.0) 43 (3.9) χ21 = 10.25, p = 0.001 1049 (96.1)
model of the multivariate logistic regression analyses were presented in Table 2. These results indicated that, among the parental mental health variables, depression was still significantly associated with the child's IA. After adjusting for potential confounding factors, adolescents in the moderate to severe IA group were three times (OR = 3.03, 95% C.I. = 1.67–5.48) as likely to have their parents classified with moderate to severe depression when compared to those in the mild or normal group. No other parental mental health variables were related to the child's IA. Tests on the interaction terms between parental depression and child's mental health variables indicated none were significant. These results suggested that there was an independent relationship between parental depression and their children's IA.
4. Discussion The results of the study have provided some empirical evidence for a significant association between parental depression and the IA status of their children. This is the first attempt to investigate such a relationship and the results could be considered as unique. The finding provides an opportunity for researchers to gain a better insight into the dynamics of parental and personal factors in the IA of adolescents. A possible explanation of the results is that, since parental depression and the depression of their children are correlated as suggested by some
Table 2 Odd ratios (95% C.I.) of variables retained in the final logistic regression model. Variables
OR (95% C.I.)
Parent's depression Moderate/severe Mild/normal
3.03 (1.67–5.48) 1.00⁎
Child characteristics Days accessing the Internet in the past week Everyday 4–6 days 3 days or less Average time spent on the Internet 3 h or more/day b3 h/day Average time spent playing online games 3 h or more/day b3 h/day Depression Moderate/severe Mild/normal Stress Moderate/severe Mild/normal Anxiety level Moderate/severe Mild/normal ⁎ Reference group.
3.16 (1.76–5.67) 1.90 (0.89–4.05) 1.00 2.54 (1.80–3.57) 1.00 2.07 (1.41–3.05) 1.00 2.56 (1.65–3.99) 1.00 2.07 (1.26–3.40) 1.00 2.32 (1.50–3.60) 1.00
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gene–environment studies (Wilkinson et al., 2013), the association between parental depression and adolescent IA might be a reflection of the relationship of the children's depression state and their own IA. In other words, there is a parental depression effect on the child's IA and such an effect consists of direct and indirect components on the child's depression. Such interpretation assumes a directional pathway between depression and IA in which depression precedes IA. However, as far as the study design is concerned there is no substantiating evidence for a directional pathway interpretation of the results. The results of the study indicate that IA is not an individual matter of young people, but a familial problem. Parental mental health also plays a significant role in the problem. Hence, any treatment options that clinicians may consider, such as Cognitive Behavioural Therapy or other alternative approaches (Greydanus & Greydanus, 2012) need to take into consideration familial and parental components and address these factors as part of the treatment regime. Whilst applying family therapy, parental mental health status should also be examined and, if necessary, treatment should also be provided to parents as part of a more holistic approach. For prevention, parents need to be informed and educated on the impact of their own mental health, particularly depression, on their children in terms of their Internet use behaviour. Limitations identified in this study included: first, information on the exposure and outcome is obtained via a self-reported questionnaire that potentially constitutes a report bias in these variables although it would most likely be a non-differential bias; second, a cross-sectional study and the evidence provided from such a study can only be considered as associative and is insufficient to draw any causal inference (Rothman & Greenland, 1998). Future studies could be conducted with a better design such as a longitudinal cohort study, which includes important potential confounding factors, such as family function and parent–child relationship, to elucidate whether the association is of a causal nature. Role of Funding Sources The author declares the study has not received any funding support from any organisations. Contributors Lawrence T. Lam is the principal investigator who formulated the research question, developed the study protocol, obtained institutional ethics approval, designed and piloted the survey questionnaire, conducted data analyses, and wrote the manuscript. Conflict of Interest The author declares no conflict of interest of any kind in the production of this publication. Acknowledgements The author would like to acknowledge the valuable assistance of Dr. Li Yang in supervising the field work during data collection.
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