Comparative efficacy of gait training for balance outcomes in patients with stroke: A systematic review and network meta-analysis

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Comparative efficacy of gait training for balance outcomes in patients with stroke: A systematic review and network meta-analysis

Tianyi Lyu^1†, Kang Yan^1†, Jiaxuan Lyu¹, Xirui Zhao¹, Ruoshui Wang¹, Chaoyang Zhang¹, Meng Liu¹, Chao Xiong², Chengjiang Liu³ and Yulong Wei^1*

• ¹School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
• ²L3 & Maintenance Solutions, SUSE Software (Beijing) Co., Ltd., Beijing, China
• ³Department of General Medicine, Affiliated Anqing First People’s Hospital of Anhui Medical University, HeFei, Anhui, China

Background: Growing evidence suggests that gait training can improve stroke patients’ balance outcomes. However, it remains unclear which type of gait training is more effective in improving certain types of balance outcomes in patients with stroke. Thus, this network meta-analysis (NMA) included six types of gait training (treadmill, body-weight-supported treadmill, virtual reality gait training, robotic-assisted gait training, overground walking training, and conventional gait training) and four types of balance outcomes (static steady-state balance, dynamic steady-state balance, proactive balance, and balance test batteries), aiming to compare the efficacy of different gait training on specific types of balance outcomes in stroke patients and determine the most effective gait training.

Method: We searched PubMed, Embase, Medline, Web of Science, and Cochrane Library databases from inception until 25 April 2022. Randomized controlled trials (RCTs) of gait training for the treatment of balance outcomes after stroke were included. RoB2 was used to assess the risk of bias in the included studies. Frequentist random-effects network meta-analysis (NMA) was used to evaluate the effect of gait training on four categories of balance outcomes.

Result: A total of 61 RCTs from 2,551 citations, encompassing 2,328 stroke patients, were included in this study. Pooled results showed that body-weight-support treadmill (SMD = 0.30, 95% CI [0.01, 0.58]) and treadmill (SMD = 0.25, 95% CI [0.00, 0.49]) could improve the dynamic steady-state balance. Virtual reality gait training (SMD = 0.41, 95% CI [0.10, 0.71]) and body-weight-supported treadmill (SMD = 0.41, 95% CI [0.02, 0.80]) demonstrated better effects in improving balance test batteries. However, none of included gait training showed a significant effect on static steady-state balance and proactive balance.

Conclusion: Gait training is an effective treatment for improving stroke patients’ dynamic steady-state balance and balance test batteries. However, gait training had no significant effect on static steady-state balance and proactive balance. To achieve maximum efficacy, clinicians should consider this evidence when recommending rehabilitation training to stroke patients. Considering body-weight-supported treadmill is not common for chronic stroke patients in clinical practice, the treadmill is recommended for those who want to improve dynamic steady-state balance, and virtual reality gait training is recommended for those who want to improve balance test batteries.

Limitation: Missing evidence in relation to some types of gait training is supposed to be taken into consideration. Moreover, we fail to assess reactive balance in this NMA since few included trials reported this outcome.

Systematic Review Registration: PROSPERO, identifier CRD42022349965.

Introduction

Recently, stroke is the leading cause of death in China and the second leading cause of death worldwide (1, 2). Despite the fact that stroke mortality, prevalence, and incidence have decreased in the past 20 years, its prevalence is increasing in young individuals (3). Thus, the significance of stroke rehabilitation has grown. Balance disorders, one of the most common symptoms after a stroke, can affect patients’ physiological and social functions (4, 5). Therefore, balance disorders place a heavy burden on both individuals and society.

Balance is one of the main functional goals of postural control and involves the coordination of movement strategies to stabilize the center of body mass during self-initiated and externally triggered stability perturbations (6). Balance disorders account for a series of gait-related disabilities, including problems with transferring, maintaining body posture, and locomotion (7, 8). Therefore, balance is an important component of gait to stabilize one’s body during mobility. Meanwhile, growing evidence suggests that gait training can improve balance outcomes (9, 10).

Gait training refers to specific types of physical therapies that help individuals strengthen and improve their walking capacity (11). Treadmill, body-weight-supported treadmill, robot-assisted gait training, virtual reality gait training, conventional gait training, and overground walking training are common types of gait training that have the potential to improve balance capacity (details in Table 1). Several studies have suggested that the aforementioned gait training could counteract the balance dysfunction caused by various diseases, such as stroke and Parkinson’s disease (9, 10). However, the effects of gait training on balance rehabilitation after stroke have been inconclusive. It remains unclear which type of gait training is the most effective. Canadian Guideline revealed that gait training (e.g., body-weight-supported treadmill) might improve dynamic balance in the subacute phase after stroke (12). A recent meta-analysis suggested that overground walking training and robotic-assisted gait training showed no significant effect on balance outcomes while treadmill showed a significant effect on balance outcomes (13). Another systematic review and meta-analysis reported that no significant balance gains were obtained from gait training (e.g., body-weight-supported treadmill and robot-assisted gait training) (14).

TABLE 1

Table 1. Characteristics of included gait training.

According to Shumway-Cook and Woollacott (15), balance performance can be divided into four types, including dynamic steady-state balance, static steady-state balance, proactive balance, and reactive balance. In addition, there are only small-sized correlations between different types of balance performance (16). With reference to these findings, balance outcome measures are further subdivided into five types, including static steady-state balance, dynamic steady-state balance, proactive balance, reactive balance, and balance test batteries (17). The first four types of balance outcome measures correspond, one by one, to the four types of balance performance, and the fifth type of balance outcome measure (balance test batteries) is added to assess the overall balance performance (18, 19). This classification has been used in several types of research to assess balance status and changes in response to exercise (20, 21). Thus, investigating the effect of gait training in specific type of balance outcomes might provide more comprehensive evidence in this field.

As mentioned earlier, evaluating the effects of gait training in post-stroke patients is of particular importance. However, selecting the optimal gait training to improve specific balance outcomes poses a challenge to clinicians. Because many gait training methods have not been directly compared in clinical trials, typical pairwise meta-analysis cannot be performed on them. Even when direct comparisons are available, the evidence is inadequate to make any conclusions. Therefore, we performed an NMA to compare the effects of different types of gait training on each type of balance outcome, thus identifying the optimal gait training for stroke survivors.

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