AIMC Topic: Internet Addiction Disorder

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Ambivalent User Needs as a Challenge and Chance for the Design of a Web-Based Intervention for Gaming Disorder: Qualitative Interview Study With Adolescents and Young Adults.

JMIR formative research
BACKGROUND: In Germany, there are still many young people with gaming disorder (GD) who do not use or cannot access existing treatment services. Given the increasing prevalence of internet use disorders and GD, especially among young people in German...

Machine Learning(s) in gaming disorder through the user-avatar bond: A step towards conceptual and methodological clarity.

Journal of behavioral addictions
In response to our study, the commentary by Infanti et al. (2024) raised critical points regarding (i) the conceptualization and utility of the user-avatar bond in addressing gaming disorder (GD) risk, and (ii) the optimization of supervised machine ...

Machine learning based classification of excessive smartphone users via neuronal cue reactivity.

Psychiatry research. Neuroimaging
Excessive Smartphone Use (ESU) poses a significant challenge in contemporary society, yet its recognition as a distinct disorder remains ambiguous. This study aims to address this gap by leveraging functional magnetic resonance imaging (fMRI) data an...

Investigating the mechanisms of internet gaming disorder and developing intelligent monitoring models using artificial intelligence technologies: protocol of a prospective cohort.

BMC public health
BACKGROUND: Internet gaming disorder (IGD), recognized by the World Health Organization (WHO), significantly impacts adolescent mental and physical health. With a global prevalence of 3.05%, rates are higher in Asia, especially among adolescents and ...

Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis.

BMC public health
BACKGROUND: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, an...

Machine learning identifies different related factors associated with depression and suicidal ideation in Chinese children and adolescents.

Journal of affective disorders
BACKGROUND: Depression and suicidal ideation often co-occur in children and adolescents, yet they possess distinct characteristics. This study sought to identify the different related factors associated with depression and suicidal ideation.

Using machine learning algorithms and techniques for defining the impact of affective temperament types, content search and activities on the internet on the development of problematic internet use in adolescents' population.

Frontiers in public health
BACKGROUND: By using algorithms and Machine Learning - ML techniques, the aim of this research was to determine the impact of the following factors on the development of Problematic Internet Use (PIU): sociodemographic factors, the intensity of using...

Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI.

Progress in neuro-psychopharmacology & biological psychiatry
It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted r...

Resting-state connectome-based support-vector-machine predictive modeling of internet gaming disorder.

Addiction biology
Internet gaming disorder (IGD), a worldwide mental health issue, has been widely studied using neuroimaging techniques during the last decade. Although dysfunctions in resting-state functional connectivity have been reported in IGD, mapping relations...