Dawe Julia, Sutherland Craig, Barco Alex, Broadbent Elizabeth
Department of Psychological Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
Department of Electrical and Computer Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand.
BMJ Paediatr Open. 2019 Jan 31;3(1):e000371. doi: 10.1136/bmjpo-2018-000371. eCollection 2019.
To review research on social robots to help children in healthcare contexts in order to describe the current state of the literature and explore future directions for research and practice.
Scoping review.
Engineering Village, IEEE Xplore, Medline, PsycINFO and Scopus databases were searched up until 10 July 2017. Only publications written in English were considered. Identified publications were initially screened by title and abstract, and the full texts of remaining publications were then subsequently screened.
Publications were included if they were journal articles, conference proceedings or conference proceedings published as monographs that described the conceptualisation, development, testing or evaluation of social robots for use with children with any mental or physical health condition or disability. Publications on autism exclusively, robots for use with children without identified health conditions, physically assistive or mechanical robots, non-physical hardware robots and surgical robots were excluded.
Seventy-three publications were included in the review, of which 50 included user studies with a range of samples. Most were feasibility studies with small sample sizes, suggesting that the robots were generally accepted. At least 26 different robots were used, with many of these still in development. The most commonly used robot was NAO. The evidence quality was low, with only one randomised controlled trial and a limited number of experimental designs.
Social robots hold significant promise and potential to help children in healthcare contexts, but higher quality research is required with experimental designs and larger sample sizes.
回顾关于社交机器人在医疗环境中帮助儿童的研究，以描述当前文献的现状，并探索研究和实践的未来方向。
范围综述。
截至2017年7月10日，检索了工程村、IEEE Xplore、Medline、PsycINFO和Scopus数据库。仅考虑用英文撰写的出版物。最初通过标题和摘要对已识别的出版物进行筛选，然后对其余出版物的全文进行筛选。
如果出版物是期刊文章、会议论文集或作为专著出版的会议论文集，描述了用于任何精神或身体健康状况或残疾儿童的社交机器人的概念化、开发、测试或评估，则纳入。专门关于自闭症的出版物、用于未确定健康状况儿童的机器人、物理辅助或机械机器人、非物理硬件机器人和手术机器人被排除。
73篇出版物被纳入综述，其中50篇包括针对一系列样本的用户研究。大多数是小样本量的可行性研究，表明这些机器人普遍被接受。至少使用了26种不同的机器人，其中许多仍在开发中。最常用的机器人是NAO。证据质量较低，只有一项随机对照试验和数量有限的实验设计。
社交机器人在医疗环境中帮助儿童方面具有巨大的前景和潜力，但需要更高质量的研究，采用实验设计和更大的样本量。
