Authors:
Alessandro Musetti Department of Humanities, Social Sciences and Cultural Industries, University of Parma, Parma, Italy

Search for other papers by Alessandro Musetti in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-4125-0068
,
Nirit Soffer-Dudek Consciousness and Psychopathology Laboratory, Department of Psychology, Ben-Gurion University of the Negev, Beer-Sheva, Israel

Search for other papers by Nirit Soffer-Dudek in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-8778-798X
,
Chiara Imperato Department of Humanities, Social Sciences and Cultural Industries, University of Parma, Parma, Italy

Search for other papers by Chiara Imperato in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-4787-0631
,
Adriano Schimmenti Faculty of Human and Social Sciences, UKE – Kore University of Enna, Enna, Italy

Search for other papers by Adriano Schimmenti in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-5181-2648
, and
Christian Franceschini Department of Medicine and Surgery, University of Parma, Parma, Italy

Search for other papers by Christian Franceschini in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-9307-5209
Open access

Abstract

Background and aims

Maladaptive Daydreaming (MD) is a suggested syndrome where individuals become addicted to fantasizing vividly for hours on end at the expense of engaging in real-world relationships and functioning. MD can be seen as a behavioral addiction. However, a paucity of longitudinal research means that there is no empirical evidence confirming the stability of this alleged addiction. Moreover, the direction of its association with psychopathology is unclear.

Methods

We examine, for the first time, long-term stability and longitudinal associations between MD, psychological distress (stress, anxiety, and depression symptoms) and COVID-19 related exposure.

Results

Participants (N = 814) completed an online survey twice, with a lag of 13 months. A two-wave structural equation model demonstrated high MD stability and positive cross-lagged pathways from MD to psychological distress. COVID-19 related exposure was not a longitudinal predictor.

Discussion and conclusions

MD is a stable condition and a risk factor for an increase in psychological distress.

Abstract

Background and aims

Maladaptive Daydreaming (MD) is a suggested syndrome where individuals become addicted to fantasizing vividly for hours on end at the expense of engaging in real-world relationships and functioning. MD can be seen as a behavioral addiction. However, a paucity of longitudinal research means that there is no empirical evidence confirming the stability of this alleged addiction. Moreover, the direction of its association with psychopathology is unclear.

Methods

We examine, for the first time, long-term stability and longitudinal associations between MD, psychological distress (stress, anxiety, and depression symptoms) and COVID-19 related exposure.

Results

Participants (N = 814) completed an online survey twice, with a lag of 13 months. A two-wave structural equation model demonstrated high MD stability and positive cross-lagged pathways from MD to psychological distress. COVID-19 related exposure was not a longitudinal predictor.

Discussion and conclusions

MD is a stable condition and a risk factor for an increase in psychological distress.

Introduction

Maladaptive daydreaming (MD) is a suggested syndrome characterized by immersing oneself in compulsive or addictive structured fictional narratives that are vivid and fanciful, and which replace human interaction (Somer, Soffer-Dudek, Ross, & Halpern, 2017; Somer, Lehrfeld, Bigelsen, & Jopp, 2016). MD differs from mind wandering which is a universal phenomenon of consciousness characterized by a shift of attention from present activity to self-generated thoughts (Valkenburg & van der Voort, 1994); turning to MD is often more purposeful, with increased awareness and intent (Theodor-Katz et al., 2022). Several studies have pointed out the benefits related to mind wandering or daydreaming (McMillan, Kaufman, & Singer, 2013), such as constructive planning, engendering creativity and providing mental breaks to relieve boredom (Mooneyham & Schooler, 2013; Smallwood & Schooler, 2015). While many scholars use the terms daydreaming and mind-wandering interchangeably, our focus is on a unique type of self-generated thoughts, which is vivid fantasizing. A minority of individuals, labeled as maladaptive daydreamers, excessively engage in this mental activity and have difficulty in controlling or taming it, which significantly interferes with their functioning, including daily, family, social and work activities. Engaging in MD is enjoyable in the short run, yet detrimental in the long run, which is why it has been conceptualized as a behavioral addiction (Pietkiewicz, Nęcki, Bańbura, & Tomalski, 2018; Soffer-Dudek, Somer, Abu-Rayya, Metin, & Schimmenti, 2020). Notably, maladaptive daydreamers often use kinaesthetic or stereotyped movements (e.g., shaking one's hands, swinging, pacing) and expose themselves to evocative music to facilitate and maintain the absorption in fictional narratives (Schimmenti, Sideli, La Marca, Gori, & Terrone, 2020; Somer, Lehrfeld, et al., 2016). These are tools that help the individual deliberately activate the desired state, implying again that there is a compulsive-addictive component to MD, different from general mind-wandering, dreaminess, or inattention (Theodor-Katz et al., 2022).

Although MD has not yet been recognized as a psychiatric nosology by major psychiatric diagnostic systems, a growing body of research is demonstrating its clinical significance (Schimmenti, 2019). MD is strongly associated with a range of psychopathological symptoms, such as anxiety and depression (Somer, Soffer-Dudek, & Ross, 2017). In a 2-week daily diary study among maladaptive daydreamers, days with heightened levels of MD were associated with elevated psychological distress and negative emotion on the same day and on the following one (Soffer-Dudek & Somer, 2018). This was explained by the guilt and shame that stem from MD. However, psychological distress is also probably an antecedent of MD. For example, many maladaptive daydreamers may use their innate tendency toward waking fantasy to escape stressful or adverse life events (Soffer-Dudek & Somer, 2018). Indeed, there is evidence that COVID-19 related exposure (e.g., self-quarantine) was associated with heightened MD levels during the COVID-19 outbreak (Metin, Somer, Abu-Rayya, Schimmenti, & Göçmen, 2021). However, to our knowledge, no study has examined MD and its associations with stressful events in long-term longitudinal designs. Addressing this gap is relevant because recent cross-sectional studies have suggested the plausible stability of MD in relation to underlying structures, such as defense styles (Musetti et al., 2022) or attachment styles (Mariani et al., 2022), or dysfunctional patterns of emotion regulation (Chirico et al., 2022; Soffer-Dudek & Somer, 2022). Moreover, although previous research has established a positive association between MD and psychological distress (Soffer-Dudek & Somer, 2018), the long-term direction of this effect needs further examination. Thus, it is still unclear: (1) whether MD represents a stable condition. Addictions are characterized by difficulty in overcoming them spontaneously. Thus, we should ask: is MD persistent? And (2) to what extent it is influenced by exposure to stressors, or conversely, to what extent it predicts the increase in psychological distress over time. On these grounds, in this study we adopted a structural equation modelling (SEM) approach to disentangle the longitudinal pathways between MD, psychological distress, and COVID-19 related exposure (Musetti et al., 2021). Such an exploration is critical for establishing the clinical significance of this construct. A SEM framework allows to differentiate shared from non-shared variance which will allow us to better understand the longitudinal link with different, inter-related, symptoms and stressors.

As MD has been suggested to represent a clinical disorder (Somer, Soffer-Dudek, Ross, et al., 2017), deserving of appropriately tailored psychological treatment (Herscu, Somer, Federman, & Soffer-Dudek, in press), it is important to establish its associations with other clinical symptoms, and to establish that it does not disappear spontaneously over the course of a year or so, i.e., it is stable. Thus, the aim of this study was to test these longitudinal associations at 13 months follow up. Based on previous theoretical and clinical knowledge (Bigelsen, Lehrfeld, Jopp, & Somer, 2016; Soffer-Dudek & Somer, 2018), we hypothesized that:

H1

MD will be a stable construct, i.e., higher MD levels at T1 would predict greater MD levels at T2.

H2

Stress and psychopathological symptoms would be etiological factors affecting MD levels, i.e., higher psychological distress at T1 would predict greater MD levels at T2.

H3

Because MD is maladaptive it will generate further difficulties, i.e., higher MD levels at T1 would predict greater psychological distress at T2.

Methods

Participants and procedure

Data reported in the current study were collected between March and May 2020 (T1) and between April and June 2021 as a part of a larger multipurpose project on the impact of the COVID-19 pandemic on the Italian population (Musetti et al., 2021). An online survey was disseminated through university communication systems and social media. All participants were informed about the purpose of the study, as well as the questionnaires being used in the study, before completing the survey. Participation was voluntary whereas giving informed consent was mandatory. Only after expressing written consent, participants provided a subject-generated identification code to link their data across two time points. The inclusion criteria were: to be 18 or older, to be an Italian speaker, to have lived in Italy during the COVID-19 lockdown. Participants did not receive any compensation for their involvement in the study.

We surveyed a snowball sample of 6277 respondents at T1 (their data were reported in a different publication; Musetti et al., 2021). Of them, 814 participants (635 females) aged 18 to 74 (M = 32.15 years, SD = 13.10) completed the two waves of the study.

Measures

Maladaptive Daydreaming: We used the Maladaptive Daydreaming Scale-16 (MDS-16) (Schimmenti et al., 2020; Somer, Lehrfeld, et al., 2016) to assess participants' levels of MD. This questionnaire consists of 16 items ranging from 0 (never/none of the time) to 100 (extremely frequent/all the time) in increments of 10. Higher mean scores indicate higher levels of MD. In the present study, Cronbach's alpha of the total scale was 0.92 at T1 and 0.93 at T2, and McDonald's omega was 0.91 at T1 and 0.92 at T2.

Psychological distress: The Depression Anxiety Stress Scale-21 (DASS-21) (Bottesi et al., 2015; Lovibond & Lovibond, 1996) consists of 21 items comprising three subscales: 1) depression (Cronbach's alpha T1 = 0.89; Cronbach's alpha T2 = 0.91; McDonald's omega T1 = 0.89; McDonald's omega T2 = 0.91); 2) anxiety (Cronbach's alpha T1 = 0.81; Cronbach's alpha T2 = 0.82; McDonald's omega T1 = 0.81; McDonald's omega T2 = 0.82); 3) and stress (Cronbach's alpha T1 = 0.89; Cronbach's alpha T2 = 0.91; McDonald's omega T1 = 0.89; McDonald's omega T2 = 0.91). Responses are rated on a 4-point Likert scale ranging from 0 (never) to 3 (almost always), with higher scores indicating more severe symptoms. In the present study, for each time point we created a latent construct representing psychological distress from these three indicators, but also allowed for the prediction of their uniquenesses. In keeping with the scale's overall purpose and structure, we found correlations which were high, but not undifferentiated, between the subscales at each time point (0.61, 0.68, 0.71 at T1 and 0.63, 0.71, 0.77 at T2, for anxiety-depression, stress-anxiety, stress-depression, respectively), suggesting that they tap onto general psychopathological distress to a great extent, but also, may carry specific variances. This justified our decision to compute a single distress latent variable per wave defined by these three indicators, yet also model relationships with specific components.

COVID-19 related exposure: We assessed COVID-19 related exposure with six ad hoc items based on existing literature: COVID-19 diagnosis (yes, no), forced quarantine (yes or no), someone close was positive for COVID-19 (yes or no), mourning related to COVID- 19 (yes or no), face-to-face and online social relationship changes (decreased, stable, increased). We included a manifest variable that captured the extent of COVID-19 related exposure. Because the items had different response scales, we computed one composite score by summing up the first four dichotomous items and multiplying that sum by the average of the last two items reversed (i.e., decreasing relationships corresponded theoretically with higher stress). We assumed an additive effect where more positive answers on these six different COVID-19 related items would imply more COVID-19 related stress.

Statistical analysis

Analyses were performed using M-PLUS, v. 8.1 statistical package (Muthén & Muthén, 2017). To test our hypotheses, we used a SEM framework to test stability paths, intra-wave associations and cross-lagged effects between COVID-19 related exposure, psychological distress (general and components), and MD. As the variables distributed normally with no significant deviations in skewness and kurtosis except for the COVID-19 related exposure variables, we did not use bootstrapping procedures. In our model, we specifically tested: stability paths (e.g., COVID-19 related exposure at T1 predicting COVID-19 related exposure at T2, stress, anxiety and depression at T1 predicting stress, anxiety and depression at T2, MD at T1 predicting MD at T2), within-time covariations among all variables at both T1 and T2, and MD cross-lagged paths (e.g., MD at T1 predicting COVID-19 related exposure, stress, anxiety, and depression at T2). In addition, autocorrelations were specified, i.e., indicators (MD parcels and distress components) were allowed to covary between T1 and T2. As far as MD parcels, we used the random parcelling technique (Little, Cunningham, Shahar, & Widaman, 2002). Specifically, we computed the first parcel by the average of six randomly selected items, and the second and the third parcel by the average of five randomly selected items. The composition of the parcels was: parcel 1 was composed by items: 13, 6, 11, 15, 1, 14; parcel 2 was composed by items: 3, 7, 4, 5, 16; parcel 3 was composed by items: 12, 9, 8, 2, 10.

Ethics

The study procedures were carried out in accordance with the Declaration of Helsinki. The Ethics Committee of the Center for Research and Psychological Intervention (CERIP) of the University of Messina approved the study. All subjects were informed about the study and all provided informed consent.

Results

T1-T2 paired comparisons

As far as differences between T1 and T2, results of paired t-tests showed that all variables significantly changed over time (see Table 1). Specifically, COVID-19 related exposure significantly decreased from T1 to T2. On the other hand, both stress, anxiety, and depression significantly increased from T1 to T2. Lastly, as far as MD, it significantly decreased from T1 to T2.

Table 1.

T1-T2 paired comparisons (n = 814)

T1T2t(df)pCohens' d
MSDMSD
COVID-19 related exposure0.561.560.210.576.09 (793)<0.0010.22
Stress16.659.7817.3910.23−2.25 (813)<0.05−0.08
Anxiety6.987.137.447.58−1.91 (813)<0.05−0.07
Depression11.959.6612.8510.69−2.69 (813)<0.01−0.09
Maladaptive Daydreaming31.1618.5528.8918.834.33 (813)<0.0010.15

Descriptive statistics

Pearson's correlations among manifest variables are presented in Table 2. As evident in the table, the T1-T2 stability correlation for MD was very high (r = 0.68, p < 0.001). Psychological distress was also stable (r = 0.55–0.56 for all scales), whereas COVID-19 related exposure was unstable (r = 0.02, p = 0.636). All symptom scales and MD were significantly interrelated whereas COVID-19 related exposure was mostly unrelated to other variables, except for a weak correlation between T2 COVID-19 related exposure and T2 anxiety.

Table 2.

Means, standard deviations and Pearson's correlations among variables (n = 814)

1.2.3.4.5.6.7.8.9.
1. COVID-19 related exposure T1
2. COVID-19 related exposure T20.02
3. Stress T10.060.05
4. Stress T20.030.060.56***
5. Anxiety T10.050.060.68***0.46***
6. Anxiety T20.000.07*0.46***0.71***0.55***
7. Depression T10.030.030.71***0.47***0.61***0.44***
8. Depression T20.040.030.46***0.77***0.38***0.63***0.56***
9. Maladaptive Daydreaming T10.030.030.32***0.28***0.32***0.29***0.35***0.28***
10. Maladaptive Daydreaming T20.000.020.24***0.32***0.25***0.37***0.28***0.35***0.68***

Notes: *p < 0.05; *** p < 0.001. Italicized correlations represent T1-T2 stability.

Testing the SEM model

Since the departure from normality was statistically significant, we performed Maximum likelihood estimation – robust (MLR). To assess the goodness of fit of our model, we considered multiple indices, including Comparative fit index (CFI), Tucker Lewis index (TLI), Root mean square error of approximation (RMSEA) and Standardized root mean square residual (SRMR). Model results are reported in Table 3 and in Fig. 1.

Table 3.

Standardized estimates, standard errors, z-scores, and 95% confidence intervals for the structural equation model (n = 814)

betaSEZ95%CI
Stability paths
 COVID_T1 → COVID_T20.010.030.40−0.055, 0.083
 DASS_T1 → DASS_T20.610.0318.62***0.547, 0.675
 MD_T1 → MD_T20.700.0229.26***0.656, 0.750
Intra-wave covariations
 COVID_T1 with
  DASS_T10.060.041.36−0.026, 0.143
  MD_T10.030.030.82−0.038, 0.092
 DASS_T1 with
  MD_T10.410.0311.48***0.337, 0.476
 COVID_T2 with
  DASS_T20.040.040.92−0.047, 0.130
  MD_T2−0.000.04−0.10−0.084, 0.076
 DASS_T2 with
  MD_T20.270.045.93***0.180, 0.357
Cross-lagged effects
 MD_T1 → COVID_T20.030.040.83−0.040, 0.099
 MD_T1 → STR_T20.070.032.06*0.003, 0.138
 MD_T1 → ANX_T20.190.042.94**0.036, 0.182
 MD_T1 → DEP_T20.080.042.26*0.011, 0.153
 COVID_T1 → MD_T2−0.020.02−0.89−0.068, 0.026
 STR_T1 → MD_T2−0.010.04−0.20−0.091, 0.074
 ANX_T1 → MD_T20.030.040.66−0.053, 0.107
 DEP_T1 → MD_T20.040.041.01−0.040, 0.126

*p < 0.05; **p < 0.01; ***p < 0.001

Fig. 1.
Fig. 1.

Standardized estimates of the model: significant paths (n = 814)

Note: T1COVID, COVID-19 related exposure measured at T1; T2COVID, COVID-19 related exposure measured at T2; T1 STRESS, Stress measured at T1; T2 STRESS, Stress measured at T2; T1 ANXIETY, Anxiety measured at T1; T2 ANXIETY, Anxiety measured at T2; T1 DEPRESS, Depression measured at T1; T2 DEPRESS, Depression measured at T2; T1 DASS, latent variable defining both stress, anxiety and depression at T1; T2 DASS, latent variable defining both stress, anxiety and depression at T2; T1 MD, Maladaptive Daydreaming measured at T1; T2 MD, Maladaptive Daydreaming measured at T2

Citation: Journal of Behavioral Addictions 12, 1; 10.1556/2006.2023.00001

The tested model showed a good fit (Byrne, 2012; Kenny, Kaniskan, & McCoach, 2015), with a statistically significant chi square (χ2 (60) = 134.416, p < 0.001, CFI = 0.99, TLI = 0.98, RMSEA = 0.04, p = 0.980, 90%CI [0.030, 0.048], SRMR = 0.025). The model explained 49.4% of the variance for MD, 84.8% for stress, 59.5% for anxiety, and 69.2% for depression.

We found stability paths to be significant and high for both the latent variable defined by stress, anxiety, and depression, and that defined by MD parcels, whereas COVID-19 related exposure at T1 did not predict COVID-19 related exposure at T2. Furthermore, cross-lagged effects indicated that MD at T1 predicted both stress, anxiety, and depression at T2, whereas distress variables did not longitudinally predict MD. As far as within-time covariations, the latent variable defined by stress, anxiety, and depression significantly and positively related to MD at both T1 and T2. No relations were found with COVID-19 related exposure, neither regarding cross-lagged effects nor regarding within-time covariations. Given the lack of relationships with COVID-19 related exposure, we re-tested the fit of our model while excluding COVID-19 related exposure variables. The fit of this alternative model was very similar to the fit of the presented one (χ2 (41) = 116.070, p < 0.001, CFI = 0.99, TLI = 0.98, RMSEA = 0.05, p = 0.645, 90%CI [0.037, 0.058], SRMR = 0.027).

Discussion

This is the first long-term longitudinal study on the stability of MD and its associations with psychological distress and COVID-19 related exposure. As expected, our findings demonstrated strong longitudinal associations between MD levels across T1 and T2, demonstrating significant stability. This finding is consistent with previous studies showing that MD can be so rewarding that a person may be caught in an addictive vicious cycle (Pietkiewicz et al., 2018). Specifically, individuals with an innate tendency for intense absorption and imaginative fantasy are reinforced to progressively replace human interaction with an uncontrollably compulsive involvement in MD (Somer, Somer, & Jopp, 2016). More broadly, our results provide empirical support for the conceptualization of MD as a stable clinical function (Marcusson-Clavertz, West, Kjell, & Somer, 2019) which is characterized by a pervasive and persistent pattern of emotion dysregulation similar to other behavioral addictions (Chirico et al., 2022). This may support the idea that MD is a stable clinical condition or disorder/syndrome (Somer, Soffer-Dudek, Ross, et al., 2017), although we cannot rule out that MD is stable as a symptom which is part of a personality disorder or another clinical syndrome. Moreover, further longitudinal studies on samples of children at risk for MD are still needed to clarify the etiology of this suggested disorder.

In addition, we found that MD levels at T1 positively predicted psychological distress of all three subtypes at T2, while controlling for baseline psychological distress levels. In other words, MD predicted an increase in psychological distress over a 13-month period, over and above what could be predicted by psychological distress itself. This finding extends the directional results by Soffer-Dudek and Somer (2018), who found negative emotion that followed MD by one day. The present study broadens that finding by showing that an adverse course following MD may manifest in the long term as well. Notably, although psychological distress co-occurred with MD at the intra-wave level, it did not precede MD. These results may be explained by an inadequate time lag, as distress may have a more immediate effect on MD. Specifically, differently from normal daydreaming, which may serve as an adaptive emotion-coping strategy, MD may be used as a means to escape from psychological distress in the short term that results in long-term impairment of psychological functioning (Pietkiewicz et al., 2018).

Our findings showed no cross-lagged associations between COVID-19 related exposure at T1, and MD and psychological distress at T2. These results are in line with previous studies on psychopathological symptoms changes during the COVID-19 pandemic (Bendau et al., 2021) and suggest that most people were able to adapt to compulsory and challenging changes due to COVID-19 over time. In addition, this finding may be related to the distal, developmental roots of MD, in that proximal stressors may not play a significant role in the onset of this disorder (Somer, Somer, & Jopp, 2016).

There are several limitations to this study. We used a snowball sampling method which limits the ability to generalize our results to the larger population. Furthermore, the results of paired t-tests may have been partially biased by the large sample size. Also, the primarily female composition of the sample may have influenced the results. In addition, we administered solely self-report measures rather than the diagnostic “gold standard” structured clinical interview for MD (Somer, Soffer-Dudek, Ross, et al., 2017). This issue is especially important considering the high comorbidity of MD and psychiatric disorders. Also, COVID-19 related exposure was assessed with mostly binary items, which may have limited its variance, making it difficult to find effects. Finally, we included only two assessment points of MD, psychological distress, and COVID-19 related exposure, thus limiting our possibility to test more complex mediation pathways between the examined variables.

These limitations notwithstanding, we conclude that this is the first long-term longitudinal study that examined 1-year stability and the relationship between MD and psychopathological symptoms. Our findings support the notion that some individuals tend to develop an excessive and chronic involvement in daydreaming which seems to be resistant to change, as addictions tend to be, and results in long-term psychological impairment. These findings suggest the need for early and timely identification of individuals at risk of developing MD.

Funding sources

This research was supported by the Israel Science Foundation (grant No. 1444/22).

Authors’ contribution

AM: conceptualization, writing – original draft, investigation; NS-D: conceptualization, writing – original draft, supervision – analysis; CI: Writing – original draft, formal analysis, methodology. AS: conceptualization, writing – original draft, supervision. CF: conceptualization, writing – original draft, investigation, supervision.

Conflict of interest

The authors report no financial or other relationship relevant to the subject of this article.

References

  • Bendau, A., Kunas, S. L., Wyka, S., Petzold, M. B., Plag, J., Asselmann, E., & Ströhle, A. (2021). Longitudinal changes of anxiety and depressive symptoms during the COVID-19 pandemic in Germany: The role of pre-existing anxiety, depressive, and other mental disorders. Journal of Anxiety Disorders, 79, 102377. https://doi.org/10.1016/j.janxdis.2021.102377.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bigelsen, J., Lehrfeld, J. M., Jopp, D. S., & Somer, E. (2016). Maladaptive daydreaming: Evidence for an under-researched mental health disorder. Consciousness and Cognition, 42, 254266. https://doi.org/10.1016/j.concog.2016.03.017.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bottesi, G., Ghisi, M., Altoè, G., Conforti, E., Melli, G., & Sica, C. (2015). The Italian version of the Depression Anxiety Stress Scales-21: Factor structure and psychometric properties on community and clinical samples. Comprehensive Psychiatry, 60, 170181. https://doi.org/10.1016/j.comppsych.2015.04.005.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Byrne, B. M. (2012). A primer of LISREL: Basic applications and programming for confirmatory factor analytic models. Springer Science & Business Media.

    • Search Google Scholar
    • Export Citation
  • Chirico, I., Volpato, E., Landi, G., Bassi, G., Mancinelli, E., Gagliardini, G., … Musetti, A. (2022). Maladaptive daydreaming and its relationship with psychopathological symptoms, emotion regulation, and problematic social networking sites use: A network analysis approach. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-022-00938-3.

    • Search Google Scholar
    • Export Citation
  • Herscu, O., Somer, E., Federman, A., & Soffer-Dudek, N. (in press). Mindfulness meditation and self-monitoring can ameliorate maladaptive daydreaming: A randomized controlled trial of a brief self-guided web-based program. Journal of Consulting and Clinical Psychology. https://doi.org/10.1037/ccp0000790.

    • Search Google Scholar
    • Export Citation
  • Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486507. https://doi.org/10.1177/0049124114543236.

    • Search Google Scholar
    • Export Citation
  • Little, T. D., Cunningham, W., Shahar, G., & Widaman, K. (2002). To parcel or not to parcel: A review of the parceling procedure in structural equation modeling. Structural Equation Modeling: An Integrative Journal, 9, 151173. https://doi.org/10.1207/S15328007SEM0902_1.

    • Search Google Scholar
    • Export Citation
  • Lovibond, S. H., & Lovibond, P. F. (1996). Manual for the depression anxiety stress scales. Psychology Foundation of Australia.

  • Marcusson-Clavertz, D., West, M., Kjell, O. N., & Somer, E. (2019). A daily diary study on maladaptive daydreaming, mind wandering, and sleep disturbances: Examining within-person and between-persons relations. Plos One, 14(11), e0225529. https://doi.org/10.1371/journal.pone.0225529.

    • Search Google Scholar
    • Export Citation
  • Mariani, R., Musetti, A., Di Monte, C., Danskin, K., Franceschini, C., & Christian, C. (2022). Maladaptive daydreaming in relation to linguistic features and attachment style. International Journal of Environmental Research and Public Health, 19(1), 386. https://doi.org/10.3390/ijerph19010386.

    • Search Google Scholar
    • Export Citation
  • McMillan, R. L., Kaufman, S. B., & Singer, J. L. (2013). Ode to positive constructive daydreaming. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00626.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Metin, B., Somer, E., Abu-Rayya, H. M., Schimmenti, A., & Göçmen, B. (2021). Perceived stress during the COVID-19 pandemic mediates the association between self-quarantine factors and psychological characteristics and elevated maladaptive daydreaming. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-021-00678-w.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Mooneyham, B. W., & Schooler, J. W. (2013). The costs and benefits of mind-wandering: A review. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 67(1), 1118. https://doi.org/10.1037/a0031569.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Musetti, A., Franceschini, C., Pingani, L., Freda, M. F., Saita, E., Vegni, E., … Schimmenti, A. (2021). Maladaptive daydreaming in an adult Italian population during the COVID-19 lockdown. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.631979.

    • Search Google Scholar
    • Export Citation
  • Musetti, A., Gori, A., Michelini, G., Di Monte, C., Franceschini, C., & Mariani, R. (2022). Are defense styles mediators between traumatic experiences and maladaptive daydreaming? Current Psychology. https://doi.org/10.1007/s12144-022-03708-5.

    • Search Google Scholar
    • Export Citation
  • Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.).

  • Pietkiewicz, I. J., Nęcki, S., Bańbura, A., & Tomalski, R. (2018). Maladaptive daydreaming as a new form of behavioral addiction. Journal of Behavioral Addictions, 7(3), 838843. https://doi.org/10.1556/2006.7.2018.95.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Schimmenti, A. (2019). Maladaptive daydreaming: Towards a nosological definition. Annales Médico-Psychologiques, Revue Psychiatrique, 177(9), 865874. https://doi.org/10.1016/j.amp.2019.08.014.

    • Search Google Scholar
    • Export Citation
  • Schimmenti, A., Sideli, L., La Marca, L., Gori, A., & Terrone, G. (2020). Reliability, validity, and factor structure of the Maladaptive Daydreaming Scale (MDS–16) in an Italian sample. Journal of Personality Assessment, 102(5), 689701. https://doi.org/10.1080/00223891.2019.1594240.

    • Search Google Scholar
    • Export Citation
  • Smallwood, J., & Schooler, J. W. (2015). The science of mind wandering: Empirically navigating the stream of consciousness. Annual Review of Psychology, 66(1), 487518. https://doi.org/10.1146/annurev-psych-010814-015331.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Soffer-Dudek, N., & Somer, E. (2018). Trapped in a daydream: Daily elevations in maladaptive daydreaming are associated with daily psychopathological symptoms. Frontiers in Psychiatry, 9, 194. https://doi.org/10.3389/fpsyt.2018.00194.

    • Search Google Scholar
    • Export Citation
  • Soffer-Dudek, N., & Somer, E. (2022). Maladaptive daydreaming is a dissociative disorder: Supporting evidence and theory. In M. Dorahy, S. Gold, & J. A. O' Neil (Eds.), Dissociation and the dissociative disorders (pp. 547563). London: Taylor & Francis. https://doi.org/10.4324/9781003057314-40.

    • Search Google Scholar
    • Export Citation
  • Soffer-Dudek, N., Somer, E., Abu-Rayya, H. M., Metin, B., & Schimmenti, A. (2020). Different cultures, similar daydream addiction? An examination of the cross-cultural measurement equivalence of the maladaptive daydreaming scale. Journal of Behavioral Addictions, 9(4), 10561067. https://doi.org/10.1556/2006.2020.00080.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Somer, E., Lehrfeld, J., Bigelsen, J., & Jopp, D. S. (2016). Development and validation of the maladaptive daydreaming scale (MDS). Consciousness and Cognition, 39, 7791. https://doi.org/10.1016/j.concog.2015.12.001.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Somer, E., Soffer-Dudek, N., & Ross, C. A. (2017). The comorbidity of daydreaming disorder (maladaptive daydreaming). The Journal of Nervous and Mental Disease, 205(7), 525530. https://doi.org/10.1097/NMD.0000000000000685.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Somer, E., Soffer-Dudek, N., Ross, C. A., & Halpern, N. (2017). Maladaptive daydreaming: Proposed diagnostic criteria and their assessment with a structured clinical interview. Psychology of Consciousness: Theory, Research, and Practice, 4(2), 176189. https://doi.org/10.1037/cns0000114.

    • Search Google Scholar
    • Export Citation
  • Somer, E., Somer, L., & Jopp, D. S. (2016). Childhood antecedents and maintaining factors in maladaptive daydreaming. The Journal of Nervous and Mental Disease, 204(6), 471478. https://doi.org/10.1097/NMD.0000000000000507.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Theodor-Katz, N., Somer, E., Hesseg, R. M., & Soffer-Dudek, N. (2022). Could immersive daydreaming underlie a deficit in attention? The prevalence and characteristics of maladaptive daydreaming in individuals with attention-deficit/hyperactivity disorder. Journal of Clinical Psychology jclp.23355 https://doi.org/10.1002/jclp.23355.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Valkenburg, P. M., & van der Voort, T. H. A. (1994). Influence of tv on daydreaming and creative imagination: A review of research. Psychological Bulletin, 116(2), 316339. https://doi.org/10.1037/0033-2909.116.2.316.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bendau, A., Kunas, S. L., Wyka, S., Petzold, M. B., Plag, J., Asselmann, E., & Ströhle, A. (2021). Longitudinal changes of anxiety and depressive symptoms during the COVID-19 pandemic in Germany: The role of pre-existing anxiety, depressive, and other mental disorders. Journal of Anxiety Disorders, 79, 102377. https://doi.org/10.1016/j.janxdis.2021.102377.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bigelsen, J., Lehrfeld, J. M., Jopp, D. S., & Somer, E. (2016). Maladaptive daydreaming: Evidence for an under-researched mental health disorder. Consciousness and Cognition, 42, 254266. https://doi.org/10.1016/j.concog.2016.03.017.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bottesi, G., Ghisi, M., Altoè, G., Conforti, E., Melli, G., & Sica, C. (2015). The Italian version of the Depression Anxiety Stress Scales-21: Factor structure and psychometric properties on community and clinical samples. Comprehensive Psychiatry, 60, 170181. https://doi.org/10.1016/j.comppsych.2015.04.005.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Byrne, B. M. (2012). A primer of LISREL: Basic applications and programming for confirmatory factor analytic models. Springer Science & Business Media.

    • Search Google Scholar
    • Export Citation
  • Chirico, I., Volpato, E., Landi, G., Bassi, G., Mancinelli, E., Gagliardini, G., … Musetti, A. (2022). Maladaptive daydreaming and its relationship with psychopathological symptoms, emotion regulation, and problematic social networking sites use: A network analysis approach. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-022-00938-3.

    • Search Google Scholar
    • Export Citation
  • Herscu, O., Somer, E., Federman, A., & Soffer-Dudek, N. (in press). Mindfulness meditation and self-monitoring can ameliorate maladaptive daydreaming: A randomized controlled trial of a brief self-guided web-based program. Journal of Consulting and Clinical Psychology. https://doi.org/10.1037/ccp0000790.

    • Search Google Scholar
    • Export Citation
  • Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486507. https://doi.org/10.1177/0049124114543236.

    • Search Google Scholar
    • Export Citation
  • Little, T. D., Cunningham, W., Shahar, G., & Widaman, K. (2002). To parcel or not to parcel: A review of the parceling procedure in structural equation modeling. Structural Equation Modeling: An Integrative Journal, 9, 151173. https://doi.org/10.1207/S15328007SEM0902_1.

    • Search Google Scholar
    • Export Citation
  • Lovibond, S. H., & Lovibond, P. F. (1996). Manual for the depression anxiety stress scales. Psychology Foundation of Australia.

  • Marcusson-Clavertz, D., West, M., Kjell, O. N., & Somer, E. (2019). A daily diary study on maladaptive daydreaming, mind wandering, and sleep disturbances: Examining within-person and between-persons relations. Plos One, 14(11), e0225529. https://doi.org/10.1371/journal.pone.0225529.

    • Search Google Scholar
    • Export Citation
  • Mariani, R., Musetti, A., Di Monte, C., Danskin, K., Franceschini, C., & Christian, C. (2022). Maladaptive daydreaming in relation to linguistic features and attachment style. International Journal of Environmental Research and Public Health, 19(1), 386. https://doi.org/10.3390/ijerph19010386.

    • Search Google Scholar
    • Export Citation
  • McMillan, R. L., Kaufman, S. B., & Singer, J. L. (2013). Ode to positive constructive daydreaming. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00626.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Metin, B., Somer, E., Abu-Rayya, H. M., Schimmenti, A., & Göçmen, B. (2021). Perceived stress during the COVID-19 pandemic mediates the association between self-quarantine factors and psychological characteristics and elevated maladaptive daydreaming. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-021-00678-w.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Mooneyham, B. W., & Schooler, J. W. (2013). The costs and benefits of mind-wandering: A review. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 67(1), 1118. https://doi.org/10.1037/a0031569.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Musetti, A., Franceschini, C., Pingani, L., Freda, M. F., Saita, E., Vegni, E., … Schimmenti, A. (2021). Maladaptive daydreaming in an adult Italian population during the COVID-19 lockdown. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.631979.

    • Search Google Scholar
    • Export Citation
  • Musetti, A., Gori, A., Michelini, G., Di Monte, C., Franceschini, C., & Mariani, R. (2022). Are defense styles mediators between traumatic experiences and maladaptive daydreaming? Current Psychology. https://doi.org/10.1007/s12144-022-03708-5.

    • Search Google Scholar
    • Export Citation
  • Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.).

  • Pietkiewicz, I. J., Nęcki, S., Bańbura, A., & Tomalski, R. (2018). Maladaptive daydreaming as a new form of behavioral addiction. Journal of Behavioral Addictions, 7(3), 838843. https://doi.org/10.1556/2006.7.2018.95.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Schimmenti, A. (2019). Maladaptive daydreaming: Towards a nosological definition. Annales Médico-Psychologiques, Revue Psychiatrique, 177(9), 865874. https://doi.org/10.1016/j.amp.2019.08.014.

    • Search Google Scholar
    • Export Citation
  • Schimmenti, A., Sideli, L., La Marca, L., Gori, A., & Terrone, G. (2020). Reliability, validity, and factor structure of the Maladaptive Daydreaming Scale (MDS–16) in an Italian sample. Journal of Personality Assessment, 102(5), 689701. https://doi.org/10.1080/00223891.2019.1594240.

    • Search Google Scholar
    • Export Citation
  • Smallwood, J., & Schooler, J. W. (2015). The science of mind wandering: Empirically navigating the stream of consciousness. Annual Review of Psychology, 66(1), 487518. https://doi.org/10.1146/annurev-psych-010814-015331.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Soffer-Dudek, N., & Somer, E. (2018). Trapped in a daydream: Daily elevations in maladaptive daydreaming are associated with daily psychopathological symptoms. Frontiers in Psychiatry, 9, 194. https://doi.org/10.3389/fpsyt.2018.00194.

    • Search Google Scholar
    • Export Citation
  • Soffer-Dudek, N., & Somer, E. (2022). Maladaptive daydreaming is a dissociative disorder: Supporting evidence and theory. In M. Dorahy, S. Gold, & J. A. O' Neil (Eds.), Dissociation and the dissociative disorders (pp. 547563). London: Taylor & Francis. https://doi.org/10.4324/9781003057314-40.

    • Search Google Scholar
    • Export Citation
  • Soffer-Dudek, N., Somer, E., Abu-Rayya, H. M., Metin, B., & Schimmenti, A. (2020). Different cultures, similar daydream addiction? An examination of the cross-cultural measurement equivalence of the maladaptive daydreaming scale. Journal of Behavioral Addictions, 9(4), 10561067. https://doi.org/10.1556/2006.2020.00080.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Somer, E., Lehrfeld, J., Bigelsen, J., & Jopp, D. S. (2016). Development and validation of the maladaptive daydreaming scale (MDS). Consciousness and Cognition, 39, 7791. https://doi.org/10.1016/j.concog.2015.12.001.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Somer, E., Soffer-Dudek, N., & Ross, C. A. (2017). The comorbidity of daydreaming disorder (maladaptive daydreaming). The Journal of Nervous and Mental Disease, 205(7), 525530. https://doi.org/10.1097/NMD.0000000000000685.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Somer, E., Soffer-Dudek, N., Ross, C. A., & Halpern, N. (2017). Maladaptive daydreaming: Proposed diagnostic criteria and their assessment with a structured clinical interview. Psychology of Consciousness: Theory, Research, and Practice, 4(2), 176189. https://doi.org/10.1037/cns0000114.

    • Search Google Scholar
    • Export Citation
  • Somer, E., Somer, L., & Jopp, D. S. (2016). Childhood antecedents and maintaining factors in maladaptive daydreaming. The Journal of Nervous and Mental Disease, 204(6), 471478. https://doi.org/10.1097/NMD.0000000000000507.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Theodor-Katz, N., Somer, E., Hesseg, R. M., & Soffer-Dudek, N. (2022). Could immersive daydreaming underlie a deficit in attention? The prevalence and characteristics of maladaptive daydreaming in individuals with attention-deficit/hyperactivity disorder. Journal of Clinical Psychology jclp.23355 https://doi.org/10.1002/jclp.23355.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Valkenburg, P. M., & van der Voort, T. H. A. (1994). Influence of tv on daydreaming and creative imagination: A review of research. Psychological Bulletin, 116(2), 316339. https://doi.org/10.1037/0033-2909.116.2.316.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
The author instructions are available in PDF.
Please, download the file from HERE

 

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

Indexing and Abstracting Services:

  • Web of Science [Science Citation Index Expanded (also known as SciSearch®)
  • Journal Citation Reports/Science Edition
  • Social Sciences Citation Index®
  • Journal Citation Reports/ Social Sciences Edition
  • Current Contents®/Social and Behavioral Sciences
  • EBSCO
  • GoogleScholar
  • PsycINFO
  • PubMed Central
  • SCOPUS
  • Medline
  • CABI
  • CABELLS Journalytics

2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

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
submission  
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 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
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

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Sophia ACHAB (Faculty of Medicine, University of Geneva, Switzerland)
  • Alex BALDACCHINO (St Andrews University, United Kingdom)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Maria BELLRINGER (Auckland University of Technology, Auckland, New Zealand)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Damien BREVERS (University of Luxembourg, Luxembourg)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Julius BURKAUSKAS (Lithuanian University of Health Sciences, Lithuania)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Silvia CASALE (University of Florence, Florence, Italy)
  • Luke CLARK (University of British Columbia, Vancouver, B.C., Canada)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Geert DOM (University of Antwerp, Belgium)
  • Nicki DOWLING (Deakin University, Geelong, Australia)
  • Hamed EKHTIARI (University of Minnesota, United States)
  • Jon ELHAI (University of Toledo, Toledo, Ohio, USA)
  • Ana ESTEVEZ (University of Deusto, Spain)
  • Fernando FERNANDEZ-ARANDA (Bellvitge University Hospital, Barcelona, Spain)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Sally GAINSBURY (The University of Sydney, Camperdown, NSW, Australia)
  • Belle GAVRIEL-FRIED (The Bob Shapell School of Social Work, Tel Aviv University, Israel)
  • Biljana GJONESKA (Macedonian Academy of Sciences and Arts, Republic of North Macedonia)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Joshua GRUBBS (University of New Mexico, Albuquerque, NM, USA)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • 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)
  • Zsolt HORVÁTH (Eötvös Loránd University, Hungary)
  • Susana JIMÉNEZ-MURCIA (Clinical Psychology Unit, Bellvitge University Hospital, Barcelona, Spain)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Chih-Hung KO (Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Taiwan)
  • Shane KRAUS (University of Nevada, Las Vegas, NV, USA)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Bernadette KUN (Eötvös Loránd University, Hungary)
  • Katerina LUKAVSKA (Charles University, Prague, Czech Republic)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Gemma MESTRE-BACH (Universidad Internacional de la Rioja, La Rioja, Spain)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Ståle PALLESEN (University of Bergen, Norway)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Michael SCHAUB (University of Zurich, Switzerland)
  • 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)
  • Hermano TAVARES (Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN (Ariel University, Israel)
  • Anise WU (University of Macau, Macao, China)

 

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Dec 2023 0 176 72
Jan 2024 0 160 36
Feb 2024 0 197 64
Mar 2024 0 143 74
Apr 2024 0 76 63
May 2024 0 67 49
Jun 2024 0 48 14