Analytical investigation
SPSS having Window (ver. 21.0; SPSS Inc., il, IL, USA) was applied to own statistical study. Market properties were stated just like the frequency and you can payment. Chi-square shot was used to compare addiction and typical teams into features from gender, socio-financial standing, members of the family construction, depression, nervousness, ADHD, puffing, and you may alcoholic beverages use. Pearson correlation study is actually did to choose the relationship ranging from smartphone addiction scores or any other details of interest. Ultimately, multivariate binary logistic regression studies was performed to assess new influence regarding gender, despair, nervousness, ADHD, puffing, and gay dating hookup apps you may alcoholic drinks have fun with toward portable addiction. The research is finished using backwards approach, having addiction group and you can normal category because the established variables and you can people gender, depression category, anxiety classification, ADHD group, smoking category, and alcoholic drinks organizations as the separate parameters. A great p property value lower than 0.05 is actually thought to suggest mathematical importance.
Abilities
One of the 5051 college students hired on studies, 539 was excluded because of unfinished responses. Ergo, a total of 4512 children (forty five.1% men, letter = 2034; 54.9% women, n = 2478) was basically one of them investigation. The latest mean age of the subjects is (SD = step 1.62). The new sociodemographic functions of your own sufferers is actually summarized when you look at the Table 1. Getting site, 4060 children (87.8%) were mobile customers (84.2% of men, n = 1718 of 2041; ninety.6% out of lady, n = 2342 out of 2584) among the many 4625 college students just who taken care of immediately the question off mobile control (426 didn’t function).
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).