MATERIALS AND METHODS: A total of 480 students from different faculties in a Malaysian public university participated in this study. They were selected by simple random sampling method. They completed self-administered questionnaires including the Malay Version of Internet Addiction Test (MVIAT)) to measure internet addiction and Adult Self-Report Scale (ASRS) Symptom Checklist, Depression Anxiety Stress Scales (DASS) and UCLA Loneliness Scale (Version 3) to assess for ADHD symptoms, depression, anxiety, stress, and loneliness respectively.
RESULTS: The prevalence of IA among university students was 33.33% (n = 160). The respondents' mean age was 21.01 ± 1.29 years old and they were predominantly females (73.1%) and Malays (59.4%). Binary logistic regression showed that gender (p = 0.002; OR = 0.463, CI = 0.284-0.754), ADHD inattention (p = 0.003; OR = 2.063, CI = 1.273-3.345), ADHD hyperactivity (p<0.0001; OR = 2.427, CI = 1.495-3.939), stress (p = 0.048; OR = 1.795, CI = 1.004-3.210) and loneliness (p = 0.022; OR = 1.741, CI = 1.084-2.794) were significantly associated with IA.
CONCLUSION: A third of university students had IA. In addition, we found that those who were at risk of IA were males, with ADHD symptoms of inattention and hyperactivity, who reported stress and loneliness. Preventive strategy to curb internet addiction and its negative sequelae may consider these factors in its development and implementation.
DESIGN AND METHODS: Participants were randomly selected using multi-stage sampling methods from three public universities in the study location. Participants completed measures of internet and smartphone addictions, depression, anxiety, stress, and suicidality, along with sociodemographic items.
FINDINGS: At bivariate level, both internet and smartphone addictions were found to have significant positive correlations with depression, anxiety, stress, and suicidality. At multivariate level, only internet addiction emerged as a consistent significant predictor for depression, anxiety, stress, and suicidality.
PRACTICE IMPLICATIONS: The present findings have implications for mental health professionals to routinely screen for psychological disturbance in young adults who have potential risks for internet addiction.
METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.
RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.
CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.