Electromechanical-assisted training for walking after stroke
Jan Mehrholz, Joachim Kugler, Marcus Pohl, Bernhard Elsner
- 发表年份
- 2025
- 引用次数
- 14
摘要
RATIONALE: Walking difficulties are common after a stroke. During rehabilitation, electromechanical and robotic gait-training devices can help improve walking. As the evidence and certainty of the evidence may have changed since our last update in 2020, we aimed to update the scientific evidence on the benefits and acceptability of these technologies to ensure they remain a viable option for stroke rehabilitation. OBJECTIVES: Primary • To determine whether electromechanical- and robot-assisted gait training versus physiotherapy (or usual care) improves walking in adults after stroke. Secondary • To determine whether electromechanical- and robot-assisted gait training versus physiotherapy (or usual care) after stroke improves walking velocity, walking capacity, acceptability, and death from all causes until the end of the intervention phase. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, and seven other databases. We handsearched relevant conference proceedings, searched trials and research registers, checked reference lists, and contacted trial authors to identify further published, unpublished, and ongoing trials. The date of the latest search was December 2023. ELIGIBILITY CRITERIA: We included all randomised controlled trials and randomised controlled cross-over trials in people over the age of 18 years diagnosed with stroke of any severity, at any stage, in any setting, evaluating electromechanical- and robot-assisted gait training versus physiotherapy (or usual care). OUTCOMES: Our critical outcome was the ability to walk independently, measured with the Functional Ambulation Category (FAC). An FAC score of 4 or 5 indicated independent walking over a 15-metre surface, irrespective of aids used, such as a cane. An FAC score less than 4 indicates dependency in walking (supervision or assistance, or both, must be given in performing walking). Important outcomes included walking velocity and capacity, as well as dropouts. RISK OF BIAS: We used Cochrane's RoB 1 tool. SYNTHESIS METHODS: Two review authors independently selected trials for inclusion, assessed methodological quality and risk of bias, and extracted data. We used random-effects models for the meta-analysis. We assessed the certainty of evidence using the GRADE approach. INCLUDED STUDIES: We included 101 studies (39 new studies plus 62 studies from previous versions) with a total of 4224 participants after stroke in our review update. SYNTHESIS OF RESULTS: Electromechanical-assisted gait training in combination with physiotherapy probably increases the odds of participants becoming independent in walking (odds ratio (OR) 1.65, 95% confidence interval (CI) 1.21 to 2.25; P = 0.001; I² = 31%; 51 studies, 2148 participants; moderate-certainty evidence); probably does not increase mean walking velocity (mean difference (MD) 0.05 m/s, 95% CI 0.02 to 0.08; P < 0.001; I² = 58%; 73 studies, 3043 participants; moderate-certainty evidence); and does not increase mean walking capacity (MD 11 metres walked in 6 minutes, 95% CI 1.8 to 20.3; P = 0.02; I² = 43%; 42 studies, 1966 participants; high-certainty evidence). Electromechanical-assisted gait training does not increase or decrease the risk of loss to the study during the intervention or the risk of death from all causes (high-certainty evidence). At follow-up after study end, electromechanical-assisted gait training in combination with physiotherapy may not increase the odds of participants becoming independent in walking (OR 1.64, 95% CI 0.77 to 3.48; P = 0.20; I² = 69%; 8 studies, 569 participants; low-certainty evidence), and probably does not increase mean walking velocity (MD 0.05 m/s, 95% CI -0.03 to 0.13; P = 0.22; I² = 66%; 17 studies, 857 participants; moderate-certainty evidence) or mean walking capacity (MD 9.6 metres walked in 6 minutes, 95% CI -14.6 to 33.7; P = 0.44; I² = 53%; 15 studies, 736 participants; moderate-certainty evidence). Our results must be interpreted with caution because (1) some trials
关键词
相关论文
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martı́n Abadi, Ashish Agarwal, Paul Barham 等 20 位作者
2016
The Organization of Behavior
D. O. Hebb
2005
The spread of true and false news online
Soroush Vosoughi, Deb Roy, Sinan Aral
2018
A review of shape memory alloy research, applications and opportunities
Jaronie Mohd Jani, Martin Leary, Aleksandar Subic 等 4 位作者
2013