1 Centre for Health Behaviours Research, The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong , Hong Kong, China.
2 School of Public Health, Zhengjiang University , Hangzhou, China.
J Behav Addict. 2018 Sep 1;7(3):633-643. doi: 10.1556/2006.7.2018.87. Epub 2018 Sep 28.
The aim of the study is to investigate (a) whether probable depression status assessed at baseline prospectively predicted new incidence of Internet addiction (IA) at the 12-month follow-up and (b) whether IA status assessed at baseline prospectively predicted new incidence of probable depression at follow-up.
We conducted a 12-month cohort study (n = 8,286) among Hong Kong secondary students, and derived two subsamples. The first subsample (n = 6,954) included students who were non-IA at baseline, using the Chen Internet Addiction Scale (≤63), and another included non-depressed cases at baseline (n = 3,589), using the Center for Epidemiological Studies Depression Scale (<16).
In the first subsample, 11.5% of the non-IA cases developed IA during follow-up, and probable depression status at baseline significantly predicted new incidence of IA [severe depression: adjusted odds ratio (ORa) = 2.50, 95% CI = 2.07, 3.01; moderate: ORa = 1.82, 95% CI = 1.45, 2.28; mild: ORa = 1.65, 95% CI = 1.32, 2.05; reference: non-depressed], after adjusting for sociodemographic factors. In the second subsample, 38.9% of those non-depressed participants developed probable depression during follow-up. Adjusted analysis showed that baseline IA status also significantly predicted new incidence of probable depression (ORa = 1.57, 95% CI = 1.18, 2.09).
The high incidence of probable depression is a concern that warrants interventions, as depression has lasting harmful effects in adolescents. Baseline probable depression predicted IA at follow-up and vice versa, among those who were free from IA/probable depression at baseline. Healthcare workers, teachers, and parents need to be made aware of this bidirectional finding. Interventions, both IA and depression prevention, should thus take both problems into consideration.
本研究旨在探究:(a)基线时评估的可能抑郁状态是否能前瞻性地预测 12 个月随访时新的网络成瘾(IA)发生率;(b)基线时评估的 IA 状态是否能前瞻性地预测随访时新的可能抑郁发生率。
我们对香港中学生进行了一项为期 12 个月的队列研究(n=8286),并得出了两个子样本。第一个子样本(n=6954)纳入了基线时非 IA 患者(使用 Chen 网络成瘾量表[≤63]),另一个子样本纳入了基线时非抑郁患者(n=3589,使用流行病学研究中心抑郁量表[<16])。
在第一个子样本中,11.5%的非 IA 患者在随访期间发展为 IA,基线时的可能抑郁状态显著预测了 IA 的新发病例[严重抑郁:调整后的优势比(ORa)=2.50,95%可信区间[CI]:2.07,3.01;中度:ORa=1.82,95%CI:1.45,2.28;轻度:ORa=1.65,95%CI:1.32,2.05;参考:非抑郁],在调整了社会人口学因素后。在第二个子样本中,38.9%的非抑郁患者在随访期间发展为可能抑郁。调整分析显示,基线时的 IA 状态也显著预测了新的可能抑郁的发生(ORa=1.57,95%CI:1.18,2.09)。
高发病率的可能抑郁是一个值得关注的问题,需要进行干预,因为抑郁会对青少年造成持久的有害影响。在基线时无 IA/可能抑郁的人群中,基线时的可能抑郁状态预测了随访时的 IA,反之亦然。卫生保健工作者、教师和家长需要意识到这一双向发现。因此,IA 和抑郁预防干预措施都应同时考虑这两个问题。