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Abstract This study explores the impact of Internet addiction (IA), age, and essential and non-essential technology usage time on the physical and mental fatigue of adolescents. The research surveyed 477 adolescents from Qatar and employed the Internet Addiction Diagnostic Questionnaire (IADQ) and Chalder's Fatigue Scale for data collection. Multiple linear regression and Mann–Whitney U tests were utilized for analysis. The findings indicate that IA, non-essential usage time, and age are positively associated with overall fatigue among adolescents. IA and non-essential usage time are positively linked to physical fatigue, while IA, non-essential usage time, and age are positive predictors of mental fatigue. However, essential usage time is negatively associated with mental fatigue. These results highlight the importance of distinguishing technology usage based on intent and necessity, as well as differentiating between physical and mental fatigue when examining problematic technology usage. This study is among the few conducted in the Middle Eastern context.

This study aimed to identify typical interactions between adolescents’ Internet addiction and family environment factors. 165 parents were surveyed about family environment and problematic Internet use for themselves and their adolescents. Three distinct clusters were identified. Cluster 1 (assertive interaction) involved non-addicted authoritative parents with adolescents at risk of addiction. These parents had occasional arguments with their adolescents. Cluster 2 (aggressive interaction) included at-risk authoritarian parents with Internet addicted adolescents. These parents often had arguments with their adolescents. Cluster 3 (lenient interaction) comprised non-addicted permissive parents with highly addicted adolescents. These parents constantly argue with their adolescents. As we have not identified a cluster where adolescents’ addiction was below minimal risk, we advocate the need to train parents along with adolescents on healthy technology use. The clusters identified can be used by professionals as a basis to produce diverse interventions that fit each of the identified family types.