Gait in patients with symptomatic osteoporotic vertebral compression fractures over 6 months of recovery

BACKGROUND: One factor related to disability in people with spinal deformity is decreased postural control and increased risk of falling. However, little is known about the effect of osteoporotic vertebral compression fractures (OVCFs) and their recovery on gait and stability. Walking characteristics of older adults with and without vertebral fractures have not yet been compared. AIMS: The purpose of the current study was to examine the spatiotemporal gait parameters and their variability in patients with an OVCF and healthy participants during treadmill walking at baseline and after 6 months of recovery. METHODS: Twelve female patients suffering a symptomatic OVCF were compared to 11 matched controls. Gait analysis was performed with a dual-belt instrumented treadmill with a 180° projection screen providing a virtual environment (computer-assisted rehabilitation environment). Results of patients with an OVCF and healthy participants were compared. Furthermore, spatiotemporal gait parameters were assessed over 6 months following the fracture. RESULTS: Patients suffering from an OVCF appeared to walk with significantly shorter, faster and wider strides compared to their healthy counterparts. Although stride time and length improved over time, the majority of the parameters analysed remained unchanged after 6 months of conservative treatment. DISCUSSION: Since patients do not fully recover to their previous level of mobility after 6 months of conservative treatment for OVCF, it appears of high clinical importance to add balance and gait training to the treatment algorithm of OVCFs. CONCLUSIONS: Patients suffering from an OVCF walk with shorter, faster and wider strides compared to their healthy counterparts adopt a less stable body configuration in the anterior direction, potentially increasing their risk of forward falls if perturbed. Although stride time and stride length improve over time even reaching healthy levels again, patients significantly deviate from normal gait patterns (e.g. in stability and step width) after 6 months of conservative treatment

Adjustments in 2011 KSS increase the clinical suitability.

The 2011 KSS is a valid clinical TKA questionnaire, but with a low completion rate (42%). Adjustments, focusing on optimizing scale features, are required to improve its clinical use. The low completion rates, non-optimal scale features, lacking rules or a combination of these factors where addressed, leading to the development of the adjusted 2011 KSS (2011 KSS-A). Four-hundred-ninety-nine primary TKA patients were addressed pre- and postoperative by mail. Clinimetric quality was evaluated. Seventy percent responded and 90% completed the scale. Internal consistency proved excellent with Cronbach’s Alpha ≥0.79 for all subscales. Strong correlations were found between the Functional Activity subscales and KOOS-PS (r= -0.63 to -0.87). All subscales improved significantly after intervention (r-range 14-33%, effect size 0.50-2.85). Postoperatively, ceiling effects were found in the subscales Symptoms (16%) and Walking & Standing (26%). Adjustments led to a shortened and simplified questionnaire while maintaining its clinimetric quality

Clinical validation of a body-fixed 3D accelerometer and algorithm for activity monitoring in orthopaedic patients

Background/Objective Activity is increasingly being recognized as a highly relevant parameter in all areas of healthcare for diagnosis, treatment, or outcome assessment, especially in orthopaedics where the movement apparatus is directly affected. Therefore, the aim of this study was to develop, describe, and clinically validate a generic activity-monitoring algorithm, satisfying a combination of three criteria. The algorithm must be able to identify, count, and time a large set of relevant daily activities. It must be validated for orthopaedic patients as well as healthy individuals, and the validation must be in a setting that mimics free-living conditions. Methods Using various technical solutions, such as a dual-axis approach, dynamic inclinometry (hip flexion), and semiautomatic calibration (gait speed), the algorithms were designed to count and time the following postures, transfers, and activities of daily living: resting/sitting, standing, walking, ascending and descending stairs, sit–stand transitions, and cycling. In addition, the number of steps per walking bout was determined. Validation was performed with healthy individuals and patients who had undergone unilateral total joint arthroplasty, representing a wide spectrum of functional capacity. Video observation was used as the gold standard to count and time activities in a validation protocol approaching free-living conditions. Results In total 992 and 390 events (activities or postures) were recorded in the healthy group and patient group, respectively. The mean error varied between 0% and 2.8% for the healthy group and between 0% and 7.5% for the patient group. The error expressed in percentage of time varied between 2.0% and 3.0% for both groups. Conclusion Activity monitoring of orthopaedic patients by counting and timing a large set of relevant daily life events is feasible in a user- and patient-friendly way and at high clinical validity using a generic three-dimensional accelerometer and algorithms based on empirical and physical methods. The algorithms performed well for healthy individuals as well as patients recovering after total joint replacement in a challenging validation set-up. With such a simple and transparent method real-life activity parameters can be collected in orthopaedic practice for diagnostics, treatments, outcome assessment, or biofeedback

Clinical validation of a body-fixed 3D accelerometer and algorithm for activity monitoring in orthopaedic patients

\u3cp\u3eBackground/Objective Activity is increasingly being recognized as a highly relevant parameter in all areas of healthcare for diagnosis, treatment, or outcome assessment, especially in orthopaedics where the movement apparatus is directly affected. Therefore, the aim of this study was to develop, describe, and clinically validate a generic activity-monitoring algorithm, satisfying a combination of three criteria. The algorithm must be able to identify, count, and time a large set of relevant daily activities. It must be validated for orthopaedic patients as well as healthy individuals, and the validation must be in a setting that mimics free-living conditions. Methods Using various technical solutions, such as a dual-axis approach, dynamic inclinometry (hip flexion), and semiautomatic calibration (gait speed), the algorithms were designed to count and time the following postures, transfers, and activities of daily living: resting/sitting, standing, walking, ascending and descending stairs, sit–stand transitions, and cycling. In addition, the number of steps per walking bout was determined. Validation was performed with healthy individuals and patients who had undergone unilateral total joint arthroplasty, representing a wide spectrum of functional capacity. Video observation was used as the gold standard to count and time activities in a validation protocol approaching free-living conditions. Results In total 992 and 390 events (activities or postures) were recorded in the healthy group and patient group, respectively. The mean error varied between 0% and 2.8% for the healthy group and between 0% and 7.5% for the patient group. The error expressed in percentage of time varied between 2.0% and 3.0% for both groups. Conclusion Activity monitoring of orthopaedic patients by counting and timing a large set of relevant daily life events is feasible in a user- and patient-friendly way and at high clinical validity using a generic three-dimensional accelerometer and algorithms based on empirical and physical methods. The algorithms performed well for healthy individuals as well as patients recovering after total joint replacement in a challenging validation set-up. With such a simple and transparent method real-life activity parameters can be collected in orthopaedic practice for diagnostics, treatments, outcome assessment, or biofeedback.\u3c/p\u3

File 26_mox files fast speed.

This data belongs to a manuscript submitted to Data in Brief, in which the content and lay-out of this data is described in detail. Data overviews (including figures and tables for age and gender groups) can be found at OSF | Normative 3D gait data of healthy subjects walking at three different speeds on an instrumented treadmill in virtual reality.A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m2) is presented and publicly shared for three walking speed conditions.Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model.Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5 m/s and every second the speed was increased wit 0.01 m/s until comfortable speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded.The 3D gait data was collected using the D-flow CAREN software. Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to xls. Processed data includes spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of both legs.The attached files include he raw data presented as .mox files for each adult for walking at fast (comfortable + 30%) speed. The .mox files contain subject data (e.g. gender, body mass, knee and ankle width), marker position and force plate data, kinematic data (joint angles), kinetic data (GRF, joint moment, joint power) generated by CAREN software (D-flow).The title of this file (26_mox files) corresponds to the associated manuscript (submitted to Data in Brief).</p

File 26_mox files comfortable speed.

This data belongs to a manuscript submitted to Data in Brief, in which the content and lay-out of this data is described in detail. Data overviews (including figures and tables for age and gender groups) can be found at OSF | Normative 3D gait data of healthy subjects walking at three different speeds on an instrumented treadmill in virtual reality.A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m2) is presented and publicly shared for three walking speed conditions.Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model.Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5 m/s and every second the speed was increased wit 0.01 m/s until comfortable speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded.The 3D gait data was collected using the D-flow CAREN software. Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to xls. Processed data includes spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of both legs.The attached files include he raw data presented as .mox files for each adult for walking at comfortable speed. The .mox files contain subject data (e.g. gender, body mass, knee and ankle width), marker position and force plate data, kinematic data (joint angles), kinetic data (GRF, joint moment, joint power) generated by CAREN software (D-flow).The title of this file (26_mox files) corresponds to the associated manuscript (submitted to Data in Brief).</p