| 51. | | | The Big Five personality traits and online gaming: A systematic review and meta-analysis
|
| 52. | | | Biomedicinális szaklapok és orvostudományi információ az interneten
|
| 53. | | | Brain network alterations in mobile phone use problem severity: A multimodal neuroimaging analysis
|
| 54. | | | Brain responses to positive and negative events in individuals with internet gaming disorder during real gaming
|
| 55. | | | Brain structural co-development is associated with internalizing symptoms two years later in the ABCD cohort
|
| 56. | | | Brain structural covariation linked to screen media activity and externalizing behaviors in children
|
| 57. | | | Breaking the habit Commentary on: Policy responses to problematic video game use: A systematic review of current measures and future possibilities (Király et al., 2018)
|
| 58. | | | Bridging the links between Big Five personality traits and problematic smartphone use: A network analysis
|
| 59. | | | The CED-le DEC: Common European Doctorate, or Doctorate Europeen Commune or dissertations on the Internet
|
| 60. | | | Changes to the playing field: A contemporary study of actual European online sports betting
|
| 61. | | | Chaos and confusion in DSM-5 diagnosis of Internet Gaming Disorder: Issues, concerns, and recommendations for clarity in the field
|
| 62. | | | Characteristics and associated factors of non-suicidal self-injury among Italian young people: A survey through a thematic website
|
| 63. | | | Characteristics and treatment response of self-identified problematic Internet users in a behavioral addiction outpatient clinic
|
| 64. | | | Chinese adaptation of the Ten-Item Internet Gaming Disorder Test and prevalence estimate of Internet gaming disorder among adolescents in Taiwan
|
| 65. | | | Classification of probable online social networking addiction: A latent profile analysis from a large-scale survey among Chinese adolescents
|
| 66. | | | Classification of problematic Internet usage types by motives and contexts with elementary and secondary school-aged counseling clients
|
| 67. | | | A clinical evaluation of the DSM-5 criteria for Internet Gaming Disorder and a pilot study on their applicability to further Internet-related disorders
|
| 68. | | | A clinical study of an online educational programme for chronic pain patients = Online oktatás krónikus fájdalom esetén
|
| 69. | | | A cohort study of patients seeking Internet gaming disorder treatment
|
| 70. | | | Comment on: Problematic online gaming and the COVID-19 pandemic - The role of exergames
|
| 71. | | | Comparative analysis of cortical anatomy in male participants with internet gaming disorder or tobacco use disorder: Insights from normative modeling
|
| 72. | | | Comparing football bettors' response to social media marketing differing in bet complexity and account type - An experimental study
|
| 73. | | | Comparing generalized and specific problematic smartphone/internet use: Longitudinal relationships between smartphone application-based addiction and social media addiction and psychological distress
|
| 74. | | | Comparison of brain connectivity between Internet gambling disorder and Internet gaming disorder: A preliminary study
|
| 75. | | | Comparison of frontostriatal circuits in adolescent nicotine addiction and internet gaming disorder
|
| 76. | | | Comparison of risk and protective factors associated with smartphone addiction and Internet addiction
|
| 77. | | | Compliance and alternative behaviors of heavy gamers in adolescents to Chinese online gaming restriction policy
|
| 78. | | | A comprehensive evaluation of the neurocognitive predictors of problematic alcohol use, eating, pornography, and internet use: A 6-month longitudinal study
|
| 79. | | | A comprehensive model to understand and assess the motivational background of video game use: The Gaming Motivation Inventory (GMI)
|
| 80. | | | Compulsivity-related behavioral features of problematic usage of the internet: A scoping review of paradigms, progress, and perspectives
|
| 81. | | | Computer playfulness, Internet dependency and their relationships with online activity types and student academic performance
|
| 82. | | | The concept of "harm" in Internet gaming disorder
|
| 83. | | | Considering existing classic and contemporary proposals for preventing online addiction problems: Some old recipes for new problems
|
| 84. | | | Construction and validation of the motives for smartphone use questionnaire
|
| 85. | | | Contribution of sexual desire and motives to the compulsive use of cybersex
|
| 86. | | | Correlations between mobile phone addiction and anxiety, depression, impulsivity, and poor sleep quality among college students: A systematic review and meta-analysis
|
| 87. | | | Covid pandémia hatásai: szorongás - internet
|
| 88. | | | COVID-19: WHO-app és az amerikai elnök COVID-19 elleni gyógyszerkoktélja
|
| 89. | | | Criteria for the establishment of a new behavioural addiction : Commentary to the debate: "Behavioral addictions in the ICD-11"
|
| 90. | | | A critical review of "Internet addiction" criteria with suggestions for the future
|
| 91. | | | Csatlakozás az internethez és az internet működése
|
| 92. | | | A cukorbetegség és az idült vesebetegség reprezentáltsága az online térben és összehasonlításuk egyéb népbetegségekkel
|
| 93. | | | Cybersex use and problematic cybersex use among young Swiss men: Associations with sociodemographic, sexual, and psychological factors
|
| 94. | | | Céltalanul a világhálón - a problémás internethasználat háttértényezőinek vizsgálata
|
| 95. | | | Daganatos betegeket támogató online lehetőségek és kihívások
|
| 96. | | | The dark side of internet: Preliminary evidence for the associations of dark personality traits with specific online activities and problematic internet use
|
| 97. | | | Dcont.hu vércukornaplók elemzése : 1. [r.]
|
| 98. | | | Decreased effective connection from the parahippocampal gyrus to the prefrontal cortex in Internet gaming disorder: A MVPA and spDCM study
|
| 99. | | | Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning
|
| 100. | | | Delay discounting, risk-taking, and rejection sensitivity among individuals with Internet and Video Gaming Disorders
|
