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RESEARCH REPORT

doi:10.1111/add.12713

Craving Facebook? Behavioral addiction to online social networking and its association with emotion regulation deficits Julia M. Hormes1, Brianna Kearns2 & C. Alix Timko2* Department of Psychology, University at Albany, State University of New York, Albany, NY, USA1and Behavioral and Social Sciences Department, University of the Sciences, Philadelphia, PA, USA2

ABSTRACT Aims To assess disordered online social networking use via modified diagnostic criteria for substance dependence, and to examine its association with difficulties with emotion regulation and substance use. Design Cross-sectional survey study targeting undergraduate students. Associations between disordered online social networking use, internet addiction, deficits in emotion regulation and alcohol use problems were examined using univariate and multivariate analyses of covariance. Setting A large University in the Northeastern United States. Participants Undergraduate students (n = 253, 62.8% female, 60.9% white, age mean= 19.68, standard deviation = 2.85), largely representative of the target population. The response rate was 100%. Measurements Disordered online social networking use, determined via modified measures of alcohol abuse and dependence, including DSMIV-TR diagnostic criteria for alcohol dependence, the Penn Alcohol Craving Scale and the Cut-down, Annoyed, Guilt, Eye-opener (CAGE) screen, along with the Young Internet Addiction Test, Alcohol Use Disorders Identification Test, Acceptance and Action Questionnaire-II, White Bear Suppression Inventory and Difficulties in Emotion Regulation Scale. Findings Disordered online social networking use was present in 9.7% [n = 23; 95% confidence interval (5.9, 13.4)] of the sample surveyed, and significantly and positively associated with scores on the Young Internet Addiction Test (P < 0.001), greater difficulties with emotion regulation (P = 0.003) and problem drinking (P = 0.03). Conclusions The use of online social networking sites is potentially addictive. Modified measures of substance abuse and dependence are suitable in assessing disordered online social networking use. Disordered online social networking use seems to arise as part of a cluster of symptoms of poor emotion regulation skills and heightened susceptibility to both substance and non-substance addiction. Keywords Addiction, alcohol, behavioral addiction, diagnosis, disordered online social networking use, emotion regulation, Facebook, internet addiction, non-substance addiction, online social networking. Correspondence to: Julia M. Hormes, Department of Psychology, University at Albany—SUNY, Social Sciences 399, 1400 Washington Ave, Albany, NY 12203, USA. E-mail: [email protected] Submitted 27 October 2013; initial review completed 3 March 2014; final version accepted 14 August 2014

INTRODUCTION Evidence to suggest significant similarities between behavioral addictions, such as pathological gambling, and substance dependence is growing [1,2]. The most recent edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) calls for research to establish with more certainty the extent to which behavioral addictions parallel substance use disorders in terms of symptoms, course and underlying mechanisms [3].

Although not specifically mentioned in the DSM-5, the proposed diagnostic category of ‘internet addiction’ has been the focus of increasing attention for several years [4,5], due to its seemingly high prevalence [6,7] and association with dysfunctional social behaviors [8] and mood and attention deficit disorders [9]. Internet addiction has been likened to other behavioral addictions [10,11], and is thought to share defining features of substance dependence [9,12,13], including excessive use, tolerance, withdrawal and negative repercussions from use [14].

*C. Alix Timko is now in the Department of Child and Adolescent Psychiatry and Behavioral Sciences at the Children’s Hospital of Philadelphia, Philadelphia, PA. © 2014 Society for the Study of Addiction

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It has been suggested that it is not simply use of the internet that is potentially addictive, but rather involvement in specific online activities such as gaming, sexual preoccupations and e-mail/text messaging [4,14]. Diagnostic labels such as ‘social network addiction’ have been proposed to capture problems related to the drastic increase in use of online social networking (OSN) sites in recent years [15–17]. With more than 800 million daily active users, Facebook is among the most popular of these sites,1 and serves as a means for social interaction, expression of identity and building and understanding youth culture [18–20]. The now ubiquitous use of Facebook raises a multitude of new and complex issues. Preliminary evidence indicates that patterns of use of Facebook and similar websites can be excessive or maladaptive [15]. Indeed, OSN has a number of characteristics thought to encourage the development of addiction, including a variable interval schedule of reinforcement from new material posted online and the presence of classically conditioned cues, such as mobile notifications about the availability of new content. Furthermore, physiological arousal and activation of appetitive pathways in response to OSN resemble that observed in other types of behavioral addiction, supporting the potential inclusion of problems related to OSN in the category of non-substance addictions [15,21]. Emotion regulation refers to attempts to alter (primarily negative) emotional experiences via the initiation, maintenance or modification of frequency, intensity or duration of emotional experiences. Difficulties with emotion regulation are believed to be risk factors for addiction [22,23]. For example, experiential avoidance has been hypothesized to act as a core mechanism in the development and maintenance of addictive disorders [24,25], with substance use serving to down-regulate unpleasant emotional states, thus facilitating avoidance. Research indicates that internet use may serve as a way of escaping reality and coping with stress, depression and worry [8,26]. It can thus be hypothesized that use of OSN sites similarly distracts from negative thoughts or feelings and aids in down-regulating feelings of loneliness, sadness or anxiety. This study was designed to systematically examine addiction-like symptoms related to OSN by focusing on the assessment and potential mechanisms underlying maladaptive patterns of use of the OSN website Facebook. Existing assessment tools designed to capture elements of addiction as they relate to OSN have been criticized for not capturing specific aspects of use that may be addictive (e.g. games played using the Facebook platform) [27,28], highlighting the need for more flexibly adaptable tools for quantifying behavioral addictions to various aspects of 1

OSN. Diagnostic criteria used to identify individuals engaging in problematic internet use behavior in general have previously been modeled after DSM diagnostic criteria for substance use and impulse control disorders, such as pathological gambling [5,9,12]. We adopted a comparable approach and modified existing, widely used and well-validated measures of symptoms of alcohol abuse and dependence to capture multiple hypothesized facets of addiction-like problems associated with the use of Facebook, including tolerance (i.e. an increase in use over time), withdrawal (i.e. unpleasant symptoms when unable to access the site) and craving or strong urges to access the site, in a population of frequent users of OSN sites. We also sought to address the relationship between excessive Facebook use and experiential avoidance, thought suppression and impulsivity. As it has been postulated previously that addiction to the internet may share a common underlying etiological framework with other addictions [15,17,29], we furthermore examined the extent of comorbidity of disordered OSN use and other substance (in this case alcohol) use disorders. METHODS The study was approved by the local Institutional Review Board. Participants Participants were undergraduate students at a large Northeastern University who received course credit in exchange for their completion of the 1-hour study questionnaire. Inclusion criteria included age 18 years or older and fluency in English. Given the number of predictors in the planned analyses and assuming α = 0.05 and a medium effect size, a sample size of n = 128 was projected to yield more than 80% power; a targeted sample size of 250 was chosen in an attempt to recruit a sufficient number of respondents with disordered OSN use. Measures Respondents completed a set of measures of alcohol craving and problem drinking that were modified to capture symptoms related to behavioral addiction to OSN. Psychometric properties of the modified measures had been evaluated in a pilot study and found to be satisfactory (see Supporting information for details). Modified DSM-IV-TR criteria for alcohol dependence The seven DSM-IV-TR criteria for alcohol dependence were adapted to assess dependence-like symptoms related to the use of Facebook (e.g. increasing amounts of time

Information provided by Facebook at http://newsroom.fb.com/Key-Facts, accessed on 19 April 2013.

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spent on Facebook, including at the expense of spending time with friends and family or engaged in other activities, irritability or anger when unable to access the website, failed efforts to cut back on the amount of time spent on the website; Cronbach’s α = 0.72), in a manner comparable to previous studies assessing addiction to indoor tanning [30,31], food [32] and binge eating [33]. Modified Penn Alcohol Craving Scale (PACS-FB) The Penn Alcohol Craving Scale (PACS) is a five-item selfreport measure that assesses frequency, intensity, duration and overall strength of craving for alcohol, and the ability to resist drinking [34]. It was modified (PACS-FB, Cronbach’s α = 0.88) to capture strong urges to log on to Facebook (e.g. ‘How often have you thought about drinking or about how good a drink would make you feel’ reworded as ‘During the past week how often have you thought about Facebook or how good checking Facebook would make you feel?’). The PACS has previously been modified to quantify cravings for a range of substances [35] and gambling [36]. Respondents also completed the PACS in its original form (Cronbach’s α = 0.90) to compare levels of craving for Facebook to the intensity of urges for alcohol and examine the association between OSN addiction and symptoms of substance dependence. Modified Cut-down, Annoyed, Guilt, Eye-opener (CAGE) (CAGE-FB) The CAGE is a brief screening instrument used to identify potential problem drinkers [37,38], assessing desire to cut down on alcohol use (C), being annoyed by people criticizing alcohol consumption (A), feeling guilty about alcohol use (G) and needing a drink first thing in the morning (‘eye opener’, E). The measure was re-worded (CAGE-FB) to capture problems related to excessive use of the social networking site Facebook (e.g. ‘Have you ever felt you should cut down on your drinking’ modified to say ‘Have you ever felt you should cut down on the amount of time spent on Facebook?’). The CAGE has previously been modified in a comparable manner, most notably in studies assessing problematic indoor tanning behavior [30,39,40]. A threshold score of two positive responses was used to identify individuals engaged in potentially problematic behaviors related to OSN. The relatively poor internal consistency reliability of the CAGE-FB (Cronbach’s α = 0.68) is consistent with previous research demonstrating weaker psychometrics of the CAGE in the general population, compared to other screening tools for problem drinking [41,42].

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modified DSM-IV-TR diagnostic criteria (one of which had to be reports of either impairment of distress), a cut-off chosen to parallel the diagnosis of alcohol dependence. Participants categorized as engaging in disordered OSN use also had to report elevated levels of craving for social networking use, defined as PACS-FB scores at or above the 75th percentile. We added craving as a diagnostic criterion based on its inclusion as a criterion for the diagnosis of alcohol use disorders in the DSM-5. The cutoff at or above the 75th percentile was equivalent to a score of 11 or higher in the present sample, which is comparable to the level of craving typically observed in alcohol-dependent individuals not currently receiving treatment [34]. Of note, the proposed criteria are consistent with recommendations suggesting that optimal diagnostic criteria for substance use disorders should yield prevalence rates between 5 and 20% [3,32]. In addition to the modified (i.e. Facebook-specific) assessments of abuse, dependence and craving, respondents also completed the following widely used and wellvalidated measures. Young Internet Addiction Test (YIAT) The YIAT is a 20-item measure of symptoms related to excessive internet use, assessing salience and anticipation of use, excessive use and lack of control over use and neglect of work and social life [12,43] (Cronbach’s α = 0.93). Alcohol Use Disorders Identification Test (AUDIT) The AUDIT is a 10-item self-report measure designed to identify hazardous or harmful alcohol consumption [44] (Cronbach’s α = 0.84). A score of eight or more (range: 0–40) is generally considered indicative of the presence of problem drinking [45]. Acceptance and Action Questionnaire—II (AAQ-II) The AAQ-II is a 10-item self-report measure that quantifies experiential avoidance or psychological inflexibility [46], for example in relation to substance use [47,48] (Cronbach’s α = 0.83). Higher scores on the AAQ-II indicate greater psychological flexibility. White Bear Suppression Inventory (WBSI) The WBSI is a 15-item measure designed to quantify thought suppression [49], which has been found to be positively related to an increase in urges to use substances such as nicotine [50] and alcohol [51] (Cronbach’s α = 0.94).

Disordered online social networking use

Difficulties in Emotion Regulation Scale (DERS)

The presence of ‘disordered OSN use’ was determined based on respondents’ endorsement of three or more

The DERS is a 36-item, six-factor self-report measure of difficulties in emotion regulation, assessing (1) lack of

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Table 1 Gender, age, and race/ethnicity in respondents with and without a current Facebook account. Current Facebook No current Facebook account (n = 253) account (n = 13) mean (SD)/% (n) mean (SD)/% (n) Statistic Gender (% female) 62.8 (159) Age 19.68 (2.85) Race/ethnicity African American 16.6 (42) White 60.9 (154) Asian 15.4 (39) American Indian/Alaskan Native 3.2 (8) Native Hawaiian/Pacific Islander 0.8 (2) Hispanic/Latino 11.9 (30)

84.6 (11) 20.54 (3.99) 15.4 (2) 53.8 (7) – – – 38.5 (5)

χ2 = 2.54, P = 0.14, ϕ = −0.10 t(256) = 1.04, P = 0.30, d = 0.25, 95% CI (−2.49, 0.77) χ2 = 0.91, P = 1.00, ϕ = −0.01 χ2 = 0.26, P = 0.77, ϕ = −0.03 χ2 = 2.35, P = 0.23, ϕ = −0.09 χ2 = 0.42, P = 1.00, ϕ = −0.04 χ2 = 0.10, P = 1.00, ϕ = −0.02 χ2 = 7.66, P = 0.02, ϕ = 0.17

CI = confidence interval; SD = standard deviation.

awareness of emotional responses; (2) lack of clarity of emotional responses; (3) non-acceptance of emotional responses; (4) limited access to emotion regulation strategies perceived as effective; (5) difficulties controlling impulses when experiencing negative emotions; and (6) difficulties engaging in goal-directed behaviors when experiencing negative emotions [52] (Cronbach’s α = 0.93). The DERS has been used in previous studies to examine the association between emotion regulation and alcohol [53], cocaine [54,55] and other substance use [56,57]. Statistical analyses Independent-samples t-tests and χ2 tests were used to compare demographic groups and individuals with and without disordered OSN use. Associations between disordered OSN use and scores on the YIAT and measures of emotion regulation and alcohol use were explored via a series of univariate analyses of covariance (ancovas) and multivariate analyses of co-variance (mancova, for the DERS, given that it contains multiple factors). Gender and race (‘white’ versus ‘non-white’) were included as covariates in all analyses but subsequently excluded if found to be non-significant. Scores on the modified PACS-FB were compared to scores on the original (i.e. alcohol) version of the measure using a paired-samples t-test. The utility of the modified CAGE-FB screen in identifying individuals meeting the proposed criteria for disordered OSN use was examined via analyses of sensitivity, specificity and positive and negative predictive values.

RESULTS A total of 269 participants completed the survey, with a response rate of 100%. Those without a current Facebook profile (5.9%, n = 16) were excluded from the analyses, resulting in a final sample of 253 respondents. © 2014 Society for the Study of Addiction

Study participants were largely representative of the gender and racial/ethnic make-up of the target population of undergraduate students in the United States. They did not differ from respondents excluded from the analyses in terms of basic demographics, with the exception of a significantly higher proportion of respondents without a current Facebook profile identifying as Hispanic or Latino (Table 1). Use of online social networking sites Almost all respondents had regular access to the internet at home (99.2%, n = 251) and/or via a ‘smart’ mobile phone (93.3%, n = 236). Mean scores on the YIAT suggested overall non-problematic internet use, with a majority of respondents describing ‘average’ patterns of use (Table 2). Most respondents had maintained a Facebook account for at least 2 years (88.9%, n = 225). Many indicated receiving Facebook notifications on their mobile phone (66.9%, n = 168). Participants reportedly spent about one-third of their time online browsing Facebook on the previous day (Table 2). Disordered online social networking use Mean scores on the modified PACS-FB suggested on average relatively high levels of craving for Facebook (Table 2), especially when compared to craving for alcohol in the same respondents, assessed using the original PACS [mean = 4.78, standard deviation (SD) =4.68; t(237) = 8.00, P < 0.001, d = 0.64, 95% confidence interval (CI) = 2.43, 4.02)]. Participants endorsed an average of two modified DSM dependence criteria (Table 2); the most commonly reported symptoms were spending a substantial amount of time (36.3%, n = 91) and more time than intended on Facebook (62.9%, n = 158). More than one-third of respondents endorsed three or more modified DSM criteria (35.1%, n = 84). Women reported Addiction, 109, 2079–2088

Male (n = 94) mean (SD)/% (n) Female (n = 159) mean (SD)/% (n) Statistic (gender)

White (n = 154) mean (SD /% (n)

© 2014 Society for the Study of Addiction

CAGE = Cut-down, Annoyed, Guilt, Eye-opener; CI = confidence interval; SD = standard deviation.

t(248) = 1.61, P = 0.11, d = 0.22, 95% CI (−7.14, 71.74) t(239) = 0.39, P = 0.67, d = 0.05, 95% CI (−13.52, 20.11) t(237) = 0.02, P = 0.99, d = 0.01, 95% CI (−0.48, 0.49) t(249) = 0.99, P = 0.33, d = 0.13, 95% CI (−2.16, 0.72) t(242) = 1.87, P = 0.06, d = 0.25, 95% CI (−0.64, 0.02) t(228) = 2.12, P = 0.04, d = 0.29, 95% CI (0.28, 7.79) χ2 = 3.97, P = 0.14, ϕ = 0.13

Non-white (n = 99) mean (SD)/% (n) Statistic (race)

Time spent on the internet 148.83 (141.84) 150.72 (159.37) 147.71 (130.88) t(248) = 0.16, P = 0.87, d = 0.02, 136.29 (116.50) 168.60 (173.34) 95% CI (−39.64, 33.61) yesterday (in minutes) 48.06 (73.50) Time spent on Facebook 46.05 (58.52) 38.71 (58.95) 50.36 (58.03) t(239) = 1.50, P = 0.14, d = 0.20, 44.77 (46.73) 95% CI (−3.70, 27.00) yesterday (in minutes) 1.97 (1.71) 1.98 (1.93) Modified DSM diagnostic criteria 1.97 (1.79) 1.61 (1.21) 2.19 (1.74) t(237) = 2.41, P = 0.02, d = 0.32, 95% CI (0.10, 1.04) 8.32 (5.64) 7.60 (5.61) Penn Alcohol Craving Scale-FB 8.04 (5.63) 6.74 (5.08) 8.82 (5.81) t(249) = 2.86, P = 0.01, d = 0.38, 95% CI (0.64, 3.50) 1.55 (1.27) 1.24 (1.26) CAGE-FB 1.43 (1.28) 1.16 (1.21) 1.59 (1.29) t(242) = 2.57, P = 0.01, d = 0.30, 95% CI (0.10, 0.76) 29.60 (14.99) Young Internet Addiction Test 27.17 (13.75) 26.00 (13.98) 27.84 (13.61) t(228) = 0.98, P = 0.33, d = 0.13, 25.57 (12.67) 95% CI (−1.87, 5.55) – – Significant problems – – – χ2 = 1.63, P = 0.44, ϕ = 0.08 related to internet use (YIAT scores ≥ 80) Occasional or frequent problems 7.8 (18) 7.1 (6) 8.2 (12) 5.0 (7) 12.1 (11) related to internet use (YIAT scores 60–79) Average patterns of internet use 66.1 (152) 61.9 (52) 68.5 (100) 69.1 (96) 61.5 (56) (YIAT scores 20–49) Below average patterns of 26.1 (60) 31.0 (26) 23.3 (34) 25.9 (36) 26.4 (24) internet use (YIAT scores 0–19)

Total (n = 253) mean (SD)/% (n)

Table 2 Time spent on the internet and on Facebook on the previous day, modified diagnostic criteria for disordered online social networking use, and scores on the modified CAGE screen and Young Internet Addiction Test (YIAT) in the combined sample and in male versus female and white versus non-white respondents with a current Facebook profile.

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significantly greater overall craving for Facebook, and endorsed significantly more modified DSM criteria and items on the modified CAGE screen, compared to men (Table 2). Applying the diagnostic criteria described above, 9.7% (n = 23; 95% CI = 5.9, 13.4) respondents met criteria for disordered OSN use, with no significant differences by gender and significantly higher rates of disordered OSN use among non-white respondents (15.7%, n = 14 versus 6.0%, n = 9, χ2 = 5.99, P = 0.01, Φ = 0.16). Those meeting diagnostic criteria spent significantly more time (in minutes) on the internet and specifically on Facebook the previous day, compared to those not endorsing diagnostic criteria (Table 3). They also scored significantly higher on the YIAT, assessing general internet addiction (Table 3). While individuals meeting criteria for disordered OSN use were about as likely as those who did not to report ‘average’ patterns of internet use on the YIAT, they were also more likely to experience ‘occasional or frequent problems’ related to their internet use (Table 3). Respondents reporting disordered OSN use were significantly more likely to score two or more on the modified CAGE-FB screen (Table 3), most commonly endorsing a feeling of needing to cut down on time spent on Facebook and a habit of checking Facebook first thing in the morning (both 78.3%, n = 18). The modified CAGE-FB screen had a specificity of 58.4%, sensitivity of 77.3%, positive predictive value of 16.4% and negative predictive value of 96.1% in the present sample. Disordered online social networking use and emotion regulation There was a significant multivariate main effect of the presence of disordered OSN use on scores on the DERS (F(6,196) = 3.39, Wilk’s λ = 0.91, P = 0.003, ηp2=0.09; gender included as a significant covariate), with respondents meeting diagnostic criteria scoring significantly higher on four of the six DERS subscales (applying a Bonferroni-corrected significance level of P = 0.01; see Table 3 for descriptive and univariate statistics). Those reporting disordered OSN use also scored significantly lower on the AAQ-II, indicating more experiential avoidance (Table 3). There were no statistically significant between-group differences in scores on the WBSI, measuring tendencies to suppress thoughts (Table 3). Disordered online social networking use and substance use Respondents endorsing the proposed criteria for disordered OSN use scored significantly higher on the AUDIT (Table 3), with gender and race as a significant covariates and significantly higher AUDIT scores in men, compared to women (mean = 10.25, SD = 6.68 versus © 2014 Society for the Study of Addiction

mean = 7.88, SD=5.96; t(231) = 2.73, P = 0.01, d = 0.37, 95% CI = −4.08, −0.66) and in whites, compared to non-white respondents (mean = 10.39, SD = 6.53 versus mean = 6.13, SD = 4.99; t(231) = 5.61, P < 0.001, d = 0.37, 95% CI = −5.76, −2.76). Respondents meeting criteria for disordered OSN use were significantly more likely to obtain AUDIT scores of eight or greater, placing them above the cut-off for hazardous drinking (Table 3). Disordered OSN use was associated with significantly higher scores on the PACS, suggesting higher levels of craving for alcohol, compared to the group of respondents not endorsing the proposed criteria for disordered OSN use (Table 3).

DISCUSSION This study examined the utility of modified criteria for substance dependence in the diagnosis of behavioral addiction to OSN use. The proposed diagnostic criteria include evidence of tolerance (i.e. an increase in time spent browsing the site), withdrawal (i.e. irritability when unable to access the site) and craving (i.e. strong urges to access the site). The prevalence rates in the present sample are comparable to the incidence of both substance use and eating disorders in the general population. However, the present study focused on a very specific demographic group (i.e. college students with regular access to the internet, completing an online survey), which significantly limits the generalizability of these prevalence estimates to the general population. Future research should focus on applying the proposed diagnostic criteria to more diverse populations to derive accurate estimates of prevalence of disordered OSN use. The association between disordered OSN use and scores on the YIAT was statistically significant, but with a small to medium effect size, suggesting that general internet addiction and problems specifically related to excessive use of OSN sites are overlapping, but not synonymous. This is consistent with the assumption that individuals develop addictions to specific online applications, such as Facebook, rather than to the internet in general. Behavioral addiction to Facebook was significantly and positively associated with a positive CAGE-FB screen; however, the relatively low specificity and positive predictive value of the modified measure suggest that more work is needed to develop suitable rapid screening tools for potential problems related to OSN. Respondents who described maladaptive patterns of use of OSN sites endorsed significantly more problems related to emotion regulation, including more experiential avoidance, lack of acceptance of emotional responses, limited access to emotion regulation strategies, poor impulse control and difficulties engaging in goal-directed behaviors, suggesting that Facebook usage Addiction, 109, 2079–2088

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Table 3 Internet use, problem drinking and emotion regulation in individuals with and without disordered online social networking (OSN) usec. Disordered OSN usec, mean (SD)/% (n)

No disordered OSN use, mean (SD)/% (n)

n Time spent on the internet yesterday (in minutes) Time spent on Facebook yesterday (in minutes) Young Internet Addiction Test (YIAT)

23 223.32 (241.55)

217 143.48 (129.10)

75.86 (64.61)

44.62 (58.56)

45.00 (16.39)

25.72 (12.32)

Significant problems related to internet use (YIAT scores ≥ 80) Occasional or frequent problems related to internet use (YIAT scores 60–79) Average patterns of internet use (YIAT scores 20–49) Below average patterns of internet use (YIAT scores 0–19) Positive CAGE-FB Screen (2 + items endorsed) Penn Alcohol Craving Scale (PACS) (original) Alcohol Use Disorders Identification Test (AUDIT) AUDIT score ≥ 8 Acceptance and Action Questionnaire—II White Bear Suppression Inventory

–

– 31.3 (5)

5.5 (11)

68.8 (11)

66.7 (134)

–

Statistic

F(1,233) = 6.22, P = 0.01, ηp2 = 0.03, 95% CI (−142.89, −16.78) F(1,225) = 5.54, P = 0.02, ηp2 = 0.02, 95% CI (−57.39, −5.09) F(1,215) = 34.48, P < 0.001, ηp2 = 0.14, 95% CI (−25.76, −12.81) χ2 = 17.82, P < 0.001, ϕ = 0.29

27.9 (56) 77.3 (17)

41.6 (87)

χ2 = 10.22, P = 0.003, ϕ = 0.21

6.57 (5.08)

4.58 (4.41)

11.76 (7.62)

8.70 (6.07)

71.4 (15) 41.23 (9.36)

46.7 (92) 50.91 (9.62)

51.52 (11.93)

47.41 (13.13)

F(1,221) = 6.51, P = 0.01, ηp2 = 0.03a,b, 95% CI (−4.51, −0.58) F(1,214) = 9.52, P = 0.002, ηp2 = 0.04a,b, 95% CI (−6.72, −1.48) χ2 = 4.64, P = 0.03, ϕ = 0.15 F(1,226) = 20.49, P < 0.001, ηp2 = 0.08a, 95% CI (5.45, 13.86) F(1,225) = 2.06, P = 0.16, ηp2 = 0.01, 95% CI (−9.75, 1.54)

2.74 (0.63) 2.35 (0.76)

2.29 (0.59) 2.73 (0.87)

2.61 (0.75)

2.36 (0.76)

Non-acceptance of emotional responses Limited access to emotion regulation strategies Difficulties controlling impulses

2.64 (0.88)

2.08 (0.93)

2.78 (0.94)

2.06 (0.80)

2.60 (0.94)

1.91 (0.77)

Difficulties engaging in goal-directed behaviors

3.56 (0.82)

2.78 (0.93)

Difficulties in Emotion Regulation Scale Total score Lack of awareness of emotional responses Lack of clarity of emotional responses

F(1,201) = 3.14, P = 0.08, ηp2 = 0.02, 95% CI (−0.04, 0.77) F(1,201) = 1.63, P = 0.20, ηp2 = 0.01, 95% CI (−0.59, 0.13) F(1,201) = 6.16, P = 0.01, ηp2 = 0.03, 95% CI (−0.99, −0.11) F(1,201) = 13.32, P < 0.001, ηp2 = 0.06, 95% CI (−1.10, −0.33) F(1,201) = 13.24, P < 0.001, ηp2 = 0.06, 95% CI (−1.07, −0.32) F(1,201) = 11.84, P = 0.001, ηp2 = 0.06, 95% CI (−1.18, −0.32)

a Gender included as a significant (P < 0.05) covariate. bRace included as a significant (P < 0.05) covariate. cDisordered OSN use was defined based on endorsement of three or more modified DSM diagnostic criteria, along with a PACS-FB score at or above the 75th percentile. CI = confidence interval; CAGE = Cut-down, Annoyed, Guilt, Eye-opener.

may be maintained via negative reinforcement. Effect sizes were in the small to medium range. Future research should examine the association between disordered OSN use and other emotional problems, including anxiety and depression, as well as explore more directly the emotional regulatory function of social networking use, for example by using ecological momentary assessment. © 2014 Society for the Study of Addiction

Our finding of a significant positive relationship between symptoms of disordered OSN use and problem drinking is consistent with previous research [29], and suggests that the presence of disordered OSN use may complicate the treatment of substance addictions and vice versa. For example, Facebook usage may be increased when individuals are intoxicated (akin to the Addiction, 109, 2079–2088

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practice of ‘drunk dialing’); similarly, Facebook may reinforce perceived social norms about practices such as underage or binge drinking. The exact manner in which the two types of disorders interact should be examined further. There are a number of limitations to the research presented here. Measures were administered via an online survey, raising the possibility of self-selection of a high-risk group of frequent internet users. The crosssectional nature of the present research does not allow for an assessment of the direction of association between the variables measured, for example in the relationship between disordered OSN use and other substance use or deficits in emotion regulation skills. The possibility that emotion regulation deficits are not merely a risk factor for disordered OSN use, but perhaps a result of excess use of social networking sites at the expense of face-to-face modeling of appropriate regulation of affect, should be explored in future research. More work is also needed to carefully evaluate the extent to which diagnostic criteria for substance dependence translate into the domain of OSN. For example, it can be argued that needing an alcoholic drink first thing in the morning is potentially much more detrimental than the need to check Facebook first thing in the morning in terms of a person’s ability to function in everyday life. Conversely, it is also possible that individuals addicted to OSN are impaired in ways that have not previously been assessed, for example by engaging in risky behaviors such as checking Facebook on mobile devices while driving. In spite of these limitations, the present research has a number of important implications. Our findings highlight the importance of continued research in the area of disordered OSN use, which may also provide a welcome opportunity to examine correlates and mechanisms underlying addictive behaviors without the confounds of ingested substances [1]. In thinking about interventions, our increasing dependence on computers and smartphones for work and social life makes complete abstinence from the internet unlikely. It may thus be useful to borrow techniques from the treatment of eating disorders which focus on ways to normalize, rather than eliminate, problematic behavior. Findings suggest that interventions targeting emotion regulation skills may be another useful approach in helping those suffering from disordered OSN use. Taken together, our findings support the recent push towards the inclusion of disordered OSN use as a type of behavioral addiction or further subcategory of internet addiction in future diagnostic classification systems. Furthermore, the diagnostic criteria presented here may serve as a framework for the flexible assessment of similar behavioral addictions involving the internet and other activities. © 2014 Society for the Study of Addiction

Declaration of interests None. Acknowledgements The authors thank Augusta Bargeron, Kelly Cuccolo, Michaella Eiss, Alana Santodonato and Priscilla Paulino for their help in conducting this study. References 1. Potenza M. N. Non-substance and substance addictions. Addiction 2009; 104: 1016–7. 2. Potenza M. N. Should addictive disorders include nonsubstance-related conditions? Addiction 2006; 101: 142– 51. 3. APA. Diagnostic and Statistical Manual of Mental Disorders, 5th edn. Washington, DC: American Psychiatric Publishing; 2013. 4. Chou C., Condron L., Belland J. C. A review of the research on internet addiction. Educ Psychol Rev 2005; 17: 363–88. 5. Byun S., Ruffini C., Mills J. E., Douglas A. C., Niang M., Stepchenkova S. et al. Internet addiction: metasynthesis of 1996–2006 quantitative research. Cyberpsychol Behav 2009; 12: 203–7. 6. Aboujaoude E., Koran L. M., Gamel N., Large M. D., Serpe R. T. Potential markers for problematic internet use: a telephone survey of 2,513 adults. CNS Spectr 2006; 11: 750–5. 7. Block J. J. Prevalence underestimated in problematic internet use study [Letter]. CNS Spectr 2007; 12: 14. 8. Whang L. S. M., Lee S. Internet over-users’ psychological profiles: a behavior sampling analysis on internet addiction. Cyberpsychol Behav 2003; 6: 143–50. 9. Aboujaoude E. Problematic internet use: an overview. World Psychiatry 2010; 9: 85–90. 10. Beard K. W., Wolf E. M. Modification in the proposed diagnostic criteria for internet addiction. Cyberpsychol Behav 2001; 4: 377–83. 11. Beard K. W. Internet addiction: a review of current techniques and potential assessment questions. Cyberpsychol Behav 2005; 8: 7–14. 12. Young K. S. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav 1998; 1: 237–44. 13. Young K. S. Internet addiction: a new clinical phenomenon and its consequences. Am Behav Sci 2004; 48: 402–15. 14. Block J. J. Issues for DSM-V: internet addiction. Am J Psychiatry 2008; 165: 306–7. 15. Kuss D. J., Griffiths M. D. Online social networking and addiction. A review of the psychological literature. Int J Environ Res Public Health 2011; 8: 3528–52. 16. Karaiskos D., Tzavellas E., Balta G., Paparrigopoulos T. Social network addiction: a new clinical disorder? Eur Psychiatry 2010; 25: 855. 17. Young K. Internet addiction: evaluation and treatment. Stud BMJ 1999; 7: 351–2. 18. Pempek T. A., Yermolayeva Y. A., Calvert S. L. College students’ social networking experiences on Facebook. J Appl Dev Psychol 2009; 30: 227–38. 19. Boyd D. Why youth (heart) social network sites: the role of networked publics in teenage social life. In: Buckingham D., editor. Youth, Identity, and Digital Media. Cambridge, MA: The MIT Press; 2008, pp. 119–42. Addiction, 109, 2079–2088

Online social networking addiction

20. Raacke J., Bonds-Raake J. MySpace and Facebook: applying the Uses and Gratification Theory to exploring friendnetworking sites. Cyberpsychol Behav 2008; 11: 169–74. 21. Wise K., Alhabash S., Park H. Emotional responses during social information seeking on Facebook. Cyberpsychol Behav Soc Netw 2010; 13: 555–62. 22. Hayes S. C., Strosahl K. D., Wilson K. G., Bissett R. T., Pistorello J., Toarmino D. et al. Measuring experiential avoidance: a preliminary test of a working model. Psychol Rec 2004; 54: 553–78. 23. Aldao A., Nolen-Hoeksema S., Schweizer S. Emotionregulation strategies across psychopathology: a metaanalytic review. Clin Psychol Rev 2010; 30: 217–37. 24. Forsyth J. P., Parker J. D., Finlay C. G. Anxiety sensitivity, controllability, and experiential avoidance and their relation to drug of choice and addiction severity in a residential sample of substance-abusing veterans. Addict Behav 2003; 28: 851–70. 25. Kashdan T. B., Barrios V., Forsyth J. P., Steger M. F. Experiential avoidance as a generalized psychological vulnerability: comparisons with coping and emotion regulation strategies. Behav Res Ther 2006; 44: 1301–20. 26. Charles K. ‘Facebook anxiety’ highlighted by study. Therapy Today [serial on the internet]. 2011; 22(2). Available at: http://www.therapytoday.net/article/show/2348 (accessed 19 September 2014) (Archived at http:// www.webcitation.org/6Si5pXsbz on 19 September 2014). 27. Andreassen C. S., Torsheim T., Brunborg G. S., Pallesen S. Development of a Facebook addiction scale. Psychol Rep 2012; 110: 501–17. 28. Griffiths M. D. Facebook addiction: concerns, criticism, and recommendations—a response to Andreassen and colleagues. Psychol Rep 2012; 110: 518–20. 29. Yen J. Y., Ko C. H., Yen C. F., Chen C. S., Chen C. C. The association between harmful alcohol use and internet addiction among college students: comparison of personality. Psychiatry Clin Neurosci 2009; 63: 218–24. 30. Mosher C. E., Danoff-Burg S. Addiction to indoor tanning. Arch Dermatol 2010; 146: 412–7. 31. Zeller S., Lazovich D., Forster J., Widome R. Do adolescent indoor tanners exhibit dependency? J Am Acad Dermatol 2006; 54: 589–96. 32. Gearhardt A. N., Corbin W. R., Brownell K. D. Preliminary validation of the Yale Food Addiction Scale. Appetite 2009; 52: 430–6. 33. Cassin S. E., von Ranson K. M. Is binge eating experienced as an addiction? Appetite 2007; 49: 687–90. 34. Flannery B. A., Volpicelli J. R., Pettinati H. M. Psychometric properties of the Penn Alcohol Craving Scale. Alcohol Clin Exp Res 1999; 23: 1289–95. 35. Chang B. H., Sommers E., Herz L. Acupuncture and relaxation response for substance use disorder recovery. J Subst Use 2010; 15: 390–401. 36. Tavares H., Zilberman M. L., Hodgins D. C., el-Guebaly N. Comparison of craving between pathological gamblers and alcoholics. Alcohol Clin Exp Res 2005; 29: 1427–31. 37. Ewing J. A. Detecting alcoholism: the CAGE questionnaire. JAMA 1984; 252: 1905–7. 38. Maisto S. A., Connors G. J., Allen J. P. Contrasting self-report screens for alcohol problems: a review. Alcohol Clin Exp Res 1995; 19: 1510–6. 39. Poorsattar S. P., Hornung R. L. UV light abuse and high-risk tanning behavior among undergraduate college students. J Am Acad Dermatol 2007; 56: 375–9. © 2014 Society for the Study of Addiction

2087

40. Warthan M. W., Uchida T., Wagner R. F. UV light tanning as a type of substance-related disorder. Arch Dermatol 2005; 141: 963–6. 41. Hays R. D., Merz J. F., Nicholas R. Response burden, reliability, and validity of the CAGE, Short MAST, and AUDIT alcohol screening measures. Behav Res Methods Instrum Comput 1995; 27: 277–80. 42. Bisson J., Nadeau L., Demers A. The validity of the CAGE scale to screen for heavy drinking and drinking problems in a general population survey. Addiction 1999; 94: 715– 22. 43. Widyanto L., McMurran M. The psychometric properties of the Internet Addiction Test. Cyberpsychology 2004; 7: 443– 50. 44. Saunders J. B., Aasland O. G., Babor T. F., De la Fuente J. R., Grant M. Development of the Alcohol Use Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption–II. Addiction 1993; 88: 791–804. 45. MacKenzie D. M., Langa A., Brown T. M. Identifying hazardous or harmful alcohol use in medical admission: a comparison of AUDIT, CAGE and Brief MAST. Alcohol Alcohol 1996; 31: 591–9. 46. Bond F. W., Hayes S. C., Baer R. A., Carpenter K. M., Guenole N., Orcutt H. K. et al. Preliminary psychometric properties of the Acceptance and Action Questionnaire-II: a revised measure of psychological inflexibility and experiential avoidance. Behav Ther 2011; 42: 676–88. 47. Levin M. E., Lillis J., Seeley J., Hayes S. C., Pistorello J., Biglan A. Exploring the relationship between experiential avoidance, alcohol use disorders, and alcohol-related problems among first-year college students. J Am Coll Health 2012; 60: 443–8. 48. Stewart S. H., Zvolensky M. J., Eifert G. H. The relations of anxiety sensitivity, experiential avoidance, and alexithymic coping to young adults’ motivations for drinking. Behav Modif 2002; 26: 274–96. 49. Wegner D. M., Zanakos S. Chronic thought suppression. J Pers 1994; 62: 615–40. 50. Toll B. A., Sobell M. B., Wagner E. F., Sobell L. The relationship between thought suppression and smoking cessation. Addict Behav 2001; 26: 509–15. 51. Palfai T. P., Monti P. M., Colby S. M., Rohsenow D. J. Effects of suppressing the urge to drink on the accessibility of alcohol outcome expectancies. Behav Res Ther 1997; 35: 59–65. 52. Gratz K. L., Roemer E. Multidimensional assessment of emotion regulation and dysregulation: development, factor structure and initial validation of the Difficulties in Emotion Regulation Scale. J Psychopathol Behav Assess 2004; 29: 231–40. 53. Fox H. C., Hong K. A., Sinha R. Difficulties in emotion regulation and impulse control in recently abstinent alcoholics compared with social drinkers. Addict Behav 2008; 33: 388–94. 54. Fox H. C., Axelrod S. R., Paliwal P., Sleeper J., Sinha R. Difficulties in emotion regulation and impulse control during cocaine abstinence. Drug Alcohol Depend 2007; 89: 298–301. 55. McDermott M. J., Tull M. T., Gratz K. L., Daughters S. B., Lejuez C. W. The role of anxiety sensitivity and difficulties in emotion regulation in posttraumatic stress disorder among crack/cocaine dependent patients in residential substance abuse treatment. J Anxiety Disord 2009; 23: 591–9. Addiction, 109, 2079–2088

2088

Julia M. Hormes et al.

56. Axelrod S. R., Perepletchikova F., Holtzman K., Sinha R. Emotion regulation and substance use frequency in women with substance dependence and borderline personality disorder receiving Dialectic Behavior Therapy. Am J Drug Alcohol Abuse 2011; 37: 37–42.

© 2014 Society for the Study of Addiction

57. Gratz K. L., Tull M. T. The relationship between emotion dysregulation and deliberate self-harm among inpatients with substance use disorders. Cognit Ther Res 2010; 34: 544–53.

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