Michael Brik

Background: Delirium is a common neuropsychiatric syndrome characterized by the acute and fluctuating impairment of cognition, attention, and consciousness, which is prevalent in older adults following surgical procedures. Despite the recognized impact of delirium on recovery, its specific effects on motor rehabilitation outcomes in the geriatric population remain underexplored. This historical cohort study aimed to evaluate the association between the presence of delirium upon admission to a rehabilitation hospital and the motor functional gain at discharge among older patients following hip fracture surgery. Methods: The collected data included socio-demographic characteristics, comorbidities, medications, Mini-Mental State Examination (MMSE) scores, and the Functional Independence Measure (FIM). Motor rehabilitation outcomes were assessed using Motor Absolute Functional Gain (mAFG), the Montebello Rehabilitation Factor Score (mMRFS), and Rehabilitation Efficiency (mRE). Results: Of the 143 hip fracture patients admitted for rehabilitation, 38 (26.6%) were diagnosed with delirium. Patients with delirium had lower MMSE scores (18.1 ± 5.8 vs. 22.4 ± 6.0, p < 0.001), higher benzodiazepine prescription rates (50.0% vs. 14.3%, p < 0.001), and longer lengths of stay in acute care and rehabilitation (42.7 ± 10.4 vs. 37.3 ± 11.2 days, p = 0.01). Despite significant improvements in the FIM scores for both groups (p < 0.001), patients with delirium had lower mAFG (11.87 ± 7.26 vs. 15.91 ± 8.73, p = 0.01), mMRFS (0.22 ± 0.14 vs. 0.31 ± 0.15, p = 0.001), and mRE (0.28 ± 0.17 vs. 0.44 ± 0.25, p < 0.001). However, the multivariate regression models showed no association between delirium and functional improvement after adjusting for confounders. Conclusions: While both patients with and without delirium showed improvement in their motor functions by the time they were discharged from a rehabilitation hospital, patients with delirium showed lower absolute and relative improvements. Tailored programs addressing the special needs of patients with delirium after hip fracture surgery may enhance outcomes for this vulnerable population. A specialized, multidisciplinary approach tailored to the patient’s cognitive status and overall condition is key to maximizing the recovery of older hip fracture patients with delirium.

An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen’s d, comparing the application’s readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89–1) and good-to-excellent reliability (ICC range, 0.77–1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58–1). Only ‘step length of the impaired leg’ showed poor-to-good validity (range, 0.37–0.84). Cohen’s d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability.

Background An affordable, easy-to-use, reliable, and valid tool is essential for gait assessment of people with various gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application (app) compared to the C-Mill VR + instrumented treadmill (Motek, Nederlands), among patients with gait asymmetry undergoing rehabilitation for lower limb disability or chronic pain. Methods Seventy patients participated in the study. The data of 35 patients were used to improve the app's algorithms, which were then validated on the remaining 35. Participants were asked to walk on a treadmill for up to 15 minutes at their own comfortable pace. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones placed on each leg. Reliability was evaluated using Pearson correlation, Cohen's d, and intra-cluster correlation coefficient (ICC), comparing the readings of the app from the two phones. Validity was assessed using the same measures, comparing readings from each phone to the data obtained from the treadmill. Bland-Altman plots were used to demonstrate the level of agreement between the app and the treadmill for each leg separately. Results Twenty-eight patients completed the validation study (seven were excluded); the median age was 45.5 years, 61% males. Walking sessions lasted on average 8.4 minutes. The ICC for consistency between the phones showed high correlation (r = 0.89 to 1) and good to excellent reliability (ICC range, 0.77 to 1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC for consistency demonstrated excellent validity for cadence and gait speed (range, 0.92 to 1), and moderate-to-excellent validity for all the other parameters (range, 0.58–0.93). Only step length of the impaired leg parameter showed poor-to-good validity (range, 0.37–0.84). Some subtle differences, though statistically significant, were found between the readings of the treadmill and the smartphone placed on the healthy leg. Cohen's d effect size was small (d < 0.5) for all the parameters. Conclusions The OneStep smartphone application demonstrated good reliability and validity for spatiotemporal gait assessment in people with gait asymmetry undergoing rehabilitation. These findings highlight the potential for smartphone-based gait analysis in a rehabilitation setting. Trial registration: NIH identifier: NCT05009303, date of registration: August 17, 2021.