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(48.3) Probable fatigue No (<4) 940(69.5) Yes (≥4) 412(30.5) Probable depression No (<10) 1,238(91.6) Yes (≥10) 114(8.4) Correlations among the variables The Pearson’s correlation analysis ( Table 2 ) showed that workaholism was positively correlated with stress

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uses. The second research question (R2) could be inferred from the results of both bivariate correlation analysis as well as EGA results. The number of associations as well as effect sizes could indicate if PSU is driven by specific platform use. While

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Journal of Behavioral Addictions
Authors: Engin Karadağ, Şule Betül Tosuntaş, Evren Erzen, Pinar Duru, Nalan Bostan, Berrak Mizrak Şahin, İlkay Çulha, and Burcu Babadağ

Background and aims

Phubbing can be described as an individual looking at his or her mobile phone during a conversation with other individuals, dealing with the mobile phone and escaping from interpersonal communication. In this research, determinants of phubbing behavior were investigated; in addition, the effects of gender, smart phone ownership and social media membership were tested as moderators.

Methods

To examine the cause–effect relations among the variables of the theoretical model, the research employs a correlational design. Participants were 409 university students who were selected via random sampling. Phubbing was obtained via the scales featuring mobile phone addiction, SMS addiction, internet addiction, social media addiction and game addiction. The obtained data were analyzed using a correlation analysis, multiple linear regression analysis and structural equation model.

Results

The results showed that the most important determinants of phubbing behavior are mobile phone, SMS, social media and internet addictions.

Discussion

Although the findings show that the highest correlation value explaining phubbing is a mobile phone addiction, the other correlation values reflect a dependency on the phone.

Conclusions

There is an increasing tendency towards mobile phone use, and this tendency prepares the basis of phubbing.

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Journal of Behavioral Addictions
Authors: Josephine Savard, Tatja Hirvikoski, Katarina Görts Öberg, Cecilia Dhejne, Christoffer Rahm, and Jussi Jokinen

Abstract

Background and aims

Impulsivity is regarded as a risk factor for sexual crime reoffending, and a suggested core feature in Compulsive Sexual Behavior Disorder. The aim of this study was to explore clinical (e.g. neurodevelopmental disorders), behavioral and neurocognitive dimensions of impulsivity in disorders of problematic sexuality, and the possible correlation between sexual compulsivity and impulsivity.

Methods

Men with Compulsive Sexual Behavior Disorder (n = 20), and Pedophilic Disorder (n = 55), enrolled in two separate drug trials in a specialized Swedish sexual medicine outpatient clinic, as well as healthy male controls (n = 57) were assessed with the Hypersexual Behavior Inventory (HBI) for sexual compulsivity, and with the Barratt Impulsiveness Scale (BIS) and Connors’ Continuous Performance Test-II (CPT-II) for impulsivity. Psychiatric comorbidity information was extracted from interviews and patient case files.

Results

Approximately a quarter of the clinical groups had Attention-Deficit/Hyperactivity Disorder (ADHD) or Autism Spectrum Disorder. Both clinical groups reported more compulsive sexuality (r = 0.73−0.75) and attentional impulsivity (r = 0.36−0.38) than controls (P < 0.05). Based on results on univariate correlation analysis, BIS attentional score, ADHD, and Commissions T-score from CPT-II were entered in a multiple linear regression model, which accounted for 15% of the variance in HBI score (P < 0.0001). BIS attentional score was the only independent positive predictor of HBI (P = 0.001).

Discussion

Self-rated attentional impulsivity is an important associated factor of compulsive sexuality, even after controlling for ADHD. Psychiatric comorbidity and compulsive sexuality are common in Pedophilic Disorder.

Conclusion

Neurodevelopmental disorders and attentional impulsivity – including suitable interventions – should be further investigated in both disorders.

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examine relationship between results of clinical assessments and functional connectivity, Pearson correlation analysis tested relations between the functional seed-target connectivity and behavioral performance. Statistical analyses were conducted by using

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correlation analysis Pre-processing images were modeled using a general linear model, which would convolve the experimental conditions with the canonical hemodynamic response function. For each subject, two conditions (within-subject factors) were defined in

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engagement. The correlation between social support and healthy dependency indicates that these two concepts may be related. The need for in-game social support also increases with age. Table 1 summarizes the results of the correlational analysis

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SAPS scores ( t 86 = −17.50, P < 0.001), impulsivity (DFII, t 86 = −3.27, P = 0.002), depressive mood (PHQ-9, t 83 = −3.86, P = 0.001) and anxiety (GAD-7, t 83 = −2.87, P = 0.009) compared with the TDC group. Partial correlation analysis

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SAPS scores ( t 86 = −17.50, P < 0.001), impulsivity (DFII, t 86 = −3.27, P = 0.002), depressive mood (PHQ-9, t 83 = −3.86, P = 0.001) and anxiety (GAD-7, t 83 = −2.87, P = 0.009) compared with the TDC group. Partial correlation analysis

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using the Mann-Whitney U test ( P < 0.05). The dependent variables in each group were compared using the Friedman test and pairwise Wilcoxon comparison (while comparing three dependent groups, P < 0.017). The correlation analysis was performed using

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