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Orthopaedic Proceedings
Vol. 100-B, Issue SUPP_3 | Pages 85 - 85
1 Apr 2018
Bolink S van Laarhoven S Lipperts M Grimm B
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Introduction

Following primary total knee arthroplasty (TKA), patients experience pain relief and report improved physical function and activity. However, there is paucity of evidence that patients are truly more active in daily life after TKA. The aims of this study were: 1) to prospectively measure physical activity with a wearable motion sensor before and after TKA; 2) to compare patient-reported levels of physical activity with objectively assessed levels of physical activity before and after TKA; 3) to investigate whether differences in physical activity after TKA are related to levels of physical function.

Methods

22 patients (age=66.6 ±9.3yrs; m/f= 12/11; BMI= 30.6 ±6.1) undergoing primary TKA (Vanguard, ZimmerBiomet), were measured preoperatively and 1–3 years postoperatively. Patient-reported outcome measures (PROMs) included KOOS-PS and SQUASH for assessment of perceived physical function and activity resp. Physical activity was assessed during 4 consecutive days in patients” home environments while wearing an accelerometer-based activity monitor (AM) at the thigh. All data were analysed using semi-automated algorithms in Matlab. AM-derived parameters included walking time (s), sitting time (s) standing time (s), sit-to-stand transfers, step count, walking bouts and walking cadence (steps/min). Objective physical function was assessed by motion analysis of gait, sit-to-stand (STS) transfers and block step-up (BS) transfers using a single inertial measurement unit (IMU) worn at the pelvis. IMU-based motion analysis was only performed postoperatively. Statistical comparisons were performed with SPSS and a per-protocol analysis was applied to present the results at follow-up.


Orthopaedic Proceedings
Vol. 95-B, Issue SUPP_9 | Pages 26 - 26
1 Feb 2013
Brunton L Bolink S van Laarhoven S Lipperts M Grimm B Heyligers I Blom A
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Accelerometer based gait analysis (AGA) is a potential alternative to the more commonly used skin marker based optical motion analysis system(OMAS). The use of gyroscopes in conjunction with accelerometers (i.e. inertial sensors), enables the assessment of position and angular movements of body segments and provides ambulatory kinematic characterisation of gait.

We investigated commonly used gait parameters and also a novel parameter, Pelvic obliquity (PO) and whether they can be used as a parameter of physical function and correlate with classic clinical outcome scores

Gait was studied in healthy subjects (n=20), in patients with end stage hip OA (n=20) and in patients with end stage knee OA (n=20). Subjects walked 20 metres in an indoor environment along a straight flat corridor at their own preferred speed. A 3D inertial sensor was positioned centrally between the posterior superior iliac spines (PSIS) overlying S1.

Comparing gait parameters of end stage hip OA patients with an age and gender matched healthy control group, significantly lower walking speed, longer step duration and shorter step length was observed. After correcting for walking speed between groups, significantly less average range of motion of PO (RoMpo) was observed for patients with end stage hip OA compared to healthy subjects and patients with end stage knee OA.

IGA allows objective assessment of physical function for everyday clinical practice and allows assessment of functional parameters beyond time only. IGA measures another dimension of physical function and could be used supplementary to monitor recovery of OA patients after TJR.


Orthopaedic Proceedings
Vol. 94-B, Issue SUPP_XXXVII | Pages 193 - 193
1 Sep 2012
Lipperts M Grimm B Van Asten W Senden R Van Laarhoven S Heyligers I
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Introduction

In orthopaedics, clinical outcome assessment (COA) is still mostly performed by questionnaires which suffer from subjectivity, a ceiling effect and pain dominance. Real life activity monitoring (AM) holds the promise to become the new standard in COA with small light weight and easy to use accelerometers. More and more activities can be identified by algorithms based on accelerometry. The identification of stair climbing for instance is important to assess the participation of patients in normal life after an orthopaedic procedure. In this study we validated a custom made algorithm to distinguish normal gait, ascending and descending stairs on a step by step basis.

Methods

A small, lightweight 3D-accelerometer taped to the lateral side of the affected (patients) or non-dominant (healthy subjects) upper leg served as the activity monitor. 13 Subjects (9 patients, 4 healthy) walked a few steps before descending a flight stairs (20 steps with a 180o turn in the middle), walked some steps more, turned around and ascended the same stairs. Templates (up, down and level) were obtained by averaging and stretching the vertical acceleration in the 4 healthy subjects. Classification parameters (low pass (0.4 Hz) horizontal (front-back) acceleration and the Euclidian distance between the vertical acceleration and each template) were obtained for each step. Accuracy is given by the percentage of correctly classified steps.


Orthopaedic Proceedings
Vol. 94-B, Issue SUPP_XXXVII | Pages 194 - 194
1 Sep 2012
Van Laarhoven S Bolink S Heyligers I Grimm B
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Introduction

Our classic outcome scores increasingly fail to distinguish interventions or to reflect rising patient demands. Scores are subjective, have a low ceiling and score pain rather than function. Objective functional assessment tools for routine clinical use are required. This study validates inertial sensor motion analysis (IMA) by differentiating patients with knee versus hip osteoarthritis in a block-step test.

Methods

Step up and down from a block (h=20cm, 3 repetitions) loading the affected (A) and unaffected (UA) leg was measured in n=59 subjects using a small inertial sensor (3D gyro and accelerometer, m=39g) attached onto the sacrum. Patients indicated for either primary unilateral THA (n=20; m/f=4/6, age=69.4yrs ±9.8) or TKA (n=16;m/f=7/9;age=67.8yrs ±8.2) were compared to healthy controls (n=23;m/f=13/10;age=61.7yrs ±6.2) and between each other to validate the test's capacity for diagnostics and as an outcome measure.

The motion parameters derived (semi-) automatically in Matlab for both legs were: front-back (FB-) sway and left-right (LR-) sway (up and down); peak-to-peak accelerations (Acc) during step down. In addition the asymmetry between both legs (ASS) was calculated for each parameter. Group differences were tested (t-test) and the diagnostic value determined by the area under the curve (AUC) of the ROC-curve.