Table 2

Impacts of adolescent personal factors on adolescent internet addiction by logistic regression analysisa,b
Risk Factors Coefficient (Standard Error) Odds Ratio 95%Confidence Interval p-value
Gender
 Female (ref.) 1.0 1.0
 Male 0.26(0.12) 1.29 1.02-1.64 0.0361
Adolescent monthly money spending levels (RMB / month)
 <100 (ref.) 1.0 1.0
 ≥300 0.41(0.16) 1.51 1.11-2.05 0.0092
 100~299 0.51(0.13) 1.66 1.29-2.14 <0.0001
Academic achievements
 Very good (ref.) 1.0 1.0
 Very & relatively bad 1.57(0.33) 4.79 2.51-9.13 <0.0001
 General 0.87(0.31) 2.38 1.29-4.41 0.0057
 Relative good 0.52(0.33) 1.68 0.88-3.20 0.1186
Total hours online for a whole week (hours /month)
 <7 (ref.) 1.0 1.0
 >28 1.45(0.17) 4.28 3.06-5.99 <0.0001
 21 ~28 1.23(0.21) 3.41 2.26-5.15 <0.0001
 14 ~21 0.96(0.19) 2.61 1.81-3.77 <0.0001
 7~14 0.89(0.15) 2.44 1.81-3.29 <0.0001
Main Purpose of using internet
 Academic learning (ref.) 1.0 1.0
 Playing game 1.94(0.34) 6.98 3.59-13.58 <.0.0001
 Real-time chatting 0.97(0.36) 2.64 1.30-5.38 0.0073
 Browsing news or e-mails only 0.17(0.40) 1.19 0.55-2.60 0.6625

a This logistic regression model was fit to model the possibility of adolescent having internet addiction, internet addiction was defined as total score ≥ 163.

b Adolescent age, gender, grade, school types, adolescent academic achievement, adolescent monthly spending levels, internet-use time, and the main purposes and places of adolescent internet use were adjusted in the models.

Xu et al.

Xu et al. BMC Public Health 2012 12:1106   doi:10.1186/1471-2458-12-1106

Open Data