Israel Medical Association Journal

Background: Gait disturbances are common in patients undergoing hemodialysis and are associated with increased fall risk, mobility decline, and adverse health outcomes. Prior research suggests that hemodialysis may impact gait parameters such as speed, stride length, and variability; however, findings are inconsistent.

Objectives: To evaluate acute changes in gait metrics before and after hemodialysis using an artificial intelligence (AI) based video gait analysis system.

Methods: We initially enrolled 38 hemodialysis patients, two were excluded due to clothing interference with video analysis (27.8% female, 72.2% male). AI-driven gait analysis was performed immediately before and after dialysis. The system extracted spatiotemporal gait and joint range of motion. Statistical analyses included the Shapiro-Wilk test for normality, Wilcoxon signed-rank tests for non-normally distributed data, and paired t-tests for normally distributed data (P < 0.05).

Results: Gait speed (0.59 m/sec pre-dialysis) remained unchanged post-dialysis (P = 0.876), as did cycle length and time. However, step length significantly decreased post-dialysis (P = 0.001), suggesting a more conservative gait pattern. Knee flexion and extension increased slightly but did not reach statistical significance.

Conclusions: Dialysis does not acutely affect overall gait speed but significantly reduces stride length. Post-dialysis fatigue or hemodynamic shifts may alter walking patterns, highlighting the need for fall prevention strategies and physical rehabilitation interventions in dialysis care. AI-based gait analysis may provide a practical tool for monitoring mobility changes in hemodialysis patients.

Background: Falls while turning are associated with increased risk of hip fracture in older adults. Reliable and clinically valid methods for turn ability assessments are needed.

Objectives: To explore the inter-observer reliability and known group validity of the TURN 180 test.

Methods: We divided 78 independent older adults (mean age 76.6 ± 6.5 years) into three groups: non-fallers, infrequent fallers (1–2 falls per year), and recurrent fallers (> 2 falls per year). Participants underwent performance-based tests: Timed Up and Go (TUG), Performance Oriented Mobility Assessment (POMA), and Berg Balance Scale (BBS). TUG was videotaped for later analysis of the TURN 180 test by two blinded observers.

Results: A significant difference was found in the TURN 180 test parameters among the groups (P < 0.04). TURN 180 was highly correlated with TUG (r = 0.81–0.89, P < 0.001) and BBS (r = -0.704–0.754, P < 0.0001) and moderately with POMA (r = -0.641–0.698, P < 0.0001). The number of steps was found to be the strongest parameter to determine fallers among older adults (specificity 96.3%, sensitivity 40%). Inter-rater reliability (intraclass correlation coefficient 0.91–0.96, P < 0.0001) was found to be excellent for the number of steps, time taken to accomplish a turn, and total test score categories.

Conclusions: The TURN 180 test is highly reliable and can identify the older adults who fall. Our results show that the TURN 180 test can serve as a good performance-based examination for research or clinical setting.

Background: Falls are a common problem among hospitalized patients, having a significant impact on quality of life and resource utilization.

Objectives: To develop and validate a fall-risk assessment tool for patients hospitalized in the department of medicine that will combine simplicity with adequate accuracy for routine use.

Methods: This observational cohort study was conducted on the medical wards of an urban tertiary teaching hospital, and included all patients who fell in the medical wards during a 1 year period (n=140) compared to other hospitalized patients.

Results: Significant correlates of falls were previous falls, impairing medical conditions, impaired mobility, and altered mental state. In multivariate logistic regression analyses, only previous falls (odds ratio 3.8 with 95% confidence interval 2.65–5.45, P < 0.0001) and acute impairing medical conditions (OR[1] 1.56, CI[2] 1.06–2.29, P < 0.05) correlated independently with a higher risk for falls. Impaired mobility retained an OR of 1.46 (CI 0.95–2.24, P = 0.084). Accordingly, defining patients with either a history of previous falls or both acute impairing medical state and impaired mobility as fall-prone patients provided a sensitivity and specificity of 67% and 63%, respectively. In a subsequent prospective validation trial on 88 patients who fell during hospitalization and 436 controls, the sensitivity and specificity of this fall-risk grouping were 64% and 68% respectively.

Conclusions: Our new simple and easy-to-use fall-risk assessment tool identified most of the fall-prone patients. These findings suggest that using this tool may enable us to prevent two-thirds of falls on the medical ward by providing effective fall-prevention facilities to only one-third of the patients.