Psychometric properties of wearable technologies to assess post-stroke gait parameters: A systematic review.

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.

Rate of torque development of paretic lower limb is an excellent predictor of walking speed in chronic stroke individuals.

BACKGROUND: Walking speed discriminates levels of functionality in stroke survivors, but its relationship with neuromuscular parameters remains unclear. We aimed to (1) verify relationships between walking speed, peak torque, and rate of torque development in individuals with chronic stroke and (2) investigate whether adjusting the predictive model for assistive device use and lower limb motor function improves its accuracy. METHODS: Twenty-nine stroke survivors (time post-stroke ≥6 months) were assessed for walking speed, motor function, torque of knee extensors, and rate of torque development. Hierarchical multiple regression was conducted to infer the contribution of assistive device use and lower limb motor function in the proportion of variance in walking speed explained by neuromuscular parameters. Adequacy of regression models was verified using Shapiro-Wilk test and visual inspection of histograms. FINDINGS: Rate of torque development measures presented higher coefficients of determination (R2 = 0.399-0.457) than peak torque (R2 = 0.333). However, no differences were observed between correlation coefficients (P > 0.05). When adjusted for assistive device use and lower limb motor function, coefficients of determination of all neuromuscular parameters were improved (P < 0.05). Regression models showed good adequacy. INTERPRETATION: Peak torque and rate of torque development from knee extensors are adequate predictors of walking speed in stroke survivors. Adjusting predictive models for assistive device use and lower limb motor function increases predictive capacity. These findings may have repercussions for assessing chronic stroke individuals and developing treatment strategies.