Cognitive-motor dual-task training on gait and balance in stroke patients: meta-analytic report and trial sequential analysis of randomized clinical trials

Abstract

Objective: Cognitive-motor dual-tasking training (CMDT) might improve limb function and motor performance in stroke patients. However, is there enough evidence to prove that it is more effective compared with conventional physical single-task training? This meta-analysis and Trial Sequential Analysis of randomized clinical trials (RCTs) aimed to evaluate the effectiveness of CMDT on balance and gait for treating hemiplegic stroke patients.

Methods: The databases were searched in PubMed, Web of Science, Ovid Database and The Cochrane Library, SinoMed database, Chinese National Knowledge Infrastructure (CNKI), Wan Fang database, and VIP database up to December 8, 2023. The Cochrane-recommended risk of bias (RoB) 2.0 tool was employed to assess risk of bias in trials. The statistical analysis was employed using R version 4.3.2. In addition, subgroup analyses and meta-regression were performed to explore the possible sources of heterogeneity. The evidence for each outcome was evaluated according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. The Copenhagen Trial Unit's Trial Sequential Analysis (version 0.9.5.10 Beta) was used for sequential analysis.

Results: Seventeen randomized clinical trials (RCTs) (n = 751 patients) were included. The results demonstrated that cognitive-motor dual-task training (CMDT) might be beneficial on stroke patients on Berg Balance Scale (BBS) (MD = 4.26, 95% CI 1.82, 6.69, p < 0.0001) (low-quality evidence). However, CMDT might not affect Time Up and Go test (TUG) (MD = -1.28, 95% CI -3.63, 1.06, p = 0.284); and single-task walking speed (MD = 1.35, 95% CI -1.56, 4.27, p = 0.413) in stroke patients (low-quality evidence). The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) results indicated that all findings were very low to low certainty. Trial Sequential Analyses demonstrated larger sample sizes are required for confirming our findings.

Conclusion: Cognitive-motor dual-task training (CMDT) compared with conventional physical single-task training might be an effective intervention for improving static balance function in stroke patients (low-quality evidence), which should be interpreted cautiously due to heterogeneity and potential biases. Nevertheless, further research is required to support the abovementioned findings. Trial Registration This protocol was registered in PROSPERO (CRD42023490530).

Keywords: Balance; Cognitive-motor; Dual Task training; Gait; Meta-analysis; Stroke; Trial Sequential Analysis.

Fig. 1

Flowchart for identification of studies

Fig. 2

The risk of bias graph in the single study

Fig. 3

The risk of bias graph in the average of all included studies

Fig. 4

A forest plot for meta-analysis of Berg Balance Scale (BBS)

Fig. 5

A forest plot for Time Up and Go Test (TUG)

Fig. 6

A forest plot for meta-analysis of single-task walking speed

Fig. 7

A forest plot for meta-analysis of DTUGT

Fig. 8

A forest plot for meta-analysis of dual-task walking speed

Fig. 9

A forest plot for meta-analysis of Activities of daily living (ADL)

Fig. 10

A forest plot for meta-analysis of Fugl-Meyer Assessment (FMA)

Fig. 11

The Trial Sequential Analysis of Berg Balance Scale (A), Time Up and Go Test (B), Single-task walking speed (C), and Fugl-Meyer Assessment (D) D: Cumulative Z-curve (solid blue lines) were constructed using a random-effects model, with the light red line showing the traditional threshold and the dark red line representing the Trial Sequential Analysis (TSA)threshold

Fig. 11

The Trial Sequential Analysis of Berg Balance Scale (A), Time Up and Go Test (B), Single-task walking speed (C), and Fugl-Meyer Assessment (D) D: Cumulative Z-curve (solid blue lines) were constructed using a random-effects model, with the light red line showing the traditional threshold and the dark red line representing the Trial Sequential Analysis (TSA)threshold