Randomized trial on the effects of a combined physical/cognitive training in aged MCI subjects: the Train the Brain study

Age-related cognitive impairment and dementia are an increasing societal burden. Epidemiological studies indicate that lifestyle factors, e.g. physical, cognitive and social activities, correlate with reduced dementia risk; moreover, positive effects on cognition of physical/cognitive training have been found in cognitively unimpaired elders. Less is known about effectiveness and action mechanisms of physical/cognitive training in elders already suffering from Mild Cognitive Impairment (MCI), a population at high risk for dementia. We assessed in 113 MCI subjects aged 65-89 years, the efficacy of combined physical-cognitive training on cognitive decline, Gray Matter (GM) volume loss and Cerebral Blood Flow (CBF) in hippocampus and parahippocampal areas, and on brain-blood-oxygenation-level-dependent (BOLD) activity elicited by a cognitive task, measured by ADAS-Cog scale, Magnetic Resonance Imaging (MRI), Arterial Spin Labeling (ASL) and fMRI, respectively, before and after 7 months of training vs. usual life. Cognitive status significantly decreased in MCI-no training and significantly increased in MCI-training subjects; training increased parahippocampal CBF, but no effect on GM volume loss was evident; BOLD activity increase, indicative of neural efficiency decline, was found only in MCI-no training subjects. These results show that a non pharmacological, multicomponent intervention improves cognitive status and indicators of brain health in MCI subjects

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions

Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Background Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. Conclusions Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987

Efficacy and safety of rituximab in HCV-related mixed cryoglobulinemia

Abstract not availabl

Assessment of early myocardial deformation changes in dyslipidemic children by three-dimensional Speckle Tracking Imaging

Background. Dyslipidemia is considered a strong risk factor for premature atherosclerotic cardiovascular disease and increased morbidity and mortality and may have an adverse effect on left ventricular (LV) performance. Three-dimensional speckle tracking imaging (3D-STI) provides information regarding different echocardiographic parameters of LV myocardial deformation. Purpose. Our aim was to assess the presence of early myocardial deformation abnormalities in nonselected dyslipidemic children free from other cardiovascular risk factors. Methods. Twenty-four consecutive nonselected hypercholesterolemic children (TC above the 95th percentile for age and gender, mean age 11.3 ± 2.16 years) and 24 healthy age-matched children were enrolled. None of them had any other cardiovascular risk factors. Obesity (body mass index >75th percentile for age and gender) as well as other diseases were excluded. Every subject underwent 2D- and 3D-STI. Volumes were measured from 3D datasets. Global longitudinal strain (GLS), global circumferential strain (GCS), global area strain (GAS), and global radial strain (GRS) were computed at end-systole. GAS was calculated as the percentage variation in the surface area defined by the longitudinal and circumferential strain vectors. Data analysis was performed offline (EchoPAC BT11, 4D Auto LVQ, GE). Results. Mean percentage intraobserver variability was 7% for GLS, 9% for GCS, 6% for GAS, and 11% for GRS. Comparison between 2D and 3D GLS showed high correspondence (r = 0.89, y = 1.13x - 0.78). The mean time of analysis was of 149 ± 27 sec for 3D analysis, which was 17% less than for 2D analysis (p<0.05). The following strain values were obtained in hyperlipidemic patients compared to controls: 3D GLS (-14.7±2.5% vs -16.8±2.7%, p <0.005), 3D GCS (-28.1±3.6% vs -29.6±4.2%, p <0.01), 3D GRS (29.6±9.2% vs 30.2±9.7%, p =NS), and 3D GAS (-39.8±3.4% vs -43.2±3.2%, p <0.001). On multivariate logistic regression analysis, the strongest relationship with dyslipidemia was found for LV GAS (β- coefficient= 0.74, r2= 0.61, p= 0.002). Conclusions. Dyslipidemia is associated with myocardial deformation changes independently from any other cardiovascular risk factor or any structural cardiac abnormalities

Assessment of right ventricular function by three-dimensional echocardiography and myocardial strain imaging in adult atrial septal defect before and after percutaneous closure

Real-time three-dimensional (3D) echocardiography allows us to measure right ventricular (RV) end-diastolic volume irrespective of its shape. Tissue Doppler imaging (TDI) and speckle tracking imaging (STI) are new tools to assess myocardial function. We sought to evaluate RV function by 3D echocardiography and myocardial strain imaging in adult patients with atrial septal defect (ASD) before and 6 months after transcatheter closure in order to assess the utility of these new indexes in comparison with standard two-dimensional (2D) and Doppler parameters. Thirty-nine ASD patients and 39 healthy age- and sex-matched controls were studied using a commercially available cardiovascular ultrasound system. 2D-Doppler parameters of RV function (fractional area change, tricuspid annular plane systolic excursion, myocardial performance index) were calculated. 3D RV volumes were also obtained. RV peak-systolic velocities, peak-systolic strain, and peak systolic and diastolic strain-rate were measured in the basal, mid and apical segments of lateral and septal walls in apical 4-chamber view by TDI and STI. In open ASD, RV ejection fraction (3D-RVEF) and global and regional RV longitudinal strain were significantly higher than control group and decreased significantly after closure. By multivariate analysis 3D-RVEF, apical strain and strain rate were independent predictors of functional class. ROC analysis showed 3D-RVEF and apical strain to be more sensitive predictors of unfavorable outcome after defect closure compared to 2D-Doppler indexes. 3D echocardiography and myocardial strain imaging give useful insights in the quantitative assessment of RV function in ASD patients before and after closure