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Internet gaming disorder is recognized as a behavioral addiction with significant clinical and public health implications. The article examines evidence-based treatment approaches and the specific challenges clinicians face in managing this condition across diverse patient populations.
This study employed machine learning algorithms on EEG data and neuropsychological assessments to distinguish internet gaming disorder from alcohol use disorder and healthy controls. The multimodal approach combining brain activity patterns with psychological measures achieved 71.2% accuracy, identifying differential neural connectivity patterns between the disorder groups.
This study examined whether social media addiction components can be classified into 'core' criteria (mood modification, relapse, withdrawal, conflict) and 'peripheral' criteria (salience, tolerance) using two established addiction scales in 2,761 adolescents. A two-factor model better explained the data than a single-factor model, with core criteria showing stronger associations with depression, anxiety, and loneliness.
This study examined resting-state quantitative EEG coherence in 29 gambling disorder patients versus 45 healthy controls, finding distinct patterns of interhemispheric connectivity disruption. Gambling disorder was characterized by frontal pole hypercoherence combined with hypocoherence in temporal, central, and parietal regions, with connectivity patterns correlating with disorder duration.
This research study investigates the prevalence and patterns of food addiction among young adults in Korea and examines its associations with substance use, behavioral addictions, and other compulsive behaviors. The study uses empirical methodology to understand how food addiction co-occurs with other addictive conditions in this demographic.