Department of Long-Term Care, GGZ Drenthe, Assen, Netherlands.
University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
Cyberpsychol Behav Soc Netw. 2023 Apr;26(4):288-299. doi: 10.1089/cyber.2022.0228.
Meta-analyses have found that social cognition training (SCT) has large effects on the emotion recognition ability of people with a psychotic disorder. Virtual reality (VR) could be a promising tool for delivering SCT. Presently, it is unknown how improvements in emotion recognition develop during (VR-)SCT, which factors impact improvement, and how improvements in VR relate to improvement outside VR. Data were extracted from task logs from a pilot study and randomized controlled trials on VR-SCT ( = 55). Using mixed-effects generalized linear models, we examined the: (a) effect of treatment session (1-5) on VR accuracy and VR response time for correct answers; (b) main effects and moderation of participant and treatment characteristics on VR accuracy; and (c) the association between baseline performance on the Ekman 60 Faces task and accuracy in VR, and the interaction of Ekman 60 Faces change scores (i.e., post-treatment - baseline) with treatment session. Accounting for the task difficulty level and the type of presented emotion, participants became more accurate at the VR task ( = 0.20, < 0.001) and faster ( = -0.10, < 0.001) at providing correct answers as treatment sessions progressed. Overall emotion recognition accuracy in VR decreased with age ( = -0.34, = 0.009); however, no significant interactions between any of the moderator variables and treatment session were found. An association between baseline Ekman 60 Faces and VR accuracy was found ( = 0.04, = 0.006), but no significant interaction between difference scores and treatment session. Emotion recognition accuracy improved during VR-SCT, but improvements in VR may not generalize to non-VR tasks and daily life.
元分析发现,社会认知训练(SCT)对精神病患者的情绪识别能力有很大影响。虚拟现实(VR)可能是提供 SCT 的有前途的工具。目前,尚不清楚在(VR-)SCT 期间情绪识别的改善是如何发展的,哪些因素影响改善,以及 VR 中的改善如何与 VR 之外的改善相关。从 VR-SCT 的试点研究和随机对照试验的任务日志中提取了数据(n=55)。使用混合效应广义线性模型,我们检查了:(a)治疗阶段(1-5)对 VR 准确性和 VR 正确答案响应时间的影响;(b)参与者和治疗特征对 VR 准确性的主要影响和调节作用;以及(c)基线表现与 VR 准确性之间的关联在 Ekman 60 张面孔任务上,以及 Ekman 60 张面孔变化分数(即,治疗后 - 基线)与治疗阶段的交互作用。考虑到任务难度水平和呈现情绪的类型,参与者在 VR 任务中变得更加准确(=0.20, <0.001),并且提供正确答案的速度更快(= -0.10, <0.001)随着治疗阶段的进展。总的来说,VR 中的整体情绪识别准确性随着年龄的增长而降低(= -0.34, =0.009);然而,没有发现任何调节变量与治疗阶段之间存在显著的相互作用。发现基线 Ekman 60 张面孔与 VR 准确性之间存在关联(=0.04, =0.006),但差异分数与治疗阶段之间没有显著的相互作用。在 VR-SCT 期间,情绪识别准确性有所提高,但 VR 中的改善可能不会推广到非 VR 任务和日常生活中。