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This study aims to analyze psychometric properties and validity of the Compulsive Internet Use Scale (CIUS) and the Internet Addiction Test (IAT) and, second, to determine a threshold for the CIUS which matches the IAT cut-off for detecting problematic Internet use. A total of 292 subjects with problematic or pathological gambling (237 men, 55 women) aged 14-63 years and with private Internet use for at least 1 h per working or weekend day were recruited via different recruitment channels. Results include that both scales were internally consistent (Cronbach's α = 0.9) and had satisfactory convergent validity (r = 0.75; 95% CI 0.70-0.80). The correlation with duration of private Internet use per week was significantly higher for the CIUS (r = 0.54) compared to the IAT (r = 0.40). Among all participants, 25.3% were classified as problematic Internet users based on the IAT with a cut-off ≥40. The highest proportion of congruent classified cases results from a CIUS cut-off ≥18 (sensitivity 79.7%, specificity 79.4%). However, a higher cut-off (≥21) seems to be more appropriate for prevalence estimation of problematic Internet use.
Background/Aims: Only a small percentage of pathological gamblers utilizes professional treatment for gambling problems. Little is known about which social and gambling-related factors are associated with treatment utilization. The aim of this study was to look for factors associated with treatment utilization for pathological gambling. Methods: The study followed a sampling design with 3 different recruitment channels, namely (1) a general population-based telephone sample, (2) a gambling location sample and (3) a project telephone hotline. Pathological gambling was diagnosed in a telephone interview. Participants with pathological gambling (n = 395) received an in-depth clinical interview concerning treatment utilization, comorbid psychiatric disorders and social characteristics. Results: Variables associated with treatment were higher age [odds ratio (OR) 1.05, 95% confidence interval (CI) 1.03-1.08], an increased number of DSM-IV criteria for pathological gambling (OR 1.34, 95% CI 1.06-1.70), more adverse consequences from gambling (OR 1.10, 95% CI 1.03-1.16) and more social pressure from significant others (OR 1.17, 95% CI 1.07-1.27). Affective disorders were associated with treatment utilization in the univariate analysis (OR 1.81, 95% CI 1.19-2.73), but multivariate analysis showed that comorbid psychiatric disorders were not independently associated. Conclusion: These results indicate that individuals with more severe gambling problems utilize treatment at an older age when more adverse consequences have occurred. Further research should focus on proactive early interventions.
Background
In combination with systematic routine screening, brief alcohol interventions have the potential to promote population health. Little is known on the optimal screening interval. Therefore, this study pursued 2 research questions: (i) How stable are screening results for at‐risk drinking over 12 months? (ii) Can the transition from low‐risk to at‐risk drinking be predicted by gender, age, school education, employment, or past week alcohol use?
Methods
A sample of 831 adults (55% female; mean age = 30.8 years) from the general population was assessed 4 times over 12 months. The Alcohol Use Disorders Identification Test—Consumption was used to screen for at‐risk drinking each time. Participants were categorized either as low‐risk or at‐risk drinkers at baseline, 3, 6, and 12 months later. Stable and instable risk status trajectories were analyzed descriptively and graphically. Transitioning from low‐risk drinking at baseline to at‐risk drinking at any follow‐up was predicted using a logistic regression model.
Results
Consistent screening results over time were observed in 509 participants (61%). Of all baseline low‐risk drinkers, 113 (21%) received a positive screening result in 1 or more follow‐up assessments. Females (vs. males; OR = 1.66; 95% confidence intervals [95% CI] = 1.04; 2.64), 18‐ to 29‐year‐olds (vs. 30‐ to 45‐year‐olds; OR = 2.30; 95% CI = 1.26; 4.20), and those reporting 2 or more drinking days (vs. less than 2; OR = 3.11; 95% CI = 1.93; 5.01) and heavy episodic drinking (vs. none; OR = 2.35; 95% CI = 1.06; 5.20) in the week prior to the baseline assessment had increased odds for a transition to at‐risk drinking.
Conclusions
Our findings suggest that the widely used time frame of 1 year may be ambiguous regarding the screening for at‐risk alcohol use although generalizability may be limited due to higher‐educated people being overrepresented in our sample.