Farmer Sybil Eleanor, Durairaj Venugopal, Swain Ian, Pandyan Anand David
Institute for Science and Technology in Medicine, Keele University, Keele, United Kingdom.
School of Design, Computing and Engineering, Bournemouth University, Bournemouth, United Kingdom; Department of Medical Physics and Clinical Engineering, Salisbury NHS Foundation Trust, Salisbury, United Kingdom.
Arch Phys Med Rehabil. 2014 May;95(5):968-85. doi: 10.1016/j.apmr.2013.12.020. Epub 2014 Jan 12.
To systematically identify, review, and explore the evidence for use of assistive technologies (ATs) in poststroke upper limb rehabilitation.
AMED, CINAHL, Cochrane Library, Compendex, CSA Illumina, EMBASE, MEDLINE, PEDro, PyscINFO, and Web of Science were last searched in September 2011.
Two independent researchers screened for inclusion criteria (adult poststroke subjects, upper limb rehabilitation with an AT). The risk of bias was assessed. Randomized controlled trials of poststroke subjects with baseline equivalence as assessed by blinded assessors were selected for data extraction.
Details of subjects, experimental and control treatments, and all outcomes were recorded in a spreadsheet.
These data were used to calculate effect sizes for all outcome measures. Impairment measures ranged from -.39 (95% confidence interval [CI], -1.14 to .62) to 1.46 (95% CI, .72-2.20). Measures of activity effect sizes were from .04 (95% CI, -.35 to .44) to .93 (95% CI, -.39 to 2.25); for Motor Activity Log, from .07 (95% CI, -.66 to .80) to 1.24 (95% CI, .47-2.01); and for participation, from -3.32 (95% CI, -4.52 to 2.11) to 1.78 (95% CI, 0-3.56).
AT treatments appear to give modest additional benefit when compared with usual care or in addition to usual care. This is most apparent for subjects early poststroke with 2 caveats: high-intensity constraint-induced movement therapy and electrical stimulation exclusively to the shoulder appear detrimental. The heterogeneity of treatment parameters and population characteristics precludes specific recommendations. Research would benefit from modeling studies to explicitly define criteria of population, intervention, comparator, and outcomes for effective treatments before the development of efficiently integrated care pathways.
系统识别、回顾并探究辅助技术（ATs）用于中风后上肢康复的证据。
对AMED、CINAHL、Cochrane图书馆、Compendex、CSA Illumina、EMBASE、MEDLINE、PEDro、PyscINFO及科学引文索引数据库的检索截至2011年9月。
两名独立研究人员筛选纳入标准（成年中风后受试者，使用辅助技术进行上肢康复）。评估偏倚风险。选择由盲法评估者评定基线具有可比性的中风后受试者的随机对照试验进行数据提取。
受试者详细信息、实验及对照治疗以及所有结局均记录在电子表格中。
这些数据用于计算所有结局指标的效应量。功能障碍指标范围为-.39（95%置信区间[CI]，-1.14至.62）至1.46（95% CI，.72 - 2.20）。活动效应量指标范围为.04（95% CI，-.35至.44）至.93（95% CI，-.39至2.25）；运动活动日志指标范围为.07（95% CI，-.66至.80）至1.24（95% CI，.47 - 2.01）；参与度指标范围为-3.32（95% CI，-4.52至2.11）至1.78（95% CI，0 - 3.56）。
与常规护理相比或在常规护理基础上，辅助技术治疗似乎能带来适度的额外益处。这在中风早期受试者中最为明显，但有两点需要注意：高强度强制性运动疗法以及仅针对肩部的电刺激似乎有害。治疗参数和人群特征的异质性使得无法给出具体建议。在开发高效整合的护理路径之前，建模研究有助于明确有效治疗的人群、干预措施、对照及结局标准，从而使研究受益。
