LAUSR

Special attention should be paid to the types of online activities in which adolescents engage, along with frequencies of the activities. Thus, we aimed to identify homogeneous subgroups of adolescents using five online activities and to examine differences in 12 characteristics across the subgroups we identified. We cross-sectionally analyzed nationally representative secondary data collected in Korea (N = 1827 adolescents who used a computer at the time of the survey). We performed latent class analysis, which is a person-centered approach, to understand the patterns of online activities in adolescence. A three-latent-class model best fit the data: frequent use for academics (FUA), non-frequent use for all activities (NFUAA), and frequent use for multiple activities (FUMA). In general, the FUA class differed from the other two classes. Specifically, the FUA class spent less time online and had better psychosocial conditions. Given these findings, health professionals should implement strategies aimed at assisting (a) adolescents to be aware that some types of online activities can potentially pose a risk to their psychosocial conditions and (b) parents to actively participate in guiding children’s Internet use.