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Although social networking services (SNSs) have become popular among young people, problematic SNS use has also increased. However, little is known about SNS addiction and its association with SNS use patterns and mental health status.
This study aims to test the mediating role of SNS addiction between SNS use patterns and mental health status among Chinese university students in Hong Kong (HK).
An online cross-sectional survey was conducted using a convenience sampling method. In total, 533 university students (323 [66.9%] female, mean age [SD]=20.87 [2.68] years) were recruited from February to March 2019. Multiple linear regression was used to assess the association between SNS use and SNS addiction. Structural equation modeling (SEM) was performed to examine the pathways and associations among SNS use, SNS addiction, psychosocial status, and mental health status (including anxiety and depressive symptoms).
A longer time spent on SNSs per day (>3 h), a longer time spent on each SNS access (≥31 min), a higher frequency of SNS access (≤every 30 min), a longer duration of SNS use before sleeping (≥61 min), and a shorter duration from waking to first SNS use (≤5 min) were significantly associated with a higher level of SNS addiction (adjusted beta [aβ]=6.03, 95% CI 4.66-7.40; aβ=4.99, 95% CI 3.14-6.83; aβ=5.89, 95% CI 4.14-7.64; aβ=5.92, 95% CI 4.19-7.65; and aβ=3.27, 95% CI 1.73-4.82, respectively). SEM showed a significant mediating effect of SNS addiction in the relationship between SNS use and psychosocial status, and mental health status, including an indirect effect (β=0.63, 95% CI 0.37-0.93) and the total effect (β=0.44, 95% CI 0.19-0.72), while the direct effect was insignificant (β=–0.19, 95% CI –0.49 to 0.08).
SNS use patterns were associated with SNS addiction, and SNS addiction mediated the associations between SNS use, psychosocial status, and mental health status of Chinese university students in HK. The findings suggest that screening for and addressing excessive SNS use are needed to prevent SNS addiction and mental distress among young people.
More than 1 billion people worldwide regularly use social networking services (SNSs), such as Facebook, Twitter, and Instagram, which are virtual communities where users interact and build online and real-life relationships [
Excessive SNS use has shown significant association with addictive behaviors [
Although recent studies have identified associations between excessive SNS use, SNS addiction, and mental health status, it remains unknown whether there is a clear pathway from excessive SNS use to SNS addiction, thus influencing users’ mental health (ie, pathway). As SNS use patterns can be differentiated by their social contexts (eg, different cultural values in interpersonal relationships on SNSs [
Based on the existing literature and knowledge gap, we developed the following hypotheses for this study and a hypothetical model (
Conceptual model and hypotheses. Ovals represent unobserved latent variables. Rectangles represent observed measured variables. SNS: social networking service.
Hypothesis 1 (H1): SNS use is positively associated with SNS addiction.
Hypothesis 2 (H2): SNS addiction is negatively associated with mental health status.
Hypothesis 3 (H3): SNS use is negatively associated with psychosocial status.
Hypothesis 4 (H4): Psychological factors are positively associated with mental health status.
Hypothesis 5 (H5): SNS addiction is negatively associated with psychosocial status.
Hypothesis 6 (H6): SNS use is negatively associated with mental health status.
A cross-sectional study design was adopted. An online survey link (Google Form) and a QR code printed on survey invitation flyers were distributed in busy public areas (eg, the entrance of library and cafeteria) within 2 major public university campuses in HK from February to March 2019. The online survey website included the study aim, respondents’ rights, and participation incentive (50 respondents were randomly selected through a lucky draw for a HK $25 [around US $3.2] cash voucher). The respondents were also invited to freely share the survey link with their peers from any HK university (ie, convenience and snowball sampling). The inclusion criteria were enrolled university students residing in HK with at least 1 SNS account. Exchange students from outside HK and respondents who did not answer more than 10 survey questions were excluded. To prevent multiple responses and confirm their identity as university students, each respondent’s mobile phone number and university email address were collected.
Demographic characteristics, including sex, age, university, degree pursued, and academic performance, were investigated. The respondents’ patterns of SNS use (eg, overall time spent on SNS per day, time spent on each SNS access, SNS access frequency, and SNS use duration before sleeping and after waking) were surveyed.
SNS addiction was measured using a revised version of the Bergen Facebook Addiction Scale (BFAS), which consists of 6 questions [
The Patient Health Questionnaire-4 (PHQ-4) was used to screen for psychological symptoms among the respondents. The PHQ-4 consists of 2 domains (ie, depression and anxiety), and each domain has 2 questions [
The respondents’ psychosocial status was measured according to their psychological well-being, social isolation, and loneliness. First, the Flourishing Scale was adopted to measure the respondents’ subjective psychological well-being [
Descriptive analysis was conducted to detail the respondents’ information about demographic characteristics, SNS use and addiction, and psychological profile. For continuous variables, the mean and SD were used, and for categorical variables, the frequency and percentage were used for analysis. Multiple linear regression was performed to assess the association between SNS use and SNS addiction. Structural equation modeling (SEM) was performed to examine the pathways from SNS use to mental health status. Multiple imputation was performed for missing data. Total, direct, and indirect effects in the hypothesized model were estimated using the maximum likelihood and bias-corrected (BC) 95% CI by the bootstrapping method with 2000 replications. Fitness indices of the SEM were considered, including the root-mean-square error of approximation (RMSEA; suggested close to or smaller than 0.06), the comparative fit index (CFI; suggested close to or larger than 0.95), the Tucker-Lewis index (TLI; suggested close to or larger than 0.95), and the standardized root-mean-square residual (SRMR; suggested close to or smaller than 0.08) [
Of the 533 respondents, 483 (90.6%) who met the eligible criteria were included in this study. The mean age was 20.87 years (SD 2.68), and 66.9% (n=323) of the respondents were female. The majority (459/483, 95.0%) were from University Grants Committee (UGC)-funded universities (ie, large public universities) and undertook higher diploma or undergraduate courses (466/483, 96.5%). With regard to SNS use, 33.7% (163/483), 42.2% (225/483), and 31.0% (165/483) of the respondents used SNSs for over 1-2 h/day, 6-15 min each time, and every 31-60 min, respectively. In addition, 46.9% (250/483) and 36.0% (192/483) of them used SNSs for 31-60 min before sleeping and within 5 min or less after waking up. The mean scores for anxiety symptoms, depressive symptoms, flourishing, social isolation, and loneliness were 2.86 (SD 1.62), 2.70 (SD 1.58), 38.87 (SD 7.42), 16.90 (SD 5.06), and 5.55 (SD 1.79), respectively. The mean score for SNS addiction was 16.4 (SD 5.03); see
Demographic characteristics and frequency of SNSa use and SNS addiction (N=483).
Variables | n (%) | SNS addiction, |
SNS addiction, |
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Male | 160 (33.1) | 15.49 (5.15) | —c |
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Female | 323 (66.9) | 16.84 (4.92) | — |
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UGCd funded | 459 (95.0) | 16.41 (0.06) | — |
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Non-UGC funded | 24 (5.0) | 16.08 (4.63) | — |
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Higher diploma and undergraduate | 466 (96.5) | 16.53 (5.03) | — |
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Postgraduate | 17 (3.5) | 12.65 (5.35) | — |
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First-class honors/quartile 4 or equivalent | 84 (17.1) | 14.92 (5.03) | — |
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Second-class honors/quartile 3 or equivalent | 192 (39.8) | 15.89 (4.90) | — |
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Second-class honors/quartile 2 or equivalent | 118 (24.4) | 18.03 (4.53) | — |
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Third-class honors/quartile 1 or equivalent | 20 (4.1) | 17.55 (0.90) | — |
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Missing | 69 (14.3) | — | — |
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≤1 h | 72 (13.5) | 12.43 (4.84) | — |
|
>1-2 h | 163 (33.7) | 16.42 (4.60) | — |
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>2-3 h | 125 (25.9) | 18.40 (4.67) | — |
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>3 h | 123 (25.5) | 20.29 (4.85) | — |
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5 min or less | 122 (22.9) | 14.89 (5.05) | — |
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6-15 min | 225 (42.2) | 16.10 (4.84) | — |
|
16-30 min | 100 (18.8) | 17.56 (4.60) | — |
|
31 min or more | 36 (6.7) | 20.08 (4.94) | — |
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Every 5 min or sooner | 30 (5.6) | 17.73 (6.73) | — |
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Every 6-30 min | 141 (26.5) | 17.85 (4.32) | — |
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Every 31-60 min | 165 (31.0) | 16.42 (4.77) | — |
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Every 61 mins or later | 147 (27.7) | 14.69 (5.08) | — |
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61 min or more | 43 (8.1) | 14.03 (5.02) | — |
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31-60 min | 94 (17.6) | 16.28 (4.55) | — |
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6-30 min | 250 (46.9) | 18.49 (4.28) | — |
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5 mins or less | 74 (13.9) | 19.26 (5.32) | — |
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5 min or less | 192 (36.0) | 16.83 (5.12) | — |
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6-30 min | 178 (33.4) | 16.92 (4.72) | — |
|
31-60 min | 64 (12.0) | 15.77 (4.57) | — |
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61 min or more | 34 (6.4) | 14.29 (5.45) | — |
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Loneliness | — | 5.55 (1.79) | <.001 |
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Flourishing | — | 38.87 (7.42) | <.001 |
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Social isolation | — | 16.90 (5.06) | <.001 |
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Anxiety symptoms | — | 2.86 (1.62) | <.001 |
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Depressive symptoms | — | 2.70 (1.58) | <.001 |
aSNS: social networking service.
b
cNot applicable.
dUGC: University Grants Committee. UGC-funded universities in HK including the University of Hong Kong (350/459, 76.3%), the Chinese University of Hong Kong (14/459, 3.1%), the Hong Kong University of Science and Technology (3/459, 0.6%), the Hong Kong Polytechnic University (81/459, 17.6%), the Education University of Hong Kong (0), the City University of Hong Kong (7/459, 1.5%), the Hong Kong Baptist University (3/459, 0.6%), and Lingnan University (1/459, 0.2%).
SNS addiction was significantly associated with SNS use patterns (
Linear regression for the association between SNSa use and SNS addiction.
Model | Crude | Adjustedb | ||||
|
β (95% CI) | β | β (95% CI) | β | ||
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≤1 h | REFc | —d | REF | — | |
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>1-2 h | 3.35 (2.07-4.64)e | 0.32 | 3.24 (1.94-4.54)e | 0.30 | |
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>2-3 h | 4.82 (3.47-6.17)e | 0.42 | 4.63 (3.26-6.00)e | 0.40 | |
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>3 h | 6.23 (4.88-7.58)e | 0.54 | 6.03 (4.66-7.40)e | 0.52 | |
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≤5 mins | REF | — | REF | — | |
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6-15 mins | 1.21 (0.14-2.28)f | 0.12 | 1.08 (–0.00 to 2.16) | 0.11 | |
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16-30 mins | 2.67 (1.38-3.95)e | 0.21 | 2.42 (1.07-3.76)e | 0.19 | |
|
≥31 mins | 5.19 (3.38-7.00)e | 0.27 | 4.99 (3.14-6.83)e | 0.26 | |
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≥Every 6 h | REF | REF | REF | REF | |
|
Every 3 h | 3.34 (1.51-5.17)e | 0.28 | 3.29 (1.48-5.11)e | 0.28 | |
|
Every 1 h | 4.28 (2.52-6.04)e | 0.40 | 4.21 (2.47-5.96)e | 0.40 | |
|
≤Every 30 min | 5.72 (3.96-7.48)e | 0.54 | 5.89 (4.14-7.64)e | 0.56 | |
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≤5 min | REF | — | REF | — | |
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6-30 min | 2.92 (1.81-4.02)e | 0.29 | 2.83 (1.69-3.98)e | 0.28 | |
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31-60 min | 5.12 (3.79-6.46)e | 0.40 | 5.10 (3.70-6.51)e | 0.40 | |
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≥61 min | 5.89 (4.20-7.58)e | 0.33 | 5.92 (4.19-7.65)e | 0.34 | |
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≥61 min | REF |
|
REF |
|
|
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31-60 min | 2.17 (0.33-4.02)f | 0.15 | 2.15 (0.32-3.99)f | 0.15 | |
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6-30 min | 3.33 (1.76-4.90)e | 0.32 | 3.28 (1.73-4.84)e | 0.31 | |
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≤5 min | 3.24 (1.69-4.80)e | 0.32 | 3.27 (1.73-4.82)e | 0.32 |
aSNS: social networking service.
bAdjusted for sex, age, academic performance.
cREF: reference group.
dNot applicable.
e
f
The hypothesized model showed good fitness indices, with X2/
Standardized regression coefficient (β) of all variables in the hypothesized model. Ovals represent unobserved latent variables. Rectangles represent observed measured variables. Values are standardized path coefficients. **
H1 (SNS use and SNS addiction; β=0.49, 95% CI 0.39-0.58), H2 (SNS addiction and mental health status; β=0.17, 95% CI 0.06-0.27), H4 (psychosocial status and mental health status; β=–0.76, 95% CI –0.88 to –0.64), and H5 (SNS addiction and psychosocial status; β=–0.29, 95% CI –0.42 to –0.16) were supported. In contrast, H3 (SNS use and psychosocial status; β=–0.09, 95% CI –0.24 to 0.06) and H6 (SNS use and mental health status; β=–0.08, 95% CI –0.21 to 0.03) were not supported (
Standardized coefficients of raised hypotheses in the hypothesized model.
Hypothesis | β | BCa 95% CI | |
H1: SNSb use → SNS addiction | 0.49 | 0.39-0.58 | .001 |
H2: SNS addiction → mental health status | 0.17 | 0.06-0.27 | .004 |
H3: SNS use → psychosocial status | –0.09 | –0.24 to 0.06 | .21 |
H4: Psychosocial status → mental health status | –0.76 | –0.88 to –0.64 | .002 |
H5: SNS addiction → psychosocial status | –0.29 | –0.42 to –0.16 | .001 |
H6: SNS use → mental health status | –0.08 | –0.21 to 0.03 | .16 |
aBC: bias-corrected.
bSNS: social networking service.
Three indirect pathways between SNS use and mental health status in SEM were investigated (
Bootstrapping analyses to examine the indirect effect estimates of pathways in the hypothesized model.
Indirect effect | β | Product of coefficients | BCa 95% CI | ||||
|
|
SE | Z |
|
|
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SNSb use → SNS addiction → mental health status | 0.20 | 0.07 | 3.02 | 0.08-0.34 | .003 | ||
SNS use → psychosocial status → mental health status | 0.17 | 0.14 | 1.20 | –0.10 to 0.47 | .21 | ||
SNS use → SNS addiction → psychosocial status → mental health status | 0.26 | 0.08 | 3.40 | 0.14-0.44 | .001 |
aBC: bias-corrected.
bSNS: social networking service.
Total, direct, and indirect effects of mediation analysis in the model.
Effect | β | Product of coefficients | BCa 95% CI | ||
SE | Z |
|
|||
Total | 0.44 | 0.14 | 3.22 | 0.19-0.72 | .002 |
Direct | –0.19 | 0.15 | 1.34 | –0.49 to 0.08 | .17 |
Indirect | 0.63 | 0.14 | 4.52 | 0.37-0.93 | .001 |
aBC: bias-corrected.
This study found that longer and more frequent use of SNSs is significantly associated with SNS addiction. This study also identified that SNS addiction and psychosocial status significantly and positively mediate the relationship between SNS use and mental health status (anxiety and depressive symptoms) in Chinese university students in HK.
SNS addiction significantly differed across the respondents’ demographics in this study. We found that females have a higher level of SNS addiction than males, corresponding with another study from HK [
We also found that SNS addiction is significantly higher among undergraduate students and those with poor academic performance, consistent with previous findings on academic performance [
Although excessive SNS use can cause SNS addiction behaviors, young people are poor at recognizing their own SNS use as excessive. In 1 study that surveyed the effects of Facebook use on the social life and behaviors of 1000 university students in Pakistan, nearly 70% of those who showed addictive Facebook use did not discern themselves as having Facebook addiction [
We found that SNS addition significantly influences mental health status (ie, anxiety and depressive symptoms), while the effect of SNS use patterns on mental health status is not significant. It should be noted that the difference between SNS use and SNS addiction is that the latter reflects more problematic behaviors and can lead to more severe outcomes that are associated with SNSs [
Furthermore, we identified that SNS addiction plays a mediating role in the pathway from SNS use to mental health status. This finding resonates with 1 study that identified that Instagram (an SNS) use predicts depression [
Of note, SNS use in moderation can positively affect users’ mental health status. SNS use can reinforce a user’s online relationships and solidify their offline connections, and this may, in turn, reduce their negative feelings and emotions (eg, anxiety, depression), positively influencing their mental health status [
As an indispensable element of today’s leisure culture, absolute abstinence from accessing SNSs is not an appropriate treatment for SNS addiction and mental distress. Restricting the excessive use of SNSs, addressing the importance of SNS addiction control to improve psychosocial and mental health status and preventing relapse by encouraging self-reflection on SNS use may be possible solutions for designing educational programs. Particularly, during the COVID-19 pandemic, which hinders face-to-face intervention delivery, internet-delivered interventions (eg, internet-delivered cognitive behavioral therapy) can be also considered to address SNS addiction. Future studies on determining the appropriate duration of SNS use with findings transferable to practice guidelines would be needed.
This study had some limitations. Cross-sectional data in this study could not provide the causality between SNS addiction and mental health status. Discussions of the magnitude of the relationship between 2 elements (SNS addiction and mental health status) were inconclusive, with different opinions that this relationship can be bidirectional [
This study provided novel information about the patterns of SNS use and its association with SNS addiction among university students. Findings from SEM also addressed that there is a significant mediating effect of SNS addiction between SNS use and mental health status, including anxiety and depressive symptoms. Further studies are suggested to demonstrate causal relationships with longitudinal data. This study helps to provide preliminary solutions for reducing SNS addiction and mental problems by conducting interventions using cognitive-behavioral approaches.
adjusted beta
bias-corrected
comparative fit index
Diagnostic and Statistical Manual 5th Edition
Hong Kong
International Classification of Diseases 11th Revision
root-mean-square error of approximation
structural equation modeling
social networking service
standardized root-mean-square residual
Tucker-Lewis index
University Grants Committee
The authors would like to thank all students who assisted with the study design and participant recruitment for this study: Mr Kwong Ming Hong Bernard, Mr Lee Tsan Ho, Ms Chan Hue Yuet Kalfanie, Ms To Yi Yan Jenny, Mr Or Ego, Ms Cheung Tsz Ching, Ms Ng Yeuk Tung, Ms Leung Ka Yan, Ms Mak Hoi Yin Tiffany, and Mr Man Anthony Gar-Ta from the University of Hong Kong.
TW and JJL undertook the data analysis. TW drafted the manuscript. JJL conceived the study design. ACYL designed the survey and helped recruit the participants. JYHW, MPW, and SSK supported the study supervision. JJL, JYHW, MPW, and SSK revised the manuscript. All the authors approved the final manuscript.
None declared.