Members had been assigned to dependency classification or regular class utilizing the aforementioned significance

Members had been assigned to dependency classification or regular class utilizing the aforementioned significance

Statistical study

SPSS to possess Window (ver. 21.0; SPSS Inc., Chicago, IL, USA) was used to own statistical investigation. Market attributes was stated once the volume and you will fee. Chi-square take to was used examine habits and regular teams into attributes away from intercourse, socio-financial standing, loved ones construction, depression, anxiety, ADHD, smoking, and you can alcoholic drinks explore. Pearson relationship study was did to find the correlation ranging from portable habits score or other details of great interest. Fundamentally, multivariate binary logistic regression analysis are performed to evaluate new influence away from sex, despair, stress, ADHD, puffing, and you will alcohol have fun with into the smartphone addiction. The research is done using backwards method, with addiction classification and you will typical group just like the established parameters and you will female intercourse, anxiety classification, anxiety category, ADHD category, puffing class, and you will alcoholic beverages teams because the separate parameters. An excellent p property value below 0.05 try thought to indicate analytical advantages.

Efficiency

Among the 5051 people recruited on the analysis, 539 were excluded due to unfinished solutions. Therefore, a maximum of 4512 people (45.1% men, n = 2034; 54.9% women, n = 2478) was among them investigation. Brand new indicate age the fresh new sufferers is (SD = step 1.62). Brand new sociodemographic attributes of the victims was described during the Dining table 1. Having resource, 4060 students (87.8%) was indeed cellular phone customers (84.2% away from men, n = 1718 off 2041; ninety.6% out-of women, n = 2342 of 2584) one of several 4625 people exactly who responded to practical question of cellular phone 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) Web dating site, 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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