The biomechanical fingerprint of hip and knee osteoarthritis patients during activities of daily living.

BACKGROUND: Osteoarthritis is a highly prevalent disease affecting the hip and knee joint and is characterized by load-mediated pain and decreased quality of life. Dependent on involved joint, patients present antalgic movement compensations, aiming to decrease loading on the involved joint. However, the associated alterations in mechanical loading of the ipsi- and contra-lateral lower limb joints, are less documented. Here, we documented the biomechanical fingerprint of end-stage hip and knee osteoarthritis patients in terms of ipsilateral and contralateral hip and knee loading during walking and stair ambulation. METHODS: Three-dimensional motion-analysis was performed in 20 hip, 18 knee osteoarthritis patients and 12 controls during level walking and stair ambulation. Joint contact forces were calculated using a standard musculoskeletal modelling workflow in Opensim. Involved and contralateral hip and knee joint loading was compared against healthy controls using independent t-tests (p < 0.05). FINDINGS: Both hip and knee cohorts significantly decreased loading of the involved joint during gait and stair ambulation. Hip osteoarthritis patients presented no signs of ipsilateral knee nor contralateral leg overloading, during walking and stair ascending. However, knee osteoarthritis patients significantly increased loading at the ipsilateral hip, and contralateral hip and knee joints during stair ambulation compared to controls. INTERPRETATION: The biomechanical fingerprint in knee and hip osteoarthritis patients confirmed antalgic movement strategies to unload the involved leg during gait. Only during stair ambulation in knee osteoarthritis patients, movement adaptations were confirmed that induced unbalanced intra- and inter-limb loading conditions, which are known risk factors for secondary osteoarthritis.

Can the Output of a Learned Classification Model Monitor a Person's Functional Recovery Status Post-Total Knee Arthroplasty?

Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models (probability of belonging to a class) is usable for monitoring a person's functional recovery status post-total knee arthroplasty (TKA) is largely unexplored. The research question is two-fold: (I) Can a learned classification model's output be used to monitor a person's recovery status post-TKA? (II) Is the output related to patient-reported functioning? We constructed a logistic regression model based on (1) pre-operative IMU-data of level walking, ascending, and descending stairs and (2) 6-week post-operative data of walking, ascending-, and descending stairs. Trained models were deployed on subjects at three, six, and 12 months post-TKA. Patient-reported functioning was assessed by the KOOS-ADL section. We found that the model trained on 6-weeks post-TKA walking data showed a decrease in the probability of belonging to the TKA class over time, with moderate to strong correlations between the model's output and patient-reported functioning. Thus, the LR-model's output can be used as a screening tool to follow-up a person's recovery status post-TKA. Person-specific relationships between the probabilities and patient-reported functioning show that the recovery process varies, favouring individual approaches in rehabilitation.

Movement Quality Parameters during Gait Assessed by a Single Accelerometer in Subjects with Osteoarthritis and Following Total Joint Arthroplasty.

This study's aim is threefold: (I) Evaluate movement quality parameters of gait in people with hip or knee osteoarthritis (OA) compared to asymptomatic controls from a single trunk-worn 3D accelerometer. (II) Evaluate the sensitivity of these parameters to capture changes at 6-weeks, 3-, 6-, and 12-months following total knee arthroplasty (TKA). (III) Investigate whether observed changes in movement quality from 6-weeks and 12-months post-TKA relates to changes in patient-reported outcome measures (PROMs). We invited 20 asymptomatic controls, 20 people with hip OA, 18 people pre- and post-TKA to our movement lap. They wore a single trunk-worn accelerometer and walked at a self-selected speed. Movement quality parameters (symmetry, complexity, smoothness, and dynamic stability) were calculated from the 3D acceleration signal. Between groups and between timepoints comparisons were made, and changes in movement quality were correlated with PROMs. We found significant differences in symmetry and stability in both OA groups. Post-TKA, most parameters reflected an initial decrease in movement quality at 6-weeks post-TKA, which mostly normalised 6-months post-TKA. Finally, improved movement quality relates to improvements in PROMs. Thus, a single accelerometer can characterise movement quality in both OA groups and post-TKA. The correlation shows the potential to monitor movement quality in a clinical setting to inform objective, data-driven personalised rehabilitation.

Joint kinematics alone can distinguish hip or knee osteoarthritis patients from asymptomatic controls with high accuracy.

Osteoarthritis (OA) is one of the leading musculoskeletal disabilities worldwide, and several interventions intend to change the gait pattern in OA patients to more healthy patterns. However, an accessible way to follow up the biomechanical changes in a clinical setting is still missing. Therefore, this study aims to evaluate whether we can use biomechanical data collected from a specific activity of daily living to help distinguish hip OA patients from controls and knee OA patients from controls using features that potentially could be measured in a clinical setting. To achieve this goal, we considered three different classes of statistical models with different levels of data complexity. Class 1 is kinematics based only (clinically applicable), class 2 includes joint kinetics (semi-applicable under the condition of access to a force plate or prediction models), and class 3 uses data from advanced musculoskeletal modeling (not clinically applicable). We used a machine learning pipeline to determine which classification model was best. We found 100% classification accuracy for KneeOA-vs-Asymptomatic and 93.9% for HipOA-vs-Asymptomatic using seven features derived from the lumbar spine and hip kinematics collected during ascending stairs. These results indicate that kinematical data alone can distinguish hip or knee OA patients from asymptomatic controls. However, to enable clinical use, we need to validate if the classifier also works with sensor-based kinematical data and whether the probabilistic outcome of the logistic regression model can be used in the follow-up of patients with OA.

Functional movement assessment by means of inertial sensor technology to discriminate between movement behaviour of healthy controls and persons with knee osteoarthritis.

BACKGROUND: Apart from biomechanical alterations in movement patterns, it is known that movement limitations in persons with knee osteoarthritis (PwKOA) are related to an individual's perception and belief regarding pain and disability. To gain more insights into the functional movement behaviour of PwKOA in a clinical setting, inertial sensor technology can be applied. This study first aims to evaluate the ability of inertial sensors to discriminate between healthy controls (HC) and PwKOA. Secondly, this study aims to determine the relationship between movement behaviour, pain-related factors and disability scores. METHODS: Twelve HC and 19 PwKOA were included. Five repetitions of six functional movement tasks (walking, forward lunge, sideward lunge, ascent and descent stairs, single leg squat and sit-to-stand) were simultaneously recorded by the inertial sensor system and a camera-based motion analysis system. Statistically significant differences in angular waveforms of the trunk, pelvis and lower limb joints between HC and PwKOA were determined using one-dimensional statistical parametric mapping (SPM1D). The Knee injury and Osteoarthritis Outcome Score and TAMPA scale for Kinesiophobia were used to evaluate the relationship between discriminating joint motion, pain-related factors and disability using spearman's correlation coefficients. RESULTS: PwKOA had significantly less trunk rotation, internal pelvis rotation and knee flexion ROM during walking. Additionally, the reduced knee flexion (i.e. at the end of the stance phase and swing phase) was related to increased level of perceived pain. During the sideward lunge, PwKOA had significantly less knee flexion, ankle plantarflexion and hip abduction. This decreased hip abduction (i.e. during stance) was related to higher fear of movement. Finally, PwKOA had significantly less knee flexion during the forward lunge, single leg squat and during ascent and descent stairs. No significant correlations were observed with disability. CONCLUSIONS: Inertial sensors were able to discriminate between movement characteristics of PwKOA and HC. Additionally, significant relationships were found between joint motion, perceived pain and fear of movement. Since inertial sensors can be used outside the laboratory setting, these results are promising as they indicate the ability to evaluate movement deviations. Further research is required to enable measurements of small movement deviations in clinically relevant tasks.

Assessment of Scapulothoracic, Glenohumeral, and Elbow Motion in Adhesive Capsulitis by Means of Inertial Sensor Technology: A Within-Session, Intra-Operator and Inter-Operator Reliability and Agreement Study.

Adhesive capsulitis (AC) is a glenohumeral (GH) joint condition, characterized by decreased GH joint range of motion (ROM) and compensatory ROM in the elbow and scapulothoracic (ST) joint. To evaluate AC progression in clinical settings, objective movement analysis by available systems would be valuable. This study aimed to assess within-session and intra- and inter-operator reliability/agreement of such a motion capture system. The MVN-Awinda® system from Xsens Technologies (Enschede, The Netherlands) was used to assess ST, GH, and elbow ROM during four tasks (GH external rotation, combing hair, grasping a seatbelt, placing a cup on a shelf) in 10 AC patients (mean age = 54 (± 6), 7 females), on two test occasions (accompanied by different operators on second occasion). Standard error of measurements (SEMs) were below 1.5° for ST pro-retraction and 4.6° for GH in-external rotation during GH external rotation; below 6.6° for ST tilt, 6.4° for GH flexion-extension, 7.1° for elbow flexion-extension during combing hair; below 4.4° for GH ab-adduction, 13° for GH in-external rotation, 6.8° for elbow flexion-extension during grasping the seatbelt; below 11° for all ST and GH joint rotations during placing a cup on a shelf. Therefore, to evaluate AC progression, inertial sensors systems can be applied during the execution of functional tasks.

Reliability and Agreement of 3D Trunk and Lower Extremity Movement Analysis by Means of Inertial Sensor Technology for Unipodal and Bipodal Tasks.

This study evaluates the reliability and agreement of the 3D range of motion (ROM) of trunk and lower limb joints, measured by inertial measurement units (MVN BIOMECH Awinda, Xsens Technologies), during a single leg squat (SLS) and sit to stand (STS) task. Furthermore, distinction was made between movement phases, to discuss the reliability and agreement for different phases of both movement tasks. Twenty healthy participants were measured on two testing days. On day one, measurements were conducted by two operators to determine the within-session and between-operator reliability and agreement. On day two, measurements were conducted by the same operator, to determine the between-session reliability and agreement. The SLS task had lower within-session reliability and agreement compared with between-session and between-operator reliability and agreement. The reliability and agreement of the hip, knee, and ankle ROM in the sagittal plane were good for both phases of the SLS task. For both phases of STS task, within-session reliability and agreement were good, and between-session and between-operator reliability and agreement were lower in all planes. As both tasks are physically demanding, differences may be explained by inconsistent movement strategies. These results show that inertial sensor systems show promise for use in further research to investigate (mal)adaptive movement strategies.

Shoulder assessment according to the international classification of functioning by means of inertial sensor technologies: A systematic review.

This review investigates current protocols using Inertial Measurement Units (IMUs) in shoulder research, and outlines future paths regarding IMU use for shoulder research. Different databases were searched for relevant articles. Criteria for study selection were (1) research in healthy persons or persons with shoulder problems, (2) IMUs applied as assessment tool for the shoulder (in healthy subjects and shoulder patients) or upper limb (in shoulder patients), (3) peer-reviewed, full-text papers in English or Dutch. Studies with less than five participants and without ethical approval were excluded. Data extraction included (1) study design, (2) participant characteristics, (3) type/brand of IMU, (4) tasks included in the assessment protocol, and (5) outcomes. Risk of bias was assessed using the Downs and Black checklist. Scapulothoracic/glenohumeral and humerothoracic kinematics were reported in respectively 10 and 27 of the 37 included papers. Only one paper in healthy persons assessed, next to scapulothoracic/glenohumeral kinematics, other upper limb joints. IMUs' validity and reliability to capture shoulder function was limited. Considering applied protocols, 39% of the protocols was located on the International-Classification-of-Functioning (ICF) function level, while 38% and 23% were on the 'capacity' and 'actual performance'-sublevel, of the ICF-activity level. Most available IMU-research regarding the shoulder is clinically less relevant, given the widely reported humerothoracic kinematics which do not add to clinical-decision-making, and the absence of protocols assessing the complete upper limb chain. Apart from knowledge on methodological pitfalls and opportunities regarding the use of IMUs, this review provides future research paths.