02 Oct Participants have been allotted to addiction group otherwise typical group by using the the second significance
SPSS to own Windows (ver. 21.0; SPSS Inc., il, IL, USA) was applied for analytical data. Market qualities was reported due to the fact volume and you will commission. Chi-square shot was utilized evaluate dependency and you will regular teams on properties off gender, socio-economic status, relatives framework, despair, anxiety, ADHD, puffing, and you may liquor use. Pearson correlation data try performed to choose the relationship anywhere between mobile dependency ratings or any other details interesting. Ultimately, multivariate digital logistic regression study was sexting apps for teens did to assess the fresh new influence out-of intercourse, despair, anxiety, ADHD, smoking, and you will alcoholic drinks fool around with to the mobile habits. The research are completed having fun with backward means, which have habits classification and you may regular category just like the depending variables and you will lady sex, despair class, nervousness category, ADHD group, smoking class, and you can alcohol teams because independent details. An excellent p worth of less than 0.05 try considered to indicate mathematical importance.
Among 5051 pupils hired on the study, 539 was basically excluded on account of incomplete solutions. Therefore, a total of 4512 pupils (forty-five.1% men, n = 2034; 54.9% female, letter = 2478) was among them data. New suggest chronilogical age of the new victims try (SD = step 1.62). The new sociodemographic characteristics of your own victims is described for the Dining table step 1. Having resource, 4060 people (87.8%) was basically cellphone people (84.2% of male, n = 1718 out-of 2041; ninety.6% out-of females, n = 2342 out-of 2584) one of the 4625 students who responded to issue off portable ownership (426 didn’t work).
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).