Hip resurfacing arthroplasty (HRA) became a popular procedure in the early 90s because of the improved wear characteristic, preserving nature of the procedure and the optimal stability and range of motion. Concerns raised since 2004 when metal ions were seen in blood and urine of patients with a MoM implant. Design of the prosthesis, acetabular component malpositioning, contact-patch-to-rim distance (CPR) and a reduced joint size all seem to play a role in elevated metal ion concentrations. Little is known about the influence of physical activity (PA) on metal ion concentrations. Implant wear is thought to be a function of use and thus of patient activity levels. Wear of polyethylene acetabular bearings was positively correlated with patient's activity in previous studies. It is hypothesized that daily habitual physical activity of patients with a unilateral resurfacing prosthesis, measured by an activity monitor, is associated with habitual physical activity.

A prospective, explorative study was conducted. Only patients with a unilateral hip resurfacing prosthesis and a follow-up of 10 ± 1 years were included. Metal ion concentrations were determined using ICP-MS. Habitual physical activity of subjects was measured in daily living using an acceleration-based activity monitor. Outcome consisted of quantitative and qualitative activity parameters.

In total, 16 patients were included. 12 males (75%) and 4 females (25%) with a median age at surgery of 55.5 ± 9.7 years [43.0 – 67.9] and median follow-up of 9.9 ± 1.0 years [9.1 – 10.9]. The median cobalt and chromium ion concentrations were 25 ± 13 and 38 ± 28 nmol/L. A significant relationship, when adjusting for age at surgery, BMI, cup size and cup inclination, between sit-stand transfers (p = .034) and high intensity peaks (p = .001) with cobalt ion concentrations were found (linear regression analysis).

This study showed that a high number of sit-stand transfers and a high number of high intensity peaks is significantly correlated with high metal ion concentrations, but results should be interpreted with care. For patients it seems save to engage in activities with low intensity peaks like walking or cycling without triggering critical wear or metal ions being able to achieve important general health benefits and quality of life, although the quality (high intensity peaks) of physical activity and behaviour of patients (sit-stand-transfers) seem to influence metal ion concentrations.

Falls and fall-related injuries can have devastating health consequences and form a growing economic burden for the healthcare system. To identify individuals at risk for preventive measures and therapies, fall risk assessment scores have been developed. However, they are costly in terms of time and effort and rely on the subjective interpretation of a skilled professional making them less suitable for frequent assessment or in a screening situation.

Small wearable sensors as activity monitor can objectively provide movement information during daily-life tasks. It is the aim of this study is to evaluate whether the activity parameters from wearable monitors correlate with fall risk scores and may predict conventional assessment scores.

Physical activity data were collected from nineteen home-dwelling frail elderly (n=19, female=10; age=81±5.6 years, GFI=5.4±1.9, MMSE=27.4±1.5) during waking hours of 4 consecutive days, wearing a wearable 9-axis activity monitor (56×40×15mm, 25g) on the lateral side of the right thigh. The signal was analysed using self-developed, previously validated algorithms (Matlab) producing the following parameters: time spent walking, step count, sit-stand-transfer counts, mean cadence (steps/min), count of stair uses and intensity counts >1.5G.

Conventional fall risk assessment was performed using the Tinetti sore (range: 0–28=best), a widely used tool directly determining the likelihood of falls and the Short Physical Performance Battery (SPPB, range: 0–12=best) which measures lower extremity performance as a validated proxy of fall risk. The anxiety to fall during activities of daily living was assessed using the self-reported Short Falls Efficacy Scale-International (FES-I, range: 7–28=worst).

Correlations between activity parameters and conventional scores were tested using Pearson's r.

The activity parameters (daily means) for the 19 participants were 70.8min (SD=28.7; min-max= 22.8–126.6) of walking, 4427 steps (SD=2344; min-max= 1391–8269) with a cadence 79.3 steps per minute (SD=17.1; min-max=52.8–103.9) and 33.3 sit-stand transfers (SD=9.7; min-max=8.8–48.0).

The average Tinetti score was 21.2 (SD=5.1; min-max=10.0–27.0), with SPPB scoring 7.8 (SD=2.4; min-max=3.0–12.0), and FES-I 4.6 (SD=5.1; min-max=7.0–23.0).

Strong (r≥0.6) and significant correlations existed between the walking cadence and the Tinetti (r=.60, p=<.01) and SPPB (r=.71, p=<.01) scores. No other correlations were found between the activity parameters and the Tinetti, SPPB and none with the psychological FES-I questionnaire.

Conventional fall risk scores and activity data are comparable to literature values and thus representative of home-dwelling frail elderly including a wide range covered for both dimensions.

No quantitative activity measure had a predictive value for fall risk assessment. Strongly correlated with Tinetti and SPPB, objectively measured cadence as a qualitative parameter seems a useful parameter for remotely identifying fall risk in frail elderly. The perceived anxiety to falls was not correlated to quantitative and qualitative activity parameters suggesting that this psychological aspect hardly affects activity.

Wearable activity monitors seem a valid tool to assess fall risk remotely and thus allow low cost, frequent and large group screening of frail elderly towards a health economically viable tool for a growing societal need. The predictive quality of activity monitored data may be increased by deriving additional qualitative measures from the activity data.

Besides eliminating pain, restoring activity is a major goal in orthopaedic interventions including joint replacement or trauma surgery following falls in frail elderly, both treatments of highest socio-economic impact.

In joint replacement and even more so in frail elderly at risk of falling, turns are assessed in clinical tests such as the TUG (Timed Get-up-and-Go), Tinetti, or SPPB so that classifying turning movements in the free field with wearable activity monitors promises clinically valuable objective diagnostic or outcome parameters.

It is the aim of this study to validate a computationally simple turn detection algorithm for a leg-worn activity monitor comprising 3D gyroscopes.

A previously developed and validated activity classification algorithm for thigh-worn accelerometers was extended by adding a turn detection algorithm to its decision tree structure and using the 3D gyroscope of a new 9-axis IMU (56×40×15mm, 25g, f=50Hz,).

Based on published principles (El-Gohary et al. Sensors 2014), the turn detection algorithm filters the x-axis (thigh) for noise and walking (Butterworth low-pass, 2^ndorder with a cut-off at 4Hz and 4^thorder with a cut-off at 0.3Hz) before using a rotational speed threshold of 15deg/s to identify a turn and taking the bi-lateral zero-crossings as start and stop markers to integrate the turning angle.

For validation, a test subject wore an activity monitor on both thighs and performed a total of 57 turns of various types (walking, on-the-spot, fast/slow), ranges (45 to 360deg) and directions (left/right) in free order while being video-taped. An independent observer annotated the video so that the algorithmic counts could be compared to n=114 turns. Video-observation was compared to the algorithmic classification in a confusion matrix and the detection accuracy (true positives) was calculated.

In addition, 4-day continuous activity measures from 4 test subjects (2 healthy, 2 frail elderly) were compared.

Overall, only 5/114 turns were undetected producing a 96% detection accuracy. No false positives were classified. However, when detection accuracy was calculated for turning angle intervals (45°: 30–67.5°; 90°: 67.5–135°; 180°: 135–270°; 360°: 270–450°), accuracy for all interval classifications combined dropped to 83.3% with equal values for left and right turns. For the 180° and 360°, accuracy was 100% while for the shorter 45° and 90° turns accuracy was 75% and 71% only, mainly because subsequent turns were not separated.

Healthy subjects performed between 470 (office worker) and 823 (house wife) turns/day while frail elderly scored 128 (high fall risk) to 487 turns/day (low fall risk). Turns/day and steps/day were not correlated. In healthy subjects ca. 50% of turns were in the 45° category compared to only ca. 35% in frail elderly.

Turn detection for a thigh-worn IMU activity monitor using a computationally simple algorithm is feasible with high general detection accuracy. The classification and separation of subsequent short turns can be further improved.

In multi-day measurement, turns/day and the distribution of short and long turns seem to be a largely independent activity parameter compared to step counts and may improve objective assessment of fall risk or arthroplasty outcome.