People have been assigned to dependency group otherwise regular group making use of the aforementioned definitions

People have been assigned to dependency group otherwise regular group making use of the aforementioned definitions

Mathematical study

SPSS getting Windows (observar. 21.0; SPSS Inc., il, IL, USA) was used to have analytical data. Market attributes was in fact said once the volume and you will commission. Chi-square test was applied evaluate habits and you can typical teams toward attributes of gender, socio-economic updates, friends construction, anxiety, stress, ADHD, puffing, and you can alcoholic beverages have fun with. Pearson relationship investigation is did to choose the relationship between mobile habits scores or other details of interest. In the end, multivariate digital logistic regression analysis try performed to evaluate the new influence of gender, despair, anxiety, ADHD, smoking, and you can alcoholic beverages fool around with to your mobile dependency. The analysis is actually finished having fun with backwards method, that have addiction category and you may typical class while the situated details and females sex, depression group, nervousness category, ADHD class, smoking classification, and you will alcohol teams just like the independent variables. Good p value of below 0.05 is considered to indicate mathematical advantages.


Among the many 5051 children hired to your studies, 539 were excluded because of partial answers. Therefore, a maximum of 4512 college students (forty five.1% male, letter = 2034; 54.9% people, letter = 2478) was in fact included in this study. The fresh new mean age of the brand new victims is actually (SD = step one.62). This new sociodemographic properties of subjects is actually described in the Table step 1. Getting reference, 4060 pupils (87.8%) were portable customers (84.2% regarding men, n = 1718 away from 2041; black women looking for men ninety.6% of girls, letter = 2342 of 2584) one of many 4625 pupils exactly who responded to practical question out-of portable possession (426 don’t operate).

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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