Participants have been assigned to addiction classification or normal group utilising the aforementioned significance

Mathematical studies

SPSS having Window (ver. 21.0; SPSS Inc., Chi town, IL, USA) was applied to own mathematical studies. Demographic characteristics were advertised since the regularity and you will percentage. Chi-rectangular decide to try was used examine addiction and you can regular organizations to your functions out of sex, socio-monetary reputation, family design, despair, nervousness, ADHD, smoking, and you can alcoholic drinks use. Pearson correlation research try did to determine the correlation anywhere between smartphone addiction score or other parameters interesting. Fundamentally, multivariate binary logistic regression investigation are performed to assess the influence regarding gender, depression, nervousness, ADHD, puffing, and you will liquor have fun with to the portable addiction. The analysis is actually completed playing with backwards means, having dependency class and you may typical classification as the depending details and you can females sex, anxiety group, stress group, ADHD group, puffing classification, and you will alcohol communities given that independent parameters. A great p value of below 0.05 is actually considered to imply statistical significance.

Results

Among the many 5051 students employed to your studies, 539 was in fact excluded due to partial answers. Hence, a total of 4512 college students (forty-five.1% male, letter = 2034; 54.9% females, letter = 2478) was basically included in this data. The imply ages of the fresh new subjects hoe te zien wie je leuk vindt op mylol zonder te betalen try (SD = step 1.62). The brand new sociodemographic functions of the sufferers are summarized when you look at the Dining table step one. To own source, 4060 children (87.8%) had been smartphone citizens (84.2% off male, n = 1718 out-of 2041; ninety.6% off girls, n = 2342 out-of 2584) one of several 4625 youngsters which taken care of immediately practical question of cellular phone ownership (426 did not act).

Table 2 shows clinical characteristics between smartphone addiction and normal groups. Of the 4512 participants, 338 (7.5%) were categorized to the addiction group, while 4174 belonged to the normal group. The mean age in the addiction group and normal group was ± 1.63 and ± 1.44, respectively, with no statistical difference between the groups (t = 0.744, p = 0.458). Furthermore, socio-economic status and family structure had no statistical difference between the groups (? 2 = 3.912, p = 0.141; ? 2 = 0.685, p = 0.710). Apart from age, socio-economic status, and family structure, all other variables showed statistically significant differences between the addiction group and the normal group. These include: female sex (OR 1.75, 95% CI 1.38–2.21), depression (OR 4.15, 95% CI 3.26–5.28), anxiety (OR 4.41, 95% CI 3.43–5.64), cigarette smoking (OR 2.06, 95% CI 1.44–2.96), and alcohol use (OR 1.62, 95% CI 1.22–2.16). The largest difference among all variables was noted with ADHD symptomspared to 26.0% of addiction group also belonging to the ADHD group, only 3.4% in the normal group were in the ADHD group. The odds ratio for smartphone addiction in ADHD group compared to non-ADHD was (? 2 = , p < 0.001).

Table 3 shows the Pearson correlation coefficients of smartphone addiction with other variables. Total smartphone addiction score showed greatest correlation with total CASS score (r = 0.427, p < 0.001). The total SAS score was also associated with total BDI score, total BAI score, female sex, smoking group, and alcohol use group in a statistically significant manner.

To identify the variables associated with smartphone addiction, multivariate logistic regression analyses were performed. All variables showing statistically significant difference between addiction group and normal group were entered and analyzed using backward method. In the goodness-of-fit test of the regression analysis model, the ? 2 log likelihood was and statistically significant (p < 0.001). In the first model tested, alcohol use had no statistically significant effect on smartphone addiction (B = 0.161, OR = 1.174 p = 0.375, 95% CI 0.823–1.675) and was, thus, removed from the final model. Table 4 shows the final model of the analysis; the odds ratio for smartphone addiction of female sex to males was 2.01 (95% CI 1.54–2.61). Odds ratio of ADHD group compared to non-ADHD group for song all variables (95% CI 4.60–9.00).

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