Does Adding Real-Time Postural Feedback During Training Boost Balance and Mobility in Individuals With Parkinson’s Disease? A Systematic Review and Meta-Analysis

ObjectiveTo evaluate the specific contribution of the real-time postural feedback component during training on balance and mobility in individuals with Parkinson's disease (PD).MethodFive databases were searched in June 2025. Studies comparing the same basic balance and gait training with versus without feedback were included. Meta-analysis was conducted and the quality of evidence was assessed.ResultEleven studies were included. Narrative analysis indicated that additional feedback may immediately improve dynamic balance and modulate gait speed but not static balance. Meta-Analysis Showed providing feedback significantly augmented post-training improvement in standing balance (SMD = 0.880, 95% CI = 0.345∼1.416, moderate-quality), but not on walking stability (SMD = 0.516, 95% CI = -0.025∼1.058, moderate-quality), Berg Balance Scale (MD = -0.079, 95% CI = -2.159∼2.000, very-low-quality), Timed-Up-and-Go (MD = 0.939, 95% CI = -1.414∼3.293, low-quality), and walking speed (MD = 0.002, 95% CI = -0.057∼0.062, low-quality).ConclusionProviding real-time postural feedback may have immediate effects on dynamic balance and walking speed and a post-training effect on standing balance in PD.RegistrationCRD42024522330.

Authors

Institutions

Publication Details

Journal
Journal of Applied Gerontology
Published
2026-01-29
DOI
https://doi.org/10.1177/07334648261419637
Primary Topic
Balance, Gait, and Falls Prevention
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Does Adding Real-Time Postural Feedback During Training Boost Balance and Mobility in Individuals With Parkinson’s Disease? A Systematic Review and Meta-Analysis

Wayne Chan, Guoshi Liang, Sin Man Agnes Lau, Meizhen Huang et al.
Journal of Applied Gerontology
Balance, Gait, and Falls Prevention
article

Does Adding Real-Time Postural Feedback During Training Boost Balance and Mobility in Individuals With Parkinson’s Disease? A Systematic Review and Meta-Analysis

Wayne Chan, Guoshi Liang, Sin Man Agnes Lau, Meizhen Huang, Hou Yee Ching, Chiu Man Fu
article en

Abstract

ObjectiveTo evaluate the specific contribution of the real-time postural feedback component during training on balance and mobility in individuals with Parkinson's disease (PD).MethodFive databases were searched in June 2025. Studies comparing the same basic balance and gait training with versus without feedback were included. Meta-analysis was conducted and the quality of evidence was assessed.ResultEleven studies were included. Narrative analysis indicated that additional feedback may immediately improve dynamic balance and modulate gait speed but not static balance. Meta-Analysis Showed providing feedback significantly augmented post-training improvement in standing balance (SMD = 0.880, 95% CI = 0.345∼1.416, moderate-quality), but not on walking stability (SMD = 0.516, 95% CI = -0.025∼1.058, moderate-quality), Berg Balance Scale (MD = -0.079, 95% CI = -2.159∼2.000, very-low-quality), Timed-Up-and-Go (MD = 0.939, 95% CI = -1.414∼3.293, low-quality), and walking speed (MD = 0.002, 95% CI = -0.057∼0.062, low-quality).ConclusionProviding real-time postural feedback may have immediate effects on dynamic balance and walking speed and a post-training effect on standing balance in PD.RegistrationCRD42024522330.

Journal of Applied Gerontology
Hong Kong Polytechnic University (HK)
Openalex Percentile: Top 78%
Balance, Gait, and Falls Prevention
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.