Internet addiction and Faceb00k addiction in Spanis wom with eating disorders

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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
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Title page
Title: Internet addiction and Facebook addiction in
Spanish women with eating disorders.
Authors:
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Almudena Martos-Sánchez 1
Nurse Research. RN.
Mail: [email protected]
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Fidel López-Espuela *2
PhD. Bachelor Psychologist. RN
Mail: [email protected]
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Isabel Panea-Pizarro *1
Nurse Research. RN.
Mail: [email protected]
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Ana Teresa Domínguez-Martín 3,
Nurse Research. RN.
Mail: [email protected]
Luis Beato-Fernández 1
MD. PhD.
Mail: [email protected]
José María Moran-García 2
PhD.
Mail: [email protected]
*
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
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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: [email protected]
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Acknowledgements
To all the participating women, without them the research would not have been possible.
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To Juan, Pilar, Irene, Jaime and Elena.
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Author Contributions: Conceptualization: IPP, FLE, LBF, ATDM; Data curation: IPP,
AMS; Methodology: AMS; FLE; Formal Analysis: JMMG; AMS; Writing- Original
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manuscript.
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Draft Preparation: JMMG, FLE, LBF, IPP, ATDM ; All authors revised and edited the
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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
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risk of Internet or Facebook addiction was compared with respect to eating disorders, no
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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
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coefficient (β) for age was -0.463 (P=<0.001), while for BMI it was 3.44; (P=0.001 being a
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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.
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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
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regarding the potential association between Internet and Facebook addiction and the presence
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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
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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)
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and first used in 1995 by psychiatrist Dr. Ivan Goldberg, who coined the term Internet addiction
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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
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Statistical Manual of Mental Disorders (DSM-5), IA is not indexed among the non-substance
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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
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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
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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
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[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
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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].
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In adolescents and young adult females Internet dependency, in an association with eating
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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
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screened by the Internet Addiction Test has been associated to eating disorders in French
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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
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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.
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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
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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
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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.
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Ethics Consideration
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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
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patients signed a written informed consent form to participate in the study. Written informed
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consent in minor patients has been obtained through their legal representatives.
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Variables and Tools
A specific questionnaire was designed in order to obtain for the following variables: age,
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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
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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.
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
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
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is recommended.
Statistical Analysis.
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Some of the studied variables were not normally distributed (Shapiro-Wilk test P<0.05), so a
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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
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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
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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
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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
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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
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variables (Table 1) and in relation to Facebook or Internet addiction is also given in Table 2.
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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
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and P=0.086, respectively) (Table 2).
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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
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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,
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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
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(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).
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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
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highly correlated (r=0.902; P<0.0001). This correlation remained statistically significant even
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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
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(r=0.194; P=0.032), but this association disappeared after further adjustment for age (P>0.05)
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while no significant correlations between BMI and BFAS scores were observed in the studied
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sample
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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
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different addiction components [10, 35-38].
It has been suggested that generational and cultural differences may exist in many aspects
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of Internet and online social network usage and addiction [10,36]. Recent findings suggest that
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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
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greater use of social networking sites is associated with body dissatisfaction but not with
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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
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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
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[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
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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
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addiction and Facebook addiction is the age of the participant, with a negative association being
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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
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observations) was a negative predictor of Internet addiction [52]. In that sample, the risk of
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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
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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
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[53]. Those authors reported in their manuscript a mean IAT score of 27.76 (15.41), with the
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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].
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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.
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No data have been reported to date about the assessment of both scales (IAT and BFAS)
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in Spanish women with eating disorders. Here, we provide initial data on Facebook and Internet
reference for further studies in this population.
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addiction assessed in Spanish women diagnosed with eating disorders, which may provide a
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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
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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
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descriptive, cross-sectional design does not allow us to establish cause-effect associations
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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
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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
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affect the “contents” of these new forms of communication instead of the “time”. In our opinion it
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is important for nurses to understand the role that both can have in patients’ everyday lives.
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behaviors and its influence on them.
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Nurses should ask patients about Internet and Facebook usage in order to detect potential risk
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Funding: This research received no external funding.
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Conflicts of Interest: The authors declare no conflict of interest.
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Journal Pre-proof
Table 1. Clinical and socio-demographic characteristics in Eating Disorder Subtypes.
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
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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
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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
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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
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