Associations of Gait Disorders and Recurrent Falls in Older People: A Prospective Population-Based Study

<b><i>Background:</i></b> Recurrent falls represent a major source of serious adverse health outcomes in the general older population. Gait impairment has been linked to recurrent falls, but there are only limited long-term data on this association. <b><i>Objectives:</i></b> The objective of the study was to investigate the association of gait disorders (GDs) and gait tests with future falls in an existing longitudinal population-based cohort. <b><i>Method:</i></b> The study was performed in participants of the Bruneck Study cohort 2010 aged 60–97 years, with prospective 5-year follow-up. At baseline, participants underwent a clinical gait assessment (to determine neurological and non-neurological GDs according to an established classification) and were also evaluated by quantitative and semiquantitative gait tests (Hauser Index, Tinetti balance and gait test, and gait speed). Logistic regression analysis adjusted for age and sex was used to determine the relationship of baseline variables with incident recurrent falls at 5-year follow-up. <b><i>Results:</i></b> Of 328 included participants, 22 (6.7%) reported recurrent falls at follow-up. Baseline presence of GDs was associated with recurrent falls at follow-up (odds ratio [OR] 4.2; 95% confidence interval [CI] 1.6–11.1; <i>p</i> = 0.004), and this effect was largely driven by neurological GDs (OR 5.5; 95% CI 1.7–17.4; <i>p</i> = 0.004). All 3 simple gait tests were predictive for incident falls (Hauser Index, <i>p</i> = 0.002; Tinetti test, <i>p</i> = 0.006; and gait speed, <i>p</i> &#x3c; 0.001). <b><i>Conclusions:</i></b> Clinical assessment of GDs and gait tests both had independent significant predictive value for recurrent falls over a 5-year follow-up period. This highlights the potential of such assessments for early fall risk screening and timely implementation of fall-preventive measures.

A-GAS: a Probabilistic Approach for Generating Automated Gait Assessment Score for Cerebral Palsy Children

<pre><p>Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This work proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate the performance of A-GAS. For the computation of A-GAS, instance abnormality index (AII) and abnormality index (AI) are calculated. AII quantifies gait abnormality of a gait cycle instance, while AI quantifies gait abnormality of a joint angle profile during walking. AII is calculated for all gait cycle instances by performing probabilistic and statistical analyses. Abnormality index (AI) is a weighted sum of AII, computed for each joint angle profile. A-GAS is a weighted sum of AI, calculated for a lower limb. Moreover, a graphical representation of the gait assessment report, including AII, AI, and A-GAS is generated for providing a better depiction of the assessment score. Furthermore, the work compares A-GAS with a present rating-based gait assessment scores to understand fundamental differences. Finally, A-GAS's performance is verified for a high-cost multi-camera set-up using nine joint angle profiles and a low-cost single camera set-up using three joint angle profiles. Results show no significant differences in performance of A-GAS for both the set-ups. Therefore, A-GAS for both the set-ups can be used interchangeably. </p> </pre>

Comparison of the Efficacy of Deep Brain Stimulation in Different Targets in Improving Gait in Parkinson's Disease: A Systematic Review and Bayesian Network Meta-Analysis

Background: Although a variety of targets for deep brain stimulation (DBS) have been found to be effective in Parkinson's disease (PD), it remains unclear which target for DBS leads to the best improvement in gait disorders in patients with PD. The purpose of this network meta-analysis (NMA) is to compare the efficacy of subthalamic nucleus (STN)-DBS, internal globus pallidus (GPi)-DBS, and pedunculopontine nucleus (PPN)-DBS, in improving gait disorders in patients with PD.Methods: We searched the PubMed database for articles published from January 1990 to December 2020. We used various languages to search for relevant documents to reduce language bias. A Bayesian NMA and systematic review of randomized and non-randomized controlled trials were conducted to explore the effects of different targets for DBS on gait damage.Result: In the 34 included studies, 538 patients with PD met the inclusion criteria. The NMA results of the effect of the DBS “on and off” on the mean change of the gait of the patients in medication-off show that GPi-DBS, STN-DBS, and PPN-DBS are significantly better than the baseline [GPi-DBS: –0.79(–1.2, –0.41), STN-DBS: –0.97(–1.1, –0.81), and PPN-DBS: –0.56(–1.1, –0.021)]. According to the surface under the cumulative ranking (SUCRA) score, the STN-DBS (SUCRA = 74.15%) ranked first, followed by the GPi-DBS (SUCRA = 48.30%), and the PPN-DBS (SUCRA = 27.20%) ranked last. The NMA results of the effect of the DBS “on and off” on the mean change of the gait of the patients in medication-on show that, compared with baseline, GPi-DBS and STN-DBS proved to be significantly effective [GPi-DBS: –0.53 (–1.0, –0.088) and STN-DBS: –0.47(–0.66, –0.29)]. The GPi-DBS ranked first (SUCRA = 59.00%), followed by STN-DBS(SUCRA = 51.70%), and PPN-DBS(SUCRA = 35.93%) ranked last.Conclusion: The meta-analysis results show that both the STN-DBS and GPi-DBS can affect certain aspects of PD gait disorder.

A-GAS: a Probabilistic Approach for Generating Automated Gait Assessment Score for Cerebral Palsy Children

Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This study proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate the performance of A-GAS. For the computation of A-GAS, instance abnormality index (AII) and abnormality index (AI) are computed. AII quantifies gait abnormality of a gait cycle instance, while AI quantifies gait abnormality of a joint angle profile. AII is calculated for all gait cycle instances by performing probabilistically and statistical tests. Abnormality index (AI) is a weighted sum of AII, computed for each joint angle profile. A-GAS is a weighted sum of AI, calculated for a lower limb. Moreover, a graphical representation of the gait assessment report, including AII, AI, and A-GAS is generated to understand the results better. Furthermore, the study compares A-GAS with a present rating-based gait assessment scores to understand fundamental differences between them. Finally, AGAS’s performance is verified for a high-cost multicamera set-up using nine joint angle profiles and a low-cost single camera set-up using three joint angle profiles. Results show no significant differences in performance of A-GAS for both the set-ups. Therefore, A-GAS for both the set-ups can be used interchangeably.

A-GAS: a Probabilistic Approach for Generating Automated Gait Assessment Score for Cerebral Palsy Children

Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This study proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate the performance of A-GAS. For the computation of A-GAS, instance abnormality index (AII) and abnormality index (AI) are computed. AII quantifies gait abnormality of a gait cycle instance, while AI quantifies gait abnormality of a joint angle profile. AII is calculated for all gait cycle instances by performing probabilistically and statistical tests. Abnormality index (AI) is a weighted sum of AII, computed for each joint angle profile. A-GAS is a weighted sum of AI, calculated for a lower limb. Moreover, a graphical representation of the gait assessment report, including AII, AI, and A-GAS is generated to understand the results better. Furthermore, the study compares A-GAS with a present rating-based gait assessment scores to understand fundamental differences between them. Finally, AGAS’s performance is verified for a high-cost multicamera set-up using nine joint angle profiles and a low-cost single camera set-up using three joint angle profiles. Results show no significant differences in performance of A-GAS for both the set-ups. Therefore, A-GAS for both the set-ups can be used interchangeably.