Lower limb fractures are commonly treated with cast immobilization, and as a main consequence of strict immobilization this typically leads to loss in muscle mass, decrease of bone density and decline in functional abilities. Body-worn sensors are increasingly used to assess outcome in clinical trials by providing objective mobility parameters in a real-world environment. The aim of this study is to investigate the usability aspects and potential changes in mobility parameters in partial-immobilization patients in real-world conditions. Six healthy young males (age 22.2 ± 1.2 years; weight 76.5 ± 6.7 kg, height 185.8 ± 6.1 cm. Mean ± standard deviation) wore a leg cylinder cast with walker boot to immobilize their dominant leg for two consecutive weeks. Subjects were asked to continuously wear a tri-axial accelerometer on the waist (actibelt) during waking hours for 6 weeks including 2 weeks before, during and after cast immobilisation. The total amount of days of continuous recording was 339 days with a total wearing time of 120 days. Software packages which allow to detect steps and to estimate real-world walking speed were used to analyse the accelerometry data. It was suspected that knee immobilization would affect strongly the wave form of the signal with an impact on the accuracy of the speed algorithm, whereas the step detection should be more robust. This effect was confirmed in a preliminary study performed to quantify the accuracy under immobilization conditions. On the other hand, step numbers are known to be sensitive to fluctuations in wearing time which was not uniform throughout the entire study. We concluded that in this setting step frequency is the most reliable parameter. Step frequency showed a systematic decrease in the values during the immobilization period which recovered to pre-immobilisation values after cast removal. This confirms the usability of accelerometry and sensitivity of its mobility parameters for clinical outcome assessment

Total knee replacement in a commonly performed procedure in the United Kingdom with more than 76000 primary procedures performed in 2010. With so many procedures performed there has to be a robust way of assessing the outcome of the procedure. Gait analysis is a valuable tool in objectively assessing the these patients. Inertial movement units (IMU's) are a fairly new development in gait analysis. The aim of our project is to use IMUs to assess the differences in gait profile between a cohort of healthy controls, a group of pre operative knee replacement patients, a group of 8 week post operative patients and finally a group of post operative knee replacement patients at 1 year. We studied a total of 47 patients. We also had data from a previous study done on healthy controls using the same measurement tool. We measured three parameters: peak swing phase flexion, peak stance phase flexion and stride duration. Our findings indicate that pre-operative patients have a significantly reduced peak flexion in swing and stance with increased stride duration. This shows no improvement at the 8 week mark. At the 1 year mark peak flexion in swing returns to pre operative levels but flexion in stance and stride duration are still poor. These findings may not have been identified without gait analysis. Gait analysis using intertial movement units will add much information to radiographs and clinical examination. This information can also be used to tailor individual patients rehabilitation.

Background. In total knee arthroplasty (TKA), patient reported outcome on pain, function or satisfaction fails to differentiate treatment options. Activity, a consequence of pain-free, well functioning TKA and a satisfied patient, may be a discriminative surrogate metric, especially when objectively measured. Methods. Habitual activity was measured in TKA patients (n=32, F/M=20/12, age: 72 ±8yrs) at long-term follow-up (9 ±1yrs) and compared to healthy, age matched controls (n=32, F/M=20/12, age: 71 ±9yrs) using a popular questionnaire (SQUASH) and accelerometry. A small 3D accelerometer (X16-mini, GCD Dataconcepts) was worn for 4 successive days during waking hours at the non-affected lateral upper leg. Data was analysed using validated algorithms (Matlab) counting and timing walking bouts, steps, sitting periods and transfers. Stair climbing events or similar activities such as walking steep slopes were classified using the higher mean hip flexion angle as a feature. Results. SQUASH scores were not sign. different between TKA (mean ±SD: 4551 ±3426) and controls (3659 ±2720, p>0.1). With accelerometry, differences between patients and controls (Median [IQR]) increased from −13% for Time Standing (3.7h [2.6–5.1] vs 4.3h [2.9–5.0], p=0.69), to −26% for Daily Steps (4939 [3796–7910] vs 6731 [5539–8270], p=0.019] and −31% for Sit-Stand Transfers (31.2 [22.1–37.0] vs 45.3 [34.9–58.4], p>0.001). For stair-up events, the difference increased to −74% (6.2 [2.8–22.4] vs 23.9 [10.8–39.1], p>0.001). Conclusions. Self-report activity could not discriminate between patients and controls. With accelerometry, sign. differences increased with rising levels of effort and difficulty. In TKA, walking alone, either by accelerometry or as the major component of self-report may not serve as powerful outcome measure in orthopaedics were energy expenditure from walking is less relevant than functionally demanding but rarer tasks such as transfer or stair climbing

Capturing objective data of the postoperative changes in the mobility of patients is expected to generate a better understanding of the effect of postoperative treatment. Until recently, the collection of gait-related data was limited to controlled clinical environments. The emergence of accurate wearable accelerometers with sufficient runtime, however, enables the long-term measurement and extraction of mobility parameters, such as “real-world walking speed”. An interim analysis of 1967 hours of actibelt data (3D accelerometer, 100 Hz) from 5 patients (planned total 20) with a femur fracture and 5 patients (planned total 20) with a humerus fracture from a geriatric population at two different sites of the university hospital of the Ludwigs-Maximilian-University in Munich was performed. Mobility data was captured during several days of stationary treatment starting directly after surgery and during a short follow-up visit six weeks after the surgery. Preliminary results show an increase of the mean walking speed between the two visits independent of the type of fracture. Patients with a humerus fracture tended to walk faster than patients with a femur fracture during both visits. The data also reveals an unexpected low level of mobility during the stationary stay. Mobile accelerometry can be used to evaluate different postoperative mobilisation strategies and even provide near-time feedback in geriatric trauma patients

Background and purpose. To identify methods used to measure free living sedentary behaviour in people with back pain and review the validity and reliability of identified measures. Methods. Databases including CINAHL, EMBASE, MEDLINE, AMED, PsycINFO, SPORTDiscus and the Sedentary Behaviour and Research Network website (. www.sedentarybehaviour.org. ) were searched for relevant published articles up to June 2014. Studies which measured sedentary behaviour in people with back pain were included. Quality of the included studies was assessed using the Newcastle Ottawa Scale. The Consensus-based Standards for the Selection of Measurement Instruments (COSMIN) Checklist was used to assess psychometric properties. Results. Six papers were identified; two of high methodological quality. The most common method of data collection was self-report, using activity diaries or questionnaires. Sedentary behaviour measured by accelerometry ranged from 6.7 to 10.7 hours per day whereas results from self-report measures ranged from 5 to 9.4 hours per day. According to the COSMIN checklist, the psychometric properties of the measurement instruments were rated fair to excellent. Conclusion. People with back pain spend a large proportion of their waking day participating in sedentary behaviour. Therefore valid and reliable sedentary behaviour measurements, such as those identified in this study, are essential for assessing the effectiveness of public health interventions and for future population monitoring. Conflicts of interest: No conflicts of interest. Sources of funding: Department for Employment and Learning

Purpose: Despite their inclusion within clinical practice, standardized radiographs may not accurately project an individual’s level of function and mobility. The purpose of this study is to examine the potential relationship between established radiographic features and lurch; a functional measure of asymmetric gait, in a group of patients who will receive total hip arthroplasty (THA). Method: Thirty-two patients (16 females, 16 males) identified as hip replacement candidates were recruited, with a mean age of 57.0 years. Lurch was obtained using the Walkabout Portable Gait Monitor (WPGM); a wireless, triaxial accelerometry device. The independent variables were comprised of the Kellgren-Lawrence Scale, and a collection of standard radiographic features, as adopted by the American Academy of Orthopaedic Surgeons (AAOS), the National Institutes of Health (NIH), and the World Health Organization (WHO). Radiographs were blinded, and the surgeon completing the rating scale was unaware of patient’s lurch values. Age-adjusted regression analyses were used to examine the potential association between each radiographic feature and lurch. Results: Increased amounts of lurch (i.e. functional impairment) were independently associated with higher Kellgren-Lawrence Scale scores (p=.047), increased Joint Space Narrowing in the mid-portion of the joint (zone 2; p=.004), the presence of acetabular wear (p=.045), an increased severity of subchondral femoral head cysts (p=.004), and higher surgeon-rated Visual Analog Scale scores for overall severity of joint degeneration (p=.008). Lurch was not significantly associated with the remaining 10 features which were examined. Further analyses revealed that lurch was not significantly associated with certain demographic factors, including sex, Body Mass Index, and co-morbid health conditions. Conclusion: Although the Kellgren-Lawrence scale was associated with an objective measure of gait, our results indicate that other radiographic features may provide a more accurate prediction of gait performance among this patient population. As lurch appears to be a robust objective measure of physical impairment, which is unaffected by BMI and co-morbidities, we believe that portable triaxial accelerometers can likely be used to conveniently collect objective gait data. This functional data may be used to supplement clinical efforts to screen and prioritize appropriate hip arthroplasty patients

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. Results. In total the subjects took 537 (41+/-8 mean+/-std) steps, 525 of which were correctly identified as step. 12 Steps were not detected, and 2 steps were incorrectly identified as step. Per subject the accuracy of the classification algorithm ranged from 57% to 97%. In only 2 subjects the accuracy was less than 75%, giving an overall accuracy of 85%. Discussion. In literature algorithms able to identify walking the stairs and normal walking have been reported with an accuracy in the range of 80–95%1,2. Our algorithm falls well within this range, and can be even further improved. The low accuracy in two subjects can be explained by the fact that the sensor was placed more to the front of the leg, which influences the low-pass horizontal acceleration. Using a combination of front-back and left-right acceleration could possibly solve this problem. In the future we are confident to identify also other activities and even distinguish different types of stair climbing (i.e. taking a step with each leg versus only taking steps with the unaffected leg and ‘dragging’ the second leg) and obtain more specific activity profiles to be used in clinical outcome assessment

Introduction. In orthopaedics, clinical outcome assessment (COA) is mostly performed by questionnaires which suffer from subjectivity, a ceiling effect and pain dominance. Real life activity monitoring (AM) can objectively assess function and becomes now feasible as technology has become smaller, lighter, cheaper and easier to use. In this study we validated a custom made algorithm based on accelerometry using different orthopaedic patients with the aim to use AM in orthopaedic COA. Methods. A small, lightweight 3D-accelerometer taped to the lateral side of the affected upper leg served as the activity monitor. AM algorithms were programmed in Matlab to classify standing, sitting, and walking. For validation a common protocol was used; subjects were asked to perform several tasks for 5 or 10 seconds in a fixed order. An observer noted the starting time of each task using a stopwatch. Accuracy was calculated for the number of bouts per activity as well as total time per activity. 10 Subjects were chosen with different pathologies (e.g. post total knee/hip arthroplasty, osteoarthritis) since the difference in movement dynamics in each pathology poses a challenge to the algorithm. Results. In total the subjects performed 267 activities (99 standing, 80 sitting, 88 walking), 258 of which (99, 73, 87 resp.) were classified correctly by the algorithm, corresponding to a sensitivity of 97%. Sensor misplacement in 1 subject caused all missed instances in sitting, and exclusion of this subject increased sensitivity to 99.9%. 5 Instances of standing were incorrectly added by the algorithm, giving a specificity of 95% for standing. In total 80 sit-stand, and 78 stand-sit transitions were performed. Subjects were standing for 792 seconds, sitting for 764 s, and walking for 905 s. The algorithm found a total duration of 739, 583 and 1056 seconds for those activities respectively, and 83 seconds of lying (misclassification of sitting). Discussion. Sensor placement is an important factor to obtain reliable results. Even so sensitivity and specificity are comparable to values found in literature [85–99%]. The added instances of standing occurred when a subject did not immediately sit after a period of walking. It is doubtful if these instances should be considered false positives. The main difference in duration is also found in sitting, which is caused by the missed instances previously described, in combination with the fact that the duration of transitions are added to the walking period in the algorithm, whereas it is divided over sitting and walking by the observer. This corresponds to a difference of less than one second per transition. The algorithm produces reliable results when challenged with different movement patterns common with orthpaedic pathologies. The device may be used as as AM in objective assessment of clinical outcome after orthopaedic procedures