Psychometric properties of wearable technologies to assess post-stroke gait parameters: A systematic review
Authors:
Silva, R. S. D., Silva, S. T. D., Cardoso, D. C. R., Quirino, M. A. F., Silva, M. H. A., Gomes, L. A., Fernandes, J. D., Oliveira, Rands, Fernandes, Abgs, and Ribeiro, T. S.
Abstract:
BACKGROUND: Wearable technologies using inertial sensors are an alternative for gait assessment. However, their psychometric properties in evaluating post-stroke patients are still being determined. This systematic review aimed to evaluate the psychometric properties of wearable technologies used to assess post-stroke gait and analyze their reliability and measurement error. The review also investigated which wearable technologies have been used to assess angular changes in post-stroke gait.
METHODS: The present review included studies in English with no publication date restrictions that evaluated the psychometric properties (e.g., validity, reliability, responsiveness, and measurement error) of wearable technologies used to assess post-stroke gait. Searches were conducted from February to March 2023 in the following databases: Cochrane Central Registry of Controlled Trials (CENTRAL), Medline/PubMed, EMBASE Ovid, CINAHL EBSCO, PsycINFO Ovid, IEEE Xplore Digital Library (IEEE), and Physiotherapy Evidence Database (PEDro); the gray literature was also verified. The Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) risk-of-bias tool was used to assess the quality of the studies that analyzed reliability and measurement error.
RESULTS: Forty-two studies investigating validity (37 studies), reliability (16 studies), and measurement error (6 studies) of wearable technologies were included. Devices presented good reliability in measuring gait speed and step count; however, the quality of the evidence supporting this was low. The evidence of measurement error in step counts was indeterminate. Moreover, only two studies obtained angular results using wearable technology.
SIGNIFICANCE: Wearable technologies have demonstrated reliability in analyzing gait parameters (gait speed and step count) among post-stroke patients. However, higher-quality studies should be conducted to improve the quality of evidence and to address the measurement error assessment. Also, few studies used wearable technology to analyze angular changes during post-stroke gait.