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  • 1 University of Miskolc, Hungary
  • | 2 University of Miskolc, Hungary
  • | 3 National University of Public Service, Hungary
  • | 4 University of Miskolc, Hungary
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Background and aims

This study examines exercise addiction (EA) in amateur runners from a multidimensional approach, including demographics (age, sex, educational attainment, and financial situation), training factors (duration of running activity, weekly time spent running, mean workout distance per session, other sports activities, and childhood physical activity), psychological features (perceived health, life satisfaction, loneliness, stress, anxiety, depression, body shape, and eating disorders), and anthropometrics (body mass index) that might predict EA.

Methods

The well-validated Exercise Dependence Scale (EDS) was applied to evaluate the prevalence of EA in amateur runners. A multinomial logistic regression was performed to find explanatory variables of risk of EA using the SPSS 24.0 statistical software.

Results

A total of 257 runners (48.9% females, Mage = 40.49, SD = 8.99 years) with at least 2 years running activity participated in an anonymous questionnaire survey. About 53.6% of respondents were characterized as non-dependent symptomatic and 37.8% as non-dependent asymptomatic. About 8.6% had prevalence of being at risk of EA. The logistic regression model displayed five variables that significantly predicted the risk of EA: (a) anxiety, (b) loneliness, (c) weekly time spent running, (d) childhood physical activity, and (e) education level.

Discussion and conclusions

Findings indicate that loneliness and anxiety may lead to withdrawal and uncontrolled behavior that in turn leads to increased amount of exercise in amateur runners. Lower level of education attainment is also a likely risk of EA development, and childhood sports activity is a predictor.

Abstract

Background and aims

This study examines exercise addiction (EA) in amateur runners from a multidimensional approach, including demographics (age, sex, educational attainment, and financial situation), training factors (duration of running activity, weekly time spent running, mean workout distance per session, other sports activities, and childhood physical activity), psychological features (perceived health, life satisfaction, loneliness, stress, anxiety, depression, body shape, and eating disorders), and anthropometrics (body mass index) that might predict EA.

Methods

The well-validated Exercise Dependence Scale (EDS) was applied to evaluate the prevalence of EA in amateur runners. A multinomial logistic regression was performed to find explanatory variables of risk of EA using the SPSS 24.0 statistical software.

Results

A total of 257 runners (48.9% females, Mage = 40.49, SD = 8.99 years) with at least 2 years running activity participated in an anonymous questionnaire survey. About 53.6% of respondents were characterized as non-dependent symptomatic and 37.8% as non-dependent asymptomatic. About 8.6% had prevalence of being at risk of EA. The logistic regression model displayed five variables that significantly predicted the risk of EA: (a) anxiety, (b) loneliness, (c) weekly time spent running, (d) childhood physical activity, and (e) education level.

Discussion and conclusions

Findings indicate that loneliness and anxiety may lead to withdrawal and uncontrolled behavior that in turn leads to increased amount of exercise in amateur runners. Lower level of education attainment is also a likely risk of EA development, and childhood sports activity is a predictor.

Introduction

Running is the most convenient way to exercise intensely. People can improve their mental and physical health, as well as their physical fitness. Running even a few minutes per day is associated with reduced risks of cardiovascular disease (Lee et al., 2014). To attain beneficial physiological adaptation, exercisers have to practice regularly. It appears that higher amounts of physical activity provide greater health benefits (O’Donovan et al., 2010); however, there is no consensus on the upper limits of intensity, frequency, and duration to exercise in an optimal way. In recent decades, a new behavioral addiction called “Exercise Addiction” (EA) has been observed when exercisers overtrain and consequently suffer severe withdrawal symptoms if they cannot exercise (Landolfi, 2013). Increasing workout or training times often ignores fatigue and also increases the risk of sometimes irreversible physical injuries. Although evidence of EA exists, it is not referenced in the latest (fifth) edition of the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association [APA], 2013). Its criteria are not well established, appropriate studies in the field are lacking, and incidence of EA is currently understood to be rare (Szabo, Griffiths, de La Vega Marcos, Mervó, & Demetrovics, 2015).

The number of amateur exercisers is increasing year by year. Some become so involved with the sports activity they are doing that the exercise slowly becomes compulsive; they feel an irresistible impulse to continue exercising, despite personal affects like fatigue, injury, or illness (Hausenblas & Downs, 2002a; Johnson, 2000). It is not fully evident how to distinguish normal amounts of exercise from excessive exercise. Hausenblas and Downs (2002b) developed a screening scale based on a modification of the criteria for substance dependence. If an individual shows three or more of the indicators (such as tolerance, withdrawal, lack of control, intention effect, time, reduction in other activities, and continuance), there is cause for concern. This psychometric instrument is widely used in research studies and has been validated in several countries. In this study, we use the term addiction instead of terms such as “dependence,” “compulsive,” “excessive,” “obligatory,” or “exercise abuse,” as they elude to the same phenomenon (Berczik et al., 2012; Szabo, 2010; Szabo et al., 2015).

EA is very complex and its emergence is still not yet clearly determined. It can manifest as a primary symptom (when EA is the main problem) or it can be a secondary symptom where EA develops as a consequence of the primary problem of an individual, for example, eating disorders, body image disorders, or weight control dysfunction (Freimuth, Moniz, & Kim, 2011; Zmijewski & Howard, 2003). Generally, regular exercise has a positive impact on an individual’s mental health, but in its addictive form, it is often associated with psychological distress (Lichtenstein, Nielsen, Gudex, Hinze, & Jørgensen, 2018) and sociocultural factors (Adams & Kirkby, 2002; Demetrovics & Kurimay, 2008). Kotbagi, Morvan, Romo, and Kern (2017) indicated that EA can serve as a short-term coping strategy to relieve negative affective states or to avoid negative emotions. We assume that people who are feeling isolated and lonely are looking for activities that can give them pleasure. Running is a sport activity that can be executed alone or in groups, but individuals suffering from loneliness (perceived social isolation or possibly lack of social reward) are more prone to unhealthy behaviors, presumably including unhealthy exercise behavior.

When evaluating the risk of EA, it is essential to measure the exercise characteristics. To date, there are still insufficient studies that assess the association between being at risk of EA in amateur runners and key parameters, such as number of years spent running activity, time spent training a week, workout distance, historical sports activity (past positive experience with exercise), and demographic and psychological factors. Therefore, studying risk of EA and its related factors from a multidimensional perspective in amateur runners can provide more insights to this behavioral problem. Amateur runners are considered as those who engage in running as a recreational activity and are not involved in international professional sports (although taking part in competitions was not an excluding factor). The main objective of this study is to find the prevalence of risk of EA and the effect of exercise characteristics, demographics, and psychological features on the probability of being at risk of EA.

Methods

Participants and procedure

Participants were contacted via two Hungarian community websites for runners (“cross-country race” and “running mates”) during a 2-month period in the summer of 2017. They received an e-mail invitation with access to an online questionnaire. All responses were anonymous and no personally identifiable information was requested. The inclusion criteria were: (a) to be at least 18 years of age, (b) physically active for at least 2 years in running on an amateur level, and (c) not an active athlete of the Hungarian National Athletics Association. The questionnaire took approximately 25–30 min to complete.

Measures

Demographics

Demographic items captured participants’ sex, age, educational level, and financial situation.

Exercising activity

Participants reported how long they had been running and how much time they spent running per week. Total weekly training time was categorized in intervals of 1–2, 3–4, 5–7, 8–10, and >10 hr. Participants indicated their mean workout distance per session, childhood physical activity, and whether they were engaged in other sports activities.

Exercise addiction (EA)

It was measured using the Exercise Dependence Scale – Revised (Hausenblas & Downs, 2002b). The authors revised the factorial structure of the original 42-item scale into 21 items (Downs, Hausenblas, & Nigg, 2004). These items were rated on a 6-point Likert scale ranging from 1 (never) to 6 (always), with three items per subscale (i.e., seven factors or subscales). A higher score indicates more exercise dependent symptoms. The scale allows both interval data and nominal categorization. For categorization, a scoring syntax file was developed by the authors. Individuals scoring 5 or 6 out of the seven DSM-IV criteria (tolerance, withdrawal effects, intention, lack of control, reduction in other activities, time, and continuance) were classified as at risk of EA, whereas those scoring in the range of 3–4 were classified as non-dependent symptomatic, and scores of 1–2 are categorized as non-dependent asymptomatic. The Exercise Dependence Scale – Revised has a psychometrically validated Hungarian version (Mónok et al., 2012). Its psychometric properties are: χ2 = 351.9, df = 168, p < .0001; CFI = 0.938; TLI = 0.922; RMSEA = 0.049 [0.042–0.056]; Cfit = 0.590; SRMR = 0.052). In our work, we used this version of the scale. In this study, the internal consistency of the scale was determined with Cronbach’s α (α = .903).

Overall life satisfaction

It was evaluated with the Cantril ladder on a scale of 1–10, where 1 represented the worst possible life satisfaction and 10 described the best (Cantril, 1965). It measured how satisfied the individuals were with their life as a whole.

Eating disorders

These were screened using the 5-item SCOFF questionnaire (S = sick, C = control, O = one stone, F = fat, F = food) One point was assigned for every “yes” response. A score greater than two indicated a possible case of anorexia, bulimia nervosa, or binge eating (Morgan, Reid, & Lacey, 1999). The questionnaire was adapted for Hungarian usage (Dukay-Szabó et al., 2016). The internal consistency of the short questionnaire was adequate in our sample (α = .702).

Loneliness

The UCLA 3-item Loneliness Scale was applied to evaluate runners’ loneliness. This scale comprised three questions that measured three dimensions of loneliness: relational connectedness, social connectedness, and self-perceived isolation. The scale used three response categories: (a) hardly ever, (b) some of the time, and (c) often. Scores were summed and a higher score indicated more loneliness (Hughes, Waite, Hawkley, & Cacioppo, 2004). The internal consistency of the inventory was good in our sample (α = .845).

Body shape concerns

Body Image Subscale was used from the Body Investment Scale to evaluate the participants’ feelings and attitudes toward their body (Orbach & Mikulincer, 1998). The six items are on a 5-point Likert scale and range from 1 (I do not agree at all) to 5 (I strongly agree). Mean scores were calculated and higher scores indicated more positive views of the body. The scale was adapted for Hungarian usage (Czeglédi, Urbán, & Csizmadia, 2010). The internal consistency of scale was good in our sample (α = .922).

Emotional profile

Depression, anxiety, and stress were evaluated using the DASS-21, which quantifies distress along the dimensions of depression (seven items), anxiety (seven items), and stress (seven items; Lovibond & Lovibond, 1995). Respondents indicated on a 4-point scale the extent to which each of 21 statements applied over the previous week with 0 (did not apply at all) to 3 (applied very much or most of the time). Higher scores on each subscale indicated increasing severity of depression, anxiety, or stress. The internal consistency of subscales in our sample was α = between .736 and .888.

Anthropometry

Body mass index (BMI) was calculated from self-reported height and weight according to the Adolphe Quetelet formula: body weight (kg)/height (m2).

Statistical analyses

Statistical Package for the Social Sciences (IBM SPSS Statistics, version 24.0 for Windows, Armonk, NY, USA) was used for data analyses. Significance level was set at p ≤ .05. Descriptive statistics (percentages, frequencies, means, and standard deviations) were used to present all variables. Cronbach’s αs were calculated to determine internal consistency of scales used in the study. A multinomial logistic regression analysis was performed to find explanatory variables of risk of EA.

Variables in the model

Dependent variables measured exercise dependence symptoms on three levels: non-dependent asymptomatic, non-dependent symptomatic, and at risk of EA.

Independent variables included categorical (gender, educational level, financial situation, childhood physical activity, eating disorders, and other sports activity besides running) and continuous variables (age, BMI, duration of running activity, weekly time spent running, mean workout distance per session, body image, perceived health, life satisfaction, loneliness, stress, anxiety, and depression).

Analysis of variance (ANOVA) with Tukey’s post hoc test was performed to find differences among three groups in subscales and items of Exercise Dependence Scale (EDS).

Ethics

The Regional Ethic Committee and the institutional review board of the University of Miskolc approved the study. According to the criteria of the Helsinki Declaration on Consent to Research and Clinical Practice, information was provided to participants about the study, its anonymity, and the voluntary nature of the participation at the beginning of the questionnaire. Participants were then required to provide their consent (trough click), which acknowledged that they had read and understood the information and agreed to take part in the study.

Results

Participants’ characteristics

A total of 285 runners completed the questionnaire. Five were incomplete and thus deleted, 16 runners did not meet the criteria of having exercised for at least 2 years or provided no data about it. Two were less than 18 years of age, and an additional five were eliminated due to BMI below 17.5 (which is the diagnostic criterion for anorexia nervosa; APA, 2013). If an eating disorder is detected, the EA may be a secondary condition. Respondents represented all geographical and economic parts of the country. Demographic data are presented in Table 1.

Table 1.

Descriptive statistics of the participants

Mean (SD) and frequencyParticipants
Sample size257
Age (years)40.49 (8.99)
BMI23.89 (3.28)
Sex ratio (%)
 Male:female51.1:48.9
Socioeconomic background (%)
 Below average:average:above average5.2:77.7:17.2
Educational attainment (%)
 Elementary:high school:higher education0:38.2:61.8
Weekly amount of training sessions (%)
 1–2:3–4:5–7:8–10:10+ hr6.3:37.9:34.0:12.1:9.8
 Running activity (years)13.18 (12.42)
Other sport activities besides running (%)
 Yes:No70.8:29.2
Childhood physical activity (%)a
 Yes:no50.2:49.8

Note. SD: standard deviation.

Yes: to be engaged in regular sports; no: not to be engaged in regular sports.

Prevalence of risk of EA in amateur runners

The prevalence of risk of EA was 8.6%, whereas 53.6% of respondents were characterized non-dependent symptomatic and 37.8% non-dependent asymptomatic.

Predicting the likelihood of categorization for EA

A multinomial logistic regression was employed to analyze the association between the polychotomous categorical (EA group classification, such as non-dependent asymptomatic, non-dependent symptomatic, and at risk group for EA) and a set of explanatory variables. The reference category for the outcome variable was “non-dependent asymptomatic” and each of the other two categories was compared to this reference group. The logistic model was statistically significant, χ2(36) = 193.38, p < .001. The distribution of Cox and Snell R2 (.358) and Nagelkerke R2 (.427) suggests that between 35.8% and 42.6% of the variance is explained by the set of variables used in this model and 68.7% of cases were correctly classified. The likelihood ratio tests indicated that five factors (weekly time spent running, childhood physical activity, educational attainment, anxiety, and loneliness) contributed meaningfully to the full effect (Table 2).

Table 2.

Multinomial logistic regression predicting likelihood of exercise addiction (N = 257)

For at risk groupB (SE)95% CI for OR
VariablesLowerORUpperp
Duration of weekly training session1.43 (0.39)1.934.178.99<.001
Childhood sport activity (1)a2.06 (0.78)1.717.8636.20.008
Educational level (3)b1.97 (0.72)1.767.1729.26.006
Anxiety0.47 (0.21)1.071.612.42.023
Loneliness0.79 (0.27)1.292.213.78.004

Note. SE: standard error; CI: confidence interval; OR: odds ratio.

aChildhood sport activity: (1): to be engaged in regular sports. bEducational level: (3): higher education (college or university degree).

Participants’ attitude toward running

The analysis of the subscales of EDS suggested that runners from all groups found it important to continually increase exercise intensity, frequency, and duration [Tolerance subscale (3.71, SD = 1.28, 95% CI = 3.55–3.87)] and to spend a significant amount of time engaging in exercise [Time subscale (3.09, SD = 1.11, 95% CI = 2.96–3.23]). We observed that the at risk group scored higher on the Lack of Control subscale (4.90, SD = 0.76, 95% CI = 4.57–5.23) and therefore these runners were less able to control the urge to exercise or to stop exercising for a significant time. All investigated groups showed fewer problems on the Intention subscale (exercising longer than intended, expected, or planned; 2.39, SD = 1.10, 95% CI = 2.25–2.52) and the Reduction in Other Activities subscale (choosing or thinking about exercise rather than spending time with family, friends, or concentrating on school or work; 1.90, SD = 0.82, 95% CI = 1.80–2.00).

The ANOVA with Tukey’s post hoc test revealed that all three groups significantly differed from each other in all subscales (Table 3) and in almost all items except Item 2 (I exercise despite recurring physical problems) and 19 (I choose to exercise so that I can get out of spending time with family/friends) between symptomatic and at risk groups.

Table 3.

One-way ANOVA for EDS subscales

SubscalesNon-dependent asymptomaticNon-dependent symptomaticAt risk of exercise addictionF testηp2
n = 97n = 137n = 23
M (SD)95% CIM (SD)95% CIM (SD)95% CI
LULULU
To2.83 (1.14)2.613.064.11 (1.01)3.944.285.06 (0.88)4.685.4463.053*.365
Ti2.43 (0.86)2.262.613.29 (0.94)3.133.454.74 (0.79)4.405.0868.147*.371
C2.21 (1.04)2.002.423.22 (1.12)3.033.414.28 (1.41)3.674.8841.578*.304
L1.86 (0.72)1.712.003.11 (0.80)2.983.244.90 (0.76)4.575.23171.509*.587
W2.03 (0.98)1.832.223.00 (1.31)2.783.224.00 (1.48)3.364.6432.757*.222
I1.73 (0.71)1.591.872.59 (0.94)2.432.753.92 (1.33)3.344.5061.963*.360
R1.39 (0.40)1.311.472.08 (0.74)1.962.213.01 (1.03)2.573.4665.249*.386

Note. To: Tolerance; Ti: Time; C: Continuance; L: Lack of Control; W: Withdrawal; I: Intention Effect; R: Reduction; M: mean; SD: standard deviation; L: lower bound; U: upper bound; EDS: Exercise Dependence Scale; ANOVA: analysis of variance; CI: confidence interval.

*p < .001.

Discussion

In this study, amateur runners were assessed based on a validated and widely used multidimensional scale, that is, the EDS categorization. The prevalence of runners at risk of EA was moderate (8.6%) in this study. The result provides some evidence that this problem exists, but to date it is not as extensive as other behavioral addictions (Sussman, Lisha, & Griffiths, 2011). The prevalence rate is greatly varied across the studies (Egorov & Szabo, 2013) and this discrepancy might possibly be related to both different questionnaires being used in the studies (Weik & Hale, 2009) and to the differences in the investigated populations, such as students (Sicilia, Alías-García, Ferriz, & Moreno-Murcia, 2013), competitive athletes (Blaydon & Lindner, 2002), or recreational exercisers (Mayolas-Pi et al., 2017). Population-based studies showed a very low prevalence of 0.09%–0.30% in the general population (Mónok et al., 2012; Müller et al., 2013). In this study, we focused specifically on the physically active adult population with at least 2 years of prior running activity. All participants were amateur runners and the male to female ratio was well-balanced in our sample.

The etiology of EA is undoubtedly multifactorial. In this study, psychological aspects (anxiety and loneliness), exercise characteristics (time spent training and childhood physical activity), as well as a demographic factor (educational level) regressed EA.

It is well known that EA can cause psychological distress. Exercisers often feel anxiety when they cannot exercise for any reason. In addition to anxiety, we found loneliness as an explanatory variable. It should not be surprising that athletes generally run alone and find this privacy pleasurable, but being alone does not necessarily mean being lonely (Presumably, personality traits have an impact on the type of sport activity the person chooses.). A systematic review was uncertain about the relationship between physical activity and loneliness. On one hand, it found that physical activity can contribute to a decrease in loneliness and on the other hand there was some evidence that physical inactivity may lead to feelings of loneliness over time (Pels & Kleinert, 2016). As sports psychology literature on loneliness is scarce, future research is needed to understand the influence of loneliness the development of risk of EA. Although Menczel (2016) did not evaluate the loneliness factor, she observed that athletes exercising alone show more symptoms of EA than those involved in team sports. However, another study failed to disclose differences in EA between individual and team exercisers (Kovacsik, Soós, de la Vega, Ruíz-Barquín, & Szabo, 2018). Logically, our findings support the theory that lonely exercisers use the sport activity as a source of joyfulness and happiness. To deal with both anxiety and loneliness, exercisers increase the time or volume of sport activity as they need more and more to achieve this feeling. It is noteworthy that exercising excessively is not necessarily maladaptive. Elite athletes perform their training with great volume and intensity, but are not at risk of EA. If they were, they would not be able to compete at a high level. Thus, the results of the instrument used among elite athletes (Szabo et al., 2015) must be interpreted with caution when considering amateur athletes. We think this sport-specific factor should be taken into consideration in future investigation of amateur/recreational runners evaluating the risk for EA.

One interesting and novel finding is that childhood engagement in sport activity predicts the likelihood of EA. The health benefits of regular physical activity are well established and it is also well known that childhood physical activity has benefits in later life (Janssen & LeBlanc, 2010) and predicts adult physical activity (Telama et al., 2005). The effect of childhood engagement in sport activity on EA needs deeper research. A theoretical approach to this relationship may include the adolescent population (both girls and boys), which may also show symptoms of EA. Downs, Savage, and DiNallo (2013) found that the majority of physically active youth was within the symptomatic range suggesting that many adolescents show early signs of exercise pathology that can lead to the development of EA later in life among the physically active population.

In our adult sample, all had high-school education and a majority (~60%) had completed their higher education degree. Lower education level is a predictor of EA. It is likely that studying at universities or colleges improves skills to deal with emotional distress and coping mechanisms, which in turn can prevent behavioral disturbance and presumably several other problems (Menczel, 2016).

There are some limitations in this study that must be acknowledged. Our survey is voluntary and response-based. The self-report EDS provides risk score and at risk categorization and not a clinical diagnosis. Although participants are from different parts of Hungary, representing all geographical and economic regions, non-respondents might differ from the respondents, which can limit the generalizability of the findings. Childhood physical activity, one of our predictors, is measured with a single item instead of a validated scale. It is supposed that a single-item measure can be used and is appropriate when the construct is narrow and unambiguous to the respondents (Carsrude & Brännback, 2014). Because of the cross-sectional design of this study, we cannot establish either the direction or the causality of the effects.

Conclusions and Implications

The present findings add to the studies that focus on risk of EA using multidimensional approach in amateur athletes. Physical activity recommendations and guidelines indicate that a high volume of physical activity needs to be performed every day for school-aged children and young adults (Janssen & LeBlanc, 2010). Given that regular physical exercise has several health benefits, it seems controversial that higher amounts of weekly workout and childhood sports activity predict EA. While examining risk of EA, it is important to consider the possible comorbidities and psychosocial problems of the individual. It is supposed that inactive youth will likely remain inactive in adulthood; they will not be engaged in sport activity in later life and never will face risk of EA. As long as the main public health concern is the physical inactivity, the guidelines will not focus on rare behavioral problems such as EA. Anxiety and loneliness are two other predictors of EA important to consider. Loneliness is a predictor of anxiety. Both feelings of loneliness and isolation are a cause and a symptom of anxiety (Muyan et al., 2016). Lonely individuals try to reduce their anxiety with sport (Phillips, Kiernanm, & King, 2003) and spending time by themselves. Running seems to be an optimal sport for them, but they may need more and more workouts to feel better. Future investigations should extend to other types of sports to examine these findings.

Risk of EA occurs among amateur runners, but research studies have contradictory findings as it relates to influencing and predicting factors, presumably due to the varying methods and investigated populations. To date, it is difficult to give unequivocal answers to the real reasons of the development of risk of EA. For prevention and treatment of this phenomenon, a methodologically well-established and longitudinal research program should address the issue. Nonetheless, we believe that our multidimensional research study contributes to the overall knowledge related to EA.

Authors’ contribution

AL and KM drafted the manuscript, contributed in study concept and design, and wrote the manuscript. KM, PS, and BV were involved in data collection. LA and BV conducted the statistical analysis. All authors discussed the results and contributed to the final version of the manuscript.

Conflict of interest

The authors declare no conflict of interest.

Acknowledgements

The study was carried out as part of the EFOP-3.6.1-16-00011 “Younger and Renewing University – Innovative Knowledge City – institutional development of the University of Miskolc aiming at intelligent specialisation” project implemented in the framework of the Szechenyi 2020 program. The realization of this project is supported by the European Union, co-financed by the European Social Fund.

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  • Kotbagi, G., Morvan, Y., Romo, L., & Kern, L. (2017). Which dimensions of impulsivity are related to problematic practice of physical exercise? Journal of Behavioral Addictions, 6(2), 221228. doi:10.1556/2006.6.2017.024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kovacsik, R., Soós, I., de la Vega, R., Ruíz-Barquín, R., & Szabo, A. (2018). Passion and exercise addiction: Healthier profiles in team than in individual sports. International Journal of Sport and Exercise Psychology. Advance online publication. doi:10.1080/1612197X.2018.1486873

    • Search Google Scholar
    • Export Citation
  • Landolfi, E. (2013). Exercise addiction. Sports Medicine, 43(2), 111119. doi:10.1007/s40279-012-0013-x

  • Lee, D. C., Pate, R. R., Lavie, C.J., Sui, X., Church, T. S., & Blair, S. N. (2014). Leisure-time running reduces all-cause and cardiovascular mortality risk. Journal of the American College of Cardiology, 64(5), 472481. doi:10.1016/j.jacc.2014.04.058

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lichtenstein, M. B., Nielsen, R. O., Gudex, C., Hinze, C. J., & Jørgensen, U. (2018). Exercise addiction is associated with emotional distress in injured and non-injured regular exercisers. Addictive Behaviors Reports, 8, 3339. doi:10.1016/j.abrep.2018.06.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales (2nd ed.). Sydney, Australia: Psychology Foundation.

    • Search Google Scholar
    • Export Citation
  • Mayolas-Pi, C., Simón-Grima, J., Peñarrubia-Lozano, C., Munguía-Izquierdo, D., Moliner-Urdiales, D., & Legaz-Arrese, A. (2017). Exercise addiction risk and health in male and female amateur endurance cyclists. Journal of Behavioral Addictions, 6(1), 7483. doi:10.1556/2006.6.2017.018

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menczel, Z. (2016). The behavioral and psychological context of exercise dependence (Doctoral dissertation). Semmelweis University, Budapest.

    • Search Google Scholar
    • Export Citation
  • Mónok, K., Berczik, K., Urbán, R., Szabo, A., Griffiths, M. D., Farkas, J., Magia, A., Eisingera, A., Kurimayf, T., Kökönyeia, G., Kuna, B., Paksig, B., & Demetrovics, Z. (2012). Psychometric properties and concurrent validity of two exercise addiction measures: A population wide study. Psychology of Sport & Exercise, 13(6), 739746. doi:10.1016/j.psychsport.2012.06.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morgan, J. F., Reid, F., & Lacey, J. H. (1999). The SCOFF Questionnaire: Assessment of a new screening tool for eating disorders. British Medical Journal, 319(7223), 14671468. doi:10.1136/bmj.319.7223.1467

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muyan, M., Chang, E. C., Jilani, Z., Yu, T., Lin, J., & Hirsch, J. K. (2016). Loneliness and negative affective conditions in adults: Is there any room for hope in predicting anxiety and depressive symptoms? The Journal of Psychology: Interdisciplinary and Applied, 150(3), 333341. doi:10.1080/00223980.2015.1039474

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Müller, A., Claes, L., Smits, D., Gefeller, O., Hilbert, A., Herberg, A., Müller, V., Hofmeister, D., & de Zwaan, M. (2013). Validation of the German version of the Exercise Dependence Scale. European Journal of Psychological Assessment, 29(3), 213219. doi:10.1027/1015-5759/a000144

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Donovan, G., Blazevich, A. J., Boreham, C., Cooper, A. R., Crank, H., Ekelund, U., Fox, K. R., Gately, P., Giles-Corti, B., Gill, J. M., Hamer, M., McDermott, I., Murphy, M., Mutrie, N., Reilly, J. J., Saxton, J. M., & Stamatakis, E. (2010). The ABC of physical activity for health: A consensus statement from the British Association of Sport and Exercise Sciences. Journal of Sports Sciences, 28, 573591. doi:10.1080/02640411003671212

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orbach, I., & Mikulincer, M. (1998). The Body Investment Scale: Construction and validation of a Body Experience Scale. Psychological Assessment, 10(4), 415425. doi:10.1037/1040-3590.10.4.415

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pels, F., & Kleinert, J. (2016). Loneliness and physical activity: A systematic review. International Review of Sport and Exercise Psychology, 9(1), 231260. doi:10.1080/1750984X.2016.1177849

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phillips, W. T., Kiernanm, M., & King, A. C. (2003). Physical activity as a nonpharmacological treatment for depression: A review. Journal of Evidence-Based Complementary and Alternative Medicine, 8(2), 139152. doi:10.1177/1076167502250792

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sicilia, Á., Alías-García, A., Ferriz, R., & Moreno-Murcia, J. A. (2013). Spanish adaptation and validation of the Exercise Addiction Inventory (EAI). Psicothema, 25, 377383. doi:10.7334/psicothema2013.21

    • Search Google Scholar
    • Export Citation
  • Sussman, S., Lisha, N., & Griffiths, M. (2011). Prevalence of the addictions: A problem of the majority or the minority? Evaluation & the Health Professions, 34(1), 356. doi:10.1177/0163278710380124

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Szabo, A. (2010). Addiction to exercise: A symptom or a disorder? New York, NY: Nova Science Publishers.

  • Szabo, A., Griffiths, M. D., de La Vega Marcos, R., Mervó, B., & Demetrovics, Zs. (2015). Methodological and conceptual limitations in exercise addiction research. The Yale Journal of Biology and Medicine, 88, 303308.

    • Search Google Scholar
    • Export Citation
  • Telama, R., Yang, X., Viikari, J., Välimäki, I., Wanne, O., & Raitakari, O. (2005). Physical activity from childhood to adulthood: A 21-year tracking study. American Journal of Preventive Medicine, 28(3), 267273. doi:10.1016/j.amepre.2004.12.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weik, M., & Hale, B. D. (2009). Contrasting gender differences on two measures of exercise dependence. British Journal of Sport Medicine, 43(3), 204207. doi:10.1136/bjsm.2007.045138

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zmijewski, C. F., & Howard, M. O. (2003). Exercise dependence and attitudes toward eating among young adults. Eating Behaviors, 4(2), 181195. doi:10.1016/S1471-0153(03)00022-9

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adams, J., & Kirkby, R. J. (2002). Excessive exercise as addiction: A review. Addiction Research and Theory, 10, 415437. doi:10.1080/1606635021000032366

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  • Blaydon, M. J., & Lindner, K. J. (2002). Eating disorders and exercise dependence in triathletes. Eating Disorders, 10(1), 4960. doi:10.1080/106402602753573559

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  • Czeglédi, E., Urbán, R., & Csizmadia, P. (2010). Measuring body image: Psychometric properties and construct validity of the Hungarian version of Body Attitude Test. Magyar Pszichológiai Szemle, 65(3), 431461. doi:10.1556/MPSzle.65.2010.3.1

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  • Demetrovics, Z., & Kurimay, T. (2008). Exercise addiction: A literature review. Psychiatria Hungarica, 23, 129141. [article in Hungarian]

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  • Downs, D. S., Hausenblas, H. A., & Nigg, C. R. (2004). Factorial validity and psychometric examination of the Exercise Dependence Scale-Revised. Measurement in Physical Education and Exercise Science, 8(4), 183201. doi:10.1207/s15327841mpee0804_1

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  • Downs, D. S., Savage, J. S., & DiNallo, J. M. (2013). Self-determined to exercise? Leisure-time exercise behavior, exercise motivation, and exercise dependence in youth. Journal of Physical Activity and Health, 10(2), 176184. doi:10.1123/jpah.10.2.176

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  • Dukay-Szabó, S., Simon, D., Varga, M., Szabó, P., Túry, F., & Rathner, G. (2016). Hungarian adaptation of a Short Eating Disorders Scale (SCOFF). Ideggyógyászati Szemle, 69, 34. doi:10.18071/isz.69.E014

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  • Egorov, A. Y., & Szabo, A. (2013). The exercise paradox: An interactional model for a clearer conceptualization of exercise addiction. Journal of Behavioral Addictions, 2(4), 199208. doi:10.1556/JBA.2.2013.4.2

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  • Hausenblas, H. A., & Downs, D. S. (2002a). Exercise dependence: A systematic review. Psychology of Sport and Exercise, 3(2), 89123. doi:10.1016/S1469-0292(00)00015-7

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  • Hausenblas, H. A., & Downs, D. S. (2002b). How much is too much? The development and validation of the Exercise Dependence Scale. Psychology and Health, 17(4), 387404. doi:10.1080/0887044022000004894

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  • Janssen, J., & LeBlanc, A. G. (2010). Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 7(1), 40. doi:10.1186/1479-5868-7-40

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  • Johnson, M. (2000). Understanding exercise addiction. New York, NY: The Rosen Publishing Group.

  • Kotbagi, G., Morvan, Y., Romo, L., & Kern, L. (2017). Which dimensions of impulsivity are related to problematic practice of physical exercise? Journal of Behavioral Addictions, 6(2), 221228. doi:10.1556/2006.6.2017.024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kovacsik, R., Soós, I., de la Vega, R., Ruíz-Barquín, R., & Szabo, A. (2018). Passion and exercise addiction: Healthier profiles in team than in individual sports. International Journal of Sport and Exercise Psychology. Advance online publication. doi:10.1080/1612197X.2018.1486873

    • Search Google Scholar
    • Export Citation
  • Landolfi, E. (2013). Exercise addiction. Sports Medicine, 43(2), 111119. doi:10.1007/s40279-012-0013-x

  • Lee, D. C., Pate, R. R., Lavie, C.J., Sui, X., Church, T. S., & Blair, S. N. (2014). Leisure-time running reduces all-cause and cardiovascular mortality risk. Journal of the American College of Cardiology, 64(5), 472481. doi:10.1016/j.jacc.2014.04.058

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lichtenstein, M. B., Nielsen, R. O., Gudex, C., Hinze, C. J., & Jørgensen, U. (2018). Exercise addiction is associated with emotional distress in injured and non-injured regular exercisers. Addictive Behaviors Reports, 8, 3339. doi:10.1016/j.abrep.2018.06.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales (2nd ed.). Sydney, Australia: Psychology Foundation.

    • Search Google Scholar
    • Export Citation
  • Mayolas-Pi, C., Simón-Grima, J., Peñarrubia-Lozano, C., Munguía-Izquierdo, D., Moliner-Urdiales, D., & Legaz-Arrese, A. (2017). Exercise addiction risk and health in male and female amateur endurance cyclists. Journal of Behavioral Addictions, 6(1), 7483. doi:10.1556/2006.6.2017.018

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menczel, Z. (2016). The behavioral and psychological context of exercise dependence (Doctoral dissertation). Semmelweis University, Budapest.

    • Search Google Scholar
    • Export Citation
  • Mónok, K., Berczik, K., Urbán, R., Szabo, A., Griffiths, M. D., Farkas, J., Magia, A., Eisingera, A., Kurimayf, T., Kökönyeia, G., Kuna, B., Paksig, B., & Demetrovics, Z. (2012). Psychometric properties and concurrent validity of two exercise addiction measures: A population wide study. Psychology of Sport & Exercise, 13(6), 739746. doi:10.1016/j.psychsport.2012.06.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morgan, J. F., Reid, F., & Lacey, J. H. (1999). The SCOFF Questionnaire: Assessment of a new screening tool for eating disorders. British Medical Journal, 319(7223), 14671468. doi:10.1136/bmj.319.7223.1467

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muyan, M., Chang, E. C., Jilani, Z., Yu, T., Lin, J., & Hirsch, J. K. (2016). Loneliness and negative affective conditions in adults: Is there any room for hope in predicting anxiety and depressive symptoms? The Journal of Psychology: Interdisciplinary and Applied, 150(3), 333341. doi:10.1080/00223980.2015.1039474

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Müller, A., Claes, L., Smits, D., Gefeller, O., Hilbert, A., Herberg, A., Müller, V., Hofmeister, D., & de Zwaan, M. (2013). Validation of the German version of the Exercise Dependence Scale. European Journal of Psychological Assessment, 29(3), 213219. doi:10.1027/1015-5759/a000144

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Donovan, G., Blazevich, A. J., Boreham, C., Cooper, A. R., Crank, H., Ekelund, U., Fox, K. R., Gately, P., Giles-Corti, B., Gill, J. M., Hamer, M., McDermott, I., Murphy, M., Mutrie, N., Reilly, J. J., Saxton, J. M., & Stamatakis, E. (2010). The ABC of physical activity for health: A consensus statement from the British Association of Sport and Exercise Sciences. Journal of Sports Sciences, 28, 573591. doi:10.1080/02640411003671212

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orbach, I., & Mikulincer, M. (1998). The Body Investment Scale: Construction and validation of a Body Experience Scale. Psychological Assessment, 10(4), 415425. doi:10.1037/1040-3590.10.4.415

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pels, F., & Kleinert, J. (2016). Loneliness and physical activity: A systematic review. International Review of Sport and Exercise Psychology, 9(1), 231260. doi:10.1080/1750984X.2016.1177849

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phillips, W. T., Kiernanm, M., & King, A. C. (2003). Physical activity as a nonpharmacological treatment for depression: A review. Journal of Evidence-Based Complementary and Alternative Medicine, 8(2), 139152. doi:10.1177/1076167502250792

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sicilia, Á., Alías-García, A., Ferriz, R., & Moreno-Murcia, J. A. (2013). Spanish adaptation and validation of the Exercise Addiction Inventory (EAI). Psicothema, 25, 377383. doi:10.7334/psicothema2013.21

    • Search Google Scholar
    • Export Citation
  • Sussman, S., Lisha, N., & Griffiths, M. (2011). Prevalence of the addictions: A problem of the majority or the minority? Evaluation & the Health Professions, 34(1), 356. doi:10.1177/0163278710380124

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Szabo, A. (2010). Addiction to exercise: A symptom or a disorder? New York, NY: Nova Science Publishers.

  • Szabo, A., Griffiths, M. D., de La Vega Marcos, R., Mervó, B., & Demetrovics, Zs. (2015). Methodological and conceptual limitations in exercise addiction research. The Yale Journal of Biology and Medicine, 88, 303308.

    • Search Google Scholar
    • Export Citation
  • Telama, R., Yang, X., Viikari, J., Välimäki, I., Wanne, O., & Raitakari, O. (2005). Physical activity from childhood to adulthood: A 21-year tracking study. American Journal of Preventive Medicine, 28(3), 267273. doi:10.1016/j.amepre.2004.12.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weik, M., & Hale, B. D. (2009). Contrasting gender differences on two measures of exercise dependence. British Journal of Sport Medicine, 43(3), 204207. doi:10.1136/bjsm.2007.045138

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zmijewski, C. F., & Howard, M. O. (2003). Exercise dependence and attitudes toward eating among young adults. Eating Behaviors, 4(2), 181195. doi:10.1016/S1471-0153(03)00022-9

    • Crossref
    • Search Google Scholar
    • Export Citation
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Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
sumbission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access
Purchase per Title  

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Publication
Programme
2021 Volume 10
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Anikó MARÁZ (Humboldt University of Berlin, Germany)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)
  • Attila SZABÓ (ELTE Eötvös Loránd University, Hungary)
  • Róbert URBÁN (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. WEINSTEIN (Ariel University, Israel)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Beáta BÖTHE (University of Montreal, Canada)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Eötvös Loránd University, Hungary)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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