Direct and Interaction Effects of Co-Existing Familial Risk Factors and Protective Factors Associated With Internet Addiction Among Chinese Students in Hong Kong

Abstract

Internet addiction (IA) is prevalent among adolescents and imposes a serious public health threat. Familial risk and protective factors may co-exist and interact with each other to determine IA. We conducted a cross-sectional survey among 9,618 Secondary 1 to 4 students in Hong Kong, China. About 16% of the surveyed students were classified as Internet addicted; nearly one third of them perceived that at least one of their family members had IA (FMIA). We found that FMIA was a risk factor (multivariate odds ratio [OR] = 2.04), and perceived family support was a protective factor (multivariate OR = 0.97) of IA. We also found a significant risk-enhancement moderation effect between these risk and protective factors, that is, the risk effect of FMIA increased with perceived family support. The finding highlights that family-based interventions, which modify familial risk and protective factors, should be effective for adolescent IA, but caution is required about potential risk-enhancement moderations between such factors.

Keywords

media and technology (music,TV,magazines,Internet,gaming,iPhones,and messaging)family support psychopathology public health

Internet addiction (IA), defined as inappropriate and/or excessive Internet use that leads to physical health problems and impaired psychological state and daily functioning (Cash, Rae, Steel, & Winkler, 2012; Shaw & Black, 2008), is a relatively new phenomenon. It has been widely reported worldwide and is considered an emergent serious public health threat in Asian countries such as South Korea and China (Block, 2008). Adolescents are at high risk of developing IA due to their greater biological vulnerability to addictive behaviors (Chambers, Taylor, & Potenza, 2003) and also the unique attractive properties of Internet use (Bremer, 2005; Livingstone, 2003; van den Eijnden, Meerkerk, Vermulst, Spijkerman, & Engels, 2008). In Hong Kong, the prevalence of adolescent IA was between 6.7% and 26.7% (Fu, Chan, Wong, & Yip, 2010; Shek & Yu, 2012; Yu & Shek, 2013). The variation in prevalence across countries and region was relatively large: 6.0% to 20.7% in Korea, Taiwan, and China; 3.7% to 8.2% in Iran, Greece, Italy, Britain, and the Netherlands (Ghassemzadeh, Shahraray, & Moradi, 2008; Ko, Yen, Chen, Yeh, & Yen, 2009; Kuss, Griffiths, & Binder, 2013; Lopez-Fernandez, Honrubia-Serrano, Gibson, & Griffiths, 2014; S. K. Park, Kim, & Cho, 2008; Poli & Agrimi, 2012; Siomos, Dafouli, Braimiotis, Mouzas, & Angelopoulos, 2008; Tang et al., 2014; van den Eijnden, Spijkerman, Vermulst, van Rooij, & Engels, 2010; H. Wang et al., 2011; Wu et al., 2013; J. Xu et al., 2012; Yen, Yen, Chen, Chen, & Ko, 2007). The variation is partially because different instruments were used in these studies.

In this transition stage of life, interpersonal influences are crucial in determining health-compromising behaviors such as use of alcohol, tobacco and illicit drugs, and sexual risk-taking behaviors (Brooks-Gunn & Furstenberg, 1989; Hawkins, Catalano, & Miller, 1992; Kandel, 1980; McPherson et al., 2013; Wood, Read, Mitchell, & Brand, 2004). Family influence as a source of interpersonal influences is highly important in determining risk behaviors, including IA, among adolescents (Ko et al., 2008; Liu & Kuo, 2007). Some researchers even hypothesized that a direct genetic link for addiction might exist (Ball, 2008). Potential influences of parental or sibling’s problem behaviors have been consistently observed in various domains of adolescent addiction, such as alcohol, tobacco, marijuana, illicit drug, and gambling (Bahr, Hoffmann, & Yang, 2005; Flay et al., 1994; García-Rodríguez, Suárez-Vázquez, Santonja-Gómez, Secades-Villa, & Sánchez-Hervás, 2011; Hawkins et al., 1992; Johnson, Shontz, & Locke, 1984; Oei & Raylu, 2004; Selya, Dierker, Rose, Hedeker, & Mermelstein, 2012; Webster, Hunter, & Keats, 1994), but there is a dearth of such studies focusing on family influences on Internet use. Given the important roles of family in enabling and facilitating Internet use among adolescents (McMillan & Morrison, 2006), adolescents are susceptible to inter-generational and familial “transmission” of IA. This study focuses on potential family influences on IA in the form of protective and risk factors, and their interplay with each other.

Perceptions about family members’ IA status or problematic Internet use are potentially important for influencing adolescent IA. A recent study found that subjective assessment of the frequency of parental Internet use was positively associated with adolescent IA (Liu, Fang, Deng, & Zhang, 2012). According to the Social Learning Theory (Bandura, 1977), young people often model after family members’ behavior and such modeling may operate through perception of family members’ Internet use. Perceived problematic Internet use, such as IA status, among significant others such as family members is therefore expected to be a determinant of IA. Also, according to the Theory of Planned Behavior (Fishbein & Ajzen, 2010), perceptions including both injunctive norm (i.e., the perception of significant others’ approval of one engaging in a behavior) and descriptive norm (i.e., the perception of significant others’ engagement in that behavior) are important determinants of one’s behaviors. Adolescents who perceive having family members with IA (FMIA) are more likely to possess norms supporting a problematic pattern of Internet use, and are hence subjected to increased risk of developing IA.

In contrast to potential risk effects of having family members with IA, perceived family support is a potential protective factor, as psychological social support in general is negatively associated with IA (Gunuc & Dogan, 2013; Yeh, Ko, Wu, & Cheng, 2008). Perceived care and support from family members were negatively associated with IA among adolescents who were recruited in clinical and school settings (Gunuc & Dogan, 2013; Siomos et al., 2012; Yeh et al., 2008). Furthermore, strong family support was associated with healthier family functioning, better communication, and less family conflicts, which are protective factors of adolescent IA (Ko, Yen, Yen, Lin, & Yang, 2007; Li, Garland, & Howard, 2014; Liu & Kuo, 2007; S. K. Park et al., 2008; J. Xu et al., 2014; Yen et al., 2007; Yu & Shek, 2013).

It is important to recognize that both familial risk and protective effects on adolescent risk behaviors are prevalent and may co-exist in a family. However, very few studies have looked at the interplay between the two types of factors. It is warranted to look at the interactions between co-existing risk factors such as perceived presence of FMIA and protective factors such as perceived family support because such interactions may result in either risk-buffering or risk-enhancement effects. In terms of risk-buffering effect, perceived social support may diminish the negative effect of a stressor in a family on addictive behaviors (Handley & Chassin, 2008; S. Park, Kim, & Kim, 2009). Perceived family support can, hence, potentially moderate the association between a risk factor and adolescent IA to result in a risk-buffering effect. If such is true, the effect of such risk factor as FMIA on IA would diminish in the presence of strong perceived family support. For instance, a significant risk-buffering effect of family functioning on the association between perceived alienation and IA was observed in a sample of Chinese adolescents (F. Z. Xu & Zhang, 2011). Other studies, however, did not detect significant risk-buffering effects of perceived social support for the relationships between risk factors and addiction (Ferguson & Xie, 2012; Mulia, Schmidt, Bond, Jacobs, & Korcha, 2008; Pabayo, Alcantara, Kawachi, Wood, & Kerr, 2013; Windle, 1992). The results testing such risk-buffering effects are, hence, inconclusive.

A risk-enhancement type of moderation effect may also exist. In that case, the risk effect of perceived FMIA on adolescent IA would increase with the level of perceived family support. For instance, a study reported a significant positive interaction between perceived parental warmth and the presence of smoking parent(s) on adolescent cigarette use, and the likelihood of adolescent smoking was found to be the highest among those with higher parental warmth and two parents who smoked (Foster et al., 2007). No study has tested moderation between perceived familial risk of IA and perceived family support on adolescent IA. Thus, there is no clue about which type of risk moderation would be detected.

Besides family influence, peer influence is a highly consistent and significant factor across research on adolescents’ problematic behaviors, including addictive behaviors such as substance use and abuse (Bahr et al., 2005; Hawkins et al., 1992; Urberg, Shyu, & Liang, 1990). Ko et al. (2008) found that adolescents with IA tended to have more friends with deviant behaviors and more friends using alcohol than non-IA adolescents. A recently published study showed that the association between perceived number of peers with IA and adolescent IA was partially mediated by some health beliefs (i.e., perceived social benefits of Internet use, perceived barriers for reducing Internet use, and perceived self-efficacy for reducing Internet use; Y. H. Wang, Wu, & Lau, 2016). As this study examined the interplay between familial risk and protective factors of IA, we would like to focus only on family factors and did not investigate peer influences on IA.

In the present study, we investigated whether the perceived presence of FMIA and perceived family support were significantly associated with IA among a large representative sample of Chinese secondary school students in Hong Kong, China. We hypothesized that perceived presence of FMIA would be a risk factor of IA, while perceived family support would be a protective factor. Furthermore, we tested significance of moderation effect of perceived family support on the association between perceived presence of FMIA and IA. We hypothesized that the effect size (odds ratio [OR]) of the association between perceived FMIA and IA would differ significantly between those with better and worse perceived social support. Because either risk-buffering or risk-enhancing types of moderation could occur, and as the interplay between perceived presence of FMIA and perceived family support on adolescent IA has not been investigated in existing literature, we used a two-sided alternate hypothesis instead of a one-sided one (i.e., we did not specify any direction for the moderation effect in the alternate hypothesis). This is, hence, an exploratory study testing the moderation hypothesis. The findings would carry significant implications in designing family-based IA-prevention programs. Such programs need to take protective factors such as perceived social support into account, and need to make use of any risk-buffering effect of perceived social support, if such exists. Yet, we also have to ensure that the risk effect of FMIA would not be enhanced by perceived family support due to a risk-enhancement type of moderation, if such moderation exists.

Method

Study Design

The present study was a cross-sectional survey, and its data collection was conducted from September 2012 to January 2013. In each of the 19 districts of Hong Kong, one school was randomly selected, and all Secondary 1 to 4 (i.e., seventh to 10th year of formal education) Chinese students of such schools were invited to participate in the study. In Hong Kong, 93.62% of the population is Chinese (Hong Kong Census and Statistics Department, 2012). Fieldworkers briefed the participants and supervised the entire data-collection process. Participants completed the self-administered, anonymous, and structured questionnaire in classroom settings in the absence of teachers. Participants were informed that their participation was completely voluntary, without incentive, and that filling out the questionnaire implied informed consent. They were guaranteed that the collected data would only be accessed by the researchers and not by the teachers. Written parental consent was obtained with teachers’ assistance. Ethics approval was obtained from the Ethics Committee of the Chinese University of Hong Kong. Two published articles were based on the same data set. They investigated health beliefs/peer influence factors of IA and psychosocial attributes associated with probable depression (Wang et al., 2016; Wu, Li, Lau, & Mo, 2016).

A total of 9,666 students took part in the survey and returned the completed questionnaire, but 48 (0.5%) cases were excluded because they were missing >25% of their item responses for the Chen Internet Addiction Scale (CIAS), which was used to define the dependent variable. The effective sample size was therefore 9,618.

Measures

Internet addiction

The CIAS is a 26-item self-reported scale assessing IA symptoms, such as compulsive use (“I can’t resist the impulse to use the Internet”) and tolerance (“I find that there is a marked increase in the duration of Internet use for me”; Chen, Weng, Su, Wu, & Yang, 2003). Each item was rated on a 4-point Likert-type scale ranging from definitely disagree (1) to definitely agree (4). The total score ranges from 26 to 104 with higher scores indicating more severe IA. The instrument showed good psychometric properties (Chen et al., 2003) and has commonly been used in studies targeting Chinese adolescents (Ko et al., 2009; Ko et al., 2005; Lin, Ko, & Wu, 2008; Yen et al., 2007). In this study, its unidimensionality was confirmed by a confirmatory factor analysis (CFA) χ²(267) = 7,425.67, comparative fit index (CFI) = .95, Tucker-Lewis index (TLI) = .94, root mean square error of approximation (RMSEA) = .05, with Cronbach’s α = .95. Because the 63/64 cutoff point of the CIAS had the highest diagnostic accuracy and satisfactory specificity and sensitivity (Ko et al., 2005), we used this cutoff point to define IA cases (and coded 1 = IA and 0 = non-IA). Other studies have also commonly used the same cutoff point for defining IA cases (Cheung & Wong, 2011; Ko et al., 2005, 2009; Tsai et al., 2009); thus, its use allows for comparisons with other studies.

Perceived family support

The four-item Family Support Subscale of the Multidimensional Scale of Perceived Social Support was used to measure the perceived level of family support (Zimet, Dahlem, Zimet, & Farley, 1988). A sample item was, “I get the emotional help and support I need from my family.” Each item was rated on a 7-point Likert-type scale ranging from very strongly disagree (1) to very strongly agree (7). The summative score of those four items was used. The instrument has been used among adolescents in Hong Kong and showed good psychometric properties (K. L. Chou, 2000). Our CFA found this scale’s unidimensional measurement model satisfactory, χ²(1) = 75.46, CFI = .99, TLI = .99, RMSEA = .09, with Cronbach’s α = .92.

Family members with Internet addiction

The perception of the presence of any FMIA was gauged by the question, “Does anyone in your family get addicted to the Internet use?” Participants answered either no (0) or yes (1). Because this is a question on perception, it did not measure family members’ actual IA status, but instead, it assessed whether participants perceived that their family members were having problematic Internet use, or at high risk of IA. It is not feasible to measure actual IA status of multiple family members without lowering the response rate and causing potential biases due to communication among members. Hence, we cannot validate this binary measure externally against other outcomes. Furthermore, perceptions of family members’ IA may be as important as actual diagnoses. Because it is a binary variable, internal reliability is not an applicable psychometric property. Its test-retest reliability based on the pilot study (n = 30) was .65, which is considered “substantial” (Landis & Goch, 1977). Further review is presented in “Discussion” section of this article about the considerations and limitations concerning this measurement.

Socio-demographics

Socio-demographic information collected included gender, grade level, father’s education, mother’s education, living arrangement with parents (with both parents or not), and residency status and duration of stay in Hong Kong.

Statistical Analyses

The prevalence of IA and its 95% confidence intervals (CI) were reported. The associations between socio-demographic factors with IA were examined by using univariate odds ratios (ORu) and their respective 95% CI. Multiple logistic regression models were fitted separately for the key independent variables of perceived family support and FMIA, adjusted for socio-demographic variables, and resulting adjusted odds ratios (ORa) were reported. The variables of perceived family support and FMIA were then entered into the same logistic regression model, similarly adjusted for socio-demographic variables, and the resulting ORs were named in the table as multivariate odds ratios (ORm). All analyses were performed using SPSS 18.0.0. Statistical significance was accepted at p < .05 in all analyses.

Results

Socio-Demographic Characteristics of the Participants

The socio-demographic characteristics of the participants are presented in Table 1. Of the participants, about half (53.3%) were male. Respectively, 16.7% and 13.4% of their father and mother had received tertiary education; 3.0% were not living with both parents; 21.3% were not born in Hong Kong. The participants were evenly recruited from Secondary 1 to 4 (i.e., about one fourth for each school grade). It is a strong limitation of this study that age was not asked because the participants (Secondary 1-4) were expected to have a relatively narrow age range (i.e., age 12 to 16 years), and age was strongly associated with school grade. The normal age range for Secondary 1 to 4 is 12 to 16 years in Hong Kong (Education Commission, 2004). We, hence, expect about half of our participants, who were Secondary 1 and 2 students, would be 12 to 14 years old, as free secondary education up to Secondary 6 has been available in Hong Kong since 2008-2009 school year.

Table 1.

Characteristics of All Participants (N = 9,618).

	%
Sex
Male	53.3
Female	46.0
Missing	0.6
School grade
Secondary 1	24.1
Secondary 2	24.5
Secondary 3	26.2
Secondary 4	25.1
Father’s education
Junior secondary or below	27.7
Senior secondary	38.1
University/college or above	16.7
Did not know	17.5
Mother’s education
Junior secondary or below	28.6
Senior secondary	41.5
University/college or above	13.4
Did not know	16.5
Living arrangement with parents
Living with both parents	82.7
Not living with both parents	3.0
Living only with mother	9.5
Living only with father	3.0
Missing	1.8
Residency and stay in Hong Kong
Born in Hong Kong	78.7
Not born in Hong Kong but lived in Hong Kong for 7 years or more	12.4
Not born in Hong Kong but lived in Hong Kong for less than 7 years	6.4
Not born in Hong Kong but unsure how long he or she had lived in Hong Kong	1.9
Missing	0.6
IA based on CIAS score (63/64)
No	83.9
Yes	16.1
Presence of FMIA
No	71.5
Yes	28.2
Missing	0.3
Perceived Family Support Scale^a: $\bar{x}$ (SD)	18.57 (6.19)

Note. IA = Internet addiction; CIAS = Chen Internet Addiction Scale; FMIA = family members with Internet addiction.

0.3% cases were classified as missing (i.e., >25% of the item responses were missing).

Information Related to Internet Addiction

Table 1 showed that 28.2% of the participants perceived the presence of FMIA. The prevalence of IA assessed by CIAS was 16.1% (95% CI = [15.4%, 16.8%]). The prevalence was 17.7% among males, which was significantly higher than that of females (14.1%; p < .001).

Associations Between Background Factors and Internet Addiction

All the background factors that are listed in Table 2 were found to be significantly associated with IA. Protective factors were negatively associated with IA, and included female sex, higher father’s education, and higher mother’s education. Risk factors were those that were positively associated with IA; they included higher school grade, not living with both parents, and not born in Hong Kong but having stayed in Hong Kong for ≥7 years.

Table 2.

Associations Between Socio-Demographic Variables and IA.

	Row (%)	ORu (95% CI)
Sex
Male	17.7	1
Female	14.1	0.76 [0.68, 0.85]***
Missing	19.4	1.12 [0.59, 2.10]
School grade
Secondary 1	11.0	1
Secondary 2	16.3	1.57 [1.33, 1.86]***
Secondary 3	17.3	1.70 [1.44, 2.00]***
Secondary 4	19.4	1.95 [1.65, 2.30]***
Father’s education
Junior secondary or below	18.6	1
Senior secondary	15.8	0.82 [0.72, 0.94]**
University/college or above	12.5	0.62 [0.52, 0.74]***
Did not know	15.9	0.82 [0.70, 0.97]*
Mother’s education
Junior secondary or below	19.6	1
Senior secondary	15.1	0.73 [0.64, 0.83]***
University/college or above	11.8	0.55 [0.45, 0.67]***
Did not know	15.9	0.78 [0.66, 0.92]**
Living arrangement with parents
Living with both parents	15.0	1
Not living with both parents	19.2	1.35 [1.00, 1.83]*
Living only with mother	20.9	1.50 [1.27, 1.78]***
Living only with father	26.7	2.07 [1.58, 2.70]***
Missing	17.9	1.24 [0.84, 1.84]
Residency and stay in Hong Kong
Born in Hong Kong	15.7	1
Not born in Hong Kong but lived in Hong Kong for 7 years or more	18.1	1.19 [1.01, 1.39]*
Not born in Hong Kong but lived in Hong Kong for less than 7 years	16.4	1.06 [0.85, 1.32]
Not born in Hong Kong but unsure how long he or she had lived in Hong Kong	15.2	0.96 [0.64, 1.45]
Missing	22.6	1.57 [0.86, 2.85]

Note. IA = Internet addiction; ORu = univariate odds ratios; CI = confidence interval.

p < .05. **p < .01. ***p < .001.

Perceived Presence of Family Members With Internet Addiction and Perceived Family Support as Factors of Internet Addiction

In a multiple logistic regression model (Table 3) containing the two key independent variables and adjusted for all the background factors measured in this study, we found that perceived presence of FMIA (ORm = 2.03, 95% CI = [1.80, 2.28]) and perceived family support (ORm = 0.97, 95% CI = [0.96, 0.98]) were both significantly associated with IA. Computation based on the coefficients of the logistic regression model showed that the ORa for the association between perceived family support (a continuous variable) and IA was .60, when comparing the two groups of participants having perceived family support scores of $\bar{x}$ ± 1 SD ( $\bar{x}$ = 18.57, SD = 6.19).

Table 3.

Associations Between FMIA/Perceived Family Support and IA.

	Row (%)	ORu (95% CI)	ORa (95% CI)^a	ORm (95% CI)^b
FMIA
No	13.2	1	1	1
Yes	23.2	1.99 [1.77, 2.22]***	2.09 [1.86, 2.35]***	2.03 [1.80, 2.28]***
Missing	28.0	2.56 [1.06, 6.14]*	2.05 [0.83, 5.09]	2.31 [0.91, 5.90]
Perceived family support	—	0.96 [0.95, 0.97]***	0.96 [0.96, 0.97]***	0.97 [0.96, 0.98]***

Note. FMIA = family members with Internet addiction; IA = Internet addiction; ORu = univariate odds ratios; CI = confidence interval; ORa = adjusted odds ratios; ORm = multivariate odds ratios.

Adjusted by all socio-demographic variables, namely, (a) Gender, (b) School grade, (c) Father’s education, (d) Mother’s education, (e) Living arrangement with parents, (f) Residency and stay in Hong Kong, and (g) School.

Stepwise forward selection process was used after adjusting (a) Gender, (b) School grade, (c) Father’s education, (d) Mother’s education, (e) Living arrangement with parents, (f) Residency and stay in Hong Kong, and (g) School.

p < .05. **p < .01. ***p < .001.

In Table 4, we found that the interaction between perceived presence of FMIA and perceived family support on IA was of statistical significance (p = .019). The direction of the interaction implies that the risk effect of FMIA (i.e., the strength of association between FMIA and IA) increased with the level of perceived family support. As an illustration, the ORs for the association between perceived presence of FMIA and IA for participants with relatively high ( $\bar{x}$ + 1 SD) and low ( $\bar{x}$ − 1 SD) Perceived Family Support Scale scores were 2.37 and 1.81, respectively. Therefore, it is a risk-enhancement type of moderation, instead of a risk-buffering type.

Table 4.

Multivariate Logistic Regression Models Entering the Two Family-Related Variables and Their Interaction Terms and Adjusted for All Background Variables.

	All
	β (SE)
FMIA
No	0
Yes	0.319 (0.176)
Missing	4.194 (2.436)
Perceived family support	−0.039 (0.006)***
FMIA × Perceived family support
(FMIA = No) × Perceived family support	0
(FMIA = Yes) × Perceived family support	0.022 (0.010)*
(FMIA = Missing) × Perceived family support	−0.207 (0.149)

Note. Adjusted by (a) Gender, (b) School grade, (c) Father’s education, (d) Mother’s education, (e) Living arrangement with parents, (f) Residency and stay in Hong Kong, and (g) School. IA = Internet addiction; FMIA = family members with Internet addiction.

p < .05. **p < .01. ***p < .001.

Discussion

We found about one sixth (16%) of the sampled students were classified as IA cases according to the criterion of CIAS. Consistent with the general observation that Chinese boys had higher IA prevalence than girls (Li et al., 2014), our results showed a statistically significant sex difference. However, the absolute gender difference was relatively mild. In Hong Kong, both male and female adolescents may be exposed to similar levels of risk factors for developing IA but various aspects of Internet use do not seem to be sex-specific.

Some significant background factors of IA are worth attention. First, we found that higher parental education level was protective against children’s IA. A previous study showed that better educated parents were more likely than other parents to monitor and discipline their children’s behaviors (Larzelere & Patterson, 1990). According to a review article, previous studies reported inconsistent results regarding the associations between parental education and IA among Chinese youth (Li et al., 2014); this study, hence, adds information to literature. Second, we found that children living with one or no parent were more vulnerable than those living with both parents to have IA. It is well known that single parenthood is associated with adolescent risk behaviors (Jablonska & Lindberg, 2007). Living with no or only one parent may result in a lower level of parental control over the child’s Internet use. Third, we found that not being born in Hong Kong was another risk factor for IA. The reason was unclear, but it is plausible that students who were immigrants faced more adverse situations, which increased their vulnerability to develop IA.

About one third of the surveyed students perceived the presence of FMIA. The high prevalence is understandable as common use of the Internet has become dominant across age groups. In a local study conducted by us (unpublished data), the age-standardized prevalence of problematic Internet use was 42.4% and 43.7% for males and females, respectively, among 15- to 54-year-old participants in Hong Kong in 2008. We expect that it has increased further in the last few years. It is, hence, not surprising that many participants reported perceived presence of FMIA. We also acknowledge that adolescents were unable to know about family members’ actual IA status and CIAS scores of family members were not available. It is very difficult to collect such data without compromising the response rate and introducing potential biases due to communication with family members. It is, hence, likely that participants were reporting perceived problematic Internet use or high perceived risk of IA among family members, instead of reporting actual IA statuses, which were unknown to them. Thus, the prevalence of actual IA among family members should be lower than that of the perceived one. As IA is a colloquial term in Hong Kong and symptoms of IA among family members are relatively easy to observe, we believe that the reported perception of FMIA was indicative of family members’ problematic Internet use and high risk level of IA. Importantly, our results showed that the perception was sufficient in influencing adolescent IA. Perception is, hence, as important as objective measures of family members’ IA status. Subjective perceptions of parental behaviors have shown to be strong determinants of adolescent health-risk behaviors (Carter, Bingham, Zakrajsek, Shope, & Sayer, 2014; Cestac, Paran, & Delhomme, 2014), independent of the effect of actual parental behaviors (Carter et al., 2014). Another similar study also reported a positive association between students’ perception about frequency of parental Internet use and IA status (Liu et al., 2012).

The association between perceived presence of FMIA and adolescent IA is also keeping with the substance use literature that addiction disorders are potentially transmittable among family members. Various plausible mechanisms might explain such potential inter-generational and within-family transmissions, including but not limited to inadequate social control, behavioral modeling, and high Internet accessibility in the same living environment. It is a limitation of this study that we did not test the mechanisms involved in the association between perceived FMIA and IA. Further research is warranted to reveal such mechanisms.

The contention that familial risk factors may co-exist with familial protective factors is supported by our observed data, as perceived family support, a protective factor was also negatively associated with IA in this study. The negative association between perceived family support and IA corroborates those between perceived family support and other risk behaviors such as anti-social behaviors, substance use, and unsafe sexual behaviors (Ary et al., 1999; Windle, 1992). For instance, a previous study showed that family function significantly explained adolescent substance use after adjusting for family member’s alcohol use (Yen et al., 2007). The observed significant protective association between family support and IA supports the use of family-based approaches to tackle problems of adolescent IA; preliminary evidence found that such interventions appear to be effective (Zhong et al., 2011).

Consistent with Foster et al.’s (2007) finding on adolescent smoking, our findings suggest existence of a risk-enhancement type of moderation, instead of a risk-buffering type of moderation, on perceived family support for the association between FMIA and adolescent IA. Although IA may cause less physical harm than smoking, both qualitative and quantitative literature consistently reported serious negative consequences of IA, including harm on one’s physical and psychosocial well-being (e.g., eye problems, disturbed sleep, poor grades, and depression; Cheung & Wong, 2011; C. Chou, 2001; C. Chou, Condron, & Belland, 2005; Yang & Tung, 2007). We found that, as compared with those students with low perceived family support, the potential adverse impact of FMIA on IA was larger among those with high perceived family support. In families with good mutual support, family members’ problematic behaviors may have an even stronger negative influence on adolescents than in families with poor relationships and less support. Interventions targeting adolescents with IA may also consider how to reduce their family members’ problematic Internet use and pay attention to families with good mutual support but having FMIA.

Our data did not test explanations behind the observed risk-enhancement moderation. The Social Control Theory (Hirschi, 1969) may, however, provide some preliminary insights to help understand such a moderation effect. The theory can potentially explain the protective effect of perceived social or family support on IA. It assumes that one’s significant others create and hold conventional, healthy, and prosocial norms, and those with strong affective ties with such significant others would be more motivated and more likely to conform to those norms, and, hence, stay away from misbehaviors (Hirschi, 1969). However, with perceived presence of FMIA, the students’ significant others (family members) may, instead, be potential role models of misbehaviors and may present unhealthy norms to the student. The stronger perceived family support, and hence the stronger affectionate ties with family members with IA, may drive adolescents to conform to the unhealthy norms toward excessive Internet use, and increase their vulnerability to develop IA. A risk-enhancement type of moderation would, thus, be resulted. Another plausible reason for the observed risk-enhancement type of moderation is that adolescents with good family support might find it easier to identify with family members’ similar behavioral pattern of problematic Internet use, and have a stronger tendency to act similarly in terms of excessive Internet use. These plausible explanations have not been tested in literature, and further studies are required to understand the mechanism of the observed significant risk-enhancement type of moderation effect.

The study has a number of important limitations. This study did not investigate the association between having peers with IA and IA among adolescents. The topic was investigated in another recently published article (Y. H. Wang et al., 2016). Because we are looking at the interplay between familial risk and protective factors, we would like to confine the scope of this report to focus on familial factors and discuss the findings in some depth. The limitation related to the assessment and definition of FMIA and inability to assess family members’ IA status has been acknowledged in a previous part of this article. We have emphasized that perception of FMIA is as important as objective measure of IA provided by parents, as perceptions are strong predictors of behaviors (Fishbein & Ajzen, 2010). Participants were not given an explanation about the definition of IA when answering the question about perceived FMIA. In fact, such an explanation is not feasible in a survey setting, and as mentioned, we were assessing perception instead of objective data. A related limitation is that a single-item measure was used to assess the perception on familial risk of IA. As it is a single-item measure, internal reliability does not apply to this case. It is also not possible to validate the question as we did not assess family members’ IA status. Its test-retest reliability was acceptable. In future research, a new behavioral checklist may be used to assess the perceived severity of problematic Internet use of family members to improve the measure’s reliability and validity. Another important limitation is that grade, but not age, of each participant was recorded as we thought that age was relatively homogeneous with school grades in this large student sample in Hong Kong. However, some students such as those who have repeated a grade would be older. Self-reported data of IA symptoms were susceptible to reporting bias. Last, but not the least, the study’s cross-sectional design limits our ability to establish causal relationships.

There is no doubt that IA development is strongly associated with adolescents’ family environment. Such associations can, however, be positive or negative. The present study investigated the interplay between familial risk and protective factors, that is, perceived family support and perceived FMIA, on adolescent IA. It generates some critical insights for designing intervention programs for prevention and treatment of IA among adolescents. First, family-based intervention programs are potentially useful to tackle adolescent IA problems. Second, the robust impact of family socialization toward problematic Internet use needs to be considered in family-based IA-intervention programs. Special attention should also be given to those students perceiving presence of FMIA. Family therapy, which allows precise examination of interaction among members within the family system, is recommended. Parents with young children should be made aware of potential familial transmission of problematic Internet use. In those programs, family support still needs to be cultivated as it is protective against IA, as well as other adolescent misbehaviors such as smoking, alcohol problems, and delinquent activities (Ary et al., 1999; Windle, 1992). Parents with problematic Internet use should be made aware that their support given to the adolescent might enhance, instead of dampen the risk of, the adolescent’s IA. An insight of this study is that IA can be considered an emerging family problem.

Footnotes

Acknowledgements

The authors thank the participating schools and students for their cooperation in this study.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Health and Health Services Research Fund (HHSRF) in Hong Kong [09100591].

Author Biographies

Anise M. S. Wu is an associate professor in the Department of Psychology at the University of Macau, and her research interest is concerned with both individual and public health, with specific focuses on addictive behaviors, organ donation, volunteerism, and older adults’ well-being.

Joseph T. F. Lau is currently a professor, the head of the Division of Behavioral Health and Health Promotion, and the director of the Centre of Health Behaviors Research at the School of Public Health and Primary Care, the Chinese University of Hong Kong. He specializes in behavioral health research and is the founding president of the Hong Kong Society of Behavioral Health.

Kit-man Cheng joined the Centre for Health Behaviours Research as a research assistant to support the statistics analysis after attaining BSc in the Chinese University of Hong Kong.

Rita W. Law received training in clinical psychology at the University of Arizona and graduated with a PhD in psychology in 2011, upon obtaining a BSSc and an MPhil from the Chinese University of Hong Kong. Her research interests revolve around how compassion and mindfulness may affect well-being and the underlying psychophysiological mechanisms.

Vincent W. S. Tse is a senior lecturer at the Department of Educational Psychology in the Chinese University of Hong Kong, and his research interest lies in examining interpersonal theory of depression and schizophrenia.

Mason M. C. Lau is a project coordinator at the School of Public Health and Primary Care, the Chinese University of Hong Kong. He holds an MSc in applied mathematics and is very experienced in implementation of large-scale projects.

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