Aims

To estimate the difference in delay discounting (DD) between subjects with Internet addiction (IA) and those without as well as to identify significant variables involved in DD.

Methods

Using the keywords related to IA (e.g., “excessive Internet use”, “Internet dependence”) AND “delayed reward discounting” OR “delay discounting” OR “temporal discounting” OR “delayed gratification” OR time discounting OR intertemporal choice OR impulsive choice, the PubMed, Embase, and PsycINFO databases were searched from inception to June 2020 for English articles with comparison between subjects with IA and those without. Effect sizes were calculated by group means from the k value or area under the curve (AUC). The random-effects models were used.

Results

Fourteen studies in total were eligible for the current meta-analysis that involved 696 subjects with IA (mean age = 22.71) and 2,394 subjects without (mean age = 21.91). Subjects with IA had a steeper DD rate (g = 1.10, 95% CI: 0.57–1.64; p ≤ 0.01) compared with that in those without. Regarding DD data, the difference between k value and AUC was significant (p < 0.01; AUC > k). Additionally, the estimation of DD by the paper-and-pencil task was larger than that by the computerized task (p < 0.01). Significant difference in the DD rate was also noted between subjects with Internet gaming disorder (IGD) and those with unspecified IA (p = 0.00; IGD > IA). The percentage of men and task variables were significantly associated with the DD rate (all p < 0.01), suggesting impaired DD in subjects with IA.

Conclusions

Our results suggested the feasibility of utilizing the DD rate as a therapeutic index for cognitive control in IA. Nevertheless, judicious use is recommended taking into consideration the significant difference between k value and AUC.

中文翻译：

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延迟折扣与网络成瘾之间的关系：系统评价和荟萃分析

目标

估计有网络成瘾 (IA) 和没有网络成瘾的受试者之间延迟折扣 (DD) 的差异，并确定 DD 中涉及的重要变量。

方法

使用与 IA 相关的关键字（例如，“过度使用互联网”、“互联网依赖”）和“延迟奖励折扣”或“延迟折扣”或“时间折扣”或“延迟满足”或时间折扣或跨期选择或冲动选择，在 PubMed、Embase 和 PsycINFO 数据库中搜索了从开始到 2020 年 6 月的英文文章，并在有 IA 的受试者和没有 IA 的受试者之间进行了比较。通过组平均值从k值或曲线下面积 (AUC) 计算效果大小。使用随机效应模型。

结果

共有 14 项研究符合当前荟萃分析的条件，涉及 696 名 IA 受试者（平均年龄 = 22.71）和 2,394 名无 IA 受试者（平均年龄 = 21.91）。与没有 IA 的受试者相比，患有 IA 的受试者的 DD 率更高（g  = 1.10，95% CI：0.57-1.64；p  ≤ 0.01）。关于 DD 数据，k值和 AUC 之间的差异是显着的（p  < 0.01；AUC >  k）。此外，纸笔任务对 DD 的估计大于计算机任务（p  < 0.01）。网络游戏障碍 (IGD) 和未指定 IA ( p = 0.00; IGD > IA)。男性和任务变量的百分比与 DD 率显着相关（所有p  < 0.01），表明 IA 受试者的 DD 受损。

结论

我们的结果表明，利用 DD 率作为 IA 认知控制的治疗指标是可行的。尽管如此，考虑到k值和 AUC 之间的显着差异，建议明智地使用。