Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105062
Title: Game-based digital interventions for depression therapy : a systematic review and meta-analysis
Authors: Li, Jinhui
Theng, Yin-Leng
Foo, Schubert
Keywords: DRNTU::Social sciences::Psychology::Behaviorism
Issue Date: 2014
Source: Li, J., Theng, Y.-L., & Foo, S. (2014). Game-Based Digital Interventions for Depression Therapy: A Systematic Review and Meta-Analysis. Cyberpsychology, Behavior, and Social Networking, 17(8), 519-527.
Series/Report no.: Cyberpsychology, behavior, and social networking
Abstract: The aim of this study was to review the existing literature on game-based digital interventions for depression systematically and examine their effectiveness through a meta-analysis of randomized controlled trials (RCTs). Database searching was conducted using specific search terms and inclusion criteria. A standard meta-analysis was also conducted of available RCT studies with a random effects model. The standard mean difference (Cohen's d) was used to calculate the effect size of each study. Nineteen studies were included in the review, and 10 RCTs (eight studies) were included in the meta-analysis. Four types of game interventions—psycho-education and training, virtual reality exposure therapy, exercising, and entertainment—were identified, with various types of support delivered and populations targeted. The meta-analysis revealed a moderate effect size of the game interventions for depression therapy at posttreatment (d=−0.47 [95% CI −0.69 to −0.24]). A subgroup analysis showed that interventions based on psycho-education and training had a smaller effect than those based on the other forms, and that self-help interventions yielded better outcomes than supported interventions. A higher effect was achieved when a waiting list was used as the control. The review and meta-analysis support the effectiveness of game-based digital interventions for depression. More large-scale, high-quality RCT studies with sufficient long-term data for treatment evaluation are needed.
URI: https://hdl.handle.net/10356/105062
http://hdl.handle.net/10220/20412
ISSN: 2152-2715
DOI: 10.1089/cyber.2013.0481
Rights: © 2014 Mary Ann Liebert, Inc. This paper was published in Cyberpsychology, Behavior, and Social Networking and is made available as an electronic reprint (preprint) with permission of Mary Ann Liebert, Inc. The paper can be found at the following official DOI: http://dx.doi.org/10.1089/cyber.2013.0481.  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:WKWSCI Journal Articles

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