Journal Pre-proof Internet addiction and Facebook addiction in Spanish women with eating disorders Isabel Panea-Pizarro, Fidel López-Espuela, Almudena MartosSánchez, Ana Teresa Domínguez-Martín, Luis Beato-Fernández, José María Moran-García PII: S0883-9417(20)30217-X DOI: https://doi.org/10.1016/j.apnu.2020.07.023 Reference: YAPNU 51304 To appear in: Archives of Psychiatric Nursing Please cite this article as: I. Panea-Pizarro, F. López-Espuela, A. Martos-Sánchez, et al., Internet addiction and Facebook addiction in Spanish women with eating disorders, Archives of Psychiatric Nursing (2020), https://doi.org/10.1016/j.apnu.2020.07.023 This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier. Journal Pre-proof Title page Title: Internet addiction and Facebook addiction in Spanish women with eating disorders. Authors: ro -p na lP Almudena Martos-Sánchez 1 Nurse Research. RN. Mail: amartoss@sescam.jccm.es re Fidel López-Espuela *2 PhD. Bachelor Psychologist. RN Mail: fidel.lopez.es@gmail.com of Isabel Panea-Pizarro *1 Nurse Research. RN. Mail: isabelpanea.pizarro@hotmail.com Jo ur Ana Teresa Domínguez-Martín 3, Nurse Research. RN. Mail: anat_dm@hotmail.com Luis Beato-Fernández 1 MD. PhD. Mail: lbeato@sescam.jccm.es José María Moran-García 2 PhD. Mail: jmmorang@unex.es * 1 Equal contribution Mental Health Department, Hospital General Universitario, Ciudad Real, Castilla la Mancha, Spain. 2 Nursing Department, Nursing and Occupational Therapy College, University of Extremadura, Caceres, Caceres, Spain Journal Pre-proof 3 Nursing Department, Complejo Hospitalario Universitario de Cáceres, Cáceres, Cáceres, Spain Corresponding Author: López-Espuela, Fidel Avenida de la Universidad S/N. Cáceres, Cáceres, CP: 10003, Spain Email address: fidellopez@unex.es of Acknowledgements To all the participating women, without them the research would not have been possible. -p ro To Juan, Pilar, Irene, Jaime and Elena. re Author Contributions: Conceptualization: IPP, FLE, LBF, ATDM; Data curation: IPP, AMS; Methodology: AMS; FLE; Formal Analysis: JMMG; AMS; Writing- Original na Jo ur manuscript. lP Draft Preparation: JMMG, FLE, LBF, IPP, ATDM ; All authors revised and edited the Journal Pre-proof Internet Addiction and Facebook Addiction in Spanish Women with Eating Disorders. Abstract: Aim: We aim to investigate the association between the presence of eating disorders and both Internet addiction (IA) and Facebook addiction (FA)in women suffering from eating disorders. Methods: A total of 124 women completed three instruments: the Internet Addiction Test (IAT), the Bergen Facebook Addiction Scale (BFAS) and a sociodemographic questionnaire. Results: The proportion of FA was 37.9%. The distribution of risk of IA was 21.8%. When the of risk of Internet or Facebook addiction was compared with respect to eating disorders, no ro significant differences were found between groups (P=0.146 and P=0.086, respectively). Age and Body Mass Index (BMI) were predictors of BFAS scores; The standardized beta -p coefficient (β) for age was -0.463 (P=<0.001), while for BMI it was 3.44; (P=0.001 being a re positive predictor of BFAS scores. For IAT scores, β age (negatively) =-0.415; (P<0.001) and β for weight (positively) 3.657; (P<0.001) were identified. lP Conclusions: The presence of an eating disorder does not seem to be a factor that characterizes the risk of Internet or Facebook addiction in our sample. As information na regarding the potential association between Internet and Facebook addiction and the presence Jo ur of eating disorders is limited, we encourage further studies on this topic. Keywords: anorexia nervosa; binge eating disorder; bulimia nervosa; eating disorders; facebook addiction; internet addiction Journal Pre-proof Introduction The Internet, an essential contemporary way of collecting information and making connections with peers, friends and family, has become an increasingly important aspect of human life. “Healthy Internet use” has been defined as a use of the Internet that makes it possible to reach a precise objective within an adequate time frame and without conceptual or behavioral complications [1]. Although Internet usage makes life easier, it can become unhealthy in some cases [2]. The use of the Internet in an unhealthy way was first defined as Internet addiction (IA) of and first used in 1995 by psychiatrist Dr. Ivan Goldberg, who coined the term Internet addiction ro disorder along with a list of symptoms, later known as Internet dependency [3], pathological Internet use [1] and problematic Internet use [2,4]. In the fifth edition of the Diagnostic and -p Statistical Manual of Mental Disorders (DSM-5), IA is not indexed among the non-substance re addictions, but Internet gambling disorder is catalogued in the appendix as a condition that deserves further study [5]. Common characteristics used in the definition of Internet addiction lP disorder are the length of time spent on the Internet (Internet use greater than 5 hours/day has been proposed as the threshold [2]), anxiety and anger when the Internet cannot be used, and na the continuous need to progressively increase the amount of Internet connection [6]. Up to five different types of IA have been proposed [7], and although the classification has been criticized Jo ur [8], addiction to social media networks such as Facebook could be included in the cyberrelationship additions [7,9,10]. Eating disorders have become an issue of global interest. Disordered eating among adolescents and young women [11,12] is characterized by social, cultural, and psychological circumstances related to eating attitudes and behaviors. The Internet also provides access to information on a broad variety of topics including eating disorders. Not only as a way to get information about how to lose weight but also, and more worrying, as a way to expose their thinness, coach each other on using socially acceptable pretexts for refusing food, share dangerous ways to achieve their goals, compare themselves or to compete to fast together, advice on how to best induce vomiting and on using laxatives and emetics, post their weight, body measurement, details of their dietary regimen or pictures of themselves to solicit acceptance and affirmation [13]. These webpages offer not only information, but support, and Journal Pre-proof sense of community to individual with an eating disorder and explains the huge increase in our society of Pro-Ana and Pro-Mia pages [14]. Thus, it is predictable that women suffering from high levels of body image anxiety (particularly body image avoidance) might be more susceptible to Internet addiction [15]. While affective dimensions of body image are important, it has been generally shown that attitudes have a limited ability to predict behaviors [15]. Descriptive studies have reported that IA may be associated with negative body image [16] and be comorbid with body dysmorphic disorder [17]. of In adolescents and young adult females Internet dependency, in an association with eating ro disorders has been reported [18-20], with the proposed link between IA and disordered eating being the mood regulation function (as a substitute for food) [21]. More recently, cyberaddiction -p screened by the Internet Addiction Test has been associated to eating disorders in French re young population [22]. Also in young population a clear pattern of association has been reported between Social Media Networks usage and disordered eating behaviors [23], frequency of fast lP food eating [24] and a negative body image [25]. Anyway, to date, little is known about the prevalence and the role of IA beyond scholars or adolescents diagnosed of eating disorders. na Research evidence also suggests that problematic Social Media Networks use, and particularly Facebook affects large numbers of people worldwide impacting negatively in mental health and Jo ur well-being [26, 27]. Research is needed in the context to understand how Internet, and Social Media Networks are used by women with eating disorders. Because there is an association among body image concerns, disordered eating, restricted social interactions, and social avoidance [21,28], it could be hypothesized that Internet addiction might co-occur with disordered eating. We aim to screen the use of Internet and Facebook in Spanish women diagnosed of eating disorders to settle the base of further research that will analyze if such use has both a helpful or harmful component or if Internet and Facebook use has a differential component between different types of eating disorder. Methods Design and Setting Journal Pre-proof We performed an observational, descriptive and cross-sectional study of women consecutively diagnosed and treated for eating disorders at the General University Hospital of Ciudad Real (Spain) between February and November 2018. The inclusion criteria to participate in the study were as follows: being female, being over 12 years old, and undergoing treated at our hospital both in outpatient and hospitalization setting. The exclusion criterion was the presence of cognitive impairment or physical or mental disability. of Ethics Consideration ro Procedures were established in concordance with the Declaration of Helsinki and approved by the Ethics and Clinical Research Committee of Ciudad Real (Spain) (ref. 2017C/123). All -p patients signed a written informed consent form to participate in the study. Written informed re consent in minor patients has been obtained through their legal representatives. lP Variables and Tools A specific questionnaire was designed in order to obtain for the following variables: age, na education level, marital status, and clinical data including eating disorder subtype, years since diagnosis, history of previous treatments and hospitalization, weight, height, current diagnosis of Jo ur anxiety or depression, smoker status, and comorbid conditions. Internet Addiction Test The IAT consists of 20 items that are scored using a five-point Likert scale (Supplemental table 1), and the test measures the frequency with which problematic situations emerge as a result of Internet use. The test identifies two main groups of users according to the score obtained: 1) normal users or users without problems (<40 points) and 2) users with a risk of IA (≥40 points) [29]. The Internet Addiction Test was first translated into Spanish [30] and then further validated in the Spanish population [31], with the conclusion that the Spanish short version of the IAT represents a useful tool for the analysis of problems arising from misuse of the Internet in the Spanish population. Journal Pre-proof Bergen Facebook Addiction Scale The Bergen Facebook Addiction Scale (BFAS) was applied in the present study to evaluate the risk of FA [9]. The BFAS is a self-report 6-item Likert scale (see Supplemental table 2) with each item scored from 1 (very rarely) to 5 (very often). A score between 0 and 10 is normal, 11–14 indicates the possibility of Facebook addiction, and 15 and above indicates FA. The Bergen Facebook Addiction Scale has also been recently validated in a population of Spanish young adults [32], which shows that the Spanish version of the BFAS provides useful evidence for research on behavioral addictions; its inclusion in batteries that assess social network addiction ro of is recommended. Statistical Analysis. -p Some of the studied variables were not normally distributed (Shapiro-Wilk test P<0.05), so a re two-step approach was used to normalize the data before statistical analyses when appropriate [33,34], including transforming the variable into a percentile rank, followed by applying an lP inverse normal transformation to the results derived from the first step. Descriptive analyses were conducted for all variables, including the mean (SD). The following analyses examined the na transformed versions of those variables. Pearson’s correlations were used to explore the relationships between the IAT and BFAS scores with a partial correlation analysis with Jo ur adjustment for age and BMI (kg/m2). A multiple linear regression (using the enter method) was used to examine whether the studied variables, age (years), BMI (kg/m2), years since diagnosis, marital status, depression, education level, smoker status, ever hospitalized and eating disorder subtype were predictors of the IAT or BFAS scores. Some subgroup analyses were performed, and comparisons between groups were performed using the unpaired Student’s T-test or one-way ANOVA when appropriate. A p value of <0.05 was considered statistically significant. Effect sizes for differences in continuous variables are given as Cohen’s d. All statistical analyses were conducted using the IBM SPSS statistical analysis software package (version 22.0). Results Journal Pre-proof A total of 124 women diagnosed with eating disorders aged 27.3(±10.1) years were included in this study. The sociodemographic, biological and clinical characteristics of the participants are shown in Table 1. According to the IAT, the distribution of potential risk of Internet addiction was 21.8% (n=27) in the global sample. According to the BFAS, the distribution of Facebook addiction was 37.9% of the whole sample (n=47). There were statistically significant differences among the potential presence of Facebook addiction (37.9%), absence of Facebook addiction (41.1%) and risk of Facebook addiction (21%) (P= 0.013). A comparison based on the eating disorder diagnosis and clinical and socio-demographic of variables (Table 1) and in relation to Facebook or Internet addiction is also given in Table 2. ro When the potential risk of Internet or Facebook addiction was compared with respect to the presence of an eating disorder, no significant differences were found between groups (P=0.146 -p and P=0.086, respectively) (Table 2). re Mean BFAS and IAT scores are compared globally and by addiction components in Table 2. No statistically significant differences were observed in the mean scores for the BFAS and lP IAT based on the eating disorder diagnosis. Similarly, no statistically significant differences were observed in the addiction components of the BFAS between eating disorder subtypes (salience, na tolerance, mood modification, relapse, withdrawal (P=0.22) and conflict (P=0.159). Furthermore, no statistically significant differences were observed in the addiction components of the IAT Jo ur (salience, excessive use, neglect of work, anticipation, lack of control and neglect of social life. After further adjustment for age (Table 2), statistically significant differences between groups were observed in the IAT score (P=0.04), including a score difference between the group with anorexia nervosa and the group with eating disorder not otherwise specified (P=0.043) (effect size Cohen’s d=0.56). We further explored potential predictors of BFAS and IAT scores in women diagnosed with eating disorders by multiple linear regression. The results are shown in table 3. Age and BMI were predictors of BFAS scores. The standardized beta coefficient for age was -0.463 (P=<0.001) (negative predictor for BFAS score), while BMI was a positive predictor of BFAS scores (standardized beta coefficient of 3.44; (P=0.001)). IAT scores had a negative association with age and a positive association with weight, standardized beta coefficient for age =-0.415; (P<0.001); and standardized beta coefficient for weight= 3.657 (P<0.001). Journal Pre-proof The differences between groups in the mean scores for the items of the BFAS (Supplemental table 1) and the IAT (Supplemental table 2) were based on eating disorder subtype. No statistically significant differences were observed in the analysis of the BFAS items. However for the Eating Disorders Not Otherwise Specified (EDNOS) statistically significant differences between the studied groups were observed in the IAT items “Do you snap, yell, or act annoyed if someone bothers you while you are online?” (P=0.017) and the item “Do you neglect household chores to spend more time online?” (P=0.009). We finally performed a correlation study between BFAS and IAT scores. Both scores were of highly correlated (r=0.902; P<0.0001). This correlation remained statistically significant even ro after further adjustment for age (r=0.871; P<0.0001). We further explored the potential correlations of BMI with BFAS and IAT scores. BMI was positively correlated with IAT scores -p (r=0.194; P=0.032), but this association disappeared after further adjustment for age (P>0.05) re while no significant correlations between BMI and BFAS scores were observed in the studied Jo ur na lP sample Journal Pre-proof Discussion This research screened Internet use by means of the Internet Addiction Test (IAT) and Facebook use by means of the Bergen Facebook Addiction Scale (BFAS) in a cohort of women with eating disorders. Internet and online social networking addiction shares similarities with other behavioral addictions [9,10] which also have prevalent addictive symptoms [35,36]. The IAT was originally developed for screening Internet addiction, while the BFAS was developed for screening Facebook addiction. Our results confirm the previously reported positive correlation between these factors in the general population [36], although those scores reflect of different addiction components [10, 35-38]. It has been suggested that generational and cultural differences may exist in many aspects ro of Internet and online social network usage and addiction [10,36]. Recent findings suggest that -p using image-oriented online social networks such as Facebook might be associated with greater body dissatisfaction and disordered eating [39,40]; however, other studies have shown that re greater use of social networking sites is associated with body dissatisfaction but not with lP disordered eating [41], as we have observed in our study. In adolescents, it has been reported that the amount of time spent on Facebook is na associated with disordered eating behaviors [42], but it seems that the way that online social networks are used, not the time spent on them, predicts the association with eating disorders Jo ur [39,43,44,45]. Particularly in the case of Facebook, our results support the findings that intensive use was not associated with the presence of eating disorders [41,43], with the probable cause of such associations being the engagement in social media comparisons on Facebook. Thus, when Facebook is not used to compare oneself physically with others, the greater use of Facebook could even lead to stronger social and emotional support and thus less loneliness, which has been positively related to the presence of eating disorders [45,46]. The association between the presence of Internet addiction and the risk of eating disorders has been studied in populations of adolescents and students [47,48]. Different studies have reported that Internet addiction can lead to changes in lifestyle-related factors that can result in irregular dietary habits [47,49]. Those studies mostly refer to populations representing childhood, adolescence and young adulthood but little is known about the possible abusive use of the Internet in populations of women diagnosed with and undergoing treatment for an eating Journal Pre-proof disorder. In 122 females aged 12-30 diagnosed of eating disorders and recruited thought hospital-based treatment program, similarly to the method used by our research team, participants reported spending more time that controls in reading forums and blogs related to eating, weight and body image issues [50] suggesting that the internet use patters could be eating disorders-specific as we aimed to explore in our study. Compulsive internet use has also been reported in women diagnosed of eating disorders [51], contrarily, in our study there were no associations between the presence of an eating disorder and the measured IAT score, in fact, we observed that the main predictor of Internet of addiction and Facebook addiction is the age of the participant, with a negative association being ro the type of eating disorder not included in the models. The results reported in a Spanish cohort of healthy young adults (n=1011) showed that gender (female) but not age (contrarily to our -p observations) was a negative predictor of Internet addiction [52]. In that sample, the risk of re Internet addiction was observed in 5.2% of the sample (pooled results for men and women), which is lower than the risk observed in our sample (21.8%). However, the mean IAT score lP observed in our sample, 25.82 (25.51), almost matches the mean reported recently in a large study that involved the application of the IAT in a cohort of 3279 participants from nine countries na [53]. Those authors reported in their manuscript a mean IAT score of 27.76 (15.41), with the Jo ur population at risk of Internet addiction ranging from 8.4% to 45.7%. It is remarkable that the assessment of Internet addiction is so difficult due to the potential presence of concomitant disorders listed in DSM-5 [53,54]. In a cohort of 1979 Spanish female college students aged 20.3 ± 4.4 years the prevalence of problematic internet use measured by the IAT was of 6% much lower than the potential risk screened in our sample, but probably not comparable due to evident differences in the sociodemographic characteristics of both samples [55]. However, also in Spanish college students a problematic internet uses higher to the observed in our sample (36.5%) was reported being associated to a risk of developing eating disorders (adjusted odd ratio=2.33; P=0.003) [56]. Overall, recent data from meta-analysis has showed that problematic internet use is a predictor of eating disorders in childhood, adolescence and young adulthood [57]. Journal Pre-proof The prevalence of Facebook addiction in adolescents and young adults has been described as 2% to 10% worldwide, with increases related to psychological distress and decreases related to well-being [58]. The percentage reported in our study is higher than those previously reported. However, the potential role of the use of social networking sites in the eating disorder context is still under discussion. Users motivated to maintain their desire to be thin could find closed groups of similarly minded individuals. On the other hand, users in the recovery process could find groups providing support for recovery [59,60]. Thus, social networking sites such as Facebook can have either positive or negative outcomes for patients diagnosed with eating disorders. of No data have been reported to date about the assessment of both scales (IAT and BFAS) ro in Spanish women with eating disorders. Here, we provide initial data on Facebook and Internet reference for further studies in this population. -p addiction assessed in Spanish women diagnosed with eating disorders, which may provide a re As a limitation of our study, we recognize that information regarding the potential association between Internet and Facebook addiction and the presence of eating disorders is lP limited. In fact, there are no reliable data to compare with our results regarding the level of Internet addiction and Facebook addiction in populations with eating disorders. Additionally, the na descriptive, cross-sectional design does not allow us to establish cause-effect associations Jo ur between the IAT and BFAS scores and the presence of an eating disorder. Additionally, due to the lack of group control in our study we cannot assure if the scores of IAT of BFAS observed in the women diagnosed of eating disorders differ from those that could be potentially observed in control healthy women with similar sociodemographic characteristics as well as life habits. Due to the small sample size absence of statistically significant results might be an expression of type II error. Further research should take our results into account to calculate larger sample sizes that assure that enough statistical power is achieved. Moreover, the participants were recruited in a convenience sample, which might potentially limit the study generalizability due to the presence of bias in the participant recruitment. Conclusions Since Internet and social networking sites are an integral part of most eating disorder patients’ lives, especially for younger individuals, it is important for nurses to understand the role Journal Pre-proof that both can have in patients’ everyday lives. Although it has been described that their use could have a particular role in the onset of an eating disorder, we report data on their use by young adult women already diagnosed and receiving treatment which is novel in Spanish women. Our study does not allow to conclude if the screened use of Internet and Facebook in the women studied has a helpful or harmful component or any specific relationship between IA and FA in women suffering for eating disorders. So further research with appropriate healthy controls and larger samples is deserved. Taking into account that Internet is an essential contemporary way of making connections with peers, friends and family, the psychopathological expression of eating disorders will probably of affect the “contents” of these new forms of communication instead of the “time”. In our opinion it ro is important for nurses to understand the role that both can have in patients’ everyday lives. re behaviors and its influence on them. -p Nurses should ask patients about Internet and Facebook usage in order to detect potential risk lP Funding: This research received no external funding. References Jo ur na Conflicts of Interest: The authors declare no conflict of interest. 1. Davis RA. A cognitive-behavioral model of pathological Internet use. Comput Hum Behav. 2001;17(2):187-95. 2. Odacı H, Kalkan M. Problematic Internet use, loneliness and dating anxiety among young adult university students. Comput Educ. 2010;55(3):1091-7. 3. Scherer K. 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Eating disorder not otherwise specified Mean (SD) (n=19) 27.14 (7.17) Binge eating disorder Mean (SD) (n=11) 34.29 (5.61) 8.32 (5.41) 14.9 (8.01) 36.8% 63.2% 62.66 (22.65) 1.63 (0.06) 23.04 (7.69) 9.1% 90.9% 90.06 (11.62) 1.62 (0.03) 34.21 (4.18) 20.6% 79.4% 5.3% 94.7% 63.6% 36.4% <0.001 2.9% 65.7% 31.4% 5.3% 57.9% 36.8% 9.1% 36.4% 54.5% 0.546 60.0% 40.5% 52.6% 47.4% 18.2% 81.8% 0.053 28.6% 71.4% 33.3% 66.7% 45.5% 54.5% 0.580 TOTAL sample Anorexia nervosa Bulimia nervosa Mean (SD) Mean (SD) (n=59) Mean (SD) (n=35) Age (years) 27.34 (10.07) 25.57 (11.55) Years since diagnosis 10.41 (10.41) 9.94 (7.94) (years) Ever hospitalized Yes 51.6% 79.7% No 48.4% 20.3% Weight (kg) 58.4 (22.41) 45.12 (17.08) Height (m) 1.62 (0.06) 1.61 (0.07) BMI (kg/m2) 22.18 (8.13) 17.38 (6.46) Marital status Married 17.9% 11.9% Single 82.1% 88.1% Education level Low (primary school) 4.0% 3.4% High school 63.7% 69.5% University 32.3% 27.1% Smoker status Yes 45.2% 39.0% No 54.8% 61.0% Depression Yes 29.3% 25.4% No 70.7% 74.6% ANOVA and chi square test analysis. Level of significance p< .001. l a o J n r u 28.45 (8.94) f o 10.93 (8.05) ro 25.7% 74.3% 69.42 (16.18) 1.63 (0.07) 26.37 (5.41) r P p e P-value* 0.07 0.14 <0.001 <0.001 0.275 <0.001 Journal Pre-proof Table 2. Facebook and Internet Addiction in Eating Disorders Subtypes Facebook addiction as measured by the Bergen Facebook Addiction Scale (BFAS) No Facebook addiction Possibility of addiction Facebook addiction Internet addiction (IA) Non-IA At risk for IA BFAS score BFAS score adjusted by age BFAS Salience BFAS Tolerance BFAS Mood modification BFAS Relapse BFAS Withdrawal BFAS Conflict IAT score TOTAL sample Anorexia nervosa Bulimia nervosa Mean (SD) Mean (SD) (n=59) Mean (SD) (n=35) 41.1% 21.0% 37.9% 37.3% 30.5% 32.2% 78.2% 21.8% 42.68 (21.33) 86.4% 13.6% 40.77 (19.29) 38.97 (C.I. 95% 34.1243.83) 2.03 (0.86) 2.36 (1.11) 2.13 (1.01) 2.43 (1.19) o J 2.36 (1.19) 2.19 (1.14) 2.36 (1.22) 25.82 (25.51) IAT score adjusted by age IAT Salience IAT Excessive use IAT Neglect of work IAT Anticipation IAT Lack of control IAT Neglect of social 1.3 (1.31) 1.28 (1.23) 1.33 (1.32) 1.36 (1.24) 1.29 (1.29) 1.51 (1.27) f o o r p P-value* 63.6% 0% 36.4% 74.3% 25.7% 43.01 (20.96) 44.13 (C.I. 95% 37.87-50.39) 2.05 (0.99) 2.44 (1.17) 63.2% 36.8% 50.74 (24.45) 50.53 (C.I. 95% 42.0459.02) 2.66 (1.31) 2.81 (1.35) 72.7% 27.3% 37.9 (26.38) 48.00 (C.I. 95% 36.07- 59.94) 2.01 (1.12) 2.14 (1.44) 2.81 (1.12) 2.84 (1.12) 3.28 (1.35) 2.6 (1.63) 0.409 2.26 (1.09) 2.04 (0.97) 2.21 (1.11) 23.13 (23.62) 20.88 (C.I. 95% 15.2426.53) 1.13 (1.17) 1.19 (1.1) 1.14 (1.16) 1.2 (1.09) 1.15 (1.05) 1.37 (1.16) 2.37 (1.14) 2.21 (1.21) 2.29 (1.23) 26.59 (24.65) 28.05 (C.I. 95% 20.76-35.33) 1.41 (1.34) 1.22 (1.32) 1.37 (1.41) 1.39 (1.23) 1.24 (1.38) 1.45 (1.28) 2.71 (1.38) 2.67 (1.36) 2.93 (1.3) 37.37 (26.58) 36.90 (C.I. 95% 26.7647.05)a 1.74 (1.25) 1.87 (1.21) 1.97 (1.39) 1.9 (1.35) 1.96 (1.39) 2.15 (1.42) 2.28 (1.59) 2.1 (1.22) 2.37 (1.54) 18.88 (33.16) 31.47 (C.I. 95% 17.58-45.35) 1.13 (1.91) 1.03 (1.5) 1.12 (1.55) 1.19 (1.69) 1 (1.7) 1.32 (1.36) 0.569 0.22 0.159 0.158 l a 48.6% 11.4% 40.0% Binge eating disorder Mean (SD) (n=11) 26.3% 21.1% 52.6% n r u 2.87 (1.21) Eating disorder not otherwise specified Mean (SD) (n=19) e r P 0.086 0.146 0.293 0.094 0.104 0.425 0.04 0.323 0.172 0.11 0.205 0.093 0.114 Journal Pre-proof life * Between subtypes of eating disorders. Quantitative variables assessed by ANOVA and qualitative variables assessed by a chi square test. a P=0.043 vs anorexia nervosa [p-values after further adjustment by age (years) (ANCOVA test)]. f o l a o J n r u r P e o r p Journal Pre-proof Table 3. Socio-demographic and clinical characteristics and BFAS and IAT scores BFAS score Optimal model R 0.504 Selected independent variables Age (years) BMI (kg/m2) Years since diagnosis Marital status Depression Education level Smoker status Ever hospitalized Eating disorder subtype IAT score Optimal model Age (years) Weight (kg) Years since diagnosis Marital status Depression Education level Smoker status Ever hospitalized Eating disorder subtype o J n r u l a R 0.471 R2 0.254 R2 0.222 F 19.888 t -5.734 3.444 -1.2 1.337 -0.755 -0.357 1.265 0.324 0.543 P-value <0.001 P-value <0.001 0.001 0.233 0.184 0.452 0,722 0.208 0.747 0.588 Adjusted R2 0.209 Standardized B -0.412 0.297 -0.104 0.126 -0.029 -0.01 0.109 0.043 0.121 F 16.688 t -4.998 3.604 -0.767 1.257 -0.354 -0.121 1.271 0.47 1.205 P-value <0.001 P-value <0.001 <0.001 0.445 0.211 0.724 0.904 0.206 0.639 0.231 f o o r p e r P Adjusted R2 0.241 Standardized B -0.463 0.278 -0.159 0.131 -0.062 -0.03 0.106 0.029 0.054 Multiple linear regression analysis for the association between BFAS and IAT scores and age (years), BMI (Kg/m2), years since diagnosis, marital status, depression, education level, smoker status, ever hospitalized and eating disorder subtype. Journal Pre-proof Table S1. IAT items and Subtype of Eating Disorder Subtype of eating disorder Anorex Bulimi ia a IAT items nervos nervo a sa Mean Mean (SD) (SD) Do you choose to spend more time online 0.9 1.4 over going out with others? (1.3) (1.7) Do you snap, yell, or act annoyed if 0.8 1.4 someone bothers you while you are online? (1.2) (1.6) Do you fear that life without the Internet 1.1 1.7 would be boring, empty and joyless? (1.3) (1.6) Do you feel preoccupied with the Internet 1.3 0.9 (11) when offline or fantasize about being online? (1.6) Do you block disturbing thoughts about your 1.1 1.2 life with soothing thoughts of the Internet? (1.2) (1.5) Do you neglect household chores to spend 1.1 1 (1.2) more time online? (1.2) 0.9 1.4 Do you lose sleep due to late night log-ins? (1.2) (1.8) Do you feel depressed, Moody, or nervous 1.2 1.3 when you are offline, which goes away once (1.4) (1.8) you are back online? Do you find that you stay online longer than 1.3 1.2 (1) you intended? (1.1) Do you try to hide how long you’ve been 0.8 1.1 online? (1.2) (1.6) Does your work suffer (e.g., postponing 0.9 1.3 things. not meeting deadlines) because of (1.2) (1.6) the amount of time you spend online? Does your job performance or productivity 1.1 1.3 suffer because of the Internet? (1.2) (1.6) Do you become defensive or secretive when 1.1 1.3 anyone asks you what you do online? (1.3) (1.6) Do you find yourself anticipating when you 1.2 1 (1.1) will go online again? (1.5) Do you check your e-mail before something 1.2 1.5 else that you need to do? (1.2) (1.5) Do you try to cut down the amount of time 0.9 1.2 you spend online and fail? (1.1) (1.5) Do others in your life complain to you about 1.2 1.3 the amount of time you spend online? (1.2) (1.7) Do you find yourself saying “Just a few more 0.9 1.2 minutes” when online? (1.1) (1.5) Do you form new relationships with fellow 1.4 1.6 online users? (1.2) (1.4) Do you prefer the excitement of the Internet 1.2 1.2 to intimacy with your partner? (1.3) (1.5) Table 1. Supplemental Eating disorder not otherwise specified Mean (SD) Mean (SD) 1.8 (1.8) 1.5 (2.1) 2.1 (1.7) 1.2 (1.8) 1.9 (1.7) 1.5 (2.1) of ro -p re lP na Jo ur Binge Peating val disorder ue 1.8 (1.8) 1.1 (1.9) 1.9 (1.7) 1.3 (2) 2.3 (1.6) 1.3 (1.5) 1.8 (1.7) 1.1 (1.7) 1.9 (1.8) 1.5 (2) 2.1 (1.4) 1.2 (1.2) 1.8 (1.7) 1.5 (2.1) 2.1 (1.8) 1.4 (1.8) 1.9 (1.7) 1.2 (1.9) 1.9 (1.6) 1 (1.7) 1.9 (1.8) 1.3 (2.1) 2.2 (1.8) 1.2 (1.7) 1.8 (1.6) 1.3 (2.2) 2.1 (1.8) 1.3 (1.8) 1.9 (1.8) 1.2 (2) 2.5 (1.8) 1.3 (1.5) 1.9 (1.6) 1.4 (1.4) 0.2 58 0.0 17 0.2 72 0.1 84 0.2 42 0.0 09 0.1 62 0.4 08 0.1 08 0.1 01 0.1 15 0.1 82 0.1 13 0.2 73 0.1 21 0.0 85 0.2 13 0.1 31 0.1 04 0.2 3 Journal Pre-proof Table S2. BFAS items and Subtype of Eating Disorder 0.2 09 0.1 63 0.4 77 0.2 9 0.5 8 2.4 (1.8) 0.3 75 2.7 (1.2) 2.6 (1.3) 3.3 (1.4) 2.6 (1.7) 0.2 61 2.8 (1.2) 2.9 (1.3) 3.2 (1.5) 2.6 (1.6) 0.6 85 2.8 (1.3) 2.9 (1.3) 3.2 (1.4) 2.5 (1.6) 0.5 12 2.5 (1.3) 2.5 (1.3) 3.1 (1.6) 2.4 (1.6) 0.5 13 2.1 (1.2) 2.5 (1.5) 2.2 (1.7) 2.6 (1.6) 2.2 (1.6) 2.6 (1.6) 2.4 (1.7) 1.8 (1.1) 2.2 (1.4) 2.1 (1.4) 2.2 (1.5) 2.1 (1.4) 2.7 (1.7) 2.1 (1.4) 1.9 (1.2) 2.1 (1.4) 2.8 (1.6) 2 (1.3) 0.1 76 1.9 (1.2) 2 (1.4) 2.8 (1.6) 2.3 (1.7) 0.1 24 2.1 (1.3) 2.3 (1.4) 2.8 (1.5) 2.5 (1.7) 0.2 72 2.3 (1.3) 2.4 (1.5) 3.1 (1.5) 2.5 (1.8) 0.2 72 re -p 2.9 (1.6) na 2.2 (1.3) Jo ur 2.3 (1.3) Pval ue lP Spent a lot of time thinking about Facebook or planned use of Facebook? Thought about how you could free more time to spend on Facebook? Thought a lot about what has happened on Facebook recently? Spent more time on Facebook than initially intended? Felt an urge to use Facebook more and more Felt that you had to use Facebook more and more in order to get the same pleasure from it? Used Facebook in order to forget about personal problems? Used Facebook to reduce feelings of guilt, anxiety, helplessness, and depression? Used Facebook in order to reduce restlessness? Experienced that others have told you to reduce your use of Facebook but not listened to them? Tried to cut down on the use of Facebook without success? Decided to use Facebook less frequently, but not managed to do so? Become restless or troubled if you have been prohibited from using Facebook? Become irritable if you have been prohibited from using Facebook? Felt bad if you, for different reasons, could not log on to Facebook for some time? Used Facebook so much that it has a negative impact on your job/studies? Given less priority to hobbies, leisure activities, and exercise because of Facebook? Ignored your partner, family members, or friends because of Facebook? of BFAS items Subtype of eating disorder Anorex Bulimi Eating disorder Binge ia a not otherwise eating nervos nervos specified disorder a a Mean Mean Mean Mean (SD) (SD) (SD) (SD) 1.8 1.9 2.4 (1.2) 1.8 (1.1) (0.9) (1.1) 1.9 2 (1.2) 2.8 (1.6) 2.1 (1.4) (1.1) 2.1 2.1 2.7 (1.6) 2.1 (1.4) (1.1) (1.2) 2.2 2.4 2.8 (1.5) 2 (1.3) (1.1) (1.3) 2.3 2.4 2.8 (1.5) 2.3 (1.7) (1.2) (1.4) ro Table 2. Supplemental 2 (1.2) 2 (1.2) 0.8 1 0.5 44 0.6 58 0.2 79 Journal Pre-proof Highlights Internet and social networking sites are an integral part of most eating disorder patients’ lives, especially for younger individuals. Women with eating disorders may have other addictive behaviors such as Internet and online social networking addiction. These behaviors share similarities with other behavioral addictions, that must be detected early and identify various addiction components. Jo ur na lP re -p ro of