Search Results (7)

(1) Introduction: This study aimed to investigate the effects of curved-path stride gait training on the gait ability of patients with stroke. (2) Materials and Methods: Thirty patients with stroke were randomly assigned to curved-path stride gait training (n = 15) and general gait training groups (n = 15). Both groups underwent training for 30 min five times a week for 8 weeks. The gait ability of each was assessed using the Dynamic Gait Index (DGI), Timed-Up-and-Go (TUG) test, 10-meter walk test, and Figure-of-8 walk test (F8WT). (3) Results: The curved-path gait training group showed significant differences in the DGI, TUG test, 10-m walk test, and F8WT pre- versus post- intervention (p < 0.05). The general gait training group showed no significant difference in F8WT pre- versus post-intervention (p > 0.05). Additionally, there was a statistically significant intergroup difference in gait ability (p < 0.05). (4) Conclusions: Curved-path gait training resulted in greater improvement in gait ability than general gait training. Therefore, curved-path gait training can be a meaningful intervention for improving the gait ability of patients with stroke. Full article

This study aimed to determine the effect of a proprioceptive neuromuscular facilitation (PNF) pattern Kinesio taping (KT) application on the ankle dorsiflexion range of motion (DF-ROM) and balance ability in patients with chronic stroke. This crossover study included 18 patients with stroke. The subjects were randomly assigned to three interventions: barefoot, ankle KT (A-KT), and PNF-KT. The A-KT was applied to the gastrocnemius and tibialis anterior (TA) muscles, and subtalar eversion. The PNF-KT was applied on the extensor hallucis, extensor digitorum, and TA muscles. DR-ROM was measured using the iSen™, a wearable sensor. Balance ability was assessed based on static balance, measured by the Biodex Balance System (BBS), and dynamic balance, measured by the timed up and go (TUG) test and dynamic gait index (DGI). Compared with the barefoot and A-KT interventions, PNF-KT showed significant improvements in the ankle DF-ROM and BBS scores, TUG, and DGI. PNF-KT, for functional muscle synergy, improved the ankle DF-ROM and balance ability in patients with chronic stroke. Therefore, the application of PNF-KT may be a feasible therapeutic method for improving ankle movement and balance in patients with chronic stroke. Additional research is recommended to identify the long-term effects of the PNF-KT. Full article

This study examined the concurrent validity between gait parameters from the GAITRite walkway and functional balance test commonly used in fall risk assessment. Patients were sampled from one geriatric outpatient clinic. One physiotherapist evaluated the patients on the GAITRite walkway with three repetitions in both single- and dual-task conditions. Patients were further evaluated with Bergs Balance scale (BBS), Dynamic Gait index (DGI), Timed Up and Go (TUG), and Sit To Stand test (STS). Correlations between quantitative gait parameters and functional balance test were analyzed with Spearman’s rank correlations. Correlations strength was considered as follows: negligible <0.1, weak 0.10–0.39, moderate 0.40–0.69, and strong ≥0.70. We included 24 geriatric outpatients in the study with a mean age of 80.6 years (SD: 5.9). Patients received eight (SD: 4.5) different medications on average, and seven (29.2%) patients used walkers during ambulation. Correlations between quantitative gait parameters and functional balance test ranged from weak to moderate in both single- and dual-task conditions. Moderate correlations were observed for DGI, TUG, and BBS, while STS showed weak correlations with all GAITRite parameters. For outpatients analyzed on the GAITRite while using walkers, correlations showed no clear pattern across parameters with large variation within balance tests. Full article

Despite existing evidence that gait disorders are a common consequence of severe traumatic brain injury (sTBI), the literature describing gait instability in sTBI survivors is scant. Thus, the present study aims at quantifying gait patterns in sTBI through wearable inertial sensors and investigating the association of sensor-based gait quality indices with the scores of commonly administered clinical scales. Twenty healthy adults (control group, CG) and 20 people who suffered from a sTBI were recruited. The Berg balance scale, community balance and mobility scale, and dynamic gait index (DGI) were administered to sTBI participants, who were further divided into two subgroups, severe and very severe, according to their score in the DGI. Participants performed the 10 m walk, the Figure-of-8 walk, and the Fukuda stepping tests, while wearing five inertial sensors. Significant differences were found among the three groups, discriminating not only between CG and sTBI, but also for walking ability levels. Several indices displayed a significant correlation with clinical scales scores, especially in the 10 m walking and Figure-of-8 walk tests. Results show that the use of wearable sensors allows the obtainment of quantitative information about a patient’s gait disorders and discrimination between different levels of walking abilities, supporting the rehabilitative staff in designing tailored therapeutic interventions. Full article

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual’s risk for falls. Full article

Background: Recent clinical guidelines for adults with neurological disabilities suggest the need to assess measures of static and dynamic balance using the Berg Balance Scale (BBS) and Dynamic Gait Index (DGI) as core outcome measures. Given that the BBS measures both static and dynamic balance, it was unclear as to whether either of these instruments was superior in terms of its convergent and concurrent validity, and whether there was value in complementing the BBS with the DGI. Objective: The objective was to evaluate the concurrent and convergent validity of the BBS and DGI by comparing the performance of these two functional balance tests in people with multiple sclerosis (MS). Methods: Baseline cross-sectional data on 75 people with MS were collected for use in this study from 14 physical therapy clinics participating in a large pragmatic cluster-randomized trial. Convergent validity estimates between the DGI and BBS were examined by comparing the partial Spearman correlations of each test to objective lower extremity functional measures (Timed Up and Go (TUG), Six-Minute Walk Test (6MWT), Timed 25-Foot Walk (T25FW) test) and the self-reported outcomes of physical functioning and general health using the 36-Item Short Form Health Survey (SF-36). Concurrent validity was assessed by applying logistic regression with gait disability as the binary outcome (Patient Determined Disease Steps (PDDS) as the criterion measure). The predictive ability of two models, a reduced/parsimonious model including the BBS only and a second model including both the BBS and DGI, were compared using the adjusted coefficient of determinations. Results: Both the DGI and BBS were strongly correlated with lower extremity measures overall as well as across the two PDSS strata with correlations. In PDDS ≤ 2, the difference in the convergence of BBS with TUG and DGI with TUG was −0.123 (95% CI: −0.280, −0.012). While this finding was statistically significant at a type 1 error rate of 0.05, it was not significant (Hommel’s adjusted p-value = 0.465) after accounting for multiple testing corrections to control for the family-wise error rate. The BBS–SF-36 physical functioning correlation was at least moderate and significant overall and across both PDDS strata. However, the DGI–physical functioning score did not have a statistically significant correlation within PDDS ≤ 2. None of the differences in convergent and concurrent validity between the BBS and DGI were significant. The additional variation in 6MWT explained by the DGI when added to a model with the BBS was 7.78% (95% CI: 0.6%, 15%). Conclusions: These exploratory analyses on data collected in pragmatic real-world settings suggest that neither of these measures of balance is profoundly superior to the other in terms of its concurrent and convergent validity. The DGI may not have any utility for people with PDDS ≤ 2, especially if the focus is on mobility, but may be useful if the goal is to provide insight on lower extremity endurance. Further research leveraging longitudinal data from pragmatic trials and quasi-experimental designs may provide more information about the clinical usefulness of the DGI in terms of its predictive validity when compared to the BBS. Full article

Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected. Full article